Big data is transforming all kinds of endeavors, as technology leaders highlighted at Xconomy’s packed event in Boston last month, “The Future of Big Data.” Now, thanks to advances in deciphering genomics, big data is also being enlisted in the war on cancer.
Today, GNS Healthcare of Cambridge, MA, announced that it is collaborating with Dana-Farber Cancer Center in Boston and Mount Sinai School of Medicine in New York to create a computer model of multiple myeloma meant to help discover new treatments for this deadly blood cancer.
GNS said it will use its supercomputer-based platform, called Reverse Engineering and Forward Simulation, to help researchers at the two institutions discover new cellular targets and new therapies for the disease, and to help tailor the current best treatment regimens for each patient.
GNS is following on the heels of IBM, which announced in March a collaboration with Memorial Sloan-Kettering Cancer Center in New York to develop a cancer diagnostic support tool for its Watson supercomputer (the one that defeated human contestants on “Jeopardy” in February 2011). The tool will combine data from Sloan Kettering’s extensive library of tumor samples and case files with the most current treatment information to advise doctors on the best treatment regimen for individual patients with lung, breast, or prostate cancer.
GNS CEO and founder Colin Hill told me the Dana Farber collaboration represents the first time big data is being applied to multiple myeloma, which will be diagnosed in about 21,700 people in the U.S. this year. Only 40 percent of patients with the disease are still alive five years after being diagnosed.
Hill says the collaborators will also use the system to search for new biomarkers and drug targets for the disease, something that could not have been done even five years ago because there the genomic data base and the computing power was not available. “The data and the technology are really in a different place now. We are talking about remaking the whole medical landscape…we’re now in a position with these rich data sets to really pinpoint the most efficacious drug targets.”
