Results of Big Data and Machine Learning Collaboration Featured at American Society of Hematology Annual Meeting
NORWALK, Conn. and CAMBRIDGE, Mass. – Dec. 5, 2015 –The Multiple Myeloma Research Foundation (MMRF), a leader in accelerating the development of breakthrough multiple myeloma therapies, 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 initial results from a multi-year collaboration that leverages an unprecedented longitudinal observational study to speed the discovery of innovative treatments for patients with multiple myeloma.
From the largest and most comprehensive computer models ever built of molecular and clinical interactions in multiple myeloma disease, potential drivers of clinical outcomes and their associated molecular pathways, including some that may be novel, have begun to emerge. These drivers and molecular pathways may represent targets for drug discovery and development, leading to new pharmaceutical strategies that prevent progression of disease and address continued unmet treatment needs of patients with multiple myeloma.
The MMRF and GNS presented these findings in a poster session today at the American Society of Hematology (ASH) 57th Annual Meeting & Exposition in Orlando. In addition to the MMRF and GNS, authors of the paper presented at ASH, “Investigation of Mechanisms of Response in Multiple Myeloma Via Bayesian Causal Inference: An Early Analysis of the CoMMpass Study Data,” represent the Translational Genomics Research Institute, Phoenix, Ariz.; Instat Services, Chatham, N.J.; and Winship Cancer Institute of Emory University, Department of Hematology and Medical Oncology, Atlanta, Ga.
The computer models are the product of large-scale, multi-modal patient data from the MMRF’s landmark CoMMpass Study™ (NCT0145429) and revolutionary GNS causal machine learning and simulation platform REFS™ (Reverse Engineering and Forward Simulation). Results reflect an analysis by REFS of the CoMMpass Interim Analysis 7 (IA7) dataset, which is composed of extensive clinical and genomic data for a population of almost 800 enrolled patients. CoMMpass follows 1,000 newly diagnosed patients with active multiple myeloma for eight years. Its objective is to map to clinical parameters each of these patients’ myeloma cells genomic profiles, generated from specimens collected at first presentation and at progression events, to develop a more complete understanding of patient responses to treatments.
“The MMRF is committed to making rapid and meaningful progress toward a cure for multiple myeloma,” said Walter M. Capone, President and Chief Executive Officer of the MMRF. “These results are an exciting first step in our ongoing partnership with GNS and herald a new era of drug discovery and development in which the combination of vast patient data resources and transformational machine learning technologies are dramatically accelerating research.”
GNS leveraged REFS to reverse-engineer the molecular pathways that affect treatment outcomes in the CoMMpass population and to assess the significance of these pathways in treatment response. REFS employs a hypothesis-free approach, simulating every possible combination and outcome from large, heterogeneous datasets. The hypothesis-free approach of REFS identified a broad range of known disease drivers and biomarkers of response, in addition to the discovery of novel drivers of clinical outcomes and patient response, giving the research team more confidence in the importance of predicted drivers of clinical outcomes and patient response.
“GNS is honored to be working with the MMRF to transform the CoMMpass dataset into mechanistic computer models that hold the keys to unlocking medical mysteries related to how multiple myeloma patients’ genetic makeup and other factors influence the efficacy of therapeutics,” said Colin Hill, CEO and co-founder of GNS. “These insights will be tremendously useful to guiding both drug discovery and development highest impact, and ultimately connecting more patients with effective treatments and advancing precision medicine into patient care.”
The MMRF and GNS will continue to investigate the findings by using future CoMMpass Interim Analyses to validate the significance of predicted novel drivers while refining REFS models. The MMRF and GNS will soon release the computer models for use by associated researchers, clinicians and partners to facilitate future discoveries.
About the CoMMpass Study™
CoMMpass is a longitudinal study of patients with newly-diagnosed active multiple myeloma. The goal is to map the genomic profile of each patient to clinical outcomes to develop a more complete understanding of patient responses to treatments. A cornerstone of the MMRF’s Personalized Medicine Initiative, the study will collect and analyze tissue samples and genetic information from approximately 1,000 newly diagnosed multiple myeloma patients for at least eight years.
The study is designed to show what treatments are used most often as first and subsequent lines of therapy, and to correlate this information with critical therapeutic response criteria including best responses achieved, overall survival, time to disease progression and quality of life measures. It is also powered to track treatment data to correspond with genetic information such as mutations and translocations (the movement of a chromosomal segment from one position to another, a phenomenon that often occurs in cancer).
About Multiple Myeloma
Multiple myeloma is a cancer of the plasma cell. It is the second most common blood cancer. An estimated 24,050 adults (13,500 men and 10,550 women) in the United States will be diagnosed with multiple myeloma in 2014 and an estimated 11,090 people are predicted to die from the disease. The five-year survival rate for multiple myeloma is approximately 43%, versus 28% in 1998.
About the Multiple Myeloma Research Foundation (MMRF)
The Multiple Myeloma Research Foundation (MMRF) was established in 1998 as a 501(c)(3) non-profit organization by twin sisters Karen Andrews and Kathy Giusti, soon after Kathy’s diagnosis with multiple myeloma. The mission of the MMRF is to relentlessly pursue innovative means that accelerate the development of next-generation multiple myeloma treatments to extend the lives of patients and lead to a cure. As the world’s number-one private funder of multiple myeloma research, the MMRF has raised $270 million since its inception and directs nearly 90% of total budget to research and related programming. As a result, the MMRF has been awarded Charity Navigator’s coveted four-star rating for 11 consecutive years, the highest designation for outstanding fiscal responsibility and exceptional efficiency. For more information about the MMRF, please visit: www.themmrf.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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The Multiple Myeloma Research Foundation