PROJECT 3: ABSTRACT FOLFIRINOX and gemcitabine plus nab-paclitaxel (GnP) have emerged as the first-line treatment for patients with metastatic pancreatic cancer (PDAC). These two regimens have not been compared in the first-line setting, and treatment selection is guided by physician choice. Among patients with metastatic PDAC who do not respond to first-line FOLFIRINOX, a substantial proportion will have a robust response to second-line GnP. This strongly suggests that there may be a subset of patients who may have significant clinical response to GnP but not FOLFIRINOX. Matching patients to the most effective first-line therapy is a critical and unmet need. We have identified molecular subtypes of PDAC that are robust and replicable. Analysis from two clinical trials has shown that the basal subtype does not respond to FOLFIRINOX-based therapies and emphasizes the critical need to identify alternative treatments. Recent results have shown that patients with basal tumors are more responsive to GnP compared to FOLFIRINOX. Based on our findings of subtype associated treatment response, we developed a robust and replicable single-sample classifier (PurIST) that is now a CLIA-approved assay, and will be used this proposal to prospectively evaluate whether the PurIST classifier can be used to direct treatment selection. Leveraging the strength of molecular subtyping expertise at the University of North Carolina at Chapel Hill and the tremendous clinical infrastructure at the Medical College of Wisconsin’s Pancreatic Cancer Program, we propose a clinical trial where PurIST subtyping will be used to direct the initial chemotherapy. To our knowledge this will be the first trial to use molecular subtyping to direct treatment in the neoadjuvant setting. Results from this trial will demonstrate if precision oncology approaches such as PurIST may help direct treatment and improve outcome for patients that may otherwise be less responsive to either FOLFIRINOX or GnP. In parallel, we will determine if specific characteristics in the tumor and tumor microenvironment may predict response to different therapies through innovative computational approaches of cutting edge trials. Finally, through our novel proteomic approaches we have found that basal tumors have differential kinase profiles. We show that effectiveness of kinases inhibitors in PDAC may have been overlooked as ~20% of PDAC patients are basal, and that subtype-specific kinases may be promising targets.