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Alcaraz, N.* ; List, M.* ; Batra, R. ; Vandin, F.* ; Ditzel, H.J.* ; Baumbach, J.*

De novo pathway-based biomarker identification.

Nucleic Acids Res. 45:e151 (2017)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-touse web service at http:// pathclass. compbio. sdu. dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Cancer Outcome Prediction; High-throughput Data; Breast-cancer; Gene-expression; Protein Interactions; Functional Modules; Network Analysis; Random Forest; R-package; Classification
Sprache
Veröffentlichungsjahr 2017
HGF-Berichtsjahr 2017
ISSN (print) / ISBN 0305-1048
e-ISSN 1362-4962
Zeitschrift Nucleic Acids Research
Quellenangaben Band: 45, Heft: 16, Seiten: , Artikelnummer: e151 Supplement: ,
Verlag Oxford University Press
Verlagsort Oxford
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
PubMed ID 28934488
DOI 10.1093/nar/gkx642
WOS ID WOS:000411096400006
Scopus ID 85031915820
Erfassungsdatum 2017-10-06
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