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

San Duk Yang, Hyun Seok Park

Research output: Contribution to journalArticlepeer-review

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 im-munotherapy 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 ex-tant 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.

Original languageEnglish
Article numbere13
JournalGenomics and Informatics
Volume20
Issue number1
DOIs
StatePublished - Mar 2022

Bibliographical note

Publisher Copyright:
© 2022 Korea Genome Organization.

Keywords

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

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