Genomics Inform.  2022 Mar;20(1):e13. 10.5808/gi.22005.

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

Affiliations
  • 1School of Integrated Software and Design, Kyung Hee Cyber University, Seoul 02447, Korea
  • 2Bioinformatics Laboratory, ELTEC College of Engineering, Ewha Womans University, Seoul 03760, Korea
  • 3Center for Convergence Research of Advanced Technologies, Ewha Womans University, Seoul 03760, Korea

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.

Keyword

gene expression; immune checkpoint inhibitors; immunotherapy; prognosis; RNA-seq; software
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