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Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors

Nature Biotechnology volume 41, pages 239–251 (2023)Cite this article

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Abstract

Post-translational modification (PTM) of antigens provides an additional source of specificities targeted by immune responses to tumors or pathogens, but identifying antigen PTMs and assessing their role in shaping the immunopeptidome is challenging. Here we describe the Protein Modification Integrated Search Engine (PROMISE), an antigen discovery pipeline that enables the analysis of 29 different PTM combinations from multiple clinical cohorts and cell lines. We expanded the antigen landscape, uncovering human leukocyte antigen class I binding motifs defined by specific PTMs with haplotype-specific binding preferences and revealing disease-specific modified targets, including thousands of new cancer-specific antigens that can be shared between patients and across cancer types. Furthermore, we uncovered a subset of modified peptides that are specific to cancer tissue and driven by post-translational changes that occurred in the tumor proteome. Our findings highlight principles of PTM-driven antigenicity, which may have broad implications for T cell-mediated therapies in cancer and beyond.

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Fig. 1: Computational pipeline for global search of PTMs on HLA I-bound peptides enriches identifications.
Fig. 2: PTM-driven binding preference highlighted through an unbiased search of 29 modifications.
Fig. 3: PTM-driven HLA motifs.
Fig. 4: The modified HLA I landscape uncovers hundreds of tumor-associated modified antigens, including antigens unique to tumor tissue.
Fig. 5: Modified HLA-bound peptides create cancer-specific signatures.

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Data availability

MC38 immunopeptidomics data were deposited in the PRIDE archive with ID PXD017448 and standard MaxQuant95 analysis results. All public data references and accession IDs are listed in the deposited data table in the Supplementary Information.

Code availability

PROMISE is accessible at https://github.com/merbllab/PROMISE.

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Acknowledgements

We thank the members of the Merbl lab, as well as A. Solomon, O. Bartok, G. Cafri, C. Cohen and M. Besser for scientific discussion and I. Cohen, A. Eisenberg-Lerner, J. DeMartino, C. Putterman, E. Zisman and A. Erez for critical reading of the manuscript. We would like to thank S. Meril for technical help and A. Erez for MC38 cells. Y.M. is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 677748), the I-CORE Program of the Planning and Budgeting Committee, the Israel Science Foundation (grant nos. 1775/12 and 2109/18), the University of Michigan: UM/Israel Research Partnership Weizmann and the Moross Integrated Cancer Center (MICC). This research was partially supported by the Israeli Council for Higher Education (CHE) via the Weizmann Data Science Research Center and by a research grant from Madame Olga Klein—Astrachan. A.I.N. was supported by the US National Institutes of Health grants R01-GM-094231 and U24-CA210967. Y.M. is the incumbent of the Leonard and C. Berall Career Development Chair. A.K. was partially supported by the Israeli Council for Higher Education(CHE) via Weizmann Data Science Research Center and by the research grant from Madam Olga Klein Astrachan.

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Author notes

  1. These authors contributed equally: Assaf Kacen, Aaron Javitt.

Authors and Affiliations

  1. Department of Immunology, Weizmann Institute of Science, Rehovot, Israel

    Assaf Kacen, Aaron Javitt, Matthias P. Kramer, Merav D. Shmueli, Lea Eisenbach & Yifat Merbl

  2. De Botton Institute for Protein Profiling, Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel

    David Morgenstern & Yishai Levin

  3. Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel

    Tomer Tsaban & Ora Schueler-Furman

  4. Department of Pathology, University of Michigan, Ann Arbor, MI, USA

    Guo Ci Teo, Felipe da Veiga Leprevost, Fengchao Yu & Alexey I. Nesvizhskii

  5. Faculty of Biology, Technion—Israel Institute of Technology, Haifa, Israel

    Eilon Barnea & Arie Admon

  6. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    Yardena Samuels

  7. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Alexey I. Nesvizhskii

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A.K. and A.J. led the study and performed all computational analyses unless otherwise mentioned. M.P.K. carried out sampling preparation and experiment design, T.S. performed 3D modeling, D.M. and Y.L. consulted regarding MS analyses and algorithm development. E.B. generated the HLA I peptidomics data and A.K., G.C.T., F.V.L. and F.Y. performed software development. Y.S. consulted regarding assay design, O.S.F., A.A., L.E. and A.I.N. supervised the work of respective group members, A.J., A.K. and Y.M. wrote the manuscript and Y.M. guided and supervised the study.

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Correspondence to Yifat Merbl.

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Extended data

Extended Data Fig. 1 PROMISE enrichment in PSM level.

(a) Percentage of novel PSMs with modifications that were identified through PROMISE (reds), on multiple immunopeptidomics datasets, out of the PSMs identified in standard search (gray). Bottom, pie chart of PSMs enriched by PROMISE search. Out of 73,648 modified PSMs identified in the analysis, 60,640 were IDs unique to PROMISE. (dark red) and 13,008 had improved matching score compared to the standard search (light red). (b) Distribution of hyperscores for PSMs which conflicted between Standard (gray) and PROMISE (dark red). Vertical lines mark the average score (c) Examples of 4 spectra that received a better peptide match in PROMSIE (left) compared to the standard search (right).

Extended Data Fig. 2 Monoallelic binding preferences.

(a) Volcano plot showing the site score plotted against the negative log10 transformed p value from the χ2 test with Benjamini-Hochberg multiple comparison correction. Letters indicate the motifs in Fig. 3 labeled by their panels. (b) The counts of peptides containing the indicated modification per haplotype are plotted against the counts of peptides containing unmodified amino acids. The Pearson correlation and p value for the correlation are indicated on each graph. Counts of N - Deamidation are more correlated to its mimic D - unmodified (top) than its source amino acid (N - unmodified). Counts of Q - Deamidation are more correlated to its mimic E - unmodified (bottom) than its source amino acid (Q - unmodified). The haplotypes that have canonical binding motifs that contain an E or D are labeled in pink in the graphs. (c, d) Reanalysis of monoallelic HLA data recapitulates phosphoserine peptides features as described in Adán Alpízar et al. (c) HLA-B*27:05 Phosphoserine position density (top) and the sequence logo (weblogo3) of the peptides carrying phosphoserine in position 4 RRXpS motif (bottom). (d) HLA-B*07:02 Phosphoserine position density (top) and K/RPXpS motif (bottom). (e) Rosetta FlexPepDock structural model of the interaction between the modified peptide KP(ox)LKVIFV (yellow sticks) and the MHC molecule haplotype HLA-A0201 (gray surface \ cartoon). The modified amino acid (green) creates a more stable interaction with the MHC molecule as compared to the unmodified form. The effect of the modified amino acid is shown in detail in the zoom-in picture. The proline hydroxyl group at position 2 forms a stabilizing hydrogen bond with MHC receptor residue E-87 (shown as dashed yellow line, as well as other hydrogen bonds between peptide and receptor). FlexPepDock reweighted score was calculated for the interaction between the MHC and modified or unmodified peptide (n = 5 simulations, box and whiskers indicates mean and quartiles (f) The percentage of peptides in each haplotype with the indicated modification that were not considered binders in NetMHC in their unmodified forms. This indicates that the binding is due to the alteration caused by the PTM. Modifications are sorted by their average percentage of PTM-driven binding and the haplotypes that had the highest percentages are labeled.

Extended Data Fig. 3 Mouse spectra validation.

(a) Similarity score distribution for three types of PSM pairs: (top) two PSMs event taken from the same synthetic peptide in the same sample run (light red, n = 300). We compared the PSM with the highest hyperscore to the PSM with the median hyperscore. (middle) A native PSM taken from HeLa digest standard proteomics compared to a matching synthetic spectrum (dark red, n = 261). (bottom) Similarity score between two randomly chosen PSMs (gray, n = 300). (b, c) Modified HLA peptides, identified in MC38 cell line and not in healthy mouse colon tissue or reported in the IEDB dataset, were synthesized (Peptide 2.0 Inc) and their spectra were captured using mass spectrometry. For each modified peptide, a similarity score was calculated between the synthetic spectrum and the original spectrum using R package OrgMassSpecR. For a similarity score below 80%, manual annotation was done to validate the spectra. (b) summary table (c) spectrum comparison visualization and a similarity score are created by R package OrgMassSpecR, synthesized spectrum (red) in a mirror image of the original spectrum in the dataset (blue). In case manual annotation was done, visualization is created using PDV software94 including a,y,b ions and all potential losses. For the full spectra validation list see Supplementary Information.

Extended Data Fig. 4 Expression of genes encoding for testis antigens identified in PROMISE.

(a) TCGA mRNA expression data of testis genes in four different cancer type from which patient sample or cell lines immunopeptidomics data was analyzed by PROMISE: COAD – HCT116; BRCA – PXD009738, HCC1143 and HCC1937; SKCM – PXD004894; GBM – PXD003790. (b) The expression of the parent gene from 4 modified HLA I-bound peptide identified in PROMISE is shown for TCGA expression data from BRCA primary tumor and normal tissue (Tumor n = 1097, Normal n = 114, box and whiskers indicate mean and quartiles). The parent testis gene is significantly overexpressed in the tumor tissue vs. the normal (Wilcox p values for tumor vs. adjacent abundance indicated in figures).

Extended Data Fig. 5 Human spectra validation.

(a, b) Modified HLA peptides, that were shared across multiple patients, were synthesized (Peptide 2.0 Inc) and their spectra were captured using mass spectrometry. For each modified peptide, a similarity score was calculated between the synthetic spectrum and the original spectrum using R package OrgMassSpecR. For a similarity score below 80%, manual annotation was done to validate the spectra. (a) summary table (b) spectrum comparison visualization and a similarity score are created by R package OrgMassSpecR, synthesized spectrum (red) in a mirror image of the original spectrum in the dataset (blue). In case manual annotation was done, visualization is created using PDV software94 including a,y,b ions and all potential losses. For the full spectra validation list see Supplementary Information.

Supplementary information

Supplementary Information

Supplementary pipeline description, mouse spectra validation, anchor versus middle positions per haplotype table, human spectra validation, reagents table, and deposited data table.

Reporting Summary

Supplementary Data 1

Modified peptides from PROMISE analysis (210 raw files; tab 1: modified peptides subgroup FDR; tab 2: all peptides, sorted by spectrum counts). Each MS replica is prefixed by its PRIDE dataset identifier and cancer type. For each modified peptide, PROMISE documents the following information: PSM information from MSFragger and Philosopher, and the best spectrum. The intensity value is presented for peptides that passed the FDR threshold in at least one cohort. From the prioritizing stage: the spectrum validation data, PTM localization window and PTM alternative solution, external database matches for the modification site from dbPTM12 and PhosphoSitePlus13, IEDB (ref. 45) match, CancerMine (ref. 59) annotation and testis genes hit (CT Antigens Database).

Supplementary Data 2

Modified peptides from PROMISE analysis (210 raw files) with global FDR cutoff.

Supplementary Data 3

HLA haplotype motifs from NetMHCpan are presented at the top of the page, followed by a histogram of the site distribution for each identified modification type. The histogram represents the modified amino acid frequency in each position (red) compared to the unmodified amino acid background (gray). Each histogram contains positions 1–7 from the N terminus and the C terminus and the preceding position (C-1). Overall, 9-mer epitopes are presented naturally with all their positions, positions 7 and C-1 are identical for 8-mer epitopes and peptides longer than nine residues are truncated accordingly.

Supplementary Data 4

Modified peptides from PROMISE analysis on immunopeptidome data of MC38 cells (PXD017448). Peptides are sorted by spectrum counts. For each modified peptide PROMISE documents the following information: PSM information from MSFragger and Philosopher, the intensity in each replica and the best spectrum. From the prioritizing stage: the spectrum validation data, PTM localization window and PTM alternative solution, external database matches for the modification site from dbPTM12 and PhosphoSitePlus13 and IEDB (ref. 45) match.

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Kacen, A., Javitt, A., Kramer, M.P. et al. Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors. Nat Biotechnol 41, 239–251 (2023). https://doi.org/10.1038/s41587-022-01464-2

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