IRnet:利用路径知识图神经网络预测免疫疗法反应。

Yuexu Jiang, Manish Sridhar Immadi, Duolin Wang, Shuai Zeng, Yen On Chan, Jing Zhou, Dong Xu, Trupti Joshi
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引用次数: 0

摘要

简介免疫检查点抑制剂(ICIs)是治疗各种癌症的有效而精确的疗法,可显著提高对其反应积极的患者的生存率。然而,只有少数患者能从 ICI 治疗中获益:目标:在治疗前识别 ICI 反应者可大大节约医疗资源,最大限度地减少潜在的药物副作用,并加快寻找替代疗法。我们的目标是引入一种新的深度学习方法来预测癌症患者的 ICI 治疗反应:我们提出的深度学习框架利用了图神经网络和生物通路知识。我们使用来自黑色素瘤、胃癌和膀胱癌等多项临床试验的 ICI 治疗患者数据,对我们的方法进行了训练和测试:我们的结果表明,该预测模型优于目前最先进的方法和基于肿瘤微环境的预测方法。此外,该模型还量化了通路、通路相互作用和基因在预测中的重要性。IRnet 的网络服务器已经开发和部署完毕,用户可以通过 https://irnet.missouri.edu.Conclusion 广泛访问:IRnet 是预测病人对免疫疗法(特别是 ICIs)反应的一种有竞争力的工具。它的可解释性还为了解 ICI 治疗的基本机制提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network.

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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