A Century of Fractionated Radiotherapy: How Mathematical Oncology Can Break the Rules

Ghaderi, Nima; Jung, Joseph H.; Brüningk, Sarah C.; Subramanian, Ajay; Nassour, Lauren; Peacock, Jeffrey

doi:10.3390/ijms23031316

Cited by 11 publications

(9 citation statements)

References 84 publications

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“…However, to our best knowledge, there are very little preclinical experimental data on in vitro radiobiology of hypofractionation for any type of tumor, including our recent research on triple-negative breast cancer cell-line model ( 16 ). The bulk research on this subject deals with modelling radiobiological effects ( 37 , 38 ). Direct comparison of fractionation schedules for NSCLC cell models in radiobiological approach in two recent studies reflects the clinical condition with some early benefits of hypofractionation in regard to a less-aggressive growth pattern ( 39 , 40 ).…”

Section: Discussionmentioning

confidence: 99%

Efficiency of moderately hypofractionated radiotherapy in NSCLC cell model

Lüdeking,

Stemwedel,

Ramachandran

et al. 2024

Front. Oncol.

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BackgroundThe current standard of radiotherapy for inoperable locally advanced NSCLCs with single fraction doses of 2.0 Gy, results in poor outcomes. Several fractionation schedules have been explored that developed over the past decades to increasingly more hypofractionated treatments. Moderate hypofractionated radiotherapy, as an alternative treatment, has gained clinical importance due to shorter duration and higher patient convenience. However, clinical trials show controversial results, adding to the need for pre-clinical radiobiological studies of this schedule.MethodsWe examined in comparative analysis the efficiency of moderate hypofractionation and normofractionation in four different NSCLC cell lines and fibroblasts using several molecular-biological approaches. Cells were daily irradiated with 24x2.75 Gy (moderate hypofractionation) or with 30x2 Gy (normofractionation), imitating the clinical situation. Proliferation and growth rate via direct counting of cell numbers, MTT assay and measurements of DNA-synthesizing cells (EdU assay), DNA repair efficiency via immunocytochemical staining of residual γH2AX/53BP1 foci and cell surviving via clonogenic assay (CSA) were experimentally evaluated.ResultsOverall, the four tumor cell lines and fibroblasts showed different sensitivity to both radiation regimes, indicating cell specificity of the effect. The absolute cell numbers and the CSA revealed significant differences between schedules (P < 0.0001 for all employed cell lines and both assays) with a stronger effect of moderate hypofractionation.ConclusionOur results provide evidence for the similar effectiveness and toxicity of both regimes, with some favorable evidence towards a moderate hypofractionation. This indicates that increasing the dose per fraction may improve patient survival and therapy outcomes.

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Section: Discussionmentioning

confidence: 99%

Efficiency of moderately hypofractionated radiotherapy in NSCLC cell model

Lüdeking,

Stemwedel,

Ramachandran

et al. 2024

Front. Oncol.

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“…To our best knowledge, there are fewer studies providing some preclinical investigations on the in vitro radiobiological comparison of hypofractionation and conventional fractionation for any tumor type, mimicking the clinical situation. Most research studies in this field deal either with clinical trials and meta-analysis ( 14 , 26 – 32 ) or with modeling radiobiological effects ( 33 , 34 ). Regarding biological effects of exposure to ionizing radiation, most studies either utilize a single-irradiation dose or focus on fractionated irradiation, applying more fractions over a short time period, to establish surviving/resistant cell lines ( 35 ).…”

Section: Discussionmentioning

confidence: 99%

Effectiveness of hypofractionated and normofractionated radiotherapy in a triple‐negative breast cancer model

et al. 2022

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Breast cancer (BC) is one of the most diagnosed malignant carcinomas in women with a triple-negative breast cancer (TNBC) phenotype being correlated with poorer prognosis. Fractionated radiotherapy (RT) is a central component of breast cancer management, especially after breast conserving surgery and is increasingly important for TNBC subtype prognosis. In recent years, moderately hypofractionated radiation schedules are established as a standard of care, but many professionals remain skeptical and are concerned about their efficiency and side effects. In the present study, two different triple-negative breast cancer cell lines, a non-malignant breast epithelial cell line and fibroblasts, were irradiated daily under normofractionated and hypofractionated schedules to evaluate the impact of different irradiation regimens on radiation-induced cell-biological effects. During the series of radiotherapy, proliferation, growth rate, double-strand DNA break-repair (DDR), cellular senescence, and cell survival were measured. Investigated normal and cancer cells differed in their responses and receptivity to different irradiation regimens, indicating cell line/cell type specificity of the effect. At the end of both therapy concepts, normal and malignant cells reach almost the same endpoint of cell count and proliferation inhibition, confirming the clinical observations in the follow-up at the cellular level. These result in cell lines closely replicating the irradiation schedules in clinical practice and, to some extent, contributing to the understanding of growth rate or remission of tumors and the development of fibrosis.

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“…Knowledge-driven modeling has successfully been applied to investigate different aspects of cancer including somatic cancer evolution and treatment. We refer the interested reader to recent review articles ( 18 , 19 ) covering for instance different fractionation schemes for radiotherapy ( 20 , 21 ), the onset and influence of treatment-induced tumor resistance ( 22 ), or cancer evolution ( 23 ). A popular application of knowledge-driven models is the simulation of in silico trials for hypothesis generation in simulated cohorts ( 24 – 26 ).…”

Section: Contrasting “Knowledge-driven” and “Data-driven” Modeling”mentioning

confidence: 99%

A review of mechanistic learning in mathematical oncology

Metzcar,

Jutzeler,

Macklin

et al. 2024

Front. Immunol.

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Mechanistic learning refers to the synergistic combination of mechanistic mathematical modeling and data-driven machine or deep learning. This emerging field finds increasing applications in (mathematical) oncology. This review aims to capture the current state of the field and provides a perspective on how mechanistic learning may progress in the oncology domain. We highlight the synergistic potential of mechanistic learning and point out similarities and differences between purely data-driven and mechanistic approaches concerning model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Four categories of mechanistic learning (sequential, parallel, extrinsic, intrinsic) of mechanistic learning are presented with specific examples. We discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. Example applications address complex problems predominantly from the domain of oncology research such as longitudinal tumor response predictions or time-to-event modeling. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input data which are embraced in a mechanistic learning framework

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A Century of Fractionated Radiotherapy: How Mathematical Oncology Can Break the Rules

Cited by 11 publications

References 84 publications

Efficiency of moderately hypofractionated radiotherapy in NSCLC cell model

Efficiency of moderately hypofractionated radiotherapy in NSCLC cell model

Effectiveness of hypofractionated and normofractionated radiotherapy in a triple‐negative breast cancer model

A review of mechanistic learning in mathematical oncology

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