PURPOSE: Several prognostic gene expression profiles have been identified in breast cancer. In spite of this progress in prognostic classification, the underlying mechanisms that drive these gene expression patterns remain unknown. Specific genomic alterations, such as copy number alterations, are an important factor in tumor development and progression and are also associated with changes in gene expression. EXPERIMENTAL DESIGN: We carried out array comparative genomic hybridization in 68 human breast carcinomas for which gene expression and clinical data were available. We used a two-class supervised algorithm, Supervised Identification of Regions of Aberration in aCGH data sets, for the identification of regions of chromosomal alterations that are associated with specific sample labeling. Using gene expression data from the same tumors, we identified genes in the altered regions for which the expression level is significantly correlated with the copy number and validated our results in public available data sets. RESULTS: Specific chromosomal aberrations are related to clinicopathologic characteristics and prognostic gene expression signatures. The previously identified poor prognosis, 70-gene expression signature is associated with the gain of 3q26.33-27.1, 8q22.1-24.21, and 17q24.3-25.1; the 70-gene good prognosis profile is associated with the loss at 16q12.1-13 and 16q22.1-24.1; basal-like tumors are associated with the gain of 6p12.3-23, 8q24.21-22, and 10p12.33-14 and losses at 4p15.31, 5q12.3-13.1, 5q33.1, 10q23.33, 12q13.13-3, 15q15.1, and 15q21.1; HER2+ breast show amplification at 17q11.1-12 and 17q21.31-23.2 (including HER2 gene). CONCLUSIONS: There is a strong correlation between the different gene expression signatures and underlying genomic changes. These findings help to establish a link between genomic changes and gene expression signatures, enabling a better understanding of the tumor biology that causes poor prognosis.
PURPOSE: Several prognostic gene expression profiles have been identified in breast cancer. In spite of this progress in prognostic classification, the underlying mechanisms that drive these gene expression patterns remain unknown. Specific genomic alterations, such as copy number alterations, are an important factor in tumor development and progression and are also associated with changes in gene expression. EXPERIMENTAL DESIGN: We carried out array comparative genomic hybridization in 68 humanbreast carcinomas for which gene expression and clinical data were available. We used a two-class supervised algorithm, Supervised Identification of Regions of Aberration in aCGH data sets, for the identification of regions of chromosomal alterations that are associated with specific sample labeling. Using gene expression data from the same tumors, we identified genes in the altered regions for which the expression level is significantly correlated with the copy number and validated our results in public available data sets. RESULTS: Specific chromosomal aberrations are related to clinicopathologic characteristics and prognostic gene expression signatures. The previously identified poor prognosis, 70-gene expression signature is associated with the gain of 3q26.33-27.1, 8q22.1-24.21, and 17q24.3-25.1; the 70-gene good prognosis profile is associated with the loss at 16q12.1-13 and 16q22.1-24.1; basal-like tumors are associated with the gain of 6p12.3-23, 8q24.21-22, and 10p12.33-14 and losses at 4p15.31, 5q12.3-13.1, 5q33.1, 10q23.33, 12q13.13-3, 15q15.1, and 15q21.1; HER2+ breast show amplification at 17q11.1-12 and 17q21.31-23.2 (including HER2 gene). CONCLUSIONS: There is a strong correlation between the different gene expression signatures and underlying genomic changes. These findings help to establish a link between genomic changes and gene expression signatures, enabling a better understanding of the tumor biology that causes poor prognosis.
Authors: Lenora W M Loo; Maarit Tiirikainen; Iona Cheng; Annette Lum-Jones; Ann Seifried; James M Church; Robert Gryfe; Daniel J Weisenberger; Noralane M Lindor; Steven Gallinger; Robert W Haile; David J Duggan; Stephen N Thibodeau; Graham Casey; Loïc Le Marchand Journal: Genes Chromosomes Cancer Date: 2013-01-23 Impact factor: 5.006
Authors: Marcin Imielinski; Sangwon Cha; Tomas Rejtar; Elizabeth A Richardson; Barry L Karger; Dennis C Sgroi Journal: Mol Cell Proteomics Date: 2012-01-12 Impact factor: 5.911
Authors: Nimisha Chaturvedi; Jelle J Goeman; Judith M Boer; Wessel N van Wieringen; Renée X de Menezes Journal: BMC Bioinformatics Date: 2014-07-08 Impact factor: 3.169