Open AccessIODNE: An integrated optimization method for identifying the deregulated subnetwork for precision medicine in cancer
Author(s) -
Mounika Inavolu S,
Renbarger J,
Radovich M,
Vasudevaraja V,
Kinnebrew GH,
Zhang S,
Cheng L
Publication year - 2017
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12167
Subject(s) - subnetwork , hras , breast cancer , interaction network , computational biology , computer science , cancer , gene , gene regulatory network , bioinformatics , biology , gene expression , genetics , computer network , kras , colorectal cancer
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 .