Inhibition of Cdc42-intersectin interaction by small molecule ZCL367 impedes cancer cell cycle progression, proliferation, migration, and tumor growth

Cdc42 is a member of the Rho family of small GTPases that are at the crossroads of major oncogenic signaling pathways involved in both lung and prostate cancers. However, the therapeutic potential of Cdc42 regulation is still unclear due to the lack of pharmacological tools. Herein, we report that ZCL367 is a bona fide Cdc42 inhibitor that suppressed cancer development and ZCL278 can act as a partial Cdc42 agonist. In lung cancer cell lines with varying EGFR and Ras mutations as well as both androgen-independent and androgen-dependent prostate cancer cell lines, ZCL367 impeded cell cycle progression, reduced proliferation, and suppressed migration. ZCL367 decreased Cdc42-intersectin interactions and reduced Cdc42-mediated filopodia formation. ZCL367 showed increased potency and selectivity for Cdc42 when compared to Rac1 and RhoA. ZCL367 reduced A549 tumorigenesis in a xenograft mouse model. Altogether, ZCL367 is a selective Cdc42 inhibitor and an excellent candidate for lead compound optimization for further anticancer studies.

Discovery of dominant and dormant genes from expression data using a novel generalization of SNR for multi-class problems

Background

The Signal-to-Noise-Ratio (SNR) is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs). These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of individual gene and 2-D Sammon's projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems.

Results and discussion

To evaluate the effectiveness of the GDIs, we have used four multiclass cancer data sets (Small Round Blue Cell Tumors, Leukemia, Central Nervous System Tumors, and Lung Cancer). For each data set we demonstrate that the new indices can find biologically meaningful genes that can act as biomarkers. We then use six machine learning tools, Nearest Neighbor Classifier (NNC), Nearest Mean Classifier (NMC), Support Vector Machine (SVM) classifier with linear kernel, and SVM classifier with Gaussian kernel, where both SVMs are used in conjunction with one-vs-all (OVA) and one-vs-one (OVO) strategies. We found GDIs to be very effective in identifying biomarkers with strong class specific signatures. With all six tools and for all data sets we could achieve better or comparable prediction accuracies usually with fewer marker genes than results reported in the literature using the same computational protocols. The dominant genes are usually easy to find while good dormant genes may not always be available as dormant genes require stronger constraints to be satisfied; but when they are available, they can be used for authentication of diagnosis.

Conclusion

Since GDI based schemes can find a small set of dominant/dormant biomarkers that is adequate to design diagnostic prediction systems, it opens up the possibility of using real-time qPCR assays or antibody based methods such as ELISA for an easy and low cost diagnosis of diseases. The dominant and dormant genes found by GDIs can be used in different ways to design more reliable diagnostic prediction systems.