oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data Article Swipe

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biomarker biomarker discovery drug discovery drug drug response in vivo cancer cell lines drug development computational biology computer science anticancer drug medicine bioinformatics cancer pharmacology biology internal medicine proteomics cancer cell biochemistry biotechnology gene

Danielle Maeser , Robert F. Gruener , R. Stephanie Huang ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1093/bib/bbab260 · OA: W3182293965

Cell line drug screening datasets can be utilized for a range of different drug discovery applications from drug biomarker discovery to building translational models of drug response. Previously, we described three separate methodologies to (1) correct for general levels of drug sensitivity to enable drug-specific biomarker discovery, (2) predict clinical drug response in patients and (3) associate these predictions with clinical features to perform in vivo drug biomarker discovery. Here, we unite and update these methodologies into one R package (oncoPredict) to facilitate the development and adoption of these tools. This new OncoPredict R package can be applied to various in vitro and in vivo contexts for drug and biomarker discovery.