Mots-Clés
Pancreatic Cancer
Microenvironment
CAFs
Bulk RNAseq
Single Cell RNAseq
Description
Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer related death in Europe with a 5 year overall survival rate of 13%. This cancer is characterized by a dense tumoral microenvironment (TME) that can represent up to 80% of the total tumor mass. This highly fibrotic microenvironment is characterized by hypoxia, immune suppression, excessive extracellular matrix deposition, and resistance to therapy.
Within this TME, cancer-associated fibroblasts (CAFs) are the most abundant cell type. Studying CAFs in PDAC is essential, as they constitute a heterogeneous population with distinct functions in tumor progression and chemoresistance.
Three main CAF subtypes are described in the literature, the myofibroblastic CAFs (myCAFs), known to produce extracellular matrix, inflammatory CAFs (iCAFs) characterized by cytokine secretion and antigen presenting CAFs (apCAFs) which express MHC class II genes. However, many more subtypes are reported in the literature with no clear consensus regarding their identity or biological significance.
Therefore, our team performed an integrated analysis of multiple single cell RNAseq (scRNAseq) datasets to identify eight robust CAF subtypes across 158 PDAC patient samples. Among these, the three canonical CAF populations were identified along with five additional subtypes each associated with specific markers. Although some of these subtypes have already been mentioned in the literature, the biological and clinical roles of these CAF populations remain to be further characterized.
The primary objective of this internship will be to investigate the clinical relevance of these CAF subtypes by determining whether they are associated with pro-tumoral or anti-tumoral phenotypes. This objective is readily achievable by analyzing publicly available bulk RNA-seq cohorts (TCGA, ICGC, and Puleo), applying gene signature enrichment scoring to estimate CAF subtype abundance, and assessing its association with patient survival.
A second, longer term objective, would be to begin the functional characterization of the most relevant CAF subtypes, notably those associated with favorable patient outcomes. This analysis will rely on pathway enrichment approaches and cell-cell communication analyses using the corresponding scRNAseq datasets currently analyzed within the team.
Overall, this project will contribute to a more comprehensive characterization of CAF heterogeneity in PDAC and help establish a biologically and clinically relevant classification of CAF subtypes.