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Toward Integrated Clinical and Gene- Expression Profiles For Breast Cancer Prognosis: A Review Paper

Author(s): Farzana Kabir Ahmad | Safaai Deris | Nor Hayati Othman

Journal: International Journal of Biometric and Bioinformatics
ISSN 1985-2347

Volume: 3;
Issue: 4;
Start page: 31;
Date: 2009;
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Keywords: Breast cancer | Prognosis | Gene-Expression Profiles | Feature selection | Classification

Breast cancer patients with the same diagnostic and clinical prognostics profilecan have markedly different clinical outcomes. This difference is possibly causedby the limitation of current breast cancer prognostic indices, which groupmolecularly distinct patients into similar clinical classes based mainly on themorphology of diseases. Traditional clinical-based prognosis models werediscovered to contain some restrictions to address the heterogeneity of breastcancer. The invention of microarray technology and its ability to simultaneouslyinterrogate thousands of genes has changed the paradigm of molecularclassification of human cancers as well as shifting clinical prognosis models to abroader prospect. Numerous studies have revealed the potential value of geneexpressionsignatures in examining the risk of disease recurrence. However,most of these studies attempted to implement genetic-marker based prognosticmodels to replace the traditional clinical markers, yet neglecting the richinformation contained in clinical information. Therefore, this research took theeffort to integrate both clinical and microarray data in order to obtain accuratebreast cancer prognosis, by taking into account that these data complement eachother. This article presents a review of the development of breast cancerprognosis models, concentrating precisely on clinical and gene-expressionprofiles. The literature is reviewed in an explicit machine-learning framework,which includes the elements of feature selection and classification techniques.

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