Agreement in breast cancer classification between microarray and quantitative reverse transcription PCR from fresh-frozen and formalin-fixed, paraffin-embedded tissues
Agreement in breast cancer classification between microarray and quantitative reverse transcription PCR from fresh-frozen and formalin-fixed, paraffin-embedded tissues
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DOI：
10.1373/clinchem.2006.083725
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发表时间：
2007-07-01
影响因子：
9.3
通讯作者：
Bernard, Philip S.
中科院分区：
文献类型：
--
作者：
Mullins, Michael;Perreard, Laurent;Bernard, Philip S.
背景：微阵列研究已经确定了具有预后意义的不同分子亚型乳腺癌。为了将这些分类转化为临床实验室，我们开发了一种实时定量反转录(qRT)-PCR方法，从新鲜冷冻（FF）和福尔马林固定石蜡包埋（FFPE）组织中诊断乳腺癌的生物学亚型。方法：我们使用来自124个乳腺样本的微阵列数据作为训练集，将肿瘤分类为4种先前定义的分子亚型：Luminal， HER2(+)/ER-， basal-like和normal-like。我们使用两种不同的基于质心的算法的训练集数据来预测35个乳腺肿瘤（测试集）作为FF和FFPE组织（70个样本）的样本类别。我们根据大的和最小的基因集对样本进行分类。我们在实时qRT-PCR检测中使用最小化的基因集来预测FF和FFPE组织的样本亚型。我们通过使用几种协议措施来评估采购方法之间的引物集性能。结果：使用qRT-PCR和最小化的“内在”基因集（40个分类器），基于质心的算法在FFPE组织分类方面完全一致。当使用微阵列（大型和最小化的基因集）和qRT-PCR数据比较FF组织的亚型分类时，诊断算法之间有94%（33 / 35）的一致性。我们发现，对角线SD与动态范围的比值是评估基因间一致性的最佳方法。结论：基于质心的算法是跨平台和采购条件的乳腺癌亚型分配的鲁棒分类器。(c) 2007美国临床化学协会。
Background: Microarray studies have identified different molecular subtypes of breast cancer with prognostic significance. To transition these classifications into the clinical laboratory, we have developed a real-time quantitative reverse transcription (qRT)-PCR assay to diagnose the biological subtypes of breast cancer from fresh-frozen (FF) and formalin-fixed, paraffin-embedded (FFPE) tissues.Methods: We used microarray data from 124 breast samples as a training set for classifying tumors into 4 previously defined molecular subtypes: Luminal, HER2(+)/ER-, basal-like, and normal-like. We used the training set data in 2 different centroid-based algorithms to predict sample class on 35 breast tumors (test set) procured as FF and FFPE tissues (70 samples). We classified samples on the basis of large and minimized gene sets. We used the minimized gene set in a real-time qRT-PCR assay to predict sample subtype from the FF and FFPE tissues. We evaluated primer set performance between procurement methods by use of several measures of agreement.Results: The centroid-based algorithms were in complete agreement in classification from FFPE tissues by use of qRT-PCR and the minimized "intrinsic" gene set (40 classifiers). There was 94% (33 of 35) concordance between the diagnostic algorithms when comparing subtype classification from FF tissue by use of microarray (large and minimized gene set) and qRT-PCR data. We found that the ratio of the diagonal SD to the dynamic range was the best method for assessing agreement on a gene-by-gene basis.Conclusions: Centroid-based algorithms are robust classifiers for breast cancer subtype assignment across platforms and procurement conditions. (c) 2007 American Association for Clinical Chemistry.