Ma Yan, Qian Yong, Wei Liang, Abraham Jame, Shi Xianglin, Castranova Vincent, Harner E James, Flynn Daniel C, Guo Lan
Mary Babb Randolph Cancer Center, Department of Statistics, Division of Hematology, West Virginia University, Morgantown, WV 26506-9300, USA.
Clin Cancer Res. 2007 Apr 1;13(7):2014-22. doi: 10.1158/1078-0432.CCR-06-2222.
The purpose of this study is to predict breast cancer recurrence and metastases and to identify gene signatures indicative of clinicopathologic characteristics using gene expression patterns derived from cDNA microarray.
Expression profiles of 7,650 genes were investigated on an unselected group of 99 node-negative and node-positive breast cancer patients to identify prognostic gene signature of recurrence and metastases. The identified gene signature was validated on independent 78 patients with primary invasive carcinoma (T(1)/T(2) and N(0)) and on 58 patients with locally advanced breast cancer (T(3)/T(4) and/or N(2)). The gene predictors were identified using a combination of random forests and linear discriminant analysis function.
This study identified a new 28-gene signature that achieved highly accurate disease-free survival and overall survival (both at P < 0.001, time-dependent receiver operating characteristic analysis) in individual breast cancer patients. Patients categorized into high-risk, intermediate-risk, and low-risk groups had distinct disease-free survival (P < 0.005, Kaplan-Meier analysis, log-rank test) in three patient cohorts. A strong association (P < 0.05) was identified between risk groups and tumor size, tumor grade, estrogen receptor and progesterone receptor status, and HER2/neu overexpression in the studied cohorts. We also identified 14-gene predictors of nodal status and 9-gene predictors of tumor grade.
This study has established a population-based approach to predicting breast cancer outcomes at the individual level exclusively based on gene expression patterns. The 28-gene recurrence signature has been validated as quantifying the probability of recurrence and metastases in patients with heterogeneous histology and disease stage.
本研究旨在利用来自cDNA微阵列的基因表达模式预测乳腺癌的复发和转移，并识别指示临床病理特征的基因特征。
对99例淋巴结阴性和阳性乳腺癌患者的未选择组进行了7650个基因的表达谱研究，以确定复发和转移的预后基因特征。在78例原发性浸润性癌（T(1)/T(2)和N(0)）患者和58例局部晚期乳腺癌（T(3)/T(4)和/或N(2)）患者中对鉴定出的基因特征进行了验证。使用随机森林和线性判别分析函数的组合来识别基因预测因子。
本研究确定了一种新的28基因特征，在个体乳腺癌患者中实现了高度准确的无病生存率和总生存率（均在P < 0.001，时间依赖性受试者操作特征分析）。在三个患者队列中，分为高风险、中风险和低风险组的患者具有不同的无病生存率（P < 0.005，Kaplan-Meier分析，对数秩检验）。在研究队列中，风险组与肿瘤大小、肿瘤分级、雌激素受体和孕激素受体状态以及HER2/neu过表达之间存在强关联（P < 0.05）。我们还确定了淋巴结状态的14基因预测因子和肿瘤分级的9基因预测因子。
本研究建立了一种基于人群的方法，仅基于基因表达模式在个体水平上预测乳腺癌的预后。28基因复发特征已被验证可量化组织学和疾病阶段异质性患者的复发和转移概率。
