Headlines & Publications

Oracle & Ci4CC Partner to Accelerate Oncology Innovation

Sat, 06 Dec 2025 16:31:35 GMT

Press Release November 2025: Oracle and Ci4CC Partner to Accelerate Oncology Innovation

Strategic collaboration to leverage Ci4CC’s national network and Oracle’s technology to help advance AI in oncology, EHR interoperability, next-gen clinical trials, and personalized medicine

24th Cancer Center Informatics Society Symposium - MIAMI BEACH, Florida – November 7, 2025 – Oracle Health and Life Sciences and the Cancer Center Informatics Society (Ci4CC), a nonprofit uniting cancer centers, researchers, and industry innovators, are collaborating to advance AI innovation in oncology care and research.

The collaboration aims to bring together Oracle’s technology with Ci4CC’s Initiatives Program and national network of NCI-Designated and Community Care Cancer Centers. The two organizations plan to collaborate to design an EHR optimized for cancer care.

“Cancer remains a leading cause of death worldwide, but AI and data science have become powerful allies in the fight,” said Seema Verma, executive vice president and general manager, Oracle Health and Life Sciences. “Combining Ci4CC’s unparalleled expertise and network with our state-of-the-art proven AI-powered healthcare applications, we have the opportunity to accelerate discoveries that can help cure cancer.”

The strategic collaboration intends to develop initiatives across oncology care and clinical research. This includes integrating clinical and genomic data for personalized medicine solutions, pioneering AI-driven approaches for clinical trial innovation and drug development, establishing robust real-world evidence frameworks, and advancing precision oncology platforms.

“This alliance with Oracle marks a significant milestone for Ci4CC and our mission to further cancer informatics,” said Sorena Nadaf-Rahrov, MS, MMI, PhDc, President & CEO - Cancer Center Informatics Society. “Advancements in cancer research and clinical care are not achieved in isolation. That’s why fostering such collaborative networks is fundamental to our Initiatives Program, and central to the Society’s mission.

By integrating Oracle’s cutting-edge and AI-enabled technology with real-world data from our national network of NCI-Designated and Community Cancer Centers, we hope to incubate and operationalize initiatives that will redefine cancer research and patient care. Turning data into knowledge, and knowledge into health continues to drive the Cancer Center Informatics Society forward.”

Oracle NewsRoom: https://www.oracle.com/news/announcement/oracle-and-ci4cc-partner-to-accelerate-oncology-innovation-2025-11-07/

PR Newswire: https://www.prnewswire.com/news-releases/oracle-and-ci4cc-partner-to-accelerate-oncology-innovation-302608712.html

DeepMind’s new AlphaGenome AI tackles the ‘dark matter’ in our DNA

Thu, 03 Jul 2025 17:40:01 GMT

Tool aims to solve the mystery of non-coding sequences — but is still in its infancy.

Nearly 25 years after scientists completed a draft human genome sequence, many of its 3.1 billion letters remain a puzzle. The 98% of the genome that is not made of protein-coding genes — but which can influence their activity — is especially vexing.

An artificial intelligence (AI) model developed by Google DeepMind in London could help scientists to make sense of this ‘dark matter’, and see how it might contribute to diseases such as cancer and influence the inner workings of cells. The model, called AlphaGenome, is described in a 25 June preprint .

“This is one of the most fundamental problems not just in biology — in all of science,” said Pushmeet Kohli, the company’s head of AI for science, at a press briefing.

The ‘sequence to function’ model takes long stretches of DNA and predicts various properties, such as the expression levels of the genes they contain and how those levels could be affected by mutations.

“I think it is an exciting leap forward,” says Anshul Kundaje, a computational genomicist at Stanford University in Palo Alto, California, who has had early access to AlphaGenome. “It is a genuine improvement in pretty much all current state-of-the-art sequence-to-function models.”

Link to full article: https://www.nature.com/articles/d41586-025-01998-w

As Trump decimates NIH funding, a daring proposal to issue $750 billion in bonds for medical cures garners attention

Fri, 27 Jun 2025 20:13:55 GMT

The Cancer Letter

June 20th Issue

As biomedical research at NIH faces an existential threat from the Trump administration, an entrepreneur is winning over allies for what he describes as a “simple idea” that could introduce a massive new infusion of money for innovation in medicine.

Lou Weisbach , a former CEO of a Fortune 500 company and a political insider, wants the federal government to issue $750 billion in bonds over six years to fund an enterprise dubbed the American Centers for Cures , and he is working to bring this issue to the White House. Should Trump wish to call this initiative the Trump American Center for Cures, that’s definitely on the table, Weisbach says.

The idea is not new. Weisbach has been working with Richard J. Boxer, a urologist at the University of California, Los Angeles, David Geffen School of Medicine and a member of the National Cancer Advisory Board, have been pushing for legislation to float the bond and establish the centers for close to a quarter century, getting some (but not enough) traction.

However, today, as the Trump administration and its Department of Government Efficiency are remaking the U.S. biomedical research enterprise, support for the idea appears to be on the rise, as former skeptics and those who had been too busy to pay attention say that the plan for the American Centers for Cures warrants serious consideration.

“The response over the last three or four weeks from the medical community and the research community has been remarkable to me as somebody who’s worked on this for a long time,” Weisbach said to The Cancer Letter soon after presenting the concept at the annual meeting of the American Society of Clinical Oncology in Chicago. “People get it. People get that we haven’t gotten the job done, and not because we don’t have great people working on it, but we are in a system that doesn’t work.

“I thought that what he was proposing was intriguing,” Dr. Steve Rosen said to The Cancer Letter . “Obviously, it was something that was very ambitious, but the potential benefits to humanity were enormous. And the key issue was coming up with the appropriate funding and to do it in a manner that was almost like the Manhattan Project, where you’re going to have a devoted team with a business mentality, with goals, deadlines, and the appropriate resources and metrics to follow with the ultimate goal of commercializing discoveries that would be a return on the investment from the bonds that were going to be issued to generate the revenue."

Another supporter, Sorena Nadaf-Rahrov , said that he agrees that the U.S. biomedical research and the healthcare system is long overdue for an overhaul. More than a liferaft, Nadaf-Rahrov describes the American Center for Cures as a way to turn this period of chaos into an opportunity to change biomedical research for the better. " Politics aside, I get it. Resources are tight. This country is trillions of dollars in debt, but our largest expense in this country is tied to healthcare. And so, if we continue in the same direction we’ve been going for years and years and years, is there going to be any significant change? Especially with the funding rates on the decline, potentially evaporating, it would be the worst day for any patient suffering from any disease when research for their disease has gone away. I can’t even imagine. These experiments must happen. In my opinion, for healthcare and academia and organizations like ours—and people like me who stand behind them—"

Link to full Article on The Cancer Letter: https://cancerletter.com/the-cancer-letter/20250620_1/

American Center for Cures

Fri, 06 Jun 2025 21:56:05 GMT

Cancer Center Informatics Society Working Group Announcement and Endorsement of the American Center for Cures

Following Its 23rd Cancer Center Summit, the Cancer Center Informatics Society Issues Statement of Support for The American Center for Cures and Bipartisan Efforts to Accelerate Prevention and Cures for Major Diseases

Los Angeles, CA — The Cancer Center Informatics Society (Ci4CC), the leading international data science and clinical informatics organization dedicated to advancing AI and translational research in oncology, today announced its strong endorsement of The American Center for Cures (ACC) — a bold, bipartisan initiative committed to preventing and curing major diseases, including cancer.

The statement of support follows Ci4CC’s 23rd Cancer Center Summit , which convened leaders in oncology, data science, and AI innovation. Ci4CC reaffirmed the urgent need for national collaboration and visionary leadership to drive real-world, equitable impact. The Society emphasized the transformative role of AI, data science, and cross-sector coordination in reshaping cancer research, care delivery, and public health.

“An ambitious vision with the potential for a profound impact,” said Dr. Steven T. Rosen , renowned oncologist and Executive Vice President and Director Emeritus of City of Hope Comprehensive Cancer Center.

“As both a researcher and healthcare advocate, I’ve seen firsthand how devastating and complex cancer can be for patients and their families,” said Sorena Nadaf-Rahrov , President and CEO of Ci4CC. “Cancer is emblematic of the broader crisis we face with chronic and life-threatening diseases — many of which remain underserved. The economic burden of cancer exceeds $200 billion annually in the U.S. alone, factoring in both direct medical costs and lost productivity. That’s why national frameworks like The American Center for Cures are essential. We must foster innovation, connect institutions, and accelerate real solutions for patients.”

Lou Weisbach , founder and leader of The American Center for Cures, added: . We deeply appreciate the endorsement of the Cancer Center Informatics Society. AI and data are at the center of our commitment to bring together the five business essentials necessary to prevent and cure disease once and for all: full funding, dynamic proven leadership, process, accountability, and a sense of urgency. By allocating $108 billion to the AI and data entity within ACC, we recognize the critical role your industry plays in helping us cross the finish line — on behalf of all patients and their families.”

Cancer remains one of the most urgent public health challenges of our time. According to Cancer Statistics 2025, recently published by the American Cancer Society:

An estimated 2 million new cancer cases will be diagnosed in the U.S. in 2025
Over 618,000 cancer-related deaths are projected
Stark racial and ethnic disparities in outcomes continue, especially among underserved populations

Ci4CC’s work has consistently demonstrated that AI and data science are essential to accelerating innovation in early detection, precision oncology, and clinical trial optimization — yet widespread adoption remains elusive.

Nadaf-Rahrov concluded :

“The mission of The American Center for Cures aligns with Ci4CC’s commitment to transforming oncology through the power of data and AI. We are proud to support this national initiative — one that holds real promise for delivering faster, better outcomes for all.”

For more information, visit

Ci4CC ACC Initiative Working Group: https://www.ci4cc.org/american-center-for-cures
American Center for Cures: https://theamericancenterforcures.org .

Caught in the crossfire

Thu, 17 Apr 2025 16:10:26 GMT

Caught in the crossfire: The critical threats facing cancer centers, research, and patient care. Shannon McWeeney PhD, & Sorena Nadaf-Rahrov MS, MMI

NCI-designated cancer centers and academic medical institutions (AMCs) are facing unprecedented threats that jeopardize their ability to conduct groundbreaking research, deliver cutting-edge care, and sustain clinical trials essential to patient treatment.

Funding instability, including freezes on federal grants and sudden cuts to indirect cost rates, is creating a ripple effect that disrupts the entire cancer research ecosystem. These challenges threaten not only the infrastructure that supports lifesaving discoveries, but also the long-term sustainability of cancer innovation in the United States.

With new research funding announcements stalled and critical programs at risk, cancer centers are being forced to divert focus from their mission to maintain basic operations. This instability undermines the progress made in reducing cancer mortality rates and risks ceding global leadership in biomedical research. Protecting these institutions is vital—not just for scientific advancement but for the millions of patients who rely on them for hope and cutting-edge care.

Link to full article: https://cancerletter.com/guest-editorial/20250321_2/

The Year of Artificial Intelligence

Thu, 13 Feb 2025 19:53:02 GMT

January 2025 Newsletter: The Year of Artificial Intelligence

Sorena Nadaf-Rahrov, MS, MMI, PhDc

Dear Cancer Center Friends & Colleagues,

Happy New Year! It’s hard to believe we’ve already stepped into 2025!

As I look back, I’m struck by the extraordinary journey we experienced in 2024. It was a year defined by remarkable achievements and progress, highlighted by what truly became

The Year of Artificial Intelligence

On behalf of the Cancer Center Informatics Society, I extend my heartfelt thanks for your dedication, expertise, and relentless efforts. Your contributions bring hope to patients, caregivers, and families, paving the way for a brighter and healthier future. As we move into 2025, I hope we all take a moment to express gratitude for our blessings, cherish those who matter most, and look ahead with optimism to a new year filled with happiness and success.

Thanks to your unwavering dedication, steadfast support, and shared vision—along with the commitment of our remarkable volunteer staff and leadership—2024 was a year of significant milestones. It was, without a doubt, our most successful year since the Society’s inception. Notably, we proudly showcased our very first booth at the ASCO 2024 Annual Meeting , marking an exciting new chapter for us.

Here are highlights that underscore the remarkable progress we achieved together in 2024:

1] Spring 2024 Cancer Center Summit: The 21st Ci4CC event was chaired by Dr. Karyn Goodman of the Tisch Cancer Institute and Mrs. Lauren Hacket of the Montefiore Einstein Cancer Center. Dr. Cornelia Ulrich from the Huntsman Cancer Institute delivered the NCI-Designated Cancer Center Director Keynote. In addition to representatives from designated cancer centers, the event also featured participation from the National Cancer Institute, continuing our tradition of robust collaboration.

The Summit focused on “Cultivating the Application of Informatics, Digital Health, and Artificial Intelligence to Enhance Cancer Clinical Trials.” and featured key sessions on:

· Clinical trial matching and recruitment

· Data science and analytics

· Leveraging real-world data to advance cancer research

· The impact of generative AI and machine learning on clinical trials

· Bridging the gap between operational needs and informatics solutions

· Decentralized and hybrid clinical trials

· Catchment area informatics and data science

· Advancing diversity, equity, and inclusion in cancer research and clinical trials

For the full agenda, session overviews, presentations, and videos, visit: Spring 2024 Society Conference

2] Fall 2024 Cancer Center Symposium: The 22nd Ci4CC event was chaired by Drs. David Jaffray and Kristy Brock of MD Anderson Cancer Center, with the Cancer Center Directors Keynote delivered by Dr. Peter Pisters, President of MD Anderson. As usual, in addition to representatives from many NCI-Designated cancer centers, the event also featured a keynote from the National Cancer Institute, continuing its tradition of robust collaboration and impactful discussions.

The Symposium centered on “Digitally Enabled Cancer Interventions” and featured key sessions on:

· Advanced Interventional Technologies

· Surgical Data Science

· The Cancer History Project

· Cancer Technology Innovations and ARPA-H

· Advances Through Open-Source Software

· Adaptive Radiation Therapy

· Data Science in Oncology

· Cybersecurity & Business Continuity in Oncology

· Evolution of Next-Generation CCSG Cores

· Tech Download Showcase: Highlights across Oncology

· Post-Symposium Cancer Center Alliance Initiatives Workshop

For the full agenda, session overviews, presentations, and videos, visit

3] 2024 marked the launch of two additional webinar series: Clinical Informatics Grand Rounds (CiGR) and Technology Download (TechDownload), chaired by Dr. Doug Fridsma and Laura Hilty, respectively.

a- Clinical Informatics Grand Rounds: CiGR serves as a dynamic forum to foster knowledge exchange, promote collaborations, and drive advancements in clinical informatics to enhance cancer care, improve patient outcomes, and support translational research and biomarker discovery. Key focus areas include the intersection of clinical informatics and cancer care; Data science, technology, and clinical applications tailored to oncology; Addressing challenges and proposing innovative strategies to advance cancer research, diagnosis, and treatment. This series is designed to discuss progress, overcome barriers, and explore informatics and data science solutions, ultimately improving outcomes for patients.

CiGR 2024 Highlights:

· Health Universe: AI History in three steps? What is next? - Doug Fridsma, MD, PhD

· Duke: Accelerating Adoption of Precision Cancer Medicine Therapies - Ryne Ramaker, MD, PhD

· OHSU: The Last Mile of Precision Oncology - Shannon McWeeney, PhD

· UCSD: Knowing which patients are Diseases - Mike Hogarth, MD

· Texas Oncology: Informatics Tools to Improve Care Delivery in Practice - Debra Patt, MD, PhD

· MD Anderson: Rethinking the Data Supply Chain Informing Cancer Control – David Jaffray, PhD

· MSKCC: AI Governance in Oncology and AI Enabled Curation – Peter Stetson, MD, MA

· Mount Sinai: AI Governance at Mount Sinai – Bruce Darrow, MD, PhD

· Ci4CC: Clinical Informatics Grand Rounds Year in Review – Sorena Nadaf-Rahrov MS, MMI

b- Technology Download: TechDownload explores the application of emerging technologies in cancer research, diagnosis, treatment, and overall patient care. This forum facilitates the exchange of ideas, fosters collaboration, and accelerates innovation to elevate patient outcomes and further translational research. Designed to supplement the monthly Ci4CC community calls, TechDownload connects a diverse group of stakeholders, including start-ups, investors, cancer centers, and advisors. Together, this ecosystem aims to drive transformative innovations and bring treatments to patients faster by combining creativity, expertise, and execution. Monthly Agenda Highlights include Cancer Center and Industry Perspectives on Innovation; Tech Spotlight: 1-2 companies showcase their innovations; Audience Feedback: Interactive app-based voting for real-time engagement.

Tech Download 2024 Highlights:

· Tech Download Innovation Workshop Panel: Buy vs Build Trends with Hartford Healthcare Cancer Institute, Florida Cancer Specialists, UCSD Health

· Tech Spotlights: Featured companies included Epic, Tempus, Varian-Siemens, Guardant Health, Triomics, IntegraConnect, Manifold AI, Mendel AI, Medeloop, Vibrent Health, Dyania Health, Health Universe, Geno.me, CancerIQ, GenomOncology, nCartes, Omega Healthcare, Beekeeper AI, and more

· Buy vs Build vs Partner: A two-part series with NCI-Designated Cancer Centers including Emory University Winship Comprehensive Cancer Center and the City of Hope Comprehensive Cancer Center

4] Cancer Center Community Calls: The CiGR and TechDownload series, launched in 2024, were created to enhance collaboration within the cancer center research community, driving impactful advancements in oncology. These series build on the foundation of our original monthly webinar series, the Cancer Center Informatics Community Call—a dynamic open forum that has thrived for nearly a decade.

This forum has brought together a diverse group of professionals, including Cancer Center Shared Resources teams, Clinical Informatics faculty and staff, Basic Scientists, Chief Data and Informatics Officers, Biostatisticians, Chief Research Information Officers, Chief Medical Officers, CCSG Technology Directors, and many others involved in applied informatics and innovation. Together, they have fostered collaboration in the rapidly evolving fields of Precision Medicine, AI, and Data Science.

With the successful launch of CiGR and TechDownload, 2025 marks the re-launch of the Community Call format, now with a fresh approach that takes us back to our roots. This new iteration will center around open mic discussions and special fireside chat interviews, creating a more engaging and interactive experience. We are thrilled to announce that Dr. Fred Lee will chair the monthly Community Calls, continuing the tradition on the third Thursday of each month. We look forward to the exciting discussions and insights that lie ahead!

5] Golf for Cancer Informatics: Speaking of Dr. Fred Lee, I am thrilled to announce that the Cancer Center Informatics Society now has its first Chief Golf Officer! Many of you are already familiar with Golf for Cancer Informatics (Golf4Ci). Launched during the Society’s five-year anniversary symposium in Maui, Hawaii, Golf4Ci is an initiative dedicated to bringing together golf enthusiasts, researchers, clinicians, and community supporters in the fight against cancer.

Golf4Ci is more than just a day of golf and camaraderie—it’s a dynamic platform for raising critical funds to advance cancer research. Thanks to the generosity of our participants and sponsors, Golf4Ci supports groundbreaking efforts in cancer research and precision oncology, leveraging Artificial Intelligence, Data Science, and Translational Bioinformatics to uncover innovative strategies for cancer treatment and prevention. Every swing, putt, and cheer contributes to transformative discoveries, ultimately improving outcomes for patients worldwide.

We’re excited to have Dr. Fred Lee lead this initiative as our Chief Golf Officer, championing our mission both on and off the course. As part of his leadership, Fred has quietly unveiled his first blog, “Now on the Tee” – A Blog by the Chief Golf Officer, offering insights and updates on this exciting initiative.

6] The Initiatives Program: A Home for Breakthrough Collaborative Advancements in Cancer Research. The Initiatives Program holds a special place in my heart as a cornerstone of the Cancer Center Informatics Society, serving as a hub for transformative collaborations in cancer research.

As stated in my 2024 mission statement: “Advancements in cancer research and clinical care are not achieved in isolation. Fostering collaborative networks is fundamental to our initiatives and central to the Society’s mission.”

Reflecting the collaborative spirit that many of us have experienced throughout our careers in Cancer Centers, the Initiatives Program embarks on a visionary journey to unite the diverse fields of Oncology Informatics and Data Science. Strategically, it extends its reach to include partnerships with Biotech and Pharma, positioning itself as a critical bridge between these sectors.

In addressing the rapidly evolving landscape of Cancer Center digital infrastructure, the program takes a comprehensive approach, focusing on:

• Translational Research and Bioinformatics

• Precision Oncology

• Artificial Intelligence

• Cancer Registries

• Data Science

• Genomics

• Digital Platforms

• Clinical Trials

• Real-World Evidence

At the same time, the program fosters dynamic collaborations with Biotech and Pharma to drive innovation in:

• Technology integration

• Data sharing

• Joint research initiatives

• Advancements in drug development

This dual-pronged strategy positions the Cancer Center Informatics Society at the forefront of transformative, oncology-focused initiatives. By seamlessly integrating Cancer Center programs with industry collaboration, the Initiatives Program aims to redefine the boundaries of cancer research, enhance patient outcomes, and accelerate innovation across the spectrum of cancer care.

Looking ahead to 2025, the Initiatives Program is expanding toward a future where technological advancements and collaborative partnerships converge to elevate cancer research and care to unprecedented heights. Together, we are crafting a roadmap that prioritizes innovation, integration, and meaningful impact, paving the way for lasting progress.

7] 2024 Highlights from the Initiatives Program:

• The Digital Patient

• OncoLLM

• Autonomous AI Research Agents

• Cancer Center AI Alliance & Next Gen Cores

• The Cancer Passport

• Equity, Diversity, and Inclusion across Cancer Informatics

Now onto 2025!

I am thrilled to announce that the 23rd Cancer Center Informatics Society Summit will be chaired by Dr. Suresh Ramalingam, Executive Director, and Dr. Adam Marcus, Deputy Director of the Winship Comprehensive Cancer Center at Emory University.

Save the Dates: Spring 2025 Cancer Center Summit

May 16–18, 2025

Location: Beautiful San Diego, CA

The summit will focus on “Accelerating Innovation in Oncology through AI” and will feature an exciting lineup of sessions, including:

· Prevention & Early Detection

· Translational Research

· Diagnosis & Risk Prediction

· Clinical Decision Making

· Clinical Trials

· Data Science: Perspectives from Cancer Center Chief AI Officers

· Clinical Informatics Grand Rounds & Technology Download

· Cancer Center Collaborative Initiatives

· CCSG Data & AI focused Shared Resources Poster Session Part II

· Golf for Cancer Informatics Event

Stay tuned for the upcoming call for abstracts and participation—additional details will be announced soon. For registration and the latest agenda updates, please visit: Cancer Center Informatics Society

Sorena

The Expanding Role of AI in Oncology: Insights from the Largest Study to Date

sorena@ci4cc.org (Sorena Nadaf) — Wed, 05 Feb 2025 10:04:20 GMT

A recent Lancet study demonstrated that AI implementation led to a 29% increase in cancer detection, with no increase in false positives and a reduced workload compared to radiologists without AI assistance. While emerging evidence supports AI’s potential to enhance cancer detection in mammography screening and reduce screen-reading workload, further research is needed to fully understand its clinical impact.

Link to full article :

https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00267-X/fulltext

AI-Powered Prediction Tool Outperforms Standard Scoring System

sorena@ci4cc.org (Sorena Nadaf) — Tue, 10 Dec 2024 22:44:46 GMT

Artificial intelligence to empower diagnosis of myelodysplastic syndromes by multiparametric flow cytometry

Abstract: The diagnosis of myelodysplastic syndromes (MDS) might be challenging and relies on the convergence of cytological, cytogenetic, and molecular factors. Multiparametric flow cytometry (MFC) helps diagnose MDS, especially when other features do not contribute to the decision-making process, but its usefulness remains underestimated, mostly due to a lack of standardization of cytometers. We present here an innovative model integrating artificial intelligence (AI) with MFC to improve the diagnosis and the classification of MDS. We develop a machine learning model through an elasticnet algorithm directed on a cohort of 191 patients, only based on flow cytometry parameters selected by the Boruta algorithm, to build a simple but reliable prediction score with five parameters. Our AI-assisted MDS prediction score greatly improves the sensitivity of the Ogata score while keeping an excellent specificity validated on an external cohort of 89 patients with an Area Under the Curve of 0.935. This model allows the diagnosis of both high- and low-risk MDS with 91.8% sensitivity and 92.5% specificity. Interestingly, it highlights a progressive evolution of the score from clonal hematopoiesis of indeterminate potential (CHIP) to high-risk MDS, suggesting a linear evolution between these different stages. By significantly decreasing the overall misclassification of 52% for patients with MDS and of 31.3% for those without MDS (P=0.02), our AI-assisted prediction score outperforms the Ogata score and positions itself as a reliable tool to help diagnose MDS.

Link to full article: https://pmc.ncbi.nlm.nih.gov/articles/PMC10483367/

Remembering Brady Davis

Mon, 11 Nov 2024 17:09:06 GMT

With heavy hearts, we remember and honor Brady Davis, whose sudden passing leaves an immense void. Brady was a devoted supporter and invaluable contributor to the Cancer Center Informatics Society, dedicating countless hours to advancing our mission and strengthening our community. His expertise, enthusiasm, and unwavering commitment shaped our initiatives and inspired everyone fortunate enough to work alongside him. Brady’s legacy will live on through the progress he championed and the connections he fostered. We extend our deepest sympathies to his family, friends, and all who knew him. He will be greatly missed.

In honor of Brady’s legacy, Ci4CC will be forming a committee to explore meaningful ways to memorialize him within our society for years to come. We plan to announce the committee’s recommendations at our Spring Summit in San Diego, CA, on March 31, 2025.

Please find his obituary here , and visit his memorial page on MyKeeper to leave a tribute.

Support the Davis family in Brady’s memory via GoFundMe

----------

Cancer Center Informatics Society (Ci4CC)

Sorena Nadaf-Rahrov & Warren Kibbe

Co-Founders, Ci4CC

Nature Digital Medicine: PRISM: Patient Records Interpretation for Semantic clinical trial Matching system using large language models

sorena@ci4cc.org (Sorena Nadaf) — Tue, 29 Oct 2024 12:56:54 GMT

Nature Digital Medicine

PRISM: Patient Records Interpretation for Semantic clinical trial Matching system using large language models

Shashi Gupta , Aditya Basu , Mauro Nievas , Jerrin Thomas , Nathan Wolfrath , Adhitya Ramamurthi , Bradley Taylor , Anai N. Kothari , Regina Schwind ,

Therica M. Miller , Sorena Nadaf-Rahrov , Yanshan Wang & Hrituraj Singh

Clinical trial matching is the task of identifying trials for which patients may be eligible. Typically, this task is labor-intensive and requires detailed verification of patient electronic health records (EHRs) against the stringent inclusion and exclusion criteria of clinical trials. This process also results in many patients missing out on potential therapeutic options. Recent advancements in Large Language Models (LLMs) have made automating patient-trial matching possible, as shown in multiple concurrent research studies. However, the current approaches are confined to constrained, often synthetic, datasets that do not adequately mirror the complexities encountered in real-world medical data. In this study, we present an end-to-end large-scale empirical evaluation of a clinical trial matching system and validate it using real-world EHRs. We perform comprehensive experiments with proprietary LLMs and our custom fine-tuned model called OncoLLM and show that OncoLLM outperforms GPT-3.5 and matches the performance of qualified medical doctors for clinical trial matching.

Link to full Nature Publication

Expanding Access in Cancer Care

Wed, 04 Sep 2024 18:48:50 GMT

American Cancer Society and Color Health to Provide Free At-Home Colorectal Cancer Screening in Underserved Rural Communities

The American Cancer Society (ACS) and Color Health today announced the launch of their new pilot program to provide free at-home colorectal cancer screening kits to individuals in rural areas and other underserved communities, where barriers to healthcare access often hinder timely screening and early detection. The program, which was recognized by the White House Office of Science and Technology Policy , builds on the ACS and Color broader collaboration to change cancer outcomes through better access to cancer screening, education, and clinical and non-clinical management from diagnosis through treatment and survivorship.

Link to full Article: https://pressroom.cancer.org/RuralHealth

NCI has announced its Fiscal Year 2026 Annual Plan

Wed, 04 Sep 2024 17:12:52 GMT

Leading Progress Against Cancer

The NCI has announced the release of the Fiscal Year 2026 Annual Plan and Professional Judgement Budget Proposal: Leading Progress Against Cancer. This plan presents "NCI's assessment of the optimal funding needed to fully capitalize on the extraordinary scientific opportunities"

At A Glance: https://www.cancer.gov/research/leading-progress/2026-professional-judgment-budget-proposal-aag.pdf

Cancer Rates Are Rising in Young People. Here’s What You Need to Know

Sat, 24 Aug 2024 21:02:38 GMT

By Drs Karen Knudsen & Othman Laraki.

Do you think you are too young to get screened for cancer? Think again.

It might save your lifeToday a woman in her 30s faces higher odds of a cancer diagnosis than her grandmother did at her age two generations ago. Cancer incidence and mortality are rising in millennials and even younger populations, according to American Cancer Society (ACS) data, while rates among older Americans are declining. In July, a study found that both members of Generation X and Millennials face a higher risk than older generations of 17 types of cancer . Cancer spares no one. Not a month goes by without the news of a celebrity, an acquaintance, a friend or a family member learning what each of us dreads to hear from a doctor: “You have cancer.” Just this March global attention was captured by the news of 42-year-old Catherine, Princess of Wales, who is married to the heir to the British throne, sharing the news of her cancer diagnosis .

Link to full article in Scientific American

NCI's new Deputy Director for Data Science

Thu, 18 Jul 2024 15:56:02 GMT

Conversation with The Cancer Letter: NCI’s new chief data scientist Warren Kibbe tells us about efforts to get “AI-ready” - July 12, 2024

“All research now involves data science at some level. And I can’t think of some aspect of science where we don’t want to analyze the results.”

What can NCI accomplish in data science?

In the informatics world, the institute’s resources would be considered paltry by comparison with private companies.

The institute’s total fiscal year 2024 budget of $7.2 billion would constitute less than 25% of the R&D budget of Microsoft Corp. and would be $2.3 billion lower than this year’s research and development spend of the artificial intelligence giant NVIDIA Corp.

“I think maybe the better question is how can we take what is being developed by those companies for really broad use, and how do we bring that into cancer research and make cancer research happen faster, better?” said Warren A. Kibbe, NCI’s inaugural deputy director for data science and strategy.

Kibbe said his primary goals as NCI’s top data scientist include improving access to data, enhancing the scientists’ abilities to apply visualization techniques for cancer research, and using technologies, such as artificial intelligence and machine learning, to advance our understanding of the basic mechanisms and etiology of cancer.

Over the past seven years, Kibbe served as chief for translational biomedical informatics and professor and vice chair in the Department of Biostatistics and Bioinformatics at Duke University and as the chief data officer for the Duke Cancer Institute.

Prior to that, Kibbe headed the NCI Center for Biomedical Informatics and Information Technology, known as CBIIT, for four years ( The Cancer Letter , June 16 , 2017; June 26 , 2013). At NCI, Kibbe spearheaded development of the Genomic Data Commons, a big data project for comprehensive, raw genomics information ( The Cancer Letter , April 29 , 2016).

In addition to consulting across NIH on data science issues during his first stint at NCI, Kibbe played a role in establishing partnerships with the U.S. Department of Energy and was involved in putting together collaborations in precision medicine and the Cancer Moonshot.

Kibbe is also a co-founder, with Sorena Nadaf-Rahrov, of the Cancer Center Informatics Society ( CI4CC ).

“No one is better suited to help the NCI innovate in cancer data science and biomedical informatics than Warren,” Nadaf-Rahrov said to The Cancer Letter . “His background and temperament provide an excellent balance to the needs and vision of the NCI. I look forward to continuing to collaborate with Warren on various projects, both as a long-standing colleague and as the president & CEO of the Cancer Center Informatics Society.”

Link to full article: https://cancerletter.com/conversation-with-the-cancer-letter/20240712_1/

Study Finds Nearly Half of Adult Cancer Deaths in the US Could Be Prevented Through Lifestyle Changes

sorena@ci4cc.org (Sorena Nadaf) — Thu, 11 Jul 2024 18:27:31 GMT

"Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States, 2019"

American Cancer Society

In 2018, the authors reported estimates of the number and proportion of cancers attributable to potentially modifiable risk factors in 2014 in the United States. These data are useful for advocating for and informing cancer prevention and control. Herein, based on up-to-date relative risk and cancer occurrence data, the authors estimated the proportion and number of invasive cancer cases (excluding nonmelanoma skin cancers) and deaths, overall and for 30 cancer types among adults who were aged 30 years and older in 2019 in the United States, that were attributable to potentially modifiable risk factors. These included cigarette smoking; second-hand smoke; excess body weight; alcohol consumption; consumption of red and processed meat; low consumption of fruits and vegetables, dietary fiber, and dietary calcium; physical inactivity; ultraviolet radiation; and seven carcinogenic infections. Numbers of cancer cases and deaths were obtained from data sources with complete national coverage, risk factor prevalence estimates from nationally representative surveys, and associated relative risks of cancer from published large-scale pooled or meta-analyses. In 2019, an estimated 40.0% (713,340 of 1,781,649) of all incident cancers (excluding nonmelanoma skin cancers) and 44.0% (262,120 of 595,737) of all cancer deaths in adults aged 30 years and older in the United States were attributable to the evaluated risk factors. Cigarette smoking was the leading risk factor contributing to cancer cases and deaths overall (19.3% and 28.5%, respectively), followed by excess body weight (7.6% and 7.3%, respectively), and alcohol consumption (5.4% and 4.1%, respectively). For 19 of 30 evaluated cancer types, more than one half of the cancer cases and deaths were attributable to the potentially modifiable risk factors considered in this study. Lung cancer had the highest number of cancer cases (201,660) and deaths (122,740) attributable to evaluated risk factors, followed by female breast cancer (83,840 cases), skin melanoma (82,710), and colorectal cancer (78,440) for attributable cases and by colorectal (25,800 deaths), liver (14,720), and esophageal (13,600) cancer for attributable deaths. Large numbers of cancer cases and deaths in the United States are attributable to potentially modifiable risk factors, underscoring the potential to substantially reduce the cancer burden through broad and equitable implementation of preventive initiatives.

Link to Full Article: https://acsjournals.onlinelibrary.wiley.com/doi/full/10.3322/caac.21858

UCSF and UCSF Health Receive Pivotal Donation to Support the First Continuous AI-monitoring Platform in Clinical Care

Wed, 29 May 2024 19:07:26 GMT

The UCSF Division of Clinical Informatics and Digital Transformation (DoC-IT) and UCSF Health have received a $5 million gift from Ken and Kathy Hao to develop a cutting-edge, real-time, continuous, and automated artificial intelligence (AI) monitoring platform for clinical care.

The Impact Monitoring Platform for AI in Clinical Care (IMPACC) aims to bridge the gap between the rapid evolution of AI technologies used by clinicians and the essential need for robust, ongoing assessment of their efficacy, safety, and equity.

Julia Adler-Milstein, PhD , chief of the UCSF Division of Clinical Informatics and Digital Transformation (DoC-IT), and Sara Murray, MD, MAS , chief health AI officer at UCSF Health, will lead the pioneering collaboration.

“This philanthropic gift is transformative in many ways,” said Adler-Milstein. “It comes at a critical juncture as the healthcare industry more broadly integrates AI into clinical practice. Through IMPACC and this collaborative effort, we are poised to improve patient care at UCSF while advancing the science of how to assess AI tools in real-world use.”

Currently, the healthcare field lacks established protocols for ongoing AI monitoring, leading to risks of adverse outcomes for patients and healthcare providers that go undetected. While assessments are conducted to determine the suitability of new AI technologies for safe integration into clinical environments before deployment, once they are deployed, health systems need a way to promptly identify any issues in their real-world performance.

IMPACC will fill this urgent need by shifting from planned, periodic, manual monitoring of a focused set of measures to real-time, continuous, automated, and longitudinal monitoring across a broad measure set with specified criteria for escalation to human review and intervention.

Full Article: https://docit.ucsf.edu/news/ucsf-and-ucsf-health-receive-pivotal-donation-support-first-continuous-ai-monitoring-platform

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Dr. Tony Kerlavage Reflects on His Time at NCI

Fri, 24 May 2024 16:53:10 GMT

I’m most proud of how my colleagues and I evolved CBIIT’s focus and activities to enable NCI’s scientific mission. My approach had always been to collaborate with colleagues from various NCI divisions, offices, and centers (DOCs). In doing this, we understood each other’s specific needs and ensured that our work together aligned with those needs. This wasn’t always CBIIT’s approach though, and while the change started before I became director, creating the necessary collaborations across the institute and developing the projects to support our scientific partners took consistent and intentional focus.

CBIIT has been working closely with the other NCI DOCs for years, supporting individual projects and concerted efforts in major programs (such as the NCI Cancer Research Data Commons [CRDC]), data sharing, and NCI Intramural Research Program data management. In hindsight, I might have tried to initiate this outreach and move these relationships forward more broadly and even more quickly. I also would have ensured that we invested more time and some dedicated funding in innovation.

To Article: https://datascience.cancer.gov/news-events/blog/dr-tony-kerlavage-reflects-his-time-nci-cbiit

Automated Artificial Intelligence Model Trained on a Large Data Sets

Fri, 08 Mar 2024 09:10:25 GMT

The aims of our case-control study were (1) to develop an automated 3-dimensional (3D) Convolutional Neural Network (CNN) for detection of pancreatic ductal adenocarcinoma (PDA) on diagnostic computed tomography scans (CTs), (2) evaluate its generalizability on multi-institutional public data sets, (3) its utility as a potential screening tool using a simulated cohort with high pretest probability, and (4) its ability to detect visually occult preinvasive cancer on prediagnostic CTs.

Link to full article

Predicting Medication Responses using Machine Learning Models

Fri, 08 Mar 2024 09:06:30 GMT

Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress

Rapid proliferation is a hallmark of cancer associated with sensitivity to therapeutics that cause DNA replication stress (RS). Many tumors exhibit drug resistance, however, via molecular pathways that are incompletely understood. Here, we develop an ensemble of predictive models that elucidate how cancer mutations impact the response to common RS-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multidrug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK–JAK–STAT assembly governing resistance. This study defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine.

Link to full article

Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal

Fri, 08 Mar 2024 09:00:44 GMT

International cancer registries make real-world genomic and clinical data available, but their joint analysis remains a challenge. AACR Project GENIE, an international cancer registry collecting data from 19 cancer centers, makes data from >130,000 patients publicly available through the cBioPortal for Cancer Genomics (https://genie.cbioportal.org). For 25,000 patients, additional real-world longitudinal clinical data, including treatment and outcome data, are being collected by the AACR Project GENIE Biopharma Collaborative using the PRISSMM data curation model. Several thousand of these cases are now also available in cBioPortal. We have significantly enhanced the functionalities of cBioPortal to support the visualization and analysis of this rich clinico-genomic linked dataset, as well as datasets generated by other centers and consortia. Examples of these enhancements include (i) visualization of the longitudinal clinical and genomic data at the patient level, including timelines for diagnoses, treatments, and outcomes; (ii) the ability to select samples based on treatment status, facilitating a comparison of molecular and clinical attributes between samples before and after a specific treatment; and (iii) survival analysis estimates based on individual treatment regimens received. Together, these features provide cBioPortal users with a toolkit to interactively investigate complex clinico-genomic data to generate hypotheses and make discoveries about the impact of specific genomic variants on prognosis and therapeutic sensitivities in cancer.

Link to full article

FORGEdb: a tool for identifying candidate functional variants and uncovering target genes and mechanisms for complex diseases

Fri, 08 Mar 2024 08:56:30 GMT

The majority of disease-associated variants identified through genome-wide association studies are located outside of protein-coding regions. Prioritizing candidate regulatory variants and gene targets to identify potential biological mechanisms for further functional experiments can be challenging. To address this challenge, we developed FORGEdb, a standalone and web-based tool that integrates multiple datasets, delivering information on associated regulatory elements, transcription factor binding sites, and target genes for over 37 million variants. FORGEdb scores provide researchers with a quantitative assessment of the relative importance of each variant for targeted functional experiments.

Link to full Article

Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach

Fri, 08 Mar 2024 08:47:34 GMT

Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data-driven care remains a challenge. Artificial intelligence (AI), which is used to predict and automate many cancers, has emerged as a promising option for improving healthcare accuracy and patient outcomes. AI applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI and ML have demonstrated greater accuracy in predicting cancer than clinicians. These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer. Therefore, it is important to improve current AI and ML technologies and to develop new programs to benefit patients. This article examines the use of AI and ML algorithms in cancer prediction, including their current applications, limitations, and future prospects.

Lead Author: Bo Zhang

Link to Full Article

Artificial intelligence in oncology: current applications and future perspectives

Fri, 08 Mar 2024 08:42:44 GMT

Artificial intelligence (AI) is concretely reshaping the landscape and horizons of oncology, opening new important opportunities for improving the management of cancer patients. Analysing the AI-based devices that have already obtained the official approval by the Federal Drug Administration (FDA), here we show that cancer diagnostics is the oncology-related area in which AI is already entered with the largest impact into clinical practice. Furthermore, breast, lung and prostate cancers represent the specific cancer types that now are experiencing more advantages from AI-based devices. The future perspectives of AI in oncology are discussed: the creation of multidisciplinary platforms, the comprehension of the importance of all neoplasms, including rare tumours and the continuous support for guaranteeing its growth represent in this time the most important challenges for finalising the ‘AI-revolution’ in oncology.

First Author: Claudio Luchini,

Artificial intelligence (AI) is concretely reshaping our lives and it is time to understand its evolution and achievements to model future development strategies. This is true also for oncology and related fields, where AI is now opening new important opportunities for improving the management of cancer patients, as will be highlighted in this perspective paper.

Link to Full Article

Using electronic health record data: a machine learning approach with a 2-year horizon

Fri, 08 Mar 2024 08:34:27 GMT

In the United States, end-stage kidney disease (ESKD) is responsible for high mortality and significant healthcare costs, with the number of cases sharply increasing in the past 2 decades. In this study, we aimed to reduce these impacts by developing an ESKD model for predicting its occurrence in a 2-year period.

Lead Author: Panayiotis Petousis, PhD

End-stage kidney disease (ESKD) poses a substantial burden for mortality rate and healthcare costs in the United States. We developed and

evaluated a machine learning (ML) model for predicting ESKD in 2 years using electronic health record (EHR) data. Various models were tested

by leveraging EHR data and employing an ML pipeline. The developed model outperforms existing kidney failure models. Through a chart

review, expert nephrologists affirmed the clinical utility of the model in predicting the outcome of complex cases. This model has been successfully

integrated into our academic institution as part of a dashboard with visualizations and explainability for the model’s predictions. In conclusion,

the developed ESKD prediction model demonstrates the ability to identify individuals at risk for ESKD. Any future reduction in mortality

and healthcare costs would showcase the effectiveness of our model.

Key words: machine learning deployment; early prediction ESKD model; electronic health record; end-stage kidney disease (ESKD).

Link to Full Article

Multimodal AI-powered approaches in prevention and management

Fri, 08 Mar 2024 08:19:38 GMT

Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management

Lead Author: Evan D. Muse

The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.

Link to Full Article

Promise and Perils of Large Language Models for Cancer Survivorship and Supportive Care

Fri, 08 Mar 2024 07:43:22 GMT

Current status and future applications of LLMs for cancer symptom management and call for cross-disciplinary collaboration that centers the needs of patients and caregivers

L ead Author: Danielle S. Bitterman, MD

Cancer symptom toxicity affects patients and caregivers of all cancer types and stages, with effects on physiologic, psychologic, financial, and social well-being.1 Barriers to symptom assessment and management of patients with cancer include lack of survivorship care access and resources, incomplete data for risk-stratification, inadequate means for timely communication about symptoms, fragmented health care across multiple specialists, misconceptions and insufficient patient and clinician education, and changing needs over the cancer trajectory.1-7 Large language models (LLMs) are uniquely poised to address these barriers because of their ability to process, transfer, and transform high-dimensional knowledge, providing more individualized management and enabling multilevel communication that can adapt to users' needs.

Full Article

Can A.I. Treat Mental Illness?

Wed, 01 Mar 2023 07:39:58 GMT

New computer systems aim to peer inside our heads—and to help us fix what they find there

In the nineteen-sixties, Joseph Weizenbaum, a computer scientist at M.I.T., created a computer program called Eliza. It was designed to simulate Rogerian therapy, in which the patient directs the conversation and the therapist often repeats her language back to her:

U ser : My boyfriend made me come here.

E liza : Your boyfriend made you come here.

U ser : He says I’m depressed much of the time.

E liza : I am sorry to hear you are depressed.

U ser : It’s true. I am unhappy.

E liza : Do you think coming here will help you not to be unhappy?

Weizenbaum made Eliza as satire. He doubted that computers could simulate meaningful human interaction. He was alarmed, therefore, when many people who tried the program found it both useful and captivating. His own secretary asked him to leave the room so that she could spend time alone with Eliza. Worse, doctors saw it as a potentially transformative tool. “Several hundred patients an hour could be handled by a computer system designed for this purpose,” three psychiatrists wrote in The Journal of Nervous and Mental Disease, in 1966. “The human therapist, involved in the design and operation of this system, would not be replaced, but would become a much more efficient man since his efforts would no longer be limited to the one-to-one patient-therapist ratio as now exists.”

Weizenbaum became an outspoken critic of artificial intelligence. “But the genie was out of the bottle,” Brian Christian, who chronicled the episode in his book “The Most Human Human,” told me. A few years later, a Stanford psychiatrist named Kenneth Colby created Parry, a program that attempted to simulate the language of a person with paranoid schizophrenia, to train students before they cared for real patients. Psychiatrists given transcripts of therapy sessions often couldn’t tell the difference between Parry and humans; in this narrow sense, the chatbot passed the Turing test. In 1972, Parry and Eliza met up for a therapy session:

P arry : Do you know anything about bookies?

E liza : What makes you think I know anything about bookies?

P arry : A bookie didn’t pay me off once.

E liza : What does that suggest to you?

P arry : I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me.

Over time, programmers developed Jabberwacky, Dr. Sbaitso, and alice (the Artificial Linguistic Internet Computer Entity). Exchanges with these chatbots were often engaging, sometimes comical, and occasionally nonsensical. But the idea that computers could serve as human confidants, expanding therapy’s reach beyond the limits of its overworked practitioners, persisted through the decades.

In 2017, Alison Darcy, a clinical research psychologist at Stanford, founded Woebot, a company that provides automated mental-health support through a smartphone app. Its approach is based on cognitive behavioral therapy, or C.B.T.—a treatment that aims to change patterns in people’s thinking. The app uses a form of artificial intelligence called natural language processing to interpret what users say, guiding them through sequences of pre-written responses that spur them to consider how their minds could work differently. When Darcy was in graduate school, she treated dozens of hospitalized patients using C.B.T.; many experienced striking improvements but relapsed after they left the hospital. C.B.T. is “best done in small quantities over and over and over again,” she told me. In the analog world, that sort of consistent, ongoing care is hard to find: more than half of U.S. counties don’t have a single psychiatrist, and, last year, a survey conducted by the American Psychological Association found that sixty per cent of mental-health practitioners don’t have openings for new patients. “No therapist can be there with you all day, every day,” Darcy said. Although the company employs only about a hundred people, it has counseled nearly a million and a half, the majority of whom live in areas with a shortage of mental-health providers.

Link to original article on The New Yorker

Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography

sorena@ci4cc.org (Sorena Nadaf) — Tue, 28 Feb 2023 22:51:54 GMT

PURPOSE

Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data.

METHODS

We developed a model called Sybil using LDCTs from the National Lung Screening Trial (NLST). Sybil requires only one LDCT and does not require clinical data or radiologist annotations; it can run in real time in the background on a radiology reading station. Sybil was validated on three independent data sets: a heldout set of 6,282 LDCTs from NLST participants, 8,821 LDCTs from Massachusetts General Hospital (MGH), and 12,280 LDCTs from Chang Gung Memorial Hospital (CGMH, which included people with a range of smoking history including nonsmokers).

RESULTS

Sybil achieved area under the receiver-operator curves for lung cancer prediction at 1 year of 0.92 (95% CI, 0.88 to 0.95) on NLST, 0.86 (95% CI, 0.82 to 0.90) on MGH, and 0.94 (95% CI, 0.91 to 1.00) on CGMH external validation sets. Concordance indices over 6 years were 0.75 (95% CI, 0.72 to 0.78), 0.81 (95% CI, 0.77 to 0.85), and 0.80 (95% CI, 0.75 to 0.86) for NLST, MGH, and CGMH, respectively.

CONCLUSION

Sybil can accurately predict an individual's future lung cancer risk from a single LDCT scan to further enable personalized screening. Future study is required to understand Sybil's clinical applications. Our model and annotations are publicly available.

Link to full article

Pfizer Partners With Tempus to Advance Oncology Drug Development

Tue, 28 Feb 2023 21:37:18 GMT

NEW YORK – Tempus said on Tuesday that it is collaborating with Pfizer to use Tempus' artificial intelligence platform to advance cancer drug discovery and development.

Under the multiyear strategic collaboration, Pfizer will have access to Tempus' AI-based platform and multimodal data library, along with its companion diagnostic development capabilities and clinical trial matching program. Financial and other details about the collaboration were not disclosed.

"This is the third strategic collaboration Tempus has established with a global pharmaceutical leader in the last year, as we believe that combining our technological capabilities with pharma's deep R&D expertise will get us much closer in realizing the full potential of precision medicine," Tempus CEO Eric Lefkofsky said in a statement.

The Pfizer agreement comes less than a week after Tempus announced a partnership with Actuate Therapeutics to discover biomarkers of response to a cancer drug.

Last year, Tempus also partnered with GSK to advance oncology drug development, building on a previous collaboration with the pharma company to support clinical trial enrollment for a Phase II study of GSK's PARP inhibitor Zejula (niraparib). The firm also has ongoing partnerships with AstraZeneca focused on biomarker discovery and with Eli Lilly to increase access to genomic testing.

Precision Oncology News

Gifts from Walther, Regenstrief foundations create cancer informatics chair at IU Simon Comprehensive Cancer Center

Thu, 16 Feb 2023 08:03:59 GMT

IU School of Medicine. Jan 11, 2023

INDIANAPOLIS–Gifts totaling $3 million will create an endowed chair in cancer informatics at the Indiana University Melvin and Bren Simon Comprehensive Cancer Center.

The chair was created through gifts from the Walther Cancer Foundation Inc. and the Regenstrief Foundation Inc. The jointly recruited chair holder will be a research scientist at the Regenstrief Institute and cancer center and a faculty member at Indiana University.

Cancer informatics aids researchers in analyzing huge amounts of data—often called “big data”—that can help identify those at risk of developing cancer, optimize prevention and detection, improve outcomes, and identify the most effective treatments.

Data can also be used to help researchers identify health disparities and inequities in cancer care and identify ways to lessen the impact on individuals with historically minoritized racial and ethnic identities. Data on where people live, their educational backgrounds, and their economic stability—conditions that play a role in people’s health and quality of life—provides valuable information to researchers and clinicians that can ultimately be used to benefit people.

“This chair is a wonderful example of how big things can come about through identification of mutual interests among several parties,” said D. Craig Brater, MD, president and CEO of the Regenstrief Foundation and vice president of programs at the Walther Cancer Foundation. “This chair will enable researchers to better understand the biology of cancer and also to address issues of equity in cancer care.”

Rapid developments in technology have given rise to the copious amounts of data that exist. Organizing and storing that data, developing analytics to mine that data, and producing information beneficial to researchers and clinicians is an enormous undertaking. Data ranges from that contained in electronic medical records (EMRs) to data generated from clinical, basic and translational population research. The incoming chair will lead the effort in collecting data accurately, effectively and ethically, while joining an already strong group of researchers at IU and Regenstrief in bioinformatics, data science and statistics.

“Regenstrief Institute research scientists have a long history of leveraging big data to support discovery, leading to better outcomes for patients at the individual and the population levels,” said Susan Hickman, PhD, interim president and CEO of Regenstrief Institute. “We are fortunate to have partners who are both generous and visionary, enabling us to endow a chair focused specifically on cancer informatics that will invigorate collaboration and innovation in this critical area.”

“I am grateful for the extraordinary partnership with the Regenstrief Foundation and the Walther Cancer Foundation that led to the creation of this chair,” said Kelvin Lee, MD , director of the IU Simon Comprehensive Cancer Center. “We routinely generate enormous data sets, but the information needs to be effectively organized and analyzed to make a real difference. With these gifts, we’ll be able to recruit an expert who can lead us through the complexities of informatics and position IU as a leader in the developing field of cancer informatics.”The body content of your post goes here. To edit this text, click on it and delete this default text and start typing your own or paste your own from a different source.

Link to original article

Appointment of Dr. Monica Bertagnolli as Director of the National Cancer Institute

Thu, 16 Feb 2023 07:56:17 GMT

NIH Director

I am pleased with today’s White House announcement that President Biden intends to appoint Monica M. Bertagnolli, M.D., as the 16th Director and first female Director of the National Cancer Institute (NCI). Dr. Bertagnolli’s decades of clinical and leadership experience make her ideally suited to lead NCI going forward, including spearheading President Biden’s Cancer MoonshotSM Initiative.

Dr. Bertagnolli is currently the Richard E. Wilson professor of surgery in the field of surgical oncology at Harvard Medical School, as well as a surgeon at Brigham and Women’s Hospital and a member of the Gastrointestinal Cancer and Sarcoma Disease Center at Dana-Farber Cancer Institute. She received her undergraduate degree from Princeton University, her medical degree from the University of Utah School of Medicine and performed her residency at Brigham and Women’s Hospital. Dr. Bertagnolli served as an associate surgeon at the Strang Cancer Prevention Institute in New York and as an attending surgeon at New York Presbyterian Hospital–Weill Cornell Medical Center before joining the Dana-Farber Cancer Institute.

A surgeon oncologist, Dr. Bertagnolli specializes in treating gastrointestinal cancers and advocates for increasing the diversity of patients enrolled in clinical trials. She currently serves as vice president of Coalition of Cancer Cooperative Groups, chair of Alliance for Clinical Trials in Oncology, president of the Alliance for Clinical Trials in Oncology Foundation, and CEO of Alliance Foundation Trials, LLC. She formerly served as president of the American Society of Clinical Oncology and was recently elected as a National Academy of Medicine Fellow.

I would like to acknowledge the outstanding leadership of Douglas R. Lowy, M.D., who has been Acting NCI Director since Norman E. “Ned” Sharpless, M.D., stepped down in April 2022 . Dr. Lowy’s willingness to serve in this role on three separate occasions is testament to his unwavering commitment to furthering the mission of NCI.

Please join me in congratulating Dr. Bertagnolli on her historic appointment and in thanking Dr. Lowy for his remarkable service as he resumes his position as NCI Deputy Director.

Lawrence A. Tabak, D.D.S., Ph.D.
Performing the Duties of the Director of NIH

An Overview: Genetic Tumor Markers for Early Detection and Current Gene Therapy Strategies

sorena@ci4cc.org (Sorena Nadaf) — Thu, 16 Feb 2023 07:30:53 GMT

First published online February 1, 2023

Reeshan ul Quraish, Tetsuyuki Hirahata, Afraz ul Quraish, and Shahan ul Quraish

Abstract

Genomic instability is considered a fundamental factor involved in any neoplastic disease. Consequently, the genetically unstable cells contribute to intratumoral genetic heterogeneity and phenotypic diversity of cancer. These genetic alterations can be detected by several diagnostic techniques of molecular biology and the detection of alteration in genomic integrity may serve as reliable genetic molecular markers for the early detection of cancer or cancer-related abnormal changes in the body cells. These genetic molecular markers can detect cancer earlier than any other method of cancer diagnosis, once a tumor is diagnosed, then replacement or therapeutic manipulation of these cancer-related abnormal genetic changes can be possible, which leads toward effective and target-specific cancer treatment and in many cases, personalized treatment of cancer could be performed without the adverse effects of chemotherapy and radiotherapy. In this review, we describe how these genetic molecular markers can be detected and the possible ways for the application of this gene diagnosis for gene therapy that can attack cancerous cells, directly or indirectly, which lead to overall improved management and quality of life for a cancer patient.

Introduction

In our previous article, we described how liquid biopsy can be used for the detection of genetic tumor markers for the early diagnosis and prognosis of cancer. 1 In this current article, we will review various nucleic acid based biomarkers for gene diagnosis and clinical methods for gene therapy to cure cancer disease. Cancer is a multi-factorial genetic disorder and a complex group of diseases in which cells grow out of control and do not die after being damaged or useless, and show immortality. 2 There are many factors involved in the process of cancer generation such as; environmental factors, lifestyles, and infections, which can then lead to abnormalities in cancer-related genes at a molecular biology level. Abnormal changes in body cell molecules (genes) and their products can be detected and serve as genetic tumor biomarkers. 3 , 4 These direct tumor biomarkers of genetic origin offer an earlier and more precise diagnosis of cancer than the conventional protein-based tumor markers. 4 Another advantage of these gene-based tumor biomarkers is that they can also be used to see the prognosis of the disease if one or more of these biomarkers are monitored for changes during and after the therapy. 5 In addition, molecular abnormalities in the genes or DNA offer targets (cancerous cells) that can also be utilized to develop a specific gene-drug which recognizes and attack only such abnormal targets (cancerous cells) to cure cancer without producing side effects at all or minimum. 6 , 7 These genetic biomarkers can also be used to enhance the immune cell therapy against cancer, therefore, more effective and specific immune cells can be engineered in the laboratory by the application of gene transfer technology, consequently, these immune cells become effective cancer-attacking cells, then these engineered cells can be re-introduced into the body of a patient to attack and kill the cancer cells. 7 - 9 In this review, we explain briefly about gene tests such as concentration and fragment length of free DNA, DNA mutation detection test, DNA methylation test, and Gene expression test for early detection, diagnosis, and prognosis of cancer, we also describe different types of gene therapies performed to either directly attack cancer cells or to induce the immune system of a cancer patient to fight cancer. In the field of oncology, molecular genetics of cancer is becoming an important tool for screening, surveillance, treatment, and management.

Future research and improvements in cancer genetics will increase the ability to precisely and cost-effectively diagnose and treat cancer patients. The purpose of this report is to present the clinical work performed in our laboratory (HIC Clinic) and to review it with the current research in this area.

Gene Test for the Early Detection of Cancer

As mentioned above, there are cancer-producing alterations in genes and these abnormalities can be detected as genetic tumor markers to diagnose the presence of cancer in the body. 3 , 10 The progression of cancer involves multiple genetic and epigenetic events that disrupt the balance between cell division and apoptosis. Genes that affect cancer progression are known as cancer driver genes, 11 which can be classified as tumor suppressor genes (TSGs) and oncogenes (OGs) based on their roles in cancer progression. 12 Oncogenes are usually activated by gain-of-function mutations that stimulate cell growth and division. Whereas, tumor suppressor genes are recessive, anti-proliferative, and frequently found inactivated or mutated in cancer. 13 These genes are inactivated by loss-of-function (LoF) mutations (insertions/deletions and nonsense mutations) that block tumor suppressor gene functions in inhibiting cell proliferation, promoting DNA repair, and activating cell cycle checkpoints. 14

Depending upon the technology, sensitivity of the method, and analytical techniques used, these genetic cancer markers can be detected in various types of samples from a patient’s body, such as blood, urine, tissue, saliva, or a biopsy sample. Minimally invasive detection of the tumor markers by the body fluids (blood or urine) will also prove a useful method for the follow-up monitoring of the therapeutic effects or prognosis of the disease. 15 - 17 Several gene test methods are used in Hirahata International Cancer Clinic (HIC Clinic) and we find these genetic markers are high in their sensitivity and specificity for cancer detection. Table 1 shows various gene diagnosis strategies in molecular biology to detect the nucleic acid tumor markers in a blood sample.The body content of your post goes here. To edit this text, click on it and delete this default text and start typing your own or paste your own from a different source.

Link to Full Article

Studies Test CAR T-Cell Therapies Designed to Overcome Key Limitations

Wed, 15 Feb 2023 19:23:50 GMT

February 8, 2023, by Sharon Reynolds

Over the last decade, the idea of engineering a personalized immune response to cancer has gone from theory to reality. CAR T-cell therapies, which are made using patients’ own immune cells , have been transformative for some types of aggressive leukemias and other blood cancers. In some cases, they’ve even cured people whose cancer has come back after many other treatments.

But CAR T cells don’t yet lead to long-term survival for most people. And making the leap from treating cancers of the blood to treating solid tumors , like pancreatic, lung, or colorectal cancer, has proven daunting.

Immune cells face a range of challenges when attacking solid tumors. These include an environment full of molecules that can block or disarm immune cells, competition from other cells for scarce nutrients, and over time, a diminished ability to kill other cells, a phenomenon often referred to as exhaustion.

Two research teams have now developed novel ways for overcoming these challenges. One team created CAR T cells that can produce their own fuel upon contact with a tumor. The other engineered CAR T cells in which certain cellular functions can be turned on and off at specific times in response to the administration of certain drugs.

In mice, both types of souped-up CAR T cells shrank solid tumors, including pancreatic cancer and melanoma, much more effectively than standard CAR T cells.

What’s especially exciting, explained Grégoire Altan-Bonnet, Ph.D., of NCI's Laboratory of Integrative Cancer Immunology , who co-wrote an editorial on the studies, is the potential for combining these two techniques into a single therapy, which could be adjusted in individual people over time.

Such an application would require years more work, but “now that we have these tools, it’s going to open up a lot of possibilities,” Dr. Altan-Bonnet said.

Link to full article

Childhood Cancer Data Initiative Updates

sorena@ci4cc.org (Sorena Nadaf) — Wed, 15 Feb 2023 19:15:30 GMT

Today, February 15, is International Childhood Cancer Day (ICCD), a global collaborative campaign to raise awareness about childhood cancer and its unique challenges, while showing support for young people with cancer and their families worldwide. NCI shares this goal today and every day of the year and commits to it through continued research in the causes, drivers, and treatment of childhood cancers and efforts to improve childhood data sharing.

The ICCD campaign quotes a World Health Organization fact on the need for childhood cancer data systems to drive continuous improvements in the quality of care and to inform policy decisions. Through the Childhood Cancer Data Initiative (CCDI), NCI is creating such a system that connects new and existing data kept in various institutions and databases and makes them more easily accessible. Doctors and researchers can then use them in treating and caring for children with cancer and to speed up research progress.

This network is the CCDI Data Ecosystem, and it includes various platforms and tools to maximize the use of the data it connects. It also keeps growing. For example, recently, more data was added from the CCDI Molecular Characterization Initiative, which has returned results to more than 600 participants.

Another way to learn more about CCDI and contribute to its progress is by attending the CCDI Annual Symposium on March 24, 2023. The symposium will gather experts from around the country to discuss CCDI’s progress and opportunities for the future. Learn more and register.

Link to Cancer Data Initiative Data Ecosystem

NCI Cancer Data Atlas System

thos@oneelevendigital.com (Thomas Conner) — Fri, 14 Sep 2018 22:14:55 GMT

The Cancer Data Access System ("CDAS") is a website where you may request data recorded from various research studies. For some studies, you may also request images or biospecimens.

CDAS provides extensive public documentation for each study, including a trial summary, an overview of the data collected, and a searchable database of research projects and publications.

If you are interested in obtaining study data, you may begin a CDAS project for that study. All projects are reviewed by NCI trial leadership. Upon approval, you will be granted access to the requested data and/or materials for a limited period.

The following studies are currently available on CDAS:

NLST: National Lung Screening Trial

This trial compared two ways of detecting lung cancer: low-dose helical computed tomography (CT) and standard chest X-ray. Both chest X-rays and low-dose helical CT scans have been used to find lung cancer early, but the effects of these screening techniques on lung cancer mortality rates had not been determined. NLST enrolled approximately 54,000 current or former heavy smokers from 33 sites and coordinating centers across the United States.

You can submit requests for: Study data, CT scan and digital pathology images. Biospecimens may be requested via an EEMS biospecimens request.

Learn more about NLST

Begin New NLST Project

PLCO: Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial

This is a large-scale, randomized study to determine whether certain screening tests will reduce the number of deaths from these cancers. PLCO is being conducted at ten sites, geographically and demographically disparate, around the U.S. The Trial enrolled approximately 155,000 male and female participants between the ages of 55 and 74 from 1993 to 2001.

You can submit requests for: Study data, chest x-ray and digital pathology images, biospecimens, and preliminary applications for biospecimen project funding.

Learn more about PLCO

Begin New PLCO Project

IDATA: Interactive Diet and Activity Tracking in AARP

This study examines the measurement error properties of instruments used for dietary and physical activity assessment in cohort studies. The investigators administered four dietary assessment instruments.

You can submit requests for: Study data and biospecimens.

Learn more about IDATA
Begin New IDATA Project

The CDAS development team would like to acknowledge the National Heart, Lung, and Blood Institute and its BioLINCC Program for the programming code used in the implementation of this website. The CDAS code was based on the BioLINCC framework, which shares many of the same advanced features and a well-defined yet flexible workflow for request submission and fulfillment.

Stanford Medicine’s 2020 Health Trends Report spotlights the rise of the data-driven physician

Wed, 28 Mar 2018 06:38:00 GMT

The report documents key trends steering the industry’s future, including a maturing digital health market, new health laws opening patient access to data, and artificial intelligence gaining regulatory traction for medical use.

In a health care sector now awash with data and digital technologies, physicians are actively preparing for the transformation of patient care, according to the 2020 Health Trends Report published by Stanford Medicine.

Stanford Medicine’s 2020 Health Trends Report once again documents key trends steering the industry’s future, including a maturing digital health market, new health laws opening patient access to data, and artificial intelligence gaining regulatory traction for medical use.

To understand how these trends will reach the doctor’s office and ultimately shape patient care, Stanford Medicine commissioned a national survey of more than 700 physicians, residents, and medical students. As a proxy for the health care delivery system writ large, these individuals were polled for their thoughts about the future of medical practice and how they are preparing for it.

“We found that current and future physicians are not only open to new technologies but are actively seeking training in subjects such as data science to enhance care for their patients,” said Lloyd Minor , MD, dean of the Stanford University School of Medicine . “We are encouraged by these findings and the opportunity they present to improve patient outcomes. At the same time, we must be clear-eyed about the challenges that may stymie progress.”

The survey’s findings have major implications for patients, their future experiences of health care, and the services to which they will have access in the next decade.

Key Findings

1. Health care providers adapting to new developments

· Physicians, residents, and students expect that almost a third of their duties could be automated by technology in the next 20 years.

· Nearly half of all physicians (47%) and three quarters of medical students (73%) are currently seeking out additional training to better prepare themselves for innovations in health care. These pursuits gravitate toward data-oriented subjects such as advanced statistics, genetic counseling, population health, and coding.

· Among physicians who are seeking additional training, 34% are pursuing classes in artificial intelligence.

2. Health care providers are digital health users and see clinical value in patient-generated sources of health data

· Nearly half of all physicians, students, and residents use a wearable health monitoring device. Among those who wear them, a majority say they use the data to inform their personal health care decisions (71% of physicians, 60% of students and residents).

· A majority of students and residents (78%) and physicians (80%) say that self-reported data from a patient’s health app would be clinically valuable in supporting their care.

· The group also sees clinical value in data received from sources such as a patient wearable device (79% students and residents; 83% physicians) and data from consumer genetic testing reports (63% students and residents, 65% physicians).

3. A transformation gap: survey responses among current and future physicians reveal significant gaps in readiness to implement emerging technologies

· There are large gaps in readiness for some of the most critical new health care developments such as telemedicine, personalized medicine, and genetic screening.

· When asked to rate the effectiveness of their education to prepare them for these developments, only 18% of current medical students and residents surveyed said that their education was “very helpful,” while 44% of physicians surveyed said that their education was either “not very helpful” or “not helpful at all.”

· This gap can be closed by modernizing the appropriate curriculum and training programs so that both current and future physicians can effectively use and make the most of new technologies.

4. Under pressure

· Physicians and those in training are struggling under medical practice burdens.

· Among physicians and residents surveyed, nearly one in five would change their career path if they were given the opportunity to do so, citing poor work-life balance and administrative burdens as the top reasons for reconsidering a medical career.

The rise of the data-driven physician represents an opportunity to positively transform medicine and improve health outcomes by bringing new technologies and insights to the patient bedside. However, as it stands today, medical professionals still feel insufficiently trained to do so. Moreover, promising medical talent is being held back by challenges such as achieving work-life balance and student debt.

As a new decade gets underway, Stanford Medicine’s 2020 Health Trends Report offers insights for health educators, employers, government officials, and private industry to take action and help tomorrow’s health care workforce reach its full potential.

Methodology: In addition to conducting a secondary review of news articles, white papers and peer-review research for the 2020 Health Trends Report, Stanford Medicine worked with Brunswick Insight to conduct a comprehensive survey of 523 current physicians and 210 medical students and residents. Respondents were contacted between September and October of 2019. Respondents were contacted through a list of American Medical Association-verified physicians and survey-sample panels of medical professionals. Respondents were compensated for their involvement in the survey and were informed that their responses would be used to inform public-facing research. Respondents were given the opportunity to opt out of any and all questions in the survey.

Media Contacts

Julie Greicius
Tel 650-723-4598
jgreicius@stanford.edu

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

F.D.A. Approves Second Gene-Altering Treatment for Cancer

thos@oneelevendigital.com (Thomas Conner) — Fri, 20 Oct 2017 04:57:00 GMT

The Food and Drug Administration on Wednesday approved the second in a radically new class of treatments that genetically reboot a patient’s own immune cells to kill cancer.

The new therapy, Yescarta, made by Kite Pharma , was approved for adults with aggressive forms of a blood cancer, non-Hodgkin’s lymphoma, who have undergone two regimens of chemotherapy that failed.

The treatment, considered a form of gene therapy, transforms the patient’s cells into what researchers call a “living drug” that attacks cancer cells. It is part of the rapidly growing field of immunotherapy, which uses drugs or genetic tinkering to turbocharge the immune system to fight disease. In some cases the treatments have led to long remissions.

“The results are pretty remarkable,” said Dr. Frederick L. Locke, a specialist in blood cancers at the Moffitt Cancer Center in Tampa, and a leader of a study of the new treatment. “We’re excited. We think there are many patients who may need this therapy.”

He added, “These patients don’t have other options.”

About 3,500 people a year in the United States may be candidates for Yescarta. It is meant to be given once, infused into a vein, and must be manufactured individually for each patient. The cost will be $373,000.

The treatment was originally developed at the National Cancer Institute, by a team Dr. Steven Rosenberg led. The institute entered an agreement with Kite in 2012, in which the company helped pay for research and received rights to commercialize the results.

Largely on the strength of the new treatment and related research, the drug giant Gilead purchased Kite in August, for $11.9 billion.

“Today marks another milestone in the development of a whole new scientific paradigm for the treatment of serious diseases,” the F.D.A. commissioner, Dr. Scott Gottlieb, said in a statement. “In just several decades, gene therapy has gone from being a promising concept to a practical solution to deadly and largely untreatable forms of cancer.”

Side effects can be life-threatening, however. They include high fevers, crashing blood pressure, lung congestion and neurological problems. In some cases, patients have required treatment in an intensive care unit. In the study that led to the approval, two patients died from side effects. Doctors have learned to manage them better, but it takes training and experience.

Partly for that reason, Yescarta, like Kymriah, will be introduced gradually, and will be available only at centers where doctors and nurses have been trained in using it.

“Ten to 15 authorized institutions will be ready to go at the time of the launch,” a spokeswoman for Kite, Christine Cassiano, said. “In 12 months, we expect to have 70 to 90. There’s a lot that goes into it, making sure each institution is ready to go.”

Companies have been racing to develop new forms of immunotherapy. The first cell-based cancer treatment — Kymriah, made by Novartis — was approved in August for children and young adults with an aggressive type of acute leukemia. It will cost $475,000, but the company has said it will not charge patients who do not respond within the first month after treatment. Novartis is expected to ask the F.D.A. to approve Kymriah for lymphoma and other blood cancers as well, and may vary its price depending on how well it works for those diseases.

Kite also plans to seek approval for other blood cancers, but does not plan to vary Yescarta’s price, said Ms. Cassiano.

The company also hopes that Yescarta will eventually be approved for earlier stages of lymphoma, rather than being limited to patients with advanced disease who have been debilitated by multiple types of chemotherapy that did not work, said Dr. David D. Chang, Kite’s chief medical officer and executive vice president for research and development.

“This is the beginning of many developments in cell therapy in the next few years,” Dr. Chang said in an interview.

He said the F.D.A. had “embraced” the concept of cell therapy, designating it a breakthrough and accelerating the approval process to speed its availability to cancer patients, many of whom do not have time to wait.

Kite and Novartis also hope to produce cell therapies for so-called solid tumors — like those of the lung, prostate, breast and colon — which account for about 90 percent of all deaths from cancer.

Before it was approved and named Yescarta, Kite’s treatment was known by other names: axi-cel, axicabtagene ciloleucel, or KTE-C19.

The study that led to approval enrolled 111 patients at 22 hospitals; 101 of them received Yescarta. They had one of three diseases: diffuse large B-cell lymphoma, primary mediastinal B-cell lymphoma or transformed follicular lymphoma.

Initially, 54 percent had complete remissions, meaning that their tumors disappeared. Another 28 percent had partial remissions, in which tumors shrank or appeared less active on scans. After six months, 80 percent of the 101 were still alive.

With a median follow-up of 8.7 months, 39 percent of the 101 were still in complete remission — a much higher rate than achieved with earlier treatments — and 5 percent still had partial remissions.

“Many patients were seriously contemplating their own mortality,” said Dr. Caron A. Jacobson, who helped conduct the study at the Dana-Farber Cancer Institute and Brigham and Women’s Cancer Center in Boston. “We would be talking to them about other clinical trials, but also about hospice care and quality of life and comfort. You’re really seeing people get their life back. After a couple weeks in the hospital and a couple weeks at home, they go back to work. On its face, it’s quite remarkable and revolutionary.”

The treatment requires removing millions of a patient’s T-cells — a type of white blood cell that is critical to the immune system — freezing them and shipping them to Kite to be genetically engineered to kill cancer cells. The process reprograms the T-cells to attack B-cells, normal parts of the immune system that turn malignant in certain blood cancers. The revved-up T-cells — now known as “CAR-T cells” — are then frozen again and shipped back to the hospital to be dripped into the patient. The turnaround time is about 17 days.

Kite’s cell-processing facility, in El Segundo, Calif., can provide the treatment for 4,000 to 5,000 patients a year, Ms. Cassiano said, adding that the company has applied for approval in Europe, and if it is granted, will probably build a plant there.

Tina Bureau, a fifth-grade teacher from Queensbury, N.Y., was one of the lymphoma patients in the study. Previously, she’d had several types of chemotherapy.

“The cancer would shrink but then it would come right back,” she said.

Last spring, she had the T-cell treatment at the Dana-Farber Cancer Institute and Brigham and Women’s Hospital in Boston. The side effects were ferocious.

“You don’t even recognize your family members,” Ms. Bureau said. “I had some bleeding on my brain, and had to be put in intensive care. The week it was happening, I don’t remember a lot. It was much more difficult for my family than me.”

Within a month, she had a complete remission, which has continued. She is back at work, full time.

“Yes, it can pose life-threatening problems,” Ms. Bureau said. “But when you’re in a situation where your life’s threatened anyway, I don’t feel you have anything to lose.”

ASCO CancerLinQ LLC and CI4CC Launch Collaboration to Transform Cancer Data

thos@oneelevendigital.com (Thomas Conner) — Tue, 07 Jun 2016 22:09:00 GMT

CancerLinQ LLC, a wholly owned nonprofit subsidiary of the American Society of Clinical Oncology, Inc. (ASCO®), has announced a collaborative effort with the Cancer Informatics for Cancer Centers (CI4CC). This initiative will bring the nation’s leading clinical, genomics, and biomedical informaticists, academicians, applied research information technology (IT) professionals, and data scientists together with the oncology community to help improve cancer care.

CancerLinQ™ is a project of CancerLinQ LLC. For more information, please visit CancerLinQ.org .

Foundation Medicine Launches Precision Medicine Partnership Program

thos@oneelevendigital.com (Thomas Conner) — Fri, 18 Sep 2015 07:54:00 GMT

Foundation Medicine Launches Precision Medicine Exchange Consortium(tm) (PMEC) To Advance The Integration Of Molecular Information In Clinical Oncology And Accelerate Adoption Of Precision Care

Precision Medicine Initiative and Cancer Research

thos@oneelevendigital.com (Thomas Conner) — Sun, 08 Feb 2015 17:31:00 GMT

This is a transformational moment for cancer research. Thanks to the investment by Congress in NCI and NIH research, we have arrived at a new understanding of cancer.

We now recognize that cancers are fundamentally diseases of the genome and that understanding cancer begins by identifying the abnormal genes and proteins that confer the risk of developing cancer. Identifying and analyzing these abnormalities will define how we diagnose cancer, determine how we develop and use targeted therapies to treat cancer, and shape the strategies we use to prevent cancer.

After decades of research, we are poised to enter a new era of medical practice where detailed genetic and other molecular information about a patient's cancer is routinely used to deploy effective, patient-specific remedies to treat it. We are about to enter the era of precision medicine.

FY 2016 Initiative on Precision Medicine

The President’s Budget for NCI for Fiscal Year (FY) 2016 contains funding increases for priority research to advance the field of precision medicine. As NCI focuses on oncology research under this FY 2016 initiative, increases proposed for other NIH institutes will advance precision medicine in other fields of science.

Under the Precision Medicine Initiative, NCI will assemble and analyze additional genomic data sets to increase our understanding of cancer genomes and their relationship to gene variants that a patient may have inherited. Based on the genomic information we uncover, NCI will test new therapies against childhood cancers and several common adult cancers. NCI will also develop better animal and cell-based models of cancer, study mechanisms of drug resistance, and identify new therapies and therapy combinations to overcome drug resistance. NCI will build on what it has already learned in ways that will accelerate the pace of discovery and deliver benefits to patients through clinical practice.

Under the Precision Medicine Initiative, NCI will target funding increases to three broad areas. NCI will conduct the priority activities of the initiative through competitive grants, cooperative clinical research, and research and development contracts.

For more details about how NCI will work with NIH on the Precision Medicine Initiative, see the New England Journal of Medicine editorial entitled A New Initiative on Precision Medicine .

NCI Clinical Trials to Advance Precision Medicine

Cancer presents an exceptionally promising opportunity to refine the principles and practices that will serve as the foundation for precision medicine. With additional resources in its FY 2016 budget, NCI will increase funding to support clinical trials that offer two distinct approaches to advancing precision medicine . The first approach recruits patients with all types of cancer, and then selects a targeted drug based on the specific genetic abnormalities of the patient's tumor. NCI is using this approach in its Molecular Analysis for Therapy Choice ( NCI-MATCH ) Program, which enrolls pediatric and adult patients with tumors that no longer respond to standard therapy. The second approach, demonstrated in the NCI Lung-MAP study, recruits patients with one type of cancer and then subdivides patients into genomically defined subsets, treating each subset with a different targeted drug.

Both types of trials have important and achievable goals:

making the molecular characterization of cancers the clinical standard for accurate diagnosis and treatment
identifying or developing an array of treatments that can be matched to the molecular features of a tumor to successfully control the disease.

Another feature of these trials is the opportunity to link their genomic findings to clinical data, lifestyle risks such as tobacco use and obesity, environmental mutagens, and viral infections. This additional feature will become a resource to support further discovery and identify new strategies for cancer prevention, early diagnosis and early intervention.

Overcoming Drug Resistance in Cancer Treatment

A major obstacle to successfully treating cancer is the challenge of resistance either to new, targeted therapies or to traditional chemotherapies. To address this challenge, NCI will support additional research to uncover the mechanisms of resistance by:

developing cancer models from tissues obtained at the time of diagnosis and at relapse to uncover mechanisms of resistance to treatment
analyzing tumor DNA and tumor cells circulating in blood samples to develop methods to predict relapse before this problem is identified clinically or in radiologic studies
testing combinations of targeted agents in clinical trials to identify approaches to overcome drug resistance.

Knowledge System to Support Precision Medicine

NCI will also build information platforms to support the integration of genetic information about tumors with data on how the tumors respond to therapy. This is the knowledge system envisioned in a 2011 Institute of Medicine (IOM) report entitled Toward Precision Medicine . As the IOM recommends, the NCI system will integrate molecular and clinical knowledge in ways that are useful to both scientists and clinicians.

NCI’s knowledge system will incorporate genetic, biochemical, environmental and clinical data from patients to define molecular subtypes and identify the approaches to cancer care that will improve patient outcomes. This component of the Precision Medicine Initiative is based on the expectation that the “big data” that emerges from precision medicine trials can be used to construct models that predict, with increasing accuracy, response to treatment.

Ongoing NCI Programs that Support Precision Medicine

This FY 2016 initiative rests on a solid foundation of NCI programs that support and advance precision medicine. NCI will continue this ongoing work as it also supports new and expanded precision medicine research under the FY 2016 initiative.

NCI Translational and Therapeutic Studies : The most conspicuous application of the principles of precision medicine appears in ongoing NCI programs to identify targeted therapeutics, such as the National Clinical Trials Network ( NCTN ), the NCI Community Oncology Research Program ( NCORP ), the NCI Early Therapeutics Clinical Trials Network ( ETCTN ), and the NCI-MATCH Program. These programs use molecular methods to identify drugs that will deliver optimum results for treating tumors. In addition, NCI is also focusing on patients identified as exceptional responders based on their strong and favorable response to investigational drugs or conventional chemotherapy. NCI is conducting genetic assessments of tumors from these exceptional responders to learn about the basis of their favorable response.

NCI Genomics and Cancer Biology Research : In its established programs in genomics research, NCI is using high-throughput techniques to identify and study small mutations, rearrangements, and chemical modifications of DNA and to detect changes in the production of RNA and proteins. Through programs such as The Cancer Genome Atlas ( TCGA ), the Therapeutically Applicable Research to Generate Effective Treatments ( TARGET ) Initiative, and the Cancer Target Discovery and Development ( CTD2 ) Network, NCI is supporting research and related translational and clinical science activities that are making important advances in precision medicine. NCI also supports precision medicine research through programs such as the Tumor Microenvironment Initiative ( TMEN ), the Integrative Cancer Biology Program ( ICBP ), and through NCI intramural programs such as those focusing on genomics and chromosome biology.

NCI Immunology and Immunotherapy Research: As NCI identifies the potential of tumors to alter their genetic code and generate potent antigens, immunotherapy is becoming an integral part of precision medicine. NCI continues to support many extramural grantees and intramural investigators who are working to treat cancers using immunotherapy.

NCI Cancer Imaging Research: The technology to precisely measure, monitor and diagnose tumor progression and regression is essential to affirm the success of precision medicine. Through programs such as NCI's Quantitative Imaging Network and The Cancer Imaging Archive, NCI is developing and refining tools to help oncologists make decisions about the optimum cancer treatment pathways for individual patients.

Finally, components of other NCI basic and translational science programs, including investigator-initiated competitive grants, NCI research and development contracts, and programs such as the RAS Initiative are also making important contributions to precision medicine, which demonstrates the breadth of NCI efforts in this field.

A New Initiative on Precision Medicine

thos@oneelevendigital.com (Thomas Conner) — Fri, 30 Jan 2015 17:31:00 GMT

“Tonight, I'm launching a new Precision Medicine Initiative to bring us closer to curing diseases like cancer and diabetes — and to give all of us access to the personalized information we need to keep ourselves and our families healthier.”

— President Barack Obama, State of the Union Address, January 20, 2015

President Obama has long expressed a strong conviction that science offers great potential for improving health. Now, the President has announced a research initiative that aims to accelerate progress toward a new era of precision medicine ( www.whitehouse.gov/precisionmedicine ). We believe that the time is right for this visionary initiative, and the National Institutes of Health (NIH) and other partners will work to achieve this vision.

The concept of precision medicine — prevention and treatment strategies that take individual variability into account — is not new1; blood typing, for instance, has been used to guide blood transfusions for more than a century. But the prospect of applying this concept broadly has been dramatically improved by the recent development of large-scale biologic databases (such as the human genome sequence), powerful methods for characterizing patients (such as proteomics, metabolomics, genomics, diverse cellular assays, and even mobile health technology), and computational tools for analyzing large sets of data. What is needed now is a broad research program to encourage creative approaches to precision medicine, test them rigorously, and ultimately use them to build the evidence base needed to guide clinical practice.

The proposed initiative has two main components: a near-term focus on cancers and a longer-term aim to generate knowledge applicable to the whole range of health and disease. Both components are now within our reach because of advances in basic research, including molecular biology, genomics, and bioinformatics. Furthermore, the initiative taps into converging trends of increased connectivity, through social media and mobile devices, and Americans' growing desire to be active partners in medical research.

Oncology is the clear choice for enhancing the near-term impact of precision medicine. Cancers are common diseases; in the aggregate, they are among the leading causes of death nationally and worldwide, and their incidence is increasing as the population ages. They are also especially feared, because of their lethality, their symptoms, and the often toxic or disfiguring therapies used to treat them. Research has already revealed many of the molecular lesions that drive cancers, showing that each cancer has its own genomic signature, with some tumor-specific features and some features common to multiple types. Although cancers are largely a consequence of accumulating genomic damage during life, inherited genetic variations contribute to cancer risk, sometimes profoundly. This new understanding of oncogenic mechanisms has begun to influence risk assessment, diagnostic categories, and therapeutic strategies, with increasing use of drugs and antibodies designed to counter the influence of specific molecular drivers. Many targeted therapies have been (and are being) developed, and several have been shown to confer benefits, some of them spectacular.2 In addition, novel immunologic approaches have recently produced some profound responses, with signs that molecular signatures may be strong predictors of benefit.3

These features make efforts to improve the ways we anticipate, prevent, diagnose, and treat cancers both urgent and promising. Realizing that promise, however, will require the many different efforts reflected in the President's initiative. To achieve a deeper understanding of cancers and discover additional tools for molecular diagnosis, we will need to analyze many more cancer genomes. To hasten the adoption of new therapies, we will need more clinical trials with novel designs4 conducted in adult and pediatric patients and more reliable models for preclinical testing. We will also need to build a “cancer knowledge network” to store the resulting molecular and medical data in digital form and to deliver them, in comprehensible ways, to scientists, health care workers, and patients.

The cancer-focused component of this initiative will be designed to address some of the obstacles that have already been encountered in “precision oncology”: unexplained drug resistance, genomic heterogeneity of tumors, insufficient means for monitoring responses and tumor recurrence, and limited knowledge about the use of drug combinations.

Precision medicine's more individualized, molecular approach to cancer will enrich and modify, but not replace, the successful staples of oncology — prevention, diagnostics, some screening methods, and effective treatments — while providing a strong framework for accelerating the adoption of precision medicine in other spheres. The most obvious of those spheres are inherited genetic disorders and infectious diseases, but there is promise for many other diseases and environmental responses.

The initiative's second component entails pursuing research advances that will enable better assessment of disease risk, understanding of disease mechanisms, and prediction of optimal therapy for many more diseases, with the goal of expanding the benefits of precision medicine into myriad aspects of health and health care.

The initiative will encourage and support the next generation of scientists to develop creative new approaches for detecting, measuring, and analyzing a wide range of biomedical information — including molecular, genomic, cellular, clinical, behavioral, physiological, and environmental parameters. Many possibilities for future applications spring to mind: today's blood counts might be replaced by a census of hundreds of distinct types of immune cells; data from mobile devices might provide real-time monitoring of glucose, blood pressure, and cardiac rhythm; genotyping might reveal particular genetic variants that confer protection against specific diseases; fecal sampling might identify patterns of gut microbes that contribute to obesity; or blood tests might detect circulating tumor cells or tumor DNA that permit early detection of cancer or its recurrence.

Such innovations will first need to be tested in pilot studies. We will initially want to take advantage of the rare settings where it is already possible to collect rich information through clinical trials, electronic medical records, and other means.

Ultimately, we will need to evaluate the most promising approaches in much larger numbers of people over longer periods. Toward this end, we envisage assembling over time a longitudinal “cohort” of 1 million or more Americans who have volunteered to participate in research. Participants will be asked to give consent for extensive characterization of biologic specimens (cell populations, proteins, metabolites, RNA, and DNA — including whole-genome sequencing, when costs permit) and behavioral data, all linked to their electronic health records. Qualified researchers from many organizations will, with appropriate protection of patient confidentiality, have access to the cohort's data, so that the world's brightest scientific and clinical minds can contribute insights and analysis. These data will also enable observational studies of drugs and devices and potentially prompt more rigorous interventional studies that address specific questions.

Such a varied array of research activities will propel our understanding of diseases — their origins and mechanisms, and opportunities for prevention and treatment — laying a firm, broad foundation for precision medicine. It will also pioneer new models for doing science that emphasize engaged participants and open, responsible data sharing. Moreover, the participants themselves will be able to access their health information and information about research that uses their data.

The research cohort will be assembled in part from some existing cohort studies (many funded by the NIH) that have already collected or are well positioned to collect data from participants willing to be involved in the new initiative. Creating this resource will require extensive planning to achieve the appropriate balance of participants, develop new approaches to participation and consent, and forge strong partnerships among existing cohorts, patient groups, and the private sector. It will also be crucial to carefully examine the successes and shortfalls of other longitudinal cohort studies.

Achieving the goals of precision medicine will also require advancing the nation's regulatory frameworks. To unleash the power of people to participate in research in innovative ways, the NIH is working with the Department of Health and Human Services to bring the Common Rule, a decades-old rule originally designed to protect research participants,5 more in line with participants' desire to be active partners in modern science. To help speed the translation of such discoveries, the Food and Drug Administration is working with the scientific community to make sure its oversight of genomic technology supports innovation, while ensuring that the public can be confident that the technology is safe and effective.

Although the precision medicine initiative will probably yield its greatest benefits years down the road, there should be some notable near-term successes. In addition to the results of the cancer studies described above, studies of a large research cohort exposed to many kinds of therapies may provide early insights into pharmacogenomics — enabling the provision of the right drug at the right dose to the right patient. Opportunities to identify persons with rare loss-of-function mutations that protect against common diseases may point to attractive drug targets for broad patient populations. And observations of beneficial use of mobile health technologies may improve strategies for preventing and managing chronic diseases.

Ambitious projects like this one cannot be planned entirely in advance; they should evolve in response to scientific and medical findings. Much of the necessary methodology remains to be invented and will require the creative and energetic involvement of biologists, physicians, technology developers, data scientists, patient groups, and others. The efforts should ideally extend beyond our borders, through collaborations with related projects around the world. Worldwide interest in the initiative's goals should motivate and attract visionary scientists from many disciplines.

This initiative will also require new resources; these should not compete with support of existing programs, especially in a difficult fiscal climate. With sufficient resources and a strong, sustained commitment of time, energy, and ingenuity from the scientific, medical, and patient communities, the full potential of precision medicine can ultimately be realized to give everyone the best chance at good health.

Identification of cell surface targets through meta-analysis of microarray data.

thos@oneelevendigital.com (Thomas Conner) — Wed, 29 Oct 2014 16:30:00 GMT

Haeberle H1 , Dudley JT , Liu JT , Butte AJ , Contag CH .

Study Link

High-resolution image guidance for resection of residual tumor cells would enable more precise and complete excision for more effective treatment of cancers, such as medulloblastoma, the most common pediatric brain cancer. Numerous studies have shown that brain tumor patient outcomes correlate with the precision of resection. To enable guided resection with molecular specificity and cellular resolution, molecular probes that effectively delineate brain tumor boundaries are essential. Therefore, we developed a bioinformatics approach to analyze micro-array datasets for the identification of transcripts that encode candidate cell surface biomarkers that are highly enriched in medulloblastoma. The results identified 380 genes with greater than a two-fold increase in the expression in the medulloblastoma compared with that in the normal cerebellum. To enrich for targets with accessibility for extracellular molecular probes, we further refined this list by filtering it with gene ontology to identify genes with protein localization on, or within, the plasma membrane. To validate this meta-analysis, the top 10 candidates were evaluated with immunohistochemistry. We identified two targets, fibrillin 2 and EphA3, which specifically stain medulloblastoma. These results demonstrate a novel bioinformatics approach that successfully identified cell surface and extracellular candidate markers enriched in medulloblastoma versus adjacent cerebellum. These two proteins are high-value targets for the development of tumor-specific probes in medulloblastoma. This bioinformatics method has broad utility for the identification of accessible molecular targets in a variety of cancers and will enable probe development for guided resection.

Effective surgical resection of brain tumors aims to remove as much diseased tissue as possible while sparing the critical neural tissue immediately adjacent to the malignancy. Whereas some cancers have clearly visible margins, others, such as medulloblastoma, have margins that can be difficult to identify [1]. While preoperative magnetic resonance imaging (MRI) with stereotactic positioning is commonly used to guide resection, poor contrast and loss of position registration due to intraoperative tissue deformation limit this image guidance technique. In addition, the few open-configuration intraoperative MRI units that exist have insufficient resolution, sensitivity, and contrast to delineate tumor margins accurately. Recent advances in micro-optical technologies have enabled the development of miniaturized microscopes to image tumor cells during surgery [2–4]. These devices have been shown to provide high-resolution information that complements wide-field fluorescence image-guided surgery techniques that have been gaining popularity among neurosurgical researchers [3–6]. For instance, Sanai et al. [4] have reported that, in low-grade gliomas, wide-field imaging based on 5-aminolevulinic acid-induced tumor fluorescence is unable to distinguish between tumor and normal regions in human patients but that individual tumor cells are identified and quantified using a real-time surgical confocal microscope. Among this research community, there is consensus about the potential benefits from the use of molecularly specific optical contrast agents that are capable of highlighting tumor tissues relative to normal brain to improve tumor resection while minimizing the destruction of healthy brain tissue.

The development of molecular probes with sufficient contrast to delineate tumor margins requires the identification of targets highly enriched in tumor tissues compared with adjacent, normal tissue. Unlike pharmaceutical development, where drug targets must ideally be present specifically in diseased tissue only, optical probe targets may be highly expressed in other tissues so long as expression in nontumor tissues occurs far from the surgical site. Therefore, bioinformatics techniques that compare gene transcript levels in tumor versus adjacent normal tissue can be used to mine the rich data sets available from human tumors and from animal models as a means of identifying molecular targets for optical probe development.

One attractive disease target for optical probe development is medulloblastoma, the most common brain tumor in children. Medulloblastoma is a solid tumor whose effective resection is both key to prolonged disease-free survival, yet its margins are difficult to visualize [7,8]. Consequently, 20% of children who are cured of the disease develop severe, sometimes irreversible neurologic deficits from overaggressive tumor resection [9,10]. Because of these risks, aggressive resections are often avoided. Real-time and precise image guidance is therefore necessary to allow for complete and accurate resection of medulloblastoma.

Here we describe the use of a novel method to identify transcripts highly enriched in medulloblastoma. Because there is no publicly available data set containing both medulloblastoma samples and normal cerebellum samples drawn from the same patient, we compared medulloblastoma studies published by the Versteeg and the Gilbertson groups [11,12] to normal cerebellum arrays ( GSE3526 and GSE13162 ). Because cell surface or extracellular proteins are preferred targets for an injected or topically applied optical probe, we selected genes coding for proteins with known localization to the plasma membrane or extracellular space. To validate these results, we performed immunohistochemistry on human tissue micro-arrays to determine protein expression of the top 10 candidates coded by the transcripts, and identified two genes, fibrillin-2 and Eph receptor A3, with the gene products of these genes have significantly enhanced immunoreactivity in the medulloblastoma versus that in the cerebellum. Although immunohistochemistry is not the method to be used for image-guided resection, it is a method of target validation. These targets can be used for the development of specific probes, either the antibodies shown here or other molecules, for use in intraoperative image guidance over a range of scales from high-resolution to wide-field imaging.

MATERIALS AND METHODS

Meta-analysis of Medulloblastoma Gene Expression Microarray Experiments

Gene expression microarray samples of medulloblastoma were obtained from NCBI Gene Expression Omnibus (GEO) experiment GSE10327 and also from the supplementary data accompanying Thompson et al. [12], which was downloaded from the St. Jude Research Data Web site (http://www.stjuderesearch.org/data/medulloblastoma). Microarray samples of normal cerebellum were obtained from NCBI GEO experiment GSE13162 . The probe set annotations for micro-array platforms were updated using AILUN [13].

Meta-analysis of genes differentially expressed between normal cerebellum and medulloblastoma was conducted using the Rank Products method [14] implemented in the RankProd package for the R statistical language ( http://r-project.org ). To compensate for the fact that disease and normal control samples did not come from the same experiment, each set of disease samples was matched with the combined set of normal samples to form a pseudomatched case versus control experiment. Each of these parings was assigned a unique origin identifier in the RankProd analysis to enable aggregate rank-based meta-analysis compensating for per-experiment variance. False discovery rates were estimated by the software using 1000 randomization rounds and differentially expressed genes exhibiting a fold change greater than 2 in medulloblastoma at a false discovery rate less than 5% were selected. The remaining genes were mapped to Gene Ontology cellular component categories using DAVID [15], and genes annotated to be localized on the cell membrane were retained as putative surface biomarkers for medulloblastoma.

Immunohistochemistry

Human tissue microarrays containing human medulloblastoma and normal human cerebellum (GL2082 and GL1001 from US Biomax, Inc, Rockville, MD) were removed from paraffin and rehydrated in water. Slides were treated with 3% hydrogen peroxide to block endogenous peroxidase for 10 minutes. Slides were treated with 0.1 M citric acid at 100°C for 5 minutes and then allowed to cool to room temperature for 40 minutes. Slides were then washed with phosphate-buffered saline (PBS) and then blocked with 2% bovine serum albumin for 30 minutes. Slides were washed with PBS and incubated with primary antibody for 2 hours at room temperature. Slides were washed with PBS and incubated in a biotinylated goat anti-rat or rabbit antibody at 1:500 for 30 minutes at room temperature. Slides were washed with PBS and incubated with streptavidin-HRP at 1:1000 for 30 minutes at room temperature. Slides were washed in PBS and treated with diaminobenzidine. Slides were washed with PBS and counterstained with hematoxylin. Slides were washed in water, dehydrated, cleared, and mounted in permanent mounting media.

Primary antibodies were rabbit anti-Eph Receptor A3 (PAB3005; Abnova, Cambridge, MA), rabbit anti-Fibrillin (HPA012853; Sigma-Aldrich, Seelze, Germany) and rat monoclonal anti-Laminin beta 1 antibody (Abcam, Cambridge, MA), anti-Frizzled-2 (181-46110749; GenWay, San Diego, CA), mouse anti-protocadherin 8 (H00005100; Abnova), rabbit anti-transforming growth factor beta receptor I (PAB3503; Abnova), anti-connective tissue growth factor (PAB3503; Abnova), anti-villin 2 (MAB3822; Millipore, Billerica, MA), anti-poliovirus receptor-related 3 (HPA011038; Sigma-Aldrich), anti-F2R (PAB11822; Abnova). Secondary antibodies were purchased from Jackson Immunoresearch (West Grove, PA).

Images were acquired with a Zeiss AxioVert microscope (Zeiss, Oberkochen, Germany) with a 60x 1.4 NA objective.

RESULTS

To identify candidate cell surface biomarkers for medulloblastoma and building from a previously published method [16], we developed a meta-analysis method called Public Microarray Integration of Cases and Control (PuMiCC) to find disparate microarray data sets for cases and controls, integrate these using rank products, and filter the differentially expressed genes based on the localization of their coded proteins within the cell. For cases of medulloblastoma, we used GSE10327 from NCBI GEO (62 samples) and a medulloblastoma data set obtained from St. Jude Hospital (40 samples) for a total of 102 human medulloblastoma microarray samples. Normal human cerebellum arrays were derived from GSE3526 (10 samples) and GSE13162 (6 samples).

The results identified 380 genes that exhibited a greater-than-twofold change in expression in the medulloblastoma relative to the normal cerebellum at a false-positive rate less than 0.05. This list was then filtered using Gene Ontology cellular component annotations to derive a list of 95 genes known to be located on or within the cell membrane. The top 30 candidate medulloblastoma cell surface markers are shown in Table 1.

Table 1

Top 30 Most Enriched Transcripts in Medulloblastoma Verse Cerebellum.

Ten candidate biomarkers were selected for validation by immunohistochemistry ( Table 2 ). We focused on candidate genes coding for proteins with known localization in the outer leaflet of the plasma membrane or in the extracellular space. Genes that had commercially available antibodies reactive against human antigen were selected for immunohistochemistry analysis. These antibodies were used to label commercially available human tissue microarrays (US Biomax, Inc) containing biopsies from 10 cases of medulloblastoma, 10 cases of normal cerebellum, and 80 cases of other brain disorders. Staining intensity was scored on a 0 to 3+ scale. Of the 10 candidate biomarkers, fibrillin 2 was deeply stained (3+) in 4 of 10 medulloblastoma cases and EphA3 was highly stained in 3 of 10 medulloblastoma cases, whereas normal cerebellum was nonreactive ( Figure 1 ). Isotype controls did not stain the medulloblastoma tissue ( Figure 2 ).

Figure 1

Medulloblastoma but not cerebellum expresses the Eph receptor A3 and fibrillin 2. Bright-field micrographs show immunohistochemical staining of the human medulloblastoma and the normal cerebellum. An anti-Eph A3 antibody (top) and an anti-fibrillin 2-antibody ...

Figure 2

Medulloblastoma and cerebellum stain lightly with rabbit IgG antibody serum labeled with biotinylated goat anti-rabbit secondary antibody. Bright-field micrographs show immunohistochemical staining of the human medulloblastoma and the normal cerebellum. ...

Table 2

Candidate Biomarkers Selected for Immunohistochemistry Validation.

DISCUSSION

Taken together, we have demonstrated a novel bioinformatics approach that successfully identified cell surface and extracellular candidate markers enriched in medulloblastoma versus adjacent cerebellum. Furthermore, these candidate markers were validated using immunohistochemistry on human tissue microarrays, revealing two genes, fibrillin 2 and Eph receptor A3.

Fibrillins interact with integrins and heparin-sulphated proteoglycans and likely play important roles in cell migration, adhesion, signaling, and cell differentiation—all important processes in tumor growth and metastasis [17]. Fibrillin 2 transcript and protein is also densely present in rhabdomyosarcoma [18]. The related protein fibrillin 1 is present in some glioblastomas [19], suggesting that fibrillins could be a common marker of invasive tumors. Whereas fibrillin 2 has been shown to have a widespread distribution in the extracellular matrix of healthy skin, lung, liver, and other tissues, it is not highly expressed in the brain [20], making it an excellent specific target for medulloblastoma marker development.

Eph receptors are tyrosine kinases that play a role in cell-cell interaction and cell migration [21]. Both Ephrin and Eph receptor over-expression may promote tumor growth, survival, angiogenesis, and metastasis. Mutations in Eph receptors have also been identified in breast cancer (EPHA1, EPHA6, EPHA10) [22] and in lung cancer (EPHA3, EPHA5, EPHA6, EPHB2, EPHB3, EPHB4) [23]. Interestingly, soluble forms of Eph A receptors (EPHA2 and EPHA3) appear to inhibit tumor angiogenesis and tumor progression [24], suggesting that EphA3 may serve as both an optical imaging agent and a therapeutic target for medulloblastoma.

Because Fibrillin 2 and Eph receptor A3 have previously implicated in tumor progression and metastasis, it is possible that optical probes for these targets will be effective against other brain tumors as well. In addition, as there are many tumor resection surgeries that would benefit from improved surgical visualization, the techniques described here have broad application to the identification of targets for molecular probe development

FOOTNOTES

1This work was funded in part through grants from the National Institutes of Health (U54CA136465 to C.H.C., R01GM079719 to A.J.B., and K99EB008557 to J.T.C.L.) and support from the Center for Children's Brain Tumors at Stanford University. J.T.D. was supported by the NLM Biomedical Informatics Training Grant (T15 LM007033) to Stanford University. H.H. was a fellow in the Stanford Molecular Imaging Scholars Program.

NIH issues finalized policy on genomic data sharing

thos@oneelevendigital.com (Thomas Conner) — Wed, 29 Oct 2014 16:30:00 GMT

The National Institutes of Health has issued a final NIH Genomic Data Sharing (GDS) policy to promote data sharing as a way to speed the translation of data into knowledge, products and procedures that improve health while protecting the privacy of research participants. The final policy was posted in the Federal Register Aug. 26, 2014 and published in the NIH Guide for Grants and Contracts Aug. 27, 2014.

Genomic Data Sharing Policy

Starting with funding applications submitted for a Jan. 25, 2015, receipt date, the policy will apply to all NIH-funded, large-scale human and non-human projects that generate genomic data. This includes research conducted with the support of NIH grants and contracts and within the NIH Intramural Research Program. NIH officials finalized the policy after reviewing public comments on a draft released in September 2013.

The GDS policy can be traced to the Human Genome Project, completed in 2003, which required rapid and broad data release during its mapping and sequencing of the human genome. The GDS policy is an extension of and replaces the Genome-Wide Association Studies (GWAS) data sharing policy. Since 2007, the GWAS policy has governed biomedical researchers’ submission and access to human data through the NIH database for Genotypes and Phenotypes (dbGaP). Its two-tiered data distribution system has made some information and data available to the public without restrictions. Access to other data has been controlled and made available only for research purposes consistent with the consent provided by participants in the original study.

Under the GWAS policy, more than 2,200 investigators from 41 different countries have received access to dbGaP data from 304 studies and produced more than 900 publications. A report on genomic data sharing through dbGaP under the GWAS policy appears in the Aug. 27, 2014, advance online issue of Nature Genetics. The report was written by members of the NIH Genomic Data Sharing policy team.

“Everyone is eager to see the incredible deluge of molecular discoveries about disease translated into prevention, diagnostics, and therapeutics for patients,” said Kathy Hudson, Ph.D., NIH deputy director for science, outreach and policy. “The collective knowledge achieved through data sharing benefits researchers and patients alike, but it must be done carefully. The GDS policy outlines the responsibilities of investigators and institutions that are using the data and also encourages researchers to get consent from participants for future unspecified use of their genomic data.”

Along with statistics about the use of dbGaP data, the Nature Genetics report outlines the challenges facing the field, such as the increased volume and complexity of genomic data.

“Advances in DNA sequencing technologies have enabled NIH to conduct and fund research that generates ever-greater volumes of GWAS and other types of genomic data,” said Eric Green, M.D., Ph.D., NHGRI director, report co-author and a co-chair of the trans-NIH committee that developed the GDS policy. “Access to these data through dbGaP and according to the data management practices laid out in the policy allows researchers to accelerate research by combining and comparing large and information-rich datasets.”

A key tenet of the GDS policy is the expectation that researchers obtain the informed consent of study participants for the potential future use of their de-identified data for research and for broad sharing. NIH also has similar expectations for studies that involve the use of de-identified cell lines or clinical specimens.

The two-tiered system for providing access to human data is based on data sensitivity and privacy concerns developed under the GWAS policy will continue. For controlled-access data, investigators will be expected to use data only for the approved research, protect data confidentiality (including not sharing the data with unauthorized people), and acknowledge data-submitting investigators in presentations and publications.

NIH expects any institution submitting data to certify that the data were collected in a legal and ethically appropriate manner and that personal identifiers, such as name or address, have been removed. The NIH GDS policy also expects investigators and their institutions to provide basic plans for following the GDS policy as part of funding proposals and applications.

Other highlights of the GDS policy

Investigators are encouraged to seek the broadest possible sharing permissions from participants for future research use of their data.
Timelines for data submission and access should promote timely and broad data sharing while being mindful of the significant effort by investigators to generate and prepare data for release, as well as to realize benefit from engaging research participants.
In general, investigators should make non-human genomic data publicly available no later than the date of initial publication. Non-human data can be deposited into widely used NIH-designated repositories other than dbGaP.
The NIH GDS policy explicitly encourages the broadest possible use of findings and development of products/technologies from the use of NIH-funded genomic data to promote maximum public benefit.
The scope is intentionally general enough to accommodate the evolving nature of genomic technologies, with more specific expectations provided through accompanying Supplemental Information to the NIH Genomic Data Sharing Policy.

The NIH GDS governance structure, described at http://gds.nih.gov/04po2.html , will be responsible for oversight of the GDS policy, including policy needs and issues related to data submission and access. The committees will also monitor scientific and ethical or legal developments that might warrant further policy development. Compliance with the policy will become a special condition in the grant or contract award. Failure to comply could lead to enforcement actions.

Investigators should submit data-sharing plans that meet the expectations of the GDS policy with the extramural funding application or prior to the start of intramural research projectsNIH advises investigators seeking funding to contact relevant extramural program directors or an NIH institute or center Genomic Program Administrator (GPA) as early as possible to discuss data sharing expectations and timelines that would apply to their proposed studies. For a list of GPAs, visit http://gds.nih.gov/04po2_2GPA.html.

For complete information about genomic data sharing and a link to the GDS policy, see http://gds.nih.gov .

Paltoo, DN, Rodriguez, LL, Feolo, M, et al. Data Use under the NIH GWAS Data Sharing Policy and Future Directions. Nature Genetics 46, 934–938 (2014) doi:10.1038/ng.3062 Published online 27 August 2014: http://www.nature.com/ng/journal/v46/n9/full/ng.3062.html .

About the National Institutes of Health (NIH) : NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

The gene mutations driving cancer have been tracked for the first time

thos@oneelevendigital.com (Thomas Conner) — Sat, 17 May 2014 16:29:00 GMT

The gene mutations driving cancer have been tracked for the first time in patients back to a distinct set of cells at the root of cancer -- cancer stem cells. The international research team studied a group of patients with myelodysplastic syndromes -- a malignant blood condition which frequently develops into acute myeloid leukemia. The researchers say their findings offer conclusive evidence for the existence of cancer stem cells.

The international research team, led by scientists at the University of Oxford and the Karolinska Institutet in Sweden, studied a group of patients with myelodysplastic syndromes -- a malignant blood condition which frequently develops into acute myeloid leukemia. The researchers say their findings, reported in the journal Cancer Cell, offer conclusive evidence for the existence of cancer stem cells.

HIGHLIGHTS

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MDS stem cells and progenitors are distinct and hierarchically related

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Mutations in low-risk MDS originate exclusively in distinct and rare MDS stem cells

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Mutations preceding AML transformation might confer self-renewal to MDS progenitors

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del(5q) precedes acquisition of recurrent driver mutations in isolated del(5q) MDS

SUMMARY

Evidence for distinct human cancer stem cells (CSCs) remains contentious and the degree to which different cancer cells contribute to propagating malignancies in patients remains unexplored. In low- to intermediate-risk myelodysplastic syndromes (MDS), we establish the existence of rare multipotent MDS stem cells (MDS-SCs), and their hierarchical relationship to lineage-restricted MDS progenitors. All identified somatically acquired genetic lesions were backtracked to distinct MDS-SCs, establishing their distinct MDS-propagating function in vivo. In isolated del(5q)-MDS, acquisition of del(5q) preceded diverse recurrent driver mutations. Sequential analysis in del(5q)-MDS revealed genetic evolution in MDS-SCs and MDS-progenitors prior to leukemic transformation. These findings provide definitive evidence for rare human MDS-SCs in vivo, with extensive implications for the targeting of the cells required and sufficient for MDS-propagation.

GRAPHICAL ABSTRACT

SIGNIFICANCE

E xperimental evidence supporting the existence of human cancer stem cells (CSCs) remain extensively contested and in vivo fate mapping of candidate human CSCs in patients has not been possible. Through establishment of molecularly and functionally distinct and hierarchically organized stem and progenitor cell compartments in myelodysplastic syndromes (MDS) and backtracking of identified somatic genetic lesions, we establish that rare Lin−CD34+CD38−CD90+CD45RA−MDS cells function as MDS-propagating cells in patients with low- to intermediate-risk MDS. Because their elimination will be essential, and possibly also sufficient, toward eradication of the entire MDS clone, the definitive identification of rare but distinct MDS stem cells will now facilitate development of therapies specifically targeting MDS stem cells.

INTRODUCTION

The concept that human cancers might be propagated exclusively by rare self-renewing cancer stem cells (CSCs), replenishing nontumorigenic cancer cells, has extensive implications for the development of targeted cancer therapies ( Clevers, 2011 and Magee et al., 2012 ). Whereas the existence of cells with human CSC-potential has been supported experimentally in some hematological malignancies ( Bonnet and Dick, 1997 , Goardon et al., 2011 , Jamieson et al., 2004 and Nilsson et al., 2000 ) and solid tumors ( Al-Hajj et al., 2003 and Schatton et al., 2008 ), the CSC concept has recently been contested in mouse and man (reviewed in Clevers, 2011 and Magee et al., 2012 ) through multiple studies suggesting that cells with CSC potential, including leukemic stem cells (LSCs), might be neither rare ( Kelly et al., 2007 and Quintana et al., 2008 ) nor phenotypically or molecularly distinct ( le Viseur et al., 2008 and Quintana et al., 2008 ). Of particular relevance, compelling evidence has demonstrated significant, intrinsic limitations of existing human CSC and LSC in vivo assays ( Clevers, 2011 , Kelly et al., 2007 , Magee et al., 2012 and Taussig et al., 2008 ), failing to reveal the cancer-propagating potential of investigated cancer cell populations as illustrated by a large fraction of patients with acute myeloid leukemia (AML) having no leukemic cells reading out in LSC assays ( Pearce et al., 2006 ).

Regardless, in vitro or in vivo CSC/LSC assays cannot establish to what degree different cancer cells act to propagate the cancer in patients, which ultimately is the most biologically and clinically relevant CSC property. Proposed identities of human CSCs/LSCs therefore await definitive verifications in patients ( Clevers, 2011 and Magee et al., 2012 ), although it will be difficult to apply the genetically engineered lineage-tracing technologies in human malignancies, which recently allowed the definitive identification and fate-mapping of mouse CSCs in vivo ( Chen et al., 2012 , Driessens et al., 2012 and Schepers et al., 2012 ).

Myelodysplastic syndromes (MDS) are clonal hematopoietic disorders characterized by inefficient hematopoiesis and frequent progression to AML ( Nimer, 2008 ). Even in low-risk MDS, clonal hematopoiesis already dominates at diagnosis, and clones found in secondary AML originate from the MDS stage of disease ( Walter et al., 2012 ), highlighting the need to specifically target the MDS-initiating clone. Previous studies provided support for del(5q)-MDS originating in hematopoietic stem cells (HSCs; Nilsson et al., 2000 ), and in vitro and in vivo stem cell (SC) assays have supported that rare CD34+CD38− cells possess MDS-SC potential in low- to intermediate-risk MDS ( Nilsson et al., 2000 , Nilsson et al., 2002 , Pang et al., 2013 and Tehranchi et al., 2010 ). However, it remains to be established whether CD34+CD38− cells are the only cells with SC potential in MDS because other MDS progenitors, including distinct myeloid progenitor subsets ( Manz et al., 2002 ), have yet to be explored for their MDS-SC potential ( Agarwal, 2012 , ASH-Workshop, 2010 , Nilsson et al., 2000 and Pang et al., 2013 ). This is particularly relevant because genomic lesions might potentially confer in vivo self-renewal ability to otherwise short-lived myeloid progenitors.

We hypothesized that recently identified recurrent somatic driver-mutations in low- to intermediate-risk MDS ( Bejar et al., 2011 , Papaemmanuil et al., 2011 , Papaemmanuil et al., 2013 and Yoshida et al., 2011 ) should provide genetic tools to map the identity and fate of MDS-propagating cells in patients in vivo.

RESULTS

Conservation of a Hierarchy of Molecularly and Functionally Distinct Stem and Progenitor Cells in Low- to Intermediate-Risk MDS

Because candidate CSCs/LSCs have frequently not been documented to be molecularly and functionally distinct or hierarchically related to other cancer/leukemic cells ( Clevers, 2011 , le Viseur et al., 2008 and Magee et al., 2012 ), a requirement for our fate mapping approach to be informative, we first compared the phenotypic, molecular and functional properties as well as hierarchical relationships of Lineage− (Lin−)CD34+CD38−CD90+CD45RA− candidate MDS-SCs with myeloid-restricted granulocyte-macrophage and megakaryocyte-erythroid progenitors (GMPs and MEPs, respectively; Majeti et al., 2007 and Manz et al., 2002 ).

In all investigated MDS cases ( Table S1 available online), phenotypically defined SCs, GMPs, MEPs, and common myeloid progenitors (CMPs) were identified in agreement with previous studies ( Pang et al., 2013 and Will et al., 2012 ). Each population was preserved at a low frequency despite high clonal involvement ( Figures 1 A–1D). Absolute numbers of phenotypic SCs and CMPs were expanded and GMPs reduced compared to age-matched controls in isolated del(5q) MDS, other patients with low- to intermediate-risk MDS with del(5q), and in non-del(5q) MDS ( Figure 1 B). As reported recently for patients with low-risk MDS ( Pang et al., 2013 ), the suppression of GMPs and increase in CMPs became clearer in del(5q) and non-del(5q) MDS cases when assessed relative to the whole Lin−CD34+CD38+ progenitor compartment ( Figure 1 B). This analysis also revealed that MEPs were relatively suppressed in non-del(5q) cases, but not in cases with del(5q) ( Figure 1 B). In MDS cases with del(5q), the mean frequency of Lin−CD34+CD38−CD90+CD45RA− SCs with del(5q) was as high as 93.7% and 98.9% as determined by fluorescence in situ hybridization (FISH) and sequencing, respectively ( Figures 1 C and 1D), suggesting that del(5q) MDS-SCs outcompete normal HSCs. With FISH, del(5q) was found to be high in CMPs, GMPs, MEPs, and SCs, although slightly lower in GMPs and MEPs as determined by sequencing ( Figure 1 D), suggesting perhaps a slight disadvantage for generation or maintenance of del(5q) GMPs and MEPs.

Figure 1.

Conservation of Molecularly and Functionally Distinct Hematopoietic Stem and Progenitor Cells in MDS

(A) FACS profiles of bone marrow stem and progenitor cells in normal age-matched control (top row), and representative del(5q) MDS (patient 1, middle row), and non-del(5q) MDS (patient 68, bottom row) patients. Numbers indicate percentages of total nucleated BM cells.

(B) Mean (SEM) stem/progenitor cell percentages in normal age-matched controls (n = 7) and low- to intermediate-risk isolated del(5q) (n = 9), del(5q) RCMD/RAEB-1 (n = 11) and non-del(5q) (n = 12) MDS in total BM (top row), and Lin−CD34+CD38+ myeloid progenitor compartment (p values against normal controls are shown, ∗p < 0.05).

(C and D) Mean (SEM) del(5q)/trisomy 8 involvement as determined by FISH (C) (del(5q) n = 5, +8 n = 2) and DNA sequencing (D) (n = 7), p values against SCs.

(E and F) Mean (SEM) expression of myeloid (E) and erythroid (F) transcripts within GMPs and MEPs from normal BM (n = 7), del(5q) MDS (n = 6), and non-del(5q) MDS (n = 4). ∗p < 0.05.

(G) Mean (SEM) myeloid and erythroid colony formation of purified GMPs and MEPs from del(5q) MDS (n = 17) and non-del(5q) MDS (n = 7).

(H) Mean (SEM) expression of stem cell transcripts in normal age-matched BM (n = 7), del(5q) MDS (n = 6) and non-del5q MDS (n = 4) stem/progenitor cells. ∗p < 0.05; #, too low to depict.

(I) Principle component analysis (log-transformed) of normal control, del(5q) and non-del(5q) MDS-SCs, GMPs, and MEPs. Each dot represents the indicated cell population from one subject.

(J) Mean (SEM) LTC-CFC formation of purified stem (Lin−CD34+CD38−CD90+CD45RA−) and progenitor cells from del(5q) MDS (n = 15) and non-del(5q) MDS (n = 11).

Hierarchical Organization of MDS Stem and Progenitor Cells

T he distinct molecularly and functionally “lineage-restricted” signatures of MEPs and GMPs in all investigated MDS patients, distinguishing them from the SC-signature of Lin−CD34+CD38−CD90+CD45RA− SCs, implicated a hierarchical relationship between Lin−CD34+CD38−CD90+CD45RA− SCs and myeloid-restricted MEPs and GMPs. We further explored this by investigating the ability of del(5q) Lin−CD34+CD38−CD90+CD45RA− cells to replenish MEPs and GMPs. Because a high fraction of human AMLs fail to reconstitute in xenograft assays ( Pearce et al., 2006 ), it is not surprising that also in most cases of MDS the bone marrow (BM) cells fail to reconstitute immune-deficient mice ( Nilsson et al., 2002 and Thanopoulou et al., 2004 ), although in a recent study SCs from a few monosomy 7 MDS cases did engraft ( Pang et al., 2013 ). In two del(5q) MDS cases, with distinct MEPs, GMPs, and Lin−CD34+CD38−CD90+CD45RA−candidate SCs ( Figures 2 A–2D; Figures S2 A–S2C), multiple mice reconstituted with Lin−CD34+CD38−CD90+CD45RA− cells ( Figure 2 E; Figure S2 D). Not previously investigated ( Pang et al., 2013 ), but of decisive importance for proposing that Lin−CD34+CD38−CD90+CD45RA− MDS cells might be the only MDS-SCs, no reconstitution was observed with purified CMPs, GMPs, MEPs, or CD34neg cells ( Figure 2 E; Figure S2 D). In both patients, Lin−CD34+CD38−CD90+CD45RA− cells only reconstituted myelopoiesis long term, in agreement with B lymphopoiesis being suppressed in MDS ( Figure 2 F; Figure S2 E; Pang et al., 2013 and Sternberg et al., 2005 ). Reconstituting Lin−CD34+CD38−CD90+CD45RA−SCs sustained a high fraction of CD34+ cells, including clonally involved Lin−CD34+CD38−CD90+CD45RA−SCs, CMPs, GMPs, and MEPs ( Figures 2 G–2J, S2 F, and S2G). Each stem/progenitor population regenerated from transplanted Lin−CD34+CD38−CD90+CD45RA− cells revealed the same molecular signatures as the same populations isolated directly from the patients ( Figure 2 K; Figure S2 H), establishing the ability of multipotent Lin−CD34+CD38−D90+CD45RA− candidate MDS-SCs to replenish lineage-restricted MDS progenitors.

Figure 2.

Hierarchically Organized Stem and Progenitor Cells in del(5q) MDS

(A) FACS profiles and percentages of stem and progenitor cells in total BM from patient 37.

(B) Percentage del(5q) by FISH in purified stem and progenitor cells.

(D) RNA sequencing analysis (reads per kilobase per million mapped reads, RPKM) of myeloid, erythroid, and stem cell transcripts in stem/progenitor cells.

(E) Mean (SEM) human (CD45+) engraftment in BM of NSG mice (two to three mice/cell population) transplanted with purified stem/progenitor cells from patient 37.

(F) Mean (SEM) distribution of myeloid (CD15+/CD33+/CD66b+), B cell (CD19+), and CD34+ stem/progenitor cells in MDS SC-derived hCD45 cells (n = two to three mice/donor).

(G) Stem and progenitor profiles in NSG BM of three mice 17–26 weeks after transplantation of purified SCs from patient 37. Percentages within total CD45+ cells are shown.

(H) Mean percentages (SEM) of stem and progenitor cell populations within human CD45+ engrafted cells after transplantation of purified MDS-SCs into NSG mice.

(I) Frequencies of del(5q) by FISH in MDS stem/progenitor cells purified from BM of NSG mice transplanted with purified MDS-SCs.

(J) Variant read frequencies for del(5q) and JAK2V617F in FACS sorted human myeloid (CD15+/CD33+/CD66b+) cells engrafted in NSG mouse 17 weeks after transplantation of purified MDS Lin−CD34+CD38−CD90+CD45RA− SCs.

(K) Mean (SEM) gene expression in stem/progenitor cells purified from BM of NSG mice transplanted with purified MDS-SCs (n = two to three for each population).

Diverse Genetic Lesions in Low- to Intermediate-Risk MDS Originate Exclusively in Rare Lin−CD34+CD38−CD90+CD45RA− MDS-SCs

Recent studies have highlighted the inability of established CSC assays to reliably identify tumor-propagating cells in patients ( Clevers, 2011 , Kelly et al., 2007 , Magee et al., 2012 , Quintana et al., 2008 and Taussig et al., 2008 ), including in MDS ( Agarwal, 2012 and ASH-Workshop, 2010 ). Therefore, to validate MDS-SC identity and activity within patients, we tracked the origin of candidate driver mutations identified in bulk MDS BM cells. Considering the short lifespan of normal myeloid progenitors ( Orkin and Zon, 2008 ), we argued that any stable mutations, contributing to the MDS clone would have been acquired in cells with self-renewal ability. From this it follows that if Lin−CD34+CD38−CD90+CD45RA− cells are the only SCs in low- to intermediate-risk MDS, then the origin of every somatic mutation identified should be traced to this compartment ( Figure 3 A). If however downstream MDS progenitors have acquired self-renewal ability upon acquisition of driver mutations, we should identify mutations mapped to progenitors but not the upstream Lin−CD34+CD38−CD90+CD45RA− SC compartment ( Figures 3 B and 3C). Thus, we performed targeted screening for somatic DNA mutations in 83 genes frequently mutated in MDS and other myeloid malignancies ( Bejar et al., 2011 , Papaemmanuil et al., 2011 , Papaemmanuil et al., 2013 and Yoshida et al., 2011 ).

Figure 3.

Alternative Models for Sequential Acquisition of Genetic Lesions in Hierarchically Organized MDS Stem and Progenitor Cells

(A) Only Lin−CD34+CD38−CD90+CD45RA− SCs have self-renewal (inherent; black arrow) ability; and thus all somatic genetic lesions (a–d) can be traced back to the rare SCs regardless whether acquired in a linear (top) or branching (bottom) pattern. As MDS-SCs replenish downstream progenitors, mutations acquired in MDS-SCs are also inherited by their downstream progenitors but without conferring self-renewal potential.

(B) Self-renewal potential has been conferred (red arrow) to a multipotent progenitor cell, which therefore can acquire new and stable genetic lesions (d) not found in the upstream SC compartment but are found in downstream GMPs and MEPs.

(C) Self-renewal potential has been acquired by GMPs and MEPs in addition to the multipotent progenitors thus allowing the occurrence of new stable mutations (e) and (f) in GMPs and MEPs, respectively.

W e identified 34 lesions, including del(5q) and mutations in candidate driver-genes in bulk BM cells from 15 patients with low- to intermediate-risk MDS ( Figures 4 A and 4B; Table S2 ), including those encoding transcription factors (RUNX1, ETV6), components of signaling pathways (JAK2, CSF3R), epigenetic regulators (TET2, ASXL1), apoptosis regulators (TP53), and spliceosome components (SF3B1, SRSF2,U2AF2, SRSF6). Importantly, these mutations were typically present in the dominant MDS clone as evidenced by a mean variant allele frequency (VAF) in whole BM cells of 30.8% (±3.3%; Figure 4 B; Table S2 ), demonstrating that they must indeed have originated in cells actively propagating the MDS clone in vivo, fulfilling the strictest definition of CSCs ( Clevers, 2011 and Magee et al., 2012 ). All these genomic lesions were tracked back to the rare Lin−CD34+CD38−CD90+CD45RA− SC compartment, including Lin−CD34+CD38−CD90+CD45RA−-derived long-term colonies ( Figure 4 B and S3 ) and NSG-reconstituting Lin−CD34+CD38−CD90+CD45RA− cells ( Figure 2 J). The mean VAF of these somatic mutations was as high as 43.4% (±4.2%) in the Lin−CD34+CD38−CD90+CD45RA− SC compartment, and in all patients, the majority of individual long-term colonies contained all identified genetic driver-lesions, including del(5q) ( Figure 4 B). In agreement with this, based on VAF for SNPs in the 5q common deleted region (CDR), a mean of 97.3% (±1.7%) of Lin−CD34+CD38−CD90+CD45RA− SCs in these del(5q) patients were del(5q) ( Figure 4 B; Table S2 ; Supplemental Experimental Procedures ). Thus, the clonal advantage of del(5q) MDS over normal hematopoiesis occurs predominantly at the SC-level. As expected based on their hierarchical relationship to Lin−CD34+CD38−D90+CD45RA− MDS-SCs, identified mutations were also found in purified GMPs and MEPs ( Figure 4 C). Finally, even if not identified in the bulk BM cells, we screened the purified Lin-CD34+CD38−CD90+CD45RA− SC compartment for reported recurrent driver mutations in MDS ( Bejar et al., 2011 , Papaemmanuil et al., 2011 and Yoshida et al., 2011 ), but never identified a mutation in the Lin−CD34+CD38−CD90+CD45RA− MDS-SC compartment, which was not also represented in bulk MDS cells (P.S.W. and S.E.W.J., unpublished observations).

Figure 4.

Mapping of Somatic Genetic Lesions to Rare and Distinct MDS Stem Cells

(A) del(5q) and genes with mutations in unfractionated (bulk) BM of specified MDS patients identified by targeted sequencing of recurrently mutated genes.

(B) Tracking of mutations identified in whole BM in (A) to Lin−CD34+CD38−CD90+CD45RA− MDS-SCs (stem) and individual SC-derived (LTC-CFC) clones; y-axis numbers indicate percent variant reads for identified mutations. For most patients typical LTC-CFCs produced from CD34+CD38−CD90+CD45RA− MDS-SCs are also shown, harboring all identified driver lesions as indicated by +.

del(5q) Precedes Recurrent Driver Mutations in Isolated del(5q) MDS

d el(5q) is one of the most frequent cytogenetic aberrations in MDS ( Haase, 2008 ), observed in all subgroups of MDS including, but not restricted to, the distinct subgroup of MDS with isolated del(5q) (previously termed 5q− syndrome; Nimer, 2008 ). While recent targeted sequencing studies of MDS patients ( Bejar et al., 2011 , Fernandez-Mercado et al., 2013 and Papaemmanuil et al., 2013 ) and our data ( Figure 4 A) suggest that isolated del(5q) MDS cases frequently harbor additional driver mutations, the order of the mutation acquisition relative to del(5q) has not been examined in detail.

To explore this further, we utilized three independent data sets from low- to intermediate-risk MDS cases with del(5q): (1) in-house targeted resequencing of 17 new cases; (2) computational analysis of 36 cases from a previous study ( Papaemmanuil et al., 2013 ), including assessment of the frequencies of cells with del(5q); and (3) nine cases with whole exome sequencing data ( Tables S1 and S3 ). In 19 (36%) of the 53 targeted resequencing cases, no further recurrent driver mutations were identified, a pattern confirmed in five of the nine whole exome-sequenced cases ( Figure 5 A; Table S3 ). In 12 of the additional 16 cases with one or more recurrent driver mutations and in which computational analysis allowed high confidence prediction (18 cases did not; Figure S4 A), del(5q) was predicted to have occurred as the first genetic lesion, whereas in only four cases was a recurrent driver mutation predicted to have preceded del(5q) ( Figure 5 B). Notably, in all high-confidence cases where the diagnosis was isolated del(5q) (n = 18) or RAEB-1/RCMD (n = 10), del(5q) was predicted to be the first (or only) genetic lesion, regardless of the identity or number of recurrent driver mutations identified ( Figure 5 C).

Figure 5.

With Exception of SF3B1-Mutated Cases with Ring Sideroblasts, del(5q) Precedes Recurrent Driver Mutations in Low- to Intermediate-Risk MDS

(A) Number of low- to intermediate-risk del(5q) MDS cases analyzed by targeted (left) or exome (right) sequencing in which no or additional recurrent driver mutations were identified.

(B) High confidence computational prediction of whether or not del(5q) occurred as the first identifiable genetic lesion in low- to intermediate-risk del(5q) MDS cases with one or more candidate driver mutation(s). Error bars represent 95% CI.

(C) Summary of frequencies of del(5q) MDS patient subcategories in which del(5q) was identified as the only, or predicted to be the first or a secondary genetic lesion. Inconclusive, overlap in 95% CI.

(D) Representative single cell (colony) analysis of reference (REF) and variant (VAR) reads for del(5q) and identified driver mutations in four patients with overlapping 95% CI. Red indicates presence and blue absence of del(5q) and the specified mutations in individual colonies (each colony represented by vertical columns).

Evolution of Genetic Lesions in del(5q) MDS Stem Cells during Disease Progression

W e next monitored rare Lin−CD34+CD38−CD90+CD45RA− MDS-SCs in sequential BM samples to gain insights into the potential genomic evolution and impact of somatic mutations on the MDS stem and progenitor cell hierarchy, in stable disease as well as prior to disease progression (see Supplemental Experimental Procedures and Table S1 ). Moreover, to establish a more complete and unbiased picture of somatic exonic mutations, and to what degree diverse mutations (recurrent driver mutations and nonrecurrent, most likely passenger mutations) in the active MDS clones could all be backtracked to the CD34+CD38−CD90+ MDS-SC compartment, we exome-sequenced bulk BM cells of four patients with del(5q) ( Figures 6 and 7 ).

Figure 6.

Mapping of Somatic Mutations to MDS Stem Cells in Sequential Samples in del(5q) MDS without Evidence of Disease Progression

(A) FACS profiles of SCs and progenitors in BM MNCs in serial samples at diagnosis (Diag) and months since diagnosis.

(B) del(5q) clonal involvement of Lin−CD34+CD38−CD90+ MDS-SCs determined by FISH and targeted sequencing.

(D) Frequency of exome variant reads in BM MNC. Dotted lines represent 5% variant read cut-off. Recurrent driver mutations are indicated in bold.

(E) Presence (red) and absence (blue) of variant reads for identified mutations in SCs (purified Lin−CD34+CD38−CD90+cells and/or individual LTC-CFCs derived from Lin−CD34+CD38−CD90+ cells). Synonymous mutations are indicated by no specified amino acid change.

(F) del(5q) and mutational status in representative individual Lin−CD34+CD38−CD90+-derived long-term colonies (gray, nonconclusive; orange, primer failure). Colonies shown at the furthest right were analyzed for presence of recurrent driver mutations only. Each vertical column shows mutation status in an individual LTC-CFC.

See also Figure S5 and Tables S4 and S5 .

Figure 7.

Genetic Evolution in MDS Stem and Progenitor Cells Preceding AML Transformation

Stem and progenitor cell characterization, and mutational status in sequential BM samples, purified MDS-SCs, and LTC-CFCs from patients 3 (A–G) and 19 (H–R).

(A and H) FACS profiles of SCs and progenitors in BM MNCs in serial samples (at diagnosis and months since diagnosis).

(B and I) del(5q) clonal involvement of Lin−CD34+CD38−CD90+CD45RA− MDS-SCs.

(C and J) Mean (SEM) LTC-CFCs generated from Lin−CD34+CD38−CD90+CD45RA− MDS-SCs, GMPs and MEPs.

(D and K) Frequency of exome variant reads in BM MNC at diagnosis and 23 months (patient 3) and at 170 months (patient 19). Dotted lines represent 5% variant read cut-off. Recurrent driver mutations are indicated in bold.

(E and L) Presence (red) and absence (blue) of variant reads for identified mutations in Lin−CD34+CD38−CD90+ SCs and/or individual LTC-CFCs (gray, nonconclusive).

(F and M) del(5q) and mutational status in representative individual Lin−CD34+CD38−CD90+CD45RA−-derived long-term colonies. Each vertical column shows mutation status in an individual LTC-CFC.

(N–Q) Presence or absence of TP53, IKZF1, SPRED2, and PIFO mutations in BM MNCs (N), MEPs (O), and MPPs (P); two replicate samples analyzed for MEPs and MPPs; percent variant reads at 170 months, are shown to the right, and from representative single cell clones from short-term expanded SCs, MPPs, and MEPs (Q).

(G and R) Proposed sequential acquisition of genetic events based on analysis of sequential samples, individual LTC-CFC, and single-cell SC, MPP, and MEP clones.

See also Supplemental Experimental Procedures , Figure S6 , and Tables S6 and S7 .

Two of the patients (patients 2 and 39) had stable disease, and exome-sequencing was performed at diagnosis as well as 30–32 months later when they remained transfusion-independent on long-term lenalidomide. At this time, the MDS-SCs, MEPs, and GMPs retained distinct phenotypic, functional, and molecular signatures ( Figures 6 A–6C; Figure S5 ). Exome-sequencing identified no (patient 2) and two (patient 39; Figure 6 D) recurrent driver mutations, and in both cases a number of predicted somatic passenger mutations ( Table S4 ), which in both patients could all be back-tracked to the Lin−CD34+CD38−CD90+ MDS-SC compartment, including LTC-CFCs, already at diagnosis and none had been eliminated despite the patients being in clinical remission 30–32 months later ( Figure 6 E and Tables S4 and S5 ). Sequencing of individual long-term colonies derived from Lin−CD34+CD38−CD90+ SCs established in both patients that all identified mutations had occurred in a linear manner ( Figure 6 F, Table S5 ). Notably, neither of these two patients have transformed to AML in the 2–4 years since the last analysis.

In a third, isolated del(5q) case (patient 3) with distinct SC and progenitor signatures ( Figures 7 A–7C, S6 ), who transformed to AML only 13 months later (no AML sample available), exome-sequencing of BM cells (23 months after diagnosis) obtained when the patient was responding well to lenalidomide, identified six somatic mutations, including a recurrent TP53 mutation, associated with poor prognosis ( Jädersten et al., 2011 ). None of these could be confidently identified in bulk BM cells at diagnosis neither by exome ( Figure 7 D; Table S6 ) nor targeted ( Table S7 ) sequencing. However, all identified mutations could be backtracked to the MDS-SC compartment at 23 months ( Figure 7 E), and individual LTC-CFC analysis confirmed that del(5q) along with a nonrecurrent MINA mutation preceded the other mutations, including theTP53 mutation ( Figures 7 F, 7G, S3 , and S6 ; Table S7 ).

A fourth patient with isolated del(5q) was analyzed before and following lenalidomide treatment, as well as at disease progression preceding AML transformation ( Figures 7 H–7Q; Figure S6 ; Table S1 ). Exome-sequencing identified 15 somatic mutations at disease progression, including a recurrent JAK2V617F and a recurrent TP53 mutation. The JAK2V617F and ten other nonrecurrent mutations were confidently identified in the bulk BM cells as well as in purified Lin−CD34+CD38−CD90+CD45RA− SCs in the initial BM sample more than 9 years earlier ( Figures 7 K and 7L; Tables S6 and S7 ). Despite reduced transfusion needs on lenalidomide treatment, 5 years later a previously undetectable recurrent TP53 mutation was now detected in bulk BM cells as well as in Lin−CD34+CD38−CD90+CD45RA− SCs ( Figures 7 L–7N; Tables S6 and S7 ). The patient later lost lenalidomide responsiveness and progressed to higher risk MDS (RAEB-1; 9% BM blasts), at which time exome-sequencing identified three additional mutations ( Figures 7 K–7N; Tables S6 and S7 ). Whereas the origin of the new IKZF1 and SPRED2 mutations as other mutations could be mapped to purified Lin−CD34+CD38−CD90+ SCs as well as SC-derived LTC-CFCs, the last mutation (PIFO) detected in 8.5% and 5.8% reads in whole BM with exome and targeted sequencing, respectively ( Figures 7 K and 7N; Tables S6 and S7 ), was undetectable in purified Lin−CD34+CD38−CD90+ SCs and SC-derived LTC-CFCs ( Figures 7 L–7M; Table S7 ). This suggested that the PIFO mutation might have occurred in a progenitor population outside the Lin−CD34+CD38−CD90+ SC compartment, which was likely to have acquired self-renewal potential. Because MEPs and a Lin−CD34+CD38−CD90− multipotent progenitor (MPP) population ( Majeti et al., 2007 ) had expanded at the time of progression ( Figure 7 H), we also performed targeted resequencing of these progenitor populations ( Figures 7 O and 7P), and identified, at progression but not in the preceding sample, PIFO variant reads at similar high frequencies as for the TP53 mutation ( Figures 7 O and 7P; Table S7 ). These findings were compatible with the preceding recurrent TP53 mutation that occurred in the Lin−CD34+CD38−CD90+ MDS-SC-compartment, conferring progenitor self-renewal and expansion at disease progression, as signified by the high reads for the predicted silent PIFO mutation exclusively outside the Lin−CD34+CD38−CD90+ compartment, thus here acting as a molecular marker for acquired self-renewal potential outside the Lin−CD34+CD38−CD90+ SC compartment. In further support of this, single cell analysis confirmed the mutually exclusive relationship of the IKZF1 (and SPRED2) and PIFO mutations, representing distinct del(5q) subclones, which prior to this branching had sequentially acquired recurrent del(5q), JAK2 V617F, and TP53 genetic lesions ( Figures 7 Q and 7R; Table S7 ). Notably, this patient transformed to AML 7 months later (from which time no BM or blood sample was available).

DISCUSSION

Efforts to identify distinct human CSCs have become a major focus in translational and clinical cancer research. Consequently, it was a considerable setback, yet to be resolved, when many studies established the inability of in vivo CSC assays to reliably uncover the tumorigenic potential of many cancer cell populations, including in hematological malignancies ( Clevers, 2011 , Kelly et al., 2007 , le Viseur et al., 2008 , Magee et al., 2012 , Pearce et al., 2006 , Quintana et al., 2008 and Taussig et al., 2008 ). Moreover, the identification of distinct human CSCs exerting their potential in patients ( Clevers, 2011 and Magee et al., 2012 ), including in MDS ( Agarwal, 2012 and ASH-Workshop, 2010 ), has remained elusive.

Here, we provide evidence of rare Lin−CD34+CD38−CD90+ cells functioning as MDS-SCs in patients with low- to intermediate-risk MDS in vivo. Lin−CD34+CD38−CD90+ MDS-SCs are molecularly and functionally distinct from clonally involved GMPs and MEPs. Importantly, Lin−CD34+CD38−CD90+ cells replenish GMPs and MEPs, establishing their hierarchical relationship. Although it cannot formally be ruled out, this hierarchical relationship argues against the possibility of driver mutations occurring outside the Lin−CD34+CD38−CD90+ SC compartment inducing self-renewal potential in progenitors and consequently a Lin−CD34+CD38−CD90+ SC-immunophenotype. Together with these findings, our backtracking of all identified somatic genetic lesions in the bulk BM from patients with MDS to Lin−CD34+CD38−CD90+ MDS-SCs provides evidence for the existence of rare human CSCs in vivo, and that these are the only MDS-SCs in vivo in low-risk MDS. This finding has wide clinical implications, highlighting that propagation and evolution of genetic lesions in the MDS clone is strictly dependent on a rare SC at early stages of MDS. In all investigated cases, the candidate driver mutations were all part of the same dominating SC clone, suggesting that they provide MDS-SCs with a competitive advantage over normal HSCs and the parental MDS clone, but even in combination fail to confer self-renewal potential to downstream progenitors. Even at diagnosis, MDS-SCs typically harbored multiple somatic mutations, including recurrent driver mutations, acquired in a linear, rather than branching, manner within the Lin−CD34+CD38−CD90+ SC compartment.

Sequencing analysis provided insights into the relationship between del(5q) and somatically acquired mutations, in particular in isolated del(5q) MDS. In more than 50% of isolated del(5q) cases, no recurrent driver mutation was identified by targeted or exome sequencing. Although our sequencing strategies may have missed recently identified recurrent mutations in MDS ( Klampfl et al., 2013 , Nangalia et al., 2013 and Papaemmanuil et al., 2013 ) and additional significant mutations are likely to be identified in the future, this is unlikely to explain why the fraction of cases without identified recurrent driver mutations is higher in patients with isolated del(5q) than in other groups with low- to intermediate-risk MDS and del(5q). In further support of del(5q) being the initiating genomic lesion in isolated del(5q) MDS, in all cases with at least one identifiable recurrent driver mutation where high confidence sequence of event prediction could be made, del(5q) preceded the acquired mutations. These findings, combined with our lack of evidence for a pre-MDS SC population, and the absence of deletions involving the 5q CDR in a large-scale screen for frequent copy number variations in the blood from older normal individuals ( Jacobs et al., 2012 ), are compatible with del(5q) being an initiating and potentially also the only genomic lesion required to develop the distinct clinical entity of isolated del(5q) MDS. However, further genomic analysis of patients and genetic modeling will be required to unequivocally establish if del(5q) is sufficient to induce isolated del(5q) MDS. Recent studies have shed some light on candidate genes within the 5q CDR responsible for causing some of the phenotypes associated with isolated del(5q) MDS (reviewed in Komrokji et al., 2013 ). It will be important to explore to what degree haploinsufficiency of the same or distinct genes in the 5q CDR is responsible for the competitive advantage of del(5q) over normal HSCs.

The only low- to intermediate-risk cases in which del(5q) was not the first identified genomic lesion were four cases of ring sideroblastic anemia in which del(5q) was preceded by a recurrent SF3B1 mutation. Intriguingly, just as del(5q) might largely define the phenotype of isolated del(5q) MDS, SF3B1 mutations are thought to be an early and potentially initiating event defining ring sideroblastic anemia ( Papaemmanuil et al., 2011 and Yoshida et al., 2011 ).

Although limited to four MDS cases with del(5q), tracking CD34+CD38−CD90+MDS-SCs and progenitors by sequential genomic and functional analysis provided insights into the landscape of somatic mutations in stable disease and preceding disease progression, and how they might impact on the MDS stem and progenitor cell hierarchy. In two patients with del(5q) who remained transfusion-independent on long-term lenalidomide treatment, the majority of Lin−CD34+CD38−CD90+ SCs remained part of the del(5q) clone, and no mutations present at diagnosis had been eliminated, nor had any new been acquired. This suggests that in patients with low-risk del(5q) with stable disease, the landscape of somatic mutations in MDS-SCs may be quite stable. However, in two other patients with low-risk isolated del(5q) MDS who later transformed to AML, additional mutations were identified in the SC-compartment while BM blasts remained <5%. In both cases, a recurrent TP53 mutation emerged, conferring worse prognosis ( Jädersten et al., 2011 ). When one of these patients progressed to RAEB-1 with a higher blast count, additional mutations were mapped to the MDS-SC compartment. However, a predicted passenger mutation (PIFO) could not be mapped to the SC compartment but was instead observed at a high frequency in expanded progenitor compartments along with the precedingTP53 mutation, compatible with acquisition of self-renewal potential by the MPPs (or a progenitor positioned between the Lin−CD34+CD38−CD90+CD45RA− and MPP compartment) at the time of disease progression following acquisition of a TP53 mutation. Although limited to one patient, and awaiting further validation and genetic modeling, this finding provides compelling in vivo evidence in support of sequentially acquired driver genomic lesions (del(5q), JAK2V617F, and TP53) in a small human CSC compartment, eventually conferring self-renewal and thus expansion-potential to downstream progenitors. This is of considerable relevance to the observed disease progression and subsequent AML transformation frequently seen in MDS patients, and in agreement with previous studies implicating that AML frequently contains candidate LSCs with a progenitor rather than SC phenotype ( Goardon et al., 2011 and Jamieson et al., 2004 ).

From the outset it could not be excluded that progenitors positioned between the Lin−CD34+CD38−CD90+SCs and myeloid-restricted MEPs and GMPs, including MPPs, might also act as MDS-SCs in vivo. However, backtracking of all identified somatic mutations to the Lin-CD34+CD38-CD90+ SC compartment in patients with low-risk MDS suggests that this is rarely the case in early MDS. Our conclusions are limited by the number of patients analyzed, and the fact that patients with MDS are highly heterogeneous with regard to their number and type of recurrent driver mutations ( Papaemmanuil et al., 2013 ). Recent studies in mice have implicated the existence of lineage-biased and perhaps even platelet-myeloid-restricted HSCs ( Sanjuan-Pla et al., 2013 and Yamamoto et al., 2013 ). When corresponding human HSC subsets can be identified, it will be of considerable interest to determine whether mutational targeting of such myeloid-biased or restricted stem cells may explain the largely myeloid-restricted phenotype of MDS.

Our findings suggest that MDS therapies with curative ambitions may best be applied in early MDS when MDS-SCs remain restricted to rare Lin−CD34+CD38−CD90+ cells. Although possessing properties predicting that they will be challenging to target effectively ( Tehranchi et al., 2010 ), the identification of distinct and rare MDS-SCs will facilitate identification of molecular targets and development of targeted therapies to eliminate MDS-SCs in a disease currently notoriously difficult to cure.

Although CSCs in more aggressive human cancers may not be rare, the distinct functional and molecular identity of MDS-SCs established here highlights the critical importance of continued efforts to identify in vivo, monitor and therapeutically target distinct CSCs, as their elimination should not only be essential but, in principle, also sufficient for development of curative cancer therapies.

EXPERIMENTAL PROCEDURES

Patients

Patients with low- to intermediate-risk MDS were included (see Table S1 for clinical details). BM from age-matched healthy individuals was always used for controls unless otherwise specified. All patients provided written informed consent and the study was approved by the ethics committees at the Karolinska Institute, The Norwegian Radiumhospital, Aarhus University Hospital, University of Pavia, Hôpital Avicenne Assistance Publique-Hôpitaux de Paris (AP-HP), University of Dundee, St. James Hospital, University of Oxford, University of New South Wales and Skåne University Hospital.

Flow Cytometry

Stem/progenitor analysis/isolation in BM mononuclear cells was performed as described ( Tehranchi et al., 2010 ) and outlined in the Supplemental Experimental Procedures .

Gene Expression Analysis

Gene expression was analyzed by Fluidigm Dynamic Arrays and global RNA sequencing ( Supplemental Experimental Procedures ).

FISH

Interphase FISH for del(5q) and +8 was performed as described in the Supplemental Experimental Procedures .

In Vitro Assays

Detailed methods for colony-forming cell (CFC), long-term-culture-CFC (LTC-CFC), and B cell potential, as well as short-term expansion of single cells are described in the Supplemental Experimental Procedures .

Xenograft Transplantation

Cells were transplanted into irradiated NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ (NSG) mice, and analyzed as outlined in the Supplemental Experimental Procedures . Experiments were performed in accordance with UK Home Office Project License 30/2570.

DNA Mutational Analysis

Targeted sequencing and exome sequencing for detection, tracking and quantification of genetic lesions were performed and analyzed as described in the Supplemental Experimental Procedures . Briefly, mutations were identified in bulk MDS BM by targeted sequencing of coding region of genes recurrently mutated in myeloid malignancies using a HaloPlex kit (Agilent Technologies) or custom designed RNA baits ( Papaemmanuil et al., 2013 ), as well as by whole exome sequencing using a SureSelect kit (Agilent Technologies) according to the manufacturer’s instructions. Mutations were back-tracked to stem and progenitor compartments by targeted sequencing, either using HaloPlex kit or Access Array chip (Fluidigm) with custom designed PCR primers.

Statistical Analysis

Statistical analysis was performed using Graphpad Prism 6 software. For individual comparisons nonparametric Mann-Whitney test was used and p values less than 0.05 were considered significant. Details of the bioinformatic analysis are included in the Supplemental Experimental Procedures .

AUTHOR CONTRIBUTIONS

S.E.W.J. and P.S.W., with input from S.L., E.P., E.H.-L., and R.S., designed and conceptualized the overall research, analyzed the data, and wrote the manuscript. U.K., O.C., and H.D. contributed equally to these studies and performed and analyzed experiments. D.W., H.D., E.P., S.T., S.L., U.K., R.E., and P.J.C. analyzed and interpreted DNA sequencing data; M.N., R.S., E.G., S.T., Q.D., and I.M. analyzed RNA sequencing data; and K.A., G.G., and B.S. performed FISH analysis. L.S., A.G., and S.D. performed experiments. A.J.M., M.K., C.S., T.M.B., S.N.C., and C.N. advised on experiments. S.-A.C. performed FACS sorting. I.D., D.J., P.F., P.H., M.S.H., M.C., L.M, S.T., D.B., J.B., A.P., J.E.P., A.U., P.V., M.T., G.K., L.N., and E.H.-L. provided patient samples and clinical data. All authors read and approved the final manuscript.

ACKNOWLEDGMENTS

This work was supported by CDP Fellowship from the Leukemia and Lymphoma Society (to P.S.W.), a Wellcome Trust Clinical Fellowship (to O.C.), a Leukemia and Lymphoma Research (LLR) project grant (to S.E.W.J), and a grant from Knut and Alice Wallenberg Foundation to Wallenberg Institute for Regenerative Medicine (to S.E.W.J.). H.D. acknowledges support from a Oxfordshire Healthcare Services Research Training Fellowship and a Wellcome Trust Clinical Research Training Fellowship. J.B. and A.P. acknowledge support from LLR and S.L. acknowledges support from the Swedish Research Council (STARGET) and European Research Council (BRAINCELL, 261063). P.V. is supported by a MRC Disease Team Award and the NIHR Oxford BRC. The authors thank the BMS at Oxford University and the Tayside Tissue Bank, Bishan Wu, Tiphaine Bouriez-Jones, Lillian Wittman, Gunilla Walldin, Monika Jansson, Susan Bray,

Noreen Keenan, Ann Hyslop, Michael Groves Adam Burns, Joanne Mason, Tim Rostron, and Anna Schuh for technical assistance.

ACCESSION NUMBERS

T he Gene Expression Omnibus accession number for the RNA sequencing reported in this paper is GSE55689 and the sequence read archive accession number for the DNA sequencing reported in this paper is SRP039353 .

Implementation of the NCI’s National Clinical Trials Network

thos@oneelevendigital.com (Thomas Conner) — Wed, 07 May 2014 16:29:00 GMT

The National Cancer Institute is launching a new clinical trials research network intended to improve treatment for the more than 1.6 million Americans diagnosed with cancer each year. The new system, NCI’s National Clinical Trials Network (NCTN), will facilitate the rapid initiation and completion of cancer clinical trials based on improvements in data management infrastructure, the development of a standardized process for prioritization of new studies, consolidation of its component research groups to improve efficiency, and the implementation of a unified system of research subject protection at over 3,000 clinical trials sites. Grants to fund the program will be awarded early in the spring of 2014.

Despite its solid record of accomplishment, the individual components of the NCI’s previous national clinical trials program (the Cooperative Groups) had become less efficient, necessitating changes to several of its operating procedures over the past decade. A 2010 report from the Institute of Medicine (see http://iom.edu/Reports/2010/A-National-Cancer-Clinical-Trials-System-for-the-21st-Century-Reinvigorating-the-NCI-Cooperative.aspx ) validated and refined the nature of the changes that needed to be undertaken. The Institute of Medicine suggested four overarching goals to guide improvement efforts:

Improving the speed and efficiency of the design, launch, and conduct of clinical trials
Making optimal use of scientific innovations
Improving selection, prioritization, support, and completion of clinical trials
Fostering expanded participation of both patients and physicians.

In particular, recommendations from the Institute of Medicine and others stressed that it was important for NCI to consolidate its late-phase clinical trials program into a smaller number of groups, each with greater capabilities and appropriate incentives to promote better overall system integration and cooperation. Those recommendations led NCI to develop the new NCTN, focusing on four overarching goals that will guide the new system: integration, prioritization, efficiency, and innovation.

The NCTN features many changes that will enhance national cancer clinical trials research activities, specifically:

The new system will use a single, common IT data management system (called Medidata RAVE) for all trials, facilitating participation by member sites in all studies, irrespective of the NCTN organization leading the trial. The uniformity of a single clinical trials system used by all of the members of the network will aid in the rapid development and conduct of studies as well as analysis of clinical trial findings.
The NCI Central Institutional Review Board (CIRB) will be expanded to cover studies conducted by the entire system, which should help sites open more trials. Previously, individual sites had to conduct their own ethics review. Having a single, centralized ethics review conducted by the NCI CIRB will harmonize the system, thereby lowering cost, time, and personnel needed for ethics review.
Tumor specimen banks and the informatics systems that will allow for the banks to be efficiently integrated have both been redesigned so that they can be used more efficiently in support of the development of predictive molecular tests to guide treatment decisions for patients.

“The new network represents an unmatched effort to integrate and streamline the process of cancer clinical trials research,” said James Doroshow, M.D., deputy director for clinical and translational research at NCI. “The conduct of NCI-supported trials, which are publicly funded, involves a complex system of designing, reviewing, and initiating studies. The new NCTN replaces a structure that was more than 55 years old.”

NCTN employs an inclusive process for generating studies and conducting clinical trials using broad representation from the oncology field, including academic researchers, as well as professional organizations, patients, and advocates. In particular, community-based clinical trials play an important role in expanding the implementation of research findings to encompass all phases of cancer care delivery. A new system to support clinical trials research in the community setting, the NCI Community Oncology Research Program (NCORP), which will play a critical, complementary role to the NCTN, is being launched later this year and will involve both cancer treatment and cancer care delivery research.

The NCTN will focus on phase 3 trials, the gold standard for establishing new treatments. It is anticipated that grant support for the NCTN program could be in the range of $150 million per year with additional funds for central administrative support made available through other NCI support contracts and grant programs.

Following the results of peer review, four adult Network groups and one pediatric group, each with its own operations and statistical centers, will be funded. The new Network groups will be engaged in closely knit collaborations to study and evaluate new cancer treatments and advanced approaches to imaging. By comparison, the former program contained nine adult groups, each with its own administrative and statistical centers. The NCTN will also include a Canadian group in the Network as NCI has had long-standing collaborations with Canadian investigators in clinical trials.

Lead Academic Participating Site (LAPS) grants, created specifically for the NCTN, target many of NCI’s designated Cancer Centers. LAPS will provide scientific leadership in development and conduct of clinical trials in association with the adult clinical trial groups. The LAPS will receive increased funding because of higher patient enrollment costs, but must maintain a high performance standard in patient accrual. About 30 LAPS will work together with one or more of the new NCTN adult groups, expanding the reach of the network.

One important outcome of this new Network will be the ability to facilitate the conduct of trials in rare tumors where patient accrual has always been very difficult. The availability of a national network of clinical trials sites to locate and enroll patients with unusual cancers should enhance the feasibility of conducting such studies. Also, as more cancers are molecularly defined and classified into smaller subsets, the new network structure will support the molecular screening studies needed to define and locate the smaller groups of patients who might be eligible for such studies.

“NCTN investigators are better poised than ever to address the most critical clinical research questions,” said Jeff Abrams, M.D., associate director of NCI’s Cancer Therapy Evaluation Program. “Employing the newly revitalized and efficient NCTN, scientists can conduct trials concentrating on specific cancers, specific populations, or particular methods, such as genetic screening of tumors, imaging, radiation, and surgery.”

The Genetic Basis of Cancer - TCGA

thos@oneelevendigital.com (Thomas Conner) — Wed, 07 May 2014 16:28:00 GMT

The Genetic Basis of Cancer (02:49)

Video Link: Click Here

The New Backbone of Clinical Trial Design

thos@oneelevendigital.com (Thomas Conner) — Wed, 07 May 2014 16:28:00 GMT

Dr. Douglas A. Levine

2012: Most scientists would now agree that The Cancer Genome Atlas (TCGA) is a transformative program in cancer biology, at least in defining the genomic landscape for a variety of malignancies in a reliable and robust manner. Research begets research and TCGA is no exception. On a computational level, there has been relatively little exploitation of the available germline data to examine quantitative trait loci (QTLs), and on the biologic level, there has been a lack of proteomic integration with TCGA – except for the recent inclusion of the reverse phase protein arrays on available residual material. Even the most nascent scientist can imagine countless additional outstanding analyses to be performed with TCGA data.

A major contribution that TCGA has made to date is in the design of molecularly targeted clinical trials. Prior to the widespread availability of reference genomic data, clinical trials of targeted therapeutics were based on a comprehensive review of the limited literature, generally indicating specific genomic events identified in a relatively small and sometimes non-uniform patient population. With TCGA’s data, now the starting point is often a survey of relevant events in the specified disease. As characterizations of more cancer types are completed, similarities and differences in event types for a given pathway or target can be highlighted across varied tumors types. For example, breast, endometrial, and colon cancers have many somatic mutations in the PIK3CA and PTEN genes. However, in ovarian cancer, copy number alterations in these genes predominate, and in lung squamous cell carcinoma, there is a plethora of amplification events in PIK3CA and mixed mutation and somatic copy number alterations (SCNAs) in PTEN. These varied genomic differences can now be (and are) considered when planning clinical trials.

MK-2206 is a selective allosteric inhibitor of AKT and is equally potent against AKT1 and AKT2, but less so against AKT3. MK-2206 inhibits phosphorylation of AKT1/2, which is a likely consequence of PI3K pathway activation through varied mechanisms, including PTEN or INPP4B loss, mutation in PIK3CA, PIK3R1, or AKT, and activation by other receptor tyrosine kinases. The PI3K Stand Up to Cancer Dream Team has recently opened two clinical trials in ovarian and endometrial cancers using MK-2206 as monotherapy in patients with recurrent disease. In designing these trials, TCGA’s data regarding the genomic heterogeneity have played a key role. It has been well known for many years that endometrial tumors have frequent somatic mutations in PTEN, PIK3CA, and other pathway members. However, for high-grade serous ovarian cancer, TCGA has identified activation of the PI3K/AKT pathway in approximately 40 percent of cases, mostly through SCNA and not through somatic mutation. Based on these genomic data, the endometrial trial is accruing ‘all-comers’ and stratifying them to treatment arms based on the results of a patient’s PIK3CA mutation testing. The ovarian trial is restricted to patients with an event in the PI3K/AKT pathway evidenced by either prior mutation detection, which is unlikely, or through PTEN loss as measured by immunohistochemistry (IHC). This loss is expected to be identified in approximately 20 percent of cases (unpublished data). The TCGA ovarian data directly informed this trial design. In contrast to mutational event testing for the PI3K/AKT pathway, which is becoming increasingly available in many CLIA-compliant laboratories, SCNA assays are hard to develop for patient selection on clinical trials. IHC is a widely available assay that can be applied to PTEN at academic centers with potential trial patients. Both trials will have sufficient correlative studies to survey all major events within the PI3K/AKT pathway once patient accrual is complete. Similar patient inclusion criteria could not be used for the same drug being tested in two different diseases due to the heterogeneous genomic background.

This example of the integration of TCGA data is just one of several with similar merit that could have been discussed. I believe that in the future of clinical trial design for targeted agents, after established pre-clinical biology, we will begin to search for a disease with sufficient genomic events to make a clinical trial worthwhile. TCGA will continue to play a major role in defining appropriate diseases for targeted agents. I hope to see genomics-directed trials that are somewhat agnostic to tumor site of origin after further proof of principle studies help to turn this admirable goal into reality.

Trials mentioned in this article:

PIK3CA Mutation Stratified Trial of MK-2206 in Recurrent or Advanced Endometrial Cancer - NCT01312753

M K-2206 in the Treatment of Recurrent Platinum-Resistant Ovarian, Fallopian Tube, or Peritoneal Cancer - NCT01283035

The Center for Cancer Genomics Data Portal Launches

thos@oneelevendigital.com (Thomas Conner) — Wed, 07 May 2014 16:27:00 GMT

The new Data Portal for the Center for Cancer Genomics (CCG ) was launched today. CCG is a recently created center to unify NCI’s activities in cancer genomics. The CCG Data Portal will serve as the access point for data generated by CCG programs, collaborations or member offices. Data are available for The Cancer Genome Atlas (TCGA) , The Cancer Cell Line Encyclopedia (CCLE) , Therapeutically Applicable Research to Generate Effective Treatments (TARGET) , Cancer Genome Characterization Initiative (CGCI) , Cancer Target Discovery and Development (CTD2) , and will soon be available for The Adjuvant Lung Cancer Enrichment Marker Identification and Sequencing Trial (ALChEMIST ) and The Exceptional Responders initiative. Lower level sequence data will continue to be available at the Cancer Genomics Hub and NCBI’s database of Genotypes and Phenotypes (dbGaP) .

TCGA's Pan-Cancer Efforts and Expansion to Include Whole Genome Sequence

thos@oneelevendigital.com (Thomas Conner) — Wed, 07 May 2014 16:27:00 GMT

Carolyn Hutter, Ph.D.

Program Director of the Division of Genomic Medicine at the National Human Genome Research Institute (NHGRI)

In 2013, TCGA’s ‘Pan-Cancer’ analysis on over 5,000 cases from 12 tumor projects (see figure) was featured in Nature Genetics with a complementary focus website, which presented over 15 papers and 5 thematic threads. The threads highlight key findings for mutational drivers, network models, exposures and pathogens, data discovery and future directions.

TCGA is currently expanding efforts to characterize commonalities, differences, and emergent themes across cancer types in collaboration with the International Cancer Genome Consortium (ICGC) through the Pan-Cancer Analysis of Whole Genomes (PAWG) project. The goal is to analyze the genomes, including genome-wide sequence data, of approximately 2000 pairs of tumor and normal samples, and integrate those results with clinical and other molecular data on the same cases. The genomic sequence data will be available to the research community through the TCGA Data Portal , CGHub , and the ICGC Data Repository . Investigators around the globe will lead analysis in a number of scientific areas, including: integration of transcriptome and genome analyses, patterns of structural variations, novel somatic mutation-calling methods, evolution and heterogeneity, and germline cancer genome variation.

Figure 1: Integrated data set for comparing and contrasting multiple tumor types. The Cancer Genome Atlas Research Network, Weinstein, J.N., Collisson, E.A., Mills, G.B., Shaw, K.M., Ozenberger, B.A., Ellrott, K., Shmulevich, I., Sander, C., and Stuart, J.M. (2013) The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. doi:10.1038/ng.2764. Read the full article .

The TCGA/ICGC PAWG will capitalize on existing TCGA data and infrastructure, and will incorporate information from other NIH-funded projects, such as the Encyclopedia of DNA Elements (ENCODE) , the Genotype-Tissue Expression (GTEx) Program and the Roadmap Epigenomics Program . As with other TCGA Pan-Cancer efforts to date, this work represents a significant effort and underscores the importance of team science. Using integrative approaches, investigators will be better able to distinguish the signal from the noise and focus on functionally relevant genomic alterations, pathways and mechanisms. However, whole genome analysis also poses a number of key challenges and research needs, such as improved approaches for computing on petabytes of data, more robust standards for cross-project mutation calling, and more effective methods for analyzing and interpreting non-coding variation.

O verall, combining whole genome sequence analysis and comprehensive genomic characterization in this coordinated cross-cancer analysis will enhance our knowledge of cancer genomics and biology. Such work will move TCGA closer towards our goal to improve our ability to diagnose, treat and prevent cancer. Furthermore, the advances in this project will extend beyond cancer research, as the improved capabilities in whole genome sequence analysis and interpretation will be applicable to studies of other diseases and of biology in general.

TCGA Bladder Cancer Study Reveals Potential Drug Targets, Similarities to Several Cancers

thos@oneelevendigital.com (Thomas Conner) — Wed, 07 May 2014 16:27:00 GMT

Investigators with The Cancer Genome Atlas (TCGA) Research Network have identified new potential therapeutic targets for a major form of bladder cancer, including important genes and pathways that are disrupted in the disease. They also discovered that, at the molecular level, some subtypes of bladder cancer – also known as urothelial carcinoma – resemble subtypes of breast, head and neck and lung cancers, suggesting similar routes of development.

T he researchers' findings provide important insights into the mechanisms underlying bladder cancer, which is estimated to cause more than 15,000 deaths in the United States in 2014. TCGA is a collaboration jointly supported and managed by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI), both parts of the National Institutes of Health.

"TCGA Research Network scientists continue to unravel the genomic intricacies of many common and often intractable cancers, and these findings are defining new research directions and accelerating the development of new cancer therapies," said NIH Director Francis Collins, M.D., Ph.D.

In this study, published online Jan. 29, 2014 in Nature, investigators examined bladder cancer that invades the muscle of the bladder, the deadliest form of the disease. The current standard treatments for muscle-invasive bladder cancer include surgery and radiation combined with chemotherapy. There are no recognized second-line therapies – second choices for treatments when the initial therapy does not work – and no approved targeted agents for this type of bladder cancer. Approximately 72,000 new cases of bladder cancer will be diagnosed in the United States in 2014.

"This project has dramatically improved our understanding of the molecular basis of bladder cancers and their relationship to other cancer types," said lead author John Weinstein, M.D., Ph.D., professor and chair of the Department of Bioinformatics and Computational Biology at The University of Texas M.D. Anderson Cancer Center in Houston. "In the long run, the potential molecular targets identified may help us to personalize therapy based on the characteristics of each patient's tumor."

"The real excitement about this project is that we now have a menu of treatment and research directions to pursue," said Seth Lerner, M.D., professor and chair in urologic oncology at Baylor College of Medicine in Houston, and one of the senior authors of the paper. "The field is poised to use this information to make new advances toward therapies for a very difficult to treat form of bladder cancer."

The research team analyzed DNA, RNA and protein data generated from the study of 131 muscle-invasive bladder cancer from patients who had not yet been treated with chemotherapy, radiation or any type of therapy. The scientists found recurrent mutations in 32 genes, including nine that were not previously known to be significantly mutated. They discovered mutations in the TP53 gene in nearly half of the tumor samples, and mutations and other aberrations in the RTK/RAS pathway (which is commonly affected in cancers) in 44 percent of tumors. TP53 makes the p53 tumor suppressor protein, which helps regulate cell division. RTK/RAS is involved in regulating cell growth and development.

The investigators also showed that genes that regulate chromatin – a combination of DNA and protein within a cell's nucleus that determines how genes are expressed – were more frequently mutated in bladder cancer than in any other common cancer studied to date. These findings suggest the possibility of developing therapies to target alterations in chromatin remodeling.

Overall, the researchers identified potential drug targets in 69 percent of the tumors evaluated. They found frequent mutations in the ERBB2, or HER2, gene. The researchers also identified recurring mutations as well as fusions involving other genes such as FGFR3 and in the PI3-kinase/AKT/mTOR pathway, which help control cell division and growth and for which targeted drugs already exist.

Because the HER2 gene and its encoded protein, HER2 – which affects cell growth and development – are implicated in a significant portion of breast cancers, scientists would like to find out if new agents under development against breast cancer can also be effective in treating subsets of bladder cancer patients.

"We've organized our medical care around the affected organ system," Dr. Lerner said. "We have thought of each of these cancers as having its own characteristics unique to the affected organ. Increasingly, we are finding that cancers cross those lines at the molecular level, where some individual cancers affecting different organs look very similar. As targeted drug agents go through preclinical and clinical development, we hope that rather than treating 10 percent of breast cancers or 5 percent of bladder cancers, it eventually will make sense to treat multiple cancer types where the target is expressed." The same theme runs through TCGA's Pan-Cancer project, which is aimed at identifying genomic similarities across cancer types, with the goal of gaining a more global understanding of cancer behavior and development.

"It is increasingly evident that there are genomic commonalities among cancers that we can take advantage of in the future," said NHGRI Director Eric D. Green, M.D., Ph.D. “TCGA is providing us with a repertoire of possibilities for developing new cancer therapeutics."

The scientists also uncovered a potential viral connection to bladder cancer. It is known that animal papilloma viruses can cause bladder cancer. In a small number of cases, DNA from viruses – notably, from HPV16, a form of the virus responsible for cervical cancer – was found in bladder tumors. This suggests that viral infection can contribute to bladder cancer development.

Tobacco is a major risk factor for bladder cancer; more than 70 percent of the cases analyzed in this study occurred in former or current smokers. However, the analysis did not identify major molecular differences between the tumors that developed in patients with or without a history of smoking.

"The definitive molecular portrait of bladder cancer by the TCGA Network has uncovered a promising array of potential therapeutic targets that provides a blueprint for investigations into the activity of existing and novel therapeutic agents in this cancer," said Louis Staudt, M.D., Ph.D., director, NCI Center for Cancer Genomics.

TCGA data are freely available prepublication to the research community through the TCGA Data Portal http://tcga-data.nci.nih.gov/tcga and CGHub https://cghub.ucsc.edu/

To date, TCGA Research Network has published analyses on these cancers:

glioblastoma multiforme ( http://cancergenome.nih.gov/newsevents/newsannouncements/news_9_4_2008 )
ovarian serous adenocarcinoma ( http://cancergenome.nih.gov/newsevents/newsannouncements/ovarianpaper )
colorectal adenocarcinoma ( http://www.cancer.gov/newscenter/pressreleases/2012/TCGAcolorectal )
lung squamous cell carcinoma ( http://www.cancer.gov/newscenter/newsfromnci/2012/LungSquamousTCGA )
invasive breast cancer ( http://cancergenome.nih.gov/newsevents/newsannouncements/breastserovca )
endometrial cancer ( http://www.cancer.gov/newscenter/newsfromnci/2013/TCGAendometrial )
acute myeloid leukemia ( http://www.cancer.gov/newscenter/newsfromnci/2013/TCGA_AML )
kidney (clear cell renal cell carcinoma) cancer: ( http://www.cancer.gov/newscenter/newsfromnci/2013/TCGArenalClearCell ).

###

This work was supported by the following grants from NIH: U54HG003273, U54HG003067, U54HG003079, U24CA143799, U24CA143835, U24CA143840, U24CA143843, U24CA143845, U24CA143848, U24CA143858, U24CA143866, U24CA143867, U24CA143882, U24CA143883, and U24CA144025.

Reference: The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of urothelial bladder carcinoma. Nature. Online January 29, 2014. DOI: 10.1038/nature12965.

The TCGA Research Network consists of more than 150 researchers at dozens of institutions across the nation. A list of participants is available at http://cancergenome.nih.gov/abouttcga/overview . More details about The Cancer Genome Atlas, including Quick Facts, Q&A, graphics, glossary, a brief guide to genomics and a media library of available images can be found at http://cancergenome.nih.gov .

NCI leads the National Cancer Program and the NIH effort to dramatically reduce the prevalence of cancer and improve the lives of cancer patients and their families, through research into prevention and cancer biology, the development of new interventions, and the training and mentoring of new researchers. For more information about cancer, please visit the NCI website at http://www.cancer.gov or call NCI's Cancer Information Service at 1-800-4-CANCER (1-800-422-6237).

NHGRI is one of the 27 institutes and centers at the National Institutes of Health. The NHGRI Extramural Research Program supports grants for research and training and career development at sites nationwide. Additional information about NHGRI can be found at http://www.genome.gov .

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 institutes and centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov .

The ICGC-TCGA DREAM Somatic Mutation Calling (SMC) Challenge

thos@oneelevendigital.com (Thomas Conner) — Wed, 07 May 2014 16:26:00 GMT

The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), in collaboration with Sage Bionetworks and IBM-DREAM, recently launched the ICGC-TCGA DREAM Somatic Mutation Calling (SMC) Challenge. The SMC Challenge is an open, crowd-sourced initiative to identify the most accurate and robust methods for detecting cancer-associated mutations in whole-genome sequencing data. These techniques will hopefully become the standard approaches for analyzing cancer genomic data in both research and clinical practice. Algorithms from participating teams will use real data and their performance will be assessed independently. The winning algorithms will be included in publications with Nature Publishing Group and may help advance the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes project, the next Pan-Cancer effort analyzing whole-genome sequencing data currently underway. For more information, please visit the ICGC-TCGA DREAM Somatic Mutation Calling Challenge website.

CASE STUDY: The Cancer Digital Slide Archive: A Web Platform for Accessing TCGA Data

thos@oneelevendigital.com (Thomas Conner) — Wed, 07 May 2014 16:26:00 GMT

Pathology slides are thin slices of tissue from a sample, prepared and stained on a piece of glass for examination under a microscope. Reviewing and analyzing these slides is a skillful task often performed by several pathologists to develop consensus around a diagnosis. In the past, the original slides had been mailed to the other pathologists, risking damage to the delicate pieces of glass and tissue. In the digital age, slides can be scanned to create image files, but this has presented a different set of problems. The massive image files may be several hundred megabytes in size, taking several hours to download.

These were the challenges David Gutman, M.D., Ph.D., faced when he started at Emory University's Center for Comprehensive Informatics in 2009. Dr. Gutman and his colleague, Lee Cooper, Ph.D., were examining whole-slide images from The Cancer Genome Atlas's (TCGA) glioblastoma multiforme (GBM) samples. Their goal was to better understand this deadly brain cancer by performing quantitative analysis on the slide images. Finding, downloading and annotating the slides were, in Dr. Gutman's words, "a real challenge." Each image is large, difficult to interpret, and not digitally connected to other data, like radiology reports and genomic information. To facilitate their research project, Dr. Gutman applied his expertise from one of his hobbies – programming. "I have been a computer nerd –for lack of a better word – my whole life. My dad was an electrical engineer and we built computers together." He continues, "I'm largely self-taught, but I've been playing around with websites for years."

To more quickly achieve their main research goal, Dr. Gutman and Dr. Cooper, along with their colleagues Dhanajaya Somanna, Ph.D., and Jake Cobb, M.S., developed a website to allow quick online access to slides, without needing to download them. "We realized [that] we did all the work setting up the site for GBM – we can basically roll it out for all the tumor types without that much added work," says Dr. Gutman. With Dr. Gutman's medical background and Dr. Cooper's skill in developing computer algorithms, they knew researchers all over Emory and could work in concert with them to expand the website to other types of TCGA's cancers selected for study. One of the benefits of collaboration, Dr. Gutman says, is that "we didn't have to develop our tools in isolation, which can be a huge problem."

With the foundation laid for what has become The Cancer Digital Slide Archive (CDSA), Dr. Gutman saw the underlying problems that it might solve, particularly related to team science, the term for the cooperation of multidisciplinary groups across institutions to answer the same research question. He explains, "One of the challenges I often face is that if you want to collaborate with people and you want to get quick feedback, the web seems to me the best way to do that.... The way we would do it before is to send a hard drive with a couple gigabytes of images or make someone download them." This is problematic not only because of the large file size, but also the installation of companion software. This can be especially due to the stringent security programs installed on computers owned and managed by a medical center, where pathology specialist collaborators often work.

"You need to be able to look at stuff on the web to get people's feedback. You shouldn't have to install or download anything," says Dr. Gutman. "Otherwise, you don't get feedback."

In its contemporary form, the CDSA is a user-friendly tool for navigating TCGA data, using pathology slides as the starting point. From there, a user is presented with a multitude of ways to investigate, annotate and share the data. Each slide is linked to TCGA's open access data . Within the website, a user can navigate to the genomics, clinical data, radiology information and more. Each slide can be digitally annotated. In addition to pan and zoom, a user can "mask" part of the slide for analysis, meaning that the section won't be used in an algorithmic analysis. Touring the slide images and coming across an abnormal slide with a black smear and a red mark, Dr. Gutman comments, "This razor blade had some shmutz on it, and here's a pen artifact." He continues, "We just tell the computer, 'Don't look here [during analysis]'... If you don't have the ability to quickly do these things, it becomes a real hassle."

One of the most useful features of CDSA is the "deep linking technology." A user can post a comment on an area of the slide or even a specific cell, and create a link to the comment and image and send it to a colleague. "It's like Google Maps," says Dr. Gutman. "You can send someone a latitude and a longitude on of the slides, they click the link, and it goes right to the spot the other person was looking at."

Dr. Gutman found for himself how useful deep linking technology is. He says that, while browsing the data pathology slides of lung cancer samples, "I found all of these interesting nuclei in the bottom right corner of a slide - I was excited!" After annotating the slide and sending the CDSA link to a colleague, Dr. Gutman was told that the nuclei were just run-of-the-mill lymphocytes, a type of white blood cell and a normal part of the tissue. "I did go to medical school, but I don't think pathology was one of my strongest subjects. Fifteen years after I learned what a lymphocyte looks like - apparently, I'd forgot[ten]!"

The CDSA website is both simple to use and intuitive. Its usability is a testament to its easy-to-navigate interface. Dr. Gutman was surprised and pleased to hear that the pathologist who trained Dr. Gutman's mentor maneuvered the website without instruction. "To get someone who didn't grow up in the digital age, like Lee and me, to use the website is pretty cool," says Dr. Gutman. Additionally, the pathologist was impressed by the image quality. "From someone who’s been using expensive glass microscopes and doing this for 40 years! That was the thing that made me most happy."

The ease of data accessibility is especially important for TCGA, which make data available as they are generated to the entire cancer research community. Dr. Cooper says, "It's important to expose [the data] to as broad an audience as possible." He continues, "The CDSA puts this information at people's fingertips. There's no barrier to getting in and looking at the data." Dr. Gutman adds that, "Making as much information available to people as easily as possible is how team science needs to be done and how consensus is built."

Considering potential applications for the CDSA, Dr. Gutman and Dr. Cooper realize that they have built a technology structure that can be utilized to view slides from any source, not just human tumors. Dr. Gutman collaborates with a veterinary pathologist at the University of Georgia. Dr. Gutman says, "He wanted to scan a bunch of canine slides, look at them on the web, and use them as a teaching resource for his vet students." Dr. Gutman was able to rework a version of CDSA to suit his colleague's goals. Dr. Cooper adds that the flexibility of the software means it could be of great value in other fields such as Parkinson's and Alzheimer's research where Dr. Gutman notes that pathology may play an important role.

In considering the future of team science and other such partnerships, Dr. Gutman summarizes his goal in a single sentence: “The idea is everything should be able to talk to each other and should make as much data available to as many people with as few barriers as possible.”

Gutman, D.A., Cobb, J., Somanna, D., Park, Y., Wang, F., Kurc, T., Saltz, J.H., Brat, D.J. and Cooper, L.A. (2013) Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data. J Am Med Inform Assoc. doi: 10.1136/amiajnl-2012-001469. View PubMed abstract

Dartmouth awarded lead role in NCI clinical trials network

thos@oneelevendigital.com (Thomas Conner) — Wed, 30 Apr 2014 16:26:00 GMT

Dartmouth has been awarded one of 30 grants from the National Cancer Institute (NCI) to serve as a Lead Academic Participating Site in its new National Clinical Trials Network (NCTN). Award recipients are a select groups of investigators charged with distributing resources in a more effective way across fewer cooperative groups.

The NCTN grant system reflects recommendations from a 2010 Institute of Medicine Report . It streamlines operations to achieve four goals:

Faster design, launch, and completion of clinical trials
Optimal use of scientific innovations
Strategic prioritization of studies
Expanded participation of patients and physicians

Through a consolidation of operational resources, the NCTN untangles behind-the-scenes red tape by using:

A common IT data management systems
One Central Institutional Review Board
Integrated specimen banks and informatics systems

"Everyone—patients, providers, and family members—wants to see faster access to new treatments for cancer," said Konstantin H. Dragnev, MD, principal investigator for the Dartmouth-Hitchcock Norris Cotton Cancer Center site. "This new framework will cut the startup time for a clinical trial by 75 percent in some cases. It removes obstacles we used to face for reporting and oversight, so we can now offer therapeutic advances to patients sooner."

As a Lead Academic Participating Site, Norris Cotton Cancer Center will be charged with enhancing participation in NCI randomized phase three clinical trials, the gold standard in cancer research for establishing new treatments for a five year period. Participating sites provide scientific leadership in the development of clinical trials, while meeting performance benchmarks for quality clinical research. Dartmouth will oversee involvement and participation from affiliated patient enrollment settings in New Hampshire, Vermont, and other states.

"As a group we can achieve more than we can individually," said Dragnev of the co-operative groups in the nationwide network. "Urban and rural residents can participate in the same study, which expands our ability to include a more diverse population in an individual study. The Dartmouth-led affiliations also mean greater access to new therapies in relatively remote areas served in northern New England."

The new network represents an unmatched effort to integrate and streamline the process of cancer clinical trials research," said James Doroshow, MD, deputy director for clinical and translational research at NCI. "The conduct of NCI-supported trials, which are publicly funded, involves a complex system of designing, reviewing, and initiating studies. The new NCTN replaces a structure that was more than 55 years old."

The Network has combined smaller cooperative groups with specialized foci, such as, pediatric, breast/bowel, or gynecological cancers. These consolidated cooperative groups allow for closer communication and collaboration among researchers doing work in the same area. The newly announced Lead Academic Participating Sites will serve as common outlets for offering trials originating from any of these groups.

" Norris Cotton Cancer Center's selection as a Lead Participating Site affirms our role as a national leader in cancer research," said Mark Israel, MD, director, Norris Cotton Cancer Center. "We are among an elite group of investigators conducting nationally the highest caliber of scientific research."

Rescuing US biomedical research from its systemic flaws

thos@oneelevendigital.com (Thomas Conner) — Thu, 17 Apr 2014 16:25:00 GMT

Bruce Alberts, Marc W. Kirschner, Shirley Tilghman, and Harold Varmus

The long-held but erroneous assumption of never-ending rapid growth in biomedical science has created an unsustainable hypercompetitive system that is discouraging even the most outstanding prospective students from entering our profession—and making it difficult for seasoned investigators to produce their best work. This is a recipe for long-term decline, and the problems cannot be solved with simplistic approaches. Instead, it is time to confront the dangers at hand and rethink some fundamental features of the US biomedicalresearch ecosystem.

Full Article: http://www.pnas.org/content/early/2014/04/09/1404402111.full.pdf+html

NCI Cancer Genomics Cloud Pilots

thos@oneelevendigital.com (Thomas Conner) — Mon, 14 Apr 2014 16:25:00 GMT

CURRENT NEEDS IN CANCER RESEARCH

The challenges posed by the need to disseminate, manage, and interpret large, multi-scale data pervade efforts to advance understanding of cancer biology and apply that knowledge in the clinic. For several years, the volume of data routinely generated by high-throughput research technologies has grown exponentially . The storage, transmission, and analysis of these data have become too costly for individual laboratories and most small to medium research organizations to support. For optimal progress to occur, access to large, valuable data collections and advanced computational capacity must be readily available to the widest possible audience.

On April 7, 2013, Dr. Harold Varmus and other members of the Institute's senior leadership issued a letter to NCI grantees seeking input on these and other computational challenges they encounter on an almost daily basis. Dr. Varmus stated that the NCI, as part of its ongoing investigations into next-generation computational capabilities to serve the research community, has begun exploring the possibility of creating one or more public "cancer knowledge clouds" in which data repositories would be co-located with advanced computing resources, thereby enabling researchers to bring their analytical tools and methods to the data. Reactions to this informal request for information were generally positive, with respondents focusing on six general themes : data access; computing capacity and infrastructure; data interoperability; training; usability; and governance.

Based in part on this information, Dr. George Komatsoulis, then interim director of the Center for Biomedical Informatics and Information Technology (CBIIT), which administers the National Cancer Informatics Program (NCIP), led the creation of a concept document describing a project to develop up to three cancer genomics cloud pilots for review by the cancer-research community. Dr. Komatsoulis presented the concept (time reference 05:58:00) at a joint meeting of the NCI Board of Scientific Advisors (BSA) and the National Cancer Advisory Board (NCAB) on June 24, 2013, where it received unanimous approval .

Soon after the concept was approved, NCI issued a Research and Development Sources Sought Notice providing a synopsis of requirements and asking respondents to submit capability statements. The deadline for submissions was July 24, 2013.

Simultaneously with beginning the procurement process, the NCIP established an IdeaScale site on August 8, 2013 to allow the community to contribute critical use cases that the cloud pilots will need to support. For more detailed information, consult the official Request for Information: IdeaScale for Cancer Genomics Cloud Pilots on FedBizOpps. A recent posting on the NCI Biomedical Informatics Blog explains the rationale behind the decision to use IdeaScale.

The IdeaScale site will be locked from additional input to coincide with the release of the BAA. It will remain open, however, as a reference for potential offerors.

THE CONTRACTING AND AWARD PROCESS

In preparation for the BAA, the NCI posted a pre-solicitation notice on November 25, 2013, on FedBizOpps that announced the online pre-proposal conference described above in the gray box.

The BAA is the specific contract mechanism that will support development of the cancer genomics cloud pilots. The project will go through three phases:

Design
Implementation
Evaluation

The organizations selected to develop the clouds will be expected to collaborate with each other and with the groups managing the NCI Center for Cancer Genomics (CCG) Data Coordinating Center. NCIP activities are being conducted in concert with the CCG Data Coordinating Center, which will provide an authoritative public data set for use in the cloud pilots. Interchange among the organizations involved will help ensure adherence to a common set of data elements and vocabularies among the cloud pilots in support of operations that may span cloud implementations.

Areas of Focus

The research and development activities sponsored by the NCIP span four areas: Cancer Biology and Genomics, Clinical and Translational Research, Computational Genomics Research, and Semantic Infrastructure and Interoperability.

CANCER BIOLOGY AND GENOMICS

Multi-dimensional characterization data sets that compare tumor and normal tissue at the molecular level are providing unprecedented detail about the molecular alterations that lead to cancer. The ability to manage and analyze these data, and to integrate the results with the corresponding biological and clinical information, is providing new directions for developing treatment strategies that target the specific molecular changes in a patient’s disease. To provide support, CBIIT informatics priorities in the areas of cancer biology and genomics include

Management and analysis of primary data sets for cancer biology and genomics
Aggregation of translational research data and annotations
Dissemination and support for cancer biology and genomics data, tools, and standards.
Application of computational methods in support of knowledge discovery

CLINICAL AND TRANSLATIONAL RESEARCH

The Clinical and Translational Research domain provides targeted bioinformatics capabilities facilitating interoperability, collaboration, and integration across applications. Relevant applications are also designed to ease clinical trial reporting burdens through the consolidation of mechanisms for reporting and harmonizing implementable data standards for next-generation study designs. Specific project areas include the following:

Clinical Research Management, Adaptive Trial Management, and Enterprise Trial Reporting comprise centralized research subject management, centralized study data management (including accrual data), adverse-event reporting, next-generation clinical trials management, and enterprise portfolio management.
Clinical Research Data Modeling and Case Report Form Harmonization provide the semantic tools needed to integrate and interoperate data across multiple systems and entities at both NCI and the extramural and pharmacologic communities.
Imaging and Biospecimen Data Management systems address the need to manage large sets of clinical and preclinical research data in the form of diagnostic images and biospecimens. Both types of data sets require the association of multiple metadata elements with individual and across-study-specific images and specimens in order to allow researchers to share and manage not only research information but also valuable specimens that may be reused beyond the initial research.
Clinical Research Regulatory Management addresses NCI’s desire to streamline the management of regulatory requirements in order to accelerate the initiation of clinical trials research.

COMPUTATIONAL GENOMICS RESEARCH

Current biomedical informatics technologies permit the genome-wide generation of multidimensional molecular data sets that researchers can use to assess copy number alterations, nucleotide substitutions, insertions or deletions, rearrangements, and epigenetic changes. Furthermore, next-generation sequencing (NGS) provides researchers with complete gene and genome sequences.

The CBIIT Computational Genomics Research Group (CCRG) creates analytical methods and applications designed to integrate, display, and interpret such diverse, systems-wide data sets. The goal is to translate genetic and genomic observations into insights concerning the biology of human cancers, including disease etiology. As part of its collaborative development efforts, the CCRG has provided tools, analytical capacity, and bioinformatics support to researchers participating in The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) projects as well as to investigators in the intramural NCI community.

INTEROPERABILITY AND SEMANTICS

T hrough a semantic infrastructure and an interoperability framework, CBIIT supports multidisciplinary science by enabling data integration across different specialties and institutions. The semantic infrastructure provides standard vocabularies, common data elements, clinical case-report forms, data models, and definitions. The interoperability framework follows a widely used approach of employing services that are independent of any particular information-technology platform.

New SU2C-Lustgarten Foundation Pancreatic Cancer Convergence Dream Team Announced

thos@oneelevendigital.com (Thomas Conner) — Wed, 09 Apr 2014 16:24:00 GMT

Fox Family Cancer Research Funding Trust Is Contributing to Support Team

$8 million grant over three years will fund research focusing on novel immunotherapies for pancreatic cancer

SAN DIEGO — Stand Up To Cancer (SU2C), The Lustgarten Foundation , and the Fox Family Cancer Research Funding Trust, along with the American Association for Cancer Research (AACR), SU2C’s Scientific Partner, announced the formation of a Dream Team dedicated to pancreatic cancer research during a press event today at the AACR Annual Meeting 2014, held here April 5-9.

Elizabeth M. Jaffee, M.D., professor of oncology at the Johns Hopkins University School of Medicine and co-director of the Gastrointestinal Cancers Program at the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins in Baltimore, Md., will lead the Dream Team. Robert H. Vonderheide, M.D., D.Phil., associate director for translational research at the Abramson Cancer Center of the University of Pennsylvania and the Hanna Wise Professor in Cancer Research at Penn’s Perelman School of Medicine in Philadelphia, Pa., will co-lead the project, which is titled, “Transforming Pancreatic Cancer to a Treatable Disease.”

The SU2C-Lustgarten Foundation Pancreatic Cancer Convergence Dream Team Translational Research Grant will provide $8 million in funding over three years for this innovative project that will develop new therapies to exploit patients’ own immune cells to treat their cancers. The team will be supported in part by a gift to SU2C from the Fox Family Cancer Research Funding Trust.

“Pancreatic cancer suppresses the body’s antitumor immune response,” said Jaffee. “These tumors do not allow immune cells that can recognize and kill them to even enter the pancreas. We think we can use vaccination to activate antitumor immune cells and then use other agents to get those cells into the pancreas, where they can attack the tumor.

“We intend to convert the immune-suppressive environment of the tumor into one that fosters rejection of the tumor by the immune system,” she said.

Researchers on the Dream Team represent nine institutions: Johns Hopkins University; Abramson Cancer Center of the University of Pennsylvania; Washington University in St. Louis; University of California, San Francisco; Oregon Health & Science University; New York University Langone Medical Center; Stanford University; University of Cambridge, United Kingdom; and Memorial Sloan Kettering Cancer Center.

“Pancreatic cancer is among the most deadly types of cancer,” said Vonderheide, “but we believe that we are on the cusp of developing and delivering care that has the potential to make real headway. We have a new understanding that the immune system can be a powerful therapy for cancer and we know that to exploit it to treat pancreatic cancer, we need to activate the immune system in better and more robust ways.

“It’s time to act,” Vonderheide added, “and this grant will allow us to do just that.”

The joint venture between SU2C, formed in 2008 to accelerate the translation of cancer research into meaningful advances in patient care, and The Lustgarten Foundation, the nation’s largest private funder of pancreatic cancer research, brings together leading cancer research fundraising groups. Formation of the team will fulfill the mission of both organizations and that of the Fox Family Cancer Research Funding Trust to fund the most promising research to find new treatment options for pancreatic cancer and ultimately enable patients to lead long and healthy lives.

“With only two percent of federal funding directed toward pancreatic cancer research, organizations like Stand Up To Cancer and The Lustgarten Foundation serve a critical role in helping to combat this lethal disease,” said Kerri Kaplan, executive director of The Lustgarten Foundation. “New approaches like immunotherapy are urgently needed to help identify more effective treatment options for patients and save lives.”

“While we are making progress with many other forms of cancer, breakthroughs are needed in pancreatic cancer to improve survival time and allow people to live their lives as normally as possible,” said Sung Poblete, Ph.D., R.N., SU2C’s president and chief executive officer. “The SU2C-Lustgarten Foundation Dream Team is bringing together some wonderfully talented researchers from top cancer centers who will work collaboratively to bring us closer to the day when pancreatic cancer can be managed, if not cured.”

The SU2C–Lustgarten Foundation Pancreatic Cancer Convergence Research Project

Pancreatic ductal adenocarcinoma (PDA) is resistant to most forms of therapy and is one of the most deadly types of cancer. The environment that surrounds cancer cells is referred to as the tumor microenvironment, and studies in mice and humans have shown that the PDA tumor microenvironment has unique characteristics that are thought to limit the efficacy of treatment. By understanding the obstacles that prevent the tumor from responding to treatments, it should be possible to develop therapeutic agents to eliminate these barriers, resulting in the effective treatment of PDA.

T cell-based cancer immunotherapy has shown promise for the treatment of a variety of cancer types and was hailed by the journal Science as “Breakthrough of the Year” in 2013. Despite its emerging promise, clinical efforts for immune therapy in PDA have lagged behind. Recent advances in PDA mouse models and in technologies to study cancer-associated immune processes at tumor sites have revealed that major anti-PDA immune responses can occur if antitumor T cell-generating approaches are combined with drugs that block immune suppression in the tumor. Based on promising initial clinical trials, this Dream Team’s goal is to “reprogram” the tumor microenvironment to fuel clinically meaningful anticancer immune responses in patients with PDA.

The Dream Team will use a “convergence” approach by bringing together leading individuals in the fields of immunotherapy, genetics, informatics, biostatistics, regulatory/clinical trials, cancer biology, and pathology. This group of experts will apply their efforts toward understanding and treating PDA.

“Pancreatic cancer is particularly difficult to treat because it is often not detected until it has reached an advanced stage,” said Poblete. “We are very pleased that SU2C is working with The Lustgarten Foundation and the Fox Family Cancer Research Funding Trust to support promising new research that has the potential to extend the lives of people with pancreatic cancer.”

The Dream Team will conduct combination clinical trials and establish biomarkers of tumor microenvironment reprogramming. Trials will focus on novel immune-suppressive pathways within the tumor, either in combination with a T cell-activating vaccine or chemotherapy. These trials will also establish a national PDA biobank for identification of immune biomarkers. Preclinical studies in PDA mouse models will be conducted to establish novel multiagent approaches and develop biomarkers that will drive the next generation of clinical trials.

The project is expected to start July 2014, with clinical trials scheduled to open within the first year.

Dream Team Selected Through Unique, Rigorous Process

A SU2C-Lustgarten Foundation Joint Scientific Advisory Committee (JSAC) conducted a unique, rapid, and rigorous evaluation of the applications via a multistep scientific review process.

The committee is chaired by Nobel Laureate Phillip A. Sharp, Ph.D., institute professor at the David H. Koch Institute for Integrative Cancer Research at the Massachusetts Institute of Technology in Cambridge, Mass. It is co-chaired by SU2C representative Michael B. Kastan, M.D., Ph.D., executive director of the Duke Cancer Institute in Durham, N.C., and David A. Tuveson, M.D., Ph.D., director of The Lustgarten Foundation Pancreatic Cancer Research Laboratory at Cold Spring Harbor Laboratory in Cold Spring Harbor, N.Y., director of research for The Lustgarten Foundation, and professor and deputy director for the Cancer Center at Cold Spring Harbor Laboratory. The JSAC comprises highly accomplished senior laboratory researchers and physician-scientists as well as advocates.

The review process began with a call for ideas by the AACR in September 2013. The committee then chose four teams, each of which met with the JSAC to present the plans for their research and respond to questions about their projects—a level of interaction between applicants and reviewers that is unique in a scientific review process.

The AACR is responsible for administering the grant and provides ongoing scientific oversight to ensure that progress is being made. Since the launch of SU2C, the AACR has played an integral role as SU2C’s Scientific Partner by providing scientific leadership, expert peer review, grants administration, and oversight of progress.

Dream Team Principals and Advocate Members

The “Transforming Pancreatic Cancer to a Treatable Disease” Dream Team consists of a multidisciplinary group of experts that includes laboratory and clinical researchers, young investigators and senior scientists who have not worked together in the past, and patient advocates. In addition to Jaffee and Vonderheide, team members are:

Principals:

Margaret A. Tempero, M.D., University of California, San Francisco;
Lisa M. Coussens, Ph.D., Oregon Health & Science University;
David C. Linehan, M.D., Washington University in St. Louis;
Dafna Bar-Sagi, Ph.D., NYU Langone Medical Center;
Irving L. Weissman, M.D., Stanford University;
Douglas T. Fearon, M.D., University of Cambridge, United Kingdom; and
Steven D. Leach, M.D., Memorial Sloan Kettering Cancer Center.

Advocates:

Stuart Rickerson, University of California, San Francisco, patient advocate;
Richard Vague, University of Pennsylvania School of Medicine board member and managing partner, Gabriel Investments; and
Betty Booher, pancreatic cancer advocate.

Including today’s announcement, SU2C has now awarded grants to 12 Dream Teams and two Translational Cancer Research Grants. Twenty-six Innovative Research Grants have been awarded to individual young investigators. Together, these recipients comprise more than 700 scientists from more than 100 institutions.

Nearly $5 Million in Research Grants Awarded for Innovative Pancreatic Cancer Research

thos@oneelevendigital.com (Thomas Conner) — Wed, 09 Apr 2014 16:23:00 GMT

SAN DIEGO — The Pancreatic Cancer Action Network and the American Association for Cancer Research (AACR) awarded 14 grants through the 2014 Research Grants Program here today to outstanding scientists throughout the country, supporting their innovative research in the field of pancreatic cancer.

The grants support research into high-priority areas in an effort to reach the Pancreatic Cancer Action Network’s goal to double pancreatic cancer survival by 2020. Pancreatic cancer has historically been understudied and underfunded, yet it is the fourth leading cause of cancer death in the United States, and has the lowest survival rate of major cancers, at just 6 percent.

The diverse research topics funded this year include determining whether sudden-onset diabetes could be an early indicator of pancreatic cancer, strategies to starve pancreatic cancer cells of necessary nutrients, and a treatment method that involves harnessing the patient’s immune system to fight the tumor.

“Pancreatic cancer is among the most deadly of cancers,” said Margaret Foti, Ph.D., M.D. (h.c.), chief executive officer of the AACR. “With death rates steadily climbing over the past decade, more research into pancreatic cancer is urgently needed. The AACR is, therefore, proud to be partnering with the Pancreatic Cancer Action Network to support cutting-edge scientific research projects that have the potential to lead to major breakthroughs in the prevention, detection, and treatment of this devastating disease.”

“The most promising science has been selected for funding through a rigorous peer-review process. This year’s grant recipients hail from leading institutions throughout the country and range from early career investigators continuing to build the field of pancreatic cancer leaders to more senior scientists,” said Julie Fleshman, president and CEO of the Pancreatic Cancer Action Network. “Their collective efforts have the potential to answer important questions that could lead to significant scientific advances for pancreatic cancer, and ultimately improve patient outcomes. We look forward to working with our new grantees and welcoming them to our team.”

The Pancreatic Cancer Action Network, in collaboration with the AACR, introduced the grants program in 2003, and has since awarded 108 research grants totaling more than $22 million to bright and motivated scientists across the country with the goals of developing a pipeline of researchers dedicated to studying the disease, supporting innovative ideas and approaches, and enabling the organization to reach its 2020 goal.

This year’s recipients will be honored today at the AACR Annual Meeting 2014 , held April 5-9.

Meet the grant recipients and learn more about their funded projects.

The 2014 Pancreatic Cancer Action Network-AACR Research Acceleration Network Grants are three-year grants totaling $1 million each. These grants offer strategic funding and project management services to high-priority projects already underway within the pancreatic cancer research community. This year’s recipients are:

Giulio F. Draetta, M.D., Ph.D. , The University of Texas MD Anderson Cancer Center, Houston, Texas; and, Co-PI, Lewis C. Cantley, Ph.D. , Joan and Sanford I. Weill Medical College of Cornell University, New York, N.Y.
“Developing a novel oxidative phosphorylation inhibitor in pancreatic cancer”
Dung T. Le, M.D ., Johns Hopkins Kimmel Cancer Center, Baltimore, Md.; and, Co-PI, Todd S. Crocenzi, M.D. , Providence Portland Medical Center, Portland, Ore.
“GVAX + CRS-207 heterologous prime boost vaccination with PD-1 blockade”
Supported by the Fredman Family Foundation

The 2014 Pancreatic Cancer Action Network-AACR Pathway to Leadership Grant is a five-year grant totaling $600,000. This grant is designed to support the future leadership of pancreatic cancer research by funding an outstanding early-career investigator beginning a postdoctoral, mentored research position and continuing through a successful transition to independence. This year’s recipient is:

Gina M. DeNicola, Ph.D. , Joan and Sanford I. Weill Medical College of Cornell University, New York, N.Y.
“Therapeutic targeting of NRF2-regulated metabolism in pancreatic cancer”

The 2014 Pancreatic Cancer Action Network-AACR Innovative Grants are intended to promote the development and study of novel ideas and approaches in basic, translational, clinical, or epidemiological research that have direct application and relevance to pancreatic cancer. These two-year grants provide $200,000 over the grant term. This year’s recipients are:

Michael Thomas Barrett, Ph.D. , Translational Genomics Research Institute, Scottsdale, Ariz.
“Genomic drivers of therapeutic responses in metastatic disease”
Dafna Bar-Sagi, Ph.D. , New York University School of Medicine New York, N.Y.
“PDA development: Heads or tails?”
Anirban Maitra, MBBS , The University of Texas MD Anderson Cancer Center, Houston, Texas
“Macrophage function in pancreatic cancer-associated diabetes”
George Miller, M.D. , New York University School of Medicine, New York, N.Y. “Regulation of pancreatic tumorigenesis by necroptosis”
Supported by Celgene Corporation
Diane M. Simeone, M.D. , University of Michigan Medical Center, Ann Arbor, Mich.
“Mesenchymal stem cells in pancreatic cancer biology and therapeutic development”

The 2014 Pancreatic Cancer Action Network-AACR Career Development Awards are two-year grants of $200,000 that are designed to attract and support early-career scientists as they conduct pancreatic cancer research and establish successful career paths in the field. This year’s recipients are:

David G. DeNardo, Ph.D. , Washington University in St. Louis, St. Louis, Mo.
“Origins and impact of macrophages in pancreatic cancer”
Eugene J. Koay, M.D., Ph.D ., The University of Texas MD Anderson Cancer Center, Houston, Texas
“Changes in mass transport as a biomarker of response in pancreatic cancer”
Florencia McAllister, M.D., The University of Texas MD Anderson Cancer Center, Houston, Texas
“Targeting IL-17 signaling axis in pancreatic ductal adenocarcinoma”
Kenneth L. Scott, Ph.D. , Baylor College of Medicine, Houston, Texas
“Functionalizing metabolic pathway driver aberrations in pancreatic cancer”
Kathryn E. Wellen, Ph.D. , University of Pennsylvania, Philadelphia, Pa.
“Understanding metabolic control of the pancreatic cancer epigenome”

The 2014 Pancreatic Cancer Action Network-AACR Fellowship is a one-year grant of $45,000 designed to support a postdoctoral investigator’s work in pancreatic cancer research. This year’s recipient is:

Barbara M. Grüner, Ph.D. , Stanford University, Stanford, Calif.
“Multiplexed in vivo drug screening: Inhibitors of metastatic seeding”
Supported in memory of Samuel Stroum

The shifting model in clinical diagnostics: how next-generation sequencing and families are altering the way rare diseases are discovered, studied, and treated

thos@oneelevendigital.com (Thomas Conner) — Sat, 05 Apr 2014 16:22:00 GMT

We are the fathers of two patients with a newly diagnosed syndrome that is highlighted in the study by Enns et al.1 Our children are two among a handful of others in the world with this disease caused by mutations in the NGLY1 gene. It is the first recognized disorder of deglycosylation. We fully anticipate that NGLY1 will generate many interesting studies for years to come, but promoting NGLY1 is not our aim here. Instead, we would like to provide you with our perspective on a shift that is occurring in clinical diagnostics. Families of children with serious genetic diseases often enter a diagnostic odyssey, moving from gene to gene in the hope of finding an explanation for the condition. Two new developments in genetics promise to dramatically shorten the time to reach a successful diagnosis: next-generation sequencing (NGS) and family engagement through social media. The very speed with which Need et al.2 and Enns et al.1 were published suggests a new model for clinicians and researchers. In this model, families, patients, and scientists work jointly to find new patients, confirm or refute hypotheses, exchange clinical information, enhance collaboration methods, and support research toward understanding and treatment.

Our diagnostic quest started in the Fall of 2010, when a team at Duke University sequenced one of our children in an effort to measure the efficacy of exome sequencing in patients whose diagnostic journeys had become intractable. The team concluded that NGLY1 could be causal. This was game changing because the gene had not been implicated in any prior condition. It was difficult to know for sure whether the mutations Duke found were causal or not because there was only one affected patient. Beyond this limitation, many geneticists have raised concern that weak standards in declaring genetic diagnoses using NGS data could do real clinical harm. So why did the Duke team communicate with the family in the absence of a definitive diagnosis? They felt the family could understand this uncertainty (as many families can) and that sharing the NGLY1 suspicion would make the family partners in the discovery efforts. Following a demonstration in a molecular biology laboratory that the mutations found in the child eliminated the protein associated with the NGLY1 gene (this does not happen in the general population), Need et al.2 shared their nomination of NGLY1 as a likely (but by no means certain) cause of the condition.

Until very recently, the fragmented distribution of patients across institutions hindered the discovery of new rare diseases. Clinicians working with a single, isolated patient could steadily eliminate known disorders but do little more. Families would seek clinicians with the longest history and largest clinic volume to increase their chances of finding a second case, but what does a physician do when N = 1 or if the phenotype is inconsistent across patients? These challenges are driving an increase in the use of NGS. Yet this technological advance presents new challenges of its own. Perhaps the most daunting, in our opinion, is the inability to share sequencing data quickly and universally. Standards and bioinformatic tools are needed that allow for a national repository where families or scientists can bring clinical results and NGS data for comparison. This challenge can be circumvented by tools already created for and by the Internet and social media.

To illustrate our point, six of the eight patients presented in the accompanying article were linked together after parents, physicians, or scientists working on isolated cases searched online for “NGLY1.” They found a blog post3 describing the disorder written by the parents of the first confirmed patient. The blog chronicles the boy’s journey (initial evaluation, visits to multiple specialists, incorrect diagnoses, and ultimately the discovery of heterozygous mutations in NGLY1). It was this personal account that allowed the ordering physician, who had been tracking a second patient with NGLY1 variants, to feel confident that the two patients were suffering from the same disorder. Another patient was discovered, on a distant continent, when a parent’s Internet search for his/her child’s symptoms stumbled upon the aforementioned blog. This prompted the parents to suggest targeted NGLY1 sequencing to their child’s physician. Parent/patient-to-physician collaboration such as this is remarkable and is likely happening in other rare diseases with the advent of NGS.

As untrained people, we are not qualified to analyze whole-exome/whole-genome data. We cannot develop a therapeutic compound. We cannot design a diagnostic assay. That being said, parents can offer observations and ideas, and we can push for solutions. Nineteen months after the initial report by Need et al.,2 five viable approaches to treatment are under active consideration, thanks to relentless digging by afflicted families. One parent found a compound that seems to have measurably raised the quality of life in one NGLY1 child. Another parent read about a novel (but relevant) fluorescent assay and shared it with the NGLY1 team. The team had not heard about it, but it has become a fundamental tool in the functional analysis of NGLY1. One parent has formed and funded a multi-institutional network of researchers to tackle specific projects. The capabilities of parents and the social media are frequently underestimated; we are here to say: join us! As the discovery of new diseases explodes with the deployment of NGS, we hope clinicians will consider this model seriously.

It would be easy to write off this experience as one-off or not scalable, but it is quite real and we predict recurrence in your clinical and scientific work in the years ahead. The real pioneers of this shift were the Odones (The Myelin Project and adrenoleukodystrophy work featured in “Lorenzo’s Oil”), the Crowleys (Pompe disease, highlighted in “The Cure” and “Extraordinary Measures”), and the Marguses (Genome Bridge at Broad and leaders in ataxia–telangiectasia research), but they were just the beginning. More patients and parents are teaching themselves genetics and subtle nuances of medicine (e.g., glycobiology). This is not simple patient advocacy or grantsmanship. Those activities are present, but they are minor compared to the larger macro trend we are seeing in the democratization of rare disease discovery and treatment.

Although one of us comes from business and the other from academia, we share a common bond in information technology. We believe that information technology is transforming medical science. There are several information technology best practices in other industries that we can apply to the NGLY1 team:

Share early, share often. Collaborate across institutions, disciplines, patients, and parents. We recognize that this is easier said than done, but it is essential. Share data. Share negative results. For findings too small to be publishable, turn to the Web and publish them in short blog posts. Get the information out there. When Stanford and Baylor did whole-genome sequencing in the Fall of 2011 on an NGLY1 patient, they produced eight candidate genes (including NGLY1) to explain the phenotype. Stanford and Baylor shared their list of variant candidates quickly. It was a dialogue with clinicians, other researchers, parents, and bioinformatics experts. The teams triaged the list and worked systematically to close certain doors. Stanford and Baylor initially thought another gene was the main culprit, but functional work did not support this. Both teams continued to refine until NGLY1 was confirmed via functional assays. The lesson is that genetics is an iterative process.

Balance the bottom-up with the top-down. We recognize that much of science happens “bottom-up,” with open-ended investigations uncovering basic truths. Balance this approach with “top-down,” measurable goals that are grounded in patient’s needs. We know science takes random turns and there will be setbacks and pitfalls, but no successful business has been built without measurable goals and targeted objectives. Strike a balance.

Move fast and break things. This motto is common in Silicon Valley, but has been popularized by Facebook CEO Mark Zuckerberg. Genetics, like any branch of medicine, is justly conservative when it comes to patients (“Do no harm”). We do not suggest that you should be less thorough or loosen protocols. We simply suggest that you take risks to make discoveries, even if ideas or assays fail along the way. Failure is good. We can all build upon those failures if we share.

We leave you with this conclusion outside of NGLY1. In order to diagnose patients, we must admit the limitations of our medical knowledge. Sometimes the best ideas come from individuals “outside the box” (i.e., patients and parents). Pay special attention to the smallest details (e.g., lack of tear production); sometimes the least likely gene candidate is the answer. Last but not least, thank you. What you do is truly remarkable. When you have a bad day in the clinic or the laboratory, please remember that there are patients and parents out there who you do not know and who are dreaming of finding you, supporting you, and counting on you.

http://www.nature.com/gim/journal/vaop/ncurrent/full/gim201423a.html

Forthcoming Changes in NCI's Clinical Trials Program

thos@oneelevendigital.com (Thomas Conner) — Sat, 05 Apr 2014 16:22:00 GMT

The Cooperative Groups

The major clinical trials enterprise in the United States, called the Clinical Trials Cooperative Group Program, has grown, but its design has remained largely unchanged for 55 years. Recognizing that our clinical trials must keep pace with advances in the scientific understanding of cancer, the Institute of Medicine (IOM) issued a report Exit Disclaimer at NCI’s request in 2010 that outlined necessary, systematic changes to more efficiently design, review, and conduct studies.

In keeping with the recommendations outlined in the IOM report and with the advice of clinical scientists from across the country, NCI has created a new National Clinical Trials Network (NCTN). The NCTN will improve the speed and efficiency of cancer clinical trials, using fewer but larger groups of investigators and distributing resources in a more effective way. In addition to supporting the new network’s clinical trial groups with awards for operations and statistical analysis, the NCTN provides funding for a centralized Institutional Review Board (IRB); for correlative studies (through the Biomarker, Imaging, and Quality-of-Life Studies Funding Program ); for new awards created specifically for the NCTN, including Lead Academic Participating Site (LAPS) grants and Integrated Translational Science Awards (ITSA); and for additional contracts to support the infrastructure of the network (e.g., pharmacy services and regulatory support).

Despite the current budget environment in which NCI is operating—at levels of appropriated funds below those of FY2010—our monetary support for clinical trials remains strong. In FY2013, NCI singled out its national clinical trials program as an area that would not incur any budget reductions, despite sequestration. This year (FY2014), funding levels for clinical trials are unchanged from FY2013.

The formal awards for the NCTN are expected to be made in the coming weeks. The capacity of the NCTN to conduct clinical trials will reflect the difficulties of supporting a large program that is constrained by higher mandatory costs and by the rate of biomedical inflation, despite approximately constant dollars. The number of trials that can be funded and the network’s total enrollment (estimated to be about 17,000 patients in interventional trials and about 2,500 patients in trials that use molecular markers to screen for tumors) will be lower than in the past. These operational concessions are required to provide higher reimbursement rates per patient to the academic sites that are developing and performing trials, as recommended by the IOM report. (These higher rates will be approximately $4,000 per patient for about 50 percent of the patients accrued through the network accrual, compared with approximately $2,000 previously.)

Community-based Clinical Trials

NCI also has a decades-long commitment to clinical cancer research in the community, where most cancer patients are treated and followed. Through programs such as the Community Clinical Oncology Program (CCOPs), the Minority-based Community Clinical Oncology Program (MCCOPs), and the NCI Community Cancer Centers Program (NCCCP), NCI has developed and maintained the availability of state-of-the-art clinical research throughout the United States.

The value of community-based practices for providing access to clinical trials is fundamental to testing promising new interventions in settings in which most patients are seen. Because advances in oncology are occurring rapidly, community-based care must also adapt swiftly, requiring changes in the manner in which clinical trials are conducted in the community. Therefore, NCI is consolidating its several existing community-based programs into an NCI Community Oncology Research Program (NCORP), in a way that enables community-based programs to benefit from intellectual and operational resources available through the NCTN.

NCORP is designed to become an integral component of the overall NCI NCTN. It will provide access to studies of cancer control, prevention, screening, treatment, and cancer care delivery in the communities in which individuals live. NCORP will be comprised of some of the sites formerly funded through the CCOPs, MCCOPs, and NCCCP, as well new grantee institutions, in accord with advice received from many sectors during the planning process.

During the transition to NCORP, we expect to announce most awards before the planned September 2014 date, avoiding disruption in existing community-based sites that successfully compete for NCORP awards. Existing sites that are not successful in the competition for NCORP awards will receive funds needed to ensure a smooth closeout of operations. In accord with traditional NCI practice, no patients will be removed from a trial as a result of the reorganization, and accrual into existing studies will continue. The NCI’s long-term goal remains the maintenance of a strong program for community-based clinical research.

For more information, please visit http://www.cancer.gov/newscenter/newsfromnci/2014/NCTNlaunch .

Cancer Research UK teams up with Winton and others to help solve 'big data' challenges

thos@oneelevendigital.com (Thomas Conner) — Tue, 01 Apr 2014 16:21:00 GMT

Cancer Research UK is holding its first conference to address the ever increasing challenges of ‘big data’ – the massive amount of information generated from a variety of different domains, including cancer research, that is hard to analyse – today (Tuesday).

"There’s been an explosion of data generated in cancer research and we need to look to the big players such as Google, Ipsos MORI and Winton to find new ways to analyse the data." - Harpal Kumar, Cancer Research UK

‘Multidisciplinary Challenges of Big Data’ is the first meeting of the ‘Big Data Analytics Conference Series’, an annual conference series sponsored by Winton Capital Management and organised by Cancer Research UK.

The charity is running huge scientific research projects* which pump out masses of data, similar to the levels generated by the biggest players in this field.

Recent technology developments have allowed cancer researchers to ask questions that will generate huge amounts of data, but to analyse this data effectively will take new skills. In order to do this Cancer Research UK will look to other fields which have a long standing interest in big data generation such as finance and web analytics.

By bringing together the best minds facing similar big data challenges – Winton, Google, Ipsos MORI , and others – with cancer scientists, biologists, physicists, mathematicians and statisticians, the aim is to share lessons learned across these various disciplines in order to better store, share, analyse,

visualise and interpret the ever increasing volumes of data.

This initiative comes alongside the Government’s recent announcement providing £42 million over five years for a Big Data Institute – the Alan Turing Institute – a national institute which will enable the UK to lead the way in, and reap the benefits from, Big Data science.

The speakers** at the conference will discuss the opportunities and challenges in big data within their respective fields which include cancer genomics, physics and astronomy, market research, web-based analysis and finance.

To promote discussion and collaborations, there will be an interactive panel session with world-leading experts spanning a variety of disciplines who will spark discussion around the issue of ‘Selection Bias in Research.’ Delegates from around the world will be in attendance hoping to foster multidisciplinary collaboration to tackle big data challenges.

Dr Harpal Kumar, Cancer Research UK’s chief executive, said: “This is an exciting new avenue for Cancer Research UK to explore and is the first time we’ve brought together such a range of expertise to see if we can learn from other disciplines. There’s been an explosion of data generated in cancer research and we need to look to the big players such as Google, Ipsos MORI and Winton to find new ways to analyse the data.

“Some of our biggest, most ambitious projects such as the International Cancer Genome Consortium and Stratified Medicine Programme could benefit from this type of analysis.”

David Harding, Winton’s founder and chairman and a board member of Cancer Research UK’s Create The Change Campaign for the Crick , said: “Big data offers radical new perspectives and opportunities for solving real problems. It is a core part of what we do at Winton, which is a global quantitative investment manager employing scientific research into financial markets. Our experience has shown how important it is to bring together people from different disciplines to tackle big data challenges. By understanding them and working together we can start to further create the change.”

Universities and Science Minister David Willetts said: “Big-data is one of the eight great technologies of the future with the potential to propel UK growth. It will help us gain faster and deeper insights from the vast amount of experimental, theoretical and clinical data we collect. That's why we have committed £42 million for the Alan Turing Institute to ensure we reap the benefits of Big Data science for the UK economy.

“The cross disciplinary approach demonstrated by Cancer Research UK and its new partners support our ambition to make the UK a world leader in the analysis and application of big data.”

The new Francis Crick Institute, opening in 2015 and set to be the largest biomedical research institute in Europe will generate a huge amount of data and will benefit from this type of data analysis.

The Crick is a partnership of six leading UK scientific and academic organisations — the Medical Research Council, Cancer Research UK, the Wellcome Trust, UCL, Imperial College London and King's College London.

R ussell Delew, Cancer Research UK’s director of major giving and appeals, said: “As one of the best places globally for scientific research, the new Francis Crick Institute will also generate huge amounts of scientific data. Set to open next year, the Institute will take innovative approaches in carrying out biomedical research aimed at speeding up scientific discovery. But we need the funds to make this a reality. So far Cancer Research UK’s Create The Change Campaign has raised £45million – nearly half our £100million target.”

Circulating tumor DNA: A new generation of cancer biomarkers

thos@oneelevendigital.com (Thomas Conner) — Sat, 29 Mar 2014 16:21:00 GMT

For many years, scientists have been on a quest to identify a non-invasive cancer biomarker - a biological molecule found in the blood that indicates the presence of disease. Their hope was that it would provide a more effective and patient-friendly method for the detection, monitoring and treatment of cancer. However, due to the very nature of cancer, identifying an effective biomarker has proven to be quite a challenge.

Now, with the recent advances in sequencing technology, a solution has been found. A large number of cancer genome sequencing studies have collectively identified the genetic changes that make human tumors grow and progress. As a result of their findings, scientists have discovered that virtually all cancers carry somatic DNA mutations. Unlike hereditary mutations that are passed from parent to child and are present in every cell in the body, somatic mutations form in the DNA of individual cells during a person's life. Because these somatic mutations are only present in tumor cell DNA, they provide an extremely specific biomarker that can be detected and tracked.

Although a tumor itself is the major source of tumor DNA, acquiring DNA through a biopsy is invasive, risky and often not possible. Fortunately, scientists have discovered that dying tumor cells release small pieces of their DNA into the bloodstream. These pieces are called cell-free circulating tumor DNA (ctDNA). February's Genome Advance of the Month describes a new study, published in the February 19, 2014, issue of Science Translational Medicine, which examines the potential of screening ctDNA for somatic mutations as a way to detect and follow the progression of a patient's tumor.

The team of scientists began by identifying at least one somatic mutation present in the tumors of 187 patients with 17 different types of advanced cancer. To do this, they used a tiered approach for each patient: they started by sequencing several genes commonly mutated in cancer, and if no mutation was found, they widened their search and sequenced all of the protein coding regions of the genome or the entire genome itself. The ctDNA present in the patients' blood was then examined for the specific somatic mutation identified in their tumor. The scientists were able to detect the ctDNA somatic mutation for 82 percent of the patients with metastatic tumors outside the brain. In comparison, 55 percent of patients with early stages of cancer had detectable levels of ctDNA mutations in their blood.

From these findings, they discovered that the percentage of patients with detectable levels of ctDNA in their blood correlated with the stage of cancer. While only 47 percent of patients with stage I cancers had detectable ctDNA, the percentage of patients with stage II, III and IV cancers was 55, 69 and 82 percent, respectively. Moreover, the scientists found that the concentration of ctDNA in blood increased as the cancer stage increased. This suggests that simply measuring the level of ctDNA in a patient's blood could be used in the future as a way to determine how advanced their cancer is. In fact, when the scientists measured the ctDNA concentration in 206 colorectal cancer patients, they saw that patients with lower blood levels of ctDNA lived significantly longer than those with higher levels of ctDNA.

In addition to its potential role as a detection and prognostic method, ctDNA was also evaluated as a way of monitoring tumor progression and testing whether a patient's tumor would respond to targeted drug treatments. The scientists examined the ctDNA present in the blood of 24 colorectal cancer patients whose tumors had first responded to a specific gene targeted therapy, but then progressed while still being treated. The patients' ctDNA was screened for mutations both before and after therapy. The scientists found new somatic mutations that prevent the drug from working formed during the patients' treatment. Because the patients saw an initial tumor response to the drug, this suggests that the drug was initially effective in killing tumor cells but that the formation of the new mutations stopped the drug from continuing to work. This valuable information would indicate to doctors that the patients' tumors are no longer responsive and different treatment is necessary.

The authors of this study demonstrated that ctDNA testing could be applied to every stage of cancer patient care. They showed that ctDNA is detected in most types of cancer at both early and advanced stages, suggesting it could be used as an effective screening method for most patients. A measurement of the levels of ctDNA in blood may also be used to quickly estimate a patient's stage of cancer and survival chances. Finally, the authors exhibited the utility of ctDNA in monitoring the response of tumors to treatment and determining which drugs may be effective in the future.

Overall, ctDNA appears to be an extremely effective and advantageous biomarker. Because it is found in the blood, it provides a non-invasive, and thus less risky, alternative method to repeated tumor biopsies to monitor tumor progression. While further studies will need to be performed before it reaches the clinic, the evidence provided in this study demonstrates the immense potential of ctDNA to improve the early detection and treatment of cancer.

Global Alliance for Genomics & Health

thos@oneelevendigital.com (Thomas Conner) — Sat, 29 Mar 2014 16:21:00 GMT

The work of the Global Alliance is critical to realizing the potential of recent technological advances that make possible the large-scale collection of data on genome sequencing and clinical outcomes. To seize this extraordinary opportunity, it is often necessary to ask questions that span individual datasets. The Global Alliance is working to alter the current reality where data are kept and studied in silos, and tools and methods are non-standardized and incompatible.

Engaging collaboratively with its stakeholders, the Global Alliance works to establish, broadly disseminate, and advocate for the use of interoperable technical standards for managing and sharing genomic and clinical data.

The Global Alliance acts as a convener, bringing together global stakeholders across sectors to share and establish best practices and to cross-pollinate ideas and learning, fostering a culture of innovation and discovery. Global Alliance stakeholders work together to promote the highest standards for ethics, ensuring that participants have the choice to responsibly and securely share their genomic and clinical data to advance progress in science and medicine.

The Future of the Pathway Interaction Database (PID)

thos@oneelevendigital.com (Thomas Conner) — Sat, 29 Mar 2014 16:20:00 GMT

The Pathway Interaction Database (PID) is a collection of biomolecular interactions and events involving signaling and regulatory pathways that have been curated from the peer-reviewed literature. Users are able to browse through biomolecular interactions, run computational analyses, or synthesize and combine interactions to establish novel pathways. Begun as a collaborative project between the National Cancer Institute (NCI) and the Nature Publishing Group (NPG) in 2006, the database serves as a freely available resource for the cancer-research community and other researchers interested in cellular pathways.

There has been some concern expressed about the longevity of PID. Although the collaboration between NCI and NPG ended in September 2012, NCI has no intention of retiring PID and will continue its maintenance, thereby retaining it as an operational resource for researchers. The following interview with Dr. Daoud Meerzaman highlights PID’s continued focus and its future direction.

NCI’s CPTAC Releases an Openly Accessible Data Set Primed for Multi-omic Integration

thos@oneelevendigital.com (Thomas Conner) — Sat, 29 Mar 2014 16:20:00 GMT

On September 4, 2013, NCI’s Clinical Proteomics Tumor Analysis Consortium (CPTAC) publicly released proteomic data produced from colorectal tumor samples previously analyzed as part of The Cancer Genome Atlas (TCGA) initiative. This is the first release of proteomic tumor data designed to complement genomic data from the same tumors.

Since its launch in 2006, the Consortium has “aimed at improving proteomic analysis platforms to reliably identify, quantify, and compare proteins and peptides in complex biological mixtures.” After five years of technology development, inter-laboratory studies, and statistical analyses of variation, the program entered a second phase, this time focused on data production. CPTAC obtained access to a subset of tumors analyzed by TCGA, with a view toward generating proteomic data sets to enable integration with the genomic data .

T he CPTAC consists of five teams that create a network of Proteome Characterization Centers (PCCs):

Broad Institute; Fred Hutchinson Cancer Research Center
Johns Hopkins University
Pacific Northwest National Laboratory
Vanderbilt University
Washington University; University of North Carolina, Chapel Hill

Genomic analysis of diffuse pediatric low-grade gliomas identifies recurrent oncogenic truncating rearrangements in the transcription factor MYBL1

thos@oneelevendigital.com (Thomas Conner) — Sat, 29 Mar 2014 16:19:00 GMT

We performed high-resolution copy-number analysis on 44 formalin-fixed, paraffin-embedded diffuse PLGGs to identify recurrent alterations. Diffuse PLGGs exhibited fewer such alterations than adult low-grade gliomas, but we identified several significantly recurrent events. The most significant event, 8q13.1 gain, was observed in 28% of diffuse astrocytoma grade IIs and resulted in partial duplication of the transcription factor MYBL1 with truncation of its C-terminal negative-regulatory domain. A similar recurrent deletion-truncation breakpoint was identified in two angiocentric gliomas in the related gene v-myb avian myeloblastosis viral oncogene homolog (MYB) on 6q23.3. Whole-genome sequencing of a MYBL1-rearranged diffuse astrocytoma grade II demonstrated MYBL1 tandem duplication and few other events. TruncatedMYBL1 transcripts identified in this tumor induced anchorage-independent growth in 3T3 cells and tumor formation in nude mice. Truncated transcripts were also expressed in two additional tumors with MYBL1partial duplication. Our results define clinically relevant molecular subclasses of diffuse PLGGs and highlight a potential role for the MYB family in the biology of low-grade gliomas.

Pediatric low-grade gliomas (PLGGs) are the most common brain tumors in children and, collectively with other CNS tumors, have surpassed leukemias as the leading cause of cancer-related deaths in children and young adults (1). PLGGs are generally categorized as “nondiffuse” or “diffuse” based on their extent of brain infiltration. Nondiffuse tumors exhibit minimal infiltration and are predominantly benign World Health Organization (WHO) grade I pilocytic astrocytomas (PAs), which are most often cured by surgery alone. In contrast, diffuse gliomas are associated with less favorable clinical outcomes, including recurrence after initial resection, by virtue of their extensive infiltration and invasion into the brain. These tumors are also more likely to progress to glioblastoma. PLGGs with diffuse growth patterns are further subclassified histologically as diffuse astrocytoma grade IIs (DA2s), gangliogliomas (GGs), angiocentric gliomas (AGs), pleomorphic xanthoastrocytomas (PXAs), and several other rare glioma types (2). However, these subclasses exhibit extensive heterogeneity and histologic overlap, often precluding categorical diagnosis. PLGGs that cannot be categorized are often referred to as low-grade gliomas, not otherwise specified (LGG-NOS) and represent nearly one-third of all PLGGs. Moreover, these histologic categories do not reliably predict biologic behavior and risk of malignant transformation.

Unifying genetic events have been identified in some PLGG subtypes, including v-raf murine sarcoma viral oncogene homolog B1 (BRAF) fusions in PAs and BRAF V600E mutations in PXAs and GGs, with substantial diagnostic, prognostic, and therapeutic implications (3⇓⇓⇓⇓⇓–9). Identification of genetic alterations in diffuse PLGGs would increase biologic understanding of tumor behavior as well as define diagnostic molecular subclasses. However, unlike pilocytic astrocytomas, the rarity and diversity of diffuse PLGGs combined with the scarcity of frozen tissue available for genomic analyses has historically impeded identification of genetic alterations specific to these tumors. Prior studies have found that BRAF-KIAA1549fusions are rare to nonexistent in diffuse PLGGs, particularly in DA2s (10). Diffuse PLGGs included in large cohorts of low- and high-grade gliomas were suggested to have increased expression of the proto-oncogenev-myb avian myeloblastosis viral oncogene homolog (MYB), including rare cases with genomic aberrations involving the gene (11), but no unifying recurrent genetic events have been identified.

Here we describe high-resolution copy-number profiles of 44 diffuse PLGGs, the largest collection ever to have been analyzed, and whole-genome sequencing of a diffuse PLGG. These studies reveal a recurrent rearrangement of the transcription factor v-myb avian myeloblastosis viral oncogene homolog-like 1 (MYBL1) that induces anchorage-independent growth of 3T3 cells as well as tumor growth in vivo. These findings indicate oncogenic events that define subclasses of diffuse PLGGs.

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RESULTS

Characteristics of the Diffuse PLGG Cohort. To focus our studies on diffuse PLGGs, we collected a diverse set of carefully screened tumors through an international consortium of seven institutions (Table S1and Fig. S1). Our cohort specifically excluded the more common, nondiffuse pilocytic astrocytomas, which are known to be driven primarily by BRAF alterations and are the focus of separate ongoing international collaborative sequencing efforts [International Cancer Genome Consortium (Germany)]. Our cohort included 18 DA2, three AG, three desmoplastic infantile ganglioglioma, nine GG, one subependymal giant cell astrocytoma, and 10 LGG-NOS tumors. Given the infiltrative growth and rarity of certain categories of diffuse PLGGs, the samples that we acquired were mostly archival formalin-fixed, paraffin-embedded (FFPE) tissue. We recently developed a method for reliable performance of array comparative genomic hybridization (aCGH) on FFPE archival samples (12) and used this technique to determine copy-number status at 1 million loci genome-wide. We also performed deep whole-genome or whole-exome sequencing to define and validate recurrent genetic events that drive tumorigenesis in these rare pediatric cancers.

Genomic Identification of Significant Targets in Cancer Analysis Identifies Significant Recurrent Events in Specific PLGG Subtypes. The percentage of the genome altered by copy-number alterations (CNAs) in diffuse PLGGs was significantly lower than among previously profiled adult low- and high-grade gliomas (P < 10−6, Mann–Whitney test, Fig. 1A) (13, 14). Few (12/44; 27%) of these tumors harbored alterations affecting more than 90% of the length of a chromosome arm (Fig. 1B), compared with an 83–97% rate among adult low- and high-grade tumors (15). One of the PLGG samples exhibited chromothripsis on chromosome 8 (chr8) (highlighted in Fig. 2A, PLGG27). The most significantly recurrent arm-level CNAs were gains of chromosomes 7 (11% of tumors), 8 (7%), and 5q (5%) and loss of 1p (2%) (Fig. 1C). These events have all been described in pediatric high-grade gliomas and adult gliomas with varying frequencies (15, 16).

Fig. 1.

CNAs among diffuse PLGGs. (A) Fraction of the genome altered by CNAs is lower among diffuse PLGGs compared with adult LGGs and high-grade gliomas (P < 10−6). Blue bars indicate medians. (B) Amplifications (red) and deletions (blue) among 44 diffuse PLGGs (x axis) across the genome (y axis) ordered by copy-number status. Significance (x axis) of (C) arm-level and (D) focal deletions (Left, blue) and amplifications (Right, red) across the genome (y axes). Putative gene targets within the peak regions are indicated where known.

Fig. 2.

DA2 samples with focal 8q gains identified by aCGH share a common centromeric breakpoint within MYBL1. (A) aCGH data for the five DA2 samples with 8q gain, magnified on the right. Red: copy-number gain; green: loss. The highlighted sample (PLGG27, blue box) exhibits chromothripsis of chr8 but shows no other copy-number changes on other chromosomes. (B) FISH probes corresponding to sequences immediately distal (red) and proximal (green) of MYBL1 confirm that the single-copy gain identified by aCGH is a duplication involving oneMYBL1 allele. The D8Z2 centromere enumeration probe (aqua) was used as a control. (C) Schematic representation of the proto-oncogene MYB family and breakpoints observed in our cohort in relation to the viral oncogene v-MYB.

We found 6 significantly recurrent regions of focal deletion and 17 significantly recurrent regions of focal amplification ( Fig. 1D and Table S2 ). One deleted region on 9p21.3 contained cyclin-dependent kinase inhibitor 2A and 2B (CDKN2A and CDKN2B), known tumor suppressors that had previously been reported in diffuse PLGGs ( 17 ); a second region was immediately adjacent to this one. A third region (6q26) contained 252 genes, including the proto-oncogene MYB. Two regions (10q21.3 and 8p22) contained single genes with no known relation to cancer or neural development, catenin (cadherin-associated protein), alpha 3 (CTNNA3) and zeta sarcoglycan (SGCZ), respectively. The sixth region (13q31.3) contained 48 genes and was adjacent to the known tumor suppressor RB1. We did not identify any focal deletions of other known tumor suppressors involved in adult or pediatric brain tumors such as neurofibromin 1 (NF1), phosphatase and tensin homolog(PTEN), or cyclin-dependent kinase inhibitor 1C (CDKN1C).

One of the 17 focally gained regions contained BRAF. However, the canonical BRAF-KIAA1549 duplication-fusion was detected in only four samples: two GGs and two LGG-NOS. This is in contrast to pilocytic astrocytomas, among which >80% of tumors harbor a BRAF duplication ( 18 ) (P < 0.0001, Fisher’s exact test). We also determined BRAF V600E mutation status in 24 tumors with sufficient DNA for sequencing. We found mutations in 54% of the diffuse PLGGs ( Fig. 1A and Table S1 ), consistent with previously published rates for diffuse PLGGs ( 8 ).

A second focally gained region (3q26.33) contained the stem cell and glial transcription factor sex determining region Y-box 2 (SOX2), which is amplified in adult glioblastomas ( 19 ). Two additional regions (2q12.1 and 5q14.3) contained factors that control telencephalic neural progenitor proliferation and differentiation: POU class 3 homeobox 3 (POU3F3) (also known as BRN1) and microRNA 9-2 ( 20 , 21 ). A fifth region (1q21.3) contained myeloid cell leukemia sequence 1 (MCL1), a known oncogene amplified in several cancer types ( 22 ). Twelve regions either contained over 150 genes or did not contain genes with known roles in cancer or neural development. We did not observe any high-level amplification of receptor tyrosine kinases (e.g., EGFR,PDGFRA), which are observed frequently in both adult and pediatric high-grade gliomas ( 19 , 23 ).

The most statistically significant recurrent focal aberration (q = 3.37 × 10−6) was a gain on chromosome 8q involving the transcription factor MYBL1. Although MYBL1 is not a known oncogene, it is closely related to the proto-oncogene MYB. In contrast to prior reports ( 11 ), no amplifications or gains of the proto-oncogene MYBwere identified in our study set. All of the focal 8q gains occurred in DA2s (P = 0.0057, Fisher’s exact test), comprising 28% (5/18) of this histologic subtype. In contrast, MYBL1 was not in a significant amplification peak across 3,131 cancers comprising multiple other cancer types that we had previously analyzed ( 22 ) or, specifically, among adult low- or high-grade gliomas ( 15 ).

All five DA2 samples with 8q focal gains exhibited a common centromeric breakpoint within MYBL1 after exon 9, including the sample with chromothripsis of chr8 ( Fig. 2A ). To confirm the MYBL1 centromeric breakpoint, we performed fluorescence in situ hybridization (FISH) using a probe slightly telomeric to the breakpoint on all eight DA2 samples with sufficient tissue available ( Fig. 2B and Fig. S2 ). All DA2 samples with 8q gain (3/3) demonstrated duplication of one allele in more than 60% of the nuclei in each tumor whereas none of the other DA2 samples showed duplication (0/5) (P = 0.018, Fisher’s exact test).

The tight clustering of these breakpoint sites, and particularly their location immediately preceding the C-terminal negative regulatory domains of MYBL1 and MYB, suggested a mechanism for rearrangement and creation of functional, truncated genes reminiscent of the viral oncogene v-MYB ( Fig. 2C ). Indeed, we also identified a homologous breakpoint between exons 10 and 11 of MYB on 6q in one angiocentric glioma with a focal 6q deletion ( Fig. S3 ) similar to that previously reported in a single angiocentric glioma with a 6q deletion ( 11 ). An additional angiocentric glioma (PLGG45), not included in our initial diffuse PLGG cohort and genomic identification of significant targets in cancer (GISTIC) analysis, also exhibited a deletion in 6q at the same location in MYB as seen in PLGG29 ( Fig. S3 ).

W hole-Genome Sequencing of a DA2 with 8q Focal Gain Defines a Tandem Duplication–Truncation of MYBL1. To further characterize the MYBL1 amplicon and its genetic context, we performed 90× whole-genome sequencing of a DA2 sample with MYBL1 gain but no other CNAs (PLGG24, Table S3 ). Whole-genome sequencing of PLGG24 determined the centromeric breakpoint of the 8q amplicon to single-base resolution between exons 9 and 10 of MYBL1 ( Fig. 3A ). The telomeric sequence was located in an intergenic region 38 kb from matrix metallopeptidase 16 (MMP16). We validated the breakpoint locations in this sample using PCR on native genomic DNA from the same tumor ( Fig. S2 C and D ). Taken together, our data define a tandem duplication–truncation of MYBL1.

Fig. 3.

Characterization of the MYBL1-truncating rearrangement in a DA2 sample (PLGG24). (A) Circos plot (Left) of 90× whole-genome sequence data showing a single-copy-number gain on 8q (red, inner heatmap ring) and corresponding intrachromosomal rearrangement (green) involving MYBL1 and an intergenic region outside MMP16. A schematic diagram represents the 8q tandem duplication (Lower Right). All nonsynonymous mutations and insertions and deletions are also listed (Upper Right). (B) Truncated MYBL1 transcripts (MYBL1-trunc1 andMYBL1-trunc2) identified by 3′-RACE. (C) RT-PCR demonstrating MYBL1-trunc2 expression in two additional PLGG samples with MYBL1 gain.

Apart from this event, whole-genome sequencing of PLGG24 revealed a sparsely altered genome. No other CNAs or fusion events were identified, and the BRAF V600E mutation was not present. Three nonsynonymous mutations in exons were identified ( Table S4 ); none of these have been reported in association with cancer. The genome-wide mutation rate (1.48/Mb) and the number of nonsynonymous mutations in exons (three per genome) were low compared with pediatric and adult high-grade astrocytomas (mutation count means: 15 and 47.3 per genome, respectively) ( 16 ). The low rate of CNAs, mutations, and translocations in this sample highlight the potential biological impact of MYBL1 duplication–truncation.

Tandem Duplication–Truncation of MYBL1 Results in Expression of Oncogenic Transcripts. To determine whether the MYBL1 duplication–truncation resulted in expression of fused transcripts, we performed 3′-rapid amplification of cDNA ends (3′-RACE) on cDNA generated from RNA of PLGG24. We identified two populations of MYBL1 transcripts, both of which contained MYBL1 exons 1–9 but also acquired short noncanonical sequences fused to the 3′ ends, leading to premature translation stops ( Fig. 3B ). The first transcript (MYBL1-trunc1) contained an additional 15 bp of intronic sequence, and the second transcript (MYBL1-trunc2) acquired 36 bp from the intergenic region near MMP16. MYBL1-trunc1 was retrieved more frequently (74%) than MYBL1-trunc2 (26%); the wild-type transcript was not observed. We then performed RT-PCR to detect MYBL1-trunc1 and MYBL1-trunc2 in the two other PLGGs with 8q focal amplification for which we had sufficient RNA (PLGG25 and PLGG28). We detected MYBL1-trunc2 in both of these samples ( Fig. 3C ), indicating that this transcript is recurrently expressed in DA2s.

T o assess the oncogenic potential of these transcripts, we transduced 3T3 cells with lentiviruses containingMYBL1-trunc1, MYBL1-trunc2, full-length MYBL1-wt, or GFP control constructs and plated them in soft agar. Both aberrantly truncated MYBL1 sequences produced soft agar colony growth indicative of transformation ( Fig. 4A ). No evidence of colony formation was noted with full-length MYBL1-wt or GFP control. Both MYBL1-trunc1– and MYBL1-trunc2–transformed 3T3 cells were also able to form tumors with malignant histology in Nude mice, whereas cells transduced with full-length MYBL1-wt constructs showed no evidence of tumor formation ( Fig. 4 B–D ). These results suggest that the truncation of MYBL1 is oncogenic.

Fig. 4.

(A) Anchorage-independent growth of 3T3 cells transduced with MYBL1-wt, MYBL1-trunc1, or MYBL1-trunc2 retroviruses, relative to GFP controls. (B) In vivo tumor formation after injection of 3T3 cells transduced withMYBL1-wt (n = 5), MYBL1-trunc1 (n = 5), or MYBL1-trunc2 (n = 5) (arrows) into the flanks of Nude mice. Tumor volume was calculated 6 wk post injection (C), and tumors were subjected to histologic analysis by H&E staining (D).

DISCUSSION

O ur data identify several recurrent somatic genetic events in PLGG, some of which track with specific histologic types. The most significant of these was recurrent focal amplification of MYBL1, found exclusively in DA2 samples, none of which had other PLGG-associated lesions such as a BRAF duplication. Focal deletion of 6q involving the MYB locus was also observed in two angiocentric gliomas in our dataset, similar to deletions in two individual cases of angiocentric glioma noted in prior studies. Such aberrations may therefore be useful in identifying this tumor type ( 11 , 24 ). In other cases, genetic events span histologic subtypes and tumor grades, such as duplications of BRAF in GG and NOS samples in our diffuse PLGG cohort as well as deletions involving CDKN2A/B ( Fig. 5 ). Although previous work had identified high-level amplification of MYBand 6q deletions involving MYB in several DA2 tumors, we detected no such events in our DA2 cohort ( 11 ).

Fig. 5.

Schematic summarizing the highly characteristic genomic aberrations detected in our diffuse PLGG cohort.

These findings provide genetic and functional evidence of a role for MYBL1 and the MYB proto-oncogene family of transcription factors in low-grade gliomagenesis. MYB transcription factors are known to regulate cell-cycle progression in multiple cellular contexts, and MYB has been identified as an oncogene in T-ALL and adenoid cystic carcinomas ( 25 ). The MYB family members share extensive homology in their DNA-binding domains; however, they differ in their C-terminal domains. In vitro and in vivo studies have demonstrated roles for each transcription factor in cell-cycle regulation mediated by interactions with and modifications of the C terminus ( 26 ). Truncating deletions may affect the transcriptional activity of MYB transcription factors by loss of a conserved negative regulatory domain or may result in increased gene expression due to loss of a 3′ UTR targeted by microRNA 15a/16 and microRNA 150 ( 25 ). Our results suggest that truncations affecting the C terminus of MYB family transcription factors may be sufficient to drive oncogenesis in a discrete molecular subclass of diffuse PLGGs by acting as gain-of-function mutations.

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METHODS

P atients and Samples. Institutional review board approval from all institutions (Boston Children's Hospital, Dana-Farber Cancer Institute, The University of Texas School of Medicine Southwestern, Children's Cancer Hospital-Egypt, The Johns Hopkins University School of Medicine, Children's National Medical Center, Hospital for Sick Children, and the Mayo Clinic) was obtained, and all samples were from patients who provided informed consent or were studied with waiver of the requirement for informed consent by the Dana-Farber Cancer Institutional review board. Samples of various histologic subtypes were identified, collected at multiple institutions, and central histopathologic review was performed by at least three board-certified neuropathologists using WHO criteria (K.L.L., S.S., S.H.R., or J.A.C.).

aCGH and Data Processing. DNA extraction from archival FFPE samples and aCGH were performed as previously described ( 12 ). GC-normalized copy-number data for the samples were then cleaned of known germ-line copy-number variations. Circular Binary Segmentation was used to segment the copy-number data, using parameters (α = 0.001, undo.splits = sdundo, undo.SD = 1.5, minimum width = 5). Segmented data were analyzed with GISTIC 2.0 to determine statistically significant recurrent broad and focal CNAs using the following parameters: minimum segment size = 8, lesion amplitude threshold = 0.2, focal/broad cutoff = 0.9× chromosome arm length, q-value threshold = 0.10, and gene confidence level = 0.95. For comparison of diffuse PLGG data to previously published adult low-grade glioma (LGG) and high-grade glioma (HGG) data ( 13 , 14 ), previously segmented copy-number data were subjected to the same GISTIC analysis parameters as above.

Whole-Genome Sequencing and Data Processing. DNA from fresh-frozen tissue from PLGG24 and paired blood was extracted using the QIAGEN DNA Blood and Tissue kit. A target depth of 90× in blood and tumor was set for Illumina sequencing, using two different insert-size libraries (500 and 800 bp) to maximize detection of rearrangements ( 27 ). Sequencing quality control (QC) metrics are shown in Table S3 .

Sequence data were aligned to the hg19 (b37) reference genome with the Burrows-Wheeler Aligner ( 28 ) with parameters [-q 5 -l 32 -k 2 -t 4 -o 1]. Aligned data were sorted, normalized, mate-fixed, duplicate-marked, and indexed with Samtools and Picard tools ( 29 ). Base-quality score recalibration and local realignment around insertions and deletions was achieved with the Genome Analysis Toolkit ( 30 , 31 ).

Somatic mutations and small insertions-deletions were called with MuTect and Indelocator, filtered against a panel of normals, and annotated to genes with Oncotator ( 29 , 32 , 33 ). CNAs were called with SegSeq and standard parameters ( 34 ). Somatic rearrangements were identified with dRanger and BreakPointer algorithms ( 32 , 33 ) with the cutoff SN parameter increased to 1,000 bp (reflecting the larger-than-normal insert sizes used for sequencing). Results are reported with high confidence if the dRanger score was ≥8 and the BreakPointer algorithm identified the exact breakpoints on both ends (equivalent to 8× high-confidence coverage of read pairs spanning the breakpoint).

Whole-Exome Sequencing. DNA was extracted from tumors as above, and 250-bp libraries were prepared by Covaris sonication, followed by double-size selection (Agencourt AMPure XP beads) and ligation to specific barcoded adaptors (Illumina TruSeq) for multiplexed analysis. Exome hybrid capture was performed with the Agilent Human All Exon v2 (44 Mb) or Illumina TruSeq bait sets, and samples were sequenced as above. Tumors were manually reviewed for the presence of the BRAF V600E mutation.

FISH and PCR. FISH was performed on 4-μm tissue sections using methods described previously ( 35 ) and Homebrew probes RP11-110J18 (5′ to MYBL1; directly labeled in SpectrumOrange) and RP11-707M3 (3′ toMYBL1; directly labeled in SpectrumGreen) that map to 8q13.1. MYBL1 status was assessed in 50 tumor nuclei per sample. PCR was performed on genomic DNA from PLGG0024 and control samples to confirm breakpoint sequences identified by dRanger. Primer sequences (5′-AATGCTATCCCTCCCCACTC-3′ and 5′-GAGGGAGCTTGGAAATTTGA-3′) targeting MYBL1 and intergenic sequences, respectively, amplified a 450-bp fragment. The band was gel-purified, cloned (TOPO TA Cloning; Invitrogen, and sequenced by Sanger sequencing to validate the fusion.

RT-PCR and 3′-RACE. MYBL1-trunc1 and MYBL1-trunc2 were cloned from PLGG24 frozen tumor tissue using a 3′ RACE kit (Invitrogen) per manufacturer’s instructions. To amplify all MYBL1 specific transcripts, primers targeting the 5′ end of MYBL1 (5′-AAAACCCTGCAGGAGACTG-3′) were used in conjunction with a universal amplification primer (UAP). A second PCR was performed using a nested MYBL1 primer (5′-TGCGGTACTTGAAGGATGG-3′) along with the UAP. PCR products were subjected to Sanger sequencing, and results were aligned to the hg19 reference genome. For RT-PCR reactions, RNA was extracted from FFPE samples of PLGG09, PLGG25, and PLGG28 using RNeasy FFPE kit (Qiagen). cDNA was generated from 500 ng RNA using the iScript cDNA synthesis kit (Bio-Rad). PCR to detect the presence of MYBL1-trunc2 was performed using primers targeting the MYBL1 exon 7–8 junction (5′-ATTGTATAGAACATGTTCAGCCT-3′) and the MYBL1-trunc2 sequence (5′-GGTCCTCTGCCTCTAGAATAGATTC-3′).

Expression Constructs and Lentiviral Production. Full-length MYBL1-wt (Open Biosystems), MYBL1-trunc1, and MYBL1-trunc2 cDNA sequences were subcloned into the pLenti7.3/V5 vector using the Gateway system (Invitrogen). For retroviral production, 293FT packaging cells were cotransfected with pBabe expression clones, Gag-pol, and vs.v-g. Viral supernatant was harvested 48 h after transfection, filtered through a 45-μm filter, and concentrated by ultracentrifugation.

Anchorage-Independent Growth Assay. Wild-type 3T3 mouse embryonic fibroblasts (ATCC) were transduced by addition of viral supernatant to the growth medium. Twenty-four hours later, infection efficiency was evaluated based on GFP expression and determined to be >80%. Cells were harvested and mixed with growth medium containing 0.33% bactoagar, and 1 × 104 cells were plated in triplicate onto a bottom layer of medium with 0.5% agar in a six-well plate. Soft agar colonies were counted 2 wk later. Images were acquired using the AlphaInnotch FluorChem HD2 Imager.

Flank Tumor Growth Assay. The Institutional Animal Care and Use Committee at Dana-Farber Cancer Institute preapproved all animal experiments. 3T3-MYBL1-wt, 3T3-MYBL1-trunc1, or 3T3-MYBL1-trunc2 cells (106) were suspended in 150 μL PBS, mixed with 150 μL Matrigel (BD Biosciences), and then injected into the flank of 6-wk-old male Nude (NU-Foxn1, Charles River) mice. Mice were then monitored for signs of distress or tumor growth. Six weeks post injection the mice were euthanized and analyzed for tumor growth. Tumors were subjected to standard histologic analysis.

Sequence analysis of mutations and translocations across breast cancer subtypes

thos@oneelevendigital.com (Thomas Conner) — Sat, 29 Mar 2014 16:19:00 GMT

Breast carcinoma is the leading cause of cancer-related mortality in women worldwide, with an estimated 1.38 million new cases and 458,000 deaths in 2008 alone1. This malignancy represents a heterogeneous group of tumours with characteristic molecular features, prognosis and responses to available therapy2, 3, 4. Recurrent somatic alterations in breast cancer have been described, including mutations and copy number alterations, notablyERBB2 amplifications, the first successful therapy target defined by a genomic aberration5. Previous DNA sequencing studies of breast cancer genomes have revealed additional candidate mutations and gene rearrangements6, 7, 8, 9, 10. Here we report the whole-exome sequences of DNA from 103 human breast cancers of diverse subtypes from patients in Mexico and Vietnam compared to matched-normal DNA, together with whole-genome sequences of 22 breast cancer/normal pairs. Beyond confirming recurrent somatic mutations in PIK3CA11, TP536, AKT112, GATA313 and MAP3K110, we discovered recurrent mutations in the CBFB transcription factor gene and deletions of its partner RUNX1. Furthermore, we have identified a recurrent MAGI3–AKT3 fusion enriched in triple-negative breast cancer lacking oestrogen and progesterone receptors and ERBB2 expression. The MAGI3–AKT3 fusion leads to constitutive activation of AKT kinase, which is abolished by treatment with an ATP-competitive AKT small-molecule inhibitor.

The National Cancer Advisory Board (NCAB) ad hoc Informatics Working Group (IWG)

thos@oneelevendigital.com (Thomas Conner) — Thu, 26 Sep 2013 16:17:00 GMT

Established in mid-2011 by the NCAB , the IWG is charged with providing strategic guidance and direction on NCI informatics investments that support the Institute’s scientific goals. The IWG provides a venue for identifying high-priority biomedical informatics needs, harmonizing ongoing and proposed informatics projects across NCI programs, both intramural and extramural, and reducing redundancies wherever possible. The IWG will explore informatics efforts, potential initiatives, and business models that are in accord with the changes that have occurred in the conduct of cancer research, the practice of oncology, and research administration.

The NCIP Informatics Council

Established in September 2012, the Informatics Council will ensure strategic alignment across NCI award and training programs in biomedical informatics and provide guidance so that NCI CBIIT and its principal program initiative, the NCIP, can prioritize projects and activities. The Council will also serve as a venue for CBIIT and NCIP Working Groups to bring forward strategic recommendations based on interactions with the wider biomedical informatics community, which will further the Institute’s bioinformatics goals in support of scientific research.

NCIP Working Groups (WGs)

CBIIT will convene WGs in focused subject areas. The goals of these groups will be to

facilitate extramural input into NCIP priorities,
provide subject-matter expertise in the specific areas around which the groups are convened, and
support the definition of informatics requirements and priorities in specific domains.

These groups may be established and dissolved over time, as needs dictate, based on the recommendations of the Council. WGs will have representation from one or more appropriate NCI DOCs (Divisions, Offices, and Centers), as well as from key extramural scientific and informatics programs in the cancer research community.

The first three WGs established by the Council are in the areas of High-throughput Molecular Data, Imaging Informatics, and Next-Generation Semantic Infrastructure.

The High-throughput Molecular Data WG will examine NCI program needs and priorities for the management, analysis, aggregation, and integration of data generated by high-throughput technologies; both basic research and clinical applications of these data will be considered. The initial focus will be on informatics initiatives for next-generation sequencing data, given the rapid advances and significant needs pertaining to these rapidly growing data collections. The group’s long-term objectives include

Lowering barriers to accessing and using data and high-throughput analytical tools
Encouraging a philosophy of sharing tools, data sets, and expertise
Providing essential resources for NCI research
Fostering partnerships to sustain collaborations in the community

The Imaging Informatics WG has the goal of examining NCI program needs and priorities for imaging informatics solutions across the cancer research continuum, from basic and preclinical studies (including small-animal imaging) through translational and clinical research. This continuum also requires standardizing and validating research algorithms and technologies for analyzing enormous amounts of imaging data and metadata in ways needed to satisfy the regulatory requirements set by the U.S. Food and Drug Administration (FDA). The group’s long-term objectives include

Lowering barriers to performing imaging research with new modalities and analytical tools
Encouraging a philosophy of sharing tools, data sets, and expertise
Providing essential resources for NCI (and eventually the larger community)
Fostering partnerships to sustain collaborations in the community
Building a sustainable, collaborative community within NCI and subsequently the larger research community

T he Next-Generation Semantic Infrastructure WG will identify and prioritize high-level needs as the semantic infrastructure is implemented. As its components are developed, the group will validate the infrastructure through application to a number of selected biomedical research projects. The group will identify projects with specific, unmet needs that can be addressed by the semantic infrastructure, and validate that it is effective in meeting those needs. Where needs are not met by existing semantic infrastructure, the working group will assess the generalizability of the need across multiple projects, and determine whether creating new or evolving existing infrastructure to meet these needs is warranted. The group’s long-term objectives include

Identifying needs in the NCI research community that the semantic infrastructure can address, with a focus on enabling interoperability and data-sharing that expedite and facilitate projects within and across scientific domains
Identifying scientific projects where project teams can apply the semantic infrastructure to validate its practicality and applicability
Lowering barriers to the access and use of data through the provision of semantic infrastructure tools and methodologies
Encouraging a philosophy of sharing tools, data sets, and expertise
Providing essential resources to NCI (and eventually the larger community)
Fostering partnerships to sustain collaborations in the community

Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy

thos@oneelevendigital.com (Thomas Conner) — Thu, 26 Sep 2013 16:15:00 GMT

Mani S, Chen Y, Li X, Arlinghaus L, Chakravarthy AB, Abramson V, Bhave SR, Levy MA, Xu H, Yankeelov TE.

J Am Med Inform Assoc. 2013 Jul-Aug;20(4):688-95. doi: 10.1136/amiajnl-2012-001332. Epub 2013 Apr 24.

PMID: http://ww.ncbi.nlm.nih.gov/pubmed/23616206

O bjective : To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC).

Materials and methods : Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118 semiquantitative and quantitative parameters were derived from these data and combined with 11 clinical variables. We used Bayesian logistic regression in combination with feature selection using a machine-learning framework for predictive model building.

Results: The best predictive models using feature selection obtained an area under the curve of 0.86 and an accuracy of 0.86, with a sensitivity of 0.88 and a specificity of 0.82.

Discussion : With the numerous options for NAC available, development of a method to predict response early in the course of therapy is needed. Unfortunately, by the time most patients are found not to be responding, their disease may no longer be surgically resectable, and this situation could be avoided by the development of techniques to assess response earlier in the treatment regimen. The method outlined here is one possible solution to this important clinical problem.

Conclusions : Predictive modeling approaches based on machine learning using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC.

Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data

thos@oneelevendigital.com (Thomas Conner) — Thu, 26 Sep 2013 16:13:00 GMT

Gutman DA, Cobb J, Somanna D, Park Y, Wang F, Kurc T, Saltz JH, Brat DJ, Cooper LA.

J Am Med Inform Assoc. 2013 Jul 25. doi: 10.1136/amiajnl-2012-001469. [Epub ahead of print]

PMID: http://www.ncbi.nlm.nih.gov/pubmed/23893318

Background : The integration and visualization of multimodal datasets is a common challenge in biomedical informatics. Several recent studies of The Cancer Genome Atlas (TCGA) data have illustrated important relationships between morphology observed in whole-slide images, outcome, and genetic events. The pairing of genomics and rich clinical descriptions with whole-slide imaging provided by TCGA presents a unique opportunity to perform these correlative studies. However, better tools are needed to integrate the vast and disparate data types.

Objective : To build an integrated web-based platform supporting whole-slide pathology image visualization and data integration.

Materials and methods : All images and genomic data were directly obtained from the TCGA and National Cancer Institute (NCI) websites.

Results : The Cancer Digital Slide Archive (CDSA) produced is accessible to the public (http://cancer.digitalslidearchive.net) and currently hosts more than 20 000 whole-slide images from 22 cancer types.

Discussion : The capabilities of CDSA are demonstrated using TCGA datasets to integrate pathology imaging with associated clinical, genomic and MRI measurements in glioblastomas and can be extended to other tumor types. CDSA also allows URL-based sharing of whole-slide images, and has preliminary support for directly sharing regions of interest and other annotations. Images can also be selected on the basis of other metadata, such as mutational profile, patient age, and other relevant characteristics.

Conclusions : With the increasing availability of whole-slide scanners, analysis of digitized pathology images will become increasingly important in linking morphologic observations with genomic and clinical endpoints.

NCI Cancer Genomics Cloud Pilots. Current Needs in Cancer Research

thos@oneelevendigital.com (Thomas Conner) — Sun, 07 Apr 2013 16:01:00 GMT

The challenges posed by the need to disseminate, manage, and interpret large, multi-scale data pervade efforts to advance understanding of cancer biology and apply that knowledge in the clinic. For several years, the volume of data routinely generated by high-throughput research technologies has grown exponentially. The storage, transmission, and analysis of these data have become too costly for individual laboratories and most small to medium research organizations to support. For optimal progress to occur, access to large, valuable data collections and advanced computational capacity must be readily available to the widest possible audience.

On April 7, 2013, Dr. Harold Varmus and other members of the Institute's senior leadership issued a letter to NCI grantees seeking input on these and other computational challenges they encounter on an almost daily basis. Dr. Varmus stated that the NCI, as part of its ongoing investigations into next-generation computational capabilities to serve the research community, has begun exploring the possibility of creating one or more public "cancer knowledge clouds" in which data repositories would be co-located with advanced computing resources, thereby enabling researchers to bring their analytical tools and methods to the data. Reactions to this informal request for information were generally positive, with respondents focusing on six general themes: data access; computing capacity and infrastructure; data interoperability; training; usability; and governance.

VICC Launches Genome-Driven Therapy - New Test Helps Personalize Care Choices for Melanoma, Some Lung Tumors

thos@oneelevendigital.com (Thomas Conner) — Wed, 04 Aug 2010 16:00:00 GMT

Vanderbilt-Ingram Cancer Center (VICC) has launched its new Personalized Cancer Medicine Initiative, becoming the first cancer center in the Southeast and one of the first in the nation to offer adult cancer patients routine “genotyping” of their tumors at the DNA level.

This information will then be used to personalize treatment by matching the appropriate therapy to the genetic changes, or mutations, that are driving the cancer’s growth.

The first tumors to be tested are types of non-small cell lung cancer and melanoma, an aggressive form of skin cancer. Both have been notoriously difficult to treat but new therapies that target specific genetic alterations in the tumors have shown promising results.

Vanderbilt is further leading the nation in personalizing medicine by leveraging its sophisticated Electronic Medical Record (EMR) to use the genotype information in point-of-care decision-making.

“The EMR for each patient is automatically updated to contain the latest genome-based treatment information, so that all healthcare providers at Vanderbilt caring for the patient are fully informed and guided by the latest decision support on these advanced therapies,” said Dan Masys, M.D. , chair of the Department of Biomedical Informatics.

“We know that genetic differences in humans at the molecular level not only contribute to the disease process, but can also significantly impact an individual’s ability to respond optimally to drug therapy,” said Jeff Balser, M.D. , Ph.D., vice chancellor for Health Affairs and dean of the School of Medicine. “We are rapidly expanding our ability to precisely identify genetic differences between patients, and make rational treatment decisions at the bedside.

T hrough a unique and cohesive set of advances that combine innovations in healthcare informatics, genomics, and drug discovery, we are beginning to ‘deliver’ on the promise of the Human Genome Project, with highly personalized therapy for our patients.”

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GRAPHICAL ABSTRACT ﻿

Hierarchical Organization of MDS Stem and Progenitor Cells

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GRAPHICAL ABSTRACT