Chen Jiamin, Rebibo Daniel, Cao Jianquan, Mok Simon Yat-Man, Patel Neel, Tseng Po-Cheng, Zhang Zhenghao, Yip Kevin Y
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR.
Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.
NAR Cancer. 2023 Mar 3;5(1):zcad012. doi: 10.1093/narcan/zcad012. eCollection 2023 Mar.
Immune checkpoint inhibitors (ICIs) have led to durable responses in cancer patients, yet their efficacy varies significantly across cancer types and patients. To stratify patients based on their potential clinical benefits, there have been substantial research efforts in identifying biomarkers and computational models that can predict the efficacy of ICIs, and it has become difficult to keep track of all of them. It is also difficult to compare findings of different studies since they involve different cancer types, ICIs, and various other details. To make it easy to access the latest information about ICI efficacy, we have developed a knowledgebase and a corresponding web-based portal (https://iciefficacy.org/). Our knowledgebase systematically records information about latest publications related to ICI efficacy, predictors proposed, and datasets used to test them. All information recorded is checked carefully by a manual curation process. The web-based portal provides functions to browse, search, filter, and sort the information. Digests of method details are provided based on the original descriptions in the publications. Evaluation results of the effectiveness of the predictors reported in the publications are summarized for quick overviews. Overall, our resource provides centralized access to the burst of information produced by the vibrant research on ICI efficacy.
免疫检查点抑制剂（ICI）已在癌症患者中产生了持久的反应，但其疗效在不同癌症类型和患者之间存在显著差异。为了根据患者的潜在临床获益进行分层，人们在识别能够预测ICI疗效的生物标志物和计算模型方面付出了大量研究努力，以至于很难追踪所有这些研究。由于不同研究涉及不同的癌症类型、ICI以及各种其他细节，因此也难以比较不同研究的结果。为了便于获取有关ICI疗效的最新信息，我们开发了一个知识库和一个相应的基于网络的门户网站（https://iciefficacy.org/）。我们的知识库系统地记录了有关与ICI疗效相关的最新出版物、提出的预测指标以及用于测试这些指标的数据集的信息。通过人工整理过程对记录的所有信息进行仔细检查。基于网络的门户网站提供了浏览、搜索、筛选和分类信息的功能。根据出版物中的原始描述提供方法细节摘要。总结了出版物中报道的预测指标有效性的评估结果，以便快速概览。总体而言，我们的资源提供了对ICI疗效活跃研究产生的大量信息的集中访问。
