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Integrated molecular analysis of adult T cell leukemia/lymphoma

Nature Genetics volume 47, pages 1304–1315 (2015)Cite this article

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Abstract

Adult T cell leukemia/lymphoma (ATL) is a peripheral T cell neoplasm of largely unknown genetic basis, associated with human T cell leukemia virus type-1 (HTLV-1) infection. Here we describe an integrated molecular study in which we performed whole-genome, exome, transcriptome and targeted resequencing, as well as array-based copy number and methylation analyses, in a total of 426 ATL cases. The identified alterations overlap significantly with the HTLV-1 Tax interactome and are highly enriched for T cell receptor–NF-κB signaling, T cell trafficking and other T cell–related pathways as well as immunosurveillance. Other notable features include a predominance of activating mutations (in PLCG1, PRKCB, CARD11, VAV1, IRF4, FYN, CCR4 and CCR7) and gene fusions (CTLA4-CD28 and ICOS-CD28). We also discovered frequent intragenic deletions involving IKZF2, CARD11 and TP73 and mutations in GATA3, HNRNPA2B1, GPR183, CSNK2A1, CSNK2B and CSNK1A1. Our findings not only provide unique insights into key molecules in T cell signaling but will also guide the development of new diagnostics and therapeutics in this intractable tumor.

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Figure 1: Overview of the ATL genome and HTLV-1 integration.
Figure 2: Landscape of somatic alterations in ATL.
Figure 3: TCR–NF-κB signaling is systematically altered in ATL.
Figure 4: Activating alterations in TCR–NF-κB signaling.
Figure 5: Deregulated B7-CD28 co-stimulatory signaling.
Figure 6: Mutations in G protein–coupled receptors.
Figure 7: Frequent intragenic deletions and mutations inducing abnormal splicing.
Figure 8: Hypermethylation in promoter-associated CpG islands in ATL.

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References

  1. Ishitsuka, K. & Tamura, K. Human T-cell leukaemia virus type I and adult T-cell leukaemia-lymphoma. Lancet Oncol. 15, e517–e526 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Matsuoka, M. & Jeang, K.T. Human T-cell leukaemia virus type 1 (HTLV-1) infectivity and cellular transformation. Nat. Rev. Cancer 7, 270–280 (2007).

    Article  CAS  PubMed  Google Scholar 

  3. Yamagishi, M. & Watanabe, T. Molecular hallmarks of adult T cell leukemia. Front. Microbiol. 3, 334 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Nakagawa, M. et al. Gain-of-function CCR4 mutations in adult T cell leukemia/lymphoma. J. Exp. Med. 211, 2497–2505 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Swerdllow, S., Campo, E. & Harris, N.L. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues (IARC Press, 2008).

  6. Chiu, Y.L. & Greene, W.C. The APOBEC3 cytidine deaminases: an innate defensive network opposing exogenous retroviruses and endogenous retroelements. Annu. Rev. Immunol. 26, 317–353 (2008).

    Article  CAS  PubMed  Google Scholar 

  7. Zhang, J. et al. The genetic basis of early T-cell precursor acute lymphoblastic leukaemia. Nature 481, 157–163 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).

  9. Boxus, M. et al. The HTLV-1 Tax interactome. Retrovirology 5, 76 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Thome, M., Charton, J.E., Pelzer, C. & Hailfinger, S. Antigen receptor signaling to NF-κB via CARMA1, BCL10, and MALT1. Cold Spring Harb. Perspect. Biol. 2, a003004 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Vallabhapurapu, S. & Karin, M. Regulation and function of NF-κB transcription factors in the immune system. Annu. Rev. Immunol. 27, 693–733 (2009).

    Article  CAS  PubMed  Google Scholar 

  12. Paul, S. & Schaefer, B.C. A new look at T cell receptor signaling to nuclear factor–κB. Trends Immunol. 34, 269–281 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Chen, L. & Flies, D.B. Molecular mechanisms of T cell co-stimulation and co-inhibition. Nat. Rev. Immunol. 13, 227–242 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Bidère, N. et al. Casein kinase 1α governs antigen-receptor-induced NF-κB activation and human lymphoma cell survival. Nature 458, 92–96 (2009).

    Article  PubMed  CAS  Google Scholar 

  15. Orel, L., Neumeier, H., Hochrainer, K., Binder, B.R. & Schmid, J.A. Crosstalk between the NF-κB activating IKK-complex and the CSN signalosome. J. Cell. Mol. Med. 14, 1555–1568 (2010).

    Article  CAS  PubMed  Google Scholar 

  16. Phillips-Mason, P.J., Kaur, H., Burden-Gulley, S.M., Craig, S.E. & Brady-Kalnay, S.M. Identification of phospholipase C γ1 as a protein tyrosine phosphatase μ substrate that regulates cell migration. J. Cell. Biochem. 112, 39–48 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wall, K.A., Pierce, J.D. & Elbein, A.D. Inhibitors of glycoprotein processing alter T-cell proliferative responses to antigen and to interleukin 2. Proc. Natl. Acad. Sci. USA 85, 5644–5648 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Fu, J. et al. The tumor suppressor gene WWOX links the canonical and noncanonical NF-κB pathways in HTLV-I Tax-mediated tumorigenesis. Blood 117, 1652–1661 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Koss, H., Bunney, T.D., Behjati, S. & Katan, M. Dysfunction of phospholipase Cγ in immune disorders and cancer. Trends Biochem. Sci. 39, 603–611 (2014).

    Article  CAS  PubMed  Google Scholar 

  20. Antal, C.E. et al. Cancer-associated protein kinase C mutations reveal kinase's role as tumor suppressor. Cell 160, 489–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Leonard, T.A., Rozycki, B., Saidi, L.F., Hummer, G. & Hurley, J.H. Crystal structure and allosteric activation of protein kinase C βII. Cell 144, 55–66 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Shaffer, A.L. III, Young, R.M. & Staudt, L.M. Pathogenesis of human B cell lymphomas. Annu. Rev. Immunol. 30, 565–610 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Sommer, K. et al. Phosphorylation of the CARMA1 linker controls NF-κB activation. Immunity 23, 561–574 (2005).

    Article  CAS  PubMed  Google Scholar 

  24. Palomero, T. et al. Recurrent mutations in epigenetic regulators, RHOA and FYN kinase in peripheral T cell lymphomas. Nat. Genet. 46, 166–170 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bustelo, X.R. Regulatory and signaling properties of the Vav family. Mol. Cell. Biol. 20, 1461–1477 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Rudd, C.E., Taylor, A. & Schneider, H. CD28 and CTLA-4 coreceptor expression and signal transduction. Immunol. Rev. 229, 12–26 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Griffith, J.W., Sokol, C.L. & Luster, A.D. Chemokines and chemokine receptors: positioning cells for host defense and immunity. Annu. Rev. Immunol. 32, 659–702 (2014).

    Article  CAS  PubMed  Google Scholar 

  28. Bangham, C.R. & Osame, M. Cellular immune response to HTLV-1. Oncogene 24, 6035–6046 (2005).

    Article  CAS  PubMed  Google Scholar 

  29. Bhatia, A. & Kumar, Y. Cellular and molecular mechanisms in cancer immune escape: a comprehensive review. Expert Rev. Clin. Immunol. 10, 41–62 (2014).

    Article  CAS  PubMed  Google Scholar 

  30. Asanuma, S. et al. Adult T-cell leukemia cells are characterized by abnormalities of Helios expression that promote T cell growth. Cancer Sci. 104, 1097–1106 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhang, Z. et al. Expression of a non-DNA-binding isoform of Helios induces T-cell lymphoma in mice. Blood 109, 2190–2197 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Melino, G., De Laurenzi, V. & Vousden, K.H. p73: friend or foe in tumorigenesis. Nat. Rev. Cancer 2, 605–615 (2002).

    Article  CAS  PubMed  Google Scholar 

  33. Supek, F., Minana, B., Valcarcel, J., Gabaldon, T. & Lehner, B. Synonymous mutations frequently act as driver mutations in human cancers. Cell 156, 1324–1335 (2014).

    Article  CAS  PubMed  Google Scholar 

  34. Feschotte, C. & Gilbert, C. Endogenous viruses: insights into viral evolution and impact on host biology. Nat. Rev. Genet. 13, 283–296 (2012).

    Article  CAS  PubMed  Google Scholar 

  35. Najafabadi, H.S. et al. C2H2 zinc finger proteins greatly expand the human regulatory lexicon. Nat. Biotechnol. 33, 555–562 (2015).

    Article  CAS  PubMed  Google Scholar 

  36. Oeckinghaus, A., Hayden, M.S. & Ghosh, S. Crosstalk in NF-κB signaling pathways. Nat. Immunol. 12, 695–708 (2011).

    Article  CAS  PubMed  Google Scholar 

  37. Tong, X. et al. Ataxin-1 and Brother of ataxin-1 are components of the Notch signalling pathway. EMBO Rep. 12, 428–435 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Hodson, D.J. et al. Deletion of the RNA-binding proteins ZFP36L1 and ZFP36L2 leads to perturbed thymic development and T lymphoblastic leukemia. Nat. Immunol. 11, 717–724 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Litchfield, D.W. Protein kinase CK2: structure, regulation and role in cellular decisions of life and death. Biochem. J. 369, 1–15 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Perissi, V., Aggarwal, A., Glass, C.K., Rose, D.W. & Rosenfeld, M.G. A corepressor/coactivator exchange complex required for transcriptional activation by nuclear receptors and other regulated transcription factors. Cell 116, 511–526 (2004).

    Article  CAS  PubMed  Google Scholar 

  41. Childs, K.S. & Goodbourn, S. Identification of novel co-repressor molecules for Interferon Regulatory Factor-2. Nucleic Acids Res. 31, 3016–3026 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kaida, D., Schneider-Poetsch, T. & Yoshida, M. Splicing in oncogenesis and tumor suppression. Cancer Sci. 103, 1611–1616 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Mallory, M.J. et al. Signal- and development-dependent alternative splicing of LEF1 in T cells is controlled by CELF2. Mol. Cell. Biol. 31, 2184–2195 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Ramsay, A.J. et al. POT1 mutations cause telomere dysfunction in chronic lymphocytic leukemia. Nat. Genet. 45, 526–530 (2013).

    Article  CAS  PubMed  Google Scholar 

  45. You, J.S. & Jones, P.A. Cancer genetics and epigenetics: two sides of the same coin? Cancer Cell 22, 9–20 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Kinpara, S. et al. Involvement of double-stranded RNA-dependent protein kinase and antisense viral RNA in the constitutive NFκB activation in adult T-cell leukemia/lymphoma cells. Leukemia 29, 1425–1429 (2015).

    Article  CAS  PubMed  Google Scholar 

  47. Mackay, H.J. & Twelves, C.J. Targeting the protein kinase C family: are we there yet? Nat. Rev. Cancer 7, 554–562 (2007).

    Article  CAS  PubMed  Google Scholar 

  48. Tsukasaki, K. et al. Definition, prognostic factors, treatment, and response criteria of adult T-cell leukemia-lymphoma: a proposal from an international consensus meeting. J. Clin. Oncol. 27, 453–459 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Iwanaga, M. et al. Human T-cell leukemia virus type I (HTLV-1) proviral load and disease progression in asymptomatic HTLV-1 carriers: a nationwide prospective study in Japan. Blood 116, 1211–1219 (2010).

    Article  CAS  PubMed  Google Scholar 

  50. Yoshida, K. et al. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature 478, 64–69 (2011).

    Article  CAS  PubMed  Google Scholar 

  51. Sato, Y. et al. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat. Genet. 45, 860–867 (2013).

    Article  CAS  PubMed  Google Scholar 

  52. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Nannya, Y. et al. A robust algorithm for copy number detection using high-density oligonucleotide single nucleotide polymorphism genotyping arrays. Cancer Res. 65, 6071–6079 (2005).

    Article  CAS  PubMed  Google Scholar 

  54. Yamamoto, G. et al. Highly sensitive method for genomewide detection of allelic composition in nonpaired, primary tumor specimens by use of Affymetrix single-nucleotide-polymorphism genotyping microarrays. Am. J. Hum. Genet. 81, 114–126 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Van Loo, P. et al. Allele-specific copy number analysis of tumors. Proc. Natl. Acad. Sci. USA 107, 16910–16915 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Mermel, C.H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Shiraishi, Y. et al. An empirical Bayesian framework for somatic mutation detection from cancer genome sequencing data. Nucleic Acids Res. 41, e89 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Haferlach, T. et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia 28, 241–247 (2014).

    Article  CAS  PubMed  Google Scholar 

  59. Lawrence, M.S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Accomando, W.P., Wiencke, J.K., Houseman, E.A., Nelson, H.H. & Kelsey, K.T. Quantitative reconstruction of leukocyte subsets using DNA methylation. Genome Biol. 15, R50 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Teschendorff, A.E. et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 29, 189–196 (2013).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank S. Sakaguchi for expert opinion and M. Sago, M. Nakamura, H. Higashi, Y. Ogino, Y. Mori and S. Baba for technical assistance. This work was supported by Grants-in-Aid from the Ministry of Health, Labour and Welfare of Japan and the Japanese Agency for Medical Research and Development (Health and Labour Sciences Research Expenses for Commission and Applied Research for Innovative Treatment of Cancer), Japanese Society for the Promotion of Science (JSPS) KAKENHI (22134006), National Cancer Center Research and Development Funds (26-A-6) and the Funding Program for World-Leading Innovative Research and Development on Science and Technology (FIRST). The National Cancer Center Biobank was supported by the National Cancer Center Research and Development Fund, Japan. This research used computational resources of the K computer provided by the RIKEN Advanced Institute for Computational Science through the HPCI System Research project (hp140230). Supercomputing resources were also provided by the Human Genome Center, the Institute of Medical Science, The University of Tokyo.

Author information

Author notes

  1. Yasunobu Nagata, Akira Kitanaka, Yuichi Shiraishi, Teppei Shimamura, Jun-ichirou Yasunaga and Yasushi Totoki: These authors contributed equally to this work.

  2. Kazuya Shimoda and Seishi Ogawa: These authors jointly supervised this work.

Authors and Affiliations

  1. Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Keisuke Kataoka, Yasunobu Nagata, Aiko Sato-Otsubo, Shinichi Kotani, Yosaku Watatani, June Takeda, Masashi Sanada, Hiromichi Suzuki, Yusuke Sato, Yusuke Shiozawa, Tetsuichi Yoshizato, Kenichi Yoshida, Hideki Makishima & Seishi Ogawa

  2. Department of Gastroenterology and Hematology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan

    Akira Kitanaka, Kotaro Shide, Yoko Kubuki, Tomonori Hidaka, Takuro Kameda & Kazuya Shimoda

  3. Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Yuichi Shiraishi, Kenichi Chiba, Hiroko Tanaka & Satoru Miyano

  4. Division of Systems Biology, Center for Neurological Disease and Cancer, Graduate School of Medicine, Nagoya University, Nagoya, Japan

    Teppei Shimamura

  5. Laboratory of Virus Control, Institute for Virus Research, Kyoto University, Kyoto, Japan

    Jun-ichirou Yasunaga, Guangyong Ma & Masao Matsuoka

  6. Division of Cancer Genomics, National Cancer Center Research Institute, Tokyo, Japan

    Yasushi Totoki, Hiromi Nakamura, Natsuko Hama & Tatsuhiro Shibata

  7. Genome Science Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan

    Genta Nagae & Hiroyuki Aburatani

  8. Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan

    Ryohei Ishii & Osamu Nureki

  9. Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

    Satsuki Muto & Toshiki Watanabe

  10. Department of Advanced Diagnosis, Clinical Research Center, Nagoya Medical Center, Nagoya, Japan

    Masashi Sanada

  11. Department of Frontier Life Science, Nagasaki University Graduate School of Biomedical Science, Nagasaki, Japan

    Masako Iwanaga

  12. Department of Hematology, Kumamoto University School of Medicine, Kumamoto, Japan

    Kisato Nosaka

  13. Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Masakatsu Hishizawa & Akifumi Takaori-Kondo

  14. Department of Hematology, Sasebo City General Hospital, Sasebo, Japan

    Hidehiro Itonaga

  15. Department of Hematology, Atomic Bomb Disease and Hibakusya Medicine Unit, Atomic Bomb Disease Institute, Nagasaki University, Nagasaki, Japan

    Yoshitaka Imaizumi & Yasushi Miyazaki

  16. Department of Hematology, National Cancer Center Hospital, Tokyo, Japan

    Wataru Munakata & Kensei Tobinai

  17. KAN Research Institute, Inc., Kobe, Japan

    Hideaki Ogasawara, Toshitaka Sato, Ken Sasai & Kenzo Muramoto

  18. Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Marina Penova, Takahisa Kawaguchi & Fumihiko Matsuda

  19. Division of Hematology, Department of Medicine, Showa University School of Medicine, Tokyo, Japan

    Tsuyoshi Nakamaki

  20. Department of Hematology and Oncology, Kanazawa University Hospital, Kanazawa, Japan

    Ken Ishiyama

  21. Division of Hematology, Department of Internal Medicine, Tokyo Metropolitan Ohtsuka Hospital, Tokyo, Japan

    Shuichi Miyawaki

  22. Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea

    Sung-Soo Yoon

  23. Pathology Project for Molecular Targets, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan

    Kengo Takeuchi

  24. Laboratory of Molecular Medicine, Human Genome Center, The institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Tatsuhiro Shibata

Authors
  1. Keisuke Kataoka
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  2. Yasunobu Nagata
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  4. Yuichi Shiraishi
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  59. Seishi Ogawa
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Contributions

Y. Shiraishi, K.C., H.T. and S. Miyano developed bioinformatics pipelines. K.K., Y.N., Y.T., H.S., Y. Shiozawa, T.Y., H.N., N.H. and T. Shibata performed sequencing data analyses. K.K., Y.N., S.K., Y.W., J.T. and K.Y. performed sequencing experiments. K.K., A.S.-O., S. Muto, Y. Sato, M.P., T. Kawaguchi and F.M. performed SNP array analysis. K.K., T. Shimamura, G.N. and H.A. performed methylation analysis. K.K., J.Y., R.I., G.M., H.O., T. Sato, K. Sasai, K.M., K. Takeuchi, O.N. and M.M. performed functional assays. K.K., M.S., H.M. and S.O. interpreted the results. A.K., J.Y., K.N., M.I., M.H., H.I., Y.I., W.M., K. Shide, Y.K., T.H., T. Kameda, T.N., K.I., S. Miyawaki, S.-S.Y., K. Tobinai, Y.M., A.T.-K., T.W., M.M. and K. Shimoda collected specimens. K.K. and S.O. generated figures and tables and wrote the manuscript. K. Shimoda and S.O. co-led the entire project. All authors participated in discussions and interpretation of the data and results.

Corresponding author

Correspondence to Seishi Ogawa.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Somatic coding mutations identified by WES/WGS for 83 ATL cases.

(a) The percentage of targeted bases covered by at least 2×, 10×, 20× and 30× sequencing reads (top) and average read depth (bottom) are shown for 81 paired tumor (T) and normal (N) WES samples. (b) Mutational signature identified in 81 WES cases, showing predominant age-related C>T transitions. (c) Correlation of the number of coding mutations (including synonymous SNVs) with patient age in 83 WES/WGS cases. (d–g) All panels are aligned, with the vertical tracks representing 83 WES/WGS cases. The data are sorted by number of coding mutations (d) and disease subtype (f). The relative frequency of nucleotide substitutions (e), a heat map showing the distribution of mutations in significantly mutated genes (q < 0.1) identified by the MutSigCV algorithm (f) and the variant allele frequency (VAF) of mutations located in diploid regions (g) are depicted.

Supplementary Figure 2 Results of WGS for 48 ATL cases.

(a) The percentage of targeted bases covered by at least 2×, 10×, 20× and 30× sequencing reads (top) and average read depth (bottom) are shown for 11 full-pass WGS and 37 low-pass WGS samples. (b) Venn diagram showing the number of coding mutations identified in WES and full-pass WGS (left), the correlation of VAFs for coding mutations between WES and full-pass WGS (middle) and comparison of the VAFs for coding mutations detected by both WES and full-pass WGS, only WES and only full-pass WGS (right) are shown (n = 9). (c) Number and type of somatic SNVs and indels detected by full-pass WGS (n = 11). (d) Rainfall plots represent the intermutational distances on the indicated chromosome, for which several genomic regions with localized hypermutations are shown. (e) APOBEC3B and APOBEC3G expression for CD4+ T cells from healthy controls (n = 3), mononuclear cells from HTLV-1 carriers (n = 3) and tumor cells from ATL cases (n = 57). (f) Mutational signature identified in 11 full-pass WGS cases, showing predominant age-related C>T transitions. (g) Number and type of SVs detected by full-pass WGS (n = 11) and low-pass WGS (n = 37).

Supplementary Figure 3 Frequent deletions within known fragile sites in ATL.

(a) Genome-wide distribution of deletion breakpoints in cases undergoing full-pass WGS (n = 11; top) and low-pass WGS (n = 37; bottom), showing very frequent deletions in known fragile sites. The y axis represents the frequency of cases with deletion breakpoints. (b) Frequent deletions in known fragile sites detected by SNP array analysis for 426 ATL cases. Segmented copy number data are shown. Each row represents a patient; deleted regions are shown in blue. (c) Comparison across different hematological malignancies of the frequency of cases showing copy number breakpoints within the indicated fragile sites or genes as detected by SNP array karyotyping. T-ALL, T cell acute lymphoblastic leukemia; B-ALL, B cell acute lymphoblastic leukemia; CLL, chronic lymphocytic leukemia; DLBCL, diffuse large B cell lymphoma; MCL, mantle cell lymphoma; FL, follicular lymphoma; MALT, mucosa-associated lymphoid tissue lymphoma; AML, acute myeloid leukemia; CML, chronic myeloid leukemia; MDS, myelodysplastic syndrome; CMML, chronic myelomonocytic leukemia; MPN, myeloproliferative neoplasm. SNP array data were obtained from GEO (GSE15187, GSE47682 and GSE12906) or our in-house database. (d) Multiple deletions within known fragile sites detected by full-pass and low-pass WGS (n = 48). Different colors represent different cases.

Supplementary Figure 4 HTLV-1 integration and expression.

(a) HTLV-1 integration sites (blue boxes; n = 62) detected by full-pass (n = 11) and low-pass (n = 37) WGS. (b) Clonal structure of ATL in five representative cases. The allele frequencies of driver mutations and HTLV-1 integrations are shown in filled circles and red bars, respectively. Only mutations residing in copy number–neutral segments are presented. (c) RNA-seq data are visualized with IGV, in which cumulative numbers of sequencing reads are displayed along genomic positions. Additional representative cases (ATL009, ATL011, ATL014 and ATL019) showing antisense-predominant HTLV-1 transcription found in most ATL cases are presented. Abnormal tax transcripts with deletions are indicated by asterisks. Boxes show ORFs. (d) Ratio of antisense to sense transcripts in 57 cases analyzed by RNA-seq, evaluated in the two regions in pX showing unidirectional transcription. (e) Box plots (median and interquartile values) of HTLV-1 gene expression levels across 57 ATL cases. FPKM was calculated for each region where HTLV-1 genes were located. (f) Expression levels of read-through transcripts at 53 integration sites in 41 cases with WGS and RNA-seq data. (g) Frequency of integrations showing aberrantly spliced transcripts depending on the orientation of transcription for the cellular gene with respect to the HTLV-1 genome. (h) Summary of gene expression levels for 23 genes located adjacent to HTLV-1 integrations. Log2 (FPKM + 1) was converted to a z score and plotted according to the orientation of transcription for the cellular gene with respect to the HTLV-1 genome. (i) The expression of 12 sense-oriented (left) and 11 antisense-oriented (right) genes found to be expressed in close proximity to a viral integration site was measured in 57 ATL cases analyzed by RNA-seq. Expression in the tumor sample having the relevant integration site for each gene is highlighted in red. The presence (+) or absence (–) of aberrantly spliced fusions between the cellular and viral genomes is shown (bottom). (j) Transcripts from around the viral integration site in the C14orf159 locus in a representative case (ATL016), as compared to those in a control (ATL017). Read-through antisense transcripts transcribed from the 5′ LTR into the juxtaposed cellular genome, a fusion transcript between R in the 3′ LTR and exon 10 of C14orf159, and a fusion transcript between HBZ and exon 9 of C14orf159 are shown with fused sequences.

Supplementary Figure 5 Recurring somatic mutations detected by targeted capture sequencing for 370 ATL cases.

(a) Comparison of WES/WGS (n = 83) and targeted capture sequencing (n = 370), showing a comparable frequency for 50 significant somatic mutations. (b) Box plots (median and interquartile values) of expression levels (log2 (FPKM + 1)) for 50 significantly mutated genes across 57 ATL cases. Genes with FPKM <1 were filtered out from the list of significantly mutated genes. (c) Number of cases with copy number gain, copy number loss or UPD coexisting with gain-of-function (left) and loss-of-function (right) mutations of the indicated genes in 370 ATL cases. (d) VAFs of CARD11, IRF4, FAS, TP53, TBL1XR1, HLA-B, CD58 and GPR183 mutations in ATL cases with or without copy number gain or loss or UPD, indicating preferential amplification of mutant alleles. (e) The locations and types of somatic mutations in significantly mutated genes. NCBI protein reference sequences are shown in Supplementary Table 24. Nonsense, frameshift and splice-site mutations were distributed across the entire gene for TBL1XR1, CD58, POT1, IRF2BP2, EP300, CSNK2B and CSNK2A1, suggesting a loss-of-function nature for these mutations. Site-specific mutations were observed in NOTCH1, suggesting a gain-of-function nature for these mutations. Site-specific mutations were also found in CSNK1A1, including a known dominant-negative mutation encoding p.Asp136Asn in one case.

Supplementary Figure 6 CNVs detected by SNP array karyotyping for 426 ATL cases.

(a) Comparison of Affymetrix 250K (n = 282) and Illumina 610K SNP arrays (n = 144) showing a comparable frequency of significantly altered regions. (b) Frequency of arm-level gains and losses in 426 ATL cases. Chromosome arms with estimated copy number ≥2.5 were considered to represent copy number gain, whereas those with estimated copy number <1.5 were considered to represent copy number loss. (c) The heat map shows somatic CNVs in each tumor (horizontal axis) plotted by chromosomal location (vertical axis). Unsupervised hierarchical clustering was performed with Manhattan distance and Ward’s linkage algorithm. (d) Significant focal amplifications and deletions detected by GISTIC 2.0 analysis. Segmented copy number data from SNP arrays are shown. Each row represents a patient; amplified and deleted regions are shown in red and blue, respectively.

Supplementary Figure 7 The landscape of somatic mutations and CNVs in ATL.

Experimental platforms, ploidy, disease subtype and CD28 fusion status (top), somatic mutations in significantly mutated genes (middle), and significant focal and broad CNVs (bottom) are shown across samples (n = 370).

Supplementary Figure 8 Deregulated functional pathways in ATL.

(a) Major driver alterations, including mutations, CNVs and SVs (asterisks), are summarized according to their functionalities. Alteration frequencies are expressed as the percentage of examined cases with the alteration; 370 cases were analyzed for mutations, except for genes examined only by WES/WGS (83 cases; crosses) and 426 cases were analyzed for CNVs. Components of the Tax interactome are highlighted by red boxes. (b) Significant enrichments of the antigen presentation pathway in GSEA of expression data comparing ATL cases (n = 57) to healthy controls (n = 3) and HTLV-1 carriers (n = 3).

Supplementary Figure 9 Biological significance of PRKCB and CARD11 mutations.

(a) Amino acid sequence alignment of the Homo sapiens PKCβ protein with those from various other organisms and other Homo sapiens PKCs using the ClustalW algorithm. The mutation and evolutionarily conserved sites are shown in red and blue, respectively. (b) Immunoblot of PKCβ and/or phosphorylated PKCβ expression in HEK293T (left) and Jurkat (right) cells expressing WT or Asp427Asn PKCβ. Blots representative of at least three independent experiments are shown. (c) Immunoblot analysis of PKCβ was performed for HEK293T cells transduced with the indicated PKCβ mutants after exposure to PMA and ionomycin for 15 min. Cytosolic PKCβ and β-tubulin protein levels were quantified using Photoshop software (n = 3). Data represent means ± s.d. *P < 0.05, Student’s t test. (d) Box plots (median and interquartile values) of the relative expression levels of CARD11 exon 15 and CARD11 exon 1 in cases with or without CARD11 intragenic deletion. Student’s t test was performed to compare expression (FPKM). (e) RNA-seq data are visualized with IGV for representative cases with (ATL026) and without (ATL027) an intragenic deletion of CARD11 that results in skipping of exons 15–17. The cumulative numbers of sequencing reads (depths) and junctional reads are shown along genomic positions. (f) Immunoblot of CARD11 expression in HEK293T cells expressing WT or Glu626Lys CARD11. Blots representative of three independent experiments are shown. (g,h) HEK293T (g) and Jurkat (h) cells expressing Glu626Lys CARD11 showed augmented NF-κB transcription, as compared with cells expressing WT CARD11, in luciferase assays (n = 3). Data represent means ± s.d. *P < 0.05, **P < 0.005, ***P < 0.0005, Student’s t test.

Supplementary Figure 10 Biological significance of CCR4 and CCR7 mutations.

(a,b) Chemotaxis induced by CCL22 (a) or CCL19 (b) in B300-19 cells (a mouse pre-B cell line) expressing WT or Tyr331* CCR4 (a) or WT or Trp355* CCR7 (b) in Transwell assays (n = 3). The number of viable cells was assessed using CellTiter-Glo assays and normalized to the value for cells expressing WT without ligand. Data represent means ± s.d. *P < 0.05, **P < 0.005, ***P < 0.0005, Student’s t test.

Supplementary Figure 11 Effect of CIMP status on overall survival in acute ATL.

Kaplan-Meier survival curves for 44 acute ATL cases stratified by CIMP status (log-rank test).

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Kataoka, K., Nagata, Y., Kitanaka, A. et al. Integrated molecular analysis of adult T cell leukemia/lymphoma. Nat Genet 47, 1304–1315 (2015). https://doi.org/10.1038/ng.3415

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