Identifying cell-type-resolved gene expression changes in pancreatic ductal adenocarcinoma from bulk RNA-seq data

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Pancreatic ductal adenocarcinoma (PDAC) contain a low but variable number of tumor cells. These are embedded in a tumor microenvironment (TME) that contains in addition to the tumor cells a variety of stromal- and and immune cells with bot pro- anti-tumorigenic functions. The low number of tumor cells confounds the the identification of tumor-specific gene expression subtypes and differentially expressed genes from bulk transcriptomic data. However, such bulk transcriptomic data also harbors information about the gene expression patterns of the cells of the TME. Uncovering this additional information may lead to better understanding of the complex interplay between tumor cells and their TME which facilitates immune system evasion and the resistance to treatment. Computational methods enable the dissection of tumor as well as stromal- and immune cell gene expression patterns from bulk transcriptomic data. However, most established method operate on a small subset of signature genes and/or require uploading the data to a webserver. We recently developed a novel method that operates transcriptome wide. It is based on a probabilistic non-negative matrix factorization approach, which allows to assign gene expression changes to cell types. We plan to apply this approach to the CRU’s PDAC RNA-seq data to a) infer the abundances of tumor-, stromal-, and immune cells; b) identify cell-type-resolved gene expression changes dependent on clinically relevant endpoints, such as survival, or grouping based on biomarkers, such as the presence of a gain-of-function mutations in KRAS, which are present in most PDACs. We leverage on single cell RNA-seq data from PDACs to obtain suitable full transcriptome cell-type-specific gene expression patterns for cell types relevant for the subprojects in this CRU, e.g. CD8+ cytotoxic T cells or inflammatory cancer associated fibroblasts. These cell-type-specific gene expression patterns serve as seed to infer and statistically assess cell-type-resolved gene expression changes. In this way we hope to shed light into the complex interplay between tumor cells and their TME, which should lead to a better understanding of the tumor supporting TME and possible avenues for novel treatments of this deadly disease.