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The T-cell receptor (TCR) repertoire is highly diverse among the population and plays an essential role in initiating multiple immune processes. TCR sequencing (TCR-seq) has been developed to profile the T cell repertoire. Similar to other high-throughput experiments, contamination can happen during several steps of TCR-seq, including sample collection, preparation and sequencing. Such contamination creates artifacts in the data, leading to inaccurate or even biased results. Most existing methods assume 'clean' TCR-seq data as the starting point with no ability to handle data contamination. Here, we develop a novel statistical model to systematically detect and remove contamination in TCR-seq data. We summarize the observed contamination into two sources, pairwise and cross-cohort. For both sources, we provide visualizations and summary statistics to help users assess the severity of the contamination. Incorporating prior information from 14 existing TCR-seq datasets with minimum contamination, we develop a straightforward Bayesian model to statistically identify contaminated samples. We further provide strategies for removing the impacted sequences to allow for downstream analysis, thus avoiding any need to repeat experiments. Our proposed model shows robustness in contamination detection compared with a few off-the-shelf detection methods in simulation studies. We illustrate the use of our proposed method on two TCR-seq datasets generated locally. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article is a response to a comment on a previous publication about benchmarking T cell receptor repertoire profiling methods for cancer RNA sequencing. The authors address concerns raised by Davydov et al. regarding the comparison of different tools and the inclusion of Phred nucleotide quality filters. They also discuss the issue of clonotype abundance calculation in TRUST4 and the evaluation of immune repertoire extraction tools. The authors explain their methodology and choices, emphasizing the use of default settings, adherence to best practices, and the focus on quantitative measures. They also defend their decision to exclude singletons from the analysis to enhance the reliability of the results. [Extracted from the article]

Transcriptome sequencing has become common in cancer research, resulting in the generation of a substantial volume of RNA sequencing (RNA-Seq) data. The ability to extract immune repertoires from these data is crucial for obtaining information on infiltrating T- and B-lymphocyte clones when dedicated amplicon T-cell/B-cell receptors sequencing (TCR-Seq/BCR-Seq) methods are unavailable. In response to this demand, several dedicated computational methods have been developed, including MiXCR, TRUST and ImRep. In the recent publication in Briefings in Bioinformatics, Peng et al. have conducted an intensive, systematic comparison of the three previously mentioned tools. Although their effort is commendable, we do have a few constructive critiques regarding technical elements of their analysis. [ABSTRACT FROM AUTHOR]