Open AccessoncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data
Author(s) -
Danielle Maeser,
Robert F. Gruener,
R. Stephanie Huang
Publication year - 2021
Publication title -
briefings in bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.204
H-Index - 113
eISSN - 1477-4054
pISSN - 1467-5463
DOI - 10.1093/bib/bbab260
Subject(s) - biomarker , drug discovery , biomarker discovery , drug , drug response , in vivo , cancer cell lines , computational biology , drug development , computer science , anticancer drug , bioinformatics , medicine , cancer , pharmacology , biology , proteomics , cancer cell , biochemistry , microbiology and biotechnology , gene
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.