Person:

Tian, Ze

Platelet-derived growth factor receptors (PDGFRs) have been implicated in a wide array of human malignancies, including medulloblastoma (MB), the most common brain tumor of childhood. Although significant progress in MB biology and therapeutics has been achieved during the past decades, MB remains a horrible challenge to the physicians and researchers. Therefore, novel inhibitors targeting PDGFR signaling pathway may offer great promise for the treatment of MB. In the present study, we investigated the cytotoxicity and mechanisms of cambogin in Daoy MB cells. Our results show that cambogin triggers significant S phase cell cycle arrest and apoptosis via down regulation of cyclin A and E, and activation of caspases. More importantly, further mechanistic studies demonstrated that cambogin inhibits PDGFR signaling in Daoy and genetically defined mouse embryo fibroblast (MEF) cell lines. These results suggest that cambogin is preferentially cytotoxic to cells expressing PDGFR. Our findings may provide a novel approach by targeting PDGFR signaling against MB.

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call ‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies.