Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy

Abstract

CD8+ T cell–dependent killing of cancer cells requires efficient presentation of tumor antigens by human leukocyte antigen class I (HLA-I) molecules. However, the extent to which patient-specific HLA-I genotype influences response to anti–programmed cell death protein 1 or anti–cytotoxic T lymphocyte–associated protein 4 is currently unknown. We determined the HLA-I genotype of 1535 advanced cancer patients treated with immune checkpoint blockade (ICB). Maximal heterozygosity at HLA-I loci (“A,” “B,” and “C”) improved overall survival after ICB compared with patients who were homozygous for at least one HLA locus. In two independent melanoma cohorts, patients with the HLA-B44 supertype had extended survival, whereas the HLA-B62 supertype (including HLA-B*15:01) or somatic loss of heterozygosity at HLA-I was associated with poor outcome. Molecular dynamics simulations of HLA-B*15:01 revealed different elements that may impair CD8+ T cell recognition of neoantigens. Our results have important implications for predicting response to ICB and for the design of neoantigen-based therapeutic vaccines.

Evolutionary Pressure against MHC Class II Binding Cancer Mutations

“…Passenger mutations, defined as mutations occurring only 1-2 times across all tumors in non-cancer genes, had a PHBR-II score distribution very similar to that of random mutations with an enrichment for PHBR-II scores near 0, suggesting that many passengers are likely to be effectively presented. This enrichment of presented passenger mutations is consistent with recent reports that HLA loss of heterozygosity is frequent in some tumor types (Chowell et al, 2017; McGranahan et al, 2017; Ryschich et al, 2004) and is associated with the accumulation of mutations that would have been effectively presented by the lost allele (McGranahan et al, 2017). Consequently, 25% percent of the passenger mutation PHBR-II scores fall below the PHBR-II cutoff of 6 (Figure 3D).…”

Immunopharmacogenomics towards personalized cancer immunotherapy targeting neoantigens

“…We previously reported that the expression levels of granzyme A and human leukocyte antigen (HLA)‐A in melanoma tissues before treated with anti‐PD‐1 antibody were significantly higher in responders compared to non‐responders, which could indicate their value as potential biomarkers to predict clinical responses. Recent reports suggest that loss of heterozygosity of HLA class I locus is an immune escape mechanism that is related with poor outcome in patients who received immune checkpoint inhibitor therapy . Furthermore, we showed that the T‐cell receptor (TCR) repertoire of tumor‐infiltrated T cells might be useful information for the evaluation or assessment of the immune microenvironment .…”

Immunotherapy for osteosarcoma: Where do we go from here?

“…A major challenge for immunotherapy is the identification of biomarkers that predict response, so that treatments can be tailored for the greatest benefit. For checkpoint inhibitors, various proposed predictors of response include the tumor immune phenotype (expression of PD‐1 and PD‐L1, as well as the presence of TILs), the somatic genomic features such as mutational burden and microsatellite instability, the gut microbiome, and the HLA class I genotype …”

“…For checkpoint inhibitors, various proposed predictors of response include the tumor immune phenotype (expression of PD-1 and PD-L1, as well as the presence of TILs), the somatic genomic features such as mutational burden and microsatellite instability, the gut microbiome, 77 and the HLA class I genotype. 78 Unfortunately, PD-L1 expression has not been consistently predictive of response in either melanoma or NSCLC. 79,80 Adding further complexity, PD-L1 expression is heterogeneous in both primary and metastatic lesions.…”