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

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Genomics Inform. 2022;20.e13

Publication date (electronic) : 2022 March 31

doi : https://doi.org/10.5808/gi.22005

San-Duk Yang¹, Hyun-Seok Park^2,3,

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

^*Corresponding author: E-mail: neo@ewha.ac.kr

Received 2022 January 17; Revised 2022 March 12; Accepted 2022 March 18.

Abstract

Introduction

Cancer cells express programmed death ligand 1 as a signal related to T cell unresponsiveness. Immunotherapies targeting immune checkpoints (e.g., anti–cytotoxic T lymphocyte associated antigen-4 and anti–programmed death-1 antibodies) are a standard component of care for patients with advanced cancers. Immune checkpoint inhibitors (ICIs) have led to improvements in the survival rate, but only a subset of patients respond to ICIs. Recent studies have reported that the efficacy of ICIs in cancer patients is determined by the T cell inflamed tumor microenvironment [1-3]. The molecular mechanisms of resistance have not yet been elucidated in detail. Nevertheless, previous studies have reported scoring methods for distinguishing non–T cell inflamed from T cell inflamed tumors based on gene expression data [4,5].

Unfortunately, general researchers who are unfamiliar with the detailed calculations involved in the equations can experience difficulties using these scoring formulas to conduct analyses. For this reason, we recently developed TcellInflamedDetector, an R package that predicts T cell inflamed tumors when given RNA-sequencing expression data. This package will be beneficial to optimize the selection of patients predicted to benefit from ICIs. TcellInflamedDetector implements the equation developed by Spranger et al. [5] to differentiate non–T cell inflamed and T cell inflamed tumor subtypes.

Input Data and Processing

As shown in Fig. 1, TcellInflamedDetector requires RNA-sequencing count input data with genes and sample identifiers. Users follow the steps for data processing that are summarized in the TcellInflamedDetector manual on GitHub [6]. The input CSV file is RNA sequencing log count per million (CPM) data. The count matrix file is converted by EdgeR aveLogCPM() and the calcNormFactor function using the trimmed mean of the m-values method. Users can extract previously established gene signatures indicative of a T cell inflamed tumor microenvironment, which include the cytotoxic T lymphocyte (CTL) signature genes CD8A, CD8B, GZMA, GZMB, and PRF1 using R code [7-10]. The established gene signatures were referenced with the Gajewski T cell-inflamed signature, interferon-gamma related signature, T cell effector signature, and immune cytolytic activity signature [4,5].

Fig. 1.

An exemplary code for extraction of T cell effector gene subset.

Estimating T Cell Inflamed and Non–T Cell Inflamed Samples

As shown in Fig. 2, gene expression values were converted to a score S_i = µ_i ± β_iσ_i (i = 1, 2, … n), where µ and σ represent the mean and standard deviation (SD) of the i^th gene’s expression across all samples, n is the total number of genes, β represents the distance between the i^th gene’s expression in a sample and the mean in units of the SD (equivalent to a z-score). The threshold for non–T cell inflamed and T cell inflamed tumors was β₀ = 0.1. The algorithm is described in detail below:

Fig. 2.

An exemplary usage of R code.

If the z-score value β_i is greater than the threshold (β₀ = 0.1), then +1 is assigned. Otherwise, if the z-score value β_i is less than the threshold (β₀ = 0.1), then ‒1 is assigned. If the sum of the column of genes with assigned values is greater than half of the number of CTL genes, then the output is a classification of “T cell inflamed.” If the sum of a column of genes with assigned values is less than half of the number of CTL genes, then the classification is “non–T cell inflamed.” Otherwise, the sample is classified as “intermediate.”

Users of the R package can obtain results in the format of a .csv file that contains data on the classification of samples as T cell inflamed, non–T cell inflamed, and intermediate. If users want to modify the CTL gene list when running the R package, they do not have to modify the complex R code. Instead, they can simply revise the gene list contained in the CTL.csv file.

Output

Five output formats are available: CTL_Selected_Inputfile.csv, Tcell_NonTcell_Result.csv, zscore_convert.csv, Zscore_convert_sum.csv, and zscore_data.csv. Fig. 3 presents examples of the prediction results of T cell inflamed, intermediate, and non–T cell inflamed groups. Users can check the expression patterns of specific genes through a heatmap. We also confirmed that T cell inflamed samples showed high expression of T cell effector gene signatures [10].

Fig. 3.

(A) An input file and generated output files. (B) Processing diagram for T cell inflamed function prediction. (C) T cell inflamed annotation of Heatmap expression in The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD) samples.

Finally, we conducted a test to demonstrate our tool’s flexibility; we tested it on The Cancer Genome Atlas (TCGA) lung adenocarcinoma RNA-sequencing dataset available through the TCGA Research Network [11]. Each sample was labeled according to the TCGA barcode, which contained gene names. Our package successfully selected subsets of gene expression data from the raw count data. Thus, TcellInflamedDetector can be beneficial for future cancer immunotherapy vaccine developers and researchers.

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