Mathematical Modeling of the Effects of Tumor Heterogeneity on the Efficiency of Radiation Treatment Schedule

Forouzannia, Farinaz; Enderling, Heiko; Kohandel, Mohammad

doi:10.1007/s11538-017-0371-5

Cited by 15 publications

(23 citation statements)

References 23 publications

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“…These studies suggest that PDT is most likely successful in tumors with high surface-to-volume ratios, and that PDT is unlikely to provide control in fast proliferating deep tumor tissues, which supports previous model results for low-penetrating red-light PDT [86]. As PDT and radiation therapy share similar biological responses and routine involvement of medical physics, modeling approaches for radiotherapy response may be readily translatable to identify optimal PDT protocols [59, 61, 63, 87–93]. In this Perspective, we suggest integrated mathematical oncology as a computational platform for developing quantitative models and simulations of PDT dosimetry to optimize local tumor control, tumor-focused drug release, spatiotemporal dynamics, and photodynamic priming of systemic modes of therapy.…”

Section: Integrated Mathematical Oncologysupporting

confidence: 73%

Illuminating the Numbers: Integrating Mathematical Models to Optimize Photomedicine Dosimetry and Combination Therapies

et al. 2019

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Cancer photomedicine offers unique mechanisms for inducing local tumor damage with the potential to stimulate local and systemic anti-tumor immunity. Optically-active nanomedicine offers these features as well as spatiotemporal control of tumor-focused drug release to realize synergistic combination therapies. Achieving quantitative dosimetry is a major challenge, and dosimetry is fundamental to photomedicine for personalizing and tailoring therapeutic regimens to specific patients and anatomical locations. The challenge of dosimetry is perhaps greater for photomedicine than many standard therapies given the complexity of light delivery and light-tissue interactions as well as the resulting photochemistry responsible for tumor damage and drug-release, in addition to the usual intricacies of therapeutic agent delivery. An emerging multidisciplinary approach in oncology utilizes mathematical and computational models to iteratively and quantitively analyze complex dosimetry, and biological response parameters. These models are parameterized by preclinical and clinical observations and then tested against previously unseen data. Such calibrated and validated models can be deployed to simulate treatment doses, protocols, and combinations that have not yet been experimentally or clinically evaluated and can provide testable optimal treatment outcomes in a practical workflow. Here, we foresee the utility of these computational approaches to guide adaptive therapy, and how mathematical models might be further developed and integrated as a novel methodology to guide precision photomedicine.

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Section: Integrated Mathematical Oncologysupporting

confidence: 73%

Illuminating the Numbers: Integrating Mathematical Models to Optimize Photomedicine Dosimetry and Combination Therapies

et al. 2019

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“…These models neglect tissue spatial structure and focus on the dynamics of the involved cell populations. They can address a variety of phenomena such as tumor clonal heterogeneity [20–22], tumor-host cell interactions [23–25] and response to therapy [26–29].…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study

et al. 2019

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Background Contemporary biological observations have revealed a large variety of mechanisms acting during the expansion of a tumor. However, there are still many qualitative and quantitative aspects of the phenomenon that remain largely unknown. In this context, mathematical and computational modeling appears as an invaluable tool providing the means for conducting in silico experiments, which are cheaper and less tedious than real laboratory experiments. Results This paper aims at developing an extensible and computationally efficient framework for in silico modeling of tumor growth in a 3-dimensional, inhomogeneous and time-varying chemical environment. The resulting model consists of a set of mathematically derived and algorithmically defined operators, each one addressing the effects of a particular biological mechanism on the state of the system. These operators may be extended or re-adjusted, in case a different set of starting assumptions or a different simulation scenario needs to be considered. Conclusion In silico modeling provides an alternative means for testing hypotheses and simulating scenarios for which exact biological knowledge remains elusive. However, finer tuning of pertinent methods presupposes qualitative and quantitative enrichment of available biological evidence. Validation in a strict sense would further require comprehensive, case-specific simulations and detailed comparisons with biomedical observations.

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“…Cooke et al demonstrated that intratumor heterogeneity was associated with poor chemoradiotherapy response in cervical cancer 4 . Also, tumor heterogeneity could result in radio‐resistance of patients 5 …”

Section: Introductionmentioning

confidence: 99%

“…4 Also, tumor heterogeneity could result in radio-resistance of patients. 5 Intratumor heterogeneity leads to startling differences in many morphological and physiological features, such as cell proliferative and angiogenic potential. 6 Tumor heterogeneity refers to diversity of tumor cells, requiring detection methods in single-cell level.…”

Section: Introductionmentioning

confidence: 99%

Cellular heterogeneity landscape in laryngeal squamous cell carcinoma

Song

Zhang

et al. 2020

Intl Journal of Cancer

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Laryngeal squamous cell carcinoma (LSCC) is a highly malignant tumor originated from respiratory system. Although there have been many improvements in therapy until now, reducing the high mortality remains difficult. Understanding the cellular heterogeneity of LSCC could contribute to improve this problem. Single-cell RNA sequencing was applied to dissect the cell composition and molecular characteristics of LSCC tissues. Immunohistochemistry staining of the LSCC tissues was performed to identify the spatial location of tumor cells. Survival analysis of marker genes was executed in The Cancer Genome Atlas to verify the correlation between each cell clusters and patients' prognosis. The LSCC tissue cells were finely grouped into various clusters, including tumor cells, immune cells, epithelial cells, fibroblasts and endothelial cells. Notably, in tumor cells, keratinocyte-like cells were in the core of tumor while malignant proliferating cells were located at the tumor edge. The malignant proliferating cells were correlated with poor prognosis. In summary, this is the first study to delineate a landscape of the LSCC intratumor heterogeneity. Our work might help researchers have a better understanding for tumor progression.

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Mathematical Modeling of the Effects of Tumor Heterogeneity on the Efficiency of Radiation Treatment Schedule

Cited by 15 publications

References 23 publications

Illuminating the Numbers: Integrating Mathematical Models to Optimize Photomedicine Dosimetry and Combination Therapies

Illuminating the Numbers: Integrating Mathematical Models to Optimize Photomedicine Dosimetry and Combination Therapies

Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study

Cellular heterogeneity landscape in laryngeal squamous cell carcinoma

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