Immunophenotype Integration for Monitoring T Cell Dynamics in Pancreatic Cancers

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PROJECT SUMMARY The overarching goal of our proposal is to accelerate the discovery of biomarkers that identify the rare group of pancreatic ductal adenocarcinoma (PDAC) patients who respond to immunotherapy by employing a novel high- throughput immune analysis pipeline. The majority of PDAC patients present with metastatic disease, and treatment options are limited, yielding a dismal 5-year survival of 8%. Despite the recent remarkable progress in immunotherapy in many cancer types, studies have failed thus far to yield substantial benefit in this stereotypically immune-restricted disease. Our research group has long pioneered immunotherapeutic strategies against PDAC, demonstrating that inciting immune responses against PDAC is in fact possible. Notably, we have observed instances in which exceptional clinical responses take place. A critical challenge in discovering biomarkers that identify these rare responders is having to undertake high-parameter characterization of the immune responses in largely negative trials despite the lack of cost-effective high-throughput methods. Our proposed study is uniquely suited to address this challenge for the following reasons. First, our team has established an unparalleled resource of biospecimens from PDAC patients who have undergone a variety of immunotherapeutic modalities including PDAC-specific vaccines, checkpoint inhibitors (anti-PD-1, anti-CTLA-4), an epigenetic modifier (entinostat), and an IDO1 inhibitor (epacadostat). Second, our work has already led to the discovery of key determinants of immunotherapy responses in PDAC patients: specific myeloid cell types and germline genetics, e.g. mismatch repair defects. Third, we have recently developed high-parameter (30+ marker) immune profiling panels for T and myeloid cell types using mass cytometry (CyTOF). Our CyTOF workflow involves multiplexing of samples, significantly reducing the cost burden and batch-related biases during analysis. Fourth, we have recently developed a novel computational pipeline to integrate the CyTOF-based high- parameter T cell profiles into simplified pseudotime-based metrics that reflect the T cell states in a given sample. This method overcomes the analytic bottleneck by obviating the need for iterative, detailed annotation of cell types, and also by facilitating comparisons with other immunologic parameters and across disparate clinical trials. Our progress now prompts our central hypothesis that this CyTOF-based pipeline will enhance the understanding of T cell responses to (i) distinct immunotherapies, (ii) clinical outcomes, and (iii) other immunomodulatory factors. Thus, using our biobank representing seven early-phase immunotherapy clinical trials in PDAC patients, we will establish the utility of our pipeline in determining and comparing T cell dynamics specific to each immunotherapy regimen and how they correlate with clinical outcomes. Using CyTOF-based myeloid cell profiles and an already available germline variant dataset from PDAC patients, we will also determine the effects of other immunomodulatory factors on T cell responses. This work will provide a new tool for biomarker discovery as well as bring forth an extensive immune profile database of PDAC patients that will empower future inquiries.