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TMEscore

1.Introduction

TME infiltration patterns were determined and systematically correlated with TME cell phenotypes, genomic traits, and patient clinicopathological features to establish the TMEscore: Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures.

TMEscore is an R package to estimate tumor microenvironment score. Provides functionality to calculate Tumor microenvironment (TME) score using PCA or z-score.

2.Installation

The package is not yet on CRAN. You can install from Github:

# install.packages("devtools")
if (!requireNamespace("TMEscore", quietly = TRUE))
  devtools::install_github("DongqiangZeng0808/TMEscore")

3.Usage

Main documentation is on the tmescore function in the package:

library('TMEscore')
#> 载入需要的程辑包：survival
#> Warning: 程辑包'survival'是用R版本4.2.1 来建造的
#> 载入需要的程辑包：survminer
#> 载入需要的程辑包：ggplot2
#> 载入需要的程辑包：ggpubr
#> 
#> 载入程辑包：'survminer'
#> The following object is masked from 'package:survival':
#> 
#>     myeloma
#> TMEscore v0.1.4  For help: https://github.com/DongqiangZeng0808/TMEscore
#> 
#>  If you use TMEscore in published research, please cite:
#>  --------------------------------
#>  Tumor microenvironment evaluation promotes precise checkpoint immunotherapy of advanced gastric cancer.
#>  Journal for ImmunoTherapy of Cancer, 2021, 9(8), e002467
#>  DOI: 10.1136/jitc-2021-002467
#>  PMID: 34376552
#>  --------------------------------
#>  Tumor microenvironment characterization in gastric cancer identifies prognostic and imunotherapeutically relevant gene signatures.
#>  Cancer Immunology Research, 2019, 7(5), 737-750
#>  DOI: 10.1158/2326-6066.CIR-18-0436 
#>  PMID: 30842092
#>  --------------------------------
library("ggplot2")
library("patchwork")

Example

tmescore<-tmescore(eset     = eset_stad, #expression data
                   pdata    = pdata_stad, #phenotype data
                   method   = "PCA", #default
                   classify = T) #if true, survival data must be provided in pdata
head(tmescore)
#>               ID subtype   time status TMEscoreA TMEscoreB  TMEscore
#> 284 TCGA-RD-A8N2    <NA> 118.00      0 -6.705998  11.66689 -18.37289
#> 95  TCGA-BR-A4IV      GS  28.97      1 -6.376907  10.91756 -17.29446
#> 66  TCGA-BR-8371      GS  11.97      1 -6.258413  10.94738 -17.20580
#> 69  TCGA-BR-8380      GS     NA      1 -5.213597  11.38528 -16.59887
#> 101 TCGA-BR-A4J9      GS   0.47      0 -5.463828  10.55516 -16.01899
#> 82  TCGA-BR-8592      GS   6.37      1 -5.003108  10.84967 -15.85278
#>     TMEscore_binary
#> 284             Low
#> 95              Low
#> 66              Low
#> 69              Low
#> 101             Low
#> 82              Low

#remove observation with missing value
tmescore<-tmescore[!is.na(tmescore$subtype),]

p<-ggplot(tmescore,aes(x= subtype,y=TMEscore,fill=subtype))+
  geom_boxplot(notch = F,outlier.shape = 1,outlier.size = 0.5)+
  scale_fill_manual(values= c('#374E55FF', '#DF8F44FF', '#00A1D5FF', '#B24745FF'))

comparision<-combn(unique(as.character(tmescore$subtype)), 2, simplify=F)

p1<-p+theme_light()+
    stat_compare_means(comparisons = comparision,size=2.5)+
    stat_compare_means(size=2.5)

# survival analysis
colnames(tmescore)[which(colnames(tmescore)=="TMEscore_binary")]<-"score"
fit<- survfit(Surv(time, status) ~ score, data = tmescore)
p2<-ggsurvplot(fit, 
               conf.int = FALSE,
               palette = c('#374E55FF', '#DF8F44FF'),
               risk.table = TRUE, 
               pval = TRUE,
               risk.table.col = "strata")
p2<-list(p2)
p2 <- arrange_ggsurvplots(p2, print = FALSE, ncol = 1, nrow = 1)

# print plots
(p1|p2)+plot_layout(ncol = 2, widths = c(1,2))

<img src="man/figuresunnamed-chunk-5-1.png" width="100%" />

Citation

If you use TMEscore in published research, please cite:

1. Tumor microenvironment evaluation promotes precise checkpoint immunotherapy of advanced gastric cancer. Journal for ImmunoTherapy of Cancer, 2021, 9(8), e002467. DOI: 10.1136/jitc-2021-002467, PMID: 34376552

2. Tumor microenvironment characterization in gastric cancer identifies prognostic and imunotherapeutically relevant gene signatures. Cancer Immunology Research, 2019, 7(5), 737-750. DOI: 10.1158/2326-6066.CIR-18-0436, PMID: 30842092

Contact

E-mail any questions to dongqiangzeng0808@gmail.com or interlaken0808@163.com