Evaluation of next‐generation sequencing‐based clonality analysis of T‐cell receptor gamma gene rearrangements based on a new interpretation algorithm

Abstract

Introduction T‐cell receptor gene (TRG) rearrangement profiling is an essential component of the workup at diagnosis of T‐cell malignancies. TRG amplification by polymerase chain reaction (PCR) and analysis by capillary electrophoresis (PCR‐CE) is mostly widely used but is hampered by a subjective interpretation of its results and possible false‐positive interpretation of clonality. Several studies evaluated the advantage of TRG rearrangement analysis by Next Generation Sequencing (TRG‐NGS), however few have proposed an adequate data interpretation algorithm. Methods Eighty five fresh and 36 formalin‐fixed paraffin embedded (FFPE) diagnostic samples suspected for a lymphoproliferative disorder were analyzed by PCR‐CE and TRG NGS. Final clinical diagnosis was available for all fresh samples. Reproducibility, analytical specificity and sensitivity of the TRG NGS analysis was evaluated. Results We propose a new interpretation algorithm for TRG NGS data analysis. PCR‐CE and TRG NGS showed identical results in 66/85 (78%) of fresh samples. Sensitivities to detect T‐cell malignancies were comparable (96% versus 92%, respectively). The analysis of FFPE material was significantly more successful by TRG NGS (34/36 cases) in respect to PCR‐CE (16/36 cases), most likely due to the small size of the amplicons. Conclusion Assessment of T‐cell clonality by TRG NGS has a significant added value in the diagnosis of T‐cell disorders as an adjunct to PCR‐CE, particularly in difficult to interpret cases or when analyzing FFPE samples.

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“…T-cell receptor (TCR) beta and gamma chain clonality were determined by BIOMED-2 polymerase chain reaction until nextgeneration sequencing was determined to be superior. 24,25 This next-generation sequencing analysis was done on an Illumina MiSeq platform (Illumina, San Diego, CA, USA) using primers targeting conserved regions within the Vc and Jc regions for identification of clonal rearrangements of the TCR gamma chain, and conserved regions within the Vb and Jb regions for identification of clonal rearrangements of the TCR beta chain. Bioinformatics analysis was performed using Lym-phoTrack software (Invivoscribe, San Diego, CA, USA).…”

“…Sézary cell population was determined as the percentage of cells with a CD3 + CD4 + and CD26 − or CD7 − immunophenotype on peripheral blood flow cytometry. T‐cell receptor (TCR) beta and gamma chain clonality were determined by BIOMED‐2 polymerase chain reaction until next‐generation sequencing was determined to be superior 24,25 . This next‐generation sequencing analysis was done on an Illumina MiSeq platform (Illumina, San Diego, CA, USA) using primers targeting conserved regions within the Vγ and Jγ regions for identification of clonal rearrangements of the TCR gamma chain, and conserved regions within the Vβ and Jβ regions for identification of clonal rearrangements of the TCR beta chain.…”

Diagnostic and prognostic molecular pathology of lymphoid malignancies

“…Similar to conventional clonality analysis, NGS-based clonality analysis can be used to distinguish reactive lymphoid proliferations from neoplasia. Studies with different assays have shown that NGS-based IG clonality analysis is at least as sensitive as conventional BIOMED-2 clonality analysis [ 6 , 30 , 71 ]. In conventional clonality analysis, small clones are often inapparent in the polyclonal background, especially if the size of the clone is near the center of the Gaussian curve.…”

“…NGS-based clonality analysis has the potential to make interpretation of the results more objective as it provides quantitative and qualitative (i.e., actual sequence) data on the clonotypes that are present. Quantitative criteria for interpretation have been proposed which are based on a combination of the percentage of reads attributed to the most prominent clonotype and the ratio between the most prominent clonotype and the background [ 6 , 30 , 71 ]. When using a quantitative approach to NGS-based clonality interpretation, it is important to keep in mind that many variables are involved; results should always be interpreted in the histological and clinical context and taking into consideration results from other molecular analyses.…”

Development and implementation of an automated and highly accurate reporting process for NGS-based clonality testing

“…A polyclonal background was not explicitly defined but was referred to as the 3rd most dominant clone [23]. For T-cell clonality, Nollet (2018) and Schumaker (2014) evaluated TRG by NGS; a dominant clone was defined as equal to or greater than 4% of total reads, and this value is 4.5× the polyclonal background [24,25]. The polyclonal background was defined as the highest clone percent 2× less than the next most frequent clone in the ten most frequent merged clones.…”