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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

Left: Kaplan‐Meier curve for observed times to tumor growth together with a model‐based confidence interval. Right: Predicted minimal tumor sizes vs. observed minimal tumor sizes. The vertical lines represent a tolerance of 25% relative to tumor size at treatment onset. Both times to tumor growth and minimal tumor size were predicted using the observations up to the end of the 3rd month of treatment.
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psp454-fig-0004: Left: Kaplan‐Meier curve for observed times to tumor growth together with a model‐based confidence interval. Right: Predicted minimal tumor sizes vs. observed minimal tumor sizes. The vertical lines represent a tolerance of 25% relative to tumor size at treatment onset. Both times to tumor growth and minimal tumor size were predicted using the observations up to the end of the 3rd month of treatment.

Mentions: Figure4 shows predictions regarding individual patients' response durations (left‐hand side), represented by Kaplan‐Meier curves, together with observed response durations that fall in the 95% confidence interval (CI) for almost 2 years after treatment onset. Beyond 2 years, the model predictions are incorrect, which is not surprising given that only information until month 3 is taken into account. Notably, beyond 2 years predicted times to progression are earlier than the actual times to progression. In this respect, the modeling framework shows a tendency for underestimating the effect of the treatment. The early part of the Kaplan‐Meier curve also indicates a tendency to predict progression at a very early time. For a small subset of patients (n = 4), the unique MTD point during the first 3 months of treatment was greater than the MTD at treatment onset, while successive MTD points showed a significant response. Integrating this point in our modeling framework resulted in predicting very early progression. However, removing these four patients resulted in correcting the early part of the Kaplan‐Meier curve.


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)

Left: Kaplan‐Meier curve for observed times to tumor growth together with a model‐based confidence interval. Right: Predicted minimal tumor sizes vs. observed minimal tumor sizes. The vertical lines represent a tolerance of 25% relative to tumor size at treatment onset. Both times to tumor growth and minimal tumor size were predicted using the observations up to the end of the 3rd month of treatment.
© Copyright Policy - creativeCommonsBy-nc-nd
Related In: Results  -  Collection

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

psp454-fig-0004: Left: Kaplan‐Meier curve for observed times to tumor growth together with a model‐based confidence interval. Right: Predicted minimal tumor sizes vs. observed minimal tumor sizes. The vertical lines represent a tolerance of 25% relative to tumor size at treatment onset. Both times to tumor growth and minimal tumor size were predicted using the observations up to the end of the 3rd month of treatment.
Mentions: Figure4 shows predictions regarding individual patients' response durations (left‐hand side), represented by Kaplan‐Meier curves, together with observed response durations that fall in the 95% confidence interval (CI) for almost 2 years after treatment onset. Beyond 2 years, the model predictions are incorrect, which is not surprising given that only information until month 3 is taken into account. Notably, beyond 2 years predicted times to progression are earlier than the actual times to progression. In this respect, the modeling framework shows a tendency for underestimating the effect of the treatment. The early part of the Kaplan‐Meier curve also indicates a tendency to predict progression at a very early time. For a small subset of patients (n = 4), the unique MTD point during the first 3 months of treatment was greater than the MTD at treatment onset, while successive MTD points showed a significant response. Integrating this point in our modeling framework resulted in predicting very early progression. However, removing these four patients resulted in correcting the early part of the Kaplan‐Meier curve.

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