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Network analysis offers a powerful technique to model the relationships between genes within biological regulatory networks. Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput “omics” data typically available. To overcome this challenge, we exploit known organizing principles of biological networks that are sparse, modular, and likely share a large portion of their underlying architecture. We present SHINE - Structure Learning for Hierarchical Networks - a framework for defining data-driven structural constraints and incorporating a shared learning paradigm for efficiently learning multiple networks from high-dimensional data. We show through simulations SHINE improves performance when relatively few samples are available and multiple networks are desired, by reducing the complexity of the graphical search space and by taking advantage of shared structural information. We evaluated SHINE on TCGA Pan-Cancer data and found learned tumor-specific networks exhibit expected graph properties of real biological networks, recapture previously validated interactions, and recapitulate findings in literature. Application of SHINE to the analysis of subtype-specific breast cancer networks identified key genes and biological processes for tumor maintenance and survival as well as potential therapeutic targets for modulating known breast cancer disease genes.

Downregulation of the cylindromatosis (CYLD) tumor suppressor has been associated with breast cancer development and progression. Here, we report a critical role for CYLD in maintaining the phenotype of mammary epithelial cells in vitro and in vivo. CYLD downregulation or inactivation induced an epithelial to mesenchymal transition of mammary epithelial cells that was dependent on the concomitant activation of the transcription factors Yes-associated protein (YAP)/transcriptional coactivator with PDZ-binding motif (TAZ) and transforming growth factor beta (TGFβ)signaling. CYLD inactivation enhanced the nuclear localization of YAP/TAZ and the phosphorylation of Small Mothers Against Decapentaplegic (SMAD)2/3 proteins in confluent cell culture conditions. Consistent with these findings were the hyperplastic alterations of CYLD-deficient mouse mammary epithelia, which were associated with enhanced nuclear expression of the YAP/TAZ transcription factors. Furthermore, in human breast cancer samples, downregulation of CYLD expression correlates with enhanced YAP/TAZ-regulated target gene expression. Our results identify CYLD as a critical regulator of a signaling node that prevents the coordinated activation of YAP/TAZ and the TGFβ pathway in mammary epithelial cells, in order to maintain their phenotypic identity and homeostasis. Consequently, they provide a novel conceptual framework that supports and explains a causal implication of deficient CYLD expression in aggressive human breast cancers.

The tricarboxylic acid (TCA) cycle is a central route for oxidative phosphorylation in cells, and fulfills their bioenergetic, biosynthetic, and redox balance requirements. Despite early dogma that cancer cells bypass the TCA cycle and primarily utilize aerobic glycolysis, emerging evidence demonstrates that certain cancer cells, especially those with deregulated oncogene and tumor suppressor expression, rely heavily on the TCA cycle for energy production and macromolecule synthesis. As the field progresses, the importance of aberrant TCA cycle function in tumorigenesis and the potentials of applying small molecule inhibitors to perturb the enhanced cycle function for cancer treatment start to evolve. In this review, we summarize current knowledge about the fuels feeding the cycle, effects of oncogenes and tumor suppressors on fuel and cycle usage, common genetic alterations and deregulation of cycle enzymes, and potential therapeutic opportunities for targeting the TCA cycle in cancer cells. With the application of advanced technology and in vivo model organism studies, it is our hope that studies of this previously overlooked biochemical hub will provide fresh insights into cancer metabolism and tumorigenesis, subsequently revealing vulnerabilities for therapeutic interventions in various cancer types.

New mechanisms by which cells communicate to specify cell fate during animal development are continuously being discovered. In a recently published article in Cell, Xia et al. (2019) identify the transcriptional regulator TAZ as a novel mediator of lateral inhibition in zebrafish oogenesis that directs cell fate through mechanical cues.

Identifying complementary genetic drivers of a given phenotypic outcome is a challenging task that is important to gaining new biological insight and discovering targets for disease therapy. Existing methods aimed at achieving this task lack analytical flexibility. We developed Candidate Driver Analysis or CaDrA, a framework to identify functionally-relevant subsets of binary genomic features that, together, are associated with a specific outcome of interest. We evaluate CaDrA’s sensitivity and specificity for typically-sized multi-omic datasets, and demonstrate CaDrA’s ability to identify both known and novel drivers of oncogenic activity in cancer cell lines and primary tumors.