A tumor progression model for hepatocellular carcinoma: Bioinformatic analysis of genomic data

Background & Aims: it is widely recognized that genomic abnormalities underpin the development of human cancers. Aberrant patterns of chromosomal changes may represent useful information that can be used in classifying the complex traits of liver cancer cases for the genetic events involved in tumor carcinogenesis, tumor progression, and prognosis. Methods: Genome-wide chromosomal aberrations of 158 hepatitis B virus-associated hepatocellular carcinoma (HCC) were studied by comparative genomic hybridization (CGH). By application of a self-organizing tree algorithm, statistically significant CGH events were used to construct an evolutionary tree that could infer patient subgroups with different degrees of tumor progression. The key CGH events in the subgroups were identified. The clinical significance of the groupings and the key CGH events were examined. Results: Based on the patterns of significant chromosomal aberrations derived, 3 HCC subgroups organized in an evolutionary tree were identified. The groupings possessed information reflecting the degrees of tumor progression, including numbers of chromosomal aberrations, tumor stages, tumor sizes, and disease outcome. Gains of 1q21-23 and 8q22-24 were identified as genomic events associated with the early development of HCC. Gain of 3q22-24, however, was identified as 1 of the late genomic events found to be associated with tumor recurrence and poor overall patient survival. Conclusions: A tumor progression model for HCC was constructed and revealed chromosomal imbalances that were significantly associated with clinical pathologic characteristics of the disease. This model explains a significant part of the variations in clinical outcome among HCC patients.