PACpAInt: a deep learning approach to identify molecular subtypes of pancreatic adenocarcinoma on histology slides
Saillard, C.; Delecourt, F.; Schmauch, B.; Moindrot, O.; Svrcek, M.; Bardier-Dupas, A.; Emile, J.-F.; Ayadi, M.; Rebours, V.; de Mestier, L.; Hammel, P.; Neuzillet, C.; Bachet, J.-B.; Iovanna, J.; Dusetti, N.; Blum, Y.; Richard, M.; Kermezli, Y.; Paradis, V.; Zaslavskiy, M.; Courtiol, P.; Kamoun, A.; Nicolle, R.; Cros, J.
Show abstract
Pancreatic ductal adenocarcinoma (PAC) is a highly heterogeneous and plastic tumor with different transcriptomic molecular subtypes that hold great prognostic and theranostic values. We developed PACpAInt, a multistep approach using deep learning models to determine tumor cell type and their molecular phenotype on routine histological preparation at a resolution enabling to decipher complete intratumor heterogeneity on a massive scale never achieved before. PACpAInt effectively identified molecular subtypes at the slide level in three validation cohorts and had an independent prognostic value. It identified an interslide heterogeneity within a case in 39% of tumors that impacted survival. Diving at the cell level, PACpAInt identified "pure" classical and basal-like main subtypes as well as an intermediary phenotype and hybrid tumors that co-carried both classical and basal-like phenotypes. These novel artificial intelligence-based subtypes, together with the proportion of basal-like cells within a tumor had a strong prognostic impact.
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