Abstract
Abstract 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.
Keywords
BiomarkerBiomarker discoveryDrug discoveryDrugDrug responseIn vivoCancer cell linesDrug developmentComputational biologyComputer scienceAnticancer drugMedicineBioinformaticsCancerPharmacologyBiologyInternal medicineProteomicsCancer cell
MeSH Terms
Antineoplastic AgentsBiomarkersTumorCell LineTumorHumansSoftware
Affiliated Institutions
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Publication Info
- Year
- 2021
- Type
- article
- Volume
- 22
- Issue
- 6
- Citations
- 1489
- Access
- Closed
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Cite This
Danielle Maeser,
Robert F. Gruener,
R. Stephanie Huang
(2021).
oncoPredict: an R package for predicting <i>in vivo</i> or cancer patient drug response and biomarkers from cell line screening data.
Briefings in Bioinformatics
, 22
(6)
.
https://doi.org/10.1093/bib/bbab260
Identifiers
- DOI
- 10.1093/bib/bbab260
- PMID
- 34260682
- PMCID
- PMC8574972