Abstract
BackgroundNetwork 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 FindingsWe 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/SignificanceThe clinical and FDA cancer networks are differentially connected, 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.
Highlights
Cancer is a complex disease, with many subtypes, affecting various tissues in diverse ways, giving rise to an abundance of chemotherapies
Breast cancer, lung cancer, and lymphoma have the highest number of drug approvals and the highest number of clinical trials (Table 2, Figure S1)
The percentage of clinical trials or Food and Drug Administration (FDA) approvals for the different cancers were calculated as the number of clinical drug trials or FDA drug approvals for a specific cancer type, divided by the total number of clinical drug trials or FDA drug approvals for the 23 cancers analyzed in this study
Summary
Cancer is a complex disease, with many subtypes, affecting various tissues in diverse ways, giving rise to an abundance of chemotherapies. This study was concerned with the global description of the network, and found that only a few diseases are highly connected by drugs, while most diseases are less connected; and most diseases, even those unrelated to each other, are connected by a few links [6] These studies constitute the global topological analysis aspect of the emerging areas of network medicine [7] and network pharmacology [8]. 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
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