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Lack of detectable neoantigen depletion signals in the untreated cancer genome

Nature Genetics volume 51, pages 1741–1748 (2019)Cite this article

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Abstract

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 (https://doi.org/10.5281/zenodo.2621365 and https://doi.org/10.5281/zenodo.3461642, respectively).

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Acknowledgements

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 https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga. 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.).

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Authors and Affiliations

  1. Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

    Jimmy Van den Eynden & Erik Larsson

  2. Department of Human Structure and Repair, Anatomy and Embryology Unit, Ghent University, Ghent, Belgium

    Jimmy Van den Eynden

  3. Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, UK

    Jimmy Van den Eynden, Alejandro Jiménez-Sánchez, Martin L. Miller & Erik Larsson

  4. Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Alejandro Jiménez-Sánchez

Authors
  1. Jimmy Van den Eynden
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  4. Erik Larsson
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Contributions

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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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). https://doi.org/10.1038/s41588-019-0532-6

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