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MHC II immunogenicity shapes the neoepitope landscape in human tumors

Nature Genetics volume 55, pages 221–231 (2023)Cite this article

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Abstract

Despite advances in predicting physical peptide-major histocompatibility complex I (pMHC I) binding, it remains challenging to identify functionally immunogenic neoepitopes, especially for MHC II. By using the results of >36,000 immunogenicity assay, we developed a method to identify pMHC whose structural alignment facilitates T cell reaction. Our method predicted neoepitopes for MHC II and MHC I that were responsive to checkpoint blockade when applied to >1,200 samples of various tumor types. To investigate selection by spontaneous immunity at the single epitope level, we analyzed the frequency spectrum of >25 million mutations in >9,000 treatment-naive tumors with >100 immune phenotypes. MHC II immunogenicity specifically lowered variant frequencies in tumors under high immune pressure, particularly with high TCR clonality and MHC II expression. A similar trend was shown for MHC I neoepitopes, but only in particular tissue types. In summary, we report immune selection imposed by MHC II-restricted natural or therapeutic T cell reactivity.

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Fig. 1: Construction and validation of DeepNeo-TCR model.
Fig. 2: Association of predicted neoantigen load with clinical benefits in multiple ICB cohorts.
Fig. 3: Response of predicted neoantigens to ICB treatment.
Fig. 4: Magnitude of depletion of immunogenic MHC II neoantigens according to the strength of immune phenotypes.
Fig. 5: Magnitude of depletion of nonimmunogenic MHC II neoantigens according to the strength of immune phenotypes.
Fig. 6: Magnitude of neoantigen depletion according to the predicted immunogenicity of mutations.
Fig. 7: Relative contribution of the predicted immunogenicity of mutations to neoantigen depletion within samples.

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Data availability

Raw sequencing data of cohorts that received immune checkpoint blockade (ICB) therapy were obtained from each publication. More specifically, data were retrieved with the following accession numbers: Rizvi et al.’s dataset16(phs000980), Van Allen et al.’s dataset18(phs000452), Riaz et al.’s dataset19(SRP094781), Snyder et al.’s dataset20(phs001041), Hugo et al.’s dataset21(SRP067938, SRP090294), Roh et al.’s dataset22(phs001425), Sade-Feldman et al.’s dataset23,24(phs001427, phs001680), Mariathasan et al.’s dataset26(EGAS00001002556) Miao et al.’s dataset28(phs001493). The Cancer Genome Atlas data were obtained from the GDC Data Portal (https://portal.gdc.cancer.gov/).

The raw exome sequencing data for our lung cancer ICB cohort is available at the European Genome-Phenome Archive (EGAD00001009101) under controlled access because it contains germline data as sensitive data. Somatic mutations called from the data is available at European Variation Archive (EVA) under PRJEB57400.

Code availability

Codes for implementing prediction of peptide-MHC binding and prediction of immunogenicity are deposited at https://github.com/kaistomics/DeepNeo.

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Acknowledgements

This work was supported by the Bio and Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (NRF-2019M3A9B6064688, NRF-2017M3A9A7050612, and NRF-2022R1A4A5028131). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea

    Jeong Yeon Kim, Kyeonghui Kim, Changhwan Sung, Jinhyeon An, Hyoeun Bang, Jin Ok Yang & Jung Kyoon Choi

  2. Penta Medix Co., Ltd., Seongnam-si, Republic of Korea

    Jeong Yeon Kim, Hyoeun Bang, Hyungjoo Kim, Seung-Jae Noh, Inkyung Shin, Dae-Yeon Cho & Jung Kyoon Choi

  3. Division of Hematology–Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Hongui Cha & Se-Hoon Lee

  4. Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea

    Changhwan Sung

  5. Department of Life Science, Hanyang University, Seoul, Republic of Korea

    Hyungjoo Kim & Incheol Shin

  6. Korea Bioinformation Center, KRIBB, Daejeon, Republic of Korea

    Jin Ok Yang

  7. Department of Biomedical Sciences, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea

    Suhwan Chang

  8. Natural Science Institute, Hanyang University, Seoul, Republic of Korea

    Incheol Shin

  9. Department of Health Sciences and Technology, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Se-Hoon Lee

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  1. Jeong Yeon Kim
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Contributions

J.Y.K. performed all data analyses and wrote the manuscript. H.C. generated and managed the cohort data. K.K., C.H.S., J.H.A., H.B. and J.O.Y. participated in the data analysis. H.K., S.C. and I.S. conducted the mouse experiments. S.-J.N., I.S. and D.-Y.C. designed the mouse experiments and analyzed the data. D.-Y.C. supervised the mouse experiments. S.-H.L. supervised the cohort analysis. J.K.C. conceived the whole study.

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Correspondence to Dae-Yeon Cho, Se-Hoon Lee or Jung Kyoon Choi.

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Extended data

Extended Data Fig. 1 Overall pipeline of data processing and scoring.

a, Overview of the pipeline used to process ICB cohorts and TCGA pan-cancer samples. Colored steps indicate that they were only used in the processing of ICB cohorts. b, Schemes for scoring pMHC complexes. Immunogenic neoepitope load per sample was calculated as the unique number of mutant peptides regardless of HLA genes. Neoepitope immunogenicity score per mutation was calculated as the mean or maximum of all pMHC pairs derived from the mutation.

Extended Data Fig. 2 PR curve of DeepNeo models.

a, PR (precision recall) curve of DeepNeo-mhc (green) and DeepNeo-tcr (blue) for MHC I (left) and MHC II (right). b, F1 score of DeepNeo and other tools in predicting the PRIME dataset of MHC I epitope immunogenicity. c, PR curve and F1 score of DeepNeo and other tools in predicting MHC II epitopes derived from external data (n = 812). d, ROC (receiver operating characteristic) curve of DeepNeo and other tools in predicting MHC II epitopes based on the HLA-DRB restricted subset of data used in c (n = 227).

Extended Data Fig. 3 ELISpot results for individual DeepNeoII(+) peptides.

For each murine tumor model, DeepNeoII(+) epitopes that passed thresholds were tested for their ability to stimulate immune responses in vivo. Colored dots indicate individual mice in which the epitopes were tested. Negative control is marked as the red dashed line, and positive control is plotted in the last column. The experiments were individually plotted with a, the first set of experiments on EMT6, n = 29 peptides examined over n = 188 independent experiments b, the second set of experiments on EMT6, which was conducted for n = 19 peptides examined over n = 114 independent experiments c, LLC1, which was conducted for n = 12 peptides examined over n = 72 independent experiments and d, B16F10, which was conducted for n = 15 peptides over n = 90 independent experiments. In all boxplots, center bar represents median box represent 25th and 75th percentile, and whiskers represent furthest outlier ≤1.5× the interquartile range from the box. Outliers of the boxplots are replaced with the actual data point represented as colored dots.

Extended Data Fig. 4 Correlation of DeepNeo predictions with TCR scores in TCGA samples.

a, Comparison of the TCR (T cell receptor) signature score between groups of high- and low- immunogenic neoantigen load as predicted by DeepNeo-MHC I and -MHC II. The difference between the two groups is calculated by the two-sided Welch t test. The boxplot’s box represents 25th and 75th percentile with center bar as median value and whiskers represent furthest outlier ≤1.5× the interquartile range from the box. b-c, Correlation of the TCR signature score with b, immunogenic neoantigen load and c, TMB across TCGA cancer types. For the TMB analyses, we used matching TCGA samples with available MHC I or II genotypes. The size and color of dots represent the number of samples per cancer type used in the analysis. The shaded area represents 95 percent confidence interval for fitted line.

Extended Data Fig. 5 Survival analysis of our lung cancer cohort.

The samples of our lung cancer cohort (n = 335) were divided into high- and low- neoantigen load group, and the progression free survival of the two groups was compared. P values were generated from the Kaplan-Meier estimation.

Extended Data Fig. 6 Forest plots for ICB cohorts with >100 samples.

The hazard ratio predicted by each neoantigen prediction method was plotted for a, our cohort in lung cancer, b, Mariathasan cohort in bladder cancer, and c, Van Allen cohort in melanoma. High immunogenic neoantigen load predicted by DeepNeo class II demonstrated the lowest hazard ratio, indicating the highest correlation of predicted neoantigen load with the survival outcome.

Extended Data Fig. 7 Schematics of the between-group and within-group analysis of TCGA untreated samples.

In the between-group analysis, the samples are divided into high-and low-immune groups, and the corresponding group’s VAF difference was calculated for immunogenic and nonimmunogenic mutations. For the within-group analysis, VAF differences between immunogenic and nonimmunogenic mutations were calculated for each high-and low-immune group.

Extended Data Fig. 8 Magnitude of neoantigen depletion according to the predicted immunogenicity of class II neoantigens.

According to contrasting terms, namely, a, TCR richness versus TCR evenness and b, Th1 cells versus Th2 cells, the distributions of VAFs comparing immunogenic and nonimmunogenic mutations were plotted for DeepNeo and NetMHCpan. The differential distribution between the two types of variants were estimated using Kolmogorov’s D statistic.

Supplementary information

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Supplementary Figs. 1–4.

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Supplementary Tables

Supplementary Table 1. Datasets used for DeepNeo-TCR model, Supplementary Table 2. Data used for DeepNeo-TCR MHC I model, Supplementary Table 3. Data used for DeepNeo-TCR MHC II model, Supplementary Table 4. Mouse ELISpot results, Supplementary Table 5. List of immune phenotype and proliferation terms for TCGA samples, Supplementary Table 6. MHC II genotype of TCGA samples.

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Kim, J.Y., Cha, H., Kim, K. et al. MHC II immunogenicity shapes the neoepitope landscape in human tumors. Nat Genet 55, 221–231 (2023). https://doi.org/10.1038/s41588-022-01273-y

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