Glazar Daniel J, Grass G Daniel, Arrington John A, Forsyth Peter A, Raghunand Natarajan, Yu Hsiang-Hsuan Michael, Sahebjam Solmaz, Enderling Heiko
Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
J Clin Med. 2020 Jun 27;9(7):2019. doi: 10.3390/jcm9072019.
Recurrent high-grade glioma (HGG) remains incurable with inevitable evolution of resistance and high inter-patient heterogeneity in time to progression (TTP). Here, we evaluate if early tumor volume response dynamics can calibrate a mathematical model to predict patient-specific resistance to develop opportunities for treatment adaptation for patients with a high risk of progression. A total of 95 T1-weighted contrast-enhanced (T1post) MRIs from 14 patients treated in a phase I clinical trial with hypo-fractionated stereotactic radiation (HFSRT; 6 Gy × 5) plus pembrolizumab (100 or 200 mg, every 3 weeks) and bevacizumab (10 mg/kg, every 2 weeks; NCT02313272) were delineated to derive longitudinal tumor volumes. We developed, calibrated, and validated a mathematical model that simulates and forecasts tumor volume dynamics with rate of resistance evolution as the single patient-specific parameter. Model prediction performance is evaluated based on how early progression is predicted and the number of false-negative predictions. The model with one patient-specific parameter describing the rate of evolution of resistance to therapy fits untrained data ( R 2 = 0.70 ). In a leave-one-out study, for the nine patients that had T1post tumor volumes ≥1 cm, the model was able to predict progression on average two imaging cycles early, with a median of 9.3 (range: 3-39.3) weeks early (median progression-free survival was 27.4 weeks). Our results demonstrate that early tumor volume dynamics measured on T1post MRI has the potential to predict progression following the protocol therapy in select patients with recurrent HGG. Future work will include testing on an independent patient dataset and evaluation of the developed framework on T2/FLAIR-derived data.
复发性高级别胶质瘤（HGG）仍然无法治愈，其耐药性会不可避免地演变，且患者进展时间（TTP）存在高度个体差异。在此，我们评估早期肿瘤体积反应动态是否能够校准一个数学模型，以预测患者特异性耐药性，从而为具有高进展风险的患者制定治疗调整方案。在一项I期临床试验中，对14例接受低分割立体定向放射治疗（HFSRT；6 Gy×5）联合帕博利珠单抗（100或200 mg，每3周一次）和贝伐单抗（10 mg/kg，每2周一次；NCT02313272）治疗的患者的95张T1加权对比增强（T1post）磁共振成像（MRI）进行勾画，以得出肿瘤体积的纵向数据。我们开发、校准并验证了一个数学模型，该模型以耐药性演变速率作为单一患者特异性参数来模拟和预测肿瘤体积动态。基于对早期进展的预测情况以及假阴性预测的数量来评估模型的预测性能。该模型通过一个描述对治疗耐药性演变速率的患者特异性参数，能够拟合未训练数据（R2 = 0.70）。在一项留一法研究中，对于9例T1post肿瘤体积≥1 cm的患者，该模型平均能够提前两个成像周期预测进展，提前时间中位数为9.3周（范围：3 - 39.3周）（无进展生存期中位数为27.4周）。我们的结果表明，在接受复发性HGG治疗方案的特定患者中，通过T1post MRI测量的早期肿瘤体积动态有可能预测进展情况。未来的工作将包括在独立患者数据集上进行测试，以及对基于T2/FLAIR数据开发的框架进行评估。
