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Delecourt, F.

Publications and source records attributed to Delecourt, F..

2 recordsLinked to original sources

Redefining phenotypic intratumor heterogeneity of pancreatic ductal adenocarcinoma: a bottom-up approach

BackgroundPancreatic ductal adenocarcinoma (PDAC) tumor inter-patient heterogeneity has been well described with two major prognostic subtypes (classical and basal-like). An important intra-patient heterogeneity has been reported but has not yet been extensively studied due to the lack of standardized, reproducible and easily accessible high throughput methods. Material and MethodsWe built an immunohistochemical (IHC) tool capable of differentiating RNA-defined classical and basal-like tumors by selecting relevant antibodies using a multi-step process. The successive stages of i) an in-silico selection from a review literature and a bulk transcriptome analysis of 309 PDACs, ii) a tumor-specific selection from 30 patient-derived xenografts followed by iii) the validation on tissue microarrays in 50 PDAC were conducted. We used our final IHC panel on two independent cohorts of resected PDAC (n=95, whole-slide, n=148, tissue microarrays) for external validation. After digitization and registration of pathology slides, we performed a tile-based-analysis in tumor and pre-neoplastic epithelial areas and a k-means clustering to identify relevant marker combinations. ResultsSequential marker selection led to the following panel: GATA6, CLDN18, TFF1, MUC16, S100A2, KRT17, PanBasal. Four different phenotypes were identified: 1 classical, 1 intermediate (KRT17+) and 2 basal-like (MUC16+ vs S100A2+) with specific biological properties. The presence of a minor basal contingent drastically reduced overall survival, even in classical predominant PDACs (HR=2.36, p=0.01). Analysis of preneoplastic lesions suggested that pancreatic carcinogenesis may follow a progressive evolution from classical toward a basal through an early intermediate phenotype. ConclusionOur IHC panel redefined and easily assessed the high degree of intra- and inter-tumoral heterogeneity of PDAC.

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↗