Purpose To elucidate molecular pathways contributing to metastatic cancer progression and poor clinical outcome in serous ovarian cancer. Experimental Design Poor survival signatures from three different serous ovarian cancer datasets were compared and a common set of genes was identified. The predictive value of this gene signature was validated in independent datasets. The expression of the signature genes was evaluated in primary, metastatic, and/or recurrent cancers using qPCR and in situ hybridization. Alterations in gene expression by TGFβ1 and functional consequences of loss of COL11A1 were evaluated using pharmacologic and knockdown approaches, respectively. Results We identified and validated a 10-gene signature (AEBP1, COL11A1, COL5A1, COL6A2, LOX, POSTN, SNAI2, THBS2, TIMP3, VCAN) that is associated with poor overall survival in patients with high-grade serous ovarian cancer. The signature genes encode extracellular matrix proteins involved in collagen remodeling. Expression of the signature genes is regulated by TGFβ1 signaling and is enriched in metastases in comparison to primary ovarian tumors. We demonstrate that levels of COL11A1, one of the signature genes, continuously increase during ovarian cancer disease progression, with the highest expression in recurrent metastases. Knockdown of COL11A1 decreases in vitro cell migration and invasion and tumor progression in mice. Conclusion Our findings suggest that collagen-remodeling genes regulated by TGFβ1 signaling promote metastasis and contribute to poor overall survival in patients with serous ovarian cancer. Our 10-gene signature has both predictive value and biological relevance and thus may be useful as a therapeutic target.
Genetically engineered mouse models have significantly contributed to our understanding of cancer biology. They have proven to be useful in validating gene functions, identifying novel cancer genes and tumor biomarkers, gaining insight into the molecular and cellular mechanisms underlying tumor initiation and multistage processes of tumorigenesis, and providing better clinical models in which to test novel therapeutic strategies. However, mice still have significant limitations in modeling human cancer, including species-specific differences and inaccurate recapitulation of de novo human tumor development. Future challenges in mouse modeling include the generation of clinically relevant mouse models that recapitulate the molecular, cellular, and genomic events of human cancers and clinical response as well as the development of technologies that allow for efficient in vivo imaging and high-throughput screening in mice.
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