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

Author:

Dean Jamie A12345,Tanguturi Shyam K6,Cagney Daniel6,Shin Kee-Young13,Youssef Gilbert78,Aizer Ayal6,Rahman Rifaquat6,Hammoudeh Lubna6,Reardon David7,Lee Eudocia7,Dietrich Jorg8,Tamura Kaoru9,Aoyagi Masaru9,Wickersham Lacey6,Wen Patrick Y7,Catalano Paul13,Haas-Kogan Daphne6ORCID,Alexander Brian M6,Michor Franziska123101112

Affiliation:

1. Department of Data Science, Dana-Farber Cancer Institute , Boston, Massachusetts , USA

2. Department of Stem Cell and Regenerative Biology, Harvard University , Cambridge, Massachusetts , USA

3. Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, Massachusetts , USA

4. Department of Medical Physics and Biomedical Engineering, University College London , London , UK

5. UCL Cancer Institute, University College London , London , UK

6. Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School , Boston, Massachusetts , USA

7. Center for Neuro-Oncology, Dana-Farber Cancer Institute, Harvard Medical School , Boston, Massachusetts , USA

8. Center for Neuro-Oncology, Massachusetts General Hospital, Harvard Medical School , Boston, Massachusetts , USA

9. Department of Neurosurgery, Tokyo Medical and Dental University , Tokyo , Japan

10. The Broad Institute of MIT and Harvard , Cambridge, MA , USA

11. The Center for Cancer Evolution, Dana-Farber Cancer Institute , Boston, Massachusetts , USA

12. The Ludwig Center at Harvard , Boston, Massachusetts , USA

Abstract

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 × 7 (1 fraction/day) followed by 1.0 Gy × 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.

Funder

Dana-Farber Cancer Institute Physical Science Oncology Center

Radiation Research Unit

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Neurology (clinical),Oncology

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