Jiamin Chen, Daniel Rebibo, Jianquan Cao, Simon Yat-Man Mok, Neel Patel, Po-Cheng Tseng, Zhenghao Zhang, Kevin Y Yip
{"title":"ICI 疗效信息门户:免疫检查点抑制剂应答者预测知识库。","authors":"Jiamin Chen, Daniel Rebibo, Jianquan Cao, Simon Yat-Man Mok, Neel Patel, Po-Cheng Tseng, Zhenghao Zhang, Kevin Y Yip","doi":"10.1093/narcan/zcad012","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18879,"journal":{"name":"NAR Cancer","volume":"5 1","pages":"zcad012"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9984987/pdf/","citationCount":"0","resultStr":"{\"title\":\"ICI efficacy information portal: a knowledgebase for responder prediction to immune checkpoint inhibitors.\",\"authors\":\"Jiamin Chen, Daniel Rebibo, Jianquan Cao, Simon Yat-Man Mok, Neel Patel, Po-Cheng Tseng, Zhenghao Zhang, Kevin Y Yip\",\"doi\":\"10.1093/narcan/zcad012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":18879,\"journal\":{\"name\":\"NAR Cancer\",\"volume\":\"5 1\",\"pages\":\"zcad012\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9984987/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NAR Cancer\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/narcan/zcad012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/3/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NAR Cancer","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/narcan/zcad012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/3/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
ICI efficacy information portal: a knowledgebase for responder prediction to immune checkpoint inhibitors.
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.