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Lack of detectable neoantigen depletion signals in the untreated cancer genome


Somatic mutations can result in the formation of neoantigens, immunogenic peptides that are presented on the tumor cell surface by HLA molecules. These mutations are expected to be under negative selection pressure, but the extent of the resulting neoantigen depletion remains unclear. On the basis of HLA affinity predictions, we annotated the human genome for its translatability to HLA binding peptides and screened for reduced single nucleotide substitution rates in large genomic data sets from untreated cancers. Apparent neoantigen depletion signals become negligible when taking into consideration trinucleotide-based mutational signatures, owing to lack of power or to efficient immune evasion mechanisms that are active early during tumor evolution.

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Fig. 1: Development of an HLA-binding genomic annotation to detect somatic mutations under immunogenic selection pressure.
Fig. 2: Analysis of somatic mutation rates in HLA-binding annotated genomic regions.
Fig. 3: Association between trinucleotide substitution types and HLA-binding properties.
Fig. 4: Weak-to-absent neoantigen depletion signals after correction for trinucleotide-based mutational signature effects.
Fig. 5: An HLA genotype-specific analysis of mutated peptides confirms the absence of neoantigen depletion signals in most tumor types.

Data availability

This study is based on public data (open or controlled access) from The Cancer Genome Atlas Network. Downstream data and source code are available at zenodo ( and, respectively).


  1. 1.

    Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).

    CAS  Article  Google Scholar 

  2. 2.

    Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

    Article  Google Scholar 

  3. 3.

    Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    CAS  Article  Google Scholar 

  4. 4.

    Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    CAS  Article  Google Scholar 

  5. 5.

    Dunn, G. P., Old, L. J. & Schreiber, R. D. The three Es of cancer immunoediting. Annu. Rev. Immunol. 22, 329–360 (2004).

    CAS  Article  Google Scholar 

  6. 6.

    DuPage, M., Mazumdar, C., Schmidt, L. M., Cheung, A. F. & Jacks, T. Expression of tumour-specific antigens underlies cancer immunoediting. Nature 482, 405–409 (2012).

    CAS  Article  Google Scholar 

  7. 7.

    Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252–264 (2012).

    CAS  Article  Google Scholar 

  8. 8.

    Hodi, F. S. et al. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363, 711–723 (2010).

    CAS  Article  Google Scholar 

  9. 9.

    Sharma, P. & Allison, J. P. Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. Cell 161, 205–214 (2015).

    CAS  Article  Google Scholar 

  10. 10.

    Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).

    CAS  Article  Google Scholar 

  11. 11.

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

    CAS  Article  Google Scholar 

  12. 12.

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

    CAS  Article  Google Scholar 

  13. 13.

    Davoli, T., Uno, H., Wooten, E. C. & Elledge, S. J. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 355, eaaf8399 (2017).

    Article  Google Scholar 

  14. 14.

    Rutledge, W. C. et al. Tumor-infiltrating lymphocytes in glioblastoma are associated with specific genomic alterations and related to transcriptional class. Clin. Cancer Res. 19, 4951–4960 (2013).

    CAS  Article  Google Scholar 

  15. 15.

    Brown, S. D. et al. Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival. Genome Res. 24, 743–750 (2014).

    CAS  Article  Google Scholar 

  16. 16.

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

    CAS  Article  Google Scholar 

  17. 17.

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

    Article  Google Scholar 

  18. 18.

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

    Article  Google Scholar 

  19. 19.

    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).

    CAS  Article  Google Scholar 

  20. 20.

    Zapata, L. et al. Negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome. Genome Biol. 19, 67 (2018).

    Article  Google Scholar 

  21. 21.

    Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949.e15 (2017).

    CAS  Article  Google Scholar 

  22. 22.

    Van den Eynden, J. & Larsson, E. Mutational signatures are critical for proper estimation of purifying selection pressures in cancer somatic mutation data when using the dN/dS metric. Front. Genet. 8, 74 (2017).

    Article  Google Scholar 

  23. 23.

    Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041.e21 (2017).

    CAS  Article  Google Scholar 

  24. 24.

    Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    CAS  Article  Google Scholar 

  25. 25.

    Marty, R. et al. MHC-I genotype restricts the oncogenic mutational landscape. Cell 17, 1272–1283.e15 (2017).

    Article  Google Scholar 

  26. 26.

    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).

    CAS  Article  Google Scholar 

  27. 27.

    Chowell, D. et al. TCR contact residue hydrophobicity is a hallmark of immunogenic CD8+ T cell epitopes. Proc. Natl Acad. Sci. USA 112, E1754–E1762 (2015).

    CAS  Article  Google Scholar 

  28. 28.

    Ellrott, K. et al. Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines. Cell Syst. 6, 271–281.e7 (2018).

    CAS  Article  Google Scholar 

  29. 29.

    Turajlic, S. et al. Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis. Lancet Oncol. 18, 1009–1021 (2017).

    CAS  Article  Google Scholar 

  30. 30.

    Mandal, R. et al. Genetic diversity of tumors with mismatch repair deficiency influences anti-PD-1 immunotherapy response. Science 364, 485–491 (2019).

    CAS  Article  Google Scholar 

  31. 31.

    Stein, A. & Folprecht, G. Immunotherapy of colon cancer. Oncol. Res. Treat. 41, 282–285 (2018).

    CAS  Article  Google Scholar 

  32. 32.

    Van den Eynden, J., Basu, S. & Larsson, E. Somatic mutation patterns in hemizygous genomic regions unveil purifying selection during tumor evolution. PLoS Genet. 12, e1006506 (2016).

    Article  Google Scholar 

  33. 33.

    Weghorn, D. & Sunyaev, S. Bayesian inference of negative and positive selection in human cancers. Nat. Genet. 49, 1785–1788 (2017).

    CAS  Article  Google Scholar 

  34. 34.

    Rosenbloom, K. R. et al. The UCSC Genome Browser database: 2015 update. Nucleic Acids Res. 43, D670–D681 (2014).

    Article  Google Scholar 

  35. 35.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  Google Scholar 

  36. 36.

    Birger, C. et al. FireCloud, a scalable cloud-based platform for collaborative genome analysis: strategies for reducing and controlling costs. Preprint at bioRxiv (2017).

  37. 37.

    González-Galarza, F. F. et al. Allele frequency net 2015 update: new features for HLA epitopes, KIR and disease and HLA adverse drug reaction associations. Nucleic Acids Res. 43, D784–D788 (2015).

    Article  Google Scholar 

  38. 38.

    Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).

    CAS  Article  Google Scholar 

  39. 39.

    Nielsen, M. & Andreatta, M. NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets. Genome Med. 8, 33 (2016).

    Article  Google Scholar 

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The results published here are in whole or in part based on data generated by TCGA. Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at We are most grateful to the patients, investigators, clinicians, technical personnel, and funding bodies who contributed to TCGA, thereby making this study possible. This work was supported by grants from the Wenner-Gren Foundation (SSv2017-005 to E.L.), the Swedish Cancer Society (CAN 2015/541 to E.L.), the Knut and Alice Wallenberg Foundation (KAW 2014.0057 and KAW 2015.0144 to E.L.), the Swedish Medical Research Council (2018-02852 to E.L.), the Swedish Foundation for Strategic Research (RB13-0204 to E.L.), EMBO (STF7729 to J.V.d.E.) and Cancer Research UK (C14303/A17197 and A21141 to M.L.M.).

Author information




J.V.d.E., E.L. and M.L.M. designed and conceptualized the study. A.J.-S. provided input on study design and contributed to the interpretation of the results. J.V.d.E. was responsible for data analysis and drafted the manuscript. All authors discussed the results, edited and finalized the manuscript.

Corresponding author

Correspondence to Jimmy Van den Eynden.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Figures 1–14 and Table 1

Reporting Summary

Supplementary Table 2

Different metrics calculated for each sample and tumor type in this study. HBMR (first tab) and dNHLA/dNnonHLA (second tab for TCGA samples and third tab for different tumor types). HBMR was calculated considering the HLA-binding annotation of the prototypical HLA genotype, while dNHLA/dNnonHLA was determined using HLA affinities from mutated peptides and TCGA sample-specific HLA genotypes. Normalizations were performed under a trinucleotide substitution model. See Supplementary Table 1 for cancer type abbreviations and main Methods for other abbreviations.

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Van den Eynden, J., Jiménez-Sánchez, A., Miller, M.L. et al. Lack of detectable neoantigen depletion signals in the untreated cancer genome. Nat Genet 51, 1741–1748 (2019).

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