Abstract 4399: Systematic interrogation of cell states that drive inflammation tolerance and therapy resistance in cancer

Cancers develop and evolve by overcoming numerous selective pressures including cell-intrinsic growth-inhibitory mechanisms, microenvironmental stresses, and therapeutic interventions. In addition to mutational mechanisms, cancer cell adaptation via transcriptional plasticity remains an important but poorly understood driver of cancer cell fitness. Specifically, when disease progression occurs after targeted therapy treatment, genomically-defined mechanisms of resistance are only identified in approximately 50% of patients, suggesting that non-genomic mechanisms play an equally important role. Thus, there is a pressing need to devise systematic and scalable methods to determine essential drivers of prognostically-relevant cancer cell states, and to engineer state-relevant models that can be applied to discover new therapeutic approaches that overcome these modes of resistance. Here, we present a strategy to systematically identify transcriptional drivers of inflammation- and therapy-resistant cancer cell states. We have successfully established a high-throughput screening pipeline to 1) over-express human transcription factor (TF) isoforms in a pooled format within cancer models; 2) perform positive-selection screens under a variety of selective pressures to define transcriptional mechanisms of inflammation tolerance and therapy resistance; 3) perform single-cell RNA sequencing to define families of TFs that drive specific cell state transitions and resistance phenotypes; and 4) conduct mechanistic studies to examine the downstream signaling networks that facilitate cancer cell growth in the setting of these selective pressures. We uncover TFs and states that drive resistance in a tissue- and perturbation-specific manner, as well as states that are robust to multiple different selective pressures. A subset of TFs and associated gene programs induce tolerance to chronic interferon exposure in our screens and are reflected in vivo in the setting of immunotherapy resistance. We also identify distinct TF families that drive resistance to pharmacologic KRAS inhibition. Notably, cancer cell states that confer resistance to KRAS inhibition also exhibit cross-resistance to other MAPK pathway inhibitors, suggesting that cell state adaption may contribute to multi-drug resistance as cancers progress. More broadly, our strategy enables the systematic identification and mapping of cell states that drive cancer cell fitness to in vivo clinical atlas datasets, providing a framework for interpretation of clinical phenotypes and potential predictive modeling of cell state evolution. Furthermore, the cell state-defined models developed here provide a substrate for new therapeutic discovery using cancer cell states as biomarkers, a key next step to developing combination strategies that overcome therapeutic resistance and improve outcomes for patients. Citation Format: Srivatsan Raghavan, Evelyn Y. Tong, Aswanth H. Mahalingam, Zixin Chen, Jacob Smigiel, Tsukasa Shibue, Andrew Navia, Alex K. Shalek, Lorin Crawford, Ava Amini, Peter S. Winter. Systematic interrogation of cell states that drive inflammation tolerance and therapy resistance in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4399.