Article

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

Veridex LLC, a Johnson & Johnson Company, San Diego, CA, USA.
The Lancet (Impact Factor: 45.22). 01/2006; 365(9460):671-9. DOI: 10.1016/S0140-6736(05)17947-1
Source: PubMed

ABSTRACT

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.

Download full-text

Full-text

Available from: Els M J J Berns
  • Source
    • "TABLE 1 Summary of the Utilized Microarray Datasets Dataset # Samples a # High-risk # Low-risk Source GSE2034 286 95 169 [2] "
    [Show abstract] [Hide abstract]
    ABSTRACT: In this experiment, a gene selection technique was proposed to select a robust gene signature from microarray data for prediction of breast cancer recurrence. In this regard, a hybrid scoring criterion was designed as linear combinations of the scores that were determined in the mutual information (MI) domain and protein-protein interactions network. Whereas, the MI-based score represents the complementary information between the selected genes for outcome prediction; and the number of connections in the PPI network between the selected genes builds the PPI-based score. All genes were scored by using the proposed function in a hybrid forward-backward gene-set selection process to select the optimum biomarker-set from the gene expression microarray data. The accuracy and stability of the finally selected biomarkers were evaluated by using five-fold cross-validation (CV) to classify available data on breast cancer patients into two cohorts of poor and good prognosis. The results showed an appealing improvement in the cross-dataset accuracy in comparison with similar studies whenever we applied a primary signature, which was selected from one dataset, to predict survival in other independent datasets. Moreover, the proposed method demonstrated 58-92 percent overlap between 50-genes signatures, which were selected from seven independent datasets individually.
    Full-text · Article · Dec 2015 · IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Finding bio-markers for complex disease from gene expression profiles attracts extensive attentions for its potential use in diagnosis, therapy, and drug design. In this paper we propose a network-based method to seek high-confident bio-markers from candidate genes collected in literature. The algorithm includes three consequent steps. First, one can collect the proposed bio-markers in literature as being the preliminary candidate; Second, a spanning-tree based threshold can be used to reconstruct gene networks for normal and cancer samples; Third, by jointly using of degree changes and distribution of the candidates in communities, one can filter out the low-confident genes. The survival candidates are high-confident genes. Specially, we consider expression profiles for carcinoma of colon. A total of 34 preliminary bio-markers collected from literature are evaluated and a set of 16 genes are proposed as high confident bio-markers, which behave high performance in distinguishing normal and cancer samples. Copyright © 2015. Published by Elsevier Ltd.
    Full-text · Article · Aug 2015 · Journal of Theoretical Biology
  • Source
    • "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 . "
    [Show abstract] [Hide abstract]
    ABSTRACT: Vascular endothelial growth factor (VEGF) is a multifunctional cytokine that has important roles in angiogenesis. Our knowledge of the significance of VEGF isoforms in human cancer remains incomplete. Bioluminescence imaging and transcriptomic analysis were used to study the colonisation capacity of the human breast cancer cells MDA-MB-231 controlling or overexpressing the VEGF165 or VEGF189 isoform (named cV-B, V165-B and V189-B, respectively) in nude mice. When injected into the bloodstream, V189-B cells induced less metastasis in the lungs and bone than V165-B and cV-B control cells, consistent with longer survival of these mice and delay in tumour uptake in the mice injected with a V189-B clone. Histological analysis confirmed that there were less αSMA-positive cells in the lungs of the mice injected with V189-B. In vitro V189-B cells decreased both cell invasion and survival. Using transcriptomic analysis, we identified a subset of 18 genes expressed differentially between V189 and V165 cell lines and in 120 human breast tumours. V165 was associated with poor prognosis, whereas V189 was not, suggesting a complex regulation by VEGF isoforms. Our results showed a negative correlation between the expression pattern of VEGF189 and the levels of expression of seven genes that influence metastasis. Our findings provide the first evidence that VEGF isoforms have different effects on breast cancer cell line colonisation in vivo.British Journal of Cancer advance online publication, 21 July 2015; doi:10.1038/bjc.2015.267 www.bjcancer.com.
    Full-text · Article · Jul 2015 · British Journal of Cancer
Show more