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。
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.