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

Yuexu Jiang1, Manish Sridhar Immadi2, Duolin Wang1

  • 1Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, MO, USA; Christopher S. Bond Life Sciences Center, University of Missouri-Columbia, Columbia, MO, USA.

Journal of Advanced Research
|August 3, 2024
PubMed
Summary
This summary is machine-generated.

A new deep learning model, IRnet, accurately predicts which cancer patients will respond to immune checkpoint inhibitors (ICIs). This tool aids in conserving resources and identifying effective immunotherapy treatments.

Keywords:
Biological pathwayCheckpoint inhibitorsGraph neural networkImmunotherapy responseMachine learningModel interpretability

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Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Immune checkpoint inhibitors (ICIs) offer significant survival benefits for cancer patients.
  • However, only a fraction of patients respond to ICIs, necessitating predictive biomarkers.

Purpose of the Study:

  • To develop a novel deep learning (DL) method for predicting patient response to ICIs.
  • To improve resource allocation and minimize adverse effects by identifying responders pre-treatment.

Main Methods:

  • A DL framework integrating graph neural networks and biological pathway knowledge was developed.
  • The model was trained and validated on clinical trial data from melanoma, gastric, and bladder cancer patients treated with ICIs.

Main Results:

  • The IRnet model demonstrated superior performance compared to existing state-of-the-art and tumor microenvironment-based predictors.
  • The model provides interpretability by quantifying the importance of genes, pathways, and their interactions.
  • A publicly accessible web server (https://irnet.missouri.edu) was deployed.

Conclusions:

  • IRnet is an effective tool for predicting patient response to immune checkpoint inhibitor therapy.
  • The model's interpretability offers insights into the mechanisms of ICI treatment efficacy.