Rigorous benchmarking of T-cell receptor repertoire profiling methods for cancer RNA sequencing

Abstract

The ability to identify and track T-cell receptor (TCR) sequences from patient samples is becoming central to the field of cancer research and immunotherapy. Tracking genetically engineered T cells expressing TCRs that target specific tumor antigens is important to determine the persistence of these cells and quantify tumor responses. The available high-throughput method to profile TCR repertoires is generally referred to as TCR sequencing (TCR-Seq). However, the available TCR-Seq data are limited compared with RNA sequencing (RNA-Seq). In this paper, we have benchmarked the ability of RNA-Seq-based methods to profile TCR repertoires by examining 19 bulk RNA-Seq samples across 4 cancer cohorts including both T-cell-rich and T-cell-poor tissue types. We have performed a comprehensive evaluation of the existing RNA-Seq-based repertoire profiling methods using targeted TCR-Seq as the gold standard. We also highlighted scenarios under which the RNA-Seq approach is suitable and can provide comparable accuracy to the TCR-Seq approach. Our results show that RNA-Seq-based methods are able to effectively capture the clonotypes and estimate the diversity of TCR repertoires, as well as provide relative frequencies of clonotypes in T-cell-rich tissues and low-diversity repertoires. However, RNA-Seq-based TCR profiling methods have limited power in T-cell-poor tissues, especially in highly diverse repertoires of T-cell-poor tissues. The results of our benchmarking provide an additional appealing argument to incorporate RNA-Seq into the immune repertoire screening of cancer patients as it offers broader knowledge into the transcriptomic changes that exceed the limited information provided by TCR-Seq.

Features of the TCR repertoire associate with patients' clinical and molecular characteristics in acute myeloid leukemia

“…These tools were previously benchmarked with other computational tools to extract TCR information from RNA-seq data originating from the same samples focusing on the characterization of T-cell rich and T cell poor tissues. Both methods were found effective in capturing estimated repertoire diversity in T-cell rich tissues particularly in samples with a limited number of hyperexpanded clones ( 31 ). However, the exact accuracy of the alignment from TRUST4 and MIXCR remains unknown due to the lack of a control framework of known TCR composition both quantitatively and qualitatively for direct comparison and assessment.…”

Loss of TET2 increases B-1 cell number and IgM production while limiting CDR3 diversity

“…For BCR analysis, quality trimming was performed using fastp ( 49 ) and TRUST4 ( 55 ) was subsequently used to identify BCR repertoire in paired sequencing reads using the international ImMunoGeneTics (IMGT) information system database as a reference. Results were analyzed using R and circus plots were made using circos Bioconductor package ( 56 ).…”

Pulmonary microbiome and transcriptome signatures reveal distinct pathobiologic states associated with mortality in two cohorts of pediatric stem cell transplant patients

“…Finally, lymphocyte receptor repertoires were imputed using Imrep (Linux install), which identifies CDR3 alignments from within bulk gene expression data. 103,104…”