Bio&Health Team, Future IT R&D Laboratory, LGE Advanced Research Institute, Seoul 137-724, Korea.
Mol Cells. 2012 Oct;34(4):393-8. doi: 10.1007/s10059-012-0177-0. Epub 2012 Sep 13.
Breast cancer is a clinically heterogeneous disease characterized by distinct molecular aberrations. Understanding the heterogeneity and identifying subgroups of breast cancer are essential to improving diagnoses and predicting therapeutic responses. In this paper, we propose a classification scheme for breast cancer which integrates data on differentially expressed genes (DEGs), copy number variations (CNVs) and microRNAs (miRNAs)-regulated mRNAs. Pathway information based on the estimation of molecular pathway activity is also applied as a postprocessor to optimize the classifier. A total of 250 malignant breast tumors were analyzed by k-means clustering based on the patterns of the expression profiles of 215 intrinsic genes, and the classification performances were compared with existing breast cancer classifiers including the BluePrint and the 625-gene classifier. We show that a classification scheme which incorporates pathway information with various genetic variations achieves better performance than classifiers based on the expression levels of individual genes, and propose that the identified signature serves as a basic tool for identifying rational therapeutic opportunities for breast cancer patients.
乳腺癌是一种临床异质性疾病，其特征是明显的分子异常。了解异质性并确定乳腺癌亚组对于改善诊断和预测治疗反应至关重要。在本文中，我们提出了一种乳腺癌分类方案，该方案整合了差异表达基因（DEGs）、拷贝数变异（CNVs）和 microRNAs（miRNAs）调节的 mRNA 的数据。还基于分子途径活性的估计应用途径信息作为后处理器来优化分类器。基于 215 个内在基因表达谱的模式，对 250 个恶性乳腺肿瘤进行了 k-均值聚类分析，并将分类性能与包括 Blueprint 和 625 个基因分类器在内的现有乳腺癌分类器进行了比较。我们表明，一种整合了途径信息和各种遗传变异的分类方案比基于单个基因表达水平的分类器具有更好的性能，并提出所确定的特征可以作为识别乳腺癌患者合理治疗机会的基本工具。
