Abstract B116: Integrative proteogeonomic profiling of PDAC reveals updated epithelial subtypes and cross-omic predictors of survival

Abstract Pancreatic ductal adenocarcinoma (PDAC) chemotherapy regimens are largely uniform, tailored mainly to patient fitness or rare genomic alterations. This strategy fails to capitalize on the disease’s substantial molecular heterogeneity, including variations in copy number alterations, immune infiltration, stromal composition, and cancer cell phenotypes. These features are not independent; they reflect interconnected biological processes and co-evolving pathways that shape tumour progression. Multi-omic integration is therefore essential—not only to capture the complexity of PDAC biology, but also to clarify the molecular basis of tumours classified as ‘intermediate’ by current stromal and cancer taxonomies, thereby enabling rational therapeutic targeting. To this end, we performed imaging mass cytometry on three serial sections of a PDAC tissue microarray (221 resected tumours, ∼4 cores each), generating 800 multiplexed images (40–43 channels) focused on epithelial, immune, or stromal biomarkers. We profiled 76 immune and stromal cell types and states (hypoxic, proliferative, apoptotic, under tension), as well as six tumour phenotypes defined by expression of epithelial transcription factors (GATA6, FOXA2, PDX1), classical markers (AGR2, TFF1, CEACAM6), basal markers (TP63, KRT5, S100A2, CAV1), and other PDAC-associated proteins (S100A4, MMP7, MUC16). These six cancer cell types captured the classical and basal PDAC signatures, along with four discrete “intermediate” states with distinct associations to stromal heterogeneity, RNA subtype (n = 92), tumour ploidy (n = 182), and patient outcome. These phenotypes are also detectable in unmatched single-cell RNA-seq data (n = 163), though with more overlap in marker expression. Using matched 30X whole genome sequencing (n = 182), we identified mutations and copy number alterations linked to shifts in cancer and stromal cell phenotypes, raising the question of which molecular axis best informs clinical prognosis. To address this, we applied a modified version of Stabl, a Lasso-based machine learning approach, to compare across omic layers and identify features most strongly associated with overall survival. Per-modality analysis showed that models incorporating omics outperformed those based on clinical features alone, with imaging data slightly outperforming genomics. Cross-omic integration revealed several prognostically relevant copy number aberrations, fibroblast phenotypes, and cancer cell states. By consolidating information on tumour phenotypes, stromal niches, and genomic alterations, this work aims to focus future drug discovery on the most clinically impactful molecular features in PDAC. Citation Format: Ferris Nowlan, Noor Shakfa, Tiak Ju Tan, Sibyl Drissler, Elizabeth Sunnucks, Jennifer L. Gorman, Chengxin Yu, Sheng-Ben Liang, Barbara Gruenwald, Ayelet Borgida, Edward L. Chen, Golnaz Abazari, Miralem Mrkonjic, Julie M. Wilson, Kieran R. Campbell, Robert C. Grant, Anne-Claude Gringas, Grainne M. O'Kane, Faiyaz Notta, Steve Gallinger, Hartland W. Jackson. Integrative proteogeonomic profiling of PDAC reveals updated epithelial subtypes and cross-omic predictors of survival 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 B116.