BACKGROUND: 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. METHODS: 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. FINDINGS: 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. INTERPRETATION: 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.
BACKGROUND: 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. METHODS: 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. FINDINGS: 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. INTERPRETATION: 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.
Authors: Lauren L C Marotta; Vanessa Almendro; Andriy Marusyk; Michail Shipitsin; Janina Schemme; Sarah R Walker; Noga Bloushtain-Qimron; Jessica J Kim; Sibgat A Choudhury; Reo Maruyama; Zhenhua Wu; Mithat Gönen; Laura A Mulvey; Marina O Bessarabova; Sung Jin Huh; Serena J Silver; So Young Kim; So Yeon Park; Hee Eun Lee; Karen S Anderson; Andrea L Richardson; Tatiana Nikolskaya; Yuri Nikolsky; X Shirley Liu; David E Root; William C Hahn; David A Frank; Kornelia Polyak Journal: J Clin Invest Date: 2011-07 Impact factor: 14.808
Authors: Brian D Lehmann; Joshua A Bauer; Xi Chen; Melinda E Sanders; A Bapsi Chakravarthy; Yu Shyr; Jennifer A Pietenpol Journal: J Clin Invest Date: 2011-07 Impact factor: 14.808
Authors: Nyam-Osor Chimge; Sanjeev K Baniwal; Jingqin Luo; Simon Coetzee; Omar Khalid; Benjamin P Berman; Debu Tripathy; Matthew J Ellis; Baruch Frenkel Journal: Clin Cancer Res Date: 2011-12-06 Impact factor: 12.531
Authors: Tobin Strom; Louis B Harrison; Anna R Giuliano; Michael J Schell; Steven A Eschrich; Anders Berglund; William Fulp; Ram Thapa; Domenico Coppola; Sungjune Kim; Jessica Frakes; John Foekens; James J Mulé; Javier F Torres-Roca Journal: Eur J Cancer Date: 2017-08-29 Impact factor: 9.162
Authors: Jian Cao; Zongzhi Liu; William K C Cheung; Minghui Zhao; Sophia Y Chen; Siew Wee Chan; Carmen J Booth; Don X Nguyen; Qin Yan Journal: Cell Rep Date: 2014-02-27 Impact factor: 9.423