Abstract
Motivation: Although studies have shown that genetic alterations are causally involved in numerous human diseases, still not much is known about the molecular mechanisms involved in sporadic and hereditary ovarian tumorigenesis.
Methods: Array comparative genomic hybridization (array CGH) was performed in 8 sporadic and 5 BRCA1 related ovarian cancer patients.
Results: Chromosomal regions characterizing each group of sporadic and BRCA1 related ovarian cancer were gathered using multiple sample hidden Markov Models (HMM). The differential regions were used as features for classification. Least Squares Support Vector Machines (LS-SVM), a supervised classification method, resulted in a leave-one-out accuracy of 84.6%, sensitivity of 100% and specificity of 75%.
Conclusion: The combination of multiple sample HMMs for the detection of copy number alterations with LS-SVM classifiers offers an improved methodological approach for classification based on copy number alterations. Additionally, this approach limits the chromosomal regions necessary to distinguish sporadic from hereditary ovarian cancer.
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References
Pinkel, D., Albertson, D.G.: Array comparative genomic hybridization and its applications in cancer. Nat. Genet. 37(Suppl.), 11–17 (2005)
Lai, W.R., Johnson, M.D., et al.: Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics 21(19), 3763–3770 (2005)
Shah, S., Lam, W.L., et al.: Modeling recurrent DNA copy number alterations in array CGH data. Bioinformatics 23, i450–i458 (2007)
Shawe-Taylor, J., Cristianini, N.: Kernel methods for pattern analysis. Cambridge University Press, Cambridge (2004)
Pochet, N., De Smet, F., et al.: Systematic benchmarking of microarray data classification: assessing the role of nonlinearity and dimensionality reduction. Bioinformatics 20, 3185–3195 (2004)
Suykens, J.A.K., Van Gestel, T., et al.: Least Squares Support Vector Machines. World Scientific, Singapore (2002)
Gajewski, W., Legare, R.D.: Ovarian cancer. Surg. Oncol. Clin. N. Am. 7, 317–333 (1998)
Burke, W., Daly, M., et al.: Recommendations for follow-up care of individuals with an inherited predisposition to cancer. II. BRCA1 and BRCA2. Cancer Genetics Studies Consortium. J. Am. Med. Assoc. 277, 997–1003 (1997)
Shah, S., Xuan, X., et al.: Integrating copy number polymorphisms into array CGH analysis using a robust HMM. Bioinformatics 22(14), e431–e439 (2006)
Schölkopf, B., Tsuda, K., et al.: Kernel methods in computational biology. MIT Press, United States (2004)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Saeys, Y., Inza, I., et al.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)
Lai, C., Reinders, M.J.T., et al.: A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets. BMC Bioinformatics 7, 235–244 (2006)
Yang, Y.H., Xiao, Y., et al.: Identifying differentially expressed genes from microarray experiments via statistic synthesis. Bioinformatics 21(7), 1084–1093 (2005)
Li, W., Yang, Y.: How many genes are needed for a discriminant microarray data analysis. In: Lin, S.M., Johnson, K.F. (eds.) Methods of Microarray Data Analysis, pp. 137–150. Kluwer Academic, Dordrecht (2002)
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Daemen, A. et al. (2008). Classification of Sporadic and BRCA1 Ovarian Cancer Based on a Genome-Wide Study of Copy Number Variations. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85565-1_21
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DOI: https://doi.org/10.1007/978-3-540-85565-1_21
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