Predictors of primary breast cancers responsiveness to preoperative epirubicin/cyclophosphamide-based chemotherapy: translation of microarray data into clinically useful predictive signatures
Predictors of primary breast cancers responsiveness to preoperative epirubicin/cyclophosphamide-based chemotherapy: translation of microarray data into clinically useful predictive signatures
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DOI：
10.1186/1479-5876-3-32
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发表时间：
2005-08-09
影响因子：
7.4
通讯作者：
Bojar, H
中科院分区：
文献类型：
--
作者：
Modlich, O;Prisack, HB;Bojar, H
背景资料：我们的目标是确定基因特征预测术前全身化疗（PST）与表阿霉素/环磷酰胺（EC）在原发性breastcancer.Methods：针活检获得治疗前从83例乳腺癌和mRNA的Affytrium HG-U133 A阵列。缓解范围从病理学证实的完全缓解（pCR）到部分缓解（PR），再到疾病稳定或进展，“无变化”（NC）。在来自56名患者和5名正常健康个体的乳腺组织样品中进行初步分析，作为预测标志物鉴定的训练队列。通过结合几种统计方法和筛选标准，提取了识别最有可能对PST-EC完全应答的个体的基因签名。为了优化无应答肿瘤的预测，还应用了Student t检验和Wilcoxon检验。使用27名患者的独立队列来挑战预测特征。一个k-最近邻算法以及两个独立的线性偏最小二乘行列式分析（PLS-DA）模型的基础上的训练队列的测试样本的分类选择。这些预测的平均特异性对于pCR大于74%，对于PR大于100%，对于NC大于62%。所有三个分类模型可以识别所有pCR cases.Results：在训练和测试队列中的59个基因的差异表达表现出预测PST-EC治疗反应的能力。基于训练群组，按照决策树构建分类器。首先，鉴定了能够区分癌组织和正常组织的转录谱。然后，从癌症特异性标记中提取“有利结果标记”（31个基因）和“不良结果标记”（26个基因）。这种逐步实施可以预测pCR，并在随后的一组患者中区分NC和PR。这两个PLS-DA模型实现区分所有三个响应classes in one step.Conclusion：在这项研究中，签名被确定为能够预测在一个独立的一组接受PST-EC的原发性乳腺癌患者的临床结果。
Background: Our goal was to identify gene signatures predictive of response to preoperative systemic chemotherapy (PST) with epirubicin/cyclophosphamide (EC) in patients with primary breast cancer.Methods: Needle biopsies were obtained pre-treatment from 83 patients with breast cancer and mRNA was profiled on Affymetrix HG-U133A arrays. Response ranged from pathologically confirmed complete remission (pCR), to partial remission (PR), to stable or progressive disease, "No Change" (NC). A primary analysis was performed in breast tissue samples from 56 patients and 5 normal healthy individuals as a training cohort for predictive marker identification. Gene signatures identifying individuals most likely to respond completely to PST-EC were extracted by combining several statistical methods and filtering criteria. In order to optimize prediction of non responding tumors Student's t-test and Wilcoxon test were also applied. An independent cohort of 27 patients was used to challenge the predictive signatures. A k-Nearest neighbor algorithm as well as two independent linear partial least squares determinant analysis (PLS-DA) models based on the training cohort were selected for classification of the test samples. The average specificity of these predictions was greater than 74% for pCR, 100% for PR and greater than 62% for NC. All three classification models could identify all pCR cases.Results: The differential expression of 59 genes in the training and the test cohort demonstrated capability to predict response to PST-EC treatment. Based on the training cohort a classifier was constructed following a decision tree. First, a transcriptional profile capable to distinguish cancerous from normal tissue was identified. Then, a "favorable outcome signature" (31 genes) and a "poor outcome signature" (26 genes) were extracted from the cancer specific signatures. This stepwise implementation could predict pCR and distinguish between NC and PR in a subsequent set of patients. Both PLS-DA models were implemented to discriminate all three response classes in one step.Conclusion: In this study signatures were identified capable to predict clinical outcome in an independent set of primary breast cancer patients undergoing PST-EC.