Here, we propose a new model for understanding the Warburg effect in tumor 
metabolism. Our hypothesis is that epithelial cancer cells induce the Warburg 
effect (aerobic glycolysis) in neighboring stromal fibroblasts. These 
cancer-associated fibroblasts, then undergo myo-fibroblastic differentiation, 
and secrete lactate and pyruvate (energy metabolites resulting from aerobic 
glycolysis). Epithelial cancer cells could then take up these energy-rich 
metabolites and use them in the mitochondrial TCA cycle, thereby promoting 
efficient energy production (ATP generation via oxidative phosphorylation), 
resulting in a higher proliferative capacity. In this alternative model of 
tumorigenesis, the epithelial cancer cells instruct the normal stroma to 
transform into a wound-healing stroma, providing the necessary energy-rich 
micro-environment for facilitating tumor growth and angiogenesis. In essence, 
the fibroblastic tumor stroma would directly feed the epithelial cancer cells, 
in a type of host-parasite relationship. We have termed this new idea the 
"Reverse Warburg Effect." In this scenario, the epithelial tumor cells "corrupt" 
the normal stroma, turning it into a factory for the production of energy-rich 
metabolites. This alternative model is still consistent with Warburg's original 
observation that tumors show a metabolic shift towards aerobic glycolysis. In 
support of this idea, unbiased proteomic analysis and transcriptional profiling 
of a new model of cancer-associated fibroblasts (caveolin-1 (Cav-1) deficient 
stromal cells), shows the upregulation of both (1) myo-fibroblast markers and 
(2) glycolytic enzymes, under normoxic conditions. We validated the expression 
of these proteins in the fibroblastic stroma of human breast cancer tissues that 
lack stromal Cav-1. Importantly, a loss of stromal Cav-1 in human breast cancers 
is associated with tumor recurrence, metastasis, and poor clinical outcome. 
Thus, an absence of stromal Cav-1 may be a biomarker for the "Reverse Warburg 
Effect," explaining its powerful predictive value.