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

JungHo Kong, Doyeon Ha, Juhun Lee, Inhae Kim, Minhyuk Park, Sin-Hyeog Im, Kunyoo Shin and Sanguk Kim ()
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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
Kunyoo Shin: Pohang University of Science and Technology
Sanguk Kim: Pohang University of Science and Technology

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-31535-6

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