Abstract
Genomic data from breast cancers provide additional prognostic and predictive information that is beginning to be used for patient management. The question arises whether additional information derived from other 'omic' approaches such as metabolomics can provide additional information. In an article published this month in BMC Cancer, Borgan et al. add metabolomic information to genomic measures in breast tumours and demonstrate, for the first time, that it may be possible to further define subgroups of patients which could be of value clinically.See research article: http://www.biomedcentral.com/1471-2407/10/628
Highlights
The major problems in breast cancer are predicting women at risk more precisely and predicting the presence of micrometastases at the time of primary surgery, including whether they will grow in the future and their responsiveness to systemic therapy
The question arises whether other ‘omics’ such as proteomics and metabolomics can add to the prognostic and predictive information already available from genomics given the heterogeneity and remaining behavioural unpredictability of breast tumours, as well as whether such studies might indicate additional therapeutic targets or whether adding ‘omic’ platforms together may be clinically useful? The paper by Borgan et al [1], published this month in BMC Cancer, from two centres in Norway is the first attempt to assess the interactive value of transcriptomics and metabolomics in a series of primary breast cancers
Metabolomics Metabolomics is the study of the metabolic changes which occur in living systems as a result of gene and protein expression and may enhance the information provided by genomics and proteomics
Summary
Introduction The major problems in breast cancer are predicting women at risk more precisely and predicting the presence of micrometastases at the time of primary surgery, including whether they will grow in the future and their responsiveness to systemic therapy. The question arises whether other ‘omics’ such as proteomics and metabolomics can add to the prognostic and predictive information already available from genomics given the heterogeneity and remaining behavioural unpredictability of breast tumours, as well as whether such studies might indicate additional therapeutic targets or whether adding ‘omic’ platforms together may be clinically useful?
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