Genomics & Informatics

Korea Genome Organization (한국유전체학회)
권호 표지
DOI QR코드

DOI QR Code

Genetic Risk Prediction for Normal-Karyotype Acute Myeloid Leukemia Using Whole-Exome Sequencing

  • Heo, Seong Gu (Department of Medical Genetics, Hallym University College of Medicine) ;
  • Hong, Eun Pyo (Department of Medical Genetics, Hallym University College of Medicine) ;
  • Park, Ji Wan (Department of Medical Genetics, Hallym University College of Medicine)
  • Received : 2013.01.23
  • Accepted : 2013.02.18
  • Published : 2013.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Normal-karyotype acute myeloid leukemia (NK-AML) is a highly malignant and cytogenetically heterogeneous hematologic cancer. We searched for somatic mutations from 10 pairs of tumor and normal cells by using a highly efficient and reliable analysis workflow for whole-exome sequencing data and performed association tests between the NK-AML and somatic mutations. We identified 21 nonsynonymous single nucleotide variants (SNVs) located in a coding region of 18 genes. Among them, the SNVs of three leukemia-related genes (MUC4, CNTNAP2, and GNAS) reported in previous studies were replicated in this study. We conducted stepwise genetic risk score (GRS) models composed of the NK-AML susceptible variants and evaluated the prediction accuracy of each GRS model by computing the area under the receiver operating characteristic curve (AUC). The GRS model that was composed of five SNVs (rs75156964, rs56213454, rs6604516, rs10888338, and rs2443878) showed 100% prediction accuracy, and the combined effect of the three reported genes was validated in the current study (AUC, 0.98; 95% confidence interval, 0.92 to 1.00). Further study with large sample sizes is warranted to validate the combined effect of these somatic point mutations, and the discovery of novel markers may provide an opportunity to develop novel diagnostic and therapeutic targets for NK-AML.

Keywords

References

  1. Lowenberg B, Downing JR, Burnett A. Acute myeloid leukemia. N Engl J Med 1999;341:1051-1062. https://doi.org/10.1056/NEJM199909303411407
  2. Mrozek K, Marcucci G, Paschka P, Whitman SP, Bloomfield CD. Clinical relevance of mutations and gene-expression changes in adult acute myeloid leukemia with normal cytogenetics: are we ready for a prognostically prioritized molecular classification? Blood 2007;109:431-448. https://doi.org/10.1182/blood-2006-06-001149
  3. Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature 2009;458:719-724. https://doi.org/10.1038/nature07943
  4. Kwon SM, Cho H, Choi JH, Jee BA, Jo Y, Woo HG. Perspectives of integrative cancer genomics in next generation sequencing era. Genomics Inform 2012;10:69-73. https://doi.org/10.5808/GI.2012.10.2.69
  5. Ley TJ, Mardis ER, Ding L, Fulton B, McLellan MD, Chen K, et al. DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 2008;456:66-72. https://doi.org/10.1038/nature07485
  6. Riva L, Luzi L, Pelicci PG. Genomics of acute myeloid leukemia: the next generation. Front Oncol 2012;2:40.
  7. Mardis ER, Ding L, Dooling DJ, Larson DE, McLellan MD, Chen K, et al. Recurring mutations found by sequencing an acute myeloid leukemia genome. N Engl J Med 2009;361: 1058-1066. https://doi.org/10.1056/NEJMoa0903840
  8. Ley TJ, Ding L, Walter MJ, McLellan MD, Lamprecht T, Larson DE, et al. DNMT3A mutations in acute myeloid leukemia. N Engl J Med 2010;363:2424-2433. https://doi.org/10.1056/NEJMoa1005143
  9. De Weer A, Poppe B, Vergult S, Van Vlierberghe P, Petrick M, De Bock R, et al. Identification of two critically deleted regions within chromosome segment 7q35-q36 in EVI1 deregulated myeloid leukemia cell lines. PLoS One 2010;5:e8676. https://doi.org/10.1371/journal.pone.0008676
  10. Bejar R, Stevenson K, Abdel-Wahab O, Galili N, Nilsson B, Garcia-Manero G, et al. Clinical effect of point mutations in myelodysplastic syndromes. N Engl J Med 2011;364:2496- 2506. https://doi.org/10.1056/NEJMoa1013343
  11. Link DC, Schuettpelz LG, Shen D, Wang J, Walter MJ, Kulkarni S, et al. Identification of a novel TP53 cancer susceptibility mutation through whole-genome sequencing of a patient with therapy-related AML. JAMA 2011;305:1568-1576. https://doi.org/10.1001/jama.2011.473
  12. Yan XJ, Xu J, Gu ZH, Pan CM, Lu G, Shen Y, et al. Exome sequencing identifies somatic mutations of DNA methyltransferase gene DNMT3A in acute monocytic leukemia. Nat Genet 2011;43:309-315. https://doi.org/10.1038/ng.788
  13. Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 2010;26:589-595. https://doi.org/10.1093/bioinformatics/btp698
  14. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009;25:2078-2079. https://doi.org/10.1093/bioinformatics/btp352
  15. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010;20:1297-1303. https://doi.org/10.1101/gr.107524.110
  16. Barnett DW, Garrison EK, Quinlan AR, Stromberg MP, Marth GT. BamTools: a C++ API and toolkit for analyzing and managing BAM files. Bioinformatics 2011;27:1691-1692. https://doi.org/10.1093/bioinformatics/btr174
  17. Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 2012;6:80-92. https://doi.org/10.4161/fly.19695
  18. Harvey RC, Mullighan CG, Wang X, Dobbin KK, Davidson GS, Bedrick EJ, et al. Identification of novel cluster groups in pe diatric high-risk B-precursor acute lymphoblastic leukemia with gene expression profiling: correlation with genome-wide DNA copy number alterations, clinical characteristics, and outcome. Blood 2010;116:4874-4884. https://doi.org/10.1182/blood-2009-08-239681
  19. Zhang J, Mullighan CG, Harvey RC, Wu G, Chen X, Edmonson M, et al. Key pathways are frequently mutated in high-risk childhood acute lymphoblastic leukemia: a report from the Children's Oncology Group. Blood 2011;118:3080- 3087. https://doi.org/10.1182/blood-2011-03-341412
  20. Srivastava SK, Bhardwaj A, Singh S, Arora S, Wang B, Grizzle WE, et al. MicroRNA-150 directly targets MUC4 and suppresses growth and malignant behavior of pancreatic cancer cells. Carcinogenesis 2011;32:1832-1839. https://doi.org/10.1093/carcin/bgr223
  21. Kufe DW. Mucins in cancer: function, prognosis and therapy. Nat Rev Cancer 2009;9:874-885. https://doi.org/10.1038/nrc2761
  22. Horvath A, Boikos S, Giatzakis C, Robinson-White A, Groussin L, Griffin KJ, et al. A genome-wide scan identifies mutations in the gene encoding phosphodiesterase 11A4 (PDE11A) in individuals with adrenocortical hyperplasia. Nat Genet 2006;38:794-800. https://doi.org/10.1038/ng1809

Cited by

  1. Class III Receptor Tyrosine Kinases in Acute Leukemia – Biological Functions and Modern Laboratory Analysis vol.10s3, pp.1177-2719, 2015, https://doi.org/10.4137/BMI.S22433
  2. Identification of somatic mutations using whole-exome sequencing in Korean patients with acute myeloid leukemia vol.18, pp.1, 2017, https://doi.org/10.1186/s12881-017-0382-y
  3. Genomic data integration in chronic lymphocytic leukemia vol.19, pp.1-2, 2017, https://doi.org/10.1002/jgm.2936