Gene expression predictors of breast cancer outcomes
Gene expression predictors of breast cancer outcomes
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DOI：
10.1016/s0140-6736(03)13308-9
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发表时间：
2003-05-10
期刊：
影响因子：
168.9
通讯作者：
Huang, AT
中科院分区：
文献类型：
--
作者：
Huang, E;Cheng, SH;Huang, AT
背景
风险因素与基因组数据的相关性有望为个体患者提供特定治疗，并且需要解读基因表达数据中复杂的多变量模式，以及评估它们改善临床预测的能力。我们旨在预测乳腺癌患者的淋巴结转移状态和复发情况。
方法
我们分析了原发性乳腺肿瘤样本的DNA微阵列数据，使用非线性统计分析来评估对个体患者具有预测价值的基因组群相互作用的多种模式，涉及淋巴结转移和癌症复发方面。
发现
我们确定了与淋巴结状态和复发相关的基因表达聚合模式（元基因），并且能够以约90%的准确率预测个体患者的结果。元基因定义了不同的基因组，表明乳腺癌这两个特征背后存在不同的生物学过程。初步的外部验证来自对不同人群中一个小样本的淋巴结状态进行的同样准确的预测。
解释
基因表达谱的多种聚合测量为个体患者定义了与淋巴结转移和疾病复发有价值的预测关联。基因表达数据有可能辅助准确的个体化预后。重要的是，这些数据是根据精确的数值预测以及结果的概率范围来评估的。对患者特定风险进行精确且具有统计学有效性的评估，最终将对面临治疗决策的临床医生最有价值。
Background Correlation of risk factors with genomic data promises to provide specific treatment for individual patients, and needs interpretation of complex, multivariate patterns in gene expression data, as well as assessment of their ability to improve clinical predictions. We aimed to predict nodal metastatic states and relapse for breast cancer patients..Methods We analysed DNA microarray data from samples of primary breast tumours, using non-linear statistical analyses to assess multiple patterns of interactions of groups of genes that have predictive value for the individual patient, with respect to lymph node metastasis and cancer recurrence.Findings We identified aggregate patterns of gene expression (metagenes) that associate with lymph node status and recurrence, and that are capable of predicting outcomes in individual patients with about 90% accuracy. The metagenes defined distinct groups of genes, suggesting different biological processes underlying these two characteristics of breast cancer. Initial external validation came from similarly accurate predictions of nodal status of a small sample in a distinct population.Interpretation Multiple aggregate measures of profiles of gene expression define valuable predictive associations with lymph node metastasis and disease recurrence for individual patients. Gene expression data have the potential to aid accurate, individualised, prognosis. Importantly, these data are assessed in terms of precise numerical predictions, with ranges of probabilities of outcome. Precise and statistically valid assessments of risks specific for patients, will ultimately be of most value to clinicians faced with treatment decisions.