PURPOSE: The correlation between the frequency of spontaneous and radiation-induced apoptosis, and the precedence between those for predicting prognosis were studied at clinical level. METHODS AND MATERIALS: Twenty-one patients (mean age, 65.8 years; 16 men and 5 women) with bladder cancer (transitional cell carcinoma Grade 3, T3bN0M0, Stage IIIb) underwent intraoperative radiotherapy: single 30-Gy 12-MV electron beam irradiation to bladder, followed by total cystectomy 6 h after irradiation. The specimens of pretreatment and irradiated bladder cancer were assayed for apoptosis, using TUNEL staining with counter staining of hematoxylin. The apoptotic index (AI) was calculated by dividing the number of apoptotic cells by the total number of cells and multiplying by 100. The Pearson's linear fitting was used to test the correlation between spontaneous and radiation-induced apoptosis. The Kaplan-Meier product-limit estimation was used for overall survival (OS) and freedom from recurrence (FFR). The precedence between spontaneous and radiation-induced apoptosis for predicting the clinical prognosis was estimated using the proportional hazard regression. RESULTS: The mean AI of spontaneous and radiation-induced apoptosis was 1.18 +/- 0.16 and 2.63 +/- 0.45, respectively, which was significantly different. There was strong correlation between spontaneous and radiation-induced apoptosis (r(2) = 0.864, adjusted r(2) = 0.857). Radiation-induced apoptosis was estimated by equation: y (radiation-induced apoptosis) = 2.67 x (spontaneous apoptosis) -0.52. However, the proportional hazard regression test indicated that only spontaneous apoptosis was significant for predicting OS and FFR (&z.sfnc;t&z.sfnc; > 0.2), but radiation-induced apoptosis was not. CONCLUSION: Estimating AI in radiation-induced apoptosis from AI in spontaneous apoptosis is possible. However, spontaneous apoptosis is more accurate in predicting clinical prognosis.
PURPOSE: The correlation between the frequency of spontaneous and radiation-induced apoptosis, and the precedence between those for predicting prognosis were studied at clinical level. METHODS AND MATERIALS: Twenty-one patients (mean age, 65.8 years; 16 men and 5 women) with bladder cancer (transitional cell carcinoma Grade 3, T3bN0M0, Stage IIIb) underwent intraoperative radiotherapy: single 30-Gy 12-MV electron beam irradiation to bladder, followed by total cystectomy 6 h after irradiation. The specimens of pretreatment and irradiated bladder cancer were assayed for apoptosis, using TUNEL staining with counter staining of hematoxylin. The apoptotic index (AI) was calculated by dividing the number of apoptotic cells by the total number of cells and multiplying by 100. The Pearson's linear fitting was used to test the correlation between spontaneous and radiation-induced apoptosis. The Kaplan-Meier product-limit estimation was used for overall survival (OS) and freedom from recurrence (FFR). The precedence between spontaneous and radiation-induced apoptosis for predicting the clinical prognosis was estimated using the proportional hazard regression. RESULTS: The mean AI of spontaneous and radiation-induced apoptosis was 1.18 +/- 0.16 and 2.63 +/- 0.45, respectively, which was significantly different. There was strong correlation between spontaneous and radiation-induced apoptosis (r(2) = 0.864, adjusted r(2) = 0.857). Radiation-induced apoptosis was estimated by equation: y (radiation-induced apoptosis) = 2.67 x (spontaneous apoptosis) -0.52. However, the proportional hazard regression test indicated that only spontaneous apoptosis was significant for predicting OS and FFR (&z.sfnc;t&z.sfnc; > 0.2), but radiation-induced apoptosis was not. CONCLUSION: Estimating AI in radiation-induced apoptosis from AI in spontaneous apoptosis is possible. However, spontaneous apoptosis is more accurate in predicting clinical prognosis.
Authors: Hisashi Oshiro; Bogdan A Czerniak; Kentaro Sakamaki; Koji Tsuta; Jolanta Bondaruk; Afsaneh Keyhani; Colin P Dinney; Takeshi Nagai; Ashish M Kamat Journal: Medicine (Baltimore) Date: 2016-08 Impact factor: 1.889