Antigen immunodominance in regulation of anti-tumor CD8 T cell immunity and immunotherapy response

About This Grant

PROJECT SUMMARY Cancer immunotherapies that activate CD8 T cells to kill cancer cells have revolutionized cancer treatment in the clinic. However, most patients still do not respond to these therapies and some cancer types, like pancreatic cancer, remain highly refractory. A better understanding of the factors regulating CD8 T cell responses against tumors is needed to inform improved immunotherapies that could benefit more patients. CD8 T cells recognize and kill cancer based on the expression of mutated proteins by cancer cells, called neoantigens. Cancer cells can express up to hundreds of neoantigens, eliciting responses from different populations of CD8 T cells. Intuitively, one might expect that CD8 T cells responding to different neoantigens would work together to kill cancer cells in an additive or synergistic manner. However, our recent work revealed that CD8 T cells responding to different neoantigens compete in their effort to control tumors, limiting the overall breadth and effectiveness of the anti-tumor T cell response. Antigen immunodominance hierarchies form wherein CD8 T cells responding to one or a few dominant neoantigens saturate the response and suppress CD8 T cell expansion and effector differentiation against other subdominant neoantigens. We hypothesize that antigen immunodominance is a predictable and targetable phenomenon that restricts CD8 T cell immunity in cancer and that therapeutically circumventing antigen immunodominance could more optimally engage CD8 T cell immunity for improved tumor control. The proposed work will establish antigen immunodominance as a novel therapeutic axis for predicting, modulating, and exploiting CD8 T cell responses to cancer. In Aim 1, we will define the mechanistic basis and predictability of antigen immunodominance hierarchies in cancer. Using mouse models of lung and pancreatic cancer with precisely controlled neoantigen landscapes, we will determine how neoantigen features that can be measured in the clinic shape antigen hierarchies, including MHC binding strength, allelic restriction, expression level and clonality. We will integrate these insights into a computational framework for improved prediction of T cell-reactive neoantigens for immunotherapy design and patient stratification. In Aim 2, we will evaluate neoantigen-targeted therapeutic strategies aimed at circumventing immunodominance to achieve broader neoantigen reactivity for more effective anti-tumor CD8 T cell immunity. This work will utilize sophisticated genetic mouse models that uniquely enable the mechanistic study of complex T cell dynamics in ways not possible in human tissue and provide rigorous immunotherapy-resistant settings for preclinical testing of new therapies. We will use cutting-edge single-cell technologies to deeply characterize tumor-specific CD8 T cell phenotypes and responses to therapy, and specifically suited CD8 T cell datasets from patients to translate mechanistic discoveries to human cancer. Altogether, these studies will reveal the therapeutic potential of targeting antigen immunodominance to maximize CD8 T cell immunity against cancer and inform new immunotherapy approaches that overcome antigen immunodominance to improve patient outcomes across cancer pathologies.