Haidong Dong, Yiyi Yan, Roxana S Dronca, Svetomir N Markovic
{"title":"一个T细胞方程作为T细胞反应的概念模型,以最大化癌症免疫治疗的疗效。","authors":"Haidong Dong, Yiyi Yan, Roxana S Dronca, Svetomir N Markovic","doi":"10.15226/2372-0948/5/1/00155","DOIUrl":null,"url":null,"abstract":"<p><p>Following antigen stimulation, the net outcomes of a T cell response are shaped by integrated signals from both positive co-stimulatory and negative regulatory molecules. Recently, the blockade of negative regulatory molecules (i.e. immune checkpoint signals) demonstrates promising therapeutic effects in treatment of human cancers, but only in a fraction of cancer patients. Since this therapy is aimed to enhance T cell responses to cancers, here we devised a conceptual model by integrating both positive and negative signals in addition to antigen stimulation that can evaluate strategies to enhance T cell responses. A digital range of adjustment of each signal is formulated in our model for prediction of a final T cell response. Our model provides a rational combination strategy for maximizing the therapeutic effects of cancer immunotherapy.</p>","PeriodicalId":90344,"journal":{"name":"SOJ immunology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5884168/pdf/nihms930903.pdf","citationCount":"0","resultStr":"{\"title\":\"A T cell equation as a conceptual model of T cell responses for maximizing the efficacy of cancer immunotherapy.\",\"authors\":\"Haidong Dong, Yiyi Yan, Roxana S Dronca, Svetomir N Markovic\",\"doi\":\"10.15226/2372-0948/5/1/00155\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Following antigen stimulation, the net outcomes of a T cell response are shaped by integrated signals from both positive co-stimulatory and negative regulatory molecules. Recently, the blockade of negative regulatory molecules (i.e. immune checkpoint signals) demonstrates promising therapeutic effects in treatment of human cancers, but only in a fraction of cancer patients. Since this therapy is aimed to enhance T cell responses to cancers, here we devised a conceptual model by integrating both positive and negative signals in addition to antigen stimulation that can evaluate strategies to enhance T cell responses. A digital range of adjustment of each signal is formulated in our model for prediction of a final T cell response. Our model provides a rational combination strategy for maximizing the therapeutic effects of cancer immunotherapy.</p>\",\"PeriodicalId\":90344,\"journal\":{\"name\":\"SOJ immunology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5884168/pdf/nihms930903.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SOJ immunology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15226/2372-0948/5/1/00155\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2017/2/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SOJ immunology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15226/2372-0948/5/1/00155","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/2/25 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
A T cell equation as a conceptual model of T cell responses for maximizing the efficacy of cancer immunotherapy.
Following antigen stimulation, the net outcomes of a T cell response are shaped by integrated signals from both positive co-stimulatory and negative regulatory molecules. Recently, the blockade of negative regulatory molecules (i.e. immune checkpoint signals) demonstrates promising therapeutic effects in treatment of human cancers, but only in a fraction of cancer patients. Since this therapy is aimed to enhance T cell responses to cancers, here we devised a conceptual model by integrating both positive and negative signals in addition to antigen stimulation that can evaluate strategies to enhance T cell responses. A digital range of adjustment of each signal is formulated in our model for prediction of a final T cell response. Our model provides a rational combination strategy for maximizing the therapeutic effects of cancer immunotherapy.