1. Interactome-transcriptome integration for predicting distant metastasis in breast cancer
- Author
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Raphaelle Millat-Carus, François Bertucci, Maxime Garcia, Pascal Finetti, Daniel Birnbaum, Ghislain Bidaut, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Aix Marseille Université (AMU), and Bidaut, Ghislain
- Subjects
Statistics and Probability ,Computer science ,[SDV]Life Sciences [q-bio] ,Stability (learning theory) ,Breast Neoplasms ,Computational biology ,computer.software_genre ,Biochemistry ,Interactome ,Transcriptome ,03 medical and health sciences ,0302 clinical medicine ,Breast cancer ,Gene expression ,medicine ,Humans ,Protein Interaction Maps ,Neoplasm Metastasis ,Molecular Biology ,030304 developmental biology ,0303 health sciences ,[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] ,Training set ,Gene Expression Profiling ,Distant metastasis ,Genomic signature ,medicine.disease ,[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] ,3. Good health ,Computer Science Applications ,[SDV] Life Sciences [q-bio] ,Gene expression profiling ,Computational Mathematics ,Computational Theory and Mathematics ,Receptors, Estrogen ,030220 oncology & carcinogenesis ,Female ,Data mining ,computer ,Algorithms - Abstract
Motivation: High-throughput gene expression profiling yields genomic signatures that allow the prediction of clinical conditions including patient outcome. However, these signatures have limitations, such as dependency on the training set, and worse, lack of generalization. Results: We propose a novel algorithm called ITI (interactome–transcriptome integration), to extract a genomic signature predicting distant metastasis in breast cancer by superimposition of large-scale protein–protein interaction data over a compendium of several gene expression datasets. Training on two different compendia showed that the estrogen receptor-specific signatures obtained are more stable (11–35% stability), can be generalized on independent data and performs better than previously published methods (53–74% accuracy). Availability: The ITI algorithm source code from analysis are available under CeCILL from the ITI companion website: http://bioinformatique.marseille.inserm.fr/iti. Contact: maxime.garcia@inserm.fr; ghislain.bidaut@inserm.fr Supplementary information: Supplementary data are available at Bioinformatics online.
- Published
- 2012