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Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines

Nature Machine Intelligence volume 2, pages 764–771 (2020)Cite this article

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Abstract

Tumour-specific neoantigens play a major role for developing personal vaccines in cancer immunotherapy. We propose a personalized de novo peptide sequencing workflow to identify HLA-I and HLA-II neoantigens directly and solely from mass spectrometry data. Our workflow trains a personal deep learning model on the immunopeptidome of an individual patient and then uses it to predict mutated neoantigens of that patient. This personalized learning and mass spectrometry-based approach enables comprehensive and accurate identification of neoantigens. We applied the workflow to datasets of five patients with melanoma and expanded their predicted immunopeptidomes by 5–15%. Subsequently, we discovered neoantigens of both HLA-I and HLA-II, including those with validated T-cell responses and those that had not been reported in previous studies.

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Fig. 1: Personalized de novo peptide sequencing workflow for neoantigen discovery.
Fig. 2: Performance of personalized and generic models on five patients.
Fig. 3: Immune characteristics of de novo HLA-I peptides from patient Mel-15.

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Data availability

RAW files from ref. 14 were downloaded from the ProteomeXchange repository, accession no. PXD004894. RAW files from ref. 22 were downloaded from the MassIVE repository, accession nos. MSV000084172 and MSV000080527.

Code availability

DeepNovo and the workflow are implemented in Python. The latest version is open source and available on GitHub (https://github.com/nh2tran/DeepNovoAA and https://doi.org/10.5281/zenodo.3988787).

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Acknowledgements

This work was funded in part by the NSERC grant OGP0046506, the Canada Research Chair programme and the National Key R&D Program of China 2018YFB1003202. N.H.T. was supported by the Mitacs Elevate Fellowship. The authors thank K. Pui Choi and J. Xu for critical reading of the manuscript.

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Author notes

  1. These authors contributed equally: Ngoc Hieu Tran, Rui Qiao.

Authors and Affiliations

  1. David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada

    Ngoc Hieu Tran & Ming Li

  2. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada

    Rui Qiao

  3. Bioinformatics Solutions Inc., Waterloo, Ontario, Canada

    Lei Xin, Xin Chen & Baozhen Shan

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  1. Ngoc Hieu Tran
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Contributions

M.L. and B.S. conceived the research idea. N.H.T. designed the neoantigen discovery workflow. N.H.T. and R.Q. implemented the software and analysed the results. X.C. and L.X. contributed to model design, software development and data analysis. N.H.T., M.L. and R.Q. wrote the manuscript. M.L., B.S. and L.X. supervised the research project.

Corresponding authors

Correspondence to Baozhen Shan or Ming Li.

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Competing interests

The workflow in Fig. 1 is the subject of an application for a patent (as a USPTO provisional application by Bioinformatics Solutions Inc., Waterloo, Canada). The authors are named inventors in the patent application. L.X., X.C. and B.S. are employees of Bioinformatics Solutions.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Length distributions of HLA de novo and database peptides.

a, Mel-5 HLA-I; b, Mel-8 HLA-I; c, Mel-12 HLA-I; d, Mel-16 HLA-I; e, Mel-15 HLA-II; f, Mel-16 HLA-II.

Extended Data Fig. 2 Binding affinity of de novo and database HLA-I peptides.

Dashed lines indicate default thresholds of weak-binding (rank 2.0%) and strong-binding (rank 0.5%) of NetMHCpan.

Extended Data Fig. 3

Immunogenicity of de novo and database HLA-I peptides.

Supplementary information

Supplementary Information

Supplementary Figs. 1–6.

Reporting Summary

Supplementary Table 1

Step-by-step results of our personalized workflow for neoantigen discovery on five patients Mel-5, Mel-8, Mel-12, Mel-15 and Mel-16 from ref. 14. S2: Number of de-novo and database HLA peptides identified at 1% FDR. S3: Peptide-spectrum matches of de-novo HLA peptides at 1% FDR. S4: Performance of the personalized models versus the generic model that was trained on a dataset of 95 HLA-I alleles by Sarkizova and others22. S5: Criteria to select candidate neoantigens from de-novo HLA peptides.

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Tran, N.H., Qiao, R., Xin, L. et al. Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines. Nat Mach Intell 2, 764–771 (2020). https://doi.org/10.1038/s42256-020-00260-4

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