Gene expression profiling predicts clinical outcome of breast cancer

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.     

A large-scale RNAi screen in human cells identifies new components of the p53 pathway

RNA interference (RNAi) is a powerful new tool with which to perform loss-of-function genetic screens in lower organisms and can greatly facilitate the identification of components of cellular signalling pathways. In mammalian cells, such screens have been hampered by a lack of suitable tools that can be used on a large scale. We and others have recently developed expression vectors to direct the synthesis of short hairpin RNAs (shRNAs) that act as short interfering RNA (siRNA)-like molecules to stably suppress gene expression. Here we report the construction of a set of retroviral vectors encoding 23,742 distinct shRNAs, which target 7,914 different human genes for suppression. We use this RNAi library in human cells to identify one known and five new modulators of p53-dependent proliferation arrest. Suppression of these genes confers resistance to both p53-dependent and p19ARF-dependent proliferation arrest, and abolishes a DNA-damage-induced G1 cell-cycle arrest. Furthermore, we describe siRNA bar-code screens to rapidly identify individual siRNA vectors associated with a specific phenotype. These new tools will greatly facilitate large-scale loss-of-function genetic screens in mammalian cells.