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Identification of Novel Functional Variants of SIN3A and SRSF1 among Somatic Variants in Acute Myeloid Leukemia Patients

  • Min, Jae-Woong (Division of Biomedical Convergence, College of Biomedical Science, Institute of Bioscience & Biotechnology, Kangwon National University) ;
  • Koh, Youngil (Department of Internal Medicine, Seoul National University Hospital) ;
  • Kim, Dae-Yoon (Cancer Research Institute, Seoul National University College of Medicine) ;
  • Kim, Hyung-Lae (Department of Biochemistry, School of Medicine, Ewha Woman's University) ;
  • Han, Jeong A (Department of Biochemistry and Molecular Biology, School of Medicine, Kangwon National University) ;
  • Jung, Yu-Jin (Department of Biological Sciences, Kangwon National University) ;
  • Yoon, Sung-Soo (Department of Internal Medicine, Seoul National University Hospital) ;
  • Choi, Sun Shim (Division of Biomedical Convergence, College of Biomedical Science, Institute of Bioscience & Biotechnology, Kangwon National University)
  • Received : 2018.01.29
  • Accepted : 2018.03.08
  • Published : 2018.05.31

Abstract

The advent of massively parallel sequencing, also called next-generation sequencing (NGS), has dramatically influenced cancer genomics by accelerating the identification of novel molecular alterations. Using a whole genome sequencing (WGS) approach, we identified somatic coding and noncoding variants that may contribute to leukemogenesis in 11 adult Korean acute myeloid leukemia (AML) patients, with serial tumor samples (primary and relapse) available for 5 of them; somatic variants were identified in 187 AML-related genes, including both novel (SIN3A, C10orf53, PTPRR, and RERGL) and well-known (NPM1, RUNX1, and CEPBA) AML-related genes. Notably, SIN3A expression shows prognostic value in AML. A newly designed method, referred to as "hot-zone" analysis, detected two putative functional noncoding variants that can alter transcription factor binding affinity near PPP1R10 and SRSF1. Moreover, the functional importance of the SRSF1 noncoding variant was further investigated by luciferase assays, which showed that the variant is critical for the regulation of gene expression leading to leukemogenesis. We expect that further functional investigation of these coding and noncoding variants will contribute to a more in-depth understanding of the underlying molecular mechanisms of AML and the development of targeted anti-cancer drugs.

Keywords

References

  1. Cancer Genome Atlas Research Network. (2013). Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059-2074. https://doi.org/10.1056/NEJMoa1301689
  2. Cancer Genome Atlas Research Network. (2015). Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N. Engl. J. Med. 372, 2481-2498. https://doi.org/10.1056/NEJMoa1402121
  3. Chen, L., Wang, W., Cao, L., Li, Z., and Wang, X. (2016). Long noncoding RNA CCAT1 acts as a competing endogenous RNA to regulate cell growth and differentiation in acute myeloid leukemia. Mol. Cells 39, 330-336. https://doi.org/10.14348/molcells.2016.2308
  4. Das, S., and Krainer, A.R. (2014). Emerging functions of SRSF1, splicing factor and oncoprotein, in RNA metabolism and cancer. Mol. Cancer Res. 12, 1195-1204. https://doi.org/10.1158/1541-7786.MCR-14-0131
  5. de Voer, R.M., Hahn, M.M., Mensenkamp, A.R., Hoischen, A., Gilissen, C., Henkes, A., Spruijt, L., van Zelst-Stams, W.A., Kets, C.M., Verwiel, E.T., et al. (2015). Deleterious germline BLM mutations and the risk for early-onset colorectal cancer. Sci. Rep. 5, 14060. https://doi.org/10.1038/srep14060
  6. Deschler, B., and Lübbert, M. (2006). Acute myeloid leukemia: Epidemiology and etiology. Cancer 107, 2099-2107. https://doi.org/10.1002/cncr.22233
  7. DiNardo, C.D., Ravandi, F., Agresta, S., Konopleva, M., Takahashi, K., Kadia, T., Routbort, M., Patel, K.P., Mark Brandt., Pierce, S., et al. (2015). Characteristics, clinical outcome, and prognostic significance of IDH mutations in AML. Am. J. Hematol. 90, 732-736. https://doi.org/10.1002/ajh.24072
  8. Efron, B. (1988). Logistic regression, survival analysis, and the kaplanmeier curve. J. Am. Stat. Assoc. 83, 414-425. https://doi.org/10.1080/01621459.1988.10478612
  9. ENCODE Project Consortium. (2004). The ENCODE (ENCyclopedia of DNA elements) project. Science 306, 636-640. https://doi.org/10.1126/science.1105136
  10. Farre, D., Bellora, N., Mularoni, L., Messeguer, X., and Alba, M.M. (2007). Housekeeping genes tend to show reduced upstream sequence conservation. Genome Biol. 8, R140. https://doi.org/10.1186/gb-2007-8-7-r140
  11. Ferrara, F., and Schiffer, C.A. (2013). Acute myeloid leukaemia in adults. Lancet 381, 484-495. https://doi.org/10.1016/S0140-6736(12)61727-9
  12. Graham, T.A., and Sottoriva, A. (2017). Measuring cancer evolution from the genome. J. Pathol. 241, 183-191. https://doi.org/10.1002/path.4821
  13. Greenberg, P., Cox, C., LeBeau, M.M., Fenaux, P., Morel, P., Sanz, G., Sanz, M., Vallespi, T., Hamblin, T., Oscier, D., et al. (1997). International scoring system for evaluating prognosis in myelodysplastic syndromes. Blood 89, 2079-2088.
  14. Heideman, M.R., Lancini, C., Proost, N., Yanover, E., Jacobs, H., & Dannenberg, J.H. (2014). Sin3a-associated Hdac1 and Hdac2 are essential for hematopoietic stem cell homeostasis and contribute differentially to hematopoiesis. Haematologica 99, 1292-1303. https://doi.org/10.3324/haematol.2013.092643
  15. Hulegardh, E., Nilsson, C., Lazarevic, V., Garelius, H., Antunovic, P., Rangert Derolf, A, Mollgard, L., Uggla, B., Wennstrom, L., Wahlin, A., et al. (2015). Characterization and prognostic features of secondary acute myeloid leukemia in a population-based setting: A report from the swedish acute leukemia registry. Am. J. Hematol. 90, 208-214. https://doi.org/10.1002/ajh.23908
  16. Jiang, S., Willox, B., Zhou, H., Holthaus, A.M., Wang, A., Shi, T. T., Maruo, S., Kharchenko, P.V., Johannsen, E.C., Kieff, E., et al. (2014). Epstein-barr virus nuclear antigen 3C binds to BATF/IRF4 or SPI1/IRF4 composite sites and recruits Sin3A to repress CDKN2A. Proc. Natl. Acad. Sci. USA 111, 421-426. https://doi.org/10.1073/pnas.1321704111
  17. Jiang, L., Huang, J., Higgs, B. W., Hu, Z., Xiao, Z., Yao, X., Conley, S., Zhong, H., Liu, Z., Brohawn, P., et al. (2016). Genomic landscape survey identifies SRSF1 as a key oncodriver in small cell lung cancer. PLoS Genet. 12, e1005895. https://doi.org/10.1371/journal.pgen.1005895
  18. Juliusson, G., Antunovic, P., Derolf, A., Lehmann, S., Mollgard, L., Stockelberg, D., Tidefelt, U., Wahlin, A., and Höglund, M. (2009). Age and acute myeloid leukemia: Real world data on decision to treat and outcomes from the swedish acute leukemia registry. Blood 113, 4179-4187. https://doi.org/10.1182/blood-2008-07-172007
  19. Kandoth, C., McLellan, M.D., Vandin, F., Ye, K., Niu, B., Lu, C., Xie, M., Zhang, Q., McMichael, J.F., Wyczalkowski, M.A., et al. (2013). Mutational landscape and significance across 12 major cancer types. Nature 502, 333-339. https://doi.org/10.1038/nature12634
  20. Keightley, P.D., Lercher, M. J., and Eyre-Walker, A. (2005). Evidence for widespread degradation of gene control regions in hominid genomes. PLoS Biol. 3, e42. https://doi.org/10.1371/journal.pbio.0030042
  21. Kircher, M., Witten, D.M., Jain, P., O'Roak, B.J., Cooper, G.M., and Shendure, J. (2014). A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310-315. https://doi.org/10.1038/ng.2892
  22. Koboldt, D.C., Zhang, Q., Larson, D. E., Shen, D., McLellan, M.D., Lin, L., Miller, C.A., Mardis, E.R., Ding, L., and Wilson, R.K. (2012). VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568-576. https://doi.org/10.1101/gr.129684.111
  23. Lanotte, M., Martin-Thouvenin, V., Najman, S., Balerini, P., Valensi, F., and Berger, R. (1991). NB4, a maturation inducible cell line with t(15;17) marker isolated from a human acute promyelocytic leukemia (M3). Blood 77, 1080-1086.
  24. Lawrence, M.S., Stojanov, P., Polak, P., Kryukov, G.V., Cibulskis, K., Sivachenko, A., Carter, S.L., Stewart, C., Mermel, C.H., Roberts, S.A., et al. (2013). Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214-218. https://doi.org/10.1038/nature12213
  25. Lee, S., Chen, J., Zhou, G., Shi, R. Z., Bouffard, G.G., Kocherginsky, M., Ge, X., Sun, M., Jayathilaka, N., Kim, Y.C., et al. (2006). Gene expression profiles in acute myeloid leukemia with common translocations using SAGE. Proc. Natl. Acad. Sci. USA 103, 1030-1035. https://doi.org/10.1073/pnas.0509878103
  26. Lee, J., Lee, J., Kim, S., Kim, S., Youk, J., Park, S., An, Y., Keam, B., Kim, D.W., Heo, D.S., et al. (2017). Clonal history and genetic predictors of transformation into small-cell carcinomas from lung adenocarcinomas. J. Clin. Oncol. 35, 3065-3074. https://doi.org/10.1200/JCO.2016.71.9096
  27. Lee, L.Y., Hernandez, D., Rajkhowa, T., Smith, S.C., Raman, J.R., Nguyen, B., Small, D., and Levis, M. (2017). Preclinical studies of gilteritinib, a next-generation FLT3 inhibitor. Blood 129, 257-260. https://doi.org/10.1182/blood-2016-10-745133
  28. Ley, T. J., Mardis, E. R., Ding, L., Fulton, B., McLellan, M.D., Chen, K., Dooling, D., Dunford-Shore, B.H., McGrath, S., Hickenbotham, M., et al. (2008). DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 456, 66-72. https://doi.org/10.1038/nature07485
  29. Li, H., and Durbin, R. (2009). Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics 25, 1754-1760. https://doi.org/10.1093/bioinformatics/btp324
  30. Li, Y., Liang, M., and Zhang, Z. (2014). Regression analysis of combined gene expression regulation in acute myeloid leukemia. PLoS Comput. Biol. 10, e1003908. https://doi.org/10.1371/journal.pcbi.1003908
  31. Malakar, P., Shilo, A., Mogilevsky, A., Stein, I., Pikarsky, E., Nevo, Y., Benyamini, H., Elgavish, S., Zong, X., et al. (2017). Long noncoding RNA MALAT1 promotes hepatocellular carcinoma development by SRSF1 upregulation and mTOR activation. Cancer Res. 77, 1155-1167. https://doi.org/10.1158/0008-5472.CAN-16-1508
  32. Mathelier, A., Zhao, X., Zhang, A. W., Parcy, F., Worsley-Hunt, R., Arenillas, D.J., Buchman, S., Chen, C.Y., Chou, A., Ienasescu, H., et al. (2014). JASPAR 2014: An extensively expanded and updated openaccess database of transcription factor binding profiles. Nucleic Acids Res. 42, D142-7. https://doi.org/10.1093/nar/gkt997
  33. Mathelier, A., Fornes, O., Arenillas, D.J., Chen, C.Y., Denay, G., Lee, J., Shi, W., Shyr, C., Tan, G., Worsley-Hunt, R., et al. (2016). JASPAR 2016: A major expansion and update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 44, D110-5. https://doi.org/10.1093/nar/gkv1176
  34. McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010). The genome analysis toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297-1303. https://doi.org/10.1101/gr.107524.110
  35. McLean, C.Y., Bristor, D., Hiller, M., Clarke, S. L., Schaar, B.T., Lowe, C.B., Wenger, A.M., and Bejerano, G. (2010). GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495-501. https://doi.org/10.1038/nbt.1630
  36. Ong, C., and Corces, V.G. (2011). Enhancer function: New insights into the regulation of tissue-specific gene expression. Nat. Rev. Genet. 12, 283-293.
  37. Oran, B., and Weisdorf, D.J. (2012). Survival for older patients with acute myeloid leukemia: A population-based study. Haematologica 97, 1916-1924. https://doi.org/10.3324/haematol.2012.066100
  38. Papaemmanuil, E., Gerstung, M., Bullinger, L., Gaidzik, V.I., Paschka, P., Roberts, N.D., Potter, N.E., Heuser, M., Thol, F., Bolli, N., et al. (2016). Genomic classification and prognosis in acute myeloid leukemia. N. Engl. J. Med. 374, 2209-2221. https://doi.org/10.1056/NEJMoa1516192
  39. Patel, J.P., Gönen, M., Figueroa, M.E., Fernandez, H., Sun, Z., Racevskis, J., Van Vlierberghe, P., Dolgalev, I., Thomas, S., Aminova, O., et al. (2012). Prognostic relevance of integrated genetic profiling in acute myeloid leukemia. N. Engl. J. Med. 366, 1079-1089. https://doi.org/10.1056/NEJMoa1112304
  40. Rapin, N., Bagger, F.O., Jendholm, J., Mora-Jensen, H., Krogh, A., Kohlmann, A., Thiede, C., Borregaard, N., Bullinger, L., Winther, O., et al. (2014). Comparing cancer vs normal gene expression profiles identifies new disease entities and common transcriptional programs in AML patients. Blood 123, 894-904. https://doi.org/10.1182/blood-2013-02-485771
  41. Royston, P., and Altman, D.G. (2013). External validation of a cox prognostic model: Principles and methods. BMC Med. Res. Methodol. 13, 33-2288-13-33. https://doi.org/10.1186/1471-2288-13-33
  42. Rozen, S., and Skaletsky, H. (2000). Primer3 on the WWW for general users and for biologist programmers. Methods Mol. Biol. 132, 365-386.
  43. Stein, E.M., DiNardo, C.D., Pollyea, D.A., Fathi, A.T., Roboz, G.J., Altman, J.K., Stone, R.M., DeAngelo, D.J., Levine, R.L., Flinn, I.W., et al. (2017). Enasidenib in mutant-IDH2 relapsed or refractory acute myeloid leukemia. Blood 130, 722-731. https://doi.org/10.1182/blood-2017-04-779405
  44. Stephens, P.J., Tarpey, P.S., Davies, H., Van Loo, P., Greenman, C., Wedge, D.C., Nik-Zainal, S., Martin, S., Varela, I., Bignell, G.R., et al. (2012). The landscape of cancer genes and mutational processes in breast cancer. Nature 486, 400-404. https://doi.org/10.1038/nature11017
  45. Tan, G., and Lenhard, B. (2016). TFBSTools: An R/bioconductor package for transcription factor binding site analysis. Bioinformatics 32, 1555-1556. https://doi.org/10.1093/bioinformatics/btw024
  46. Wang, K., Li, M., and Hakonarson, H. (2010). ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res., 38, e164. https://doi.org/10.1093/nar/gkq603
  47. Ward, L.D., and Kellis, M. (2011). HaploReg: A resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930-D934.
  48. Ward, L.D., and Kellis, M. (2012). Interpreting noncoding genetic variation in complex traits and human disease. Nat. Biotechnol. 30, 1095-1106. https://doi.org/10.1038/nbt.2422
  49. Zhang, Q., Cheng, T., Jin, S., Guo, Y., Wu, Y., Liu, D., Xu, X., Sun, Y., Li, Z., He H., et al. (2017). Genome-wide open chromatin regions and their effects on the regulation of silk protein genes in bombyx mori. Sci. Rep. 7, 12919. https://doi.org/10.1038/s41598-017-13186-6