SOX4 regulates invasion of bladder cancer cells via repression of WNT5a.

Sry­Related HMG­BOX­4 (SOX4) is a developmental transcription factor that is overexpressed in as many as 23% of bladder cancer patients; however, the role of SOX4 in bladder cancer tumorigenesis is not yet well understood. Given the many roles of SOX4 in embryonic development and the context­dependent regulation of gene expression, in this study, we sought to determine the role of SOX4 in bladder cancer and to identify SOX4­regulated genes that may contribute to tumorigenesis. For this purpose, we employed a CRISPR interference (CRISPRi) method to transcriptionally repress SOX4 expression in T24 bladder cancer cell lines, 'rescued' these cell lines with the lentiviral­mediated expression of SOX4, and performed whole genome expression profiling. The cells in which SOX4 was knocked down (T24­SOX4­KD) exhibited decreased invasive capabilities, but no changes in migration or proliferation, whereas rescue experiments with SOX4 lentiviral vector restored the invasive phenotype. Gene expression profiling revealed 173 high confidence SOX4­regulated genes, including WNT5a as a potential target of repression by SOX4. Treatment of the T24­SOX4­KD cells with a WNT5a antagonist restored the invasive phenotype observed in the T24­scramble control cells and the SOX4 lentiviral­rescued cells. High WNT5a expression was associated with a decreased invasion and WNT5a expression inversely correlated with SOX4 expression, suggesting that SOX4 can negatively regulate WNT5a levels either directly or indirectly and that WNT5a likely plays a protective role against invasion in bladder cancer cells.

MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development.

Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. Here we introduce a computational method (MEDICI) to predict PPI essentiality by combining gene knockdown studies with network models of protein interaction pathways in an analytic framework. Our method uses network topology to model how gene silencing can disrupt PPIs, relating the unknown essentialities of individual PPIs to experimentally observed protein essentialities. This model is then deconvolved to recover the unknown essentialities of individual PPIs. We demonstrate the validity of our approach via prediction of sensitivities to compounds based on PPI essentiality and differences in essentiality based on genetic mutations. We further show that lung cancer patients have improved overall survival when specific PPIs are no longer present, suggesting that these PPIs may be potentially new targets for therapeutic development. Software is freely available at https://github.com/cooperlab/MEDICI. Datasets are available at https://ctd2.nci.nih.gov/dataPortal.