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Credit: Casey Greene, PhD, Perelman School of Medicine, University of Pennsylvania, Cell Reports

PHILADELPHIA – Matching unique genetic information from cancer patients’ tumors with treatment options – an emerging area of precision medicine efforts – often fails to identify all patients who may respond to certain therapies. Other molecular information from patients may reveal these so-called “hidden responders,” according to a Penn Medicine study in Cell Reports this week. The findings are published alongside several papers in other Cell journals this week examining molecular pathways using The Cancer Genome Atlas (TCGA).

“Targeted sequencing can find individuals with certain mutations that are thought to confer susceptibility to anti-cancer drugs,” said senior author Casey Greene, PhD, an assistant professor of Pharmacology in the Perelman School of Medicine at the University of Pennsylvania. “But many people may lack these mutations, and as machine learning approaches improve they may help guide these patients to appropriate therapies.”

Greene and first author and doctoral student Gregory P. Way used machine learning to classify abnormal protein activity in tumors. This branch of artificial intelligence develops computer programs that can use new data to learn and

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