Abstract：
BACKGROUND: 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.
OBJECTIVE: 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.
CONCLUSIONS: 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.
OBJECTIVE: 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.
CONCLUSIONS: 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.
摘要：
背景：免疫检查点抑制剂（ICIs）是针对各种癌症类型的有效且精确的疗法，显着提高对他们有积极反应的患者的生存率。然而,只有少数患者受益于ICI治疗。
目的：在治疗前确定ICI反应者可以极大地节省医疗资源，尽量减少潜在的药物副作用，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。
方法：提出的深度学习框架利用图神经网络和生物通路知识。我们训练和测试我们的方法使用ICI治疗患者的数据从几个临床试验涵盖黑色素瘤，胃癌,和膀胱癌。
结果：我们的结果表明，该预测模型优于当前最先进的方法和基于肿瘤微环境的预测因子。此外,该模型量化了路径的重要性，途径相互作用，和预测中的基因。已经开发并部署了IRnet的Web服务器，在https://irnet为用户提供广泛的可访问性。密苏里州.edu.
结论：IRnet是预测患者对免疫治疗反应的竞争性工具，特别是ICIs。它的可解释性也为ICI治疗的潜在机制提供了有价值的见解。
目的：在治疗前确定ICI反应者可以极大地节省医疗资源，尽量减少潜在的药物副作用，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。
方法：提出的深度学习框架利用图神经网络和生物通路知识。我们训练和测试我们的方法使用ICI治疗患者的数据从几个临床试验涵盖黑色素瘤，胃癌,和膀胱癌。
结果：我们的结果表明，该预测模型优于当前最先进的方法和基于肿瘤微环境的预测因子。此外,该模型量化了路径的重要性，途径相互作用，和预测中的基因。已经开发并部署了IRnet的Web服务器，在https://irnet为用户提供广泛的可访问性。密苏里州.edu.
结论：IRnet是预测患者对免疫治疗反应的竞争性工具，特别是ICIs。它的可解释性也为ICI治疗的潜在机制提供了有价值的见解。
