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GNS Healthcare, Dana Farber, Mount Sinai to Collaborate on In Silico Multiple Myeloma Models


GNS Healthcare said this week that it is collaborating with Dana-Farber Cancer Institute and Mount Sinai School of Medicine to build computer models of multiple myeloma using the company’s reverse engineering and forward simulation platform.

The partners will use GNS’ REFS platform to engineer network models from next-generation sequencing data as well as proteomic data, information on patient outcomes, and other clinical data.

"We have made encouraging progress at Dana-Farber Cancer Institute in using gene profiling, proteomic, and signaling studies in tumor cell samples treated with existing and novel medicines to get a better understanding of myeloma pathogenesis and to develop novel targeted therapies," Ken Anderson, director of the Jerome Lipper Multiple Myeloma Center and LeBow Institute for Myeloma Therapeutics at Dana-Farber, said in a statement.

This partnership allows the researchers to now apply “a powerful, supercomputer-driven approach using our multi-layered genomic data to develop computer models to directly define the integrated underlying circuitry of myeloma,” Nikhil Munshi, associate director of Dana Farber’s Multiple Myeloma Center, added.

The collaborators say these models could shed light on the fundamental mechanisms of multiple myeloma and help researchers identify novel intervention points in the disease for specific groups of patients and develop 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," Eric Schadt, director of the Institute for Genomics and Multiscale Biology and a professor of Genomics at Mount Sinai, said in a statement.

"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,” he said.

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