Bioinformatics Methods for the Identification of Treatment Response Prediction Markers in Pancreatic Cancer

Chemotherapeutic agents for pancreatic ductal adenocarcinoma (PDAC) are highly toxic and induce severe side effects to patients. We wanted to promote minimally invasive precision immuno-oncology interventions by bioinformatics methods, by identifying a pool of biomarkers which would predict the therapeutic efficacy prior to the administration of gemcitabine. Six PDAC patients undergoing gemcitabine treatment were stratified in two groups based on disease progression: stable disease (pre-SD) and progressive disease (pre-PD). Peripheral blood was collected before gemcitabine treatment for all PDAC patients and blood RNA used for gene expression analysis. We filtered 20,356 genes and performed BrB-ArrayTools Class Comparison analysis which allowed the identification of 1489 upregulated genes in the group of pre-SD (downregulated in group pre-PD). These genes were mostly associated to T lymphocytes immune response (Metacore Pathways Maps analysis). We performed a second Geneset Class Comparison Enrichment Analysis; the resultant genes were combined with the genes included in the top Metacore Pathways Maps, and we obtained a pool of highly differentially expressed genes predicting the efficacy of anti-Tumor immune response [Translated title: Markers and kits for predicting the response to chemotherapeutic agents for pancreatic and biliary tract cancers by blood gene expression]; Patent number: JP 2021-126107; 2021) [1]. The key biological processes associated to a favorable prognosis (pre-SD group) were related to T cell immunosurveillance, TCR alpha beta signaling pathway, Chemotaxis-CXCR3-A signaling and Cytokines/chemokines and receptors.