Cells from some tumors use an altered metabolic pattern compared with that of normal differentiated adult cells in the body. Tumor cells take up much more glucose and mainly process it through aerobic glycolysis, producing large quantities of secreted lactate with a lower use of oxidative phosphorylation that would generate more adenosine triphosphate (ATP), water, and carbon dioxide. This is the Warburg effect, which provides substrates for cell growth and division and free energy (ATP) from enhanced glucose use. This metabolic switch places the emphasis on producing intermediates for cell growth and division, and it is regulated by both oncogenes and tumor suppressor genes in a number of key cancer-producing pathways. Blocking these metabolic pathways or restoring these altered pathways could lead to a new approach in cancer treatments.

Cell growth and proliferation requires an intricate coordination between the stimulatory signals arising from nutrients and growth factors and the inhibitory signals arising from intracellular and extracellular stresses. Alteration of the coordination often causes cancer. In mammals, the mTOR (mammalian target of rapamycin) protein kinase is the central node in nutrient and growth factor signaling, and p53 plays a critical role in sensing genotoxic and other stresses. The results presented here demonstrate that activation of p53 inhibits mTOR activity and regulates its downstream targets, including autophagy, a tumor suppression process. Moreover, the mechanisms by which p53 regulates mTOR involves AMP kinase activation and requires the tuberous sclerosis (TSC) 1/TSC2 complex, both of which respond to energy deprivation in cells. In addition, glucose starvation not only signals to shut down mTOR, but also results in the transient phosphorylation of the p53 protein. Thus, p53 and mTOR signaling machineries can cross-talk and coordinately regulate cell growth, proliferation, and death.

Whereas cell cycle arrest, apoptosis, and senescence are traditionally thought of as the major functions of the tumor suppressor p53, recent studies revealed two unique functions for this protein: p53 regulates cellular energy metabolism and antioxidant defense mechanisms. Here, we identify glutaminase 2 (GLS2) as a previously uncharacterized p53 target gene to mediate these two functions of the p53 protein. GLS2 encodes a mitochondrial glutaminase catalyzing the hydrolysis of glutamine to glutamate. p53 increases the GLS2 expression under both nonstressed and stressed conditions. GLS2 regulates cellular energy metabolism by increasing production of glutamate and α-ketoglutarate, which in turn results in enhanced mitochondrial respiration and ATP generation. Furthermore, GLS2 regulates antioxidant defense function in cells by increasing reduced glutathione (GSH) levels and decreasing ROS levels, which in turn protects cells from oxidative stress (e.g., H ₂ O ₂ )-induced apoptosis. Consistent with these functions of GLS2, the activation of p53 increases the levels of glutamate and α-ketoglutarate, mitochondrial respiration rate, and GSH levels and decreases reactive oxygen species (ROS) levels in cells. Furthermore, GLS2 expression is lost or greatly decreased in hepatocellular carcinomas and the overexpression of GLS2 greatly reduced tumor cell colony formation. These results demonstrated that as a unique p53 target gene, GLS2 is a mediator of p53’s role in energy metabolism and antioxidant defense, which can contribute to its role in tumor suppression.

High-throughput biological data, whether generated as sequencing, transcriptional microarrays, proteomic, or other means, continues to require analytic methods that address its high dimensional aspects. Because the computational part of data analysis ultimately identifies shape characteristics in the organization of data sets, the mathematics of shape recognition in high dimensions continues to be a crucial part of data analysis. This article introduces a method that extracts information from high-throughput microarray data and, by using topology, provides greater depth of information than current analytic techniques. The method, termed Progression Analysis of Disease ( PAD ), first identifies robust aspects of cluster analysis, then goes deeper to find a multitude of biologically meaningful shape characteristics in these data. Additionally, because PAD incorporates a visualization tool, it provides a simple picture or graph that can be used to further explore these data. Although PAD can be applied to a wide range of high-throughput data types, it is used here as an example to analyze breast cancer transcriptional data. This identified a unique subgroup of Estrogen Receptor -positive ( ER ⁺ ) breast cancers that express high levels of c-MYB and low levels of innate inflammatory genes. These patients exhibit 100% survival and no metastasis. No supervised step beyond distinction between tumor and healthy patients was used to identify this subtype. The group has a clear and distinct, statistically significant molecular signature, it highlights coherent biology but is invisible to cluster methods, and does not fit into the accepted classification of Luminal A/B , Normal-like subtypes of ER ⁺ breast cancers. We denote the group as c-MYB ⁺ breast cancer.