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. 2018 Apr 1;34(7):1226-1228.
doi: 10.1093/bioinformatics/btx744.

GDSCTools for mining pharmacogenomic interactions in cancer

Thomas Cokelaer  1 , Elisabeth Chen  2 , Francesco Iorio  3 , Michael P Menden  4 , Howard Lightfoot  2 , Julio Saez-Rodriguez  3   5 , Mathew J Garnett  2
Affiliations

GDSCTools for mining pharmacogenomic interactions in cancer

Thomas Cokelaer et al. Bioinformatics. .

Abstract

Motivation: Large pharmacogenomic screenings integrate heterogeneous cancer genomic datasets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces.

Results: We have developed GDSCTools: a software aimed at the identification of clinically relevant genomic markers of drug response. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) integrates heterogeneous cancer genomic datasets as well as anti-cancer drug responses on a thousand cancer cell lines. Including statistical tools (analysis of variance) and predictive methods (Elastic Net), as well as common data structures, GDSCTools allows users to reproduce published results from GDSC and to implement new analytical methods. In addition, non-GDSC data resources can also be analysed since drug responses and genomic features can be encoded as CSV files.

Contact: thomas.cokelaer@pasteur.fr or saezrodriguez.rwth-aachen.de or mg12@sanger.ac.uk.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1
Fig. 1
(A) Drug response (cell viability versus drug concentrations) and derived drug response metrics (AUC and IC50s). (B) Distribution of IC50s in response to a given drug across a dichotomy of cell lines induced by the status of a genomic feature. (C) P-values from an ANOVA analysis versus signed effect sizes (all drug-genomic feature interactions). (D) Weight distributions resulting from training a sparse linear regression model of a given drug response using all the genomic features
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