Abstract B103: Defining cancer cell states that drive inflammation tolerance and KRAS inhibitor resistance in pancreatic cancer

Abstract Cancers develop and evolve by overcoming a variety of 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. For example, ctDNA analysis of disease progression samples after KRAS inhibitor (KRASi) treatment identified genomically-defined mechanisms of resistance in only 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 pancreatic 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 cell states in pancreatic cancer. We have established a robust 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 KRASi resistance; 3) perform single-cell RNA sequencing to define 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 and to microenvironmental signals, suggesting that cell state adaption may contribute to increased cancer cell fitness and 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 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 pancreatic cancer patients. Citation Format: Yuzhou Evelyn. Tong, Aswanth Mahalingam, Zixin Chen, Jacob Smigiel, Tsukasa Shibue, Andew Navia, Alex K. Shalek, Lorin Crawford, Ava Amini, Peter S. Winter, Srivatsan Raghavan. Defining cancer cell states that drive inflammation tolerance and KRAS inhibitor resistance in pancreatic cancer abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85 (18Suppl₃): Abstract nr B103.