Wang Y, Klijn JGM, Zhang Y, Sieuwerts AM, Look MP, Yang F et al.. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365: 671

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The Lancet (Impact Factor: 45.22). 01/2006; 365(9460):671-9. DOI: 10.1016/S0140-6736(05)17947-1
Source: PubMed


Genome-wide measures of gene expression can identify patterns of gene activity that subclassify tumours and might provide a better means than is currently available for individual risk assessment in patients with lymph-node-negative breast cancer.
We analysed, with Affymetrix Human U133a GeneChips, the expression of 22000 transcripts from total RNA of frozen tumour samples from 286 lymph-node-negative patients who had not received adjuvant systemic treatment.
In a training set of 115 tumours, we identified a 76-gene signature consisting of 60 genes for patients positive for oestrogen receptors (ER) and 16 genes for ER-negative patients. This signature showed 93% sensitivity and 48% specificity in a subsequent independent testing set of 171 lymph-node-negative patients. The gene profile was highly informative in identifying patients who developed distant metastases within 5 years (hazard ratio 5.67 [95% CI 2.59-12.4]), even when corrected for traditional prognostic factors in multivariate analysis (5.55 [2.46-12.5]). The 76-gene profile also represented a strong prognostic factor for the development of metastasis in the subgroups of 84 premenopausal patients (9.60 [2.28-40.5]), 87 postmenopausal patients (4.04 [1.57-10.4]), and 79 patients with tumours of 10-20 mm (14.1 [3.34-59.2]), a group of patients for whom prediction of prognosis is especially difficult.
The identified signature provides a powerful tool for identification of patients at high risk of distant recurrence. The ability to identify patients who have a favourable prognosis could, after independent confirmation, allow clinicians to avoid adjuvant systemic therapy or to choose less aggressive therapeutic options.

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    • "Different methods extract information from different viewpoints , and propose consequently sets of bio-markers which are different significantly (van de Kooy et al., 2002; Wang et al., 2005). Sometimes, there exist few overlapping genes in sets of biomarkers proposed by different methods. "
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    Journal of Theoretical Biology 08/2015; 383. DOI:10.1016/j.jtbi.2015.07.026 · 2.12 Impact Factor
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    • "Simon and Dr A . Peng Lam on a series of human breast tumours ( n ¼ 344 ) described in detail elsewhere ( Wang et al , 2005 ) . We used a filter to refine the metastasis - related gene set . "
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    • "Other two datasets refer to breast cancer samples: in the rst we have primary tumour specimens that developed metastasis or not (97 and 28 samples respectively, referred to as Met", GEO accession number GSE2990 [16] [17]), while in the second there are primary tumour biopsies that relapsed or not (107 and 179 samples respectively, referred to as Rel", GEO accession number GSE2034 [18]). "
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