Classification of Sporadic and BRCA1 Ovarian Cancer Based on a Genome-Wide Study of Copy Number Variations

Authors:
Anneleen Daemen;Olivier Gevaert;Karin Leunen;Vanessa Vanspauwen;Geneviève Michils;Eric Legius;Ignace Vergote;Bart Moor
Affiliations:
Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Leuven, Belgium;Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Leuven, Belgium;Department of Obstetrics and Gynaecology, Division of Gynaecologic Oncology Multidisciplinary Breast Centre, University Hospital Leuven, Leuven, Belgium;Department of Human Genetics, University Hospital Leuven, Leuven, Belgium;Department of Human Genetics, University Hospital Leuven, Leuven, Belgium;Department of Human Genetics, University Hospital Leuven, Leuven, Belgium;Department of Obstetrics and Gynaecology, Division of Gynaecologic Oncology Multidisciplinary Breast Centre, University Hospital Leuven, Leuven, Belgium;Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Leuven, Belgium
Venue:
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Year:
2008

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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.