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Tissue microarrays: one size does not fit all

Overview of attention for article published in Diagnostic Pathology, July 2010
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Title
Tissue microarrays: one size does not fit all
Published in
Diagnostic Pathology, July 2010
DOI 10.1186/1746-1596-5-48
Pubmed ID
Authors

Jeanette E Eckel-Passow, Christine M Lohse, Yuri Sheinin, Paul L Crispen, Christopher J Krco, Eugene D Kwon

Abstract

Although tissue microarrays (TMAs) are commonly employed in clinical and basic-science research, there are no guidelines for evaluating the appropriateness of a TMA for a given biomarker and tumor type. Furthermore, TMA performance across multiple biomarkers has not been systematically explored. A simulated TMA with between 1 and 10 cores was designed to study tumor expression of 6 biomarkers with varied expression patterns (B7-H1, B7-H3, survivin, Ki-67, CAIX, and IMP3) using 100 patients with clear cell renal cell carcinoma (RCC). We evaluated agreement between whole tissue section and TMA immunohistochemical biomarker quantification to assess how many TMA cores are necessary to adequately represent RCC whole tissue section expression. Additionally, we evaluated associations of whole tissue section and TMA expression with RCC-specific death. The number of simulated TMA cores necessary to adequately represent whole tissue section quantification is biomarker specific. Although 2-3 cores appeared adequate for B7-H3, Ki-67, CAIX, and IMP3, even as many as 10 cores resulted in poor agreement for B7-H1 and survivin compared to RCC whole tissue sections. While whole tissue section B7-H1 was significantly associated with RCC-specific death, no significant associations were detected using as many as 10 TMA cores, suggesting that TMAs can result in false-negative findings if the TMA is not optimally designed. Prior to TMA analysis, the number of TMA cores necessary to accurately represent biomarker expression on whole tissue sections should be established as there is not a one-size-fits-all TMA. We illustrate the use of a simulated TMA as a cost-effective tool for this purpose.

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Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 2%
United Kingdom 1 2%
United States 1 2%
Denmark 1 2%
Unknown 44 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 38%
Student > Master 7 15%
Researcher 4 8%
Professor 3 6%
Other 3 6%
Other 5 10%
Unknown 8 17%
Readers by discipline Count As %
Medicine and Dentistry 13 27%
Agricultural and Biological Sciences 12 25%
Biochemistry, Genetics and Molecular Biology 4 8%
Computer Science 2 4%
Neuroscience 2 4%
Other 5 10%
Unknown 10 21%