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

摘要

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.