Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma

• Published in: Nature communications Vol. 14; no. 1; pp. 3459 - 12
• Main Authors: Saillard, Charlie, Delecourt, Flore, Schmauch, Benoit, Moindrot, Olivier, Svrcek, Magali, Bardier-Dupas, Armelle, Emile, Jean Francois, Ayadi, Mira, Rebours, Vinciane, de Mestier, Louis, Hammel, Pascal, Neuzillet, Cindy, Bachet, Jean Baptiste, Iovanna, Juan, Dusetti, Nelson, Blum, Yuna, Richard, Magali, Kermezli, Yasmina, Paradis, Valerie, Zaslavskiy, Mikhail, Courtiol, Pierre, Kamoun, Aurelie, Nicolle, Remy, Cros, Jerome
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.06.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 13; 14/1; 38/91; 631/114/1305; 631/114/2401; 631/1647/1407/1555; 631/67/1504/1713; 631/67/2329; Adenocarcinoma; Adenocarcinoma - genetics; Aggression; Biopsy; Cancer; Deep Learning; Heterogeneity; Histology; Humanities and Social Sciences; Humans; Life Sciences; Medical prognosis; Microheterogeneity; multidisciplinary; Pancreas; Pancreatic cancer; Pancreatic Neoplasms; Pancreatic Neoplasms - genetics; Science; Science (multidisciplinary); Stroma; Survival; Transcriptomics; Tumor cells; Tumors
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-39026-y

Two tumor (Classical/Basal) and stroma (Inactive/active) subtypes of Pancreatic adenocarcinoma (PDAC) with prognostic and theragnostic implications have been described. These molecular subtypes were defined by RNAseq, a costly technique sensitive to sample quality and cellularity, not used in routine practice. To allow rapid PDAC molecular subtyping and study PDAC heterogeneity, we develop PACpAInt, a multi-step deep learning model. PACpAInt is trained on a multicentric cohort ( n  = 202) and validated on 4 independent cohorts including biopsies (surgical cohorts n  = 148; 97; 126 / biopsy cohort n  = 25), all with transcriptomic data ( n  = 598) to predict tumor tissue, tumor cells from stroma, and their transcriptomic molecular subtypes, either at the whole slide or tile level (112 µm squares). PACpAInt correctly predicts tumor subtypes at the whole slide level on surgical and biopsies specimens and independently predicts survival. PACpAInt highlights the presence of a minor aggressive Basal contingent that negatively impacts survival in 39% of RNA-defined classical cases. Tile-level analysis ( > 6 millions) redefines PDAC microheterogeneity showing codependencies in the distribution of tumor and stroma subtypes, and demonstrates that, in addition to the Classical and Basal tumors, there are Hybrid tumors that combine the latter subtypes, and Intermediate tumors that may represent a transition state during PDAC evolution. Rapid and effective molecular subtyping of pancreatic adenocarcinoma (PDAC) is important for prognosis and treatment. Here, the authors develop PACpAInt, a deep learning model for PDAC molecular subtyping from whole-slide histological imaging that enables the analysis of heterogeneity and prognostic predictions.