Phase I study of a novel glioblastoma radiation therapy schedule exploiting cell-state plasticity

Abstract

Background Glioblastomas comprise heterogeneous cell populations with dynamic, bidirectional plasticity between treatment-resistant stem-like and treatment-sensitive differentiated states, with treatment influencing this process. However, current treatment protocols do not account for this plasticity. Previously, we generated a mathematical model based on preclinical experiments to describe this process and optimize a radiation therapy fractionation schedule that substantially increased survival relative to standard fractionation in a murine glioblastoma model. Methods We developed statistical models to predict the survival benefit of interventions to glioblastoma patients based on the corresponding survival benefit in the mouse model used in our preclinical study. We applied our mathematical model of glioblastoma radiation response to optimize a radiation therapy fractionation schedule for patients undergoing re-irradiation for glioblastoma and developed a first-in-human trial (NCT03557372) to assess the feasibility and safety of administering our schedule. Results Our statistical modeling predicted that the hazard ratio, when comparing our novel radiation schedule with a standard schedule, would be 0.74. Our mathematical modeling suggested that a practical, near optimal schedule for re-irradiation of recurrent glioblastoma patients was 3.96 Gy x 7 (1 fraction/day) followed by 1.0 Gy x 9 (3 fractions/day). Our optimized schedule was successfully administered to 14/14 (100%) patients. Conclusions A novel radiation therapy schedule based on mathematical modeling of cell-state plasticity is feasible and safe to administer to glioblastoma patients.

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches

“…Several mathematical models have been validated through preclinical investigations and clinical trials, demonstrating their potential for shaping effective clinical trial designs [63][64][65][66][67][68][69]. Notably, Dean et al [69] engineered a mathematical model tailored to refine radiation therapy schedules for glioblastoma, a formidable brain cancer.…”

“…Several mathematical models have been validated through preclinical investigations and clinical trials, demonstrating their potential for shaping effective clinical trial designs [63][64][65][66][67][68][69]. Notably, Dean et al [69] engineered a mathematical model tailored to refine radiation therapy schedules for glioblastoma, a formidable brain cancer. The model recommended an almost-optimal re-irradiation regimen for recurrent glioblastoma patients: 7 days of 3.96 Gy (administered once per day), followed by 9 days of 1.0 Gy (administered thrice per day).…”

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and AI Algorithms

“…Additionally, these models aim to optimize treatment schedules by identifying optimal and combination regimens, thus improving treatment outcomes [35][36][37]. Several mathematical models have been validated in preclinical studies and trials, demonstrating their potential in designing successful clinical trials [38][39][40][41][42][43][44]. Leder et al [45] and Dean et al [44] developed a novel radiation therapy schedule based on mathematical modeling, which improved survival in preclinical trials using mouse models of glioblastoma (GBM).…”

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and AI Algorithms

“…Several mathematical models have been validated through preclinical investigations and clinical trials, demonstrating their potential for shaping effective clinical trial designs [59][60][61][62][63][64][65]. Notably, Dean et al [65] engineered a mathematical model tailored to refine radiation therapy schedules for glioblastoma, a formidable brain cancer.…”