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DOI: 10.1073/pnas.0932692100
¤ OpenAccess: Green
This work has “Green” OA status. This means it may cost money to access on the publisher landing page, but there is a free copy in an OA repository.

Repeated observation of breast tumor subtypes in independent gene expression data sets

Thérese Sørlie,Robert Tibshirani,Joel S. Parker,Trevor Hastie,J. S. Marron,Andrew B. Nobel,Shibing Deng,Hilde Johnsen,Robert Pesich,Stephanie Geisler,János Demeter,Charles M. Perou,Per Eystein Lønning,Patrick O. Brown,Anne Lise Børresen-Dale,David Botstein

Breast cancer
DNA microarray
Gene expression
2003
Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by hierarchical clustering based on patterns of expression of 534 "intrinsic" genes and shown to subdivide into one basal-like, one ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like subgroup. The genes used for classification were selected based on their similar expression levels between pairs of consecutive samples taken from the same tumor separated by 15 weeks of neoadjuvant treatment. Similar cluster analyses of two published, independent data sets representing different patient cohorts from different laboratories, uncovered some of the same breast cancer subtypes. In the one data set that included information on time to development of distant metastasis, subtypes were associated with significant differences in this clinical feature. By including a group of tumors from BRCA1 carriers in the analysis, we found that this genotype predisposes to the basal tumor subtype. Our results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities.
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    Repeated observation of breast tumor subtypes in independent gene expression data sets” is a paper by Thérese Sørlie Robert Tibshirani Joel S. Parker Trevor Hastie J. S. Marron Andrew B. Nobel Shibing Deng Hilde Johnsen Robert Pesich Stephanie Geisler János Demeter Charles M. Perou Per Eystein Lønning Patrick O. Brown Anne Lise Børresen-Dale David Botstein published in 2003. It has an Open Access status of “green”. You can read and download a PDF Full Text of this paper here.