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A Critical Evaluation of Network and Pathway-Based Classifiers for Outcome Prediction in Breast Cancer

Please always quote using this URN: urn:nbn:de:bvb:20-opus-131323
  • Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain inRecently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single genes classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single genes classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single genes sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single genes classifiers for predicting outcome in breast cancer.show moreshow less

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Metadaten
Author: Christine Staiger, Sidney Cadot, Raul Kooter, Marcus Dittrich, Tobias Müller, Gunnar W. Klau, Lodewyk F. A. Wessels
URN:urn:nbn:de:bvb:20-opus-131323
Document Type:Journal article
Faculties:Fakultät für Biologie / Theodor-Boveri-Institut für Biowissenschaften
Language:English
Parent Title (English):PLoS One
Year of Completion:2012
Volume:7
Issue:4
Pagenumber:e34796
Source:PLoS ONE 7(4): e34796. doi:10.1371/journal.pone.0034796
DOI:https://doi.org/10.1371/journal.pone.0034796
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Tag:classification; database; expression signature; markers; metastasis; modules; protein-interaction networks; set; stability; survival
Release Date:2016/12/13
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung