Hierarchical clustering analysis of tissue microarray immunostaining data identifies prognostically significant groups of breast carcinoma

被引:156
作者
Makretsov, NA
Huntsman, DG
Nielsen, TO
Yorida, E
Peacock, M
Cheang, MCU
Dunn, SE
Hayes, M
van de Rijn, M
Bajdik, C
Gilks, CB
机构
[1] Vancouver Gen Hosp, Dept Pathol & Lab Med, Gen Pathol Evaluat Ctr, British Columbia Canc Agcy, Vancouver, BC V5M 1Z9, Canada
[2] Vancouver Gen Hosp, Prostate Res Ctr, Gen Pathol Evaluat Ctr, British Columbia Canc Agcy, Vancouver, BC V5M 1Z9, Canada
[3] Univ British Columbia, Vancouver, BC V5Z 1M9, Canada
[4] Univ British Columbia, Lab Oncogenom Res, Dept Pediat, British Columbia Res Inst Childrens & Womens Hlth, Vancouver, BC V5Z 1M9, Canada
[5] British Columbia Canc Agcy, Dept Pathol, Vancouver, BC V5Z 4E6, Canada
[6] British Columbia Canc Agcy, Canc Control Res Program, Vancouver, BC V5Z 4E6, Canada
[7] Stanford Univ, Med Ctr, Dept Pathol, Stanford, CA 94305 USA
关键词
D O I
10.1158/1078-0432.CCR-04-0429
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Prognostically relevant cluster groups, based on gene expression profiles, have been recently identified for breast cancers, lung cancers, and lymphoma. Our aim was to determine whether hierarchical clustering analysis of multiple immunomarkers (protein expression profiles) improves prognostication in patients with invasive breast cancer. A cohort of 438 sequential cases of invasive breast cancer with median follow-up of 15.4 years was selected for tissue microarray construction. A total of 31 biomarkers were tested by immunohistochemistry on these tissue arrays. The prognostic significance of individual markers was assessed by using Kaplan-Meier survival estimates and log-rank tests. Seventeen of 31 markers showed prognostic significance in univariate analysis (P less than or equal to 0.05) and 4 markers showed a trend toward significance (P less than or equal to 0.2). Unsupervised hierarchical clustering analysis was done by using these 21 immunomarkers, and this resulted in identification of three cluster groups with significant differences in clinical outcome. chi(2) analysis showed that expression of 11 markers significantly correlated with membership in one of the three cluster groups. Unsupervised hierarchical clustering analysis with this set of 11 markers reproduced the same three prognostically significant cluster groups identified by using the larger set of markers. These cluster groups were of prognostic significance independent of lymph node metastasis, tumor size, and tumor grade in multivariate analysis (P = 0.0001). The cluster groups were as powerful a prognostic indicator as lymph node status. This work demonstrates that hierarchical clustering of immunostaining data by using multiple markers can group breast cancers into classes with clinical relevance and is superior to the use of individual prognostic markers.
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页码:6143 / 6151
页数:9
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