Swaroop S Singh1, Diana C Mehedint2, O Harris Ford2, D Antony Jeyaraj2, Elena A Pop2, Susan J Maygarden3, Anastasia Ivanova2,4, Rameela Chandrasekhar5,6, Gregory E Wilding5,6, James L Mohler1,2,7,8. 1. Department of Urology, Roswell Park Cancer Institute, Buffalo, New York. 2. Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina. 3. Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina. 4. Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina. 5. Department of Biostatistics, Roswell Park Cancer Institute, Buffalo, New York. 6. Department of Biostatistics, State University of New York, Buffalo, New York. 7. Department of Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina. 8. Department of Urology, State University of New York, Buffalo, New York.
Abstract
BACKGROUND: The balance between apoptotic and proliferative processes determines the enlargement of a tumor. Accurate measurement of apoptotic and proliferative rates from diagnostic prostate biopsies would allow calculation of tumor growth rates in a population-based prostate cancer (CaP) study. Automated image analysis may be used if proliferation and apoptotic biomarkers provide clearly resolved immunostained images. METHODS: Clinical CaP aggressiveness was assigned as low, intermediate or high using clinical criteria for 46 research subjects with newly diagnosed CaP. Diagnostic biopsy sections from the research subjects were dual-labeled for proliferation biomarker, Ki-67 and apoptotic biomarker, apoptotic chromatin condensation inducer in the nucleus (ACINUS). Apoptotic biomarkers, caspase-3 and terminal deoxyribonucleotidyltransferase mediated dUTP-biotin nick end labeling (TUNEL) were labeled separately. Images from immunostained sections were analyzed using automated image analysis and tumor growth rates computed. Association between clinical CaP aggressiveness and tumor growth rates was explored. RESULTS: Sixteen subjects had high, 17 had intermediate, and 13 had low clinical CaP aggressiveness. Positive immunostaining was localized to the nucleus for Ki-67, ACINUS, and TUNEL. A statistically significant linear trend across clinical CaP aggressiveness categories was found when tumor growth rates were calculated using ACINUS (P = 0.046). Logistic regression and ROC plots generated showed ACINUS (AUC = 0.677, P = 0.048) and caspase-3 (AUC = 0.694, P = 0.038) to be better predictors than TUNEL (AUC = 0.669, P = 0.110). CONCLUSIONS: ACINUS met the criteria for automated image analysis and for calculation of apoptotic rate. Tumor growth rates determined using automated image analysis should be evaluated for clinical prediction of CaP aggressiveness, treatment response, recurrence, and mortality.
BACKGROUND: The balance between apoptotic and proliferative processes determines the enlargement of a tumor. Accurate measurement of apoptotic and proliferative rates from diagnostic prostate biopsies would allow calculation of tumor growth rates in a population-based prostate cancer (CaP) study. Automated image analysis may be used if proliferation and apoptotic biomarkers provide clearly resolved immunostained images. METHODS: Clinical CaP aggressiveness was assigned as low, intermediate or high using clinical criteria for 46 research subjects with newly diagnosed CaP. Diagnostic biopsy sections from the research subjects were dual-labeled for proliferation biomarker, Ki-67 and apoptotic biomarker, apoptotic chromatin condensation inducer in the nucleus (ACINUS). Apoptotic biomarkers, caspase-3 and terminal deoxyribonucleotidyltransferase mediated dUTP-biotin nick end labeling (TUNEL) were labeled separately. Images from immunostained sections were analyzed using automated image analysis and tumor growth rates computed. Association between clinical CaP aggressiveness and tumor growth rates was explored. RESULTS: Sixteen subjects had high, 17 had intermediate, and 13 had low clinical CaP aggressiveness. Positive immunostaining was localized to the nucleus for Ki-67, ACINUS, and TUNEL. A statistically significant linear trend across clinical CaP aggressiveness categories was found when tumor growth rates were calculated using ACINUS (P = 0.046). Logistic regression and ROC plots generated showed ACINUS (AUC = 0.677, P = 0.048) and caspase-3 (AUC = 0.694, P = 0.038) to be better predictors than TUNEL (AUC = 0.669, P = 0.110). CONCLUSIONS:ACINUS met the criteria for automated image analysis and for calculation of apoptotic rate. Tumor growth rates determined using automated image analysis should be evaluated for clinical prediction of CaP aggressiveness, treatment response, recurrence, and mortality.
Authors: Shannon Henery; Thaddeus George; Brian Hall; David Basiji; William Ortyn; Philip Morrissey Journal: Apoptosis Date: 2008-08 Impact factor: 4.677
Authors: Wilfred D Stein; William Doug Figg; William Dahut; Aryeh D Stein; Moshe B Hoshen; Doug Price; Susan E Bates; Tito Fojo Journal: Oncologist Date: 2008-10-06
Authors: Hiroko Kuriyama; Kathleen R Lamborn; Judith R O'Fallon; N Iturria; Thomas Sebo; Paul L Schaefer; Bernd W Scheithauer; Jan C Buckner; Nagato Kuriyama; Robert B Jenkins; Mark A Israel Journal: Neuro Oncol Date: 2002-07 Impact factor: 12.300