The effort will combine the unprecedented genomic and clinical data from the MMRF’s landmark CoMMpass Study with revolutionary GNS machine learning platforms and rapid computer simulations.
The work supports the development of computer models of myeloma disease that may uncover novel molecular pathways that can prevent progression of disease and address the unmet treatment needs of patients with multiple myeloma.
CoMMpass is a longitudinal study of 1,000 newly diagnosed patients with active multiple myeloma. Its objective is to map each of these patients’ genomic profiles to clinical outcomes to develop a more complete understanding of patient responses to treatments.
"Through creative, dynamic partnerships, we continually build new research models to accelerate development of the most promising treatments for patients with multiple myeloma," says Walter M. Capone, President and Chief Executive Officer of the MMRF.
"This collaboration with GNS to apply leading-edge computer models and analytics to uncover disease pathways in the diverse CoMMpass data set exemplifies this strategy. We are excited by the real potential of this project to make rapid and meaningful progress toward a cure for multiple myeloma."
GNS will use an in silico process, applying its MAX architecture and patented REFS inference engine and simulation platforms to the CoMMpass data. REFS will identify causal drivers and underlying molecular processes of disease progression.
REFS will also discover the most likely targets for therapeutics to treat, and perhaps to predict and prevent, relapses and refractory disease. REFS will also discover predictive diagnostic biomarkers that determine which treatments will work and for which patients.
The work promises to support physicians and patients with a broader set of individualized treatment protocols mapped to specific molecular and genomic characteristics.
"This work embodies the transformative role for big data analytics to uncover specific treatment protocols that have a much better chance of success for individual patients," says Colin Hill, CEO and co-founder of GNS.
"REFS turns large datasets into computer models that reveal new molecular pathways and targets that, up until now, could not be easily identified. It ultimately accelerates many of the traditional steps in the drug discovery and development process, bypassing obstacles that often delay or prevent promising, new treatments from reaching patients."
GNS has used REFS successfully to advance the research priorities of numerous nonprofit organizations focused on diseases including Huntington’s disease, multiple sclerosis, childhood asthma, Alzheimer’s disease and others.
Its work ultimately enables next-generation care management solutions focused on reducing adverse events, slowing disease progression and improving therapeutic effectiveness through precision medicine.