Tumor Volume Dynamics as an Early Biomarker for Patient-Specific Evolution of Resistance and Progression in Recurrent High-Grade Glioma
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 develo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/9/7/2019 |
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| author | Daniel J. Glazar G. Daniel Grass John A. Arrington Peter A. Forsyth Natarajan Raghunand Hsiang-Hsuan Michael Yu Solmaz Sahebjam Heiko Enderling |
| author_facet | Daniel J. Glazar G. Daniel Grass John A. Arrington Peter A. Forsyth Natarajan Raghunand Hsiang-Hsuan Michael Yu Solmaz Sahebjam Heiko Enderling |
| author_sort | Daniel J. Glazar |
| collection | DOAJ |
| description | 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 (<inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi mathvariant="normal">R</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0.70</mn> </mrow> </semantics> </math> </inline-formula>). In a leave-one-out study, for the nine patients that had T1post tumor volumes ≥1 cm<sup>3</sup>, 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. |
| first_indexed | 2024-03-10T18:51:19Z |
| format | Article |
| id | doaj.art-5f45fdecd7724cf2b5f508c1455a69d0 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T18:51:19Z |
| publishDate | 2020-06-01 |
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| series | Journal of Clinical Medicine |
| spelling | doaj.art-5f45fdecd7724cf2b5f508c1455a69d02023-11-20T05:07:38ZengMDPI AGJournal of Clinical Medicine2077-03832020-06-0197201910.3390/jcm9072019Tumor Volume Dynamics as an Early Biomarker for Patient-Specific Evolution of Resistance and Progression in Recurrent High-Grade GliomaDaniel J. Glazar0G. Daniel Grass1John A. Arrington2Peter A. Forsyth3Natarajan Raghunand4Hsiang-Hsuan Michael Yu5Solmaz Sahebjam6Heiko Enderling7Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USADepartment of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USADepartment of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USADepartment of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USADepartment of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USADepartment of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USADepartment of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USADepartment of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USARecurrent 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 (<inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi mathvariant="normal">R</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0.70</mn> </mrow> </semantics> </math> </inline-formula>). In a leave-one-out study, for the nine patients that had T1post tumor volumes ≥1 cm<sup>3</sup>, 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.https://www.mdpi.com/2077-0383/9/7/2019mathematical modelresponse predictionhigh-grade gliomapatient-specific |
| spellingShingle | Daniel J. Glazar G. Daniel Grass John A. Arrington Peter A. Forsyth Natarajan Raghunand Hsiang-Hsuan Michael Yu Solmaz Sahebjam Heiko Enderling Tumor Volume Dynamics as an Early Biomarker for Patient-Specific Evolution of Resistance and Progression in Recurrent High-Grade Glioma Journal of Clinical Medicine mathematical model response prediction high-grade glioma patient-specific |
| title | Tumor Volume Dynamics as an Early Biomarker for Patient-Specific Evolution of Resistance and Progression in Recurrent High-Grade Glioma |
| title_full | Tumor Volume Dynamics as an Early Biomarker for Patient-Specific Evolution of Resistance and Progression in Recurrent High-Grade Glioma |
| title_fullStr | Tumor Volume Dynamics as an Early Biomarker for Patient-Specific Evolution of Resistance and Progression in Recurrent High-Grade Glioma |
| title_full_unstemmed | Tumor Volume Dynamics as an Early Biomarker for Patient-Specific Evolution of Resistance and Progression in Recurrent High-Grade Glioma |
| title_short | Tumor Volume Dynamics as an Early Biomarker for Patient-Specific Evolution of Resistance and Progression in Recurrent High-Grade Glioma |
| title_sort | tumor volume dynamics as an early biomarker for patient specific evolution of resistance and progression in recurrent high grade glioma |
| topic | mathematical model response prediction high-grade glioma patient-specific |
| url | https://www.mdpi.com/2077-0383/9/7/2019 |
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