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JMD 2005, Vol. 7, No. 2
Copyright © 2005 American Society for Investigative Pathology & Association for Molecular Pathology

Statistical Considerations for Immunohistochemistry Panel Development after Gene Expression Profiling of Human Cancers

Rebecca A. Betensky^*, Catherine L. Nutt, Tracy T. Batchelor and David N. Louis^¶

From the Department of Biostatistics, * Harvard School of Public Health, Boston; and the Departments of Neurology and Pathology, Cancer Center, and Neurosurgical Service, ^¶ Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts

In recent years there have been a number of microarray expression studies in which different types of tumors were classified by identifying a panel of differentially expressed genes. Immunohistochemistry is a practical and robust method for extending gene expression data to common pathological specimens with the advantage of being applicable to paraffin-embedded tissues. However, the number of assays required for successful immunohistochemical classification remains unclear. We propose a simulation-based method for assessing sample size for an immunohistochemistry investigation after a promising gene expression study of human tumors. The goals of such an immunohistochemistry study would be to develop and validate a marker panel that yields improved prognostic classification of cancer patients. We demonstrate how the preliminary gene expression data, coupled with certain realistic assumptions, can be used to estimate the number of immunohistochemical assays required for development. These assumptions are more tenable than alternative assumptions that would be required for crude analytic sample size calculations and that may yield underpowered and inefficient studies. We applied our methods to the design of an immunohistochemistry study for glioma classification and estimated the number of assays required to ensure satisfactory technical and prognostic validation. Simulation approaches for computing power and sample size that are based on existing gene expression data provide a powerful tool for efficient design of follow-up genomic studies.

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