Clonotype pattern in T-cell lymphomas map the cell of origin to immature lymphoid precursors

Abstract

Mature T-cell lymphomas (TCLs) are rare, clinically heterogeneous hematologic cancers with high medical need. TCLs have an inferior prognosis which is attributed to poor understanding of their pathogenesis. On the basis of phenotypic similarities between normal and neoplastic lymphocytes, it has been assumed that TCLs develop in the periphery, directly from various subtypes of normal T cells. To address the debated question of the cell of origin in TCLs, we attempted to identify the highly variable complementarity-determining regions (CDRs) of T-cell receptors (TCRs) to trace the clonal history of the T cells. We have collected previously published whole-genome, whole-exome, and whole-transcriptome sequencing data from 574 patients with TCL. TCR clonotypes were identified by de novo assembly of CDR3 regions of TCRα, TCRβ, and TCRγ. We have found that the vast majority of TCLs are clonotypically oligoclonal, although the pattern of oligoclonality varied. Anaplastic large-cell lymphoma was the most diverse comprising multiple clonotypes of TCRα, TCRβ, and TCRγ, whereas adult TCL or leukemia and peripheral TCLs often showed monoclonality for TCRβ and TCRγ but had diverse TCRα clonotypes. These patterns of rearrangements indicated that TCLs are initiated at the level of the lymphoid precursor. In keeping with this hypothesis, TCR rearrangements in TCLs resembled the pattern seen in the human thymus, which showed biased usage of V (variable) and J (joining) segments of high combinatorial probability resulting in recurrent public CDR3 sequences shared across unrelated patients and different clinical TCL entities. Clonotypically diverse initiating cells may seed target tissues that are then responsible for disease relapses after therapy.

Graphical abstract

Figure 1.

Summary of the methodological approach to TCR analysis in TCLs. The pie chart represents the percentage of sequencing samples collected from different subtypes of TCLs that were analyzed for TCF and the TCR clonotypes. Details for the diagnostic groupings are provided in supplemental Table 2. TCF was estimated on the basis of the chromosomal aberration identified using TitanCNA or IchorCNA. TCR clonotypes were identified using MiXCR. The diversity index (inverse Simpson), clonotype sharing (Jaccard index), and probability of clonotype generation (P_gen) were analyzed using Immunarch and OLGA pipelines, as described in “Methods.” SPTL, subcutaneous panniculitic TCL.

Figure 2.

Frequency of TCRβ^DNA clonotypes in TCLs. A total of 378 DNA samples from 8 subtypes of TCLs were analyzed to identify the frequency of TCF and TCRβ^DNA clonotypes using WES and WGS data. TCRβ^DNA clonotypes corresponding to the TCF are indicated by the colored bars in the descending order of relative frequencies (the most abundant, rank 1, is the bright blue bar). The data are split by diagnosis: (A) cutaneous lymphomas: MF, and SS, (B) ALCL and ATCLL, and (C) SPTL, PTCL, Other TCL, and NKTL.

Figure 3.

Frequency of TCRβ^RNA clonotypes in TCLs. A total of 196 RNA samples from 6 subtypes of TCLs were analyzed to identify the frequency of the TCRβ^RNA clonotypes. The 10 most abundant clonotypes are indicated by using the same color coding as in Figure 2; the remaining clonotypes are merged and their combined frequency is indicated by the gray bars. The data are split by diagnosis: (A) cutaneous lymphomas: MF and SS, (B) ATCLL, ALCL, Other TCLs and PTCL.

Figure 4.

Oligoclonality in TCLs. (A) Schematic presentation of the TCR gene rearrangement during T-cell development. The arrows represent rearrangement on each chromosome mapped to different stages of a normal human thymocyte. The thin arrow for TCRβ symbolizes the 60-40 rule. Predicted time points of the transformation in various subtypes of TCLs are shown at the bottom. (B-C) WES and WGS samples (n = 367) were analyzed to identify the TCRα^DNA, TCRβ^DNA, and TCRγ^DNA clonotypes. (C) The frequency of the most abundant TCRβ^DNA clonotype was plotted vs the added frequency of the 2 most frequent TCRγ^DNA clonotypes or the 2 most abundant TCRα^DNA clonotypes for each sample. (D) A similar correlation plot of the frequency of dominant TCRβ^RNA clonotypes vs the dominant TCRα^RNA clonotypes (data from 196 WTS samples). DP, double positive; ISP, immature single positive; SP, single positive.

Figure 5.

Vβ and Jβ (TRBV/TRBJ) usage in TCLs. The identified TCRβ^DNA and TCRβ^RNA clonotypes from WES, WGS, and WTS data for the TCL diagnoses and control blood lymphocytes to identify preferential TRBV and TRBJ segment usage. (A-B) TRBV and TRBJ frequencies, respectively, in the 10 most abundant clonotypes. (C-D) Plots similar to those in panels A and B with the inclusion of the single most abundant clonotype. The control group is absent in panels C and D because of the lack of a high-frequency clonotype in these samples.

Figure 6.

V and J (TRAV, TRAJ, TRGV, and TRGJ) usage in TCLs. The frequency of V and J segments of TCRα and TCRγ clonotypes are plotted as in Figure 7. The 2 most abundant TCRα^DNA and TCRγ^DNA clonotypes from TCLs and control samples were identified, and the frequency of V and J segments were plotted on the heat map. The RNA samples were used only for identification of the TCRα^RNA clonotypes. (A-B) TRAV and TRAJ segment usage; (C-D) TRGV and TRGJ segment usage.

Figure 7.

P_gen for TCRα and TCRβ clonotypes. Shared TCRα^aa and TCRβ^aa clonotypes were identified using the Immunarch pipeline and their P_gen was calculated using the OLGA algorithm (Figure 1) from DNA and RNA data. (A) P_gen histogram for the shared TCRα^DNA and TCRβ^DNA clonotypes. As a comparison, the P_gen values for the TCRβ^DNA clonotypes for the healthy control group are plotted. (B) Analogous histogram (as in panel A) showing P_gen for the shared clonotypes in TCRα^RNA and TCRβ^RNA.