Gamma-normal-gamma mixture model for detecting differentially methylated loci in three breast cancer cell lines

Cancer Informatics

Volumn 3, Issue , 2007, Pages 43-54

  Khalili, Abbas ^a   Potter, Dustin ^a   Yan, Pearlly ^a   Li, Lang ^b   Gray, Joe ^c   Huang, Tim ^a   Lin, Shili ^a  

^a OHIO STATE UNIVERSITY   (United States)

^b INDIANA UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

Author keywords

Breast cancer cell lines; CpG islands; Methylation epigenetic signature; Microarrays; Mixture modeling

Indexed keywords

ARTICLE; BREAST CANCER; CANCER CELL; CANCER CELL CULTURE; CELL HETEROGENEITY; GAMMA NORMAL GAMMA MIXTURE MODEL; GENE EXPRESSION; HUMAN; HUMAN CELL; INFORMATION PROCESSING; MEDICAL LITERATURE; METHYLATION; MICROARRAY ANALYSIS; STATISTICAL MODEL; BREAST CANCER CELL LINE; CPG ISLAND; DNA MICROARRAY; EPIGENETICS; IN VITRO STUDY; PROTEIN FINGERPRINTING;

EID: 34447557026     PISSN: 11769351     EISSN: 11769351     Source Type: Journal    
DOI: 10.1177/117693510700300012     Document Type: Article

References (34)

1. Ballestar, E., Esteller, M. and Richardson, B.C. 2006. The Epigenetic Face of Systemic Lupus Erythematosus. J. Immunol., 176(12):7143-7147.
2. Bieche, I., Tozlu, S., Girault, I., Onody, P., Driouch, K., Vidaud, M. and Lidereau, R. 2004. Expression of PEA3/E1AF/ETV4, an Ets-related transcription factor, in breast tumors: positive links to MMP2, NRG1 and CGB expression. Carcinogenesis, 25(3):405-411.
3. Boyer, L.A., Lee, T.I., Cole, M.F., Johnstone, S.E., Levine, S.S., Zucker, J.P., Guenther, M.G., Kumar, R.M., Murray, H.L., Gifford, R.G.J.D.K., Melton, D.A., Jaenisch, R. and Young, R.A. 2005. Core Transcriptional Regulatory Circuitry in Human Embryonic Stem Cells. Cell, 122(6):947-956.
4. Carlin, B. and Louis, T. 1996. Bayes and Empirical Bayes Methods for Data Analysis. New York: Chapman and Hall.
5. Dean, N. and Raftery, A. 2005. Normal uniform mixture differential gene expression detection for cDNA microarrays. BMC Bioinformatics, 6(1):173.
6. Efron, B. and Morris, C. 1973. Combining possibly related estimation problems (with discussion). Journal of the Royal Statistical Society, Series B, 35:379421.
7. Herman, J.G., Graff, J.R., Myohanen, S., Nelkin, B.D. and Baylin, S.B. 1996. Methylation-specific PCR: A novel PCR assay for methylation status of CpGislands. PNAS, 93(18):9821-9826.
8. Jaenisch, R. and Bird, A. 2003. Epigenetic regulation of gene expression: how the genome inte grates intrinsic and environmental signals. Nat. Genet., 33:245-254.
9. Jones, P.A. and Martienssen, R. 2005. A Blueprint for a Human Epigenome Project: The AACR Human Epigenome Workshop. Cancer Res., 65(24):11241-11246.
10. Karey, K. and Sirbasku, D. 1988. Differential responsiveness of human breast cancer cell lines MCF-7 and T47D to growth factors and 17 beta-estradiol. Cancer Res., 48(14):4083-4092.
11. Kriaucionis. S. and Bird, A. 2003. DNA methylation and Rett syndrome. Hum. Mol. Genet., 12(Review Issue 2):R221-227.
12. Laird, P.W. 2005. Cancer epigenetics. Hum. Mol. Genet., 14(Review issue 1):R65-76.
13. Li, L. 2006. Survival prediction of diffuse large-B-cell lymphoma based on both clinical and gene expression information. Bioinformatics, 22(4):466-471.
14. McLachlan, G.J. and Peel, D. 2000. Finite Mixture Models. New York, NY: Wiley.
15. Miyoshi, Y., Ando, A., Egawa, C., Taguchi, T., Tamaki, Y., Tamaki, H., Sugiyama, H. and Noguchi, S. 2002. High Expression of Wilms' Tumor Suppressor Gene Predicts Poor Prognosis in Breast Cancer Patients. Clin. Cancer Res., 8(5):1167-1171.
16. Nelder, J.A. and Mead, R. 1965. A Simplex Method for function minimization. Computational Journal, 7:308-313.
17. Newton, M., Kendziorski, C., Richmond, C., Blattner, F. and Tsui, K. 2001. On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data. Journal of Computational Biology, 8(1):37-52.
18. Patole, P.S., Zecher, D., Pawar, R.D., Grone, H.J., Schlondorff, D. and Anders, H.J. 2005. G-Rich DNA Suppresses Systemic Lupus. J. Am. Soc. Nephrol., 16(11):3273-3280.
19. Piotrowski, A., Benetkiewicz, M., Menzel, U., de Stahl, T.D., Mantripragada, K., Grigelionis, G., Buckley, P.G., Jankowski, M., Hoffman, J., a, D.B., S̀rutek, E., Laskowski, R., Zegarski, W. and Du manski, J.P. 2006. Microarray-based survey of CpG islands identifies concurrent hyper- and hypomethylation patterns in tissues derived from patients with breast cancer. Genes, Chromosomes and Cancer, (7):656-667.
20. Pittman, J., Huang, E., Dressman, H., Horng, C.F., Cheng, S.H., Tsou, M.H., Chen, C.M., Bild, A., Iversen, E.S., Huang, A.T., Nevins, J.R. and West, M. 2004. Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. PNAS, 101(22):8431-8436.
21. Potti, A., Mukherjee, S., Petersen, R., Dressman, H.K., Bild, A., Koontz, J., Kratzke, R., Watson, M.A., Kelley, M., Ginsburg, G.S., West, M., Harpole, J., David, H. and Nevins, J.R. 2006. A Genomic Strategy to Refine Prognosis in Early-Stage Non-Small-Cell Lung Cancer. N. Engl. J. Med., 355(6):570-580.
22. Radisavljevic, Z. 2003. Nitric oxide suppression triggers apoptosis through the FKHRL1 (FOXO3A)/ROCK kinase pathway in human breast carcinoma cells. Cancer, 97(5):1358-1363.
23. Rakyan, V.K., Hildmann, T., Novik, K.L., Lewin, J., Tost, J., Cox, A.V., Andrews, T.D., Howe, K.L., Otto, T., Olek, A., Fischer, J., Gut, I.G., Berlin, K. and Beck, S. 2004. DNA Methylation Profiling of the Human Major Histocompatibility Complex: A Pilot Study for the Human Epigenome Project. J. PLoS Biology, 2(12):e405.
24. Shen, Y., Chow, J., Wang, Z. and Fan, G. 2006. Abnormal CpG island methylation occurs during in vitro differentiation of human embryonic stem cells. Hum. Mol. Genet., 15(17):2623-2635.
25. Simonaro, C.M., Park, J.H., Eliyahu, E., Shtraizent, N., McGovern, M.M. and Schuchman, E.H. 2006. Im printing at the SMPD1 locus: implications for acid sphingomyelinase-deficient Niemann-Pick disease. Am. J. Hum. Genet., 78(5):865-870.
26. Speed, T. 2003. Statistical Analysis of Gene Expression Microarray Data. Chapman & Hall/CRC.
27. Verducci, J.S., Melfi, V.F., Lin, S., Wang, Z., Roy, S. and Sen, C.K. 2006. Microarray analysis of gene expression: considerations in data mining and statistical treatment. Physiol. Genomics, 25(3):355-363.
28. Wang, L.G., Beklemisheva, A., Liu, X.M., Ferrari, A.C., Feng, J. and Chiao, J.W. 2006. ual action on promoter demethylation and chromatin by an isothiocyanate restored GSTP1 silenced in prostate cancer. Molecular Carcinogenesis.
29. West, M., Ginsburg, G.S., Huang, A.T. and Nevins, J.R. 2006. Embracing the complexity of genomic data for personalized medicine. Genome Res., 16(5):559-566.
30. Xia, H., Qi, H., Li, Y., Pei, J., Barton, J., Blackstad, M., Xu, T. and Tao, W. 2002. LATS1 tumor suppressor regulates G2/M transition and apoptosis. Oncogene, 21(8):1233-1241.
31. Yan, P.S., Chen, C.M., Shi, H., Rahmatpanah, F., Wei, S.H. and Huang, T.H.M. 2002. Applications of CpG Island Microarrays for High-Throughput Analysis of DNA Methylation. J. Nutr., 132(8):2430S-2434.
32. Yang, H., Liu, V., Wang, Y., Tsang, P. and Ngan, H. 2006. Differential DNA methylation profiles in gynecological cancers and correlation with clinico-pathological data. BMC Cancer, 6(12).
33. Yang, Y.H., Dudoit, S., Luu, P., Lin, D.M., Peng, V., Ngai, J. and Speed. T.P. 2002. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucl. Acids Res., 30(4):e15-.
34. Zhang, B., Schmoyer, D., Kirov, S. and Snoddy, J. 2004. GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies. BMC Bioinformatics, 5(1):16.