Prediction of Response to Temozolomide in Low‐Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics

Mazzocco, Pauline; Barthélémy, Célia; Kaloshi, Gentian; Lavielle, Marc; Ricard, Damien; Idbaïh, Ahmed; Psimaras, Dimitri; Renard, M.-A; Alentorn, Agustí; Honnorat, Jérôme; Delattre, J.; Ducray, François; Ribba, Benjamin

doi:10.1002/psp4.54

Cited by 22 publications

(17 citation statements)

References 36 publications

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“…Finally, in the specific context of LGG, Mazzocco et al. developed a tumor growth inhibition model of LGG treated with TMZ, in which parameters were estimated using longitudinal tumor size measurements. In this model, however, resistance to TMZ was described using an empirical parametric function, giving no insight on resistance mechanisms, as treatment efficacy was simply considered to decrease with time.…”

Section: Discussionmentioning

confidence: 99%

Analysis of temozolomide resistance in low‐grade gliomas using a mechanistic mathematical model

Ollier

Mazzocco

Ricard

et al. 2017

Fundamemntal Clinical Pharma

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Understanding how tumors develop resistance to chemotherapy is a major issue in oncology. When treated with temozolomide (TMZ), an oral alkylating chemotherapy drug, most low-grade gliomas (LGG) show an initial volume decrease but this effect is rarely long lasting. In addition, it has been suggested that TMZ may drive tumor progression in a subset of patients as a result of acquired resistance. Using longitudinal tumor size measurements from 121 patients, the aim of this study was to develop a semi-mechanistic mathematical model to determine whether resistance of LGG to TMZ was more likely to result from primary and/or from chemotherapy-induced acquired resistance that may contribute to tumor progression. We applied the model to a series of patients treated upfront with TMZ (n = 109) or PCV (procarbazine, CCNU, vincristine) chemotherapy (n = 12) and used a population mixture approach to classify patients according to the mechanism of resistance most likely to explain individual tumor growth dynamics. Our modeling results predicted acquired resistance in 51% of LGG treated with TMZ. In agreement with the different biological effects of nitrosoureas, none of the patients treated with PCV were classified in the acquired resistance group. Consistent with the mutational analysis of recurrent LGG, analysis of growth dynamics using mathematical modeling suggested that in a subset of patients, TMZ might paradoxically contribute to tumor progression as a result of chemotherapy-induced resistance. Identification of patients at risk of developing acquired resistance is warranted to better define the role of TMZ in LGG.

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Section: Discussionmentioning

confidence: 99%

Analysis of temozolomide resistance in low‐grade gliomas using a mechanistic mathematical model

Ollier

Mazzocco

Ricard

et al. 2017

Fundamemntal Clinical Pharma

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“…The logistic growth functions are often used to characterize cell proliferation and have been previously used for TMZ in patients with brain tumors. 5,6 For simplicity, proliferation of GP and glioma stem state 0 (GS0) cells both used logistic growth functions. Cell death in the control model was attributed to physiological factors, such as limited blood flow, and the microenvironment, such as hypoxia, and again for simplicity, utilized equal first-order rate constants for each cell type.…”

Section: Cell State Modelmentioning

confidence: 99%

“…An exponential function to account for TMZ resistance has been previously used. 6 A number of other assumptions -related to tumor size, compartment volumes, and cell fractions -were applied to set parameter values prior to conducting the simulations (see Supplementary Material). All models were developed with Mlxplore (version 2016R1) model exploration and visualization program that is part of the Lixoft suite (Antony, France: Lixoft SAS, 2016).…”

Section: Cell State Modelmentioning

confidence: 99%

Modulation of Cell State to Improve Drug Therapy

Gallo

2018

CPT Pharmacom & Syst Pharma

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“…MGMT promoter methylation exists in about 35% of GBMs (16) and is associated with longer overall and progression-free survival (14,15) . Research has suggested that tumor responsiveness to TMZ is also impacted by IDH1 mutation and p53 mutation in lower grade gliomas (17,18) .…”

Section: Introductionmentioning

confidence: 99%

Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma

Massey

White

Whitmire

et al. 2019

Preprint

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Temozolomide (TMZ) has been the standard-of-care chemotherapy for glioblastoma (GBM) patients for more than a decade. Despite this long time in use, significant questions remain regarding how best to optimize TMZ therapy for individual patients. Understanding the relationship between TMZ response and factors such as number of adjuvant TMZ cycles, patient age, patient sex, and image-based tumor features, might help predict which GBM patients would benefit most from TMZ, particularly for those whose tumors are not MGMT methylated. Using a cohort of 90 newly-diagnosed GBM patients treated according to the Stupp protocol, we examined the relationships between several patient and tumor characteristics and volumetric and survival outcomes during adjuvant chemotherapy. Volumetric changes in MR imaging abnormalities during adjuvant therapy were used to assess TMZ response. T1Gd volumetric response is associated with younger patient age, increased number of TMZ cycles, longer time to nadir volume, and decreased tumor invasiveness. Moreover, increased adjuvant TMZ cycles corresponded with improved volumetric response only among more nodular tumors, and this volumetric response was associated with improved survival outcomes. Finally, in a subcohort of patients with known MGMT methylation status, MGMT methylated tumors were more diffusely invasive than unmethylated tumors, suggesting that the improved response in nodular tumors is not driven by a preponderance of MGMT methylated tumors. Our finding that less diffusely invasive tumors are associated with greater volumetric response to TMZ suggests that patients with these tumors may benefit from additional cycles of adjuvant TMZ, even for those without MGMT methylation.

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Prediction of Response to Temozolomide in Low‐Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics

Cited by 22 publications

References 36 publications

Analysis of temozolomide resistance in low‐grade gliomas using a mechanistic mathematical model

Analysis of temozolomide resistance in low‐grade gliomas using a mechanistic mathematical model

Modulation of Cell State to Improve Drug Therapy

Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma

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