Dataset Information

Assessment of T-cell receptor repertoire and clonal expansion in peripheral T-cell lymphoma using RNA-seq data.

ABSTRACT: T-cell clonality of peripheral T-cell lymphoma (PTCL) is routinely evaluated with a PCR-based method using genomic DNA. However, there are limitations with this approach. The purpose of this study was to determine the utility of RNA-seq for assessing T-cell clonality and T-cell antigen receptor (TCR) repertoire of the neoplastic T-cells in 108 PTCL samples. TCR transcripts, including complementarity-determining region 3 (CDR3) sequences, were assessed. In normal T cells, the CDR3 sequences were extremely diverse, without any clonotype representing more than 2% of the overall TCR population. Dominant clones could be identified in 65 out of 76 PTCL cases (86%) with adequate TCR transcript expression. In monoclonal cases, the dominant clone varied between 11% and 99% of TCR? transcripts. No unique V? or V? usage was observed. Small T-cell clones were often observed in T- and NK-cell tumors in a percentage higher than observed in reactive conditions. ? chain expression was very low in tumors expressing TCR??, but its expression level was high and clonality was detected in a TCR?? expressing tumor. NK cell lymphoma (NKCL) did not express significant levels of TCR V? or V? genes. RNA-seq is a useful tool for detecting and characterizing clonal TCR rearrangements in PTCL.

SUBMITTER: Gong Q

PROVIDER: S-EPMC5595876 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Publications

Assessment of T-cell receptor repertoire and clonal expansion in peripheral T-cell lymphoma using RNA-seq data.

Gong Qiang Q Wang Chao C Zhang Weiwei W Iqbal Javeed J Hu Yang Y Greiner Timothy C TC Cornish Adam A Kim Jo-Heon JH Rabadan Raul R Abate Francesco F Wang Xin X Inghirami Giorgio G GG McKeithan Timothy W TW Chan Wing C WC

Scientific reports 20170912 1

T-cell clonality of peripheral T-cell lymphoma (PTCL) is routinely evaluated with a PCR-based method using genomic DNA. However, there are limitations with this approach. The purpose of this study was to determine the utility of RNA-seq for assessing T-cell clonality and T-cell antigen receptor (TCR) repertoire of the neoplastic T-cells in 108 PTCL samples. TCR transcripts, including complementarity-determining region 3 (CDR3) sequences, were assessed. In normal T cells, the CDR3 sequences were ...[more]

PMID: 28900149

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Project description:BackgroundThe annotation of biomolecular functions is an essential step in the analysis of newly sequenced organisms. Usually, the functions are inferred from predicted genes on the genome using homology search techniques. A high quality genomic sequence is an important prerequisite which, however, is difficult to achieve for certain organisms, such as hybrids or organisms with a large genome. For functional analysis it is also possible to use a de novo transcriptome assembly but the computational requirements can be demanding. Up to now, it is unclear how much of the functional repertoire of an organism can be reliably predicted from unassembled RNA-seq short reads alone.ResultsWe have conducted a study to investigate to what degree it is possible to reconstruct the functional profile of an organism from unassembled transcriptome data. We simulated the de novo prediction of biomolecular functions for Arabidopsis thaliana using a comprehensive RNA-seq data set. We evaluated the prediction performance using several homology search methods in combination with different evidence measures. For the decision on the presence or absence of a particular function under noisy conditions we propose a statistical mixture model enabling unsupervised estimation of a detection threshold. Our results indicate that the prediction of the biomolecular functions from the KEGG database is possible with a high sensitivity up to 94 percent. In this setting, the application of the mixture model for automatic threshold calibration allowed the reduction of the falsely predicted functions down to 4 percent. Furthermore, we found that our statistical approach even outperforms the prediction from a de novo transcriptome assembly.ConclusionThe analysis of an organism's transcriptome can provide a solid basis for the prediction of biomolecular functions. Using RNA-seq short reads directly, the functional profile of an organism can be reconstructed in a computationally efficient way to provide a draft annotation in cases where the classical genome-based approaches cannot be applied.

Dataset Information

Assessment of T-cell receptor repertoire and clonal expansion in peripheral T-cell lymphoma using RNA-seq data.

Publications

Assessment of T-cell receptor repertoire and clonal expansion in peripheral T-cell lymphoma using RNA-seq data.

Similar Datasets

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