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使用实时定量逆转录聚合酶链反应检测法对浸润性乳腺癌进行分类和风险分层

Classification and risk stratification of invasive breast carcinomas using a real-time quantitative RT-PCR assay.

作者信息

Perreard Laurent, Fan Cheng, Quackenbush John F, Mullins Michael, Gauthier Nicholas P, Nelson Edward, Mone Mary, Hansen Heidi, Buys Saundra S, Rasmussen Karen, Orrico Alejandra Ruiz, Dreher Donna, Walters Rhonda, Parker Joel, Hu Zhiyuan, He Xiaping, Palazzo Juan P, Olopade Olufunmilayo I, Szabo Aniko, Perou Charles M, Bernard Philip S

机构信息

The ARUP Institute for Clinical and Experimental Pathology, SLC, Utah, USA.

出版信息

Breast Cancer Res. 2006;8(2):R23. doi: 10.1186/bcr1399. Epub 2006 Apr 20.

Abstract

INTRODUCTION

Predicting the clinical course of breast cancer is often difficult because it is a diverse disease comprised of many biological subtypes. Gene expression profiling by microarray analysis has identified breast cancer signatures that are important for prognosis and treatment. In the current article, we use microarray analysis and a real-time quantitative reverse-transcription (qRT)-PCR assay to risk-stratify breast cancers based on biological 'intrinsic' subtypes and proliferation.

METHODS

Gene sets were selected from microarray data to assess proliferation and to classify breast cancers into four different molecular subtypes, designated Luminal, Normal-like, HER2+/ER-, and Basal-like. One-hundred and twenty-three breast samples (117 invasive carcinomas, one fibroadenoma and five normal tissues) and three breast cancer cell lines were prospectively analyzed using a microarray (Agilent) and a qRT-PCR assay comprised of 53 genes. Biological subtypes were assigned from the microarray and qRT-PCR data by hierarchical clustering. A proliferation signature was used as a single meta-gene (log2 average of 14 genes) to predict outcome within the context of estrogen receptor status and biological 'intrinsic' subtype.

RESULTS

We found that the qRT-PCR assay could determine the intrinsic subtype (93% concordance with microarray-based assignments) and that the intrinsic subtypes were predictive of outcome. The proliferation meta-gene provided additional prognostic information for patients with the Luminal subtype (P = 0.0012), and for patients with estrogen receptor-positive tumors (P = 3.4 x 10-6). High proliferation in the Luminal subtype conferred a 19-fold relative risk of relapse (confidence interval = 95%) compared with Luminal tumors with low proliferation.

CONCLUSION

A real-time qRT-PCR assay can recapitulate microarray classifications of breast cancer and can risk-stratify patients using the intrinsic subtype and proliferation. The proliferation meta-gene offers an objective and quantitative measurement for grade and adds significant prognostic information to the biological subtypes.

摘要

引言

预测乳腺癌的临床病程往往很困难,因为它是一种由多种生物学亚型组成的复杂疾病。通过微阵列分析进行基因表达谱分析已确定了对预后和治疗很重要的乳腺癌特征。在本文中,我们使用微阵列分析和实时定量逆转录(qRT)-PCR检测,根据生物学“内在”亚型和增殖情况对乳腺癌进行风险分层。

方法

从微阵列数据中选择基因集,以评估增殖情况并将乳腺癌分为四种不同的分子亚型,即管腔型、正常样型、HER2+/ER-型和基底样型。对123份乳腺样本(117例浸润性癌、1例纤维腺瘤和5例正常组织)以及3种乳腺癌细胞系进行前瞻性分析,使用微阵列(安捷伦)和由53个基因组成的qRT-PCR检测。通过层次聚类从微阵列和qRT-PCR数据中确定生物学亚型。增殖特征被用作单个元基因(14个基因的log2平均值),以在雌激素受体状态和生物学“内在”亚型的背景下预测预后。

结果

我们发现qRT-PCR检测能够确定内在亚型(与基于微阵列的分类有93%的一致性),并且内在亚型可预测预后。增殖元基因为管腔型亚型患者(P = 0.0012)以及雌激素受体阳性肿瘤患者(P = 3.4×10-6)提供了额外的预后信息。与低增殖的管腔型肿瘤相比,管腔型亚型中高增殖赋予了19倍的相对复发风险(置信区间 = 95%)。

结论

实时qRT-PCR检测能够重现乳腺癌的微阵列分类,并可使用内在亚型和增殖情况对患者进行风险分层。增殖元基因为分级提供了客观定量的测量,并为生物学亚型增加了重要的预后信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a746/1557722/af5202f53de4/bcr1399-1.jpg

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