» Articles » PMID: 11553815

Gene Expression Patterns of Breast Carcinomas Distinguish Tumor Subclasses with Clinical Implications

Overview
Specialty Science
Date 2001 Sep 13
PMID 11553815
Citations 4571
Authors
Affiliations
Soon will be listed here.
Abstract

The purpose of this study was to classify breast carcinomas based on variations in gene expression patterns derived from cDNA microarrays and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing 78 cancers, three fibroadenomas, and four normal breast tissues were analyzed by hierarchical clustering. As reported previously, the cancers could be classified into a basal epithelial-like group, an ERBB2-overexpressing group and a normal breast-like group based on variations in gene expression. A novel finding was that the previously characterized luminal epithelial/estrogen receptor-positive group could be divided into at least two subgroups, each with a distinctive expression profile. These subtypes proved to be reasonably robust by clustering using two different gene sets: first, a set of 456 cDNA clones previously selected to reflect intrinsic properties of the tumors and, second, a gene set that highly correlated with patient outcome. Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.

Citing Articles

Improving patient clustering by incorporating structured variable label relationships in similarity measures.

Lambert J, Leutenegger A, Baudot A, Jannot A BMC Med Res Methodol. 2025; 25(1):72.

PMID: 40089699 DOI: 10.1186/s12874-025-02459-8.


Exercise boost after surgery improves survival in model of metastatic breast cancer.

Stagaard R, Jensen A, Schauer T, Bay M, Tavanez A, Wielsoe S Front Immunol. 2025; 16:1533798.

PMID: 40066446 PMC: 11891249. DOI: 10.3389/fimmu.2025.1533798.


Molecular Subtypes of Breast Cancer in Arab Women: Distribution and Prognostic Insights.

Elkum N, Aboussekhra A, Aboussekhra M, Aldalham H, Alshehri L, Alessy S J Epidemiol Glob Health. 2025; 15(1):36.

PMID: 40063309 PMC: 11893967. DOI: 10.1007/s44197-025-00376-z.


Breast cancer prediction based on gene expression data using interpretable machine learning techniques.

Kallah-Dagadu G, Mohammed M, Nasejje J, Mchunu N, Twabi H, Batidzirai J Sci Rep. 2025; 15(1):7594.

PMID: 40038307 PMC: 11880515. DOI: 10.1038/s41598-025-85323-5.


TCGEx: a powerful visual interface for exploring and analyzing cancer gene expression data.

Kus M, Sahin C, Kilic E, Askin A, Ozgur M, Karahanogullari G EMBO Rep. 2025; .

PMID: 40033050 DOI: 10.1038/s44319-025-00407-7.


References
1.
Hedenfalk I, Duggan D, Chen Y, Radmacher M, Bittner M, Simon R . Gene-expression profiles in hereditary breast cancer. N Engl J Med. 2001; 344(8):539-48. DOI: 10.1056/NEJM200102223440801. View

2.
Perou C, Sorlie T, Eisen M, van de Rijn M, Jeffrey S, Rees C . Molecular portraits of human breast tumours. Nature. 2000; 406(6797):747-52. DOI: 10.1038/35021093. View

3.
Tusher V, Tibshirani R, Chu G . Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001; 98(9):5116-21. PMC: 33173. DOI: 10.1073/pnas.091062498. View

4.
HOWAT J, Barnes D, Harris M, Swindell R . The association of cytosol oestrogen and progesterone receptors with histological features of breast cancer and early recurrence of disease. Br J Cancer. 1983; 47(5):629-40. PMC: 2011376. DOI: 10.1038/bjc.1983.101. View

5.
Barrelet L, Wong Y, LEMARCHAND-BERAUD T, Gomez F . The predictive value of estrogen and progesterone receptors' concentrations on the clinical behavior of breast cancer in women. Clinical correlation on 547 patients. Cancer. 1986; 57(6):1171-80. DOI: 10.1002/1097-0142(19860315)57:6<1171::aid-cncr2820570618>3.0.co;2-x. View