Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies

Abstract

Identifying reliable drug response biomarkers is a significant challenge in cancer research. We present computational analysis of resistance (CARE), a computational method focused on targeted therapies, to infer genome-wide transcriptomic signatures of drug efficacy from cell line compound screens. CARE outputs genome-scale scores to measure how the drug target gene interacts with other genes to affect the inhibitor efficacy in the compound screens. Such statistical interactions between drug targets and other genes were not considered in previous studies but are critical in identifying predictive biomarkers. When evaluated using transcriptome data from clinical studies, CARE can predict the therapy outcome better than signatures from other computational methods and genomics experiments. Moreover, the CARE signatures for the PLX4720 BRAF inhibitor are associated with an anti-programmed death 1 clinical response, suggesting a common efficacy signature between a targeted therapy and immunotherapy. When searching for genes related to lapatinib resistance, CARE identified PRKD3 as the top candidate. PRKD3 inhibition, by both small interfering RNA and compounds, significantly sensitized breast cancer cells to lapatinib. Thus, CARE should enable large-scale inference of response biomarkers and drug combinations for targeted therapies using compound screen data.

The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

“…Synergistic behavior is difficult to predict, so rational combinations may not validate experimentally (12). Hypothesis-driven studies of the mechanisms of synergy and antagonism have focused on a limited set of candidate targets (13,14). Alternatively, unbiased high-throughput screening assays (15)(16)(17) synergistic combination candidates are needed to improve the experimental cost-benefit ratio (18,19).…”

Systematic identification of non-coding pharmacogenomic landscape in cancer

“…Using cell-line-based panels, annotated with both genetic and pharmacological data, to gain insights into the mechanism of anti-cancer drug response has been considered as the cornerstone of precision cancer medicine 2 . Those large-scale high-throughput cancer pharmacogenomics efforts, mainly focusing on protein coding components of the genome, have led to many insightful discoveries 3 – 6 but also raised new questions: few new biomarkers and drivers were identified to fully explain the regulation of drug resistance in cancer 7 .…”

PATRI, a Genomics Data Integration Tool for Biomarker Discovery

“…The PATRI structure can be integrated with further analysis methods, available as R packages, making the tool a suitable platform for future implementation of innovative analysis approaches in biomarker discovery, such as the integration of novel prediction algorithms [ 83 – 86 ], possibly supporting also the identification of synergistic combinations [ 87 ], or the handling of confounding factors in preclinical cancer model variability [ 88 ]. One easy adaptation might be, for example, the emerging promising field of the identification of splicing gene isoforms or transcriptomics biomarkers as novel predictors of drug response [ 89 ].…”