Evolutionary divergence of HLA class I genotype impacts efficacy of cancer immunotherapy

Article metrics

Abstract

Functional diversity of the highly polymorphic human leukocyte antigen class I (HLA-I) genes underlies successful immunologic control of both infectious disease and cancer. The divergent allele advantage hypothesis dictates that an HLA-I genotype with two alleles with sequences that are more divergent enables presentation of more diverse immunopeptidomes1,2,3. However, the effect of sequence divergence between HLA-I alleles—a quantifiable measure of HLA-I evolution—on the efficacy of immune checkpoint inhibitor (ICI) treatment for cancer remains unknown. In the present study the germline HLA-I evolutionary divergence (HED) of patients with cancer treated with ICIs was determined by quantifying the physiochemical sequence divergence between HLA-I alleles of each patient’s genotype. HED was a strong determinant of survival after treatment with ICIs. Even among patients fully heterozygous at HLA-I, patients with an HED in the upper quartile respond better to ICIs than patients with a low HED. Furthermore, HED strongly impacts the diversity of tumor, viral and self-immunopeptidomes and intratumoral T cell receptor clonality. Similar to tumor mutation burden, HED is a fundamental metric of diversity at the major histocompatibility complex–peptide complex, which dictates ICI efficacy. The data link divergent HLA allele advantage to immunotherapy efficacy and unveil how ICI response relies on the evolved efficiency of HLA-mediated immunity.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Landscape of HEDs at HLA-A, -B and -C.
Fig. 2: High mean HED is associated with improved response and survival to ICIs.
Fig. 3: Effect of high mean HED and high TMB on efficacy of ICI treatment.
Fig. 4: Mean HED is positively correlated with diversity of the tumor, viral and human immunopeptidomes.

Data availability

The data from prior studies are available at the following accession numbers: dbGaP, phs001041.v1.p1 (Snyder et al.6); dbGaP, phs000452.v2.p1 (Van Allen et al.8); SRA, SRP067938 (Hugo et al.41) and SRP090294 (Hugo et al.41); dbGaP, phs000980.v1.p1 (Rizvi et al.7); SRA, SRP095809 and BioProject, PRJNA359359 (Riaz et al.7); SRA, PRJNA419415 (Chowell et al.10), PRJNA419422 (Chowell et al.10) and PRJNA419530 (Chowell et al.10); cBioPortal for Cancer Genomics, http://cbioportal.org/msk-impact.

References

  1. 1.

    Parham, P. & Ohta, T. Population biology of antigen presentation by MHC class I molecules. Science 272, 67–74 (1996).

  2. 2.

    Wakeland, E. K. et al. Ancestral polymorphisms of MHC class II genes: divergent allele advantage. Immunol. Res. 9, 115–122 (1990).

  3. 3.

    Pierini, F. & Lenz, T. L. Divergent allele advantage at human MHC genes: signatures of past and ongoing selection. Mol. Biol. Evol. 35, 2145–2158 (2018).

  4. 4.

    McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).

  5. 5.

    Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202–206 (2019).

  6. 6.

    Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).

  7. 7.

    Rizvi, N. A. et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).

  8. 8.

    Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).

  9. 9.

    Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).

  10. 10.

    Chowell, D. et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359, 582–587 (2018).

  11. 11.

    Carrington, M. et al. HLA and HIV-1: heterozygote advantage and B*35-Cw*04 disadvantage. Science 283, 1748–1752 (1999).

  12. 12.

    Penn, D. J., Damjanovich, K. & Potts, W. K. MHC heterozygosity confers a selective advantage against multiple-strain infections. Proc. Natl Acad. Sci. USA 99, 11260–11264 (2002).

  13. 13.

    Thursz, M. R., Thomas, H. C., Greenwood, B. M. & Hill, A. V. Heterozygote advantage for HLA class-II type in hepatitis B virus infection. Nat. Genet. 17, 11–12 (1997).

  14. 14.

    Doherty, P. C. & Zinkernagel, R. M. A biological role for the major histocompatibility antigens. Lancet i, 1406–1409 (1975).

  15. 15.

    Doherty, P. C. & Zinkernagel, R. M. Enhanced immunological surveillance in mice heterozygous at H-2 gene complex. Nature 256, 50–52 (1975).

  16. 16.

    Hughes, A. L. & Yeager, M. Natural selection at major histocompatibility complex loci of vertebrates. Annu. Rev. Genet. 32, 415–435 (1998).

  17. 17.

    Robinson, J. et al. Distinguishing functional polymorphism from random variation in the sequences of > 10,000 HLA-A, -B and -C alleles. PLoS Genet. 13, e1006862 (2017).

  18. 18.

    Gfeller, D. & Bassani-Sternberg, M. Predicting antigen presentation-what could we learn from a million peptides? Front. Immunol. 9, 1716 (2018).

  19. 19.

    Paul, S. et al. HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity. J. Immunol. 191, 5831–5839 (2013).

  20. 20.

    Marty, R. et al. MHC-I genotype restricts the oncogenic mutational landscape. Cell 171, 1272–1283.e15 (2017).

  21. 21.

    Marty, R., Thompson, W. K., Salem, R. M., Zanetti, M. & Carter, H. Evolutionary pressure against MHC Class II binding cancer mutations. Cell 175, 416–428 e413 (2018).

  22. 22.

    McGranahan, N. et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171, 1259–1271.e11 (2017).

  23. 23.

    Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019).

  24. 24.

    Potts, W. K. & Wakeland, E. K. Evolution of diversity at the major histocompatibility complex. Trends Ecol. Evol. 5, 181–187 (1990).

  25. 25.

    Lenz, T. L. Computational prediction of MHC II-antigen binding supports divergent allele advantage and explains trans-species polymorphism. Evolution 65, 2380–2390 (2011).

  26. 26.

    Grantham, R. Amino acid difference formula to help explain protein evolution. Science 185, 862–864 (1974).

  27. 27.

    McKenzie, L. M., Pecon-Slattery, J., Carrington, M. & O’Brien, S. J. Taxonomic hierarchy of HLA class I allele sequences. Genes Immun. 1, 120–129 (1999).

  28. 28.

    Buhler, S., Nunes, J. M. & Sanchez-Mazas, A. HLA class I molecular variation and peptide-binding properties suggest a model of joint divergent asymmetric selection. Immunogenetics 68, 401–416 (2016).

  29. 29.

    Grueber, C. E., Wallis, G. P. & Jamieson, I. G. Episodic positive selection in the evolution of avian toll-like receptor innate immunity genes. PLoS ONE 9, e89632 (2014).

  30. 30.

    Subramanian, S. & Kumar, S. Evolutionary anatomies of positions and types of disease-associated and neutral amino acid mutations in the human genome. BMC Genom. 7, 306 (2006).

  31. 31.

    Wain, L. V. et al. Adaptation of HIV-1 to its human host. Mol. Biol. Evol. 24, 1853–1860 (2007).

  32. 32.

    International Wheat Genome Sequencing Consortium. A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome. Science 345, 1251788 (2014).

  33. 33.

    Rentoft, M. et al. Heterozygous colon cancer-associated mutations of SAMHD1 have functional significance. Proc. Natl Acad. Sci. USA 113, 4723–4728 (2016).

  34. 34.

    Sundaram, L. et al. Predicting the clinical impact of human mutation with deep neural networks. Nat. Genet. 50, 1161–1170 (2018).

  35. 35.

    Abelin, J. G. et al. Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 46, 315–326 (2017).

  36. 36.

    Bradburn, M. J., Clark, T. G., Love, S. B. & Altman, D. G. Survival analysis part II: multivariate data analysis-an introduction to concepts and methods. Br. J. Cancer 89, 431–436 (2003).

  37. 37.

    Broström, Gr Event history analysis with R. (CRC Press, Boca Raton, FL, 2012).

  38. 38.

    Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).

  39. 39.

    Pearson, H. et al. MHC class I-associated peptides derive from selective regions of the human genome. J. Clin. Invest. 126, 4690–4701 (2016).

  40. 40.

    Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949.e16 (2017).

  41. 41.

    Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).

  42. 42.

    Shukla, S. A. et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 33, 1152–1158 (2015).

  43. 43.

    Robinson, J. et al. The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Res. 43, D423–D431 (2015).

  44. 44.

    Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Res. 46, D754–D761 (2018).

  45. 45.

    Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

  46. 46.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  47. 47.

    McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

  48. 48.

    Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

  49. 49.

    Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

  50. 50.

    Larson, D. E. et al. SomaticSniper: identification of somatic point mutations in whole genome sequencing data. Bioinformatics 28, 311–317 (2012).

  51. 51.

    Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28, 1811–1817 (2012).

  52. 52.

    Wei, L. et al. MAC: identifying and correcting annotation for multi-nucleotide variations. BMC Genom. 16, 569 (2015).

  53. 53.

    Jurtz, V. et al. NetMHCpan-4.0: improved peptide-MHC Class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199, 3360–3368 (2017).

  54. 54.

    Carlson, C. S. et al. Using synthetic templates to design an unbiased multiplex PCR assay. Nat. Commun. 4, 2680 (2013).

  55. 55.

    Robins, H. S. et al. Comprehensive assessment of T-cell receptor beta-chain diversity in alphabeta T cells. Blood 114, 4099–4107 (2009).

  56. 56.

    Shen, R. L. & Seshan, V. E. FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Res. 44, e131 (2016).

Download references

Acknowledgements

We thank the Chan lab and members of the Immunogenomics and Precision Oncology Platform for advice and input. This work was supported in part by the National Institutes of Health (NIH) grant no. R35 CA232097 (to T.A.C.), NIH grant no. RO1 CA205426 (to T.A.C. and N.A.R.), the PaineWebber Chair (to T.A.C.), and the NIH/National Cancer Institute’s Cancer Center support grant (no. P30 CA008748) and the Deutsche Forschungsgemeinschaft grant no. LE 2593/3-1 (to T.L.L.).

Author information

D.C., C.K., F.P., V.M., T.L.L., T.A.C., F.K., L.G.T.M. and N.A.R performed the data acquisition and analyses. D.C., C.K., F.P., T.L.L. and T.A.C. wrote the manuscript, with input from all authors.

Correspondence to Tobias L. Lenz or Timothy A. Chan.

Ethics declarations

Competing interests

T.A.C. is a co-founder of Gritstone Oncology and holds equity. T.A.C. holds equity in An2H. T.A.C. acknowledges grant funding from Bristol-Myers Squibb, AstraZeneca, Illumina, Pfizer, An2H and Eisai. T.A.C. has served as an advisor for Bristol-Myers, MedImmune, Squibb, Illumina, Eisai, AstraZeneca and An2H. N.A.R. is a consultant/advisory board member for AstraZeneca, BMS, Roche, Merck, Novartis, Lilly and Pfizer. T.A.C. and N.A.R are cofounders of Gritstone Oncology. T.A.C., N.A.R, L.G.T.M. and D.C. hold ownership of intellectual property on using TMB to predict immunotherapy response, with pending patent, which has been licensed to PGDx.

Additional information

Peer review information Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Hierarchical clustering of HLA-I evolutionary divergences at individual HLA class I loci.

a, Hierarchical clustering of HED at HLA-A using all HLA-A alleles from all patient cohorts. b, Hierarchical clustering of HED at HLA-B using all HLA-B alleles. c, Hierarchical clustering of HED at HLA-C using all HLA-C alleles. Heat maps shows z-score normalized HED across all alleles. Color gradient of blue to red indicates low HED between allele pairs to high HED between allele pairs, respectively.

Extended Data Fig. 2 Mean HLA-I evolutionary divergence is associated with improved benefit from immune checkpoint inhibitors.

a, Association of high mean HED (red) with improved efficacy of anti-CTLA-4 treatment in a cohort of patients with metastatic melanoma; P = 0.0072; two-sided log-rank test. Density plots indicate the distribution of mean HED and cutoff used in the survival curves. T.Q.C. = top quartile cutoff, HR = hazard ratio, CI = confidence interval. b, Association of high (top quartile) tumor mutational burden (TMB) with overall survival after anti-CTLA-4 treatment; P= 0.20; two-sided log-rank test. c, Association of high mean HED and high TMB (red) with improved overall survival after anti-CTLA4 treatment; P = 0.024; two-sided log-rank test. d, Multivariable Cox proportional-hazards model including mean HED and other clinical variables. Data show independent effect of mean HED associated with improved survival after anti-CTLA-4. HED, TMB, and fraction of copy number alterations (FCNA) are dichotomized into high (1) and low (0) groups based on the top quartile for each variable. P values calculated using two-sided log-rank test. Horizontal lines represent the 95% confidence interval.

Extended Data Fig. 3 Effect of mean HLA-I evolutionary divergence on hazard ratio from survival across all possible cutpoints.

Cutpoint analysis showing the relationship between mean HED and hazard ratio. Data show a negative relationship between mean HED and hazard ratio across all possible cutpoints for mean HED, indicating improved overall survival as mean HED increases.

Extended Data Fig. 4 Neither HLA-I heterozygosity nor HLA-I evolutionary divergence is associated with prognosis in TCGA melanoma patients.

a, Full heterozygosity at HLA-I (red) is not associated with prognosis in TCGA melanoma patients; P = 0.14, two-sided log-rank test. b, High patient mean HED (red) is not associated with prognosis in TCGA melanoma patients; P = 0.80, two-sided log-rank test. c, High mean HED (red) is not associated with prognosis in TCGA melanoma patients fully heterozygous at HLA-I; P = 0.54; two-sided log-rank test.

Extended Data Fig. 5 Neither HLA-I heterozygosity nor HLA-I evolutionary divergence is associated with prognosis in TCGA lung cancer patients.

a, Full heterozygosity at HLA-I (red) is not associated with prognosis in TCGA lung cancer patients; P = 0.38, two-sided log-rank test. b, High mean HED is not associated with prognosis in patients from Extended Data Fig. 4a; P = 0.48, log-rank test. c, High mean HED (red) is not associated with prognosis in TCGA lung cancer patients fully heterozygous at HLA-I; P = 0.51, two-sided log-rank test.

Extended Data Fig. 6 The effects of mean HLA-I evolutionary divergence and tumor mutational burden are independent of cancer type and drug class.

a, Multivariable Cox proportional-hazards model including mean HED and other clinical variables using all patients. Data show independent effect of mean HED in predicting response to ICI. Drug class P= 8.14e-06. P values calculated using two-sided log-rank test. Horizontal lines indicate 95% confidence interval. b, Multivariable Cox proportional-hazards model including mean HED and other clinical variables using patients fully heterozygous at HLA-I. Data show independent effect of mean HED associated with improved survival after ICI therapy. Mean HED P = 7.25e-04; Drug Class P = 9.32e-05. HED and TMB are dichotomized into high (1) and low (0) groups using the top quartile for each variable. P values calculated using two-sided log-rank test. Horizontal lines represent the 95% confidence interval.

Extended Data Fig. 7 Oncoprint showing mutations in genes in our patient cohorts.

Data show no difference in proportion of patients with mutations in the presented genes between patients with high mean HLA-I evolutionary divergence (HED) and low mean HED. LOH = loss of heterozygosity at HLA-I.

Extended Data Fig. 8 Combined effect of HED and TMB on survival after ICI administration and multivariable analysis of HED at individual loci.

a, Cutpoint analysis showing the association of both high mean HED and high TMB with improved survival after ICI (same distributions as Fig. 3g; n = 248). Data show a reduction in hazard ratio when combining HED and TMB compared to either variable alone. Green indicates two-sided log-rank p-value < 0.05; red indicates non-significant log-rank p-value. b, Multivariable cox regression analysis demonstrating the effect of HED at individual loci on overall survival after ICI administration. Data indicate that high HED at HLA-A and HLA-B are each associated with improved overall survival after ICI. P values calculated using two-sided log-rank test. Horizontal lines represent the 95% confidence interval.

Extended Data Fig. 9 Effect of mean HLA-I evolutionary divergence and tumor mutational burden on efficacy of immune checkpoint inhibitor treatment in an independent set of patients.

a, Association of high mean HED (red) with improved overall survival after ICI in an independent pan-cancer dataset of patients described in Chowell et al. These patients do not overlap with those presented in Figs. 2 & 3. Cutoff was determined using the median mean HED across the cohort. P = 0.02; two-sided log-rank test. HR = hazard ratio, CI = confidence interval. b, Association of high mean HED (red) with improved overall survival after ICI in an independent, pan-cancer dataset of patients described in Chowell et al. These patients do not overlap with those presented in Figs. 2 & 3. Patients in the red curve have mean HED greater than or equal to the top quartile; patients in the blue curve have mean HED less than or equal to the first quartile. P = 0.04; two-sided log-rank test c, Association of high mean HED (red) with improved overall survival after ICI in all patients described in Chowell et al. Cutoff was determined using the median mean HED across the cohort. P = 0.0012; two-sided log-rank test. d, Association of high mean HED (red) with improved overall survival after ICI in all patients described in Chowell et al. Patients in the red curve have mean HED greater than or equal to the top quartile; patients in the blue curve have mean HED less than or equal to the first quartile. P = 0.0037; two-sided log-rank test. e, Multivariable Cox proportional-hazards model including mean HED and other variables. Data show independent effect of mean HED on improved survival after ICI administration when adjusting for TMB and cancer type. Mean HED is dichotomized into high (1) and low (0) groups using the median; TMB is treated as a continuous variable. TMB P = 0.0008. P values calculated using two-sided log-rank test. Horizontal lines represent the 95% confidence interval.

Extended Data Fig. 10 Association of HLA-I evolutionary divergence at each class I locus with diversity of tumor and human immunopeptidomes.

a, Correlation of HED at HLA-A with number of unique neopeptides bound to HLA-A alleles of each patient genotype using all patients heterozygous at HLA-A from Fig. 2 (n = 118) for whom neopeptide data were available; P = 0.15; one-sided Kendall’s rank correlation. b, Correlation of HED at HLA-B with number of unique neopeptides bound to HLA-B alleles of each patient genotype using patients heterozygous at HLA-B (n= 129); P = 0.001; one-sided Kendall’s rank correlation c, Correlation of HED at HLA-C with number of unique neopeptides bound to HLA-C alleles of each patient genotype using patients heterozygous at HLA-C (n = 118); P = 0.03; one-sided Kendall’s rank correlation. d, Correlation of HED at HLA-A with number of unique viral peptides bound to HLA-A alleles of each patient genotype using patients heterozygous at HLA-A (n = 118); P = 0.01; one-sided Kendall’s rank correlation. e, Correlation of HED at HLA-B with number of unique viral peptides bound to HLA-B alleles of each patient genotype using patients heterozygous at HLA-B (n= 129); P < 0.0001; one-sided Kendall’s rank correlation. f, Correlation of HED at HLA-C with number of unique viral peptides bound to HLA-C alleles of each patient genotype using patients heterozygous at HLA-C; P < 0.0001. g, Correlation of HED at HLA-A with number of unique self peptides bound to HLA-A alleles of each patient genotype using patients heterozygous at HLA-A (n = 118); P = 0.79; two-sided Kendall’s rank correlation. h, Correlation of HED at HLA-B with number of unique self peptides bound to HLA-B alleles of each patient genotype using patients heterozygous at HLA-B (n= 129); P < 0.0001; two-sided Kendall’s rank correlation. i, Correlation of HED at HLA-C with number of unique self peptides bound to HLA-C alleles of each patient genotype using patients heterozygous at HLA-C (n = 118); P < 0.0001; two-sided Kendall’s rank correlation. Red line indicates line of best linear fit.

Supplementary information

Supplementary Information

Supplementary Figures 1 and 2

Reporting Summary

Supplementary Tables

Supplementary Tables 1–4

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chowell, D., Krishna, C., Pierini, F. et al. Evolutionary divergence of HLA class I genotype impacts efficacy of cancer immunotherapy. Nat Med 25, 1715–1720 (2019) doi:10.1038/s41591-019-0639-4

Download citation