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IODNE: An integrated optimization method for identifying the deregulated subnetwork for precision medicine in cancer

S Mounika Inavolu1,2, J Renbarger3, M Radovich1,2

  • 1Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.

CPT: Pharmacometrics & Systems Pharmacology
|March 8, 2017
PubMed

Insights

This study introduces a new method, IODNE, to find key genes in breast cancer by analyzing gene and protein networks. The method successfully identified known cancer targets, aiding drug discovery.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Subnetwork analysis is crucial for understanding complex molecular pathways in cancer.
  • Identifying reliable drug targets requires integrating gene expression data with protein-protein interaction (PPI) networks.

Purpose of the Study:

  • To develop a novel optimization algorithm, the Integrated Optimization Method to Identify Deregulated Subnetwork (IODNE), for pinpointing dysregulated subnetworks in breast cancer.
  • To validate IODNE's efficacy in identifying clinically relevant cancer genes and drug targets.

Main Methods:

  • Gene expression profiles from The Cancer Genome Atlas (TCGA) were integrated with PPI networks.
  • A novel algorithm, IODNE, was developed using simultaneous edge and node scoring for subnetwork identification.
  • IODNE was applied to Luminal-A and triple-negative breast cancer (TNBC) subtype data.

Main Results:

  • IODNE successfully identified subnetworks containing known cancer-related genes and clinical targets like Estrogen Receptor 1 (ER1)/Progesterone Receptor (PR) and Human Epidermal growth factor Receptor 2 (HER2) in Luminal-A breast cancer.
  • Application to TNBC data revealed subnetworks with genes including ERBB2, HRAS, PGR, CAD, POLE, and SLC2A1.

Conclusions:

  • The developed IODNE algorithm is effective for identifying dysregulated subnetworks and potential drug targets in breast cancer.
  • IODNE provides a valuable tool for cancer subnetwork analysis, aiding in the discovery of novel therapeutic strategies.

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