Importance The need for a more refined, molecularly-based classification model for glioblastoma (GBM) in the temozolomide era. Objective Refine the existing clinically-based recursive partitioning analysis (RPA) model by incorporating molecular variables. Design, Setting, and Participants NRG Oncology RTOG 0525 specimens (n=452) were analyzed for protein biomarkers representing key pathways in GBM by a quantitative molecular microscopy-based approach with semi-quantitative immunohistochemical validation. Prognostic significance of each protein was examined by single-marker and multi-marker Cox-regression analyses. In order to reclassify the prognostic risk groups, significant protein biomarkers upon single-marker analysis were incorporated into a RPA model consisting of the same clinical variables (age, KPS, extent of resection, and neurologic function) as the existing RTOG RPA. The new RPA model (NRG-GBM-RPA) was confirmed using traditional immunohistochemistry in an independent dataset (n=176). Main Outcomes and Measures Overall survival (OS) Results MGMT (HR=1.81, 95% CI(1.37, 2.39), p<0.001), survivin (HR=1.36, 95% CI(1.04, 1.76), p=0.02), c-Met (HR=1.53, 95% CI(1.06,2.23), p=0.02), pmTOR (HR=0.76, 95% CI(0.60,0.97), p=0.03), and Ki-67 (HR=1.40, 95% CI(1.10, 1.78), p=0.007), were found to be significant upon single-marker multivariate analysis of OS. To refine the existing RPA, significant protein biomarkers together with clinical variables (age, performance status, extent of resection, and neurological function) were incorporated into a new model. Higher MGMT protein was significantly associated with decreased MGMT promoter methylation and vice-versa. Further, MGMT protein expression had greater prognostic value for OS compared to MGMT promoter methylation. The refined NRG-GBM-RPA consisting of MGMT protein, c-Met protein, and age revealed greater separation of OS prognostic classes compared to the existing clinically-based RPA model and MGMT promoter methylation in NRG Oncology RTOG 0525. The prognostic significance of the NRG-GBM-RPA was subsequently confirmed in an independent dataset (N=176). Conclusions and Relevance The new NRG-GBM-RPA model significantly enhances outcome stratification over both the current RTOG RPA model and MGMT promoter methylation, respectively, for GBM patients treated with radiation and temozolomide and was biologically validated in an independent dataset. The revised RPA has the potential to significantly contribute to improving the accurate assessment of prognostic groups in GBM patients treated with radiation and temozolomide and also influence clinical decision making.

No abstract

Background: Glioblastoma is a rapidly proliferating tumor. Patients bear an inferior prognosis with a median survival time of 14-16 months. Proliferation and repopulation are a major resistance promoting factor for conventionally fractionated radiotherapy. Tumor-Treating-Fields (TTFields) are an antimitotic modality applying low-intensity (1-3 V/ cm), intermediate-frequency (100-300 kHz) alternating electric-fields. More recently interference of TTFields with DNAdamage-repair and synergistic effects with radiotherapy were reported in the preclinical setting. This study aims at examining the dosimetric consequences of TTFields applied during the course of radiochemotherapy. Methods: Cone-beam-computed-tomography (CBCT)-data from the first seven patients of the PriCoTTF-phase-I-trial were used in a predefined way for dosimetric verification and dose-accumulation of the non-coplanar-intensitymodulated-radiotherapy (IMRT)-treatment-plans as well as geometric analysis of the transducer-arrays by which TTFields are applied throughout the course of treatment. Transducer-array-position and contours were obtained from the low-dose CBCT's routinely made for image-guidance. Material-composition of the electrodes was determined and a respective Hounsfield-unit was assigned to the electrodes. After 6D-fusion with the planning-CT, the dose-distribution was recalculated using a Boltzmann-equation-solver (Acuros XB) and a Monte-Carlo-dose-calculation-engine.