Expression profiling predicts outcome in breast cancer

Gruvberger et al. postulate, in their commentary [1] published in this issue of Breast Cancer Research, that our “prognostic gene set may not be broadly applicable to other breast tumor cohorts”, and they suggest that “it may be important to define prognostic expression profiles separately in estrogen receptor (ER) positive and negative tumors”. This is based on two observations derived from our gene expression profiling data in breast cancer [2]: the overlap between reporter genes for prognosis and ER status, and Gruvberger et al.’s inability to confirm the prognosis prediction using a nonoptimal selection of 58 of our 231 prognosis reporter genes. The overlap between our prognosis reporter genes and the ER status genes is certainly very large, mainly because ~10 % of all genes on our microarray contain informatio

Predicting a local recurrence after breast-conserving therapy by gene expression profiling

INTRODUCTION: To tailor local treatment in breast cancer patients there is a need for predicting ipsilateral recurrences after breast-conserving therapy. After adequate treatment (excision with free margins and radiotherapy), young age and incompletely excised extensive intraductal component are predictors for local recurrence, but many local recurrences can still not be predicted. Here we have used gene expression profiling by microarray analysis to identify gene expression profiles that can help to predict local recurrence in individual patients. METHODS: By using previously established gene expression profiles with proven value in predicting metastasis-free and overall survival (wound-response signature, 70-gene prognosis profile and hypoxia-induced profile) and training towards an optimal prediction of local recurrences in a training series, we establish a classifier for local recurrence after breast-conserving therapy. RESULTS: Validation of the different gene lists shows that the wound-response signature is able to separate patients with a high (29%) or low (5%) risk of a local recurrence at 10 years (sensitivity 87.5%, specificity 75%). In multivariable analysis the classifier is an independent predictor for local recurrence. CONCLUSION: Our findings indicate that gene expression profiling can identify subgroups of patients at increased risk of developing a local recurrence after breast-conserving therapy

Predicting a local recurrence after breast-conserving therapy by gene expression profiling.

IntroductionTo tailor local treatment in breast cancer patients there is a need for predicting ipsilateral recurrences after breast-conserving therapy. After adequate treatment (excision with free margins and radiotherapy), young age and incompletely excised extensive intraductal component are predictors for local recurrence, but many local recurrences can still not be predicted. Here we have used gene expression profiling by microarray analysis to identify gene expression profiles that can help to predict local recurrence in individual patients.MethodsBy using previously established gene expression profiles with proven value in predicting metastasis-free and overall survival (wound-response signature, 70-gene prognosis profile and hypoxia-induced profile) and training towards an optimal prediction of local recurrences in a training series, we establish a classifier for local recurrence after breast-conserving therapy.ResultsValidation of the different gene lists shows that the wound-response signature is able to separate patients with a high (29%) or low (5%) risk of a local recurrence at 10 years (sensitivity 87.5%, specificity 75%). In multivariable analysis the classifier is an independent predictor for local recurrence.ConclusionOur findings indicate that gene expression profiling can identify subgroups of patients at increased risk of developing a local recurrence after breast-conserving therapy

Electroweak parameters of the z0 resonance and the standard model

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Precision Electroweak Measurements on the Z resonance.

We report on the final electroweak measurements performed with data taken at the Z resonance by the experiments operating at the electron–positron colliders SLC and LEP. The data consist of 17 million Z decays accumulated by the ALEPH, DELPHI, L3 and OPAL experiments at LEP, and 600 thousand Z decays by the SLD experiment using a polarised beam at SLC. The measurements include cross-sections, forward–backward asymmetries and polarised asymmetries. The mass and width of the Z boson, mZ and ΓZ, and its couplings to fermions, for example the ρ parameter and the effective electroweak mixing angle for leptons, are precisely measured: The number of light neutrino species is determined to be 2.9840±0.0082, in agreement with the three observed generations of fundamental fermions. The results are compared to the predictions of the Standard Model (SM). At the Z-pole, electroweak radiative corrections beyond the running of the QED and QCD coupling constants are observed with a significance of five standard deviations, and in agreement with the Standard Model. Of the many Z-pole measurements, the forward–backward asymmetry in b-quark production shows the largest difference with respect to its SM expectation, at the level of 2.8 standard deviations. Through radiative corrections evaluated in the framework of the Standard Model, the Z-pole data are also used to predict the mass of the top quark, , and the mass of the W boson, . These indirect constraints are compared to the direct measurements, providing a stringent test of the SM. Using in addition the direct measurements of mt and mW, the mass of the as yet unobserved SM Higgs boson is predicted with a relative uncertainty of about 50% and found to be less than at 95% confidence level