Software tools for high-throughput analysis and archiving of immunohistochemistry staining data obtained with tissue microarrays

被引:176
作者
Liu, CL
Prapong, W
Natkunam, Y
Alizadeh, A
Montgomery, K
Gilks, CB
van de Rijn, M
机构
[1] Stanford Univ, Med Ctr, Dept Pathol, Stanford, CA 94305 USA
[2] Stanford Univ, Med Ctr, Dept Genet, Stanford, CA 94305 USA
[3] Stanford Univ, Med Ctr, Dept Biochem, Stanford, CA 94305 USA
[4] Vancouver Gen Hosp, Dept Pathol, Vancouver, BC, Canada
[5] Vancouver Gen Hosp, Genet Pathol Evaluat Ctr, Vancouver, BC, Canada
关键词
D O I
10.1016/S0002-9440(10)64434-3
中图分类号
R36 [病理学];
学科分类号
100104 ;
摘要
The creation of tissue microarrays (TMAs) allows for the rapid immunohistochemical analysis of thousands of tissue samples, with numerous different antibodies per sample. This technical development has created a need for tools to aid in the analysis and archival storage of the large amounts of data generated. We have developed a comprehensive system for high-throughput analysis and storage of TMA immunostaining data, using a combination of commercially available systems and novel software applications developed in our laboratory specifically for this purpose. Staining results are recorded directly into an Excel worksheet and are reformatted by a novel program (TMA-Deconvoluter) into a format suitable for hierarchical clustering analysis or other statistical analysis. Hierarchical clustering analysis is a powerful means of assessing relatedness within groups of tumors, based on their immunostaining with a panel of antibodies. Other analyses, such as generation of survival curves, construction of Cox regression models, or assessment of intra- or interobserver variation, can also be done readily on the reformatted data. Finally, the immunoprofile of a specific case can be rapidly retrieved from the archives and reviewed through the use of Stainfinder, a novel web-based program that creates a direct link between the clustered data and a digital image database. An on-line demonstration of this system is available at http://genome-www.stanford.edu/TMA/xplore.sbtml.
引用
收藏
页码:1557 / 1565
页数:9
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