Abstract
Background
Traditional prognostic indicators of breast cancer, i.e. lymph node diffusion, tumor size, grading and estrogen receptor expression, are inadequate predictors of metastatic relapse. Thus, additional prognostic parameters appear urgently needed. Individual oncogenic determinants have largely failed in this endeavour. Only a few individual tumor growth drivers, e.g. mutated p53, Her-2, E-cadherin, Trops, did reach some prognostic/predictive power in clinical settings. As multiple factors are required to drive solid tumor progression, clusters of such determinants were expected to become stronger indicators of tumor aggressiveness and malignant progression than individual parameters. To identify such prognostic clusters, we went on to coordinately analyse molecular and histopathological determinants of tumor progression of post-menopausal breast cancers in the framework of a multi-institutional case series/case-control study.
Methods
A multi-institutional series of 217 breast cancer cases was analyzed. Twenty six cases (12 %) showed disease relapse during follow-up. Relapsed cases were matched with a set of control patients by tumor diameter, pathological stage, tumor histotype, age, hormone receptors and grading. Histopathological and molecular determinants of tumor development and aggressiveness were then analyzed in relapsed versus non-relapsed cases. Stepwise analyses and model structure fitness assessments were carried out to identify clusters of molecular alterations with differential impact on metastatic relapse.
Results
p53, Bcl-2 and cathepsin D were shown to be coordinately associated with unique levels of relative risk for disease relapse. As many Ras downstream targets, among them matrix metalloproteases, are synergistically upregulated by mutated p53, whole-exon sequence analyses were performed for TP53, Ki-RAS and Ha-RAS, and findings were correlated with clinical phenotypes. Notably, TP53 insertion/deletion mutations were only detected in relapsed cases. Correspondingly, Ha-RAS missense oncogenic mutations were only found in a subgroup of relapsing tumors.
Conclusions
We have identified clusters of specific molecular alterations that greatly improve prognostic assessment with respect to singularly-analysed indicators. The combined analysis of these multiple tumor-relapse risk factors promises to become a powerful approach to identify patients subgroups with unfavourable disease outcome.
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