Abstract 3287: Learning to target CDK4/6 inhibitor resistance via a breast cancer-specific atlas of cellular mechanisms

Abstract Development of resistance to targeted, chemotherapeutic, and immune-oncology treatments alike is a major barrier to long-term remission in advanced cancer patients. We developed an integrated computational-experimental platform to mitigate treatment resistance by identifying therapeutics that selectively target clinical resistance mechanisms. The key innovation is the computational framework that comprehensively maps tumor-specific mechanisms of response and resistance to a treatment based on clinicogenomic and/or preclinical pharmacogenomic data. It then screens for drugs or targets that are synthetically lethal with these resistance mechanisms. Here we present the identification of therapeutic options that mitigate resistance to CDK4/6 inhibitors as a case study. Leveraging a breast cancer-specific atlas of cellular architecture, our framework learned genetic mechanisms of CDK4/6 inhibitor resistance from a high-throughput phenotypic screen of ~700 cell lines treated with the clinical CDK4/6 inhibitor palbociclib. The learned mechanisms formed a parsimonious set of hierarchically linked protein complexes that coordinate G1-to-S transition (P=5. 6 × 10−12). The mechanisms unified distinct resistant populations such as samples resistant due to CDK4/6-Rb checkpoint bypass and those resistant due to growth factor receptor activation. Applied to 70 ER+ metastatic breast cancer patients in a real-world dataset, the learned resistance mechanisms accurately predicted patient response to palbociclib (P=3 × 10−4, log-rank test). Computationally screening 20K clinical-grade small molecules and gene targets for therapeutic options synthetically lethal with the learned resistance mechanisms identified the known CDK4/6 resistance targets CDK2, CCNE1, and E2F. It also identified a target that, to our knowledge, has not previously been rigorously evaluated for its potential to mitigate CDK4/6 inhibitor resistance. In-vitro, monotherapy inhibition of the target by siRNA selectively decreased growth of breast cancer cell lines resistant to CDK4/6 inhibitors by 52% compared to CDK4/6i sensitive cell lines (P=0. 023, Mann-Whitney U-test). As a combination therapy, inhibition of the target by siRNA selectively increased the sensitivity to palbociclib by nearly an order of magnitude in an otherwise resistant breast cancer cell line (expected vs observed IC50: 9. 7 uM vs. 1. 3 uM) while not having a substantial effect in a palbociclib-sensitive cell line (expected vs observed IC50: 0. 57 uM vs. 0. 56 uM). Finally, while monotherapy inhibition of the target and CDK4/6 induced cell cycle arrest, the combination strongly induced apoptosis in cell lines resistant to CDK4/6 inhibitors. These results highlight how computational deconvolution of tumor resistance mechanisms enable algorithmic identification of therapeutic options to target treatment-resistant cell populations. Citation Format: Adam Yaari, Eduardo Farias, Francisco Guedes, Oliver Priebe, Lee McDaniel, Tyler Earnest, Trey Ideker, Maxwell A. Sherman. Learning to target CDK4/6 inhibitor resistance via a breast cancer-specific atlas of cellular mechanisms abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 3287.