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

ABSTRACT: Promising results for prediction of outcome in breast cancer has 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 who are very difficult to differentiate 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 a group of tumors from low-risk patients was examined with gene expression profiling. An intermediate risk group of 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 accurately predicted metastases. 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 the present low- and intermediate-risk samples. The results indicated better performance of HUMAC32 among the low-malignant cancers compared to the 70-gene classifier. This may indicate that although the metastatic potential of a tumor is on the whole determined by the same genes in tumors with different characteristics and risk, some expression patterns have higher predictive power in the low-risk group. Keywords: case-control design

ORGANISM(S): Homo sapiens

PROVIDER: GSE4796 | GEO | 2007/04/30

SECONDARY ACCESSION(S): PRJNA95741

REPOSITORIES: GEO