Thomas Urup1,2, Rikke Hedegaard Dahlrot3, Kirsten Grunnet1,2, Ib Jarle Christensen4, Signe Regner Michaelsen1, Anders Toft1, Vibeke Andrée Larsen5, Helle Broholm6, Michael Kosteljanetz7, Steinbjørn Hansen3, Hans Skovgaard Poulsen1,2, Ulrik Lassen1,2,8. 1. a Department of Radiation Biology , the Finsen Center, Rigshospitalet , Copenhagen , Denmark ; 2. b Department of Oncology , the Finsen Center, Rigshospitalet , Copenhagen , Denmark ; 3. c Department of Oncology , Odense University Hospital , Odense , Denmark ; 4. d Laboratory of Gastroenterology , University of Copenhagen, Hvidovre Hospital , Copenhagen , Denmark ; 5. e Department of Radiology , Center of Diagnostic Investigation, Rigshospitalet , Copenhagen , Denmark ; 6. f Department of Neuropathology , Center of Diagnostic Investigation, Rigshospitalet , Copenhagen , Denmark ; 7. g Department of Neurosurgery , the Neurocenter, Rigshospitalet , Copenhagen , Denmark ; 8. h Phase I Unit, Finsencenter, Rigshospitalet , Copenhagen , Denmark.
Abstract
BACKGROUND: Predictive markers and prognostic models are required in order to individualize treatment of recurrent glioblastoma (GBM) patients. Here, we sought to identify clinical factors able to predict response and survival in recurrent GBM patients treated with bevacizumab (BEV) and irinotecan. MATERIAL AND METHODS: A total of 219 recurrent GBM patients treated with BEV plus irinotecan according to a previously published treatment protocol were included in the initial population. Prognostic models were generated by means of multivariate logistic and Cox regression analysis. RESULTS: In multivariate analysis, corticosteroid use had a negative predictive impact on response at first evaluation (OR 0.45; 95% CI 0.22-0.93; p = 0.03) and at best response (OR 0.51; 95% CI 0.26-1.02; p = 0.056). Three significant (p < 0.05) prognostic factors associated with reduced progression-free survival and overall survival (OS) were identified. These factors were included in the final model for OS, namely corticosteroid use (HR 1.70; 95% CI 1.18-2.45; p = 0.004), neurocognitive deficit (HR 1.40; 95% CI 1.04-1.89; p = 0.03) and multifocal disease (HR 1.56; 95% CI 1.15-2.11; p < 0.0001). Based on these results a prognostic index able to calculate the probability for OS at 6 and 12 months for the individual patient was established. The predictive value of the model for OS was validated in a separate patient cohort of 85 patients. DISCUSSION AND CONCLUSION: A prognostic model for OS was established and validated. This model can be used by physicians to risk stratify the individual patient and together with the patient decide whether to initiate BEV relapse treatment.
BACKGROUND: Predictive markers and prognostic models are required in order to individualize treatment of recurrent glioblastoma (GBM) patients. Here, we sought to identify clinical factors able to predict response and survival in recurrent GBMpatients treated with bevacizumab (BEV) and irinotecan. MATERIAL AND METHODS: A total of 219 recurrent GBMpatients treated with BEV plus irinotecan according to a previously published treatment protocol were included in the initial population. Prognostic models were generated by means of multivariate logistic and Cox regression analysis. RESULTS: In multivariate analysis, corticosteroid use had a negative predictive impact on response at first evaluation (OR 0.45; 95% CI 0.22-0.93; p = 0.03) and at best response (OR 0.51; 95% CI 0.26-1.02; p = 0.056). Three significant (p < 0.05) prognostic factors associated with reduced progression-free survival and overall survival (OS) were identified. These factors were included in the final model for OS, namely corticosteroid use (HR 1.70; 95% CI 1.18-2.45; p = 0.004), neurocognitive deficit (HR 1.40; 95% CI 1.04-1.89; p = 0.03) and multifocal disease (HR 1.56; 95% CI 1.15-2.11; p < 0.0001). Based on these results a prognostic index able to calculate the probability for OS at 6 and 12 months for the individual patient was established. The predictive value of the model for OS was validated in a separate patient cohort of 85 patients. DISCUSSION AND CONCLUSION: A prognostic model for OS was established and validated. This model can be used by physicians to risk stratify the individual patient and together with the patient decide whether to initiate BEV relapse treatment.
Authors: J N Jakobsen; T Urup; K Grunnet; A Toft; M D Johansen; S H Poulsen; I J Christensen; A Muhic; H S Poulsen Journal: J Neurooncol Date: 2018-01-12 Impact factor: 4.130
Authors: Thomas Urup; Line Mærsk Staunstrup; Signe Regner Michaelsen; Kristoffer Vitting-Seerup; Marc Bennedbæk; Anders Toft; Lars Rønn Olsen; Lars Jønson; Shohreh Issazadeh-Navikas; Helle Broholm; Petra Hamerlik; Hans Skovgaard Poulsen; Ulrik Lassen Journal: BMC Cancer Date: 2017-04-18 Impact factor: 4.430
Authors: Liang Yen Liu; Matthew S Ji; Nhung T Nguyen; Frances E Chow; Donna M Molaie; Sean T Pianka; Richard M Green; Linda M Liau; Benjamin M Ellingson; Phioanh L Nghiemphu; Timothy F Cloughesy; Albert Lai Journal: CNS Oncol Date: 2019-07-11