JungHo Kong1,4, Doyeon Ha1,4, Juhun Lee1,4, Inhae Kim2, Minhyuk Park1, Sin-Hyeog Im1,2,3, Kunyoo Shin1,3 & Sanguk Kim1,3,*
1Department of Life Sciences, Pohang University of Science and Technology, Pohang 37673, Korea. 2ImmunoBiome Inc., Pohang 37666, Korea. 3Institute of Convergence Science, Yonsei University, Seoul 03722, Korea. 4These authors contributed equally: JungHo Kong, Doyeon Ha, Juhun Lee.
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.