Genome-wide association study identifies variation at 6q25.1 associated with survival in multiple myeloma

Nature Communications

Volumn 7, Issue , 2016, Pages

  Johnson, David C ^a   Weinhold, Niels ^b,c   Mitchell, Jonathan S ^a   Chen, Bowang ^d   Kaiser, Martin ^a   Begum, Dil B ^a   Hillengass, Jens ^c   Bertsch, Uta ^c   Gregory, Walter A ^e   Cairns, David ^e   Jackson, Graham H ^f   Försti, Asta ^d,g   Nickel, Jolanta ^c   Hoffmann, Per ^h,i   Nöethen, Markus M ^h   Stephens, Owen W ^b   Barlogie, Bart ^b   Davis, Faith E ^b   Hemminki, Kari ^d,g   Goldschmidt, Hartmut ^c,j   Houlston, Richard S ^a   Morgan, Gareth J ^b   more..

^a INSTITUTE OF CANCER RESEARCH   (United Kingdom)

^b UNIVERSITY OF ARKANSAS FOR MEDICAL SCIENCES   (United States)

^c UNIVERSITY OF HEIDELBERG   (Germany)

^d GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

^e UNIVERSITY OF LEEDS   (United Kingdom)

^f NEWCASTLE UNIVERSITY   (United Kingdom)

^g LUND UNIVERSITY   (Sweden)

^h UNIVERSITY OF BONN   (Germany)

ⁱ UNIVERSITY OF BASEL   (Switzerland)

^j National Center of Tumor Diseases   (Germany)

more..

Author keywords

[No Author keywords available]

Indexed keywords

CANCER; GENETIC MARKER; GENOME; GENOTYPE; TUMOR;

AGE DISTRIBUTION; ARTICLE; CANCER GENETICS; CANCER PROGNOSIS; CANCER STAGING; CANCER SURVIVAL; CHROMOSOME 6Q; DISEASE ASSOCIATION; EUROPEAN; GENETIC ASSOCIATION; GENETIC MARKER; GENETIC VARIABILITY; GENOME; GENOTYPE; HUMAN; META ANALYSIS; MULTIPLE MYELOMA; OVERALL SURVIVAL; SEX DIFFERENCE; SINGLE NUCLEOTIDE POLYMORPHISM; AGED; CAUCASIAN; CHROMOSOME 6; FEMALE; GENETICS; GENOME-WIDE ASSOCIATION STUDY; MALE; MIDDLE AGED; MORTALITY; PROGNOSIS; PROPORTIONAL HAZARDS MODEL;

AGED; CHROMOSOMES, HUMAN, PAIR 6; EUROPEAN CONTINENTAL ANCESTRY GROUP; FEMALE; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMANS; MALE; MIDDLE AGED; MULTIPLE MYELOMA; POLYMORPHISM, SINGLE NUCLEOTIDE; PROGNOSIS; PROPORTIONAL HAZARDS MODELS;

EID: 84954094656     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms10290     Document Type: Article

References (63)

1. Palumbo, A. & Anderson, K. Multiple myeloma. N. Engl. J. Med. 364, 1046-1060 (2011
2. Kyle, R. A. & Rajkumar, S. V. Multiple myeloma. N. Engl. J. Med. 351, 1860-1873 (2004
3. Bergsagel, P. L., Mateos, M. V., Gutierrez, N. C., Rajkumar, S. V. & San Miguel, J. F. Improving overall survival and overcoming adverse prognosis in the treatment of cytogenetically high-risk multiple myeloma. Blood 121, 884-892 (2013
4. Blade, J., Rosinol, L. & Cibeira, M. T. Prognostic factors for multiple myeloma in the era of novel agents. Ann. Oncol. 19, vii117-vii120 (2008
5. Tricot, G. Prognostic factors in multiple myeloma. Clin. Adv. Hematol. Oncol. 3, 167-168 (2005
6. Bergsagel, P. L. Prognostic factors in multiple myeloma: it?s in the genes. Clin. Cancer Res. 9, 533-534 (2003
7. Rajkumar, S. V. & Greipp, P. R. Prognostic factors in multiple myeloma. Hematol. Oncol. Clin. North Am. 13, 1295-1314 (1999
8. Boyd, K. D. et al. A novel prognostic model in myeloma based on cosegregating adverse FISH lesions and the ISS: analysis of patients treated in the MRC Myeloma IX trial. Leukemia 26, 349-355 (2012
9. Lee, J. C. et al. Human SNP links differential outcomes in inflammatory and infectious disease to a FOXO3-regulated pathway. Cell 155, 57-69 (2013
10. Wu, C. et al. Genome-wide association study identifies common variants in SLC39A6 associated with length of survival in esophageal squamous-cell carcinoma. Nat. Genet. 45, 632-638 (2013
11. Van Rechem, C. et al. A coding single-nucleotide polymorphism in lysine demethylase KDM4A associates with increased sensitivity to mTOR inhibitors. Cancer Discov. 5, 245-254 (2015
12. Eslam, M. et al. Interferon-lambda rs12979860 genotype and liver fibrosis in viral and non-viral chronic liver disease. Nat. Commun. 6, 6422 (2015
13. Fasching, P. A. et al. The role of genetic breast cancer susceptibility variants as prognostic factors. Hum. Mol. Genet. 21, 3926-3939 (2012
14. Chubb, D. et al. Common variation at 3q26.2, 6p21.33, 17p11.2 and 22q13.1 influences multiple myeloma risk. Nat. Genet. 45, 1221-1225 (2013
15. Weinhold, N. et al. The CCND1 c.870G4A polymorphism is a risk factor for t(11; 14)(q13; q32) multiple myeloma. Nat. Genet. 45, 522-525 (2013
16. Broderick, P. et al. Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk. Nat. Genet. 44, 58-61 (2012
17. Erickson, S. W. et al. Genome-wide scan identifies variant in 2q12.3 associated with risk for multiple myeloma. Blood 124, 2001-2003 (2014
18. Reddington, J. P. et al. Redistribution of H3K27me3 upon DNA hypomethylation results in de-repression of Polycomb target genes. Genome Biol. 14, R25 (2013
19. Swaminathan, B. et al. Variants in ELL2 influencing immunoglobulin levels associate with multiple myeloma. Nat. Commun. 6, 7213 (2015
20. Hubacek, J. A. et al. Rs6922269 marker at the MTHFD1L gene predict cardiovascular mortality in males after acute coronary syndrome. Mol. Biol. Rep. 42, 1289-1293 (2015
21. Palmer, B. R. et al. Genetic polymorphism rs6922269 in the MTHFD1L gene is associated with survival and baseline active vitamin B12 levels in post-acute coronary syndromes patients. PLoS ONE 9, e89029 (2014
22. Naj, A. C. et al. Dementia revealed: novel chromosome 6 locus for late-onset Alzheimer disease provides genetic evidence for folate-pathway abnormalities. PLoS Genet. 6, e1001130 (2010
23. Puissant, A. et al. Targeting MYCN in neuroblastoma by BET bromodomain inhibition. Cancer Discov. 3, 308-323 (2013
24. Delmore, J. E. et al. BET bromodomain inhibition as a therapeutic strategy to target c-Myc. Cell 146, 904-917 (2011
25. Lin, X., Nelson, P. & Gelman, I. H. SSeCKS, a major protein kinase C substrate with tumor suppressor activity, regulates G(1)-4S progression by controlling the expression and cellular compartmentalization of cyclin D. Mol. Cell. Biol. 20, 7259-7272 (2000
26. Liu, W. et al. Quantitative assessment of AKAP12 promoter methylation in human prostate cancer using methylation-sensitive high-resolution melting: correlation with Gleason score. Urology 77, 1006 e1-1006 e7 (2011
27. Wu, W., Zhang, J., Yang, H., Shao, Y. & Yu, B. Examination of AKAP12 promoter methylation in skin cancer using methylation-sensitive highresolution melting analysis. Clin. Exp. Dermatol. 36, 381-385 (2011
28. Jo, U. H., Whang, Y. M., Sung, J. S. & Kim, Y. H. Methylation of AKAP12{alpha} promoter in lung cancer. Anticancer Res. 30, 4595-4600 (2010
29. Mardin, W. A. et al. SERPINB5 and AKAP12 - expression and promoter methylation of metastasis suppressor genes in pancreatic ductal adenocarcinoma. BMC Cancer 10, 549 (2010
30. Liu, W. et al. Quantitative assessment of AKAP12 promoter methylation in colorectal cancer using methylation-sensitive high resolution melting: Correlation with Duke?s stage. Cancer Biol. Ther. 9, 862-871 (2010
31. Liu, W. et al. Rapid determination of AKAP12 promoter methylation levels in peripheral blood using methylation-sensitive high resolution melting (MS-HRM) analysis: application in colorectal cancer. Clin. Chim. Acta 411, 940-946 (2010
32. Flotho, C., Paulun, A., Batz, C. & Niemeyer, C. M. AKAP12, a gene with tumour suppressor properties, is a target of promoter DNA methylation in childhood myeloid malignancies. Br. J. Haematol. 138, 644-650 (2007
33. Turtoi, A. et al. The angiogenesis suppressor gene AKAP12 is under the epigenetic control of HDAC7 in endothelial cells. Angiogenesis 15, 543-554 (2012
34. Heller, G. et al. Genome-wide transcriptional response to 5-aza-2?-deoxycytidine and trichostatin a in multiple myeloma cells. Cancer Res. 68, 44-54 (2008
35. Walker, B. A. et al. A compendium of myeloma-associated chromosomal copy number abnormalities and their prognostic value. Blood 116, e56-e65 (2010
36. Chapman, M. A. et al. Initial genome sequencing and analysis of multiple myeloma. Nature 471, 467-472 (2011
37. Lohr, J. G. et al. Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy. Cancer Cell 25, 91-101 (2014
38. Walker, B. A. et al. Mutational spectrum, copy number changes, and outcome: results of a sequencing study of patients with newly diagnosed myeloma. J. Clin. Oncol. 33, 3911-3920 (2015
39. Kaaij, L. T. et al. DNA methylation dynamics during intestinal stem cell differentiation reveals enhancers driving gene expression in the villus. Genome Biol. 14, R50 (2013
40. Bock, C. et al. DNA methylation dynamics during in vivo differentiation of blood and skin stem cells. Mol. Cell 47, 633-647 (2012
41. Lewis, A. et al. A polymorphic enhancer near GREM1 influences bowel cancer risk through differential CDX2 and TCF7L2 binding. Cell Rep. 8, 983-990 (2014
42. Wilson, T. J., Gilfillan, S. & Colonna, M. Fc receptor-like A associates with intracellular IgG and IgM but is dispensable for antigen-specific immune responses. J. Immunol. 185, 2960-2967 (2010
43. Ziv, E. et al. Genome-wide association study identifies variants at 16p13 associated with survival in multiple myeloma patients. Nat. Commun. 6, 7539 (2015
44. Morgan, G. J. et al. Long-term follow-up of MRC Myeloma IX trial: Survival outcomes with bisphosphonate and thalidomide treatment. Clin. Cancer Res. 19, 6030-6038 (2013
45. Morgan, G. J. et al. Cyclophosphamide, thalidomide, and dexamethasone as induction therapy for newly diagnosed multiple myeloma patients destined for autologous stem-cell transplantation: MRC Myeloma IX randomized trial results. Haematologica 97, 442-450 (2012
46. Merz, M. et al. Subcutaneous versus intravenous bortezomib in two different induction therapies for newly diagnosed multiple myeloma: Interim analysis from the prospective GMMG-MM5 trial. Haematologica 100, 964-969 (2015
47. Goldschmidt, H. et al. Joint HOVON-50/GMMG-HD3 randomized trial on the effect of thalidomide as part of a high-dose therapy regimen and as maintenance treatment for newly diagnosed myeloma patients. Ann. Hematol. 82, 654-659 (2003
48. Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009
49. Delaneau, O., Zagury, J. F. & Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5-6 (2013
50. R-Core-Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2013), http: //www.R-project.org/.
51. Gogarten, S. M. et al. GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies. Bioinformatics 28, 3329-3331 (2012
52. Weinhold, N. et al. The 7p15.3 (rs4487645) association for multiple myeloma shows strong allele-specific regulation of the MYC-interacting gene CDCA7L in malignant plasma cells. Haematologica 100, e110-e113 (2015
53. Consortium, G. T. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648-660 (2015
54. Westra, H. J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238-1243 (2013
55. Nica, A. C. et al. The architecture of gene regulatory variation across multiple human tissues: the MuTHER study. PLoS Genet. 7, e1002003 (2011
56. Zhang, X. et al. Identification of common genetic variants controlling transcript isoform variation in human whole blood. Nat. Genet. 47, 345-352 (2015
57. Gamazon, E. R. et al. SCAN: SNP and copy number annotation. Bioinformatics 26, 259-262 (2010
58. Chiecchio, L. et al. Deletion of chromosome 13 detected by conventional cytogenetics is a critical prognostic factor in myeloma. Leukemia 20, 1610-1617 (2006
59. Boyle, E. M. et al. A molecular diagnostic approach able to detect the recurrent genetic prognostic factors typical of presenting myeloma. Genes Chromosomes Cancer 54, 91-98 (2015
60. Neben, K. et al. Combining information regarding chromosomal aberrations t(4; 14) and del(17p13) with the International Staging System classification allows stratification of myeloma patients undergoing autologous stem cell transplantation. Haematologica 95, 1150-1157 (2010
61. Boyle, A. P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790-1797 (2012
62. Ward, L. D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930-D934 (2012
63. Cooper, G. M. et al. Single-nucleotide evolutionary constraint scores highlight disease-causing mutations. Nat. Methods 7, 250-251 (2010