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乳腺癌亚型的发现与验证。

Discovery and validation of breast cancer subtypes.

作者信息

Kapp Amy V, Jeffrey Stefanie S, Langerød Anita, Børresen-Dale Anne-Lise, Han Wonshik, Noh Dong-Young, Bukholm Ida R K, Nicolau Monica, Brown Patrick O, Tibshirani Robert

机构信息

Department of Statistics, Stanford University, Stanford, CA, USA.

出版信息

BMC Genomics. 2006 Sep 11;7:231. doi: 10.1186/1471-2164-7-231.

DOI:10.1186/1471-2164-7-231
PMID:16965636
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1574316/
Abstract

BACKGROUND

Previous studies demonstrated breast cancer tumor tissue samples could be classified into different subtypes based upon DNA microarray profiles. The most recent study presented evidence for the existence of five different subtypes: normal breast-like, basal, luminal A, luminal B, and ERBB2+.

RESULTS

Based upon the analysis of 599 microarrays (five separate cDNA microarray datasets) using a novel approach, we present evidence in support of the most consistently identifiable subtypes of breast cancer tumor tissue microarrays being: ESR1+/ERBB2-, ESR1-/ERBB2-, and ERBB2+ (collectively called the ESR1/ERBB2 subtypes). We validate all three subtypes statistically and show the subtype to which a sample belongs is a significant predictor of overall survival and distant-metastasis free probability.

CONCLUSION

As a consequence of the statistical validation procedure we have a set of centroids which can be applied to any microarray (indexed by UniGene Cluster ID) to classify it to one of the ESR1/ERBB2 subtypes. Moreover, the method used to define the ESR1/ERBB2 subtypes is not specific to the disease. The method can be used to identify subtypes in any disease for which there are at least two independent microarray datasets of disease samples.

摘要

背景

先前的研究表明，乳腺癌肿瘤组织样本可根据DNA微阵列图谱分为不同的亚型。最近的研究提供了证据，证明存在五种不同的亚型：正常乳腺样、基底样、腔面A型、腔面B型和ERBB2阳性型。

结果

基于使用一种新方法对599个微阵列（五个独立的cDNA微阵列数据集）的分析，我们提供了证据支持乳腺癌肿瘤组织微阵列中最一致可识别的亚型为：ESR1阳性/ERBB2阴性、ESR1阴性/ERBB2阴性和ERBB2阳性（统称为ESR1/ERBB2亚型）。我们对所有三种亚型进行了统计学验证，并表明样本所属的亚型是总生存期和无远处转移概率的重要预测指标。

结论

由于进行了统计验证程序，我们有了一组质心，可应用于任何微阵列（由UniGene簇ID索引），以将其分类为ESR1/ERBB2亚型之一。此外，用于定义ESR1/ERBB2亚型的方法并非特定于该疾病。该方法可用于识别任何疾病中的亚型，只要有至少两个疾病样本的独立微阵列数据集。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e68d/1574316/72bef66d8556/1471-2164-7-231-7.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e68d/1574316/72bef66d8556/1471-2164-7-231-7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e68d/1574316/27d5e814fbc4/1471-2164-7-231-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e68d/1574316/0ec76d630853/1471-2164-7-231-2.jpg
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