Article

Clinical & Experimental Metastasis

, Volume 22, Issue 7, pp 593-603

Metastasis Predictive Signature Profiles Pre-exist in Normal Tissues

  • Haiyan YangAffiliated withLaboratory of Population Genetics, National Cancer Institute
  • , Nigel CrawfordAffiliated withLaboratory of Population Genetics, National Cancer Institute
  • , Luanne LukesAffiliated withLaboratory of Population Genetics, National Cancer Institute
  • , Richard FinneyAffiliated withLaboratory of Population Genetics, National Cancer Institute
  • , Mindy LancasterAffiliated withLaboratory of Population Genetics, National Cancer Institute
  • , Kent W. HunterAffiliated withLaboratory of Population Genetics, National Cancer InstituteLaboratory of Population Genetics, CCR/NCI/NIH Email author 

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Abstract

Previous studies from our laboratory have demonstrated that metastatic propensity is significantly influenced by the genetic background upon which tumors arise. We have also established that human gene expression profiles predictive of metastasis are not only present in mouse tumors with both high and low metastatic capacity, but also correlate with genetic background. These results suggest that human metastasis-predictive gene expression signatures may be significantly driven by genetic background, rather than acquired somatic mutations. To test this hypothesis, gene expression profiling was performed on inbred mouse strains with significantly different metastatic efficiencies. Analysis of previously described human metastasis signature gene expression patterns in normal tissues permitted accurate categorization of high or low metastatic mouse genotypes. Furthermore, prospective identification of animals at high risk of metastasis was achieved by using mass spectrometry to characterize salivary peptide polymorphisms in a genetically heterogeneous population. These results strongly support the role of constitutional genetic variation in modulation of metastatic efficiency and suggest that predictive signature profiles could be developed from normal tissues in humans. The ability to identify those individuals at high risk of disseminated disease at the time of clinical manifestation of a primary cancer could have a significant impact on cancer management.

Keywords

gene expression metastasis microarrays prognostics proteomics susceptibility