Molecular portraits of human breast tumours View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2000-08

AUTHORS

Charles M. Perou, Therese Sørlie, Michael B. Eisen, Matt van de Rijn, Stefanie S. Jeffrey, Christian A. Rees, Jonathan R. Pollack, Douglas T. Ross, Hilde Johnsen, Lars A. Akslen, Øystein Fluge, Alexander Pergamenschikov, Cheryl Williams, Shirley X. Zhu, Per E. Lønning, Anne-Lise Børresen-Dale, Patrick O. Brown, David Botstein

ABSTRACT

Human breast tumours are diverse in their natural history and in their responsiveness to treatments. Variation in transcriptional programs accounts for much of the biological diversity of human cells and tumours. In each cell, signal transduction and regulatory systems transduce information from the cell's identity to its environmental status, thereby controlling the level of expression of every gene in the genome. Here we have characterized variation in gene expression patterns in a set of 65 surgical specimens of human breast tumours from 42 different individuals, using complementary DNA microarrays representing 8,102 human genes. These patterns provided a distinctive molecular portrait of each tumour. Twenty of the tumours were sampled twice, before and after a 16-week course of doxorubicin chemotherapy, and two tumours were paired with a lymph node metastasis from the same patient. Gene expression patterns in two tumour samples from the same individual were almost always more similar to each other than either was to any other sample. Sets of co-expressed genes were identified for which variation in messenger RNA levels could be related to specific features of physiological variation. The tumours could be classified into subtypes distinguished by pervasive differences in their gene expression patterns. More... »

PAGES

747

Journal

TITLE

Nature

ISSUE

6797

VOLUME

406

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/35021093

    DOI

    http://dx.doi.org/10.1038/35021093

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1033846543

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/10963602


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