To support oncologists and clinicians in the treatment decision-making process, we have developed DrugTargetInspector (DTI), an interactive assistance tool that provides rich functionality for the integrative analysis of tumor-specific -omics datasets. To reveal the characteristics of a given tumor, DTI analyzes and integrates genomic, transcriptomic, and proteomic datasets, where genomic data sets provide information on genetic alterations (mutations). These mutations can affect the eligibility of therapy options in different ways. On the one side, driver mutations induce the deregulation of certain processes and pathways and, hence, the knowledge of such mutations and the induced pathway activities is important for decisions on (targeted) therapies. On the other side, mutations can make therapies ineffective or reduce their efficacy (e.g. drugs targeting EGFR are ineffective if an activating KRAS mutation exists in the tumor). To account for these dependencies, mutation data can be uploaded to DTI and based on the GDSC database, DTI annotates given mutations with their pharmacogenomic effects across a large panel of drugs.

Transcriptomic and proteomic datasets can be used to identify deregulated signaling pathways and processes. To this end, DTI performs enrichment analyses on a large set of pathways derived from a variety of databases. Based on these pathway activities and the corresponding information on mutations, putative target pathways, drug targets and their corresponding drugs can be identified (e.g. PTEN deletion or loss of function activates the PIK/AKT/MTOR pathway, interesting drug targets could then be mTORC1 or the upstream receptor HER2 to be targeted by e.g. (a combination of) everolimus and trastuzumab). DTI also determines if these drug targets are deregulated and offers functionality to determine their effect on downstream processes. To this end, DTI performs a subgraph analysis, which reveals the most deregulated subnetwork rooted in a drug target of interest. The subnetwork is visualized along with its corresponding gene expression data, which allows for a visual assessment of how the downstream molecules might be influenced by the root node.

In summary, DTI provides a powerful integrated tool suite for cancer therapy stratification by providing in-depth analyses of tumor -omics datasets and a characterization of various aspects of dysregulation in the tumor, making DTI a valuable addition to existing clinical decision-support systems.