Interactive webtool for analyzing drug sensitivity and resistance associated with genetic signatures of cancer cell lines

Boeschen, M.; Horn, S.; Le Duc, D.; Stiller, M.; von Laffert, M.; Schönberg, T.

doi:10.1101/2022.08.22.504894

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bioRxiv · 10.1101/2022.08.22.504894

Interactive webtool for analyzing drug sensitivity and resistance associated with genetic signatures of cancer cell lines

Boeschen, M.·Horn, S.·Le Duc, D.·Stiller, M.·von Laffert, M.·Schönberg, T.

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Abstract

A wide therapeutic repertoire has become available to oncologists including radio- and chemotherapy, small molecules and monoclonal antibodies. However, drug efficacy can be limited by genetic changes that allow cancer cells to escape therapy. Here, we designed a webtool that facilitates the data analysis of the Genomics of Drug Sensitivity in Cancer (GDSC) database on 265 approved compounds in association with a plethora of genetic changes documented for 1001 cell lines in the Cancer Cell Line Encyclopedia (CCLE, cBioPortal). The webtool computes odds ratios of drug resistance for a queried set of genetic alterations. It provides results on the efficacy of single or groups of compounds assigned to cellular signaling pathways. Using this webtool we replicated known genetic drivers and identified new candidate genes, germline variants, co-mutation, and pharmacogenomic modifiers of both cancer drug resistance and drug repurposing. Webtool availability: https://tools.hornlab.org/GDSC/. Author SummaryTumors develop through uncontrolled cell growth enabled by various alterations that create tumor heterogeneity. Changes of the genome and thus cancer cells cause every patient to react differently to drugs and can lead to drug resistance in cancer therapies. To overcome drug resistance, researchers focus on developing personalized therapies. Here, we provide a straightforward tool to test public in vitro drug sensitivity data on a range of drugs for custom analyses of genetic changes. This may inform the identification of potential drug candidates and improve our understanding of signaling pathways as we can test drug response with custom sets of genetic changes according to specific research questions. The tool and underlying code can be adapted to larger drug response datasets and other data types, e.g. metabolic data, to help structure and accommodate the increasingly large biomedical knowledge base.

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Boeschen, M., Horn, S., Le Duc, D., Stiller, M., von Laffert, M., Schönberg, T.. 2022-08-25. Interactive webtool for analyzing drug sensitivity and resistance associated with genetic signatures of cancer cell lines. https://doi.org/10.1101/2022.08.22.504894

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