Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel.
BACKGROUND
Recent large-scale omics initiatives have catalogued the somatic alterations of cancer cell line panels along with their pharmacological response to hundreds of compounds. In this study, we have explored these data to advance computational approaches that enable more effective and targeted use of current and future anticancer therapeutics.


RESULTS
We modelled the 50% growth inhibition bioassay end-point (GI50) of 17,142 compounds screened against 59 cancer cell lines from the NCI60 panel (941,831 data-points, matrix 93.08% complete) by integrating the chemical and biological (cell line) information. We determine that the protein, gene transcript and miRNA abundance provide the highest predictive signal when modelling the GI50 endpoint, which significantly outperformed the DNA copy-number variation or exome sequencing data (Tukey's Honestly Significant Difference, P <0.05). We demonstrate that, within the limits of the data, our approach exhibits the ability to both interpolate and extrapolate compound bioactivities to new cell lines and tissues and, although to a lesser extent, to dissimilar compounds. Moreover, our approach outperforms previous models generated on the GDSC dataset. Finally, we determine that in the cases investigated in more detail, the predicted drug-pathway associations and growth inhibition patterns are mostly consistent with the experimental data, which also suggests the possibility of identifying genomic markers of drug sensitivity for novel compounds on novel cell lines.


BACKGROUND
terez@pasteur.fr; ab454@ac.cam.uk


BACKGROUND
Supplementary data are available at Bioinformatics online.
Unité de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 75 724 Paris, France.
Isidro
I
Cortés-Ciriano
Medicinal Chemistry, Leiden Academic Centre for Drug Research, Einsteinweg 55, 2333CC, Leiden.
Gerard J P
GJ
van Westen
Unité de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 75 724 Paris, France.
Guillaume
G
Bouvier
Unité de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 75 724 Paris, France.
Michael
M
Nilges
European Molecular Biology Laboratory European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD, Hinxton, Cambridge, UK and.
John P
JP
Overington
Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, CB2 1EW Cambridge, UK.
Andreas
A
Bender
Unité de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 75 724 Paris, France.
Thérèse E
TE
Malliavin
D001681:Biological Assay; D045744:Cell Line, Tumor; D049109:Cell Proliferation; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D008954:Models, Biological; D009369:Neoplasms / Q000473:pathology*; D010597:Pharmacogenetics; D060388:Support Vector Machine