A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks.
Authors
Lancashire, Lee JPowe, D G
Reis-Filho, J S
Rakha, E
Lemetre, Christophe
Weigelt, B
Abdel-Fatah, T M
Green, Anthony R
Mukta, R
Blamey, R
Paish, E C
Rees, Robert C
Ellis, I O
Ball, Graham R
Affiliation
Clinical and Experimental Pharmacology, Paterson Institute for Cancer Research, University of Manchester, Manchester, M20 4BX, UK, llancashire@picr.man.ac.uk.Issue Date
2009-04-04
Metadata
Show full item recordAbstract
Gene expression microarrays allow for the high throughput analysis of huge numbers of gene transcripts and this technology has been widely applied to the molecular and biological classification of cancer patients and in predicting clinical outcome. A potential handicap of such data intensive molecular technologies is the translation to clinical application in routine practice. In using an artificial neural network bioinformatic approach, we have reduced a 70 gene signature to just 9 genes capable of accurately predicting distant metastases in the original dataset. Upon validation in a follow-up cohort, this signature was an independent predictor of metastases free and overall survival in the presence of the 70 gene signature and other factors. Interestingly, the ANN signature and CA9 expression also split the groups defined by the 70 gene signature into prognostically distinct groups. Subsequently, the presence of protein for the principal prognosticator gene was categorically assessed in breast cancer tissue of an experimental and independent validation patient cohort, using immunohistochemistry. Importantly our principal prognosticator, CA9, showed that it is capable of selecting an aggressive subgroup of patients who are known to have poor prognosis.Citation
A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks. 2009: Breast Cancer Res. Treat.Journal
Breast Cancer Research and TreatmentDOI
10.1007/s10549-009-0378-1PubMed ID
19347577Type
ArticleLanguage
enISSN
1573-7217ae974a485f413a2113503eed53cd6c53
10.1007/s10549-009-0378-1
Scopus Count
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