PT  - JOURNAL ARTICLE
AU  - Cuong C. Dang
AU  - Antonio Peón
AU  - Pedro J. Ballester
TI  - Unearthing New Genomic Markers of Drug Response by Improved Measurement of Discriminative Power
AID  - 10.1101/033092
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 033092
4099  - http://biorxiv.org/content/early/2016/12/18/033092.short
4100  - http://biorxiv.org/content/early/2016/12/18/033092.full
AB  - Background Oncology drugs are only effective in a small proportion of cancer patients. To make things worse, our current ability to identify these responsive patients before treatment is still very limited. Thus, there is a pressing need to discover response markers for marketed and research oncology drugs in order to improve patient survival, reduce healthcare costs and enhance success rates in clinical trials. Screening these drugs against a large panel of cancer cell lines has been recently employed to discover new genomic markers of in vitro drug response, which can now be further evaluated on more accurate tumour models. However, while the identification of discriminative markers among thousands of candidate drug-gene associations in the data is error-prone, an appraisal of the effectiveness of such detection task is currently lacking.Results Here we present a new non-parametric method to measuring the discriminative power of a drug-gene association. This is enabled by the identification of an auxiliary threshold posing this task as a binary classification problem. Unlike parametric statistical tests, the adopted non-parametric test has the advantage of not making strong assumptions about the data distorting the identification of genomic markers. Furthermore, we introduce a new benchmark to further validate these markers in vitro using more recent data not used to identify the markers. Thus, the application of this new methodology has led to the identification of 128 new genomic markers distributed across 61% of the analysed drugs, including 5 drugs without previously known markers, which were missed by the MANOVA test initially applied to analyse data from the Genomics of Drug Sensitivity in Cancer consortium.