Abstract
Combination therapy is well established as a key intervention strategy for cancer treatment, with the potential to overcome monotherapy resistance and deliver a more durable efficacy. However, given the scale of unexplored potential target space and the resulting combinatorial explosion, identifying efficacious drug combinations is a critical unmet need that is still evolving. In this paper, we demonstrate a network biology-driven, simulation-based solution, the Simulated Cell. Integration of omics data with a curated signaling network enables the accurate and interpretable prediction of 66,348 combination-cell line pairs obtained from a large-scale combinatorial drug sensitivity screen of 684 combinations across 97 cancer cell lines (BAC= 0.62, AUC=0.7). We highlight drug combination pairs that interact with DNA Damage Response pathways and are predicted to be synergistic, and deep network insight to identify biomarkers driving combination synergy. We demonstrate that the cancer cell ‘avatars’ capture the biological complexity of their in vitro counterparts, enabling the identification of pathway-level mechanisms of combination benefit to guide clinical translatability.
Competing Interest Statement
The Authors declare no Competing Non-Financial Interests but the following Competing Financial Interests: OP, NNO, BF are full-time employees of Turbine. AB, RB, SK, SZH and VJ were full-time employees for the full course of this study. DVV is a full-time employee and shareholder of Turbine. The use of Turbine's Simulated Cell technology and the proprietary intellectual property of the platform was imperative for this study; findings are hypotheses to be confirmed through real-life validation. BS, DY, KCB, and JM are full-time employees and shareholders of AstraZeneca. JRD was employed by AstraZeneca for the full course of this study. JRD is currently an employee of Tempus, but all analysis included in this study was performed while he was employed at AstraZeneca.