Intermittent radiotherapy as alternative treatment for recurrent high grade glioma: a modeling study based on longitudinal tumor measurements

Sarah C Brüningk, Jeffrey Peacock, Christopher J Whelan, Renee Brady-Nicholls, Hsiang-Hsuan M Yu, Solmaz Sahebjam, Heiko Enderling, Sarah C Brüningk, Jeffrey Peacock, Christopher J Whelan, Renee Brady-Nicholls, Hsiang-Hsuan M Yu, Solmaz Sahebjam, Heiko Enderling

Abstract

Recurrent high grade glioma patients face a poor prognosis for which no curative treatment option currently exists. In contrast to prescribing high dose hypofractionated stereotactic radiotherapy (HFSRT, [Formula: see text] Gy [Formula: see text] 5 in daily fractions) with debulking intent, we suggest a personalized treatment strategy to improve tumor control by delivering high dose intermittent radiation treatment (iRT, [Formula: see text] Gy [Formula: see text] 1 every 6 weeks). We performed a simulation analysis to compare HFSRT, iRT and iRT plus boost ([Formula: see text] Gy [Formula: see text] 3 in daily fractions at time of progression) based on a mathematical model of tumor growth, radiation response and patient-specific evolution of resistance to additional treatments (pembrolizumab and bevacizumab). Model parameters were fitted from tumor growth curves of 16 patients enrolled in the phase 1 NCT02313272 trial that combined HFSRT with bevacizumab and pembrolizumab. Then, iRT +/- boost treatments were simulated and compared to HFSRT based on time to tumor regrowth. The modeling results demonstrated that iRT + boost(- boost) treatment was equal or superior to HFSRT in 15(11) out of 16 cases and that patients that remained responsive to pembrolizumab and bevacizumab would benefit most from iRT. Time to progression could be prolonged through the application of additional, intermittently delivered fractions. iRT hence provides a promising treatment option for recurrent high grade glioma patients for prospective clinical evaluation.

Conflict of interest statement

SS receives research support from Merck, Bristol Myers Squibb, and Brooklyn ImmunoTherapeutics and acts as advisory board member for Merck, and Boehringer Ingelheim. HY is on the advisory board of AbbVie and Novocure and is a member of the speaker bureau of BrainLab. HY also receives a honorarium from UpToDate. The remaining authors declare that they have no competing interests.

© 2021. The Author(s).

Figures

Figure 1
Figure 1
Overview of the used data. (A) Schematic of the NCT02313272 protocol indicating the imaging and treatment time point for this triple combination therapy trial. (B) Out of the 32 trial participants only those 16 with monitored tumor progression were included in this analysis.
Figure 2
Figure 2
Model fit results. (A) Grid search results to identify the optimal growth rate λ for the patient population (indicated by red arrow). Results of the sum over the median, mean and maximum RMSE are shown (denoted as RMSE score in cm3). (B) Overview of the measured vs. simulated tumor volume. (C) Correlation analysis of the surviving fraction and the PTV gEUD. The Pearson correlation coefficient ρ and corresponding p-value p are given. For S fit parameters to undisturbed data are shown with 90% confidence intervals estimated from bootstraps. See (D) for legend. (D) Correlation analysis of the logarithm of the decay rate (log(ε)) and the surviving fraction. Fit values to undisturbed data are shown with 90% confidence intervals over the bootstraps. RMSE Root mean squared error, PTV Planning target volume, gEUD generalized equivalent uniform dose.
Figure 3
Figure 3
Evaluation of non-inferiority of iRT +/− boost vs HFSRT. (A) Kaplan–Meier plot for five treatment fractions delivered as HFSRT (red), iRT (blue) or iRT + boost (green). Shaded areas correspond to the envelope of the bootstrap estimated modeling uncertainty. The logrank test p-values is given for comparison of HFSRT and iRT + boost. (B) Boxplot of the logarithm of the decay rate parameter ε for iRT responders and non-responders. (C) Boxplot of the surviving fraction S for iRT responders and non-responders. In (B) and (C) median values (red lines), 25th and 75th percentiles (box upper and lower bounds) and full ranges (whiskers) all calculated over patient fit values to undisturbed data are shown. Indicated p-values correspond to Wilcoxon signed-rank test.
Figure 4
Figure 4
Kaplan–Meier plots for treatments with increasing maximum number of iRT fractions. Shown are fitted HFSRT (red), and simulated iRT (blue) and iRT + boost (green) results. Shaded areas correspond to the envelope of the bootstrap estimated modeling uncertainty. The logrank test p-values are given for comparison of HFSRT and iRT+boost. (A) Up to seven fractions. (B) Up to nine fractions. (C) Up to eleven fractions. (D) Up to thirteen fractions.
Figure 5
Figure 5
Grouping of patient response. (A–D) Estimated growth trajectories of representative patients for fitted HFSRT (red), and simulated iRT (blue) and iRT + boost (green) treatments with up to eleven treatment fractions. Shaded areas correspond to the envelope of the bootstrap estimated modeling uncertainty. (A) Group 1, (B) Group 2, (C) Group 3, (D) Group 4. (E) Analysis of the logarithm of the decay rate ε for the different groups. Indicated p-values correspond to Wilcoxon signed rank tests between the groups comprising more than one patient. (F) Analysis of the radiotherapy surviving fraction for the different groups. There were no significant differences. In (E) and (F) median values (red lines), 25th and 75th percentiles (box upper and lower bounds) and full ranges (whiskers) are shown.

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