Targeting the engine room of the cancer cell
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IMAGE: The pipeline consists of a series of pre-computed (*) components, including a reference set of more than 13,000 tumor expression profiles representing 35 different tumor types, a collection of 28… view more 

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Credit: CUIMC/Califano Lab

NEW YORK, NY (JUNE 18, 2018)– Researchers at Columbia University Irving Medical Center (CUIMC) have developed a highly innovative computational framework that can support personalized cancer treatment by matching individual tumors with the drugs or drug combinations that are most likely to kill them.

The study, published today on Nature Genetics, by Dr. Andrea Califano of Columbia University Irving Medical Center and Dr. Irvin Modlin of Yale University and Wren Laboratories LLC, co-senior author on the study, with collaborators from 17 research centers worldwide, details a proof of concept for a novel analytical platform applicable to any cancer type and validates its predictions on gastroenteropancreatic neuroendocrine tumors (GEP-NETs). The latter represent a rare class of tumors of the digestive system that, when metastatic, are associated with poor survival.

In a comprehensive analysis of samples from 212 patients, the team first identified a new class of drug-targets, called master regulators, which are rarely if ever mutated in cancer patients, and then predicted the drugs that can specifically invert their

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