Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive and invasive tumoral lesion affecting the pancreas. Molecular analysis and classification based on gene expression landscapes is complicated by the intrinsic heterogeneity of PDAC tumors. Like all solid cancer, PDAC are made up of the ‘tumoral mass’ (predominantly epithelial cells) which is surrounded by a microenvironment composed of stromal (fibroblasts, pericytes, endothelial) and immune cells, giving support, nutrients and sometimes resistance/metastatic potential to neoplastic cells. Precise quantification of this tumor heterogeneity is of utmost importance, as these multiple components are key factors in explaining tumor progression and response to therapy. A promising approach to accurately quantify cell type heterogeneity in PDAC relies on the recent emergence of bulk deconvolution algorithms based on single-cell reference profiles [1]. One of the main limitations of these approaches is the accuracy of the single-cell based profiles, which can strongly impair the quantification and the biological interpretation of the inferred tumor composition [2]. To overcome these difficulties, we built an integrative set of PDAC cell-type specific gene markers, based on a dedicated pre-established gene markers curation and subsequent analysis of PDAC recent single-cell RNA-seq datasets. After several steps of quality control, filtration, annotation and data integration, we launched a systemic identification of integrative cell-types specific gene markers. We then intend to use these markers to revise our current understanding of cell-type heterogeneity in PDAC using single-cell based bulk deconvolution approaches. We also have a goal to build an R package dedicated to the deconvolution of PDACs, leveraging the robust cell-type specific gene markers and integrated references we have been able to establish over the last few years. References [1] Francisco Avila Cobos, Mohammad Javad Najaf Panah, Jessica Epps, et al. Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes. Genome Biology, 24(1):177, August 2023. [2] Geng Chen, Baitang Ning, and Tieliu Shi. Single-Cell RNA-Seq Technologies and Related Computational Data Analysis. Frontiers in Genetics, 10, 2019. Publisher: Frontiers.