Search bioRxiv⌕ Search

Biology subjects

de Mestier, L.

Publications and source records attributed to de Mestier, L..

2 recordsLinked to original sources

Distinct Progression Paths Underlie the Diversity of Pancreatic Papillary Cystic Neoplasms

Surgical decisions for intraductal papillary mucinous neoplasms (IPMNs), precursors of pancreatic cancer, depend on distinguishing low- from high-grade dysplasia, a distinction that molecular markers could refine. We microdissected 433 tumor regions from 135 patients and integrated transcriptomics, deep-learning histology, and KRAS/GNAS RNA-called variants. RNA programs and morphological motifs separated lesions by phenotype, intestinal versus others, rather than by dysplasia grade. Along gastric IPMN trajectories, low-grade lesions recapitulated the pit-chief polarity of normal gastric glands, whereas high-grade lesions were indistinguishable from pancreatobiliary lesions in morphology, RNA programs, and driver mutations. Intestinal IPMNs arose through two routes: a canonical GNAS-associated pathway and a KRAS-associated shift from low-grade gastric features, with lesions sharing driver mutations retaining common programs across phenotypes. Resolving these phenotypic trajectories clarified distinct progression paths and enabled phenotype-specific prediction of high-grade dysplasia in mucus aspirated from 94 resected lesions. These findings support a promising fluid-based assessment of IPMN status.

cancer biology↗

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

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.

cancer biology↗