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首页> 外文期刊>BMC Genomics >Transfer of clinically relevant gene expression signatures in breast cancer: from Affymetrix microarray to Illumina RNA-Sequencing technology
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Transfer of clinically relevant gene expression signatures in breast cancer: from Affymetrix microarray to Illumina RNA-Sequencing technology

机译：乳腺癌中临床相关基因表达特征的转移：从Affymetrix微阵列到Illumina RNA测序技术

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Microarrays have revolutionized breast cancer (BC) research by enabling studies of gene expression on a transcriptome-wide scale. Recently, RNA-Sequencing (RNA-Seq) has emerged as an alternative for precise readouts of the transcriptome. To date, no study has compared the ability of the two technologies to quantify clinically relevant individual genes and microarray-derived gene expression signatures (GES) in a set of BC samples encompassing the known molecular BC’s subtypes. To accomplish this, the RNA from 57 BCs representing the four main molecular subtypes (triple negative, HER2 positive, luminal A, luminal B), was profiled with Affymetrix HG-U133 Plus 2.0 chips and sequenced using the Illumina HiSeq 2000 platform. The correlations of three clinically relevant BC genes, six molecular subtype classifiers, and a selection of 21 GES were evaluated. 16,097 genes common to the two platforms were retained for downstream analysis. Gene-wise comparison of microarray and RNA-Seq data revealed that 52% had a Spearman’s correlation coefficient greater than 0.7 with highly correlated genes displaying significantly higher expression levels. We found excellent correlation between microarray and RNA-Seq for the estrogen receptor (ER; rs = 0.973; 95% CI: 0.971-0.975), progesterone receptor (PgR; rs = 0.95; 0.947-0.954), and human epidermal growth factor receptor 2 (HER2; rs = 0.918; 0.912-0.923), while a few discordances between ER and PgR quantified by immunohistochemistry and RNA-Seq/microarray were observed. All the subtype classifiers evaluated agreed well (Cohen’s kappa coefficients >0.8) and all the proliferation-based GES showed excellent Spearman correlations between microarray and RNA-Seq (all rs >0.965). Immune-, stroma- and pathway-based GES showed a lower correlation relative to prognostic signatures (all rs >0.6). To our knowledge, this is the first study to report a systematic comparison of RNA-Seq to microarray for the evaluation of single genes and GES clinically relevant to BC. According to our results, the vast majority of single gene biomarkers and well-established GES can be reliably evaluated using the RNA-Seq technology.
机译：微阵列通过在转录组范围内进行基因表达研究，彻底改变了乳腺癌（BC）研究。最近，RNA测序（RNA-Seq）已成为精确转录组的替代方法。迄今为止，尚无研究比较这两种技术在一组包含已知分子BC亚型的BC样本中量化临床相关个体基因和微阵列衍生基因表达特征（GES）的能力。为此，使用Affymetrix HG-U133 Plus 2.0芯片对来自代表四种主要分子亚型（三阴性，HER2阳性，管腔A，管腔B）的57个BC的RNA进行了分析，并使用Illumina HiSeq 2000平台进行了测序。评估了三个临床相关的BC基因，六个分子亚型分类器和选择的21个GES的相关性。保留了两个平台共有的16,097个基因用于下游分析。基因芯片和RNA-Seq数据的基因比较显示52％的Spearman相关系数大于0.7，高度相关的基因显示出明显更高的表达水平。我们发现雌激素受体（ER; rs = 0.973; 95％CI：0.971-0.975），孕激素受体（PgR; rs = 0.95; 0.947-0.954）和人表皮生长因子受体的微阵列与RNA-Seq之间具有良好的相关性2（HER2; rs = 0.918; 0.912-0.923），而观察到通过免疫组织化学和RNA-Seq /微阵列定量的ER和PgR之间存在一些不一致。所有评估的亚型分类器均一致（Cohen的kappa系数> 0.8），并且所有基于增殖的GES均显示出微阵列与RNA-Seq之间出色的Spearman相关性（所有rs> 0.965）。基于免疫，基质和途径的GES相对于预后特征显示出较低的相关性（所有rs> 0.6）。据我们所知，这是第一项报道将RNA-Seq与微阵列进行系统比较以评估单个基因和与BC临床相关的GES的研究。根据我们的结果，可以使用RNA-Seq技术可靠地评估绝大多数单基因生物标志物和完善的GES。

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