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Welcome to the Tcellinflamed wiki!
A major issue in immune checkpoint inhibitor is a lack of efficacy. Previous studies reported that T cell inflamed signature can help to predict immunotherapy response. Many studies have been conducted to investigate mechanisms of immunotherapy resistance by defining tumor microenvironment as T cell inflamed, Intermediate and non-T cell inflamed subsets. Although calculation method of T cell inflamed subsets exists, valid screening tools for distinguishing T cell inflamed from non-T cell inflamed subsets using gene expression data are still needed. Previous studies suggested that scoring method which distinguish non-T cell inflamed, T cell inflamed and intermediate based on gene expression data(Luke et al., 2019)(Spranger et al., 2016). However, general researchers who are unfamiliar with detailed calculation of equation can have analysis difficulties in using scoring formula. In this reason, we recently developed R package that predict T cell inflamed tumors with RNA seq expression data. TcellInflamedDetector is the first package to detect T cell inflamed subset to help interpret cancer gene expression data. This package will be beneficial to distinguish T cell inflamed hot tumor and Non-T cell inflamed cold tumor to optimize selection of right patients who will benefit from ICIs. This package will be beneficial to distinguish T cell inflamed hot tumor and Non-T cell inflamed cold tumor to optimize selection of right patients who will benefit from ICIs.
Established gene signatures were referenced by Gajewski T cell-inflamed signature, IFN-gamma related signature, T cell effector signature and Immune cytolytic activity signature (Luke et al., 2019)(Spranger et al., 2016).
Users can create TcellInflamedDetector folder in the users' local Directory(for example, C directory) and download these files from bottom URLs. inputfile_example.csv can be replaced by users RNA-seq log CPM expression data file(csv format).
TcellInflamedDetector.tar.gz : https://github.com/sandukyang/Tcellinflamed/blob/main/TcellInflamedDetector.tar.gz
inputfile_example.csv : https://github.com/sandukyang/Tcellinflamed/blob/main/inputfile_example.csv
CTL.csv : https://github.com/sandukyang/Tcellinflamed/blob/main/CTL.csv
library(devtools)
install.packages("C:\\TcellInflamedDetector\\TcellInflamedDetector.tar.gz",repos=NULL, type="source")
library(TcellInflamedDetector)
setwd("C:\\TcellinflamedDetector\")
Inputfile<-read.csv("C:\\TcellinflamedDetector\\inputfile_example.csv")
CTLfile<-read.csv("C:\\TcellinflamedDetector\\CTL.csv")
TcellInflamedDetector(Inputfile,CTLfile)
Users can simply use log CPM file(csv) which contains T cell relative Genes(row name) and it's expressions
Example file is available in the https://github.com/sandukyang/Tcellinflamed/blob/main/inputfile_example.csv
Using nrow(inputfile) code, users can easily generate the result files. or users can add or edit testing genes (T cell relative genes).
San-Duk Yang, Hyun-Seok Park
TcellInflamedDetector(inputfile_example.csv,CTLfile)
Jiang,P. et al. (2018) Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response, Nat Med, 24, 1550–1558.
Luke,J.J. et al. (2019) WNT/β-catenin pathway activation correlates with immune exclusion across human cancers, Clin Cancer Res, 25(10), 3074–3083.
Spranger,S. et al. (2016) Density of immunogenic antigens does not explain the presence or absence of the T-cell–inflamed tumor microenvironment in
melanoma, PNAS, 113 (48), E7759-E7768.
Ayers,M. et al. (2017) IFNgamma-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest,127, 2930–40.
Spranger,S. et al. (2019) Melanoma-intrinsic beta-catenin signalling prevents anti-tumour immunity. Nature,523, 231–5.
Herbst,R.S. et al. (2014) Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature, 515, 563–7.
Rooney,M.S. et al. (2015) Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell, 160, 48–61.
Shah, S. et al. (2016) Clinical Response of a Patient to Anti–PD-1 Immunotherapy and the Immune Landscape of Testicular Germ Cell Tumors, Cancer Immunol. Res.1158, 2326-6066.