PMID: identifier of
Pubmed reference: |
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 (16551658) |
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| title: |
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Identifying gene expression changes in breast cancer that distinguish early and late relapse among uncured patients. |
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| author(s): |
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Philippe Broët1, Vladimir Kuznetsov2, Jonas Bergh3, Edison Liu2, Lance Miller2 |
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| laboratory: |
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| abstract: |
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MOTIVATION: In recent years, microarray technology has revealed many tumor-expressed genes prognostic of clinical outcomes in early-stage breast cancer patients. However, in the presence of cured patients, evaluating gene effect on time to relapse is quite complex since it may affect either the probability of never experiencing a relapse (cure effect) or the time to relapse among the uncured patients (disease progression effect) or both. In this context, we propose a simple and an efficient method for identifying gene expression changes that characterize early and late recurrence for uncured patients. RESULTS: Simulation results show the good performance of the proposed statistic for detecting a disease progression effect. In a study of early-stage breast cancer, our results show that the proposed statistic provides a more powerful basis for gene selection than the classical Cox model-based statistic. From a biological perspective, many of the genes identified here as associated with the speed of disease recurrence have known roles in tumorigenesis. |
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| subject: |
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| fulltext language: |
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English |
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| ISSN: |
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1367-4803 |
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| publication format: |
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Article in peer-reviewed journal |
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| DOI: |
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10.1093/bioinformatics/btl110 |
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| journal: |
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| Bioinformatics (Bioinformatics) |
| Publisher |
Oxford University Press (OUP): Policy B - Oxford Open Option B |
| ISSN |
1367-4803 (eISSN : 1460-2059) |
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| publication date: |
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2006 |
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| volume: |
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22 |
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| page, identifiant, ...: |
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1477-85 |
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