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

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) th...

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Autores principales: Mazzocco, P, Barthélémy, C, Kaloshi, G, Lavielle, M, Ricard, D, Idbaih, A, Psimaras, D, Renard, M‐A, Alentorn, A, Honnorat, J, Delattre, J‐Y, Ducray, F, Ribba, B
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4759703/
https://www.ncbi.nlm.nih.gov/pubmed/26904387
http://dx.doi.org/10.1002/psp4.54
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author Mazzocco, P
Barthélémy, C
Kaloshi, G
Lavielle, M
Ricard, D
Idbaih, A
Psimaras, D
Renard, M‐A
Alentorn, A
Honnorat, J
Delattre, J‐Y
Ducray, F
Ribba, B
author_facet Mazzocco, P
Barthélémy, C
Kaloshi, G
Lavielle, M
Ricard, D
Idbaih, A
Psimaras, D
Renard, M‐A
Alentorn, A
Honnorat, J
Delattre, J‐Y
Ducray, F
Ribba, B
author_sort Mazzocco, P
collection PubMed
description 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.
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spelling pubmed-47597032016-02-22 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, M‐A Alentorn, A Honnorat, J Delattre, J‐Y Ducray, F Ribba, B CPT Pharmacometrics Syst Pharmacol Original Articles 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. John Wiley and Sons Inc. 2015-10-10 2015-12 /pmc/articles/PMC4759703/ /pubmed/26904387 http://dx.doi.org/10.1002/psp4.54 Text en © 2015 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs (http://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Articles
Mazzocco, P
Barthélémy, C
Kaloshi, G
Lavielle, M
Ricard, D
Idbaih, A
Psimaras, D
Renard, M‐A
Alentorn, A
Honnorat, J
Delattre, J‐Y
Ducray, F
Ribba, B
Prediction of Response to Temozolomide in Low‐Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics
title Prediction of Response to Temozolomide in Low‐Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics
title_full Prediction of Response to Temozolomide in Low‐Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics
title_fullStr Prediction of Response to Temozolomide in Low‐Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics
title_full_unstemmed Prediction of Response to Temozolomide in Low‐Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics
title_short Prediction of Response to Temozolomide in Low‐Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics
title_sort prediction of response to temozolomide in low‐grade glioma patients based on tumor size dynamics and genetic characteristics
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4759703/
https://www.ncbi.nlm.nih.gov/pubmed/26904387
http://dx.doi.org/10.1002/psp4.54
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