Genetic alterations of JAK/STAT cascade and histone modification in extranodal NK/T-cell lymphoma nasal type

Seungbok Lee, Ha Young Park, So Young Kang, Seok Jin Kim, Jinha Hwang, Seungho Lee, Soo Heon Kwak, Kyong Soo Park, Hae Yong Yoo, Won Seog Kim, Jong-Il Kim, Young Hyeh Ko, Seungbok Lee, Ha Young Park, So Young Kang, Seok Jin Kim, Jinha Hwang, Seungho Lee, Soo Heon Kwak, Kyong Soo Park, Hae Yong Yoo, Won Seog Kim, Jong-Il Kim, Young Hyeh Ko

Abstract

Extranodal NK/T-cell lymphoma nasal type (ENKL) is a rare type of non-Hodgkin lymphoma that more frequently occurs in East Asia and Latin America. Even though its molecular background has been discussed in the last few years, the current knowledge does not explain the disease pathogenesis in most cases of ENKL. Here, we performed multiple types of next-generation sequencing on 34 ENKL samples, including whole-exome sequencing (9 cancer tissues and 4 cancer cell lines), targeted sequencing (21 cancer tissues), and RNA sequencing (3 cancer tissues and 4 cancer cell lines). Mutations were found most frequently in 3 genes, STAT3, BCOR, and MLL2 (which were present in 9, 7, and 6 cancer samples, respectively), whereas there were only 2 cases of JAK3 mutation. In total, JAK/STAT pathway- and histone modification-related genes accounted for 55.9% and 38.2% of cancer samples, respectively, and their involvement in ENKL pathogenesis was also supported by gene expression analysis. In addition, we provided 177 genes upregulated only in cancer tissues, which appear to be linked with angiocentric and angiodestructive growth of ENKL. In this study, we propose several novel driver genes of ENKL, and show that these genes and their functional groups may be future therapeutic targets of this disease.

Keywords: JAK-STAT pathway; chromatin modification; extranodal NK/T-cell lymphoma nasal type; next-generation sequencing; somatic mutation.

Conflict of interest statement

CONFLICTS OF INTEREST

The authors declare no competing financial interests.

Figures

Figure 1. Distribution of mutations in ENKL
Figure 1. Distribution of mutations in ENKL
Figure 2. Locations of STAT3 mutations
Figure 2. Locations of STAT3 mutations
All 9 missense SNVs were accumulated in the SH2 domain.
Figure 3. Functional enrichment of commonly upregulated…
Figure 3. Functional enrichment of commonly upregulated or downregulated genes in both CT and CC samples
A. Hierarchical clustering and heat map of common DEGs. B. Gene ontologies enriched in up- or downregulated genes. C. Top 10 KEGG pathways and D. top 10 CGP gene sets enriched in upregulated genes.
Figure 4. Functional enrichment of genes that…
Figure 4. Functional enrichment of genes that were upregulated only in CT samples
A. Gene ontologies enriched in CT-specific upregulated genes. There were several vasculature-development- or endothelium-development-related ontologies, which were the most significant. B. Top 10 KEGG pathways and C. top 10 CGP gene sets enriched in these genes.
Figure 5. Mutation rates of BCOR according…
Figure 5. Mutation rates of BCOR according to tumor type
EBV-associated malignancies (ENKL and EBV(+) GC) showed high percentages for both BCOR mutation rate and LOF proportion. LOF, loss-of-function; GC, gastric carcinoma; UECA, uterine endometrial carcinoma; LUAD, lung adenocarcinoma; CRC, colorectal adenocarcinoma; AML, acute myeloid leukemia; MDS, myelodysplastic syndrome.

References

    1. Au WY, Weisenburger DD, Intragumtornchai T, Nakamura S, Kim WS, Sng I, Vose J, Armitage JO, Liang R. Clinical differences between nasal and extranasal natural killer/T-cell lymphoma: a study of 136 cases from the International Peripheral T-Cell Lymphoma Project. Blood. 2009;113:3931–3937.
    1. Lee J, Kim WS, Park YH, Park SH, Park KW, Kang JH, Lee SS, Lee SI, Lee SH, Kim K, Jung CW, Ahn YC, Ko YH, Park K. Nasal-type NK/T cell lymphoma: clinical features and treatment outcome. Br J Cancer. 2005;92:1226–1230.
    1. Lee J, Suh C, Park YH, Ko YH, Bang SM, Lee JH, Lee DH, Huh J, Oh SY, Kwon HC, Kim HJ, Lee SI, Kim JH, Park J, Oh SJ, Kim K, et al. Extranodal natural killer T-cell lymphoma, nasal-type: a prognostic model from a retrospective multicenter study. J Clin Oncol. 2006;24:612–618.
    1. Suzuki R, Suzumiya J, Yamaguchi M, Nakamura S, Kameoka J, Kojima H, Abe M, Kinoshita T, Yoshino T, Iwatsuki K, Kagami Y, Tsuzuki T, Kurokawa M, Ito K, Kawa K, Oshimi K. Prognostic factors for mature natural killer (NK) cell neoplasms: aggressive NK cell leukemia and extranodal NK cell lymphoma, nasal type. Ann Oncol. 2010;21:1032–1040.
    1. Yamaguchi M, Kwong YL, Kim WS, Maeda Y, Hashimoto C, Suh C, Izutsu K, Ishida F, Isobe Y, Sueoka E, Suzumiya J, Kodama T, Kimura H, Hyo R, Nakamura S, Oshimi K, et al. Phase II study of SMILE chemotherapy for newly diagnosed stage IV, relapsed, or refractory extranodal natural killer (NK)/T-cell lymphoma, nasal type: the NK-Cell Tumor Study Group study. J Clin Oncol. 2011;29:4410–4416.
    1. Jaccard A, Gachard N, Marin B, Rogez S, Audrain M, Suarez F, Tilly H, Morschhauser F, Thieblemont C, Ysebaert L, Devidas A, Petit B, de Leval L, Gaulard P, Feuillard J, Bordessoule D, et al. Efficacy of L-asparaginase with methotrexate and dexamethasone (AspaMetDex regimen) in patients with refractory or relapsing extranodal NK/T-cell lymphoma, a phase 2 study. Blood. 2011;117:1834–1839.
    1. Kanavaros P, Briere J, Emile JF, Gaulard P. Epstein-Barr virus in T and natural killer (NK) cell non-Hodgkin's lymphomas. Leukemia. 1996;10:s84–87.
    1. Aozasa K, Takakuwa T, Hongyo T, Yang WI. Nasal NK/T-cell lymphoma: epidemiology and pathogenesis. Int J Hematol. 2008;87:110–117.
    1. Nakashima Y, Tagawa H, Suzuki R, Karnan S, Karube K, Ohshima K, Muta K, Nawata H, Morishima Y, Nakamura S, Seto M. Genome-wide array-based comparative genomic hybridization of natural killer cell lymphoma/leukemia: different genomic alteration patterns of aggressive NK-cell leukemia and extranodal Nk/T-cell lymphoma, nasal type. Genes Chromosomes Cancer. 2005;44:247–255.
    1. Karube K, Nakagawa M, Tsuzuki S, Takeuchi I, Honma K, Nakashima Y, Shimizu N, Ko YH, Morishima Y, Ohshima K, Nakamura S, Seto M. Identification of FOXO3 and PRDM1 as tumor-suppressor gene candidates in NK-cell neoplasms by genomic and functional analyses. Blood. 2011;118:3195–3204.
    1. Koo GC, Tan SY, Tang T, Poon SL, Allen GE, Tan L, Chong SC, Ong WS, Tay K, Tao M, Quek R, Loong S, Yeoh KW, Yap SP, Lee KA, Lim LC, et al. Janus kinase 3-activating mutations identified in natural killer/T-cell lymphoma. Cancer discovery. 2012;2:591–597.
    1. Bouchekioua A, Scourzic L, de Wever O, Zhang Y, Cervera P, Aline-Fardin A, Mercher T, Gaulard P, Nyga R, Jeziorowska D, Douay L, Vainchenker W, Louache F, Gespach C, Solary E, Coppo P. JAK3 deregulation by activating mutations confers invasive growth advantage in extranodal nasal-type natural killer cell lymphoma. Leukemia. 2014;28:338–348.
    1. Huang Y, de Reynies A, de Leval L, Ghazi B, Martin-Garcia N, Travert M, Bosq J, Briere J, Petit B, Thomas E, Coppo P, Marafioti T, Emile JF, Delfau-Larue MH, Schmitt C, Gaulard P. Gene expression profiling identifies emerging oncogenic pathways operating in extranodal NK/T-cell lymphoma, nasal type. Blood. 2010;115:1226–1237.
    1. Ng SB, Yan J, Huang G, Selvarajan V, Tay JL, Lin B, Bi C, Tan J, Kwong YL, Shimizu N, Aozasa K, Chng WJ. Dysregulated microRNAs affect pathways and targets of biologic relevance in nasal-type natural killer/T-cell lymphoma. Blood. 2011;118:4919–4929.
    1. Huang Y, de Leval L, Gaulard P. Molecular underpinning of extranodal NK/T-cell lymphoma. Best practice & research Clinical haematology. 2013;26:57–74.
    1. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research. 2000;28:27–30.
    1. Huynh KD, Fischle W, Verdin E, Bardwell VJ. BCoR, a novel corepressor involved in BCL-6 repression. Genes & development. 2000;14:1810–1823.
    1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature genetics. 2000;25:25–29.
    1. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Jr, Kinzler KW. Cancer genome landscapes. Science. 2013;339:1546–1558.
    1. Oh ST, Simonds EF, Jones C, Hale MB, Goltsev Y, Gibbs KD, Jr, Merker JD, Zehnder JL, Nolan GP, Gotlib J. Novel mutations in the inhibitory adaptor protein LNK drive JAK-STAT signaling in patients with myeloproliferative neoplasms. Blood. 2010;116:988–992.
    1. Kimura H, Karube K, Ito Y, Hirano K, Suzuki M, Iwata S, Seto M. Rare occurrence of JAK3 mutations in natural killer cell neoplasms in Japan. Leukemia & lymphoma. 2014;55:962–963.
    1. Yu H, Pardoll D, Jove R. STATs in cancer inflammation and immunity: a leading role for STAT3. Nature reviews Cancer. 2009;9:798–809.
    1. Shair KH, Bendt KM, Edwards RH, Bedford EC, Nielsen JN, Raab-Traub N. EBV latent membrane protein 1 activates Akt, NFkappaB, and Stat in B cell lymphomas. PLoS pathogens. 2007;3:e166.
    1. Muromoto R, Ikeda O, Okabe K, Togi S, Kamitani S, Fujimuro M, Harada S, Oritani K, Matsuda T. Epstein-Barr virus-derived EBNA2 regulates STAT3 activation. Biochemical and biophysical research communications. 2009;378:439–443.
    1. Sanchez-Margalet V, Martin-Romero C. Human leptin signaling in human peripheral blood mononuclear cells: activation of the JAK-STAT pathway. Cellular immunology. 2001;211:30–36.
    1. Perez-Garcia A, Ambesi-Impiombato A, Hadler M, Rigo I, LeDuc CA, Kelly K, Jalas C, Paietta E, Racevskis J, Rowe JM, Tallman MS, Paganin M, Basso G, Tong W, Chung WK, Ferrando AA. Genetic loss of SH2B3 in acute lymphoblastic leukemia. Blood. 2013;122:2425–2432.
    1. Morin RD, Mendez-Lago M, Mungall AJ, Goya R, Mungall KL, Corbett RD, Johnson NA, Severson TM, Chiu R, Field M, Jackman S, Krzywinski M, Scott DW, Trinh DL, Tamura-Wells J, Li S, et al. Frequent mutation of histone-modifying genes in non-Hodgkin lymphoma. Nature. 2011;476:298–303.
    1. Dalgliesh GL, Furge K, Greenman C, Chen L, Bignell G, Butler A, Davies H, Edkins S, Hardy C, Latimer C, Teague J, Andrews J, Barthorpe S, Beare D, Buck G, Campbell PJ, et al. Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes. Nature. 2010;463:360–363.
    1. Parsons DW, Li M, Zhang X, Jones S, Leary RJ, Lin JC, Boca SM, Carter H, Samayoa J, Bettegowda C, Gallia GL, Jallo GI, Binder ZA, Nikolsky Y, Hartigan J, Smith DR, et al. The genetic landscape of the childhood cancer medulloblastoma. Science. 2011;331:435–439.
    1. Hilton E, Johnston J, Whalen S, Okamoto N, Hatsukawa Y, Nishio J, Kohara H, Hirano Y, Mizuno S, Torii C, Kosaki K, Manouvrier S, Boute O, Perveen R, Law C, Moore A, et al. BCOR analysis in patients with OFCD and Lenz microphthalmia syndromes, mental retardation with ocular anomalies, and cardiac laterality defects. European journal of human genetics : EJHG. 2009;17:1325–1335.
    1. Damm F, Chesnais V, Nagata Y, Yoshida K, Scourzic L, Okuno Y, Itzykson R, Sanada M, Shiraishi Y, Gelsi-Boyer V, Renneville A, Miyano S, Mori H, Shih LY, Park S, Dreyfus F, et al. BCOR and BCORL1 mutations in myelodysplastic syndromes and related disorders. Blood. 2013;122:3169–3177.
    1. Grossmann V, Tiacci E, Holmes AB, Kohlmann A, Martelli MP, Kern W, Spanhol-Rosseto A, Klein HU, Dugas M, Schindela S, Trifonov V, Schnittger S, Haferlach C, Bassan R, Wells VA, Spinelli O, et al. Whole-exome sequencing identifies somatic mutations of BCOR in acute myeloid leukemia with normal karyotype. Blood. 2011;118:6153–6163.
    1. Cancer Genome Atlas Research N. Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014;513:202–209.
    1. Cancer Genome Atlas Research N. Kandoth C, Schultz N, Cherniack AD, Akbani R, Liu Y, Shen H, Robertson AG, Pashtan I, Shen R, Benz CC, Yau C, Laird PW, Ding L, Zhang W, Mills GB, et al. Integrated genomic characterization of endometrial carcinoma. Nature. 2013;497:67–73.
    1. Imielinski M, Berger AH, Hammerman PS, Hernandez B, Pugh TJ, Hodis E, Cho J, Suh J, Capelletti M, Sivachenko A, Sougnez C, Auclair D, Lawrence MS, Stojanov P, Cibulskis K, Choi K, et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell. 2012;150:1107–1120.
    1. Hodis E, Watson IR, Kryukov GV, Arold ST, Imielinski M, Theurillat JP, Nickerson E, Auclair D, Li L, Place C, Dicara D, Ramos AH, Lawrence MS, Cibulskis K, Sivachenko A, Voet D, et al. A landscape of driver mutations in melanoma. Cell. 2012;150:251–263.
    1. Cancer Genome Atlas N. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487:330–337.
    1. Tempera I, Lieberman PM. Epigenetic regulation of EBV persistence and oncogenesis. Seminars in cancer biology. 2014;26:22–29.
    1. Steven H, Swerdlow EC, Lee Harris Nancy, Jaffe Elaine, Stefano S, Pileri A, Stein Harald, Thiele Jurgen, Vardiman James W. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. International Agency for Reasearch on Cancer: WHO PRESS; 2008.
    1. Shan Z, Li G, Zhan Q, Li D. Gadd45a inhibits cell migration and invasion by altering the global RNA expression. Cancer biology & therapy. 2012;13:1112–1122.
    1. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nature methods. 2012;9:357–359.
    1. Perez-Llamas C, Lopez-Bigas N. Gitools: analysis and visualisation of genomic data using interactive heat-maps. PloS one. 2011;6:e19541.
    1. Ju YS, Lee WC, Shin JY, Lee S, Bleazard T, Won JK, Kim YT, Kim JI, Kang JH, Seo JS. A transforming KIF5B and RET gene fusion in lung adenocarcinoma revealed from whole-genome and transcriptome sequencing. Genome Res. 2012;22:436–445.
    1. Engstrom PG, Steijger T, Sipos B, Grant GR, Kahles A, Consortium R, Ratsch G, Goldman N, Hubbard TJ, Harrow J, Guigo R, Bertone P. Systematic evaluation of spliced alignment programs for RNA-seq data. Nature methods. 2013;10:1185–1191.
    1. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.
    1. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome research. 2010;20:1297–1303.
    1. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Gabriel S, Meyerson M, Lander ES, Getz G. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature biotechnology. 2013;31:213–219.
    1. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic acids research. 2010;38:e164.
    1. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Kang HM, Marth GT, McVean GA. An integrated map of genetic variation from 1, 092 human genomes. Nature. 2012;491:56–65.
    1. Drmanac R, Sparks AB, Callow MJ, Halpern AL, Burns NL, Kermani BG, Carnevali P, Nazarenko I, Nilsen GB, Yeung G, Dahl F, Fernandez A, Staker B, Pant KP, Baccash J, Borcherding AP, et al. Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science. 2010;327:78–81.
    1. Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, Carter SL, Stewart C, Mermel CH, Roberts SA, Kiezun A, Hammerman PS, McKenna A, Drier Y, Zou L, Ramos AH, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013;499:214–218.
    1. McPherson A, Hormozdiari F, Zayed A, Giuliany R, Ha G, Sun MG, Griffith M, Heravi Moussavi A, Senz J, Melnyk N, Pacheco M, Marra MA, Hirst M, Nielsen TO, Sahinalp SC, Huntsman D, et al. deFuse: an algorithm for gene fusion discovery in tumor RNA-Seq data. PLoS Comput Biol. 2011;7:e1001138.
    1. Steidl C, Shah SP, Woolcock BW, Rui L, Kawahara M, Farinha P, Johnson NA, Zhao Y, Telenius A, Neriah SB, McPherson A, Meissner B, Okoye UC, Diepstra A, van den Berg A, Sun M, et al. MHC class II transactivator CIITA is a recurrent gene fusion partner in lymphoid cancers. Nature. 2011;471:377–381.
    1. Kangaspeska S, Hultsch S, Edgren H, Nicorici D, Murumagi A, Kallioniemi O. Reanalysis of RNA-sequencing data reveals several additional fusion genes with multiple isoforms. PLoS One. 2012;7:e48745.
    1. Kim D, Salzberg SL. TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol. 2011;12:R72.
    1. Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D, Jia M, Shepherd R, Leung K, Menzies A, Teague JW, Campbell PJ, Stratton MR, Futreal PA. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic acids research. 2011;39:D945–950.
    1. Anders S, Pyl PT, Huber W. HTSeq—A Python framework to work with high-throughput sequencing data. bioRxiv. 2014
    1. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. bioRxiv. 2014
    1. de Hoon MJ, Imoto S, Nolan J, Miyano S. Open source clustering software. Bioinformatics. 2004;20:1453–1454.
    1. Saldanha AJ. Java Treeview—extensible visualization of microarray data. Bioinformatics. 2004;20:3246–3248.
    1. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545–15550.
    1. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pages F, Trajanoski Z, Galon J. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25:1091–1093.
    1. Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, Lotia S, Pico AR, Bader GD, Ideker T. A travel guide to Cytoscape plugins. Nature methods. 2012;9:1069–1076.

Source: PubMed

Спонсоры и соавторы

Заболевания

Медикаментозные вмешательства

Подписаться