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(American Journal of Pathology. 2002;161:1557-1565.)
© 2002 American Society for Investigative Pathology

Technical Advance

Software Tools for High-Throughput Analysis and Archiving of Immunohistochemistry Staining Data Obtained with Tissue Microarrays

Chih Long Liu*, Wijan Prapong{dagger}, Yasodha Natkunam{ddagger}, Ash Alizadeh*, Kelli Montgomery{ddagger}, C. Blake Gilks§ and Matt van de Rijn{ddagger}

From the Departments of Biochemistry,* Genetics,{dagger} and Pathology,{ddagger} Stanford University Medical Center, Stanford, California; and the Department of Pathology and Genetic Pathology Evaluation Centre,§ Vancouver General Hospital, Vancouver, British Columbia

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/explore.shtml.




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