Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse

• Published in: Breast cancer research and treatment 2012-04, Vol.132 (2), p.523-535
• Main Authors: Harrell, J. Chuck, Prat, Aleix, Parker, Joel S., Fan, Cheng, He, Xiaping, Carey, Lisa, Anders, Carey, Ewend, Matthew, Perou, Charles M.
• Format: Article
• Language: English
• Subjects: Analysis; Biological and medical sciences; Brain; Brain Neoplasms - genetics; Brain Neoplasms - mortality; Brain Neoplasms - secondary; Breast cancer; Breast Neoplasms - genetics; Breast Neoplasms - mortality; Breast Neoplasms - pathology; Cancer research; Cancer therapies; Cell Line, Tumor; Cell Movement; Cluster Analysis; Data processing; Databases, Genetic; Differentiation; Disease-Free Survival; Diseases; DNA microarrays; Embryo cells; Female; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Genes; Genetic Predisposition to Disease; Genetic research; Genomic analysis; Gynecology. Andrology. Obstetrics; Humans; Hypoxia; Kaplan-Meier Estimate; Liver; Liver Neoplasms - genetics; Liver Neoplasms - mortality; Liver Neoplasms - secondary; Lung; Lung Neoplasms - genetics; Lung Neoplasms - mortality; Lung Neoplasms - secondary; Mammary gland diseases; Medical sciences; Medicine; Medicine & Public Health; Metastases; Metastasis; Multiple primary tumors; Multivariate Analysis; Neoplasm Invasiveness; Oligonucleotide Array Sequence Analysis; Oncology; Phenotype; Preclinical Study; Predictions; Predictive Value of Tests; Principal Component Analysis; Prognosis; Proportional Hazards Models; Relapse; Risk Assessment; Risk Factors; Stem cells; Time Factors; Tumors
• ISSN: 0167-6806; 1573-7217
• DOI: 10.1007/s10549-011-1619-7

The ability to predict metastatic potential could be of great clinical importance, however, it is uncertain if predicting metastasis to specific vital organs is feasible. As a first step in evaluating metastatic predictions, we analyzed multiple primary tumors and metastasis pairs and determined that >90% of 298 gene expression signatures were found to be similarly expressed between matched pairs of tumors and metastases; therefore, primary tumors may be a good predictor of metastatic propensity. Next, using a dataset of >1,000 human breast tumor gene expression microarrays we determined that HER2-enriched subtype tumors aggressively spread to the liver, while basal-like and claudin-low subtypes colonize the brain and lung. Correspondingly, brain and lung metastasis signatures, along with embryonic stem cell, tumor initiating cell, and hypoxia signatures, were also strongly expressed in the basal-like and claudin-low tumors. Interestingly, low “Differentiation Scores,” or high expression of the aforementioned signatures, further predicted for brain and lung metastases. In total, these data identify that depending upon the organ of relapse, a combination of gene expression signatures most accurately predicts metastatic behavior.