De novo pathway-based biomarker identification
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De novo pathway-based biomarker identification. / Alcaraz, Nicolas; List, Markus; Batra, Richa; Vandin, Fabio; Ditzel, Henrik J.; Baumbach, Jan.
I: Nucleic Acids Research, Bind 45, Nr. 16, e151, 19.09.2017.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › fagfællebedømt
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T1 - De novo pathway-based biomarker identification
AU - Alcaraz, Nicolas
AU - List, Markus
AU - Batra, Richa
AU - Vandin, Fabio
AU - Ditzel, Henrik J.
AU - Baumbach, Jan
PY - 2017/9/19
Y1 - 2017/9/19
N2 - 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-to-use 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.
AB - 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-to-use 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.
UR - http://www.scopus.com/inward/record.url?scp=85031915820&partnerID=8YFLogxK
U2 - 10.1093/nar/gkx642
DO - 10.1093/nar/gkx642
M3 - Journal article
C2 - 28934488
AN - SCOPUS:85031915820
VL - 45
JO - Nucleic Acids Research
JF - Nucleic Acids Research
SN - 0305-1048
IS - 16
M1 - e151
ER -
ID: 185472026