Abstract C066: Identifying Transcriptional Drivers of Plasticity in Pancreatic Cancer

Abstract

Abstract Pancreatic ductal adenocarcinoma (PDAC) remains a deadly disease with few effective treatments. Even with the advent of mutationally-directed therapies targeting the KRAS oncogene, PDAC transcriptional plasticity remains a major mechanism of therapeutic resistance. As a result, there is a desperate need to develop additional strategies to treat this deadly disease. In addition to the canonical genomic alterations in KRAS, TP53, CDKN2A, and SMAD4, PDAC tumors exhibit diverse transcriptional programs, or "cell states," with prognostic implications: PDAC tumors in the “classical state” are more responsive to upfront chemotherapy and have better outcomes, while those in the “basal state” are more aggressive with poorer prognoses. Currently, PDAC cell states are not used to direct therapy. Cell state is an integrative property shaped by both cell-intrinsic (e.g., mutations, epigenetics) and cell-extrinsic (e.g., tumor microenvironment) factors. In our prior work, single-cell analyses of clinical PDAC samples demonstrated notable cell state heterogeneity and plasticity. In addition, longitudinal monitoring of patient-derived organoids showed that cells can shift from classical to basal or coexpressor states as the environment changes, and that these cell states can drastically influence drug response. However, it remains unknown which signaling and transcriptional pathways induce basal or classical states, and whether these gene regulatory networks can be targeted therapeutically. Thus, there is a pressing need to devise scalable methods to determine essential drivers for prognostically relevant cancer cell states.Here, we aim to systematically and unbiasedly identify transcription factors (TFs) that drive basal and classical cell states in PDAC. We have successfully established a high-throughput screening pipeline to 1) over-express all human TF isoforms in a pooled format within a cohort of PDAC models; 2) sort cells using antibodies against state-specific cell surface markers; and 3) perform single-cell RNA sequencing on sorted cells to nominate individual TFs or combinations that induce specific cell state transitions. Preliminary results identify both previously reported (e.g., GATA4) and new TFs that can induce the classical state. By overexpressing TFs individually or in combination, we are able to engineer PDAC cell line models that more faithfully reflect the transcriptional phenotypes observed in patient tumors. Ultimately, we aim to use this approach to construct high-fidelity isogenic but state-variant PDAC models for use both in therapeutic screening and as a substrate for investigating mechanisms governing cancer cell state plasticity. Citation Format: Yuzhou Evelyn Tong, Aswanth Mahalingam, Walaa E Katan, Julia Joung, Alex K Shalek, Peter S Winter, Srivatsan Raghavan. Identifying Transcriptional Drivers of Plasticity in Pancreatic Cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C066.