ArticleCPT: pharmacometrics & systems pharmacology2015
Prediction of Response to Temozolomide in Low-Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics.
Article in CPT: pharmacometrics & systems pharmacology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma.PloS one · 2020Trial
- Overcoming chemotherapy resistance in low-grade gliomas: A computational approach.PLoS computational biology · 2023Article
- Surveillance imaging frequency in adult patients with lower-grade (WHO Grade 2 and 3) gliomas.Neuro-oncology · 2022Review
- Optimal Combinations of Chemotherapy and Radiotherapy in Low-Grade Gliomas: A Mathematical Approach.Journal of personalized medicine · 2021Article
- Computational design of improved standardized chemotherapy protocols for grade II oligodendrogliomas.PLoS computational biology · 2019Article
- Modulation of Cell State to Improve Drug Therapy.CPT: pharmacometrics & systems pharmacology · 2018Article
- A mathematical model describes the malignant transformation of low grade gliomas: Prognostic implications.PloS one · 2017Article
- Comprehensive Analysis Reveals a 4-Gene Signature in Predicting Response to Temozolomide in Low-Grade Glioma Patients.Cancer control : journal of the Moffitt Cancer CenterArticle
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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