T-cell differentiation stage block bias confers hypermethylation and mediastinal preference in T-cell lymphoblastic lymphoma

Abstract

Background: The clinical guideline classifies T-LBL and T-ALL jointly, differentiating them merely by the bone marrow blast cell proportion. However, their distinct clinical manifestations, genetic profiles, and specific pathogenic requirements have prompted us to reevaluate the differences between them.

Methods and Results: We established the NCH-TALL-LBL cohort, which includes flow cytometry data and somatic mutation data from our center. Additionally, we collected T-LBL samples and implemented single-cell RNA sequencing and single-cell T-cell receptor sequencing. Combining the single-cell RNA sequencing data of T-ALL, expression array data, flow cytometry data, we discovered that malignant T cells in T-LBL are predominantly in the DN- and DP-stage blocking modes (DP cells dominate). This block mode in T-LBL generates signals that drive the development of an immunosuppressive microenvironment and the mediastinum preference. Additionally, E2F2, an active transcription factor in the DP and DN stages, upregulates the expression of UHRF1, resulting in hypermethylation of tumor suppressor genes. Findings from in vivo and in vitro research clearly show that demethylation therapy targeting this mechanism effectively inhibits tumor proliferation in T-LBL.

Conclusion: From the perspective of differentiation blockage, T-LBL and T-ALL represent different stages of the same disease, and the stage block bias of T-cell contributes to their heterogeneity.

Keywords

T-cell lymphoblastic lymphoma / hypermethylation / mediastinum preference / T-cell differentiation stage

References

[1] Alaggio R, Amador C, Anagnostopoulos I, et al. The 5th edition of the World Health Organization classification of haematolymphoid tumours: lymphoid neoplasms. Leukemia. 2022; 36(7): 1720-1748.

[2] Michaux K, Bergeron C, Gandemer V, et al. Relapsed or refractory lymphoblastic lymphoma in children: results and analysis of 23 patients in the EORTC 58951 and the LMT96 protocols. Pediatr Blood Cancer. 2016; 63(7): 1214-1221.

[3] Temple WC, Mueller S, Hermiston ML, Burkhardt B. Diagnosis and management of lymphoblastic lymphoma in children, adolescents and young adults. Best Pract Res Clin Haematol. 2023; 36(1): 101449.

[4] Kroeze E, Loeffen JLC, Poort VM, Meijerink JPP. T-cell lymphoblastic lymphoma and leukemia: different diseases from a common premalignant progenitor? Blood Adv. 2020; 4(14): 3466-3473.

[5] Khanam T, Sandmann S, Seggewiss J, et al. Integrative genomic analysis of pediatric T-cell lymphoblastic lymphoma reveals candidates of clinical significance. Blood. 2021; 137(17): 2347-2359.

[6] Bonn BR, Rohde M, Zimmermann M, et al. Incidence and prognostic relevance of genetic variations in T-cell lymphoblastic lymphoma in childhood and adolescence. Blood. 2013; 121(16): 3153-3160.

[7] Balbach ST, Makarova O, Bonn BR, et al. Proposal of a genetic classifier for risk group stratification in pediatric T-cell lymphoblastic lymphoma reveals differences from adult T-cell lymphoblastic leukemia. Leukemia. 2016; 30(4): 970-973.

[8] Basso K, Mussolin L, Lettieri A, et al. T-cell lymphoblastic lymphoma shows differences and similarities with T-cell acute lymphoblastic leukemia by genomic and gene expression analyses. Genes Chromosomes Cancer. 2011; 50(12): 1063-1075.

[9] Feng H, Stachura DL, White RM, et al. T-lymphoblastic lymphoma cells express high levels of BCL2, S1P1, and ICAM1, leading to a blockade of tumor cell intravasation. Cancer Cell. 2024; 42(6): 1130-1131.

[10] Wang J, Li H, Xue Y, et al. Cell communication pathway prognostic model identified detrimental neurodevelopmental pathways in neuroblastoma. Neoplasia. 2024; 52: 100997.

[11] Chen X, Yang W, Roberts CWM, Zhang J. Developmental origins shape the paediatric cancer genome. Nat Rev Cancer. 2024; 24(6): 382-398.

[12] Liu H, Li P, Wei Z, et al. Regulation of T cell differentiation and function by epigenetic modification enzymes. Semin Immunopathol. 2019; 41(3): 315-326.

[13] Bayon-Calderon F, Toribio ML, Gonzalez-Garcia S. Facts and challenges in immunotherapy for T-cell acute lymphoblastic leukemia. Int J Mol Sci. 2020; 21(20): 7685.

[14] Burkhardt B, Mueller S, Khanam T, Perkins SL. Current status and future directions of T-lymphoblastic lymphoma in children and adolescents. Br J Haematol. 2016; 173(4): 545-559.

[15] Patel JL, Smith LM, Anderson J, et al. The immunophenotype of T-lymphoblastic lymphoma in children and adolescents: a Children's Oncology Group report. Br J Haematol. 2012; 159(4): 454-461.

[16] Touzart A, Boissel N, Belhocine M, et al. Low level CpG island promoter methylation predicts a poor outcome in adult T-cell acute lymphoblastic leukemia. Haematologica. 2020; 105(6): 1575-1581.

[17] Hetzel S, Mattei AL, Kretzmer H, et al. Acute lymphoblastic leukemia displays a distinct highly methylated genome. Nat Cancer. 2022; 3(6): 768-782.

[18] Bhasin SS, Thomas BE, Summers RJ, et al. Pediatric T-cell acute lymphoblastic leukemia blast signature and MRD associated immune environment changes defined by single cell transcriptomics analysis. Sci Rep. 2023; 13(1): 12556.

[19] Caron M, St-Onge P, Sontag T, et al. Single-cell analysis of childhood leukemia reveals a link between developmental states and ribosomal protein expression as a source of intra-individual heterogeneity. Sci Rep. 2020; 10(1): 8079.

[20] Park JE, Botting RA, Dominguez Conde C, et al. A cell atlas of human thymic development defines T cell repertoire formation. Science. 2020; 367(6480): eaay3224.

[21] Jain N, Lamb AV, O'Brien S, et al. Early T-cell precursor acute lymphoblastic leukemia/lymphoma (ETP-ALL/LBL) in adolescents and adults: a high-risk subtype. Blood. 2016; 127(15): 1863-1869.

[22] Kumar BV, Connors TJ, Farber DL. Human T cell development, localization, and function throughout life. Immunity. 2018; 48(2): 202-213.

[23] Zheng M, Li D, Zhao Z, et al. Protein phosphatase 2A has an essential role in promoting thymocyte survival during selection. Proc Natl Acad Sci U S A. 2019; 116(25): 12422-12427.

[24] Xiao ZJ, Wang SQ, Chen JJ, et al. The dual role of the NFATc2/galectin-9 axis in modulating tumor-initiating cell phenotypes and immune suppression in lung adenocarcinoma. Adv Sci (Weinh). 2024; 11(20): e2306059.

[25] Wu C, Thalhamer T, Franca RF, et al. Galectin-9-CD44 interaction enhances stability and function of adaptive regulatory T cells. Immunity. 2014; 41(2): 270-282.

[26] Kozbor D, Burioni R, Ar-Rushdi A, Zmijewski C, Croce CM. Expression of members of immunoglobulin gene family in somatic cell hybrids between human B and T cells. Proc Natl Acad Sci U S A. 1987; 84(14): 4969-4973.

[27] Mancini M, Magnani E, Macchi F, Bonapace IM. The multifunctionality of UHRF1: epigenome maintenance and preservation of genome integrity. Nucleic Acids Res. 2021; 49(11): 6053-6068.

[28] Helmin KA, Morales-Nebreda L, Torres Acosta MA, et al. Maintenance DNA methylation is essential for regulatory T cell development and stability of suppressive function. J Clin Invest. 2020; 130(12): 6571-6587.

[29] Provez L, Putteman T, Landfors M, et al. Pre-clinical evaluation of the hypomethylating agent decitabine for the treatment of T-Cell lymphoblastic lymphoma. Cancers (Basel). 2023; 15(3): 647.

[30] Li Z, Song Y, Zhang M, Wei Y, Ruan H. Genomic landscape of T-cell lymphoblastic lymphoma. Chin J Cancer Res. 2022; 34(2): 83-94.

[31] Haider Z, Landfors M, Golovleva I, et al. DNA methylation and copy number variation profiling of T-cell lymphoblastic leukemia and lymphoma. Blood Cancer J. 2020; 10(4): 45.

[32] Hao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell. 2021; 184(13): 3573-3587 e29.

[33] McGinnis CS, Murrow LM, Gartner ZJ. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 2019; 8(4): 329-337 e4.

[34] Patel AP, Tirosh I, Trombetta JJ, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014; 344(6190): 1396-1401.

[35] Malta TM, Sokolov A, Gentles AJ, et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell. 2018; 173(2): 338-354 e15.

[36] Kotliar D, Veres A, Nagy MA, et al. Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq. Elife. 2019; 8: e43803.

[37] Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013; 14: 7.

[38] Juhling F, Kretzmer H, Bernhart SH, Otto C, Stadler PF, Hoffmann S. metilene: fast and sensitive calling of differentially methylated regions from bisulfite sequencing data. Genome Res. 2016; 26(2): 256-262.

[39] Van de Sande B, Flerin C, Davie K, et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat Protoc. 2020; 15(7): 2247-2276.

[40] Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012; 16(5): 284-287.

[41] Carrasco-Padilla C, Aguilar-Sopena O, Gomez-Moron A, et al. T cell activation and effector function in the human Jurkat T cell model. Methods Cell Biol. 2023; 178: 25-41.

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2025 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.