Linked Life Data

18546481

Source:http://linkedlifedata.com/resource/pubmed/id/18546481

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2008-6-11
pubmed:abstractText
We describe a new method based on principal component analysis and robust consensus ensemble clustering to identify and elucidate the subtypes of breast cancer disease. The method was applied to microarray gene expression data using micro-dissection of samples from 36 breast cancer patients with at least two of three pathological stages of disease. Controls were normal breast epithelial cells from 3 disease free patients. Our method identified an optimum set of genes and strong, stable clusters which correlated well with clinical classification into Luminal, Basal and Her2+ subtypes based on ER, PR and Her2 status. It also revealed a hierarchical portrait of disease progression through various grades and stages and identified genes and functional pathways for each stage, grade and disease subtype. We found that gene expression heterogeneity across subtypes is much greater than the heterogeneity of progression from DCIS to IDC within a subtype, suggesting that the disease subtypes are distinct disease processes. The averaging over data perturbations and clustering methods is critical in the robust identification of subtypes and gene markers for grade and progression.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
0919-9454
pubmed:author
pubmed:issnType
Print
pubmed:volume
18
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
130-40
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Breast cancer stratification from analysis of micro-array data of micro-dissected specimens.
pubmed:affiliation
The Broad Institute of MIT and Harvard, Cambridge MA 02142, USA. galexe@broad.mit.edu
pubmed:publicationType
Journal Article