Partners to Apply Causal Machine Learning and Simulation to Combination Treatment Regimens to Discover Biomarkers of Clinical Response to Treatment
CHICAGO, Ill. and CAMBRIDGE, Mass. – Dec. 15, 2015 – The Alliance for Clinical Trials in Oncology (the Alliance), one of the five cooperative research groups in the United States sponsored by the National Cancer Institute conducting large multicenter trials in oncology, and GNS Healthcare (GNS), a leading precision medicine company that applies causal machine learning technology to match health interventions to individual patients and discover new target intervention pathways, today announced a big data initiative to identify patient subpopulations that may respond better to combination treatments for metastatic colorectal cancer. The collaboration leverages REFS™ (Reverse Engineering and Forward Simulation), the patented GNS machine learning and simulation platform.
REFS will analyze multi-modal genetic, genomic, and clinical datasets associated with a recently completed Phase III Alliance-led clinical trial of 1,137 patients that evaluated the efficacy of combination chemotherapies with biological agents. Specifically, the study evaluated Irinotecan/5-FU/Leucovorin or Oxaliplatin/5-FU/Leucovorin with Bevacizumab or Cetuximab (C225) in patients with advanced colorectal cancer. Irinotecan is sold by Pfizer (NYSE: PFE) under the brand name Camptosar and is now available as a generic. Oxaliplatin is sold under the brand name Eloxatin by Sanofi (NYSE: ADR) and is now available as a generic. Bevacizumab is sold by Genentech/Roche (OTCQX: RHHBY) under the brand name Avastin. Bristol-Myers Squibb (NYSE: BMY), Eli Lilly (NYSE: LLY), and Merck KGaA (NYSE: MRK) sell Erbitux, the brand name for Cetuximab.
“The complexity of this initiative – the robust datasets, as well as the level of scientific rigor needed to deliver results in a timely fashion – requires a partner with the most advanced computing and machine learning capabilities,” says Dr. Alan P. Venook, Study Chair for the Alliance, Madden Family Distinguished Professor of Medical Oncology and Translational Research at the University of California-San Francisco, and Shorenstein Associate Director for Program Development at the Helen Diller Family Comprehensive Cancer Center. “The GNS machine learning platform has the ability to analyze extraordinarily large and complex datasets and to quickly deliver useful answers on the efficacy of combination treatments for metastatic colorectal cancer.”
The large-scale Alliance datasets include comprehensive clinical information and molecular profiles of patients diagnosed with metastatic colorectal cancer. The clinical parameters encompass demographics, patient and disease characteristics, and outcomes (e.g., overall survival).The molecular results represent detailed analyses of tumor tissue and blood specimens. All information and specimens were collected from patients who participated in the Alliance study from 2004 through 2014.
The effort hopes to accelerate and advance research on the efficacy and value of combination treatments for colorectal cancer with a goal to deliver a reliable framework to rapidly assess the impact of these treatments on individual patients. It aims to identify biomarkers of specific patient outcomes and to predict the likelihood of those outcomes at an individual patient level. This knowledge would improve the ability to match patients to the treatment regimen with the greatest value for them – and to do so sooner.
“GNS is honored to be selected by the Alliance to discover patient response diagnostic markers from this clinical trial data and go beyond ‘one-size-fits-all’ medicine,” says Dr. Iya Khalil, Executive Vice President and Co-Founder of GNS Healthcare “This research embodies the GNS vision to bring precision medicine into mainstream medicine to improve patient outcomes. The goal is to identify patient sub-types that respond favorably to combination treatment regimens, ultimately supporting physicians and their patients with critical information needed to more precisely, quickly, and confidently choose a treatment regimen matched to the unique biological characteristics of the patient.”
About the Alliance
The Alliance for Clinical Trials in Oncology develops and conducts clinical trials with promising new cancer therapies, and utilizes the best science to develop optimal treatment and prevention strategies for cancer, as well as researches methods to alleviate side effects of cancer and cancer treatments. The Alliance has been awarded a grant by the National Cancer Institute (NCI) as a member of the NCI National Clinical Trials Network (NCTN) in addition to a grant to serve as a research base for the NCI Community Research Oncology Program (NCORP). Collectively, the Alliance comprises nearly 10,000 cancer specialists at hospitals, medical centers, and community clinics across the United States and Canada. To learn more about the Alliance, visit www.AllianceforClinicalTrialsinOncology.org
About GNS Healthcare
GNS Healthcare applies causal machine learning technology to predict which treatments will work for which patients, improving individual patient outcomes and the health of populations, while reducing costs. The GNS technology is based on its MAX™ (Meaningful Actions Accelerator) data integration architecture and patented REFS™ (Reverse Engineering and Forward Simulation) causal inference and simulation engine. Health plans, bio-pharmaceutical companies, healthcare providers, foundations, academic medical centers, and employers use these cloud-based solutions to solve pressing and costly problems including preterm birth, end-of-life care, comparative effectiveness, medication non-adherence, metabolic syndrome, diabetes, specialty care medications, drug target discovery, patient stratification in clinical trials, and more. GNS solutions focus on reducing adverse events, slowing disease progression, and improving therapeutic effectiveness through precision matching that maximizes impact on individual patient health outcomes while reducing wasteful spending and downstream medical costs.
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Alliance for Clinical Trials in Oncology