Human thymic T cell repertoire is imprinted with strong convergence to shared sequences

Heikkilä, Nelli; Vanhanen, Reetta; Yohannes, Dawit A.; Kleino, Iivari; Mattila, Ilkka; Saramäki, Jari; Arstila, T. Petteri

doi:10.1016/j.molimm.2020.09.003

Cited by 15 publications

(18 citation statements)

References 42 publications

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“…Moreover, additional supportive evidence for the lymphoid precursor model for TCLs comes from the pattern of V-J usage and clonotype sharing. TCR rearrangements in human thymocytes are characterized by biased V and J segment usage reflecting the probability of recombination (P gen ) producing shared (public) clonotypes [54,55]. A very similar situation is seen in TCLs.…”

Section: Discussionmentioning

confidence: 97%

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

Iyer

Hennessey

Gniadecki

2021

Preprint

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Background Mature T-cell lymphomas (TCLs) are rare, clinically heterogeneous hematologic cancers of high medical need. TCLs have inferior prognosis compared with their B-cell counterparts, which is attributed to poor understanding of their pathogenesis. Based on 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. Methods and findings To address the debated question of the cell of origin in TCLs we analyzed to identify the highly variable complementarity determining regions (CDR3) regions of T-cell receptor (TCR) to trace the clonal history of the T-cells. We have collected previously published whole genome -exome, and -transcriptome sequencing data from 574 TCL patients comprising five nodal lymphomas [anaplastic large cell lymphoma (n=67), peripheral T-cell lymphoma (PTCL, n=55), adult T-cell lymphoma/leukemia (n=135), natural killer T-cell lymphoma (NKCL, n=25), not specified/other (n=30)] and three extranodal, cutaneous T-cell lymphomas [mycosis fungoides (n=122), Sezary syndrome (n=130), and subcutaneous panniculitis like T-cell lymphoma (n=10)]. TCR clonotypes contained in the tumor cell fraction, representing the clonotypes of malignant cells, were identified by de novo assembly of CDR3 regions of TCRγ, β and α. We have found that the vast majority of TCLs are clonotypically oligoclonal, although the pattern oligoclonality varied. Anaplastic large cell lymphoma was most diverse comprising multiple clonotypes of TCRγ, β and α whereas adult T-cell lymphoma/leukemia and peripheral T-cell lymphomas often showed monoclonality for TCRγ and β but had diverse TCRα clonotypes. These patterns of rearrangements were not compatible with the current mature T-cell precursor model and 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 showing biased usage of V and J segments of high combinatorial probability resulting in recurrent, public CDR3 sequences shared between unrelated patients and across different clinical TCL entities. Frequencies of malignant clonotypes followed Zipf-Mandelbrot scaling law suggesting that TCLs comprise an interconnected system of expanding tumor clones. The major limitation of this study is that it is based on the analysis of the TCR clonotypes and does not directly inform about developmental trajectories of cellular clones. Conclusions Lymphoid precursors are the likely cells of origin for mature T-cell lymphomas. Anaplastic large cell lymphoma seems to be derived from the most immature precursors with germline TCR whereas peripheral T-cell lymphoma and adult T-cell lymphoma/leukemia map to the later stages after TCR lower case Greek beta rearrangement stage. Clonotypically diverse initiating cells may seed target tissues being responsible for disease relapses after therapy.

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Section: Discussionmentioning

confidence: 97%

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

Iyer

Hennessey

Gniadecki

2021

Preprint

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“…Additionally, we recovered motifs from the kernels of the neural network by limiting the values of the kernels similar to Ploenzke and Irizarry 72 , and from the hierarchical clustering based on TCRdist distance, and compare these recovered motifs with the motifs extracted by GLIPH2 55 on the same datasets. Each dataset includes a set of epitope-specific TCR β receptors downloaded from VDJdb and a set of naive, randomly paired TCR β receptors from the peripheral blood samples of 4 healthy donors 73 . Epitope-specific datasets are specific to cytomegalovirus (KLGGALQAK epitope, with 13000 paired TCR β receptors), Influenza A (GILGFVFTL epitope, with 2000 paired TCR β receptors), and Epstein-Barr virus (AVFDRKSDAK epitope, with 1700 paired TCR β receptors).…”

Section: Use Case 2: Extending Immuneml With a Deep Learning Componenmentioning

confidence: 99%

immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires

Pavlović

Scheffer

Motwani

et al. 2021

Preprint

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Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

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“…There is controversy in the literature about the relative roles of genetic background and thymic selection in determining V (D) J gene usage (32,(34)(35)(36)(37). Overall, a general pattern is shared throughout human populations, such that certain genes are always used more than others (38). However, genetic factors lead to subtle differences.…”

Section: Genetic Influences On Tcr Repertoire Selectionmentioning

confidence: 99%

High Throughput Human T Cell Receptor Sequencing: A New Window Into Repertoire Establishment and Alloreactivity

Khosravi‐Maharlooei

Sykes

2021

Front. Immunol.

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Recent advances in high throughput sequencing (HTS) of T cell receptors (TCRs) and in transcriptomic analysis, particularly at the single cell level, have opened the door to a new level of understanding of human immunology and immune-related diseases. In this article, we discuss the use of HTS of TCRs to discern the factors controlling human T cell repertoire development and how this approach can be used in combination with human immune system (HIS) mouse models to understand human repertoire selection in an unprecedented manner. An exceptionally high proportion of human T cells has alloreactive potential, which can best be understood as a consequence of the processes governing thymic selection. High throughput TCR sequencing has allowed assessment of the development, magnitude and nature of the human alloresponse at a new level and has provided a tool for tracking the fate of pre-transplant-defined donor- and host-reactive TCRs following transplantation. New insights into human allograft rejection and tolerance obtained with this method in combination with single cell transcriptional analyses are reviewed here.

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Human thymic T cell repertoire is imprinted with strong convergence to shared sequences

Cited by 15 publications

References 42 publications

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

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

immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires

High Throughput Human T Cell Receptor Sequencing: A New Window Into Repertoire Establishment and Alloreactivity

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