Summary
Human Leukocyte Antigen (HLA) expression contributes to the activation of anti-tumor immunity through interactions with T cell receptors. However, pan-cancer HLA expression in tumors has not been systematically studied. In a retrospective analysis using the Cancer Genome Atlas, we quantified HLA class I and class II expression across 33 tumor types, which strongly correlated with infiltration of various immune cell types, expression of pro-inflammatory genes, and immune checkpoint markers. Patients with high HLA allelic diversity and gene expression had better survival. Immune microenvironments could be predicted using a neural network model trained on HLA expression data with varied survival outcomes. Furthermore, we identified a subset of tumors which upregulated HLA class I but not class II genes and exploited HLA-mediated escape strategies. Our results suggest the potential of using HLA expression data to predict immunogenicity. Taken together, we emphasize the crucial role of HLA upregulation in shaping prolonged anti-tumor immunity.
Highlights
Systematic analysis of HLA gene expression across 33 cancer types.
A machine learning model trained on HLA expression data predicts immunogenicity.
HLA upregulation correlates with cell-mediated immunity and improved survival.
A subset of highly immunogenic tumors exhibit HLA-mediated tumor escape.
Competing Interest Statement
The authors have declared no competing interest.