Cancer-drug associations: A complex system

Ertugrul Dalkic, Xuewei Wang, Neil Wright, Christina Chan

    Research output: Contribution to journalArticle

    • 7 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.

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

    Profile

    neoplasms
    Neoplasms
    drugs
    Hexosaminidase A
    United States Food and Drug Administration
    Calibration
    Clinical Trials
    clinical trials
    statistics
    death
    Drug Approval
    Breast Neoplasms
    Mutation
    breast neoplasms
    mutation
    Tartronates
    Supravalvular Aortic Stenosis
    Dilatation and Curettage
    Electric network analysis
    Fibrin

    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, Vol. 5, No. 4, e10031, 2010.

    Research output: Contribution to journalArticle

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    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.",
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