Abstract
Purpose: Gene expression profiling has the potential to lead to a more accurate prognostic classification of breast cancer patients. Recently, a set of 70 genes had been identified that allowed risk classification of node-negative patients. We analysed the prognostic utility of a subset of these genes and compared it with established prognostic factors in a collective of 150 untreated node-negative breast cancer patients. Methods: cRNA of 150 untreated node-negative breast cancer patients was hybridized on the Affymetrix HG-U133A array and probe sets corresponding to the set of 70 genes were identified. A good and poor prognosis profile was generated for the expression of 52 genes. The prognostic utility of this gene-expression signature was then compared with established prognostic factors (i.e. histological grade, tumor size and steroid hormone receptor status). The prognostic utility for disease-free survival (DFS) was evaluated using univariate and multivariate statistical analyses. Results: 33 (22%) of the tumors were assigned to the good and 117 (78%) to the poor prognosis group based on the gene-expression signature. This led to a sensitivity of 93% with a specificity of 26%. 5-year DFS was 94% in the good and 78% in the poor prognosis group, respectively. However, this difference diminished with longer follow-up (p=0.167). Of the prognostic factors analysed, only histological grade was associated with DFS (p Conclusion: histological grade had a higher prognostic utility than the gene-expression signature in our cohort of untreated node-negative breast cancer patients
Published Version
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