Despite advances in diagnostic and therapeutic modalities, the 5-year survival of patients with pancreatic adenocarcinoma (PDAC) remains woefully low. Unfortunately, African Americans (AA) bear a significant brunt of this disease and both the incidence and mortality of PDAC are higher amongst them. Understanding the biological factors contributing to this pattern will help develop novel strategies to improve outcomes. Pancreatic cancer is a mutation-driven cancer. Alterations in tumor driver genes like KRAS, TP53, SMAD4 and CDKN2A alter multiple transcriptional pathways in the native pancreatic cells and their microenvironment, resulting in tumor growth, progression and metastases. Presence or absence of many of these mutations can modulate the aggressiveness of cancer and the clinical course of patients with PDAC. Studies in other cancers including colon, breast and prostate cancer, have demonstrated that genomic and transcriptional landscape can shape the incidence and the outcomes of these cancers. However, such data in the context of PDAC is lacking. Thus, we have put forth the following hypothesis to explain the observed clinical patterns in the context of PDAC: Hypothesis: Differences in the genomic and transcriptomic signatures contribute to the observed patterns of increased pancreatic cancer incidence and worse outcomes in AA patients. Our hypothesis will be tested through the two specific aims: Aim 1 will be focused on interrogating the mutational landscape of PDAC using deep whole exome sequencing (WES) on resected tumor specimens of histologically proven PDAC cases from AA patients. In addition to testing specific hypotheses about KRAS, we will screen potential pathogenic variants (PPVs) which could be contributing significantly clinical outcomes among PDAC patients. In Aim 2 Bulk RNA sequencing (RNA-seq) will be performed on the cohort of 100 PDAC specimens from AA patients (from aim 1) to identify clinically relevant transcriptional pathways in PDAC tumors in two ways. First, the PDAC cases will be compared according to our published PurIST algorithm to understand the incidence of classical/basal tumor subtypes, which predict response to frontline therapy. Additionally, we will perform denovo transcriptomic subtyping and pathway analysis to understand whether PDAC tumors harbor distinct transcriptional signatures compared to those previously identified. Successful execution of the current studies will provide, for the first time, detailed integrative analysis of genetic and transcriptional landscape of PDAC. These data will help improve our understanding of the biological factors contributing to observed clinical patterns and support the development of novel strategies to improve outcomes.