Center for Biological Sequence Analysis, Department of Systems Biolology, Technical University of Denmark, 2800 Lyngby, Denmark.
BMC Bioinformatics. 2011 Jul 28;12:310. doi: 10.1186/1471-2105-12-310.
Genome scale expression profiling of human tumor samples is likely to yield improved cancer treatment decisions. However, identification of clinically predictive or prognostic classifiers can be challenging when a large number of genes are measured in a small number of tumors.
We describe an unsupervised method to extract robust, consistent metagenes from multiple analogous data sets. We applied this method to expression profiles from five "double negative breast cancer" (DNBC) (not expressing ESR1 or HER2) cohorts and derived four metagenes. We assessed these metagenes in four similar but independent cohorts and found strong associations between three of the metagenes and agent-specific response to neoadjuvant therapy. Furthermore, we applied the method to ovarian and early stage lung cancer, two tumor types that lack reliable predictors of outcome, and found that the metagenes yield predictors of survival for both.
These results suggest that the use of multiple data sets to derive potential biomarkers can filter out data set-specific noise and can increase the efficiency in identifying clinically accurate biomarkers.
对人类肿瘤样本进行基因组规模的表达谱分析可能会改善癌症治疗决策。然而，当在少量肿瘤中测量大量基因时，识别具有临床预测或预后分类能力的基因可能具有挑战性。
我们描述了一种从多个类似数据集中提取稳健、一致的元基因的无监督方法。我们将该方法应用于五个“双阴性乳腺癌”（DNBC）（既不表达 ESR1 也不表达 HER2）队列的表达谱，并得出了四个元基因。我们在四个类似但独立的队列中评估了这些元基因，发现其中三个元基因与特定的新辅助治疗反应之间存在强烈关联。此外，我们将该方法应用于卵巢癌和早期肺癌，这两种肿瘤类型缺乏可靠的预后预测因子，发现这些元基因可以为两种肿瘤的生存提供预测因子。
这些结果表明，使用多个数据集来推导潜在的生物标志物可以滤除数据集特有的噪声，并提高识别临床准确生物标志物的效率。
