Cancer-drug associations: A complex system

Ertugrul Dalkic, Xuewei Wang, Neil Wright, Christina Chan

Research output: Contribution to journalArticle

  • 8 Citations

Abstract

Background: Network analysis has been performed on large-scale medical data, capturing the global topology of drugs, targets, and disease relationships. A smaller-scale network is amenable to a more detailed and focused analysis of the individual members and their interactions in a network, which can complement the global topological descriptions of a network system. Analysis of these smaller networks can help address questions, i.e., what governs the pairing of the different cancers and drugs, is it driven by molecular findings or other factors, such as death statistics. Methodology/Principal Findings: We defined global and local lethality values representing death rates relative to other cancers vs. within a cancer. We generated two cancer networks, one of cancer types that share Food and Drug Administration (FDA) approved drugs (FDA cancer network), and another of cancer types that share clinical trials of FDA approved drugs (clinical trial cancer network). Breast cancer is the only cancer type with significant weighted degree values in both cancer networks. Lung cancer is significantly connected in the FDA cancer network, whereas ovarian cancer and lymphoma are significantly connected in the clinical trial cancer network. Correlation and linear regression analyses showed that global lethality impacts the drug approval and trial numbers, whereas, local lethality impacts the amount of drug sharing in trials and approvals. However, this effect does not apply to pancreatic, liver, and esophagus cancers as the sharing of drugs for these cancers is very low. We also collected mutation target information to generate cancer type associations which were compared with the cancer type associations derived from the drug target information. The analysis showed a weak overlap between the mutation and drug target based networks. Conclusions/Significance: The clinical and FDA cancer networks are differentially onnected, with only breast cancer significantly connected in both networks. The networks of cancer-drug associations are moderately affected by the death statistics. A strong overlap does not exist between the cancer-drug associations and the molecular information. Overall, this analysis provides a systems level view of cancer drugs and suggests that death statistics (i.e. global vs. local lethality) have a differential impact on the number of approvals, trials and drug sharing.

LanguageEnglish (US)
Article numbere10031
JournalPLoS One
Volume5
Issue number4
DOIs
StatePublished - 2010

Profile

Large scale systems
drugs
neoplasms
Pharmaceutical Preparations
Neoplasms
United States Food and Drug Administration
Statistics
Drug Approval
clinical trials
statistics
Clinical Trials
death
breast neoplasms
Complex networks
Electric network analysis
Linear regression
Liver
Breast Neoplasms
esophageal neoplasms
mutation

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Cancer-drug associations : A complex system. / Dalkic, Ertugrul; Wang, Xuewei; Wright, Neil; Chan, Christina.

In: PLoS One, Vol. 5, No. 4, e10031, 2010.

Research output: Contribution to journalArticle

Dalkic, Ertugrul ; Wang, Xuewei ; Wright, Neil ; Chan, Christina. / Cancer-drug associations : A complex system. In: PLoS One. 2010 ; Vol. 5, No. 4.
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