期刊
NATURE MACHINE INTELLIGENCE
卷 2, 期 12, 页码 -出版社
SPRINGERNATURE
DOI: 10.1038/s42256-020-00260-4
关键词
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资金
- NSERC [OGP0046506]
- Canada Research Chair programme
- National Key R&D Program of China [2018YFB1003202]
- Mitacs Elevate Fellowship
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. Neoantigens play a critical role in cancer immunotherapy. Tran et al. show how training a personalized deep learning model for each individual patient can improve the accuracy and identification rate of mutated neoantigens.
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