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Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

Author

Listed:
  • Bo Wen

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Kai Li

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Yun Zhang

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Bing Zhang

    (Baylor College of Medicine
    Baylor College of Medicine)

Abstract

Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens.

Suggested Citation

  • Bo Wen & Kai Li & Yun Zhang & Bing Zhang, 2020. "Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15456-w
    DOI: 10.1038/s41467-020-15456-w
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    Cited by:

    1. Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Heta Desai & Katrina H. Andrews & Kristina V. Bergersen & Samuel Ofori & Fengchao Yu & Flowreen Shikwana & Mark A. Arbing & Lisa M. Boatner & Miranda Villanueva & Nicholas Ung & Elaine F. Reed & Alexe, 2024. "Chemoproteogenomic stratification of the missense variant cysteinome," Nature Communications, Nature, vol. 15(1), pages 1-24, December.
    3. Samuel Rivero-Hinojosa & Melanie Grant & Aswini Panigrahi & Huizhen Zhang & Veronika Caisova & Catherine M. Bollard & Brian R. Rood, 2021. "Proteogenomic discovery of neoantigens facilitates personalized multi-antigen targeted T cell immunotherapy for brain tumors," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    4. Bo Wen & Bing Zhang, 2023. "PepQuery2 democratizes public MS proteomics data for rapid peptide searching," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    5. Wen-Feng Zeng & Xie-Xuan Zhou & Sander Willems & Constantin Ammar & Maria Wahle & Isabell Bludau & Eugenia Voytik & Maximillian T. Strauss & Matthias Mann, 2022. "AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    6. Haodong Xu & Ruifeng Hu & Xianjun Dong & Lan Kuang & Wenchao Zhang & Chao Tu & Zhihong Li & Zhongming Zhao, 2024. "ImmuneApp for HLA-I epitope prediction and immunopeptidome analysis," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    7. Kevin L. Yang & Fengchao Yu & Guo Ci Teo & Kai Li & Vadim Demichev & Markus Ralser & Alexey I. Nesvizhskii, 2023. "MSBooster: improving peptide identification rates using deep learning-based features," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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