Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel.

ABSTRACT:

Motivation

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 outperfo

SUBMITTER: Cortes-Ciriano I 

PROVIDER: S-EPMC4681992 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature