DRUGSURV: a resource for repositioning of approved and experimental drugs in oncology based on patient survival information derived from clinical cancer expression datasets.

Drug repositioning in oncology is commonly initiated by in vitro experimental evidence that a drug exhibits anticancer cytotoxicity. Any independent verification that the observed effects in vitro may be valid in a clinical setting, and that the drug could potentially affect patient survival in vivo is of paramount importance. DRUGSURV is the first computational tool to estimate the potential effects of a drug using patient survival information derived from clinical cancer expression datasets.

Computational principles of drug repositioning

The availability of a vast amount of experimental data covering various diseases has stimulated computational efforts to identify novel potential indications for established drugs. The computational principles of drug repositioning are based on a polypharmacology paradigm: the drugs are considered in the context of all proteins (genes) affected upon treatment (i.e. the drug signature), and specific diseases are modelled by the multiple genes involved/perturbed in the disease state (i.e. disease signature). Significant similarity between drug and disease signatures is indicative of the potential application of the drug to treat the disease (see figure 1).

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Figure 1. Computational principles of drug repositioning. Drugs are considered in the context of all proteins (genes) affected upon treatment (i.e. the drug signature). Disease is modelled by genes involved/perturbed in the disease state. Significant similarity (intersection between drug signature and disease signature) is indicative of the potential application of the drug to treat the disease.

DRUGSURV: exploiting patient survival information

Effect on patient survival outcome is a key criterion of drug efficiency in clinical trials. However, none of the studies have considered patient survival information in modelling the potential of existing/new drugs in the management of cancer. In contrast to other approaches, DRUGSURV uses genes significantly associated (p-value < 0.01) with patient survival as a cancer signature specific for a cancer type or clinical condition studied in a particular dataset (see figure 2). At the moment, DRUGSURV covers 44 independent clinical cancer expression datasets (in most cases each dataset contains > 100 patients annotated with survival information).

drug_reposition

Figure 2.Cancer Signature: Genes up/down regulated in patients with poor/good survival are selected as cancer signature specific for a cancer type or clinical condition studied in a particular dataset.

DRUGSURV: drug signature

DRUGSURV covers both FDA approved drugs (~1700) and experimental drugs (~5000). The coverage of drugs by DRUGSURV significantly exceeds any previous efforts in the field. Drug signature is defined based on known drug targets. This information is integrated from DrugBank and Pubchem Bioassay databases. The proteins which are known targets of a drug, or involved in the drug transport/metabolism, or have been reported to be inhibited by the drug in high throughput screening chemical assays (Pubchem Bioassays) are referred to as direct drug targets . We also use the term indirect drug targets to refer to the proteins that interact with the direct drug targets according to the IntAct database.

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Figure 3. DRUGSURV: principles to derive Drug Signature


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