Evaluation of the antitumor effect of PCV chemotherapy on low-grade gliomas patients with a longitudinal tumor growth inhibition model
B. Ribba (1), M. Peyre (2), D. Ricard (3), B. Cajavec-Bernard (1), E. Grenier (1), M. Tod (4), V. Calvez (1), D. Frappaz (5), M-P. Sunyach (5), A. Vasiljevic (6), J. Pallud (7), S. Cartalat-Carel (2), J. Honnorat (2,8,9), F. Ducray (2,8,9)
(1) INRIA, Project-team NUMED, Ecole Normale Supérieure de Lyon, 46 allée d’Italie, 69007 Lyon Cedex 07, France; (2) Hospices Civils de Lyon, Hôpital Neurologique, Neuro-oncologie,Lyon, 69003 France; (3) Hôpital d’Instruction des Armées du Val-de-Grâce, Paris, 75005 France; (4) Université de Lyon, Lyon, F-69003, France ; Université Lyon 1, EA3738 CTO, Faculté de Médecine Lyon-Sud, Oullins, F-69600, France and Pharmacie, Hôpital de la Croix Rousse, Hospices civils de Lyon, 69004 Lyon, France; (5) Centre Léon Bérard, Neuro-oncologie, Lyon, 69003 France; (6) Hospices Civils de Lyon, Hôpital Neurologique, Neuropathologie, Lyon, 69003 France; (7) Service de Neurochirurgie, Centre Hospitalier Sainte-Anne and Université René Descartes Paris V, Paris, 75014 France; (8) Inserm U1028 ; CNRS UMR5292 ; LyonNeuroscience Research Center, Neuro-oncology and Neuro- inflammation team, Lyon,F-69000, France; (9) University Lyon 1, Lyon, F-69000, France
Objectives: To develop a tumor growth inhibition model able to describe the evolution of diffuse low-grade gliomas (LGGs) growth dynamics in patients treated with PCV (Procarbazine, Vincristin, CCNU) chemotherapy, and to use this model as a tool to suggest potential improvements of the chemotherapy regimen.
Methods: Model building was performed with longitudinal tumor size (mean tumor diameter) data assessed through imaging techniques in 21 patients representing 254 observations in total [1]. The model was formulated under a population approach as a system of ordinary differential equations distinguishing between two cell populations: one proliferative treatment-sensitive cell population and one quiescent treatment-resistant cell population that spontaneously undergoes apoptosis. Monolix was used to estimate the population and individual parameters.
Results: Consistent with LGGs biology, the model estimated that LGGs consist mostly 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. In the present series, simulating tumor growth responses with time interval between cycles lengthened to 9 months resulted in delaying tumor regrowth after treatment by more than 20 months in the mean, in comparison to the classical 6 weeks PCV regimen.
Conclusions: Based on the hypothesis that LGGs consist of proliferative treatment-sensitive cells and quiescent treatment-resistant cells that spontaneously undergo apoptosis, we propose a mixed-effect model that accurately describes the evolution of these tumors 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 this chemotherapy regimen.
References:
[1] Peyre M, Cartalat-Carel S, Meyronet D, Ricard D, Jouvet A, Pallud J, Mokhtari K, Guyotat J, Jouanneau E, Sunyach MP, Frappaz D, Honnorat J, Ducray F. Prolonged response without prolonged chemotherapy: a lesson from PCV chemotherapy in low-grade gliomas. Neuro Oncol. 2010;12(10):1078-82.