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Network-based machine learning approach to predict immunotherapy response in cancer patients

Author

Listed:
  • JungHo Kong

    (Pohang University of Science and Technology)

  • Doyeon Ha

    (Pohang University of Science and Technology)

  • Juhun Lee

    (Pohang University of Science and Technology)

  • Inhae Kim

    (ImmunoBiome Inc.)

  • Minhyuk Park

    (Pohang University of Science and Technology)

  • Sin-Hyeog Im

    (Pohang University of Science and Technology
    ImmunoBiome Inc.
    Yonsei University)

  • Kunyoo Shin

    (Pohang University of Science and Technology
    Yonsei University)

  • Sanguk Kim

    (Pohang University of Science and Technology
    Yonsei University)

Abstract

Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions. We curate more than 700 ICI-treated patient samples with clinical outcomes and transcriptomic data, and observe that NetBio-based predictions accurately predict ICI treatment responses in three different cancer types—melanoma, gastric cancer, and bladder cancer. Moreover, the NetBio-based prediction is superior to predictions based on other conventional ICI treatment biomarkers, such as ICI targets or tumor microenvironment-associated markers. This work presents a network-based method to effectively select immunotherapy-response-associated biomarkers that can make robust ML-based predictions for precision oncology.

Suggested Citation

  • JungHo Kong & Doyeon Ha & Juhun Lee & Inhae Kim & Minhyuk Park & Sin-Hyeog Im & Kunyoo Shin & Sanguk Kim, 2022. "Network-based machine learning approach to predict immunotherapy response in cancer patients," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31535-6
    DOI: 10.1038/s41467-022-31535-6
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    References listed on IDEAS

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