CiteULike: rdiaz Lai

A Tumor Progression Model for Hepatocellular Carcinoma: Bioinformatic Analysis of Genomic Data

Terence Poon — 2007-12-26T10:50:35-00:00

Gastroenterology, Vol. 131, No. 4. (October 2006), pp. 1262-1270.

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.

A statistical method to detect chromosomal regions with DNA copy number alterations using SNP-array-based CGH data.

Y Lai — 2006-11-04T18:04:47-00:00

Comput Biol Chem, Vol. 29, No. 1. (February 2005), pp. 47-54.

Single nucleotide polymorphism (SNP) arrays were used to detect chromosomal regions with DNA copy number alterations. Current statistical methods for microarray-based comparative genomic hybridization (array-CGH) analysis generally assume certain relationships among adjacent markers on the same chromosome, and these assumptions may be questionable. For an SNP-array-based CGH study, multiple normal reference SNP arrays were collected. In order to utilize these normal reference SNP arrays, we derived an empirical distribution of signal ratios for each SNP marker. With an assumed threshold value for the overall error rate control and the defined signal ratio ranges for chromosomal amplification and deletion, we proposed a procedure to identify chromosomal alteration regions based on several bootstrapped one-sample t-tests and the false discovery rate control. When we have multiple arrays for different individuals with the same disease, our method can also be used to detect SNP markers for chromosomal alteration regions that are common among these individuals. We applied our method to a published SNP array data set for breast carcinoma cell lines. For an individual with breast cancer, numerous chromosomal alteration regions were identified. Compared to results of previous studies, our method identified more chromosomal alteration regions, with some being implicated in the literature to harbor genes associated with breast cancer. For multiple cancer arrays, our results suggested the existence of common chromosomal alteration regions. However, a high proportion of false positives also indicated that genetic variations among different individuals with breast cancer can be present.

Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data.

Weil R Lai — 2005-09-19T19:34:23-00:00

Bioinformatics (4 August 2005)

MOTIVATION: Array Comparative Genomic Hybridization (CGH) can reveal chromosomal aberrations in the genomic DNA. These amplifications and deletions at the DNA level are important in the pathogenesis of cancer and other diseases. While a large number of approaches have been proposed for analyzing the large array CGH data sets, the relative merits of these methods in practice are not clear. RESULTS: We compare eleven different algorithms for analyzing array CGH data. These include both segment detection methods and smoothing methods, based on such diverse techniques as mixture models, Hidden Markov Models, maximum likelihood, regression, wavelets, genetic algorithms, and others. We compute the Receiver Operating Characteristic (ROC) curves using simulated data to quantify sensitivity and specificity for various levels of signal-to-noise ratio and different sizes of abnormalities. We also characterize their performance on chromosomal regions of interest in a real data set obtained from patients with Glioblastoma Multiforme. While comparisons of this type are difficult due to possibly sub-optimal choice of parameters in the methods, they nevertheless reveal general characteristics that are helpful to the biological investigator.