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.

Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Figures

Fig. 1.. Effect of HLA-I homozygosity on survival in patients treated with immune checkpoint inhibitors.

(A) Association between homozygosity in at least one HLA-I locus and reduced overall survival in cohort 1. (B) Association between homozygosity in at least one HLA-I locus and reduced survival in cohort 2. (C) Association between HLA-I homozygosity and decreased survival from all 1535 patients. Data show one or more HLA-I loci or individual loci (HLA-A, HLA-B, and HLA-C). Indicated are the number of patients and HR. Horizontal lines represent the 95% CI. P value was calculated by using the Log-rank test. (D) Patients in cohort 1 with heterozygosity at all HLA-I loci and a high mutation load (defined as >113 mutations) compared with patients that are homozygous for at least one HLA-I locus and have a low mutation load. (E) Patients in cohort 2 with heterozygosity at all HLA-I loci and a high tumor mutational load (defined as >16.72 mutations) compared with patients that are homozygous in at least one HLA-I locus and have a low mutation load. (F) Distribution of HRs to stratify cohort 1 patients based on tumor mutational load. The combined effect of HLA-I heterozygosity at all loci and mutation load on improved survival was greater as compared with mutation load alone. (G) Distribution of HRs to stratify cohort 2 patients based on mutation load. A range of cutoffs across the quartiles of mutation load was used. P values were calculated by using the Wilcoxon-rank sum test. (H) Survival analysis showing that LOH of heterozygous germline HLA-I is associated with decreased overall survival in patients treated with ICB. (I) Survival analysis showing that the effect of LOH of heterozygous germline HLA-I is greater in tumors with low mutation burden compared with tumors with high mutation load and without LOH. High mutation load is defined as >113 mutations.

Fig. 2.. Influence of the HLA-B44 supertype on survival of melanoma patients treated with immune checkpoint inhibitors.

(A) Prevalence of the different HLA supertypes in patients with melanoma from cohort 1. (B) Prevalence of the different HLA supertypes in the patients with melanoma from cohort 2. (C and D) Survival analysis of patients possessing the B44 alleles [B44 (+)] compared with patients without the B44 alleles [B44 (–)] from cohort 1 (C) and cohort 2 (D). (E and F) Survival analysis of patients with the B44 alleles and with high mutation burden versus patients without B44 and with low mutation load, from cohort 1 (E) and cohort 2 (F). (G) Distribution of hazard ratios to stratify cohort 1 patients based on mutation load. The combined effect of B44 and mutation load on increased survival was greater compared with simply considering mutation load alone. (H) Distribution of HRs to stratify cohort 2 patients based on mutation load. A range of cutoffs across the quartiles of mutation load was used. P values were calculated by using the Wilcoxon-rank sum test. (I) Survival analysis of melanoma patients with and without the B44 alleles from the TCGA cohort. (J) (Left) Example of peptide motif common among B44 alleles, docked in complex with HLA-B*44:02 based on an available crystal structure (PDB 1M6O). The five common residues (E2, I3, P4, V6, and Y9) of the motif were reported in (56). Peptide residues are colored according to their properties as basic (red), acidic (blue), polar (green), or hydrophobic (gray). (Center) Close-up view of an example peptide conforming to the B44 motif (54, 56). Residues at positions 2 and 9 are important for anchoring the peptide in the HLA binding groove (54). (Right) Alignment between B44 peptide motif and known immunogenic neoantigens (table S8) restricted to B44 expressed by melanomas. All neoepitopes feature Glu (E) at position 2; neoantigens are also either identical or similar to the motif at one or two additional positions. The neoantigen FAM3C: TESPFEQHI was identified in a melanoma patient with long-term response to anti–CTLA-4 from cohort 1. Sequence similarity was determined by using standard residue classes (GAVLI, FYW, CM, ST, KRH, DENQ, and P).

Fig. 3.. Effect of the HLA-B*15:01 allele on overall survival of melanoma patients treated with immune checkpoint inhibitors.

(A) Survival analysis showing reduced survival in ICB-treated melanoma patients from cohort 1 with and without the HLA-B*15:01 allele. (B) Overview of the three-dimensional structure of the peptide-binding groove of HLA-B*15:01, (light purple), bound peptide (yellow), and bridging residues (light pink). (C) Side view of the bridge-sequestration effect over bound-peptide residue positions P2 (light blue) and P3 (red). (D) MD simulation snapshots of both the isolated HLA B*15:01 molecule and its complex with a 9–amino acid UBCH6 peptide; each trajectory was run over the course of 500 ns of simulation time. (E) Observables from the MD simulations described in (D). The mean bridge distances in the HLA-B*15:01 molecule and in the HLA-B*15:01-peptide complex are comparable. The residue-position root mean square fluctuations (RMSFs) indicate that each of the bridging residues becomes more rigid in the presence of the peptide. (F) On-therapy clonality of TCR CDR3s between HLA heterozygous patients and patients who are HLA-homozygous (in at least one class I locus or at HLA-DP). (G) On-therapy clonality of TCR CDR3s per VJ combination between HLA heterozygous patients and patients with HLA homozygosity (in at least one class I locus or at HLA-DP).