Quantitative determination of expression of the prostate cancer protein alpha-methylacyl-CoA racemase using automated quantitative analysis (AQUA): a novel paradigm for automated and continuous biomarker measurements.

Despite years of discovery and attempts at validation, few molecular biomarkers achieve acceptance in the clinical setting. Tissue-based markers evaluated by immunohistochemistry suffer from a high degree of inter- and intraobserver variability. One recent advance in this field that promises to automate this process is the development of AQUA, a molecular-based method of quantitative assessment of protein expression. This system integrates a set of algorithms that allows for the rapid, automated, continuous, and quantitative analysis of tissue samples, including the separation of tumor from stromal elements and the subcellular localization of signals. This study uses the AQUA system to assess a recently described prostate cancer biomarker, alpha-methylacyl-CoA-racemase (AMACR), and to determine the effectiveness of the quantitative measurement of this marker as a means for making the diagnosis of prostate cancer. Using a prostate cancer progression tissue microarray containing a wide range of prostate tissues, AQUA was directly compared to standard immunohistochemical evaluation for AMACR protein expression using the p504s monoclonal antibody. Both methods produced similar results showing AMACR protein expression to be strongest in the clinically localized prostate cancer, followed by the metastatic tumor samples. Benign prostate tissue was categorized as negative for most tissue samples by immunohistochemistry. However, AMACR was detectable using the AQUA system at low levels using the standard 1:25 dilution but also at 1:250 dilution, which is not detectable by light microscopy. The AQUA system was also able to discriminate foamy gland prostate cancers, which are known to have a lower AMACR expression than typical acinar prostate cancers, from benign prostate tissue samples. Finally, a receiver-operating-characteristic curve was plotted to determine the specificity of the AMACR AQUA Z-score (normalized AQUA score) to predict that a given tissue microarray sample contains cancer. The area under the curve was calculated at 0.90 (P < 0.00001; 95% CI, 0.84 to 0.95). At an AMACR AQUA Z-score score of -0.3, 91% of the 70 samples classified as prostate cancer were correctly categorized without the intervention of a pathologist reviewing the tissue microarray slide. In conclusion, the AQUA system provides a continuous measurement of AMACR on a wide range of prostate tissue samples. In the future, the AMACR AQUA Z-score may be useful in the automated screening and evaluation of prostate tissue biomarkers.