Breast cancer patients with the same stage of disease can have markedly different
treatment responses and overall outcome. The strongest predictors for metastases (for
example, lymph node status and histological grade) fail to classify accurately breast
tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces
the risk of distant metastases by approximately one-third; however, 70-80% of patients
receiving this treatment would have survived without it. None of the signatures of
breast cancer gene expression reported to date allow for patient-tailored therapy
strategies. Here we used DNA microarray analysis on primary breast tumours of 117
young patients, and applied supervised classification to identify a gene expression
signature strongly predictive of a short interval to distant metastases ('poor prognosis'
signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph
node negative). In addition, we established a signature that identifies tumours of
BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle,
invasion, metastasis and angiogenesis. This gene expression profile will outperform
all currently used clinical parameters in predicting disease outcome. Our findings
provide a strategy to select patients who would benefit from adjuvant therapy.