Clinical OMICS

MAY-JUN 2018

Healthcare magazine for research scientists, labs, pathologists, hospitals, cancer centers, physicians and biopharma companies providing news articles, expert interviews and videos about molecular diagnostics in precision medicine

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Page 48 of 51 May/June 2018 Clinical OMICs 47 Researchers at the University of California, San Francisco (UCSF) have generated a detailed, quantitative gene–drug interaction map as an open resource that could help clini- cians prescribe the most effective type of chemotherapy for each cancer patient, based on their tumor 's genetic profile. The UCSF scientists say that as well as allowing research- ers to predict how genetically defined human cancer cell lines respond to different chemotherapy agents, the map has uncovered new genetic factors that appear to deter- mine how breast and ovarian tumors respond to common chemotherapy drug classes. As proof of principle, they used the gene–drug interaction map to identify two gene mutations that appeared to contribute to ovarian cancer resistance to poly (ADP-ribose) polymerase (PARP) inhib- itors, and confirmed their finding in patients participating in a clinical trial. "We know very little about how gene mutations in tumor cells can change how a tumor might respond or not to cer- tain chemotherapy drugs," said Bandy Opadhyay, a mem- ber of the UCSF Helen Diller Family Comprehensive Cancer Center and the Quantitative Biosciences Institute. "We're trying to take a systems view of chemotherapy resistance. With rarer mutations in particular, there aren't enough patients for large clinical trials to be able to identify bio- markers of resistance, but by considering all the different potential genetic factors that have been identified together in one study, we can robustly predict from experiments in laboratory dishes how cancers with different genetic muta- tions will respond to different treatments." The vast majority of cancer patients receive chemo- therapy, but the decision on which of the more than 100 chemotherapy agents to use is often based on historical average response rates, rather than on an understanding of genetic factors that may impact treatment efficacy or tumor resistance. "Choosing from multiple possible che- motherapy options can complicate clinical decision mak- ing," the researchers wrote. "Therefore, optimizing the use of chemotherapies is a significant and pressing challenge in precision oncology." The team is making their chemical–genetic interaction map publicly available, with the hope that it will provide valuable new insights into the biological basis for chemo- therapy success and failure, and potentially help researchers identify effective new chemotherapy combinations against tumors with specific genetic signatures. "This work highlights the utility of a systematic chemi- cal–genetic interaction map as a resource for the identifica- tion of clinically relevant biomarkers of drug susceptibility, as well as a foundation for integration with other cancer datasets to enhance drug and biomarker development," the authors noted. "This quantitative map is predictive of inter- actions maintained in other cell lines, identifies DNA-re- pair factors, predicts cancer cell line responses to therapy, and prioritizes synergistic drug combinations. In contrast to most standard genetic screens, this approach provides a quantitative readout that approximates genetic interaction strength and allows for the comparison of responses across many drugs." UCSF Researchers Release Gene–Drug Interaction Map for Chemotherapy BSIP/UIG / Getty Images

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