Use of a computational histology artificial intelligence-powered predictive biomarker for chemotherapy selection in advanced pancreatic cancer patients from a multi-institutional cohort including two prospective studies.

764 Background: This study used the previously developed Computational Histology Artificial Intelligence (CHAI) platform to develop and validate a pathology-derived signature to distinguish patients with advanced pancreatic ductal adenocarcinoma (PDAC) likely to benefit from first-line fluoropyrimidine-based (F-chemo) versus gemcitabine-based (G-chemo) chemotherapy regimens. Methods: Whole slide images of H OS: p=0.016). Conclusions: The CHAI-powered signature developed from a multi-institutional real-world cohort and validated on a prospectively collected cohort predicted treatment efficacy, as measured by TNTD and OS, with fluoropyrimidine- versus gemcitabine-based chemotherapy. This biomarker can guide optimal treatment selection for first-line therapy in advanced PDAC. TNTD and OS in a validation cohort composed of data from two prospective studies, stratified by the biomarker. F-Chemo Median (95% CI) G-Chemo Median (95% CI) Cox Proportional Hazards Model Biomarker-Treatment Interaction Likelihood Ratio Test p-value TNTD p= 0.003 F-pref 8.6 (7.4-11.3) 7.5 (5.8-8.7) G-pref 7.2 (6.1-8.7) 9.6 (7.1-13.6) OS p= 0.016 F-pref 14.4 (11.3-16.7) 11.7 (7.8 - 12.7) G-pref 12.4 (11.1 - 14.5) 14.3 (9.0-21.3)