Bachet, J.-B. | bioRxiv Science

bioRxiv2024

Favorable histo-molecular remodeling of pancreatic ductal adenocarcinoma after Total Neoadjuvant Therapy including Stereotactic Body Radiotherapy

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest tumors with slow progress in systemic therapies due to its peculiar and resistant tumor microenvironment. Inclusion of isotoxic high-dose stereotactic body radiation therapy (iHD-SBRT) into a total neoadjuvant strategy (TNT) is promising for the treatment of localized PDAC. However, the histo-molecular effects of iHD-SBRT are still poorly explored. In this study, we have shown that TNT, associating FOLFIRINOX [FFX] followed by iHD-SBRT, leads to significant and long-lasting remodeling of PDAC, affecting its stromal, metabolic, and molecular features. Contrary to FFX alone, TNT is able to enrich tumors with Classical and Inactive stromal signatures associated with better prognosis. Furthermore, iHD-SBRT seems capable to counteract several of the detrimental modulatory effects induced by FFX such as Epithelial-to-Mesenchymal Transition or angiogenesis. Additionally, we identified inflammatory cancer-associated fibroblasts signatures as an important prognostic factor. This work provides new rationale to sequentially combine FFX with iHD-SBRT and suggests new pathways that can be targeted in combination with a TNT. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/591890v1_ufig1.gif" ALT="Figure 1"> View larger version (80K): org.highwire.dtl.DTLVardef@155da78org.highwire.dtl.DTLVardef@17a1ab7org.highwire.dtl.DTLVardef@16fe522org.highwire.dtl.DTLVardef@12c5870_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2022

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↗