Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications.


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.

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@article{Srlie2001GeneEP, title={Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications.}, author={Th{\'e}rese S\orlie and CM Perou and Robert Tibshirani and Turid Aas and Stephanie B Geisler and Hilde Johnsen and Trevor J. Hastie and Michel B Eisen and Matt van de Rijn and SS Jeffrey and Thor Thorsen and Heidi E Quist and John C. Matese and PO Brown and David Botstein and Per Eystein L\onning and Anne-Lise B\orresen-Dale}, journal={Proceedings of the National Academy of Sciences of the United States of America}, year={2001}, volume={98 19}, pages={10869-74} }