Abstract 1905: Accelerating clinically-translatable discoveries using a network- and RNA-based precision-oncology framework

Abstract

Abstract Despite recent advances, prioritizing therapy at the individual patient level remains challenging. In fact, inter-patient tumor heterogeneity remains one of the major challenges in cancer therapy, making it difficult to optimize available treatments on an individual patient basis. Likewise, the systematic prediction of drug sensitivity in vivo is still a major challenge in translational research, where targeted therapeutics are currently selected based on the presence of either actionable oncogene dependencies or aberrant cellular mechanisms. A further challenge is the limited availability of models that faithfully recapitulate the biology, complexity, and heterogeneity of human tumors, including their interaction with a conserved microenvironment and a competent immune system. To address these challenges, we introduce OncoLoop, a highly-generalizable, network-based precision medicine framework to triangulate between available mouse models, human tumors, and large-scale drug perturbational assays with in vivo validation to predict personalized treatment. OncoLoop requires only transcriptomic data (i.e., RNA-seq profiles) and leverages regulatory network analysis to (a) identify cognate models based on conservation of patient-specific Master Regulator (MR) proteins and (b) prioritize drugs based on their ability to invert the activity of MR proteins (MR-inverters), using drug perturbation profiles in cognate cell lines. As proof-of-concept, we applied OncoLoop to prostate cancer using a series of genetically engineered mouse models (GEMMs) that capture a broad range of phenotypes, including metastatic, castration-resistant and neuroendocrine disease. Indeed, >70% of patients in published cohorts had at least one high-fidelity matched GEMM. Drugs targeting shared Master Regulator dependencies of a patient and its cognate GEMM(s) were predicted using perturbational profiles of >300 drugs in MR-matched cell lines, resulting in an 80% validation rate in GEMM allografts and human xenografts. This network-based approach is highly generalized and can be applied to both cancer and non-cancer-related contexts. Citation Format: Alessandro Vasciaveo, Min Zou, Juan M. Arriaga, Francisca Nunes de Almeida, Eugene F. Douglass, Michael Shen, Andrea Califano, Cory Abate-Shen. Accelerating clinically-translatable discoveries using a network- and RNA-based precision-oncology framework [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1905.