Roepman Paul, Kemmeren Patrick, Wessels Lodewijk F A, Slootweg Piet J, Holstege Frank C P
Department of Physiological Chemistry, University Medical Center Utrecht, Universiteitsweg, Utrecht, The Netherlands.
Cancer Res. 2006 Feb 15;66(4):2361-6. doi: 10.1158/0008-5472.CAN-05-3960.
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.
全基因组mRNA表达测量能够识别癌症的分子特征，并有望改善患者管理。目前，基于基因组成的明显不稳定性以及不同研究的特征之间有限的重叠，此类表达谱正在接受严格评估。我们最近确定了一种用于检测头颈部鳞状细胞癌淋巴结转移的原发性肿瘤特征。在启动大型多中心前瞻性验证之前，我们已对该特征的组成进行了全面评估。采用了多种训练方法来验证原始的预测基因集。基于训练样本的不同组合，评估了多个特征的预测准确性和基因组成。最初的预测基因集是具有预测能力的825个较大基因群体的一个子集。由于肿瘤样本中相似的表达模式，许多预测基因是可互换的。头颈部转移特征的基因组成比以前的预测指标更稳定。排除最强的预测基因可以通过增加特征中包含的基因数量来弥补。使用预测基因的各种子集可以设计出多个准确的预测特征。通过纳入更多预测能力较低的基因，可以弥补缺乏具有强预测能力的基因的情况。不同研究针对相同目标的预测特征之间缺乏重叠，可能是因为存在比设计准确预测指标所需更多的预测基因。
