Search

Simple Summary T-cells defend the human body from pathogenic invasion via specific recognition by T-cell receptors (TCRs). The TCR genes undergo recombination (rearrangement) in a myriad of possible ways to generate different TCRs that can recognize a wide diversity of foreign antigens. However, in patients with T-cell lymphoma (TCL), a particular T-cell becomes malignant and proliferates, resulting in a population of genetically identical cells with same TCR rearrangement pattern. To help diagnose patients with TCL, a polymerase chain reaction (PCR)-based assay is currently used to determine if neoplastic cells in patient samples are of T-cell origin and bear identical (monoclonal) TCR rearrangement pattern. Herein, we report the application of a novel segmentation and copy number computation algorithm to accurately identify different TCR rearrangement patterns using data from the whole genome sequencing of patient materials. Our approach may improve the diagnostic accuracy of TCLs and can be similarly applied to the diagnosis of B-cell lymphomas. Abstract T-cell lymphomas arise from a single neoplastic clone and exhibit identical patterns of deletions in T-cell receptor (TCR) genes. Whole genome sequencing (WGS) data represent a treasure trove of information for the development of novel clinical applications. However, the use of WGS to identify clonal T-cell proliferations has not been systematically studied. In this study, based on WGS data, we identified monoclonal rearrangements (MRs) of T-cell receptors (TCR) genes using a novel segmentation algorithm and copy number computation. We evaluated the feasibility of this technique as a marker of T-cell clonality using T-cell lymphomas (TCL, n = 44) and extranodal NK/T-cell lymphomas (ENKTLs, n = 20), and identified 98% of TCLs with one or more TCR gene MRs, against 91% detected using PCR. TCR MRs were absent in all ENKTLs and NK cell lines. Sensitivity-wise, this platform is sufficiently competent, with MRs detected in the majority of samples with tumor content under 25% and it can also distinguish monoallelic from biallelic MRs. Understanding the copy number landscape of TCR using WGS data may engender new diagnostic applications in hematolymphoid pathology, which can be readily adapted to the analysis of B-cell receptor loci for B-cell clonality determination.