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Cited by 12Cancer Immunotherapy and BiomarkersBioinformatics and Genomic NetworksComputational Drug Discovery Methods

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IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network

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Jiang, Yuexu, Immadi, Manish Sridhar, Wang, Duolin, Zeng, Shuai, On Chan, Yen, Zhou, Jing, Xu, Dong, Joshi, Trupti·2025·Journal of Advanced Research
DOI 10.1016/j.jare.2024.07.036PMC PMC12147637
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Abstract

INTRODUCTION: Immune checkpoint inhibitors (ICIs) are potent and precise therapies for various cancer types, significantly improving survival rates in patients who respond positively to them. However, only a minority of patients benefit from ICI treatments. OBJECTIVES: Identifying ICI responders before treatment could greatly conserve medical resources, minimize potential drug side effects, and expedite the search for alternative therapies. Our goal is to introduce a novel deep-learning method to predict ICI treatment responses in cancer patients. METHODS: The proposed deep-learning framework leverages graph neural network and biological pathway knowledge. We trained and tested our method using ICI-treated patients' data from several clinical trials covering melanoma, gastric cancer, and bladder cancer. RESULTS: Our results demonstrate that this predictive model outperforms current state-of-the-art methods and tumor microenvironment-based predictors. Additionally, the model quantifies the importance of pathways, pathway interactions, and genes in its predictions. A web server for IRnet has been developed and deployed, providing broad accessibility to users at https://irnet.missouri.edu. CONCLUSION: IRnet is a competitive tool for predicting patient responses to immunotherapy, specifically ICIs. Its interpretability also offers valuable insights into the mechanisms underlying ICI treatments.

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Metadata

Authors
Jiang, Y., Immadi, M. S., Wang, D., Zeng, S., On Chan, Y., Zhou, J., Xu, D., & Joshi, T.
Affiliations
University of Missouri (US)
Corresponding author
Joshi, Trupti
ORCID
Venue
Journal of Advanced Research
Publisher
Elsevier BV
Published
2025-06
Volume
72
Pages
319-331
ISSN
  • 2090-1224 (ISSN-L)
  • 2090-1232
DOI
10.1016/j.jare.2024.07.036
PMC
PMC12147637
OpenAlex
W4401317914
Cited by
12
References
7
Citations by year
2026: 6 · 2025: 6
FWCI
2.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Advanced Research (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institutes of Health
  • Missouri Department of Health and Senior Services
  • National Science Foundation
Topics
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Keywords
immune checkpoint inhibitors, immunotherapy response prediction, graph neural networks, melanoma, gastric cancer, bladder cancer, tumor microenvironment, interpretability, web server
MeSH terms
Deep Learning, Immune Checkpoint Inhibitors, Graph Neural Networks, Humans, Immunotherapy, Neoplasms, Neural Networks, Computer, Treatment Outcome, Tumor Microenvironment

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