TY - JOUR
T1 - Quantitative fluorescence imaging analysis for cancer biomarker discovery
T2 - Application to β-catenin in archived prostate specimens
AU - Huang, Dali
AU - Casale, George P.
AU - Tian, Jun
AU - Wehbi, Nizar K.
AU - Abrahams, Neil A.
AU - Kaleem, Zahid
AU - Smith, Lynette M.
AU - Johansson, Sonny L.
AU - Elkahwaji, Johny E.
AU - Hemstreet, George P.
PY - 2007/7/1
Y1 - 2007/7/1
N2 - The surprising disparity between the number of protein-encoding genes (∼30,000) in the human genome and the number of proteins (∼300,000) in the human proteome has inspired the development of translational proteomics aimed at protein expression profiling of disease states. Translational proteomics, which offers the promise of early disease detection and individualized therapy, requires new methods for the analysis of clinical specimens. We have developed quantitative flourescence imaging analysis (QFIA) for accurate, reproducible quantification of proteins in slide-mounted tissues. The method has been validated for the analysis of β-catenin in archived prostate specimens fixed in formalin. QFIA takes advantage of the linearity of fluorescence antibody signaling for tissue epitope content, a feature validated for β-catenin in methacarn-fixed prostate specimens analyzed by reverse-phase protein array analysis and QFIA (r = 0.97). QFIA of β-catenin in formaldehyde-fixed tissues correlated directly with β-catenin content (r = 0.86). Application of QFIA in a cross-sectional study of biopsies from 42 prostate cancer (PC) cases and 42 matched controls identified β-catenin as a potential field marker for PC. Receiver operating characteristic plots revealed that β-catenin expression in the normal-appearing acini of cancerous glands identified 42% (95% confidence intervals, 26-57%) of cancer cases, with 88% (95% confidence intervals, 80-96%) specificity. The marker may contribute to a PC biomarker panel. In conclusion, we report the development and validation of a new method for fluorescence quantification of proteins in archived tissues and its application to archived specimens for an evaluation of β-catenin expression as a biomarker for PC.
AB - The surprising disparity between the number of protein-encoding genes (∼30,000) in the human genome and the number of proteins (∼300,000) in the human proteome has inspired the development of translational proteomics aimed at protein expression profiling of disease states. Translational proteomics, which offers the promise of early disease detection and individualized therapy, requires new methods for the analysis of clinical specimens. We have developed quantitative flourescence imaging analysis (QFIA) for accurate, reproducible quantification of proteins in slide-mounted tissues. The method has been validated for the analysis of β-catenin in archived prostate specimens fixed in formalin. QFIA takes advantage of the linearity of fluorescence antibody signaling for tissue epitope content, a feature validated for β-catenin in methacarn-fixed prostate specimens analyzed by reverse-phase protein array analysis and QFIA (r = 0.97). QFIA of β-catenin in formaldehyde-fixed tissues correlated directly with β-catenin content (r = 0.86). Application of QFIA in a cross-sectional study of biopsies from 42 prostate cancer (PC) cases and 42 matched controls identified β-catenin as a potential field marker for PC. Receiver operating characteristic plots revealed that β-catenin expression in the normal-appearing acini of cancerous glands identified 42% (95% confidence intervals, 26-57%) of cancer cases, with 88% (95% confidence intervals, 80-96%) specificity. The marker may contribute to a PC biomarker panel. In conclusion, we report the development and validation of a new method for fluorescence quantification of proteins in archived tissues and its application to archived specimens for an evaluation of β-catenin expression as a biomarker for PC.
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U2 - 10.1158/1055-9965.EPI-06-0718
DO - 10.1158/1055-9965.EPI-06-0718
M3 - Article
C2 - 17623804
AN - SCOPUS:34447512839
SN - 1055-9965
VL - 16
SP - 1371
EP - 1381
JO - Cancer Epidemiology Biomarkers and Prevention
JF - Cancer Epidemiology Biomarkers and Prevention
IS - 7
ER -