Literature DB >> 12067990

Parallel analysis of sporadic primary ovarian carcinomas by spectral karyotyping, comparative genomic hybridization, and expression microarrays.

Jane Bayani1, James D Brenton, Pascale F Macgregor, Ben Beheshti, Monique Albert, Dhani Nallainathan, Jana Karaskova, Barry Rosen, Joan Murphy, Stephanie Laframboise, Brent Zanke, Jeremy A Squire.   

Abstract

Analysis of ovarian carcinomas has shown that karyotypes are often highly abnormal and cannot be identified with certainty by conventional cytogenetic methods. In this study, 17 tumors derived from 13 patients were analyzed by a combination of spectral karyotyping (SKY), comparative genomic hybridization (CGH), and expression microarrays. Within the study group, a total of 396 chromosomal rearrangements could be identified by SKY and CGH analysis. When the distribution of aberrations was normalized with respect to relative genomic length, chromosomes 3, 8, 11, 17, and 21 had the highest frequencies. Parallel microarray expression studies of 1718 human cDNAs were used to analyze expression profiles and to determine whether correlating gene expression with chromosomal rearrangement would identify smaller subsets of differentially expressed genes. Within the entire set of samples, microarray expression analysis grouped together poorly differentiated tumors irrespective of histological subtype. For three patients, a comparison between genomic alterations and gene expression pattern was performed on samples of primary and metastatic tumors. Their common origin was demonstrated by the close relationship of both the SKY and CGH karyotypes and the observed profiles of gene expression. In agreement with the pattern of genomic imbalance observed for chromosome 3 in ovarian cancer, the relative expression profile with respect to a normal ovary exhibited a contiguous pattern of reduced expression of genes mapping to the 3p25.5-3p21.31 and increased expression of genes from 3q13.33-3q28. This study demonstrates that SKY, CGH, and microarray analysis can in combination identify significantly smaller subsets of differentially expressed genes for future studies.

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Year:  2002        PMID: 12067990

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  23 in total

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Review 9.  Multiple mutations and cancer.

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