Abstract B027: “Form follows function”: A new paradigm of pancreatic cancer progression

Abstract

Pancreatic ductal adenocarcinoma (PDAC) frequently invades adjacent organs and major blood vessels. Although genetic and transcriptomic drivers are well described, the mechanisms by which these molecular changes translate into spatially organized invasive behavior remain poorly understood. We identify a striking level of tissue organization, characterized by spatial and geometric properties within and among tumor structures. Leveraging the first large-scale, AI-assisted, human-curated PDAC atlas from hematoxylin and eosin (H&E) images, we annotated, classified, and characterized 144,474 malignant and normal structures from treatment-naïve (n=118) and neoadjuvant-treated PDAC patients (n=50). Additionally, we developed a new computational tool, SHAPE, to investigate PDAC aggressiveness through a comprehensive “geometrization” of cancer progression. Using traditional H&E-stained slides and three-dimensional (3D) tissue reconstruction experiments, we found that invading tumor structures display an eccentric morphology with pronounced local angular coherence. These geometric and spatial properties revealed coherent architectural patterns, with invasive structures closely tracking vessels and nerves as they infiltrate surrounding tissue. To define molecular signatures of the PDAC architecture, we integrated morphologic features from 39,045 annotated tumor structures with whole-genome and RNA-sequencing data. PDACs enriched for eccentric structures exhibited increased copy-number alterations, chromosome 17p loss of heterozygosity, and basal-like/quasi-mesenchymal transcriptional programs. Spatial transcriptomic analysis of 1,650 tumor structures from six additional PDAC patients further showed enrichment of invasive cellular programs within highly eccentric structures, including epithelial-to-mesenchymal transition, angiogenesis, coagulation, and complement pathways, underscoring their infiltrative nature. Finally, cross-validation of our AI-based method enabled a fully automated, highly interpretable computational approach to support pathologists and clinicians in evaluating neoadjuvant chemotherapy response, predicting patient survival, and guiding chemotherapy in adjuvant settings. Collectively, these findings deepen our understanding of PDAC progression, identify a new hallmark of tumor architecture, and pave the way for full integration of AI-driven morphology-based approaches into clinical workflows to improve the management of PDAC patients.