Abstract 790: Coupled dynamics of drug synergy, gene expression, and alternative splicing in combination therapies in breast cancer

Abstract

Abstract Drug combination therapies in the cancer setting often succeed where mono-therapies fail, facilitating durable and robust responses that may curtail metastases and even be accompanied by milder side-effects. Predicting synergistic and antagonistic combinations based on the gene expression data of mono-therapy drug-tumor response is an important open problem wherein the role of transcriptional splicing dynamics is often ignored or too poorly correlated with phenotypes to be useful. In this work we leverage the inherent transcript/exon level resolution of RNA-seq data to infer combination-specific splicing signatures associated with additive and synergistic (subadditive) drug combinations as defined by canonical viability measurements in a time-course experiment. We integrate splicing information into a gene regulatory network (GRN) to compute leading order effects of splicing on the GRN by adaptive network refinement. Briefly, we use RNA-seq to study the transcriptional response over time (0, 3, 6, 9, 12, and 24 h) for three drugs (A, B and C) and their combinations (AB, AC and BC) in MCF-7 (ER+) breast cancer cells lines. Cell viability measurements show that one of the combinations (AB) is strongly synergistic, whereas the other two (AC and BC) are merely additive. We show via rigorous linear modeling of RNA-seq count data at the exon level that in addition to a novel transcriptional signature driven by differential expression, the combination AB transcriptional landscape is characterized by persistent alternative splicing signatures mostly comprised of genes which are not differentially expressed with respect to A or B but whose functional role has been dramatically changed by the addition (deletion) of a key regulatory protein domain encoded by the extra(missing) exon. We construct an isoform-level co-expression network (IRN) to probe the regulatory changes this dynamical splicing induces and show that it crucially contributes to the emergence of extensive transcriptional cascades by creating and removing key gene-gene correlations and altering the modular structure of the network. Using this approach we show that a key isoform of Protein beta-arrestin-1 (ARBB1), which participates in desensitization of G-protein coupled receptors through intracellular response to Estrogen, is enriched in the calcium ion-dependent exocytosis module of IRN, while ARRB1 remains functionally uninformative in the regular GRN. Our results suggest that any gene-signature based drug synergy prediction algorithm must take into account alternative splicing in order to effectively characterize the novel pathways being activated in the synergistic drug-tumor interaction. Citation Format: Xintong Chen, Gustavo Stolovitzky, Bojan Losic. Coupled dynamics of drug synergy, gene expression, and alternative splicing in combination therapies in breast cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 790.