Abstract A117: Molecular stratification of therapeutic targets in pancreatic cancer

Abstract

Abstract Precision medicine approaches using molecular tumor profiling have resulted in striking clinical benefits in certain patient populations. However, targeted therapy in pancreatic cancer has been unable to displace multi-agent chemotherapy, in part due to lack of actionable biomarkers to anchor stratification. To address this need, we have conducted a biomarker focused analysis of cancer dependencies and cell surface targets to understand specific vulnerabilities present in molecular subgroups. We started by interrogating genome scale CRISPR screening and expression data from The Cancer Dependency Map to identify lineage-enriched, selective dependencies in pancreatic cancer. Our results reveal synthetic lethal interactions such as paralog pairs (e.g., VPS4A/B), dependencies predictable by overexpression (e.g., KLF5), and dependencies predictable by genetic alterations (e.g., RAB10 with SMAD4 deletion). Our results reaffirm the importance of KRAS signaling but also highlight a diversity of downstream essential mechanisms including DOCK5 and RAF1. We also identified vulnerabilities specific to transcriptional subtypes, finding ITGA3 to be a selective dependency of not only the basal-like state in pancreas cancer but also across the spectrum of epithelial malignancies. Given the central role of KRAS mutations in tumorigenesis and the emergence of KRAS inhibitors, we studied predictors of KRAS dependency. GSEA analysis of genes overexpressed in cell lines with lower dependency on KRAS identified gene sets related to focal adhesion, EMT, and neuron development, while lines with high KRAS dependency were enriched in genes related to epithelial differentiation/development and the canonical Hallmark KRAS up signature. These gene signatures also predict resistance and sensitivity to small molecule inhibition of KRAS with a high degree of accuracy (Pearson r = 0.68, 0.94 respectively). In addition to vulnerabilities from intracellular target inhibition, cancer cells may be targeted selectively based on surface proteins using cellular and antibody based therapeutic strategies. We used bulk gene expression data from TCGA and GTEx as well as single cell expression data from The Human Cell Atlas, to calculate enriched genes in pancreas cancer versus critical normal tissues. Our approach captures targets including MSLN, TROP2, MUC1, and CLDN18, which have established tumor specificity, but also identifies PSCA, GPRC5A, and CEACAM5 as additional emerging pancreas tumor specific antigens. We charted interpatient and intratumoral heterogeneity in spatial transcriptomics data using a cohort of 21 treated and untreated pancreatic cancer tissue specimens, identifying MUC1 and GPRC5A as both expressed homogenously across patients and throughout individual tumors while scarcely expressed in normal tissues. Ultimately, these findings may prompt prioritization of new molecularly defined dependencies, and construction of rational combinatorial regimens. Citation Format: Dennis Gong, Jimmy A. Guo, Carina Shiau, Ananya D. Jambhale, Steven Wang, Scott Ginebaugh, Patrick Z. Yu, Kevin S. Kapner, Seema Chugh, Laleh Abbassi, Daniel Zhao, Westley W. Wu, Peter Chen, Harshabad Singh, William L. Hwang, Andrew J. Aguirre. Molecular stratification of therapeutic targets in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A117.