2007
DOI: 10.1186/1471-2407-7-61
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Identification of a robust gene signature that predicts breast cancer outcome in independent data sets

Abstract: Background: Breast cancer is a heterogeneous disease, presenting with a wide range of histologic, clinical, and genetic features. Microarray technology has shown promise in predicting outcome in these patients.

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Cited by 38 publications
(29 citation statements)
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“…To test the relationship between clinical outcome and the expression of genes highly correlated with h4, four independent gene profiling data sets were analyzed (19,21,22,30). This analysis is based on the hypothesis that genes whose expression correlates significantly may function in common mechanisms to affect tumor behavior.…”
Section: Resultsmentioning
confidence: 99%
“…To test the relationship between clinical outcome and the expression of genes highly correlated with h4, four independent gene profiling data sets were analyzed (19,21,22,30). This analysis is based on the hypothesis that genes whose expression correlates significantly may function in common mechanisms to affect tumor behavior.…”
Section: Resultsmentioning
confidence: 99%
“…This approach has been applied to diseases of unknown cause to create new hypotheses relating to disease pathogenesis (7,8) and is shown to have prognostic (9) and diagnostic applications (10,11).…”
mentioning
confidence: 99%
“…However, many of the other published instances are no better than the median of random tests (e.g. the Korkola et al [15] 'robust signature') or even worse than almost all (the Taube et al [16] 202-gene signature). It is also clear that sets comprising 100 or more genes are passable predictors of breast cancer survival, with p values in the range of 10 À4 -10 À5 , whatever the gene choice.…”
Section: Scanning Through Published Signaturesmentioning
confidence: 98%