The recent observation that gene expression profiles from bulk tumors can define high and low metastatic propensities prior to the formation of overt clinical secondary lesions [10
] suggests that a significant proportion of cells in a tumor must exhibit the predictive gene expression profile. Thus, it has been suggested that metastatic potential is encoded early in tumorigenesis, most likely by the oncogenic mutations themselves [13
]. However, these results are inconsistent with the commonly accepted progressive theory of metastasis [30
], which predicts that only a subpopulation of a tumor obtains full metastatic competency. We argue that our previous genetic and expression studies [8
] explain this paradox. It would therefore follow that the genetic background from which a tumor arose might significantly influence the metastatic efficiency of a primary tumor, and be a major determinant of the prognostic expression profile [11
]. If this theory was pursued to its logical conclusion, it should therefore be possible to identify those individuals at high risk using differential expression patterns in normal tissues.
To test this hypothesis, we have examined expression of the proposed metastasis predictor gene set in normal tissues. Consistent with our hypothesis, the data presented here demonstrate that a significant fraction of the predictive metastatic signature genes proposed by Ramaswamy et al.[10
] are differentially expressed in normal tissues between high- (FVB) and low- (DBA, NZB F1
) metastatic genotypes prior to the induction of malignant disease. These data, in addition to other studies demonstrating the significant impact of genetic background on gene expression [4
], suggest that tumor metastatic propensity and predictive gene expression patterns are significantly influenced by constitutional polymorphisms rather than solely by oncogenic mutations. These data are also entirely consistent with the recent report by Malins et al. [33
] who demonstrated that it was possible to distinguish metastatic and non-metastatic prostate cancer patients based on DNA structure in normal prostate epithelium, suggesting that predisposition to metastasis is constitutively encoded.
More importantly, these observations suggest that prospective identification of patients at higher risk of developing disseminated disease using readily available samples instead of the more difficult to obtain tumor tissue might well be possible. In support of this hypothesis, we have demonstrated that it is possible to use saliva peptide polymorphisms to prospectively identify individuals at high risk for metastatic disease in a genetically segregating population. This approach has enabled the protein expression pattern itself, independent of the identity of the proteins or peptides, to be employed as a discriminator of metastatic propensity. Although significant improvements in the sensitivity and specificity of the assays are obviously required to achieve the level of accuracy required for clinical use, the ability to correctly categorize ~80% of the samples based on a relatively small training set suggests this methodology, or some variant of it, might be of significant value for clinical prognosis.
To achieve the required level of accuracy, a variety of strategies are currently being pursued. Foremost is the use of more sensitive instrumentation to increase the resolution, sensitivity, reproducibility and the number of the data points used to derive signature gene profiles. Increased sensitivity will likely permit the identification of more reliable and informative markers than is currently possible with a low resolution methodology such as SELDI-TOF. Second, a much larger cohort of animals or individuals will likely be required to generate a clinically usable predictor than were used in this proof-of-principle study. Significant variability in the metastasis frequency exists in both high and low metastatic genotypes, resulting in a small fraction of animals that are highly predisposed to metastasis phenotypically resembling low metastatic strains and vice versa. Inclusion of misclassified individuals in the training set would therefore reduce the accuracy of the predictor and likely is partially responsible for the 20% inaccuracy rate observed in the current study. Significant increases in the number of samples used to generate the predictor should reduce the confounding effects of misclassified samples, thus improving the overall predictive value. Third, analysis of tissues other than saliva might provide a better prognostic profile. While perhaps the most easily available non-invasive human tissue, salivary secretions might not possess adequate complexity to provide sufficient marker variability to enable accurate prognosis in the more genetically complex human population. Subsequently, blood or serum samples may provide a richer source of polymorphisms than saliva for more accurate clinical diagnosis.
The ability to prospectively identify those patients at increased risk of tumor dissemination also raises the potential for pre-emptive use of anti-metastatic therapies. Identifying patients with a higher likelihood of developing metastases prior to detectable secondary disease and enrolling them in metastasis-prevention regimens might significantly reduce the burden of secondary lesions. Preliminary evidence of the feasibility of such a strategy is currently ongoing in our laboratory, and by using a small molecule agent, caffeine, we have demonstrated the ability to significantly reduce the efficiency of pulmonary colonization in the PyMT animal [16
]. If these studies can be translated into human populations, similar strategies and agents might eventually lessen metastasis-associated mortality and morbidity. Alternatively, identifying high risk patients would also permit better monitoring to enable earlier therapeutic intervention against metastatic disease.
Finally, it is also important to point out that while the outcome of this study might be extrapolated to have implications for the seed and soil hypothesis proposed by Paget [34
], there are a number of important distinctions. To date most studies examining metastasis-related genes have focused on factors intrinsic to the tumor cell (seed) or cells surrounding the tumor cell (soil) in the secondary site. Since normal tissues and autochthonous tumors have the same constitutional genetic information, genetic variation that modifies metastatic efficiency can operate in the tumor cell, the stroma of the secondary site, in intermediate tissues during dissemination, or any combination of the above simultaneously. These data suggests that genetic heterogeneity influences both seed and
individuals, not somatic changes within the “seed” or the interaction of the “seed” with the different “soils” that exist within
individuals. Thus, at present, this study addresses the role of genetic constitutional polymorphism on metastatic efficiency at the level of the whole organism, though individual contributions of the “seed” and “soil” are likely to be critical determinants in the efficiency of metastatic dissemination.