Prediction of metastasis from low-malignant breast cancer by gene expression profiling

Promising results for prediction of outcome in breast cancer have been obtained by genome wide gene expression profiling. Some studies have suggested that an extensive overtreatment of breast cancer patients might be reduced by risk assessment with gene expression profiling. A patient group hardly examined in these studies is the low-risk patients for whom outcome is very difficult to predict with currently used methods. These patients do not receive adjuvant treatment according to the guidelines of the Danish Breast Cancer Cooperative Group (DBCG). In this study, 26 tumors from low-risk patients were examined with gene expression profiling. An intermediate risk group of 34 low-malignant T2 tumors that fulfilled all other low-risk criteria than tumor size was included to increase statistical power. A 32-gene classifier, HUMAC32, was identified and it predicted metastases with 80% sensitivity and 77% specificity. The classifier was also validated in an independent group of high-risk tumors resulting in comparable performance of HUMAC32 and a 70-gene classifier developed for this group. Furthermore, the 70-gene signature was tested in our low- and intermediate-risk samples. The results demonstrated high cross-platform consistency of the classifiers. Higher performance of HUMAC32 was demonstrated among the low-malignant cancers compared with the 70-gene classifier. This suggests that although the metastatic potential to some extend is determined by the same genes in groups of tumors with different characteristics and risk, expression-based classification specifically developed in low-risk patients have higher predictive power in this group.