title: IODNE: An integrated optimization method for identifying the deregulated subnetwork for precision medicine in cancer.
abstract: Subnetwork analysis can explore complex patterns of entire molecular pathways for the purpose of drug target identification. In this article, the gene expression profiles of a cohort of patients with breast cancer are integrated with protein-protein interaction (PPI) networks using, simultaneously, both edge scoring and node scoring. A novel optimization algorithm, integrated optimization method to identify deregulated subnetwork (IODNE), is developed to search for the optimal dysregulated subnetwork of the merged gene and protein network. IODNE is applied to select subnetworks for Luminal-A breast cancer from The Cancer Genome Atlas (TCGA) data. A large fraction of cancer-related genes and the well-known clinical targets, ER1/PR and HER2, are found by IODNE. This validates the utility of IODNE. When applying IODNE to the triple-negative breast cancer (TNBC) subtype data, we identified subnetworks that contain genes such as ERBB2, HRAS, PGR, CAD, POLE, and SLC2A1.
authors.affiliation: Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.|Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.
authors.forename: S
authors.initials: S
authors.lastname: Mounika Inavolu
authors.affiliation: Department of Pediatrics, Hematology/Oncology, School of Medicine, Indiana University, Indianapolis, Indiana, USA.
authors.forename: J
authors.initials: J
authors.lastname: Renbarger
authors.affiliation: Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.|Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.
authors.forename: M
authors.initials: M
authors.lastname: Radovich
authors.affiliation: Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.|Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.
authors.forename: V
authors.initials: V
authors.lastname: Vasudevaraja
authors.affiliation: Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.|Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.
authors.forename: G H
authors.initials: GH
authors.lastname: Kinnebrew
authors.affiliation: Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.|Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.
authors.forename: S
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.|Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.|Department of Pediatrics, Hematology/Oncology, School of Medicine, Indiana University, Indianapolis, Indiana, USA.
authors.forename: L
authors.initials: L
authors.lastname: Cheng
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000628:therapy*; D015331:Cohort Studies; D005260:Female; D053263:Gene Regulatory Networks / Q000235:genetics*; D015316:Genetic Therapy / Q000379:methods / Q000639:trends; D006801:Humans; D057285:Precision Medicine / Q000379:methods* / Q000639:trends; D054730:Protein Interaction Domains and Motifs / Q000235:genetics; D059467:Transcriptome / Q000235:genetics*