Abstract C059: Probabilistic graph-based model uncovers druggable vulnerabilities in major solid cancers

Abstract

Abstract We present A3D3a’s Molecular Vulnerability Picker (MVP), a novel probabilistic approach to determine vulnerabilities in networks of cancer interactions for the identification of new therapeutic targets. Successes in targeted therapies driven by molecular profiling have helped 47% of cancer patients achieve long-lasting remission. However, over half of patients still lack safe, effective, and long-lasting treatment options. Successes have often clustered around exploitation of major driver mechanisms that can be clearly discerned from molecular data. Heterogeneity, rarity, and complexity of these remaining cancers mean that the signal of key molecular vulnerabilities, i.e., protein targets that can be exploited for therapy, can be drowned out by noise. We developed A3D3a’s MVP to increase the signal-to-noise ratio in cancer networks of interaction and alteration information to determine vulnerabilities toward the identification of new potential drug targets. To implement A3D3a’s MVP, we constructed protein networks of major solid cancers from TCGA data that we used as a framework for information flow. We developed a novel weight-biased Markov Chain model to highlight cooperativity of weak signals arising from related regions of the protein network, emphasizing previously hidden signal within these cancer networks. To validate A3D3a’s MVP, we examined the top genes it returned in the Dependency Map and the Genomics of Drug Sensitivity in Cancer. We further validated our findings by comparing the number of genetic dependencies and drug targets recovered by the model to that recovered by the state of the art. Finally, we applied A3D3a’s MVP to identify therapeutic opportunities across cancer indications and extended our analysis to highlight well-ranked genes whose proteins contain druggable pockets and thus, could serve as new drug targets. For the 19 cancer types included in this analysis, A3D3a’s MVP returned significantly more genetic dependencies (p < 0.01) and drug targets (p < 0.001) than genes ranked by the state of the art. We demonstrate that A3D3a’s MVP is able to increase the signal of weakly altered genes and is also able to identify genuine dependencies that themselves are not mutated or altered in any way. Using A3D3a’s MVP, we identified 56 drug repurposing opportunities and 49 potential druggable targets for solid cancers and also highlight novel potential druggable targets for future exploitation and new therapeutics. In summary, A3D3a’s MVP is a novel mathematical modeling approach that increases the signal-to-noise ratio in cancer molecular data and helps uncover previously hidden molecular vulnerabilities towards new potential drug targets to address unmet patient needs. Citation Format: Stephanie T Schmidt, Ying Zhu, Li Zhao, Chunjie Jiang, Patrizio Di Micco, Costas Mitsopoulos, Andrew Futreal, Bissan Al-Lazikani. Probabilistic graph-based model uncovers druggable vulnerabilities in major solid cancers [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C059.