Hundreds of model cancer cell lines from diverse tissues have now been comprehensively assayed using several different genome-scale technologies. Additionally, many of these model lines have been challenged with dozens of clinically available oncotherapies. Here we present a set of high-throughput technologies suitable for turning these diverse datasources into actionable, patient-specific diagnostic predictors of response.

First, we computationally integrate diverse -omics data from the DNA, mRNA and protein levels into a pathway model of the cellular state. Next, parallel compute clusters are used to develop and evaluate accurate predictive models upon these pathway activity levels. Finally, these predictive models are used to suggest therapies in a patient-specific manner.

Here we show results from applying these technologies to learning a simple 50-gene signature for response to the the tyrosine kinase inhibitor Dasatinib. This signature utilizes features from the TP53/FOXM1, FOS/JUN & MYC pathways. We note that the exceptional responders to Dasatinib are enriched for nervous-system cancer cell lines. We applied our predictive signature to glioblastoma multiforme samples to correctly indicate which specific GBM patients may have respond to Dasatinib.

Citation Format: Christopher W. Szeto, Stephen Benz, Charlies Vaske. Building patient-specific predictors of drug responses from cell line genomics. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Integrating Clinical Genomics and Cancer Therapy; Jun 13-16, 2015; Salt Lake City, UT. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(1_Suppl):Abstract nr 44.

©2016 American Association for Cancer Research.
2016