Comprehensive modeling of tumor suppressor gene-derived neoantigens in pancreatic cancer

Abstract public domain (NIH RePORTER)

PROJECT ABSTRACT Immunotherapies potentiate pre-existing immune responses, or induce de novo responses, that are capable of destroying tumor cells with high specificity. However, immunotherapies do not currently benefit the majority of patients suffering from pancreatic ductal adenocarcinoma (PDAC), one of the most lethal forms of cancer. Like other cancers, PDAC is primarily driven by somatic mutations, some of which give rise to mutated peptides. When presented on the surface of tumor cells, these peptides can be recognized by cytotoxic T cells. In this context of immune cell recognition, mutated peptides are called neoantigens and are critical targets of the anti-tumor immune response. Historically, PDAC has been considered an ‘immunologically cold’ cancer, predicted to contain only a scarcity of neoantigens. However, most research to date has studied PDAC neoantigens derived from missense mutations, with little focus on other potential sources, such as frameshift mutations. In the context of tumor suppressor genes (TSGs), frameshifts are frequent drivers of PDAC, and it is currently unknown if these mutations give rise to neoantigens shared by different patients. If confirmed, TSG-derived frameshift neoantigens could represent novel targets for immunotherapy with broad applicability. Objectives: This study will (1) predict and experimentally validate neoantigens from human pancreas cancer, (2) model frameshift-derived neoantigens in the context of a murine model of pancreas cancer, and (3) assess the therapeutic potential of these neoantigens as targets of anti-tumor cytotoxic T cells. Aim 1: Determine immunogenicity of TSG-derived frameshift neoantigens predicted from human PDAC. A computational pipeline will predict frameshift-derived neoantigens using whole-exome sequencing of pancreatic cancer organoids. Candidates will be validated through in vitro stimulation of autologous leukocytes and ELISPOT assays. Aim 2: Establish a suite of murine model neoantigens for in vivo modeling of the neoantigen-driven, anti-tumor immune response. A cohort of model neoantigens will be developed based on the murine orthologs of TSGs. A bioinformatics approach will establish a set of candidate models that will be evaluated via experimental vaccinations. Aim 3: Install TSG-derived neoantigens in an orthotopic mouse model and evaluate the antigen-specific cytotoxic T cell response. A CRISPR-based mouse model capable of installing precise frameshift mutations has been established. Pancreatic organoids will be derived from this model, edited via CRISPR to generate model neoantigens, and then orthotopically transplanted into syngeneic mice, followed by longitudinal characterization of neoantigen-specific T cells. Fellowship training plan and environment: This research project will be conducted at the Massachusetts Institute of Technology’s Koch Institute for Integrative Cancer Research, in the laboratory of Dr. Tyler Jacks. The research environment is highly collaborative, and research training will involve hands-on mentorship from postdoctoral fellows and research staff. Research training will be significantly enriched by a regular schedule of scientific seminars, research presentations, teaching experiences, and frequent meetings with Dr. Jacks and other academic advisors and faculty.

Public health relevance public domain (NIH RePORTER)

NARRATIVE Pancreas cancer is a lethal malignancy, remaining recalcitrant in most cases to all treatment modalities even after the promising advent of immunotherapy. A computational investigation of pancreas cancer genomic sequencing data has indicated a new class of neoantigens, defined by their clonality in individual tumors and their predicted occurrence in many patients, which could render some pancreatic tumors amenable to immunotherapies that generate or deliver a neoantigen-specific cytotoxic T cell response. This study aims to evaluate the therapeutic potential of this class of neoantigens using a combination of bioinformatics, in vitro assays, and a preclinical mouse model.