A mathematical model of diffuse low-grade gliomas treated with PCV chemotherapy.

Abstract

2065 Background: Several questions remain unanswered regarding the optimal use of first-line chemotherapy in low-grade gliomas (LGGs). Dynamic studies assessing the impact of first-line chemotherapy have shown that after the termination of chemotherapy, LGGs frequently continue to decrease in volume despite chemotherapy no longer being administered. Understanding this phenomenon is likely to be crucial to optimize chemotherapy for LGGs. The aim of the present study was to develop a mathematical model that could accurately describe the evolution of LGGs growth dynamics during and after first-line PCV chemotherapy. Methods: The model was formulated as systems of ordinary differential equations distinguishing between two cell populations: a proliferative treatment-sensitive cell population and a quiescent treatment-resistant cell population that spontaneously undergoes apoptosis. Model evaluation was performed in a series of 21 patients treated with first-line PCV chemotherapy. Results: Consistent with the biology of LGGs, the model estimated that LGGs consist largely of quiescent cells. Despite large inter-individual variability, the model correctly predicted individual tumor response profiles in the 21 patients. In analyzing evolution over time of proliferative and quiescent cell compartments, the model suggested that in some patients the six-week interval between PCV cycles might be suboptimal and that lengthening the time interval between cycles might improve the duration of response. Conclusions: We propose a mixed-effects model that accurately describes the evolution of LGGs during and after PCV chemotherapy. This model suggests that tailoring the time interval between PCV cycles according to the individual growth characteristics of LGGs may be a possible means by which to increase the efficacy of PCV chemotherapy.