School of Life Sciences, Institute of Precision Medicine, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060, China.
State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, China.
Nucleic Acids Res. 2022 Jul 5;50(W1):W761-W767. doi: 10.1093/nar/gkac374.
Immune checkpoint blockade (ICB) therapy has been successfully applied to clinically therapeutics in multiple cancers, but its efficacy varies greatly among different patients and cancer types. Therefore, the construction of gene signatures to identify patients who could benefit from ICB therapy is particularly important for precision cancer treatment. However, due to the lack of a user-friendly platform, the construction of such gene signatures is a great challenge for clinical investigators who have limited programming skills. In light of this challenge, we developed a web server called Tumor Immunotherapy Response Signature Finder(TIRSF) for the construction of gene signatures to predict ICB therapy response in cancer patients. TIRSF consists of three functional modules. The first module is the Signature Discovery module which provides signature construction and performance evaluation functionalities. The second is a module for response prediction based on the TIRSF signatures, which enables response prediction and prognostic analysis of immunotherapy samples. The last is a module for response prediction based on existing signatures. This module currently integrates 24 published signatures for ICB therapy response prediction. Together, all of above features can be freely accessed at http://tirsf.renlab.org/.
免疫检查点阻断（ICB）疗法已成功应用于多种癌症的临床治疗，但在不同患者和癌症类型中的疗效差异很大。因此，构建基因特征来识别可能从 ICB 治疗中受益的患者对于精准癌症治疗尤为重要。然而，由于缺乏用户友好的平台，对于编程技能有限的临床研究人员来说，构建此类基因特征是一项巨大的挑战。针对这一挑战，我们开发了一个名为 Tumor Immunotherapy Response Signature Finder（TIRSF）的网络服务器，用于构建基因特征以预测癌症患者对 ICB 治疗的反应。TIRSF 包含三个功能模块。第一个模块是 Signature Discovery 模块，提供特征构建和性能评估功能。第二个是基于 TIRSF 特征的响应预测模块，可实现免疫治疗样本的响应预测和预后分析。最后一个是基于现有特征的响应预测模块。该模块目前集成了 24 个用于 ICB 治疗反应预测的已发表特征。总之，所有上述功能均可在 http://tirsf.renlab.org/ 免费访问。
