Data


  • Gene expression profiling predicts clinical outcome of breast cancer

    van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH

    Nature. 2002 Jan 31;415(6871):530-6.

    Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases (‘poor prognosis’ signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.



    • Data can be downloaded here.



  • A gene-expression signature as a predictor of survival in breast cancer

    Marc J van de Vijver, Yudong D He, Laura J van’t Veer, Hongyue Dai, Augustinus A M Hart, Dorien W Voskuil, George J Schreiber, Johannes L Peterse, Chris Roberts, Matthew J Marton, Mark Parrish, Douwe Atsma, Anke Witteveen, Annuska Glas, Leonie Delahaye, Tony van der Velde, Harry Bartelink, Sjoerd Rodenhuis, Emiel T Rutgers, Stephen H Friend, RenĂ© Bernards

    New England Journal of Medicine 2002 vol. 347 (25) pp. 1999-2009

    BACKGROUND: A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy.
    METHODS: Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses.
    RESULTS: Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (+/-SE) overall 10-year survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6+/-4.5 percent in the group with a poor-prognosis signature and 85.2+/-4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome.
    CONCLUSIONS: The gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria.







  • Pooling breast cancer datasets has a synergetic effect on classification performance and improves signature stability

    Martin H van Vliet, Fabien Reyal, Hugo M Horlings, Marc J van de Vijver, Marcel J T Reinders, Lodewyk F A Wessels

    BMC Genomics 2008 vol. 9 pp. 375

    BACKGROUND: Michiels et al. (Lancet 2005; 365: 488-92) employed a resampling strategy to show that the genes identified as predictors of prognosis from resamplings of a single gene expression dataset are highly variable. The genes most frequently identified in the separate resamplings were put forward as a ‘gold standard’. On a higher level, breast cancer datasets collected by different institutions can be considered as resamplings from the underlying breast cancer population. The limited overlap between published prognostic signatures confirms the trend of signature instability identified by the resampling strategy. Six breast cancer datasets, totaling 947 samples, all measured on the Affymetrix platform, are currently available. This provides a unique opportunity to employ a substantial dataset to investigate the effects of pooling datasets on classifier accuracy, signature stability and enrichment of functional categories.
    RESULTS: We show that the resampling strategy produces a suboptimal ranking of genes, which can not be considered to be a ‘gold standard’. When pooling breast cancer datasets, we observed a synergetic effect on the classification performance in 73% of the cases. We also observe a significant positive correlation between the number of datasets that is pooled, the validation performance, the number of genes selected, and the enrichment of specific functional categories. In addition, we have evaluated the support for five explanations that have been postulated for the limited overlap of signatures.
    CONCLUSION: The limited overlap of current signature genes can be attributed to small sample size. Pooling datasets results in more accurate classification and a convergence of signature genes. We therefore advocate the analysis of new data within the context of a compendium, rather than analysis in isolation.

    “We used a collection of six available datasets containing microarray data of breast cancer samples. These datasets were all measured on Human Genome HG U133A Affymetrix arrays and normalized using the same protocol. The datasets were downloaded from NCBI’s Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) with the following identifiers; GSE6532, GSE3494, GSE1456, GSE7390 and GSE5327. The Chin et al. dataset was downloaded from ArrayExpress (http://www.ebi.ac.uk/, identifier E-TABM-158).”