Chen Xi, Li Jiang, Gray William H, Lehmann Brian D, Bauer Joshua A, Shyr Yu, Pietenpol Jennifer A
Department of Biostatistics, Vanderbilt University, Nashville, TN 37232.
Cancer Inform. 2012;11:147-56. doi: 10.4137/CIN.S9983. Epub 2012 Jul 24.
Triple-negative breast cancer (TNBC) is a heterogeneous breast cancer group, and identification of molecular subtypes is essential for understanding the biological characteristics and clinical behaviors of TNBC as well as for developing personalized treatments. Based on 3,247 gene expression profiles from 21 breast cancer data sets, we discovered six TNBC subtypes from 587 TNBC samples with unique gene expression patterns and ontologies. Cell line models representing each of the TNBC subtypes also displayed different sensitivities to targeted therapeutic agents. Classification of TNBC into subtypes will advance further genomic research and clinical applications.
We developed a web-based subtyping tool TNBCtype for candidate TNBC samples using our gene expression meta data and classification methods. Given a gene expression data matrix, this tool will display for each candidate sample the predicted subtype, the corresponding correlation coefficient, and the permutation P-value. We offer a user-friendly web interface to predict the subtypes for new TNBC samples that may facilitate diagnostics, biomarker selection, drug discovery, and the more tailored treatment of breast cancer.
三阴性乳腺癌（TNBC）是一组异质性乳腺癌，识别分子亚型对于理解TNBC的生物学特征和临床行为以及开发个性化治疗方法至关重要。基于来自21个乳腺癌数据集的3247个基因表达谱，我们从587个具有独特基因表达模式和本体的TNBC样本中发现了六种TNBC亚型。代表每种TNBC亚型的细胞系模型对靶向治疗药物也表现出不同的敏感性。将TNBC分类为亚型将推动进一步的基因组研究和临床应用。
我们利用基因表达元数据和分类方法为候选TNBC样本开发了一个基于网络的亚型分类工具TNBCtype。给定一个基因表达数据矩阵，该工具将为每个候选样本显示预测的亚型、相应的相关系数和排列P值。我们提供了一个用户友好的网络界面来预测新TNBC样本的亚型，这可能有助于乳腺癌的诊断、生物标志物选择、药物发现以及更具针对性的治疗。
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