Use of artificial intelligence–based digital pathology to predict outcomes for immune checkpoint inhibitor therapy in advanced gastro-esophageal cancer.

4013 Background: Accurate prediction of response and survival outcomes with anti-PD-1/PD-L1 immune checkpoint inhibition (ICI) remains a significant challenge in gastro-esophageal cancers. In this study, we use an artificial intelligence (AI)-based single-cell analysis of digitized whole-slide H median time to event: 19 months vs 9 months). For predicting objective response in the validation cohort, a multivariate model combining the spatial features achieved AUROC = 0.81 compared to AUROC = 0.65 for PD-L1 CPS (P = 0.0014), while combining the multivariate model with PD-L1 CPS achieved AUROC = 0.84. Conclusions: A single-cell computational pathology approachidentifies spatial biomarkers with predictive utility for determining ICI treatment outcomes in advanced gastro-esophageal cancer. Further validation of these findings is being pursued in additional cohorts.