In human breast cancer, loss of carcinoma cell–specific response to TGF-β signaling has been linked to poor patient prognosis. However, the mechanisms through which TGF-β regulates these processes remain largely unknown. In an effort to address this issue, we have now identified gene expression signatures associated with the TGF-β signaling pathway in human mammary carcinoma cells. The results strongly suggest that TGF-β signaling mediates intrinsic, stromal-epithelial, and host-tumor interactions during breast cancer progression, at least in part, by regulating basal and oncostatin M–induced CXCL1, CXCL5, and CCL20 chemokine expression. To determine the clinical relevance of our results, we queried our TGF-β–associated gene expression signatures in 4 human breast cancer data sets containing a total of 1,319 gene expression profiles and associated clinical outcome data. The signature representing complete abrogation of TGF-β signaling correlated with reduced relapse-free survival in all patients; however, the strongest association was observed in patients with estrogen receptor–positive (ER-positive) tumors, specifically within the luminal A subtype. Together, the results suggest that assessment of TGF-β signaling pathway status may further stratify the prognosis of ER-positive patients and provide novel therapeutic approaches in the management of breast cancer.
Brian Bierie, Christine H. Chung, Joel S. Parker, Daniel G. Stover, Nikki Cheng, Anna Chytil, Mary Aakre, Yu Shyr, Harold L. Moses
Usage data is cumulative from February 2024 through February 2025.
Usage | JCI | PMC |
---|---|---|
Text version | 446 | 37 |
71 | 29 | |
Figure | 187 | 16 |
Table | 74 | 0 |
Supplemental data | 62 | 0 |
Citation downloads | 50 | 0 |
Totals | 890 | 82 |
Total Views | 972 |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.