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

Mazzocco P, Barthélémy C, Kaloshi G, Lavielle M, Ricard D, Idbaih A, Psimaras D, Renard MA, Alentorn A, Honnorat J, Delattre JY, Ducray F, Ribba B - CPT Pharmacometrics Syst Pharmacol (2015)

Bottom Line: Herein we propose a model of tumor growth inhibition integrating a tumor's genetic characteristics (p53 mutation and 1p/19q codeletion) that successfully describes the time course of tumor size in patients with low-grade gliomas treated with first-line temozolomide chemotherapy.The model captures potential tumor progression under chemotherapy by accounting for the emergence of tissue resistance to treatment following prolonged exposure to temozolomide.Combining longitudinal tumor size quantitative modeling with a tumor''s genetic characterization appears as a promising strategy to personalize treatments in patients with low-grade gliomas.

View Article: PubMed Central - PubMed

Affiliation: Inria, project-team Numed, Ecole Normale Supérieure de Lyon Lyon France.

ABSTRACT
Both molecular profiling of tumors and longitudinal tumor size data modeling are relevant strategies to predict cancer patients' response to treatment. Herein we propose a model of tumor growth inhibition integrating a tumor's genetic characteristics (p53 mutation and 1p/19q codeletion) that successfully describes the time course of tumor size in patients with low-grade gliomas treated with first-line temozolomide chemotherapy. The model captures potential tumor progression under chemotherapy by accounting for the emergence of tissue resistance to treatment following prolonged exposure to temozolomide. Using information on individual tumors' genetic characteristics, in addition to early tumor size measurements, the model was able to predict the duration and magnitude of response, especially in those patients in whom repeated assessment of tumor response was obtained during the first 3 months of treatment. Combining longitudinal tumor size quantitative modeling with a tumor''s genetic characterization appears as a promising strategy to personalize treatments in patients with low-grade gliomas.

No MeSH data available.


Related in: MedlinePlus

Top, Left: Visual Predictive Check (VPC) diagnostics on the 77 patients included in the (internal) analysis. Dashed lines represent the 5th, 50th, and 95th percentiles from observed data. The areas represent the 90% confidence interval of the 5th, 50th, and 95th simulated percentiles. Top, Right: VPC on 43 external patients. Middle, Left: VPC for the internal patients with p53 mutation (n = 24). Middle, Right: p53 nonmutated patients (n = 35). Bottom, Left: VPC for 1p/19q codeleted patients (n = 23). Bottom, Right: VPC for 1p/19q non‐codeleted patient (n = 47).
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psp454-fig-0003: Top, Left: Visual Predictive Check (VPC) diagnostics on the 77 patients included in the (internal) analysis. Dashed lines represent the 5th, 50th, and 95th percentiles from observed data. The areas represent the 90% confidence interval of the 5th, 50th, and 95th simulated percentiles. Top, Right: VPC on 43 external patients. Middle, Left: VPC for the internal patients with p53 mutation (n = 24). Middle, Right: p53 nonmutated patients (n = 35). Bottom, Left: VPC for 1p/19q codeleted patients (n = 23). Bottom, Right: VPC for 1p/19q non‐codeleted patient (n = 47).

Mentions: Figure3 shows goodness‐of‐fit (visual predictive check) plots for the 77 patients included in the model‐building dataset and for the 43 patients included in an external dataset. These diagnostics indicate good quality of the model, with and without covariates. The proposed model is able to capture the variability in patients' response to TMZ, including prolonged response after therapy discontinuation or emergence of acquired resistance to TMZ during treatment.


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

Mazzocco P, Barthélémy C, Kaloshi G, Lavielle M, Ricard D, Idbaih A, Psimaras D, Renard MA, Alentorn A, Honnorat J, Delattre JY, Ducray F, Ribba B - CPT Pharmacometrics Syst Pharmacol (2015)

Top, Left: Visual Predictive Check (VPC) diagnostics on the 77 patients included in the (internal) analysis. Dashed lines represent the 5th, 50th, and 95th percentiles from observed data. The areas represent the 90% confidence interval of the 5th, 50th, and 95th simulated percentiles. Top, Right: VPC on 43 external patients. Middle, Left: VPC for the internal patients with p53 mutation (n = 24). Middle, Right: p53 nonmutated patients (n = 35). Bottom, Left: VPC for 1p/19q codeleted patients (n = 23). Bottom, Right: VPC for 1p/19q non‐codeleted patient (n = 47).
© Copyright Policy - creativeCommonsBy-nc-nd
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4759703&req=5

psp454-fig-0003: Top, Left: Visual Predictive Check (VPC) diagnostics on the 77 patients included in the (internal) analysis. Dashed lines represent the 5th, 50th, and 95th percentiles from observed data. The areas represent the 90% confidence interval of the 5th, 50th, and 95th simulated percentiles. Top, Right: VPC on 43 external patients. Middle, Left: VPC for the internal patients with p53 mutation (n = 24). Middle, Right: p53 nonmutated patients (n = 35). Bottom, Left: VPC for 1p/19q codeleted patients (n = 23). Bottom, Right: VPC for 1p/19q non‐codeleted patient (n = 47).
Mentions: Figure3 shows goodness‐of‐fit (visual predictive check) plots for the 77 patients included in the model‐building dataset and for the 43 patients included in an external dataset. These diagnostics indicate good quality of the model, with and without covariates. The proposed model is able to capture the variability in patients' response to TMZ, including prolonged response after therapy discontinuation or emergence of acquired resistance to TMZ during treatment.

Bottom Line: Herein we propose a model of tumor growth inhibition integrating a tumor's genetic characteristics (p53 mutation and 1p/19q codeletion) that successfully describes the time course of tumor size in patients with low-grade gliomas treated with first-line temozolomide chemotherapy.The model captures potential tumor progression under chemotherapy by accounting for the emergence of tissue resistance to treatment following prolonged exposure to temozolomide.Combining longitudinal tumor size quantitative modeling with a tumor''s genetic characterization appears as a promising strategy to personalize treatments in patients with low-grade gliomas.

View Article: PubMed Central - PubMed

Affiliation: Inria, project-team Numed, Ecole Normale Supérieure de Lyon Lyon France.

ABSTRACT
Both molecular profiling of tumors and longitudinal tumor size data modeling are relevant strategies to predict cancer patients' response to treatment. Herein we propose a model of tumor growth inhibition integrating a tumor's genetic characteristics (p53 mutation and 1p/19q codeletion) that successfully describes the time course of tumor size in patients with low-grade gliomas treated with first-line temozolomide chemotherapy. The model captures potential tumor progression under chemotherapy by accounting for the emergence of tissue resistance to treatment following prolonged exposure to temozolomide. Using information on individual tumors' genetic characteristics, in addition to early tumor size measurements, the model was able to predict the duration and magnitude of response, especially in those patients in whom repeated assessment of tumor response was obtained during the first 3 months of treatment. Combining longitudinal tumor size quantitative modeling with a tumor''s genetic characterization appears as a promising strategy to personalize treatments in patients with low-grade gliomas.

No MeSH data available.


Related in: MedlinePlus