Literature DB >> 15701881

Gene expression profiling of breast cancer in relation to estrogen receptor status and estrogen-metabolizing enzymes: clinical implications.

Vessela N Kristensen1, Therese Sørlie, Jurgen Geisler, Anita Langerød, Noriko Yoshimura, Rolf Kåresen, Nobuhiro Harada, P E Lønning, Anne-Lise Børresen-Dale.   

Abstract

Interactions between luminal epithelial cells and their surrounding microenvironment govern the normal development and function of the mammary gland. Estradiol plays a key role in abnormal intracellular signaling, which contributes to the development and progression of breast tumors. The present article summarizes the results from a microarray whole genome gene expression analysis as well as a quantitative analysis of the mRNA expression of members of the estradiol metabolic and signaling pathways in the tumors of postmenopausal breast cancer patients. The analysis of the variation in whole genome gene expression resulted in a tumor classification comprising several distinct groups with distinct expression of the estrogen receptor (ER). The parallel study on the expression of only nine mRNA transcripts of members of the estradiol pathways resulted in two main clusters, representing ER- and ER tumors. The mRNA expression of the estradiol-metabolizing enzymes did not follow the expression of the ER in all cases, leading to the recognition of several further subclasses of tumors. When the tumor classes obtained by whole genome gene expression analysis were compared with those obtained by independent quantitation of the estradiol-metabolizing enzymes, a statistically significant association between both classification groups was observed. These findings point to a possible association between development of a tumor with a particular expression profile and its capacity to synthesize estradiol as measured by the expression of the transcripts for the necessary key enzymes. Further, whole genome expression patterns were studied in 12 patients treated with anastrozole. Using significance analysis of microarrays, we identified 298 genes significantly differently expressed between partial response and progressive disease groups.

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Year:  2005        PMID: 15701881

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  12 in total

1.  SNPs associated with molecular subtypes of breast cancer: on the usefulness of stratified Genome-wide Association Studies (GWAS) in the identification of novel susceptibility loci.

Authors:  Vessela N Kristensen; Anne-Lise Børresen-Dale
Journal:  Mol Oncol       Date:  2008-03-06       Impact factor: 6.603

Review 2.  Transcriptional switches: chemical approaches to gene regulation.

Authors:  Lori W Lee; Anna K Mapp
Journal:  J Biol Chem       Date:  2010-02-10       Impact factor: 5.157

Review 3.  Photonic crystal enhanced fluorescence for early breast cancer biomarker detection.

Authors:  Brian T Cunningham; Richard C Zangar
Journal:  J Biophotonics       Date:  2012-06-27       Impact factor: 3.207

Review 4.  Assessing estrogen signaling aberrations in breast cancer risk using genetically engineered mouse models.

Authors:  Priscilla A Furth; M Carla Cabrera; Edgar S Díaz-Cruz; Sarah Millman; Rebecca E Nakles
Journal:  Ann N Y Acad Sci       Date:  2011-07       Impact factor: 5.691

5.  Plasma biomarker profiles differ depending on breast cancer subtype but RANTES is consistently increased.

Authors:  Rachel M Gonzalez; Don S Daly; Ruimin Tan; Jeffrey R Marks; Richard C Zangar
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2011-05-17       Impact factor: 4.254

6.  Inhibition of ErbB2(Her2) expression with small molecule transcription factor mimics.

Authors:  Lori W Lee; Christopher E C Taylor; Jean-Paul Desaulniers; Manchao Zhang; Jonas W Højfeldt; Quintin Pan; Anna K Mapp
Journal:  Bioorg Med Chem Lett       Date:  2009-09-01       Impact factor: 2.823

7.  Using Frequent Co-expression Network to Identify Gene Clusters for Breast Cancer Prognosis.

Authors:  Jie Zhang; Kun Huang; Yang Xiang; Ruoming Jin
Journal:  Proc Int Joint Conf Bioinforma Syst Biol Intell Comput       Date:  2009-08-03

8.  MUC1-induced alterations in a lipid metabolic gene network predict response of human breast cancers to tamoxifen treatment.

Authors:  Sean P Pitroda; Nikolai N Khodarev; Michael A Beckett; Donald W Kufe; Ralph R Weichselbaum
Journal:  Proc Natl Acad Sci U S A       Date:  2009-03-16       Impact factor: 11.205

9.  Genomic signatures of breast cancer metastasis.

Authors:  V Urquidi; S Goodison
Journal:  Cytogenet Genome Res       Date:  2007       Impact factor: 1.636

10.  Data perturbation independent diagnosis and validation of breast cancer subtypes using clustering and patterns.

Authors:  G Alexe; G S Dalgin; R Ramaswamy; C Delisi; G Bhanot
Journal:  Cancer Inform       Date:  2007-02-19
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