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Pré-Publication, Document De Travail Année : 2022

PACpAInt: a deep learning approach to identify molecular subtypes of pancreatic adenocarcinoma on histology slides

F. Delecourt
  • Fonction : Auteur
B. Schmauch
  • Fonction : Auteur
O. Moindrot
  • Fonction : Auteur
M. Svrcek
  • Fonction : Auteur
A. Bardier-Dupas
  • Fonction : Auteur
M. Ayadi
  • Fonction : Auteur
N. Dusetti
  • Fonction : Auteur
Y. Blum
M. Richard
  • Fonction : Auteur
Y. Kermezli
  • Fonction : Auteur
M. Zaslavskiy
  • Fonction : Auteur
P. Courtiol
  • Fonction : Auteur
A. Kamoun
  • Fonction : Auteur
R. Nicolle
J. Cros
  • Fonction : Auteur

Résumé

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.

Dates et versions

hal-03871989 , version 1 (25-11-2022)

Identifiants

Citer

C. Saillard, F. Delecourt, B. Schmauch, O. Moindrot, M. Svrcek, et al.. PACpAInt: a deep learning approach to identify molecular subtypes of pancreatic adenocarcinoma on histology slides. 2022. ⟨hal-03871989⟩
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