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This study identifies metabolomic discrimination between early and metastatic breast cancer. Micrometastatic disease may account for metabolomic misclassification of some early patients as metastatic. Metabolomics identifies more patients as low relapse risk compared with Adjuvantionline. Further exploration of this metabolomic fingerprint is warranted.

The key to optimising our approach in early breast cancer is to individualise care. Each patient has a tumour with innate features that dictate their chance of relapse and their responsiveness to treatment. Often patients with similar clinical and pathological tumours will have markedly different outcomes and responses to adjuvant intervention. These differences are encoded in the tumour genetic profile. Effective biomarkers may replace or complement traditional clinical and histopathological markers in assessing tumour behaviour and risk. Development of high-throughput genomic technologies is enabling the study of gene expression profiles of tumours. Genomic fingerprints may refine prediction of the course of disease and response to adjuvant interventions. This review will focus on the role of multiparameter gene expression analyses in early breast cancer, with regards to prognosis and prediction. The prognostic role of genomic signatures, particularly the Mammaprint and Rotterdam signatures, is evolving. With regard to prediction of outcome, the Oncotype Dx multigene assay is in clinical use in tamoxifen treated patients. Extensive research continues on predictive gene identification for specific chemotherapeutic agents, particularly the anthracyclines, taxanes and alkylating agents. IntroductionOver the past decade there have been exciting developments in gene expression analysis [1]. Assessment of the genetic profiles of tumours furthers our understanding of their composition and behaviour. These signatures are enabling improved diagnosis, prognostic classification and more accurate prediction of benefit from chemotherapy for individual patients. Genetic profiles also assist pharmacogenomic development by providing potential new targets for therapies.Breast cancer is a prevalent disease and a leading cause of cancer death in women. Adjuvant systemic therapy improves disease-free survival and overall survival (OS) in some women [2,3]. Patients with poor prognostic features benefit the most from adjuvant therapy and identification of these high risk women is an ongoing challenge. Individualised systemic treatment for these women should improve outcomes. Conversely, identification of women with a good prognosis, or low risk of recurrent disease, may be spared the rigours and potential complications associated with adjuvant therapy.Traditionally, patients have been stratified according to risk of recurrence by clinical and histopathological features. These features have not proven adequate to identify patients who will most benefit from adjuvant therapy. For patients and clinicians there is a fear of under-treating in the adjuvant setting, potentially resulting in recurrent, incurable metastatic disease. Consequently, over-treatment in the adjuvant setting is not uncommon. PrognosisMolecular identification and classification of tumours enables important distinctions to be made between tumours that may appear similar based on traditional clinical and histopathological systems [4]. Traditional prognostic f...

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The CHS compared favourably with VES-13 for sensitivity. However, the great variability in specificity observed with the CHS within subgroups limits its applicability in the global population.