Development and validation of a method for using breast core needle biopsies for gene expression microarray analyses.-猫眼课题宝

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2002-05

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Clinical cancer research : an official journal of the American Association for Cancer Research

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其他

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作者：

M. Ellis;Natalie Davis;A. Coop;Minetta C. Liu;L. Schumaker;Richard Lee;R. Srikanchana;C. G. Russell;Baljit Singh;W. Miller;V. Stearns;M. Pennanen;T. Tsangaris;A. Gallagher;Aiyi Liu;A. Zwart;D. Hayes;M. Lippman;Yue Wang;R. Clarke

目的基因表达微阵列技术有可能确定特定的表型(诊断)，确定患者的预期临床结果(预后)，并显示特定治疗有益效果的可能性(预测)。我们希望开发最佳的组织采集、处理和分析程序，以探索代表癌症和非癌症组织的乳房芯针活检的基因表达谱。实验设计利用人乳腺癌移植瘤来评估几种处理方法，以期收集足够数量的高质量RNA用于基因表达微阵列研究。对样本的组织结构和回收的RNA的质量和数量进行了评估。一个优化的方案被应用于一项来自患者的核心针刺乳腺活检的小规模研究，在该研究中，我们比较了来自癌症的分子图谱和来自非癌症活检的分子图谱。使用Research Genetics，Inc.命名的基因cDNA微阵列获得基因表达数据。使用简单的层次聚类和一种新的基于主成分分析的多维尺度对数据进行可视化。通过简单的统计方法降低了数据的维度。使用多层感知器建立预测神经网络，并在来自快速冷冻的乳房切除标本的独立数据集中进行评估。结果在组织病理学分析前，用冰冷的PBS冰洗5min后，通过RNAlater处理组织可保留组织结构。细胞边缘清晰，没有观察到组织折叠和碎裂，保持了核心的完整性，允许最佳的病理解释和重要诊断信息的保存。恢复了足够浓度的高质量RNA；55例活检组织中有51例产生的总RNA的中位数为1.34微克(范围从100 ng到12.60微克)。快速冷冻或使用RNAlater不会影响RNA回收或从活组织检查中获得的分子图谱。神经网络预测器准确区分了主要为癌症和非癌症的乳腺活检组织。结论这些研究提供了一种简单、安全和有效的方法，用于前瞻性地获取和处理用于基因表达研究的乳房芯针活检组织。来自这些研究的基因表达数据可以用来建立准确的预测模型，将不同的分子图谱分开。这些数据为未来的前瞻性研究确立了这些方法的使用和有效性。

PURPOSE Gene expression microarray technologies have the potential to define molecular profiles that may identify specific phenotypes(diagnosis), establish a patient's expected clinical outcome (prognosis), and indicate the likelihood of a beneficial effect of a specific therapy (prediction). We wished to develop optimal tissue acquisition, processing, and analysis procedures for exploring the gene expression profiles of breast core needle biopsies representing cancer and noncancer tissues. EXPERIMENTAL DESIGN Human breast cancer xenografts were used to evaluate several processing methods for prospectively collecting adequate amounts of high-quality RNA for gene expression microarray studies. Samples were assessed for the preservation of tissue architecture and the quality and quantity of RNA recovered. An optimized protocol was applied to a small study of core needle breast biopsies from patients, in which we compared the molecular profiles from cancer with those from noncancer biopsies. Gene expression data were obtained using Research Genetics, Inc. Named Genes cDNA microarrays. Data were visualized using simple hierarchical clustering and a novel principal component analysis-based multidimensional scaling. Data dimensionality was reduced by simple statistical approaches. Predictive neural networks were built using a multilayer perceptron and evaluated in an independent data set from snap-frozen mastectomy specimens. RESULTS Processing tissue through RNALater preserves tissue architecture when biopsies are washed for 5 min on ice with ice-cold PBS before histopathological analysis. Cell margins are clear, tissue folding and fragmentation are not observed, and integrity of the cores is maintained, allowing optimal pathological interpretation and preservation of important diagnostic information. Adequate concentrations of high-quality RNA are recovered; 51 of 55 biopsies produced a median of 1.34 microg of total RNA (range, 100 ng to 12.60 microg). Snap-freezing or the use of RNALater does not affect RNA recovery or the molecular profiles obtained from biopsies. The neural network predictors accurately discriminate between predominantly cancer and noncancer breast biopsies. CONCLUSIONS The approaches generated in these studies provide a simple, safe, and effective method for prospectively acquiring and processing breast core needle biopsies for gene expression studies. Gene expression data from these studies can be used to build accurate predictive models that separate different molecular profiles. The data establish the use and effectiveness of these approaches for future prospective studies.

Development and validation of a method for using breast core needle biopsies for gene expression microarray analyses.

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