A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks.

2.50
Hdl Handle:
http://hdl.handle.net/10541/69759
Title:
A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks.
Authors:
Lancashire, Lee J; Powe, 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
Abstract:
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.
Affiliation:
Clinical and Experimental Pharmacology, Paterson Institute for Cancer Research, University of Manchester, Manchester, M20 4BX, UK, llancashire@picr.man.ac.uk.
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 Treatment
Issue Date:
4-Apr-2009
URI:
http://hdl.handle.net/10541/69759
DOI:
10.1007/s10549-009-0378-1
PubMed ID:
19347577
Type:
Article
Language:
en
ISSN:
1573-7217
Appears in Collections:
All Paterson Institute for Cancer Research; Clinical and Experimental Pharmacology Group

Full metadata record

DC FieldValue Language
dc.contributor.authorLancashire, Lee J-
dc.contributor.authorPowe, D G-
dc.contributor.authorReis-Filho, J S-
dc.contributor.authorRakha, E-
dc.contributor.authorLemetre, Christophe-
dc.contributor.authorWeigelt, B-
dc.contributor.authorAbdel-Fatah, T M-
dc.contributor.authorGreen, Anthony R-
dc.contributor.authorMukta, R-
dc.contributor.authorBlamey, R-
dc.contributor.authorPaish, E C-
dc.contributor.authorRees, Robert C-
dc.contributor.authorEllis, I O-
dc.contributor.authorBall, Graham R-
dc.date.accessioned2009-06-05T10:02:50Z-
dc.date.available2009-06-05T10:02:50Z-
dc.date.issued2009-04-04-
dc.identifier.citationA validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks. 2009: Breast Cancer Res. Treat.en
dc.identifier.issn1573-7217-
dc.identifier.pmid19347577-
dc.identifier.doi10.1007/s10549-009-0378-1-
dc.identifier.urihttp://hdl.handle.net/10541/69759-
dc.description.abstractGene 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.en
dc.languageENG-
dc.language.isoenen
dc.subjectBreast Canceren
dc.subjectPrognosisen
dc.subjectBioinformaticsen
dc.subjectSurvivalen
dc.subjectHypoxiaen
dc.titleA validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks.en
dc.typeArticleen
dc.contributor.departmentClinical and Experimental Pharmacology, Paterson Institute for Cancer Research, University of Manchester, Manchester, M20 4BX, UK, llancashire@picr.man.ac.uk.en
dc.identifier.journalBreast Cancer Research and Treatmenten

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