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Negative trade-off between neoantigen repertoire breadth and the specificity of HLA-I molecules shapes antitumor immunity

Nature Cancer volume 2, pages 950–961 (2021)Cite this article

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Abstract

Human leukocyte antigen class I (HLA-I) genes shape our immune response against pathogens and cancer. Certain HLA-I variants can bind a wider range of peptides than others, a feature that could be favorable against a range of viral diseases. However, the implications of this phenomenon on cancer immune response are unknown. Here we quantified peptide repertoire breadth (or promiscuity) of a representative set of HLA-I alleles and found that patients with cancer who were carrying HLA-I alleles with high peptide-binding promiscuity have significantly worse prognosis after immune checkpoint inhibition. This can be explained by a reduced capacity of the immune system to discriminate tumor neopeptides from self-peptides when patients carry highly promiscuous HLA-I variants, shifting the regulation of tumor-infiltrating T cells from activation to tolerance. In summary, HLA-I peptide-binding specificity shapes neopeptide immunogenicity and the self-immunopeptidome repertoire in an antagonistic manner, and could underlie a negative trade-off between antitumor immunity and genetic susceptibility to viral infections.

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Fig. 1: Basic properties of HLA-I allelic promiscuity.
Fig. 2: High allele promiscuity is associated with more stable peptide–HLA complexes.
Fig. 3: High allele promiscuity and survival rate in patient cohorts treated with checkpoint inhibitor therapy.
Fig. 4: HLA-I allelic promiscuity and differentiation capacity between neopeptides and corresponding nonmutated peptides.
Fig. 5: Association of high HLA genotype promiscuity with an immunosuppressive cancer microenvironment.

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Data availability

Previously published clinical datasets that were reanalyzed here are available at refs. 4,23,24,25,28,54. The following databases were used in the study: the Immune Epitope Database, https://www.iedb.org/; TANTIGEN database, http://projects.met-hilab.org/tadb/; the Cancer Genome Atlas, https://portal.gdc.cancer.gov/; cBioPortal, https://www.cbioportal.org/; UniProt database, https://www.uniprot.org/. Precalculated data on T-cell dysfunction were downloaded from the TIDE server, http://tide.dfci.harvard.edu/.Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The code for HLA allele promiscuity calculation is available at https://github.com/matemanc/hla_promiscuity.

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Acknowledgements

We thank T. Lenz for earlier discussions on this topic and for providing us with the raw data on HED values of HLA-I allele pairs. We also thank the anonymous reviewers for their insightful suggestions on the manuscript, and B. Győrffy. for his suggestions on survival analysis. The results here are in part based on data generated by the TCGA Research Network (https://www.cancer.gov/tcga). We thank H. Ye for providing the HLA genotype data of TCGA patients. Similarly, we thank R. Marty, J. Font-Burgada and H. Carter for sending HLA genotype data of TCGA patients for earlier analyses. The study was supported by the following research grants: The European Research Council (H2020-ERC-2014-CoG, no. 648364 – Resistance Evolution, to C.P.); ‘Célzott Lendület’ Program of the Hungarian Academy of Sciences (LP-2017–10/2017, to C.P.); ‘Frontline’ Research Excellence Program of the National Research, Development and Innovation Office, Hungary (KKP-126506, to C.P.); EVOMER (GINOP-2.3.2–15–2016–00014, to C.P. and B.P.); MolMedEx TUMORDNS (GINOP-2.3.2–15–2016–00020 and GINOP-2.3.3–15–2016–00001, to C.P., and GINOP-2.3.2–15–2016–00026, to B.P.); and ‘Frontline’ Research Excellence Program of the National Research, Development and Innovation Office, Hungary (KKP-129814, to B.P.). The project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement 739593 (to B.P., L.K. and M.M). M.M. was supported by the New National Excellence Program of the Ministry of Human Capacities (UNKP-20-5) and by the Bolyai János Research Fellowship of the Hungarian Academy of Sciences. L.A. was supported by the New National Excellence Program of the Ministry for Innovation and Technology (UNKP-20-2). G.M.B. was supported by the New National Excellence Program of the Ministry for Innovation and Technology (UNKP-19-3). B.K. was supported by the New National Excellence Program of the Ministry for Innovation and Technology (UNKP-20-4). B.T.P. was supported by the New National Excellence Program of the Ministry for Innovation and Technology (UNKP-20-3). This research work was conducted with the support of the Szeged Scientists Academy under the sponsorship of the Hungarian Ministry of Innovation and Technology (FEIF/433-4/2020-ITM_SZERZ). We thank D. Bokor for proofreading of the manuscript.

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Author notes

  1. These authors contributed equally: Máté Manczinger, Balázs Koncz, Gergő Mihály Balogh.

Authors and Affiliations

  1. Biological Research Centre, Institute of Biochemistry, Synthetic and Systems Biology Unit, Eötvös Loránd Research Network (ELKH), Szeged, Hungary

    Máté Manczinger, Balázs Papp & Csaba Pál

  2. Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary

    Máté Manczinger, Balázs Koncz, Gergő Mihály Balogh, Benjamin Tamás Papp, Leó Asztalos & Lajos Kemény

  3. MTA-SZTE Dermatological Research Group, Eötvös Loránd Research Network (ELKH), University of Szeged, Szeged, Hungary

    Máté Manczinger & Lajos Kemény

  4. HCEMM-USZ Skin Research Group, Szeged, Hungary

    Máté Manczinger & Lajos Kemény

  5. Szeged Scientist Academy, Szeged, Hungary

    Benjamin Tamás Papp & Leó Asztalos

  6. HCEMM-BRC Metabolic Systems Biology Lab, Szeged, Hungary

    Balázs Papp

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Contributions

M.M., L.K., B.P. and C.P. undertook conceptualization. M.M., B.K., G.M.B., B.T.P. and L.A. were responsible for methodology. Software was provided by M.M. Formal analysis was performed by M.M., B.K., G.M.B., B.T.P. and L.A. Investigation was carried out by M.M., B.K., G.M.B., B.T.P., B.P. and C.P. Resources were provided by M.M., C.P. and B.P. Data curation was undertaken by M.M., B.K., G.M.B. and B.T.P. Writing of the original draft was done by M.M., B.P. and C.P. Writing, review and editing were performed by M.M., B.P. and C.P. M.M., L.K., C.P. and B.P. supervised the project. Project administration was carried out by M.M. and C.P. Funding acquisition was the responsibility of C.P. and B.P.

Corresponding authors

Correspondence to Máté Manczinger or Csaba Pál.

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The authors declare no competing interests.

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Peer review information Nature Cancer thanks Pramod Srivastava and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Calculation and reliability of HLA allele promiscuity.

a, DKL values were calculated for each peptide length group separately (see Methods for details) and (b) the weighted mean of length-specific values was determined. c, d, The number of identified peptide sequences bound by a given HLA allele is variable, which may have a significant effect on the calculated Kullback-Leibler (KL) divergence values. To test whether KL divergence can be reliably determined using a minimal peptide sequence number of 400, we first selected HLA alleles with many (more than 1000) identified peptides (n = 51 alleles). After randomly selecting 400 sequences from the repertoire of each allele, we calculated KL divergence as explained in the Methods section. We iterated this process 1000 times and compared the Pr values with those calculated using the full dataset. Panel c indicates the distribution of Spearman’s ρ values and the two-sided correlation test P values. d, An example plot indicating the relationship between promiscuity values calculated with either 400 random or all peptides bound by a given allele (n = 51 alleles). e, f, The calculated promiscuity values are independent of the reference amino acid (AA) distribution used during calculation (Q on panel a). The Pr values showed strong correlation when calculated using (e) human and viral or (f) human and bacterial reference AA distributions (Spearman’s ρ: 0.99, two-sided correlation test P < 2.2 × 10−16 for both comparisons, n = 67 alleles on both panels). On plots d to f, dashed lines indicate a smooth curve fitted using cubic smoothing spline method in R (see Methods).

Source data

Extended Data Fig. 2 Sequence diversity of peptides presented by HLA class I alleles.

Amino acid preference was determined at different peptide positions bound by each of the 67 investigated HLA class I alleles. Sequence logos were visualized for 9 amino acid long peptides only84. Alleles are ordered according to their promiscuity level. The vertical axes indicate the information content of the given position in bits. The relative sizes of characters are proportional to their frequency at the given position.

Source data

Extended Data Fig. 3 The relationship between HLA allele promiscuity and (a, b) the diversity of presented peptides or (c-e) fraction of bound neoepitopes.

a, b, We found a strong correlation between the HLA allele promiscuity and sequence diversity of peptides eluted from the surface of monoallelic cell lines in studies by (a) Abelin et al. (n = 16 alleles) and (b) Di Marco et al. (n = 15 alleles). c-e, The relationship between the promiscuity of (c) HLA-A (n = 21 alleles) (d) HLA-B (n = 31 alleles) and (e) HLA-C (n = 15 alleles) alleles and the predicted fraction of bound neopeptides. The variance of allele promiscuity is shown on panels c to e. On all plots, Spearman’s ρ and the corresponding two-sided correlation test P values are indicated and dashed red lines indicate a linear regression line.

Source data

Extended Data Fig. 4 Distribution of genotype Pr in cancer immunotherapy cohorts.

The density of genotype Pr is shown for (a) all patients (n = 316), (b) melanoma patients treated with CTLA-4 inhibitors (n = 164), (c) non-small cell lung cancer (NSCLC) patients treated with PD-1 or PD-L1 inhibitors (n = 74) and (d) melanoma patients treated with PD-1 or PD-L1 inhibitors (n = 78). Dashed black vertical lines represent the 25th and 75th percentile values, while dashed red vertical lines represent the fixed genotype Pr cutoff of 2.076 used for patient classification.

Source data

Extended Data Fig. 5 The effect of HLA promiscuity on response to ICI therapy and survival of treatment naïve patients.

a, b, Response to ICI therapy in an independent melanoma cohort. Patients were stratified into low (n = 20 patients) and high (n = 11 patients) promiscuity groups using the same cutoff as in Fig. 3. On the boxplot, the P value of a two-sided Wilcoxon’s rank-sum test is indicated. Vertical lines indicate median, boxes indicate the interquartile range (IQR), horizontal lines indicate 1st quartile - 1.5 × IQR and 3rd quartile + 1.5 × IQR. On the pie chart, the P value of a two-sided Fisher’s exact test is shown. c-h, The relationship between HLA promiscuity and progression-free survival of treatment naïve melanoma (c and f) and NSCLC patients (d, e, g and h). Similarly to previous studies5, patients were classified into low (c to e) and high (f to h) mutational burden cohorts using the median as a cutoff. In each cohort, patients were classified into low and high promiscuity groups using the median as a cutoff. Two-sided log-rank test P values are shown. The vertical axes indicate the probability of progression-free survival. LUSC: lung squamous cell carcinoma; LUAD: lung adenocarcinoma. i, j, The relationship between HLA-I genotype Pr and changes in tumor size after anti-PD-1 treatment in mismatch repair deficient (i, n = 10) and proficient (j, n = 11) cancer patients. Patients were stratified into low and high genotype Pr groups using the median value as a cutoff. P values indicate the outcome of two-sided two-sample T-tests. On boxplots, vertical lines indicate median, boxes indicate the interquartile range (IQR), horizontal lines indicate 1st quartile - 1.5 × IQR and 3rd quartile + 1.5 × IQR.

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Extended Data Fig. 6 HLA-I promiscuity is a major determinant of patient survival.

a, Carrying a single promiscuous HLA-I allele has no impact on survival. The patients in Fig. 1a to c were classified based on the number of promiscuous alleles in their genotypes. Alleles with promiscuity levels in the top quartile were considered to be promiscuous. Two-sided log-rank test P values are shown. In the second analysis, between-group differences were also tested for trend (see Methods). The vertical axes indicate the probability of survival. b, HLA alleles belonging to the B44 supergroup have low promiscuity. B44 alleles are highlighted in blue and have significantly lower promiscuity than alleles not belonging to this group (two-sided Wilcoxon’s rank-sum test P: 0.049). c-e, No significant association between HLA-I genotype Pr and (c) HLA homozygosity, (d) HLA-I evolutionary divergence (HED), (e) mutational burden. All patients from Fig. 3a to c were included in these analyses. c, There was no significant difference in genotype Pr between fully HLA heterozygous patients (n = 251) and the ones homozygous for at least one HLA-I locus (n = 65 patients). The P value for a two-sided Wilcoxon’s rank-sum test is indicated. Similarly, there was no significant association between (d) genotype Pr and mean HED (Spearman’s ρ: −0.02, two-sided correlation test P = 0.77, n = 316 patients) and (e) genotype Pr and tumor mutational burden of cancer immunotherapy patients (Spearman’s ρ: 0.02, two-sided correlation test P = 0.77, n = 316 patients). Dashed red lines indicate a smooth curve fitted using the cubic smoothing spline method in R (see Methods). On the boxplot, horizontal lines indicate median, boxes indicate the interquartile range (IQR), vertical lines indicate 1st quartile - 1.5 × IQR and 3rd quartile + 1.5 × IQR.

Extended Data Fig. 7 The relationship between genotype Pr and DAI.

a, The median and variance of DAI in different allele promiscuity groups. Alleles were grouped based on the 3rd quartile as a cutoff (n = 26 and 77 alleles in the high and low promiscuity group, respectively). High allele promiscuity was associated with lower median DAI, but it had no significant effect on the coefficient of variation. P values of two-sided Wilcoxon’s rank-sum tests are shown. On the boxplots, horizontal lines indicate median, boxes indicate the interquartile range (IQR), vertical lines indicate 1st quartile - 1.5 × IQR and 3rd quartile + 1.5 × IQR. b, The number and fraction of HLA-bound neopeptides in different DAI value ranges. The binding of 589 neopeptides to 103 HLA alleles was determined and DAI values were calculated for each neopeptide-human self-peptide pair. Alleles are shown in increasing order of promiscuity. For each allele, the fraction of peptide pairs belonging to different DAI categories is shown color-coded. c, The effect of genotype DAI on survival when determined as the average of allele-specific DAI values. Patients were stratified into groups using the 25th and 75th percentile of all values as cutoffs. A two-sided log-rank test P value is shown. Between-group differences were also tested for trend (see Methods). The vertical axis indicates the probability of survival. Higher genotype DAI value was associated with better survival. d, The maximum BLOSUM62 sequence similarity of neopeptides (n = 589) and immunogenic viral (n = 1038) peptides to the non-mutated human proteome. Only nine amino acid-long peptides were included in this analysis. P value of a two-sided Wilcoxon’s rank-sum test is indicated. On boxplots, horizontal lines indicate median, boxes indicate the interquartile range (IQR), vertical lines indicate 1st quartile - 1.5 × IQR and 3rd quartile + 1.5 × IQR.

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Extended Data Fig. 8 The effect of HLA-associated features on TIDE dysfunction score.

a, The TIDE dysfunction score is shown as a function of HLA evolutionary divergence. There was no relationship between the two variables (Spearman’s ρ: −0.01, two-sided correlation test P = 0.96, n = 35 samples). Similarly, neither HLA homozygosity (b) nor the carrier status for B44 (c, n = 17 and 18 samples in B44 positive and B44 negative groups, respectively) or B62 (d, n = 5 and 30 samples in B62 positive and negative groups, respectively) supertype alleles affected the TIDE dysfunction score. P values indicate two-sided Wilcoxon’s rank-sum test results. On plot a, the dashed red line indicates a linear regression line. On boxplots, horizontal lines indicate median, boxes indicate the interquartile range (IQR), vertical lines indicate 1st quartile - 1.5 × IQR and 3rd quartile + 1.5 × IQR.

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Manczinger, M., Koncz, B., Balogh, G.M. et al. Negative trade-off between neoantigen repertoire breadth and the specificity of HLA-I molecules shapes antitumor immunity. Nat Cancer 2, 950–961 (2021). https://doi.org/10.1038/s43018-021-00226-4

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