Clustering Algorithm-Driven Detection of TRBC1-Restricted Clonal T-Cell Populations Produces Better Results than Manual Gating Analysis

Flow cytometric (FC) immunophenotyping and T-cell receptor (TCR) gene rearrangement studies are essential ancillary methods for the characterisation of T-cell lymphomas. Traditional manual gating and polymerase chain reaction (PCR)-based analyses can be labour-intensive, operator-dependent, and have limitations in terms of sensitivity and specificity. The objective of our study was to investigate the efficacy of the Phenograph and t-SNE algorithms together with an antibody specific for the TCR β-chain constant region 1 (TRBC1) to identify monoclonal T-cell populations. FC- and PCR-based clonality analyses were performed on 275 samples of T-cell lymphomas, B-cell lymphomas, and reactive lymphocytic proliferations. Monotypic T-cell populations were identified in 65.1% of samples by manual gating and 72.4% by algorithm-driven analysis, while PCR-based analysis detected clonal T cells in 68.0%. Of the 262 monotypic populations identified, 46.6% were classified as T-cell lymphomas and 53.4% as T-cell populations of uncertain significance (T-CUS). Algorithm-driven gating identified monotypic populations that were overlooked by manual gating or PCR-based methods. The study highlights the difficulty in distinguishing monotypic populations as T-cell lymphoma or T-CUS. Further research is needed to establish criteria for distinguishing between these populations and to improve FC diagnostic accuracy.