Blog Layout

Foundation Medicine Launches Precision Medicine Partnership Program

CAMBRIDGE, Mass.--(BUSINESS WIRE)-- Foundation Medicine, Inc. (NASDAQ:FMI) today announced the launch of its precision medicine partner program, Precision Medicine Exchange Consortium™ (PMEC), to facilitate data exchange, advance research, and support education and applications of precision medicine in oncology and molecular pathology. PMEC brings together oncology thought leaders from academic medical centers, regional hospital systems, and community oncology networks, initially located in the U.S. and to be expanded internationally, who share a vision for utilizing precision medicine as a means for improving clinical outcomes in oncology treatment. The consortium intends to realize this vision through a collaborative exchange of molecular information and clinical outcomes data, and through a broader integration of comprehensive genomic profiling in cancer treatment.


Founding members of PMEC include The Cleveland Clinic's Taussig Cancer Institute, Hackensack University Medical Center, The Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Sidney Kimmel Cancer Center at Thomas Jefferson University, UC Davis Health Comprehensive Cancer Center, the University of North Carolina Lineberger Comprehensive Cancer Center, and Vanderbilt-Ingram Cancer Center and The Wake Forest Baptist Comprehensive Cancer Center.


"Progress in cancer care will be achieved by breaking down the information silos that exist in healthcare and collaborating towards clinically robust and relevant data exchange," said Brian Bolwell, MD, chairman of the Cleveland Clinic Cancer Institute and founding member of PMEC. "We applaud Foundation Medicine and our partner institutions in PMEC for their innovative thinking, for valuing the exchange of information, and for taking a leadership role to effect a positive change for our patients with cancer."


PMEC is being established on the guiding principles of innovation, education and the exchange of highly validated molecular information and outcomes data to improve cancer care. PMEC members will have access to a suite of innovative information tools and programs tailored to fit the needs of the individual cancer centers who are members of PMEC.


A selection of these planned offerings include:


  • access to a shared data exchange platform of de-identified, matched clinical outcomes and genomic data contributed by Foundation Medicine and PMEC members to support research and clinical innovation;
  • access to clinical research programs that integrate comprehensive genomic profiling to improve cancer care; and
  • programs to support the advancement of precision medicine and molecular oncology through training, education and streamlined member services.

Additionally, PMEC members can elect to participate in best practices exchanges, where members will have the opportunity to share ideas advancing precision medicine.


"The promise of precision medicine rests on these national and international initiatives, including PMEC, which enable investigators to share crucial data. Only through that data sharing can we understand the implications of genomics for patient care," said Mary Zutter, M.D., assistant vice chancellor for Integrative Diagnostics at Vanderbilt University Medical Center.


"The Precision Medicine Exchange Consortium is being developed as the most comprehensive program of its kind and will align innovators in cancer care around molecular information and clinical data curation and exchange," said Vincent A. Miller, M.D., chief medical officer at Foundation Medicine. "Collectively, PMEC is poised to fuel innovation, support cancer research and extend valuable information and education in a way that heretofore has not happened within the cancer community. We're proud to be launching this initiative with so many thought leaders across a broad spectrum of cancer institutions and practices. We're actively seeking new members who share PMEC's vision, and we welcome conversations with additional cancer centers, data and informatics organizations and payers who embrace the opportunity to collaborate for the betterment of cancer care."


About Foundation Medicine


Foundation Medicine (NASDAQ:FMI) is a molecular information company dedicated to a transformation in cancer care in which treatment is informed by a deep understanding of the genomic changes that contribute to each patient's unique cancer. The company's clinical assays, FoundationOne® for solid tumors and FoundationOne® Heme for hematologic malignancies and sarcomas, provide a comprehensive genomic profile to identify the molecular alterations in a patient's cancer and match them with relevant targeted therapies and clinical trials. Foundation Medicine's molecular information platform aims to improve day-to-day care for patients by serving the needs of clinicians, academic researchers and drug developers to help advance the science of molecular medicine in cancer. For more information, please visit http://www.FoundationMedicine.com or follow Foundation Medicine on Twitter (@FoundationATCG).


Foundation Medicine® is a registered trademark, and Precision Medicine Exchange Consortium™, PMEC CORE™, PMEC Research™, and PMEC Education™ are trademarks, of Foundation Medicine, Inc.


Cautionary Note Regarding Forward-Looking Statements


This press release contains "forward-looking statements" within the meaning of the Private Securities Litigation Reform Act of 1995, including, but not limited to, statements regarding the vision, objectives, members and offerings of Precision Medicine Exchange Consortium, the initiation of PMEC programs, and the ability of PMEC to affect cancer research and cancer care. All such forward-looking statements are based on management's current expectations of future events and are subject to a number of risks and uncertainties that could cause actual results to differ materially and adversely from those set forth in or implied by such forward-looking statements. These risks and uncertainties include the risks that PMEC is unable to successfully combine and share de-identified clinical outcomes and genomic information, to provide patients of PMEC members with access to clinical trials; and the risks described under the caption "Risk Factors" in Foundation Medicine's Quarterly Report on Form 10-Q for the three months ended March 31, 2015, which is on file with the Securities and Exchange Commission, as well as other risks detailed in subsequent filings with the Securities and Exchange Commission, may be realized. All information in this press release is as of the date of the release, and Foundation Medicine undertakes no duty to update this information unless required by law.

Share this Article with others

Automated Artificial Intelligence Model Trained on a Large Data Sets

08 Mar, 2024
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.

Predicting Medication Responses using Machine Learning Models

08 Mar, 2024
Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress

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

08 Mar, 2024
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.

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

08 Mar, 2024
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.

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

By Bo Zhang • 08 Mar, 2024
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

Artificial intelligence in oncology: current applications and future perspectives

By Claudio Luchini • 08 Mar, 2024
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,

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

By Panayiotis Petousis, PhD • 08 Mar, 2024
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

Multimodal AI-powered approaches in prevention and management

By Evan D. Muse • 08 Mar, 2024
Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management Lead Author: Evan D. Muse

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

By Danielle S. Bitterman, MD • 08 Mar, 2024
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

Can A.I. Treat Mental Illness?

01 Mar, 2023
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
Share by: