K6. Circular binary segmentation modeling of array CGH data on hepatocellular carcinoma

Abstract

Hepatocellular carcinoma (HCC) is a malignant tumor derived from hepatocytes that belong to primary malignant epithelial tumors of the liver. The outcome of HCC patients still remains dismal due to the difficulty in detecting the disease at its early stage. We propose a new approach aiming to identify new biomarkers for early diagnosis of HCC. Genomic DNA copy number alterations (CNAs) are associated with complex diseases like HCC. Array-based Comparative Genomic Hybridization (a-CGH) is a technique used to identify copy number changes in genomic DNA. We use a statistical model based on a circular binary segmentation (CBS) algorithm. Our approach makes use of a median absolute deviation model to separate outliers from their surrounding segments. We tested 35 samples of HCC patients on specific chromosome regions, then applied CBS algorithm to detect genomic DNA alternations in copy number. Our results show that a gain of 1q was detected in 63% and a gain of 20q was detected in 26% of HCC cases. Also, a loss of 4q was detected in 3%, a loss of 13q was detected in 29%, loss in 16q was detected in 9%, and loss of 17q was detected in 3% of HCC cases.