Purpose: The capability of microarray platform to interrogate thousands of genes has led to the development of molecular diagnostic tools for cancer patients. Although large-scale comparative studies on clinical samples are often limited by the access of human tissues, expression profiling databases of various human cancer types are publicly available for researchers. Given that mouse models have been instrumental to our current understanding of cancer progression, we aimed to test the hypothesis that novel gene signatures possessing predictability in clinical outcome can be derived by coupling genomic analyses in mouse models of cancer with publicly available human cancer data sets. Experimental Design: We established a complex series of syngeneic metastatic animal models using a murine breast cancer cell line. Tumor RNA was hybridized on Affymetrix MouseGenome-430A2.0 Gene-Chips. With the use of Venn logic, gene signatures that represent metastatic competency were derived and tested against publicly available human breast and lung cancer data sets. Results: Survival analyses showed that the spontaneous metastasis gene signature was significantly associated with metastasis-free and overall survival (P < 0.0005). Consequently, the six-gene model was determined and showed statistical predictability in predicting survival in breast cancer patients. In addition, the model was able to stratify poor from good prognosis for lung cancer patients in most data sets analyzed. Conclusions: Together, our data support that novel gene signature derived from mouse models of cancer can be used for predicting human cancer outcome. Our approaches set precedence that similar strategies may be used to decipher novel gene signatures for clinical utility.
ASJC Scopus subject areas
- Cancer Research