TcellInflamedDetector: an R package to distinguish T cell inflamed tumor types from non–T cell inflamed tumor types

San-Duk Yang, Hyun-Seok Park
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Abstract

A major issue in the use of immune checkpoint inhibitors is their lack of efficacy in many patients. Previous studies have reported that the T cell inflamed signature can help predict the response to immunotherapy. Thus, many studies have investigated mechanisms of immunotherapy resistance by defining the tumor microenvironment based on T cell inflamed and non–T cell inflamed subsets. Although methods of calculating T cell inflamed subsets have been developed, valid screening tools for distinguishing T cell inflamed from non–T cell inflamed subsets using gene expression data are still needed, since general researchers who are unfamiliar with the details of the equations can experience difficulties using extant scoring formulas to conduct analyses. Thus, we introduce TcellInflamedDetector, an R package for distinguishing T cell inflamed from non–T cell inflamed samples using cancer gene expression data via bulk RNA sequencing.
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TcellInflamedDetector:一个R包,用于区分T细胞炎症肿瘤类型和非T细胞炎症肿瘤类型
使用免疫检查点抑制剂的一个主要问题是它们对许多患者缺乏疗效。先前的研究报道,T细胞发炎的特征可以帮助预测免疫疗法的反应。因此,许多研究通过定义基于T细胞炎症和非T细胞炎症亚群的肿瘤微环境来研究免疫疗法耐药性的机制。尽管已经开发出计算T细胞炎症亚群的方法,但仍然需要使用基因表达数据来区分炎症T细胞和非炎症T细胞亚群的有效筛选工具,因为不熟悉方程细节的普通研究人员在使用现有的评分公式进行分析时可能会遇到困难。因此,我们引入了TcellInflamedDetector,这是一种R包,用于通过批量RNA测序,使用癌症基因表达数据区分发炎的T细胞和非发炎的T淋巴细胞样本。
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