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Multiple Robust Signatures for Detecting Lymph Node Metastasis in Head and Neck Cancer

¹ Department of Physiological Chemistry, University Medical Center Utrecht, Universiteitsweg, Utrecht, the Netherlands; ² Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg, Delft, the Netherlands; ³ Department of Pathology, The Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands; and ⁴ Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands

Requests for reprints: Frank C.P. Holstege, Department of Physiological Chemistry, Universiteitsweg 100, 3584 CG Utrecht, the Netherlands. Phone: 31-30-253-8186; Fax: 31-30-253-9035; E-mail: f.c.p.holstege{at}med.uu.nl.

Genome-wide mRNA expression measurements can identify molecular signatures of cancer and are anticipated to improve patient management. Such expression profiles are currently being critically evaluated based on an apparent instability in gene composition and the limited overlap between signatures from different studies. We have recently identified a primary tumor signature for detection of lymph node metastasis in head and neck squamous cell carcinomas. Before starting a large multicenter prospective validation, we have thoroughly evaluated the composition of this signature. A multiple training approach was used for validating the original set of predictive genes. Based on different combinations of training samples, multiple signatures were assessed for predictive accuracy and gene composition. The initial set of predictive genes is a subset of a larger group of 825 genes with predictive power. Many of the predictive genes are interchangeable because of a similar expression pattern across the tumor samples. The head and neck metastasis signature has a more stable gene composition than previous predictors. Exclusion of the strongest predictive genes could be compensated by raising the number of genes included in the signature. Multiple accurate predictive signatures can be designed using various subsets of predictive genes. The absence of genes with strong predictive power can be compensated by including more genes with lower predictive power. Lack of overlap between predictive signatures from different studies with the same goal may be explained by the fact that there are more predictive genes than required to design an accurate predictor. (Cancer Res 2006; 66(4): 2361-6)

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