Prediction of Response to Temozolomide in Low-Grade Glioma Patients Based on Tumor Size Dynamics and Genetic CharacteristicsReportar como inadecuado




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1 DRACULA - Multi-scale modelling of cell dynamics : application to hematopoiesis CGMC - Centre de génétique moléculaire et cellulaire, Inria Grenoble - Rhône-Alpes, ICJ - Institut Camille Jordan Villeurbanne, UCBL - Université Claude Bernard Lyon 1 : EA 2 POPIX - Modélisation en pharmacologie de population Inria Saclay - Ile de France 3 ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute 4 CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique 5 XPOP - Modélisation en pharmacologie de population Inria Saclay - Ile de France 6 ASCR - Czech Academy of Sciences Prague 7 Agence nationale de la sécurité des systèmes d-information ANSSI 8 Centre de recherche en neurosciences de Lyon 9 UCBL - Université Claude Bernard Lyon 1 10 NUMED - Numerical Medicine UMPA-ENSL - Unité de Mathématiques Pures et Appliquées, Inria Grenoble - Rhône-Alpes, ICJ - Institut Camille Jordan Villeurbanne

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. WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? þ First-line temozolomide is frequently used to treat low-grade gliomas LGG, which are slow-growing brain tumors. The duration of response depends on genetic characteristics such as 1p-19q chromosomal codeletion, p53 mutation, and IDH mutations. However, up to now there are no means of predicting, at the individual level, the duration of the response to TMZ and its potential benefit for a given patient. • WHAT QUESTION DID THIS STUDY ADDRESS? þ The present study assessed whether combining longitudinal tumor size quantitative modeling with a tumor-s genetic characterization could be an effective means of predicting the response to temozolomide at the individual level in LGG patients. • WHAT THIS STUDY ADDS TO OUR KNOWLEDGE þ For the first time, we developed a model of tumor growth inhibition integrating a tumor-s genetic characteristics which successfully describes the time course of tumor size and captures potential tumor progression under chemotherapy in LGG patients treated with first-line temozolomide. The present study shows that using information on individual tumors- genetic characteristics, in addition to early tumor size measurements, it is possible to predict the duration and magnitude of response to temozolomide. • HOW THIS MIGHT CHANGE CLINICAL PHARMACOLOGY AND THERAPEUTICS þ Our model constitutes a rational tool to identify patients most likely to benefit from temozolomide and to optimize in these patients the duration of temozolomide therapy in order to ensure the longest duration of response to treatment. Response evaluation criteria such as RECIST—or RANO for brain tumors—are commonly used to assess response to anticancer treatments in clinical trials. 1,2 They assign a patient-s response to one of four categories, ranging from - complete response - to - disease progression. - Yet, criticisms have been raised regarding the use of such categorical criteria in the drug development process, 3,4 and regulatory agencies have promoted the additional analysis of longitudinal tumor size measurements through the use of quantitative modeling. 5 Several mathematical models of tumor growth and response to treatment have been developed for this purpose. 6,7 These analyses have led to the





Autor: P Mazzocco - Célia Barthélémy - G Kaloshi - Marc Lavielle - D Ricard - A Idbaih - D Psimaras - M-A Renard - A Alentorn - J Hon

Fuente: https://hal.archives-ouvertes.fr/



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