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      Pan-cancer γδ TCR analysis uncovers clonotype diversity and prognostic potential

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          Summary

          Gamma-delta T cells (γδ T cells) play a crucial role in both innate and adaptive immunity within tumors, yet their presence and prognostic value in cancer remain underexplored. This study presents a large-scale analysis of γδ T cell receptor (γδ TCR) reads from 11,000 tumor samples spanning 33 cancer types, utilizing the TRUST4 algorithm. Our findings reveal extensive diversity in γδ TCR clonality and gene expression, underscoring the potential of γδ T cells as prognostic biomarkers in various cancers. We further demonstrate the utility of TCR gamma (TRG) and delta (TRD) gene expression from standard RNA-sequencing (RNA-seq) data. This comprehensive dataset offers a valuable resource for advancing γδ T cell research, with implications for enhanced immunotherapy approaches or alternative therapeutic strategies. Additionally, our centralized database supports translational research into the therapeutic significance of γδ T cells.

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          Highlights

          • Comprehensive analysis of γδ TCRs from 11,473 tumor samples

          • Significant variability and overall consistency in γδ gene expression and clonotype

          • γδ TCR expression and diversity as prognostic biomarkers across multiple cancers

          • Centralized γδ TCR repertoire database for future therapeutic discovery

          Abstract

          Yu et al. present a large-scale analysis of gamma-delta T cell receptors spanning over 11,000 tumor samples. Their findings uncover the diversity and prognostic significance of these T cell subsets, highlighting their role in predicting cancer outcomes and offering insights for improving immunotherapy and advancing cancer research.

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          Most cited references53

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Cutadapt removes adapter sequences from high-throughput sequencing reads

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              GSVA: gene set variation analysis for microarray and RNA-Seq data

              Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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                Author and article information

                Contributors
                Journal
                Cell Rep Med
                Cell Rep Med
                Cell Reports Medicine
                Elsevier
                2666-3791
                04 October 2024
                15 October 2024
                04 October 2024
                : 5
                : 10
                : 101764
                Affiliations
                [1 ]Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
                [2 ]Moffitt Cancer Center Immuno-Oncology Program, Tampa, FL 33612, USA
                [3 ]Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
                [4 ]Department of Immunology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
                [5 ]Department of Integrative Immunology, Duke University, Durham, NC 27710, USA
                Author notes
                []Corresponding author xuefeng.wang@ 123456moffitt.org
                [6]

                Present address: Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03756, USA

                [7]

                These authors contributed equally

                [8]

                Lead contact

                Article
                S2666-3791(24)00494-4 101764
                10.1016/j.xcrm.2024.101764
                11513832
                39368482
                d2a089c3-62f0-4850-a9be-e82996dce909
                © 2024 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 1 February 2024
                : 10 June 2024
                : 12 September 2024
                Categories
                Article

                gamma-delta t cells,t cell receptor,tcr clonality,immune repertoire,trust4,cancer immunotherapy,prognostic biomarkers

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