Optimal Combinations of Chemotherapy and Radiotherapy in Low-Grade Gliomas: A Mathematical Approach

Abstract

Low-grade gliomas (LGGs) are brain tumors characterized by their slow growth and infiltrative nature. Treatment options for these tumors are surgery, radiation therapy and chemotherapy. The optimal use of radiation therapy and chemotherapy is still under study. In this paper, we construct a mathematical model of LGG response to combinations of chemotherapy, specifically to the alkylating agent temozolomide and radiation therapy. Patient-specific parameters were obtained from longitudinal imaging data of the response of real LGG patients. Computer simulations showed that concurrent cycles of radiation therapy and temozolomide could provide the best therapeutic efficacy in-silico for the patients included in the study. The patient cohort was extended computationally to a set of 3000 virtual patients. This virtual cohort was subject to an in-silico trial in which matching the doses of radiotherapy to those of temozolomide in the first five days of each cycle improved overall survival over concomitant radio-chemotherapy according to RTOG 0424. Thus, the proposed treatment schedule could be investigated in a clinical setting to improve combination treatments in LGGs with substantial survival benefits.

Patient-specific forecasting of postradiotherapy prostate-specific antigen kinetics enables early prediction of biochemical relapse

“…This definition of the survival fraction could be refined by leveraging a radiobiological dose-dependent formulation, for which there exists a rich literature ( Forouzannia et al., 2018 ; Corwin et al., 2013 ; Lima et al., 2017 ; Bodgi et al., 2016 ; Rockne et al., 2015 ; Powathil et al., 2007 , 2013 ; Lewin et al., 2018 ; O’Rourke et al., 2008 ; Kal and Gellekom, 2003 ; Wang and Li, 2005 ). Hence, our modeling framework would enable to investigate alternative radiation plans and systematically select clinically feasible, optimal regimens for individual patients ( Forouzannia et al., 2018 ; Henares-Molina et al., 2017 ; Brüningk et al., 2021 ; Lipková et al., 2019 ; Ayala-Hernández et al., 2021 ). The aforementioned model extensions increase the number of parameters to be identified on a patient-specific basis.…”

“…Several studies have investigated mechanistic models of PCa growth and PSA dynamics in various scenarios, including untreated tumor growth ( Lorenzo et al., 2016 , 2019b ; Swanson et al., 2001 ; Vollmer, 2010 ; Farhat et al., 2017 ), hormone therapy ( Brady-Nicholls et al., 2021 , 2020 ; Ideta et al., 2008 ; Hirata et al., 2010 ; Jain et al., 2011 ; Morken et al., 2014 ; Phan et al., 2019 ; Jackson, 2004 ), cytotoxic and antiangiogenic therapies ( West et al., 2018 , 2019 ; Colli et al., 2020 , 2021 ), and after radical prostatectomy ( Vollmer and Humphrey, 2003 ; Truskinovsky et al., 2005 ). Since radiotherapy is used for the treatment of many types of cancer, the study of tumor response to radiation and the forecasting of patient-specific radiotherapeutic outcomes using mechanistic models constitute a rich area of research ( Corwin et al., 2013 ; Hormuth et al., 2021 ; Rockne et al., 2015 ; Lipková et al., 2019 ; Lima et al., 2017 ; Ayala-Hernández et al., 2021 ; Pérez-García et al., 2015 ; Zahid et al., 2021 ; Alfonso et al., 2021 ; Powathil et al., 2007 ). Nevertheless, there is a dearth of mechanistic models providing a coupled description of tumor and PSA dynamics following radiotherapy ( Lorenzo et al., 2019b ; Sosa-Marrero et al., 2021 ; Yamamoto et al., 2016 ).…”

Patient-specific forecasting of post-radiotherapy prostate-specific antigen kinetics enables early prediction of biochemical relapse

“…This definition of the survival fraction could be refined by leveraging a radiobiological dose-dependent formulation, for which there exists a rich literature [Forouzannia et al, 2018;Corwin et al, 2013;Lima et al, 2017;Bodgi et al, 2016;Rockne et al, 2015;Powathil et al, 2007Powathil et al, , 2013Lewin et al, 2018;O'Rourke et al, 2008;Kal and Gellekom, 2003;Wang and Li, 2005]. Hence, our modeling framework would enable to investigate alternative radiation plans and systematically select clinically-feasible, optimal regimens for individual patients [Forouzannia et al, 2018;Henares-Molina et al, 2017;Brüningk et al, 2021;Lipková et al, 2019;Ayala-Hernández et al, 2021]. The aforementioned model extensions increase the number of parameters to be identified on a patient-specific basis.…”

Data-driven simulation of Fisher-Kolmogorov tumor growth models using Dynamic Mode Decomposition

“…To facilitate the clinical use of this approach, the personalization of the forecasts usually relies on routinely collected patient-specific data (e.g., medical imaging, blood tests, biopsies), which enable to identify the model parameters, construct a virtual anatomic representation of the host organ and tumor, and validate model predictions [38,30,56]. Computational oncology has been experiencing rapid growth and recent efforts have shown promise in predicting pathological and therapeutic outcomes for individual patients suffering from multiple types of cancers, such as brain [25,36,42,6,54], breast [28,51], prostate [15,41,39,11], pancreas [55], and kidney tumors [14]. Ultimately, validated models could also be leveraged to rigorously derive optimal treatment plans for individual patients [15,28,38].…”

“…The mathematical models used to describe cancer phenomena in clinical scenarios usually rely on either ordinary differential equations (ODEs) or partial differential equations (PDEs) to respectively describe the temporal or spatiotemporal mechanisms of cancer development and response to treatments [38,30,48,56,42,29]. ODE models have met a widespread use in computational oncology due to the frequent use of scalar metrics in monitoring cancer patients (e.g., tumor volume, blood biomarkers) and their minimal computational cost, which facilitates parameter estimation, uncertainty quantification, and therapy optimization [11,6,57,40,12]. However, ODE models can only provide a limited representation of the heterogeneous tumor architecture and are not able to capture key spatially-resolved mechanisms, such as cancer cell mobility, tumor-induced mechanical deformation of host tissue, and vascular delivery of cancer therapeutics, as well as tissue-scale aspects of standard clinical interventions, such as radiotherapy and surgery [38,56,28,43,36,51].…”