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Automated subcellular localization and quantification of protein expression in tissue microarrays

Abstract

The recent development of tissue microarrays—composed of hundreds of tissue sections from different tumors arrayed on a single glass slide—facilitates rapid evaluation of large-scale outcome studies. Realization of this potential depends on the ability to rapidly and precisely quantify the protein expression within each tissue spot. We have developed a set of algorithms that allow the rapid, automated, continuous and quantitative analysis of tissue microarrays, including the separation of tumor from stromal elements and the sub-cellular localization of signals. Validation studies using estrogen receptor in breast carcinoma show that automated analysis matches or exceeds the results of conventional pathologist-based scoring. Automated analysis and sub-cellular localization of beta-catenin in colon cancer identifies two novel, prognostically significant tumor subsets, not detected by traditional pathologist-based scoring. Development of automated analysis technology empowers tissue microarrays for use in discovery-type experiments (more typical of cDNA microarrays), with the added advantage of inclusion of long-term demographic and patient outcome information.

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Figure 1: RESA allows the accurate assignment of subcellular compartments and localization of a target antigen.
Figure 2: Automated and pathologist-based scoring of ER shows a high degree of correlation and equal power in predicting outcome.
Figure 3: Increasing levels of nuclear β-catenin associate with an increasingly poor prognosis.
Figure 4: Unlike analyses of overall β-catenin expression, automated, subcellular localization of β-catenin can predict outcome in colon carcinoma.

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Acknowledgements

We thank T. D'Aquila, M. Helie, L. Charette, D. Fischer, E. Rimm and P. Lizardi for their help in this effort; and J. Costa, V. Marchesi, A. Reynolds, R. Levenson and E. Fearon for review of the manuscript. This work was supported by grants from the Patrick and Catherine Weldon Donaghue Foundation for Medical Research and grants from the NIH including: K0-8 ES11571, NIEHS (to R.L.C.), RO-1 GM57604 NCI (to D.L.R.) and US Army DAMD grant 01-000436.

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Correspondence to David L. Rimm.

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R.L.C. and D.L.R. are involved in a Yale-based effort to commercialize the AQUA technology though a company called Histometrix.

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Camp, R., Chung, G. & Rimm, D. Automated subcellular localization and quantification of protein expression in tissue microarrays. Nat Med 8, 1323–1328 (2002). https://doi.org/10.1038/nm791

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