Abstract
Abstract Drug resistance to targeted therapies, either intrinsic (existing before treatment) or acquired (resulting from adaptation to therapy), is a major challenge in patient care. Drug combinations offer a possible solution to prevent drug resistance by blocking multiple routes to tumor proliferation. Discovery of effective combinations remains a challenging task due to complexity of the underlying biological processes and inter-tumor heterogeneity. Here, we developed a statistical pathway analysis method that (i) reveals pathways involved in drug response and adaptive resistance and (ii) nominates combination targets and therapies to down-regulate the resistance pathways. The method is based on the rationale that (i) use of therapies targeting specific genomic aberrations can lead to compensatory responses (e.g. through feedback loop activation in the short term or the development of oncogenic alterations in the long term) leading to drug resistance, and (ii) collective changes in pathway activities are better predictors of resistance and mitigation strategies than individual responses. We construct a pathway model of signaling interactions for measured molecular species through automated extraction of pathway information from Pathway Commons plus manual expert curation. Next, we combine the pathway model with the cell-type-specific drug response data to calculate a TargetScore (TS) for each protein. The score quantifies the adaptive pathway responses to a perturbation by integrating the change in the level of a (phospho)protein along with its pathway neighborhood in response to the single drug. A high TS corresponds to involvement in adaptive response (e.g. upregulation of RTK expression by MEK inhibitor via a feedback loop) and a low TS corresponds to the activity of the drug (e.g. inhibition of ERK phosphorylation by MEK inhibitor). Finally, by identifying the sub-pathways with enriched, high TS values, we determine the adaptive resistance pathways. We test the resulting predictions (combinations of the original single drug with drugs targeting members of the resistance pathways) experimentally. TargetScore is amenable to calculations for hundreds of samples treated with individual (or combinations of) drugs in multiple doses and/or time points and assayed for hundreds to thousands of molecular entities (mRNA or proteomic). Analysis of longitudinal data may allow us to trace the evolution of drug resistance and, potentially, the optimum time points for intervention. The current protocol is defined with a focus on proteomic RPPA data, but can be adapted to other kinds of molecular data associated with adaptive responses. We applied our method to BET-BRD inhibition in ovarian cancer to compare resistance/response pathways in cells with varying degrees sensitivity. The analysis nominated cell-type-specific, anti-resistance combinations involving BET inhibitors. Citation Format: Augustin Luna, Özgün Babur, Gonghong Yan, Emek Demir, Chris Sander, Anil Korkut. Discovery of adaptive resistance pathways and anti-resistance combination therapies in cancer from phosphoproteomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2838.
Published Version
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