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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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2015
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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. |
format | Online Article Text |
id | pubmed-4759703 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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