Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach.

2.50
Hdl Handle:
http://hdl.handle.net/10541/67994
Title:
Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach.
Authors:
Lancashire, Lee J; Rees, Robert C; Ball, Graham R
Abstract:
OBJECTIVE: The advent of microarrays has attracted considerable interest from biologists due to the potential for high throughput analysis of hundreds of thousands of gene transcripts. Subsequent analysis of the data may identify specific features which correspond to characteristics of interest within the population, for example, analysis of gene expression profiles in cancer patients to identify molecular signatures corresponding with prognostic outcome. These high throughput technologies have resulted in an unprecedented rate of data generation, often of high complexity, highlighting the need for novel data analysis methodologies that will cope with data of this nature. METHODS: Stepwise methods using artificial neural networks (ANNs) have been developed to identify an optimal subset of predictive gene transcripts from highly dimensional microarray data. Here these methods have been applied to a gene microarray dataset to identify and validate gene signatures corresponding with estrogen receptor and lymph node status in breast cancer. RESULTS: Many gene transcripts were identified whose expression could differentiate patients to very high accuracies based upon firstly whether they were positive or negative for estrogen receptor, and secondly whether metastasis to the axillary lymph node had occurred. A number of these genes had been previously reported to have a role in cancer. Significantly fewer genes were used compared to other previous studies. The models using the optimal gene subsets were internally validated using an extensive random sample cross-validation procedure and externally validated using a follow up dataset from a different cohort of patients on a newer array chip containing the same and additional probe sets. Here, the models retained high accuracies, emphasising the potential power of this approach in analysing complex systems. These findings show how the proposed method allows for the rapid analysis and subsequent detailed interrogation of gene expression signatures to provide a further understanding of the underlying molecular mechanisms that could be important in determining novel prognostic markers associated with cancer.
Affiliation:
Clinical and Experimental Pharmacology, Paterson Institute for Cancer Research, University of Manchester, Manchester M20 4BX, United Kingdom. LLancashire@picr.man.ac.uk
Citation:
Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach. 2008, 43 (2):99-111 Artif Intell Med
Journal:
Artificial Intelligence in Medicine
Issue Date:
Jun-2008
URI:
http://hdl.handle.net/10541/67994
DOI:
10.1016/j.artmed.2008.03.001
PubMed ID:
18420392
Type:
Article
Language:
en
ISSN:
0933-3657
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.authorRees, Robert C-
dc.contributor.authorBall, Graham R-
dc.date.accessioned2009-05-12T18:25:54Z-
dc.date.available2009-05-12T18:25:54Z-
dc.date.issued2008-06-
dc.identifier.citationIdentification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach. 2008, 43 (2):99-111 Artif Intell Meden
dc.identifier.issn0933-3657-
dc.identifier.pmid18420392-
dc.identifier.doi10.1016/j.artmed.2008.03.001-
dc.identifier.urihttp://hdl.handle.net/10541/67994-
dc.description.abstractOBJECTIVE: The advent of microarrays has attracted considerable interest from biologists due to the potential for high throughput analysis of hundreds of thousands of gene transcripts. Subsequent analysis of the data may identify specific features which correspond to characteristics of interest within the population, for example, analysis of gene expression profiles in cancer patients to identify molecular signatures corresponding with prognostic outcome. These high throughput technologies have resulted in an unprecedented rate of data generation, often of high complexity, highlighting the need for novel data analysis methodologies that will cope with data of this nature. METHODS: Stepwise methods using artificial neural networks (ANNs) have been developed to identify an optimal subset of predictive gene transcripts from highly dimensional microarray data. Here these methods have been applied to a gene microarray dataset to identify and validate gene signatures corresponding with estrogen receptor and lymph node status in breast cancer. RESULTS: Many gene transcripts were identified whose expression could differentiate patients to very high accuracies based upon firstly whether they were positive or negative for estrogen receptor, and secondly whether metastasis to the axillary lymph node had occurred. A number of these genes had been previously reported to have a role in cancer. Significantly fewer genes were used compared to other previous studies. The models using the optimal gene subsets were internally validated using an extensive random sample cross-validation procedure and externally validated using a follow up dataset from a different cohort of patients on a newer array chip containing the same and additional probe sets. Here, the models retained high accuracies, emphasising the potential power of this approach in analysing complex systems. These findings show how the proposed method allows for the rapid analysis and subsequent detailed interrogation of gene expression signatures to provide a further understanding of the underlying molecular mechanisms that could be important in determining novel prognostic markers associated with cancer.en
dc.language.isoenen
dc.subjectPredictive Modellingen
dc.subjectBreast Canceren
dc.subject.meshBreast Neoplasms-
dc.subject.meshFemale-
dc.subject.meshGene Expression Profiling-
dc.subject.meshHumans-
dc.subject.meshLymphatic Metastasis-
dc.subject.meshNeural Networks (Computer)-
dc.subject.meshOligonucleotide Array Sequence Analysis-
dc.subject.meshPredictive Value of Tests-
dc.subject.meshReceptors, Estrogen-
dc.subject.meshReproducibility of Results-
dc.subject.meshTranscription, Genetic-
dc.titleIdentification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach.en
dc.typeArticleen
dc.contributor.departmentClinical and Experimental Pharmacology, Paterson Institute for Cancer Research, University of Manchester, Manchester M20 4BX, United Kingdom. LLancashire@picr.man.ac.uken
dc.identifier.journalArtificial Intelligence in Medicineen

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