GNS Healthcare, a healthcare data analytics company, has entered into a collaboration with Dana-Farber Cancer Institute and the Mount Sinai School of Medicine to create a data-driven computer model of multiple myeloma, the second most common blood cancer in the U.S.
Created using GNS's supercomputer-driven REFS (Reverse Engineering and Forward Simulation) platform, the models will be used to help researchers discover novel therapies for the disease and to help determine the best existing treatments for patients.
"GNS will apply its Big Data analytics platform to create a disease-specific computer model that will yield a powerful new resource to the multiple myeloma research and clinical community—with the ultimate aim of better outcomes for patients,” said Colin Hill, CEO and co-founder of GNS. “This collaboration with Dana-Farber and Mount Sinai will create models that will help transform the tremendous amount of data coming from new technologies, such as next-generation sequencing, into predictive computer models of disease progression and treatment response for scientists and clinicians. This project is one of many examples of our work in challenging, complex and, sometimes rare, diseases."
GNS will employ the REFS platform to reverse engineer network models from next-generation genetic sequencing, proteomic, outcomes and other clinical data. Results from millions of in silico simulations of the REFS models will provide new insights into the fundamental mechanisms of multiple myeloma, enabling the identification of novel intervention points in the disease for specific groups of patients and the development of more effective medicines.
"Prior published work has shown us that approaches like the REFS platform can develop integrated network models of disease that can be used to uncover novel drivers of disease," said Dr. Eric Schadt, director of the Institute for Genomics and Multiscale Biology, chair of the Department of Genetics and Genomics Sciences at Mount Sinai. "With the wealth of detailed biological data available in this project, we look forward to a close collaboration with GNS to build a predictive model to elucidate novel insights into this complex disease."