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

Charlie Saillard, Flore Delecourt, Benoit Schmauch, Olivier Moindrot, Magali Svrcek, Armelle Bardier-Dupas, Jean Francois Emile, Mira Ayadi, Vinciane Rebours, Louis de Mestier, Pascal Hammel, Cindy Neuzillet, Jean Baptiste Bachet, Juan Iovanna, Nelson Dusetti, Yuna Blum, Magali Richard, Yasmina Kermezli, Valerie Paradis, Mikhail Zaslavskiy, Pierre Courtiol, Aurelie Kamoun, Remy Nicolle and Jerome Cros ()
Additional contact information
Charlie Saillard: Medical Imaging Team
Flore Delecourt: Beaujon Hospital
Benoit Schmauch: Medical Imaging Team
Olivier Moindrot: Medical Imaging Team
Magali Svrcek: Saint-Antoine Hospital - Sorbonne Universités
Armelle Bardier-Dupas: Pitié-Salpêtrière Hospital - Sorbonne Universités
Jean Francois Emile: Ambroise Paré Hospital – Université Saint Quentin en Yvelines
Mira Ayadi: Genomic Services & Precision Medicine
Vinciane Rebours: Beaujon Hospital
Louis de Mestier: Beaujon Hospital
Pascal Hammel: Paul Brousse Hospital
Cindy Neuzillet: Institut Curie
Jean Baptiste Bachet: Pitié-Salpêtrière Hospital - Sorbonne Universités
Juan Iovanna: Aix Marseille Université
Nelson Dusetti: Aix Marseille Université
Yuna Blum: Université de Rennes 1
Magali Richard: Université Grenoble-Alpes
Yasmina Kermezli: Université Grenoble-Alpes
Valerie Paradis: Beaujon Hospital
Mikhail Zaslavskiy: Medical Imaging Team
Pierre Courtiol: Medical Imaging Team
Aurelie Kamoun: Medical Imaging Team
Remy Nicolle: FHU MOSAIC, Centre de Recherche sur l’Inflammation (CRI)
Jerome Cros: Beaujon Hospital

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-39026-y

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