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Breast Cancer Research and Treatment

Erschienen in:

01.08.2012 | Brief Report

Feature extraction via composite scoring and voting in breast cancer

verfasst von: Martin Koch, Markus Hanl, Michael Wiese

Erschienen in: Breast Cancer Research and Treatment | Ausgabe 1/2012

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Abstract

Identification and characterization of tumor subtypes using gene expression profiles of triple negative breast cancer patients. Microarray data of four breast cancer studies were pooled and evaluated. Molecular subtype classification was performed using random forest and a novel algorithm for feature extraction via composite scoring and voting. Biological and clinical properties were evaluated via GSEA, functional annotation clustering and clinical endpoint analysis. The subtype signatures are highly predictive for distant metastasis free survival of tamoxifen-treated patients. Consensus clustering and the novel algorithm proposed three triple negative subtypes. One subtype shows low E2F4 gene expression and is predictive for survival of ER negative breast cancer patients. The other two subtypes share commonalities with luminal B tumors. Classification of breast cancer expression profiles may reveal novel tumor subtypes, possessing clinical impact. Furthermore, subtype characterizing gene signatures might hold potential for novel strategies in cancer therapy.

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Titel: Feature extraction via composite scoring and voting in breast cancer
verfasst von: Martin Koch
Markus Hanl
Michael Wiese
Publikationsdatum: 01.08.2012
Verlag: Springer US
Erschienen in: Breast Cancer Research and Treatment / Ausgabe 1/2012
Print ISSN: 0167-6806
Elektronische ISSN: 1573-7217
DOI: https://doi.org/10.1007/s10549-012-2177-3

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Abstract

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