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首页> 外文期刊>The journal of applied laboratory medicine. >BRCA1 Variant Assessment Using a Simple Analytic Assay
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BRCA1 Variant Assessment Using a Simple Analytic Assay

机译：BRCA1变异评估使用一个简单的分析分析

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Background: We previously developed a biological assay to accurately predict BRCA1 (BRCA1 DNA repair associated) mutation status, based on gene expression profiles of Epstein-Barr virus-transformed lymphoblastoid cell lines. The original work was done using whole genome expression microarrays, and nearest shrunken centroids analysis. While these approaches are appropriate for model building, they are difficult to implement clinically, where more targeted testing and analysis are required for time and cost savings. Methods: Here, we describe adaptation of the original predictor to use the NanoString nCounter platform for testing, with analysis based on the k-top scoring pairs (k-TSP) method. Results: Assessing gene expression using the nCounter platform on a set of lymphoblastoid cell lines yielded 93.8% agreement with the microarray-derived data, and 87.5% overall correct classification of BRCA1 carriers and controls. Using the original gene expression microarray data used to develop our predictor with nearest shrunken centroids, we rebuilt a classifier based on the k-TSP method. This classifier relies on the relative expression of 10 pairs of genes, compared to the original 43 identified by nearest shrunken centroids (NSC), and was 96.2% concordant with the original training set prediction, with a 94.3% overall correct classification of BRCA1 carriers and controls. Conclusions: The k-TSP classifier was shown to accurately predict BRCA1 status using data generated on the nCounter platform and is feasible for initiating a clinical validation.
机译：背景:我们之前开发的生物分析准确预测乳腺癌易感基因1 (BRCA1 DNA基于基因修复)突变状态有关,表达谱的巴尔病毒转化lymphoblastoid细胞系。最初的工作是使用全基因组完成微阵列表达,和最近的萎缩质心分析。适合模型建立,临床上难以实现,更多有针对性的测试和分析需要时间和成本节约。适应的原始预测使用NanoString nCounter平台测试分析基于k-top得分对(k-TSP)方法。一组lymphoblastoid nCounter平台细胞株产生93.8%的协议microarray-derived数据和总体的87.5%正确的BRCA1运营商和分类控制。微阵列数据用于开发我们的预测最近的萎缩与重心,我们重建分类器基于k-TSP方法。分类器依赖于相对的表达10双的基因,而原来的43被最近的萎缩质心(NSC),和整合与原来的96.2%训练集预测,总体为94.3%正确的BRCA1运营商和分类控制。准确地预测BRCA1状态使用数据生成nCounter平台和可行的初始临床验证。

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