It is widely appreciated that reactive stroma or carcinoma-associated fibroblasts can influence epithelial tumor progression. In prostate cancer (PCa), the second most common male malignancy worldwide, the amount of reactive stroma is variable and has predictive value for tumor recurrence. By analyzing human PCa protein and RNA expression databases, we found smooth muscle cells (SMCs) are decreased in advanced tumors, whereas fibroblasts are maintained. In three mouse models of PCa, PB-MYC, ERG/PTEN and TRAMP, we found the composition of the stroma is distinct. SMCs are greatly depleted in advanced PB-MYC tumors and locally reduced in ERG/PTEN prostates, whereas in TRAMP tumors the SMC layers are increased. In addition, interductal fibroblast-like cells expand in PB-MYC and ERG/PTEN tumors, whereas in TRAMP PCa they expand little and stromal cells invade into intraductal adenomas. Fate mapping of SMCs showed that in PB-MYC tumors the cells are depleted, whereas they expand in TRAMP tumors and interestingly contribute to the stromal cells in intraductal adenomas. Hedgehog (HH) ligands secreted by epithelial cells are known to regulate prostate mesenchyme expansion differentially during development and regeneration. Any possible role of HH signaling in stromal cells during PCa progression is poorly understood. We found that HH signaling is high in SMCs and fibroblasts near tumor cells in all models, and epithelial Shh expression is decreased whereas Ihh and Dhh are increased. In human primary PCa, expression of IHH is the highest of the three HH genes, and elevated HH signaling correlates with high stromal gene expression. Moreover, increasing HH signaling in the stroma of PB-MYC PCa resulted in more intact SMC layers and decreased tumor progression (micro-invasive carcinoma). Thus, we propose HH signaling restrains tumor progression by maintaining the smooth muscle and preventing invasion by tumor cells. Our studies highlight the importance of understanding how HH signaling and stromal composition impact on PCa to optimize drug treatments.
