scholarly journals Effects of G2 checkpoint dynamics on the low-dose hyper-radiosensitivity

2017 ◽  
Author(s):  
Oluwole Olobatuyi ◽  
Gerda de Vries ◽  
Thomas Hillen
Keyword(s):  
Cell Cycle ◽  
Cell Lines ◽  
Low Dose ◽  
Radiation Treatment ◽  
Dose Range ◽  
Experimental Studies ◽  
Quadratic Model ◽  
Linear Quadratic ◽  
G2 Checkpoint ◽  
Linear Quadratic Model

AbstractWe develop and analyze a system of differential equations to investigate the effects of G2 checkpoint dynamics on the low-dose hyper-radiosensitivity. In experimental studies, it has been found that certain cell lines are more sensitive to low-dose radiation than would be expected from the classical Linear Quadratic model (LQ model). In fact, it is frequently observed that cells incur more damage at a low dose (say 0.3 Gy) than at higher dose (say 1 Gy). This effect has been termed hyper-radiosensitivity (HRS). The HRS is followed by a period of relative radioresistance (per unit dose) of cell kill over the dose range of ~ 0.5 - 1 Gy. This latter phenomenon is termed increased radioresistance (IRR). These effects depend on the type of cells and on their phase in the cell cycle. Here we focus on the HRS phenomenon by fitting a model for the cell cycle that includes G2-checkpoint dynamics and radiation treatment to surviving fraction data for different cell lines including glioma cells, prostate cancer cells, as well as to cell populations that are enriched in certain phases of the cell cycle. The HRS effect is measured in the literature through , the ratio of slope αs, of the surviving fraction curve at zero dose to slope α of the corresponding LQ model. We derive an explicit formula for this ratio and we show that it corresponds very closely to experimental observations. Finally, we can identify the dependence of this ratio on the surviving fraction at 2 Gy. It was speculated in the literature that such a relation exists. Our theoretical analysis will help to more systematically identify the HRS in cell lines and opens doors to analyze its use in cancer treatment.PACS and mathematical subject classification numbers as needed.

Evidence for dose and dose rate effects in human and animal radiation studies

2018 ◽  
Vol 47 (3-4) ◽  
pp. 97-112 ◽  
Author(s):  
M.P. Little
Keyword(s):  
Dose Rate ◽  
Dose Response ◽  
Low Dose ◽  
Atomic Bomb ◽  
Dose Range ◽  
Quadratic Model ◽  
Linear Quadratic ◽  
Dose Rates ◽  
Atomic Bomb Survivors ◽  
Linear Quadratic Model

For stochastic effects such as cancer, linear-quadratic models of dose are often used to extrapolate from the experience of the Japanese atomic bomb survivors to estimate risks from low doses and low dose rates. The low dose extrapolation factor (LDEF), which consists of the ratio of the low dose slope (as derived via fitting a linear-quadratic model) to the slope of the straight line fitted to a specific dose range, is used to derive the degree of overestimation (if LDEF > 1) or underestimation (if LDEF < 1) of low dose risk by linear extrapolation from effects at higher doses. Likewise, a dose rate extrapolation factor (DREF) can be defined, consisting of the ratio of the low dose slopes at high and low dose rates. This paper reviews a variety of human and animal data for cancer and non-cancer endpoints to assess evidence for curvature in the dose response (i.e. LDEF) and modifications of the dose response by dose rate (i.e. DREF). The JANUS mouse data imply that LDEF is approximately 0.2–0.8 and DREF is approximately 1.2–2.3 for many tumours following gamma exposure, with corresponding figures of approximately 0.1–0.9 and 0.0–0.2 following neutron exposure. This paper also cursorily reviews human data which allow direct estimates of low dose and low dose rate risk.

scholarly journals Mechanistic Modelling of Radiation Responses

Cancers ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 205 ◽  
Author(s):  
Stephen J McMahon ◽  
Kevin M Prise
Keyword(s):  
Clinical Practice ◽  
Cell Lines ◽  
Radiation Biology ◽  
Quadratic Model ◽  
Clinical Use ◽  
Linear Quadratic ◽  
Mechanistic Modelling ◽  
Linear Quadratic Model ◽  
Physical Chemical ◽  
Modelling Approaches

Radiobiological modelling has been a key part of radiation biology and therapy for many decades, and many aspects of clinical practice are guided by tools such as the linear-quadratic model. However, most of the models in regular clinical use are abstract and empirical, and do not provide significant scope for mechanistic interpretation or making predictions in novel cell lines or therapies. In this review, we will discuss the key areas of ongoing mechanistic research in radiation biology, including physical, chemical, and biological steps, and review a range of mechanistic modelling approaches which are being applied in each area, highlighting the possible opportunities and challenges presented by these techniques.

scholarly journals Resistance of Hypoxic Cells to Ionizing Radiation Is Mediated in Part via Hypoxia-Induced Quiescence

Cells ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 610
Author(s):  
Apostolos Menegakis ◽  
Rob Klompmaker ◽  
Claire Vennin ◽  
Aina Arbusà ◽  
Maartje Damen ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cell Lines ◽  
Cancer Cell ◽  
Radiation Treatment ◽  
Large Fraction ◽  
Radiation Sensitivity ◽  
Cancer Cell Lines ◽  
Cycle Phase ◽  
Multicellular Spheroids ◽  
Phase Duration

Double strand breaks (DSBs) are highly toxic to a cell, a property that is exploited in radiation therapy. A critical component for the damage induction is cellular oxygen, making hypoxic tumor areas refractory to the efficacy of radiation treatment. During a fractionated radiation regimen, these hypoxic areas can be re-oxygenated. Nonetheless, hypoxia still constitutes a negative prognostic factor for the patient’s outcome. We hypothesized that this might be attributed to specific hypoxia-induced cellular traits that are maintained upon reoxygenation. Here, we show that reoxygenation of hypoxic non-transformed RPE-1 cells fully restored induction of DSBs but the cells remain radioresistant as a consequence of hypoxia-induced quiescence. With the use of the cell cycle indicators (FUCCI), cell cycle-specific radiation sensitivity, the cell cycle phase duration with live cell imaging, and single cell tracing were assessed. We observed that RPE-1 cells experience a longer G1 phase under hypoxia and retain a large fraction of cells that are non-cycling. Expression of HPV oncoprotein E7 prevents hypoxia-induced quiescence and abolishes the radioprotective effect. In line with this, HPV-negative cancer cell lines retain radioresistance, while HPV-positive cancer cell lines are radiosensitized upon reoxygenation. Quiescence induction in hypoxia and its HPV-driven prevention was observed in 3D multicellular spheroids. Collectively, we identify a new hypoxia-dependent radioprotective phenotype due to hypoxia-induced quiescence that accounts for a global decrease in radiosensitivity that can be retained upon reoxygenation and is absent in cells expressing oncoprotein E7.

scholarly journals Simulating Radiotherapy Effect in High-Grade Glioma by Using Diffusive Modeling and Brain Atlases

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Alexandros Roniotis ◽  
Kostas Marias ◽  
Vangelis Sakkalis ◽  
Georgios C. Manikis ◽  
Michalis Zervakis
Keyword(s):  
High Grade Glioma ◽  
Quadratic Model ◽  
Normal Brain ◽  
Linear Quadratic ◽  
Linear Quadratic Model ◽  
Diffusive Model ◽  
Glioma Model ◽  
Modern Application ◽  
Diffusion Tensors ◽  
Brain Atlases

Applying diffusive models for simulating the spatiotemporal change of concentration of tumour cells is a modern application of predictive oncology. Diffusive models are used for modelling glioblastoma, the most aggressive type of glioma. This paper presents the results of applying a linear quadratic model for simulating the effects of radiotherapy on an advanced diffusive glioma model. This diffusive model takes into consideration the heterogeneous velocity of glioma in gray and white matter and the anisotropic migration of tumor cells, which is facilitated along white fibers. This work uses normal brain atlases for extracting the proportions of white and gray matter and the diffusion tensors used for anisotropy. The paper also presents the results of applying this glioma model on real clinical datasets.

scholarly journals Gene expression signatures but not cell cycle checkpoint functions distinguish AT carriers from normal individuals

2013 ◽  
Vol 45 (19) ◽  
pp. 907-916
Author(s):  
Liwen Zhang ◽  
Dennis A. Simpson ◽  
Cynthia L. Innes ◽  
Jeff Chou ◽  
Pierre R. Bushel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Cell Lines ◽  
Cell Cycle Checkpoint ◽  
G2 Checkpoint ◽  
P53 Signaling ◽  
Normal Individuals ◽  
Gene Expression Signatures ◽  
Cycle Checkpoint ◽  
Atm Cell

Ataxia telangiectasia (AT) is a rare autosomal recessive disease caused by mutations in the ataxia telangiectasia-mutated gene ( ATM). AT carriers with one mutant ATM allele are usually not severely affected although they carry an increased risk of developing cancer. There has not been an easy and reliable diagnostic method to identify AT carriers. Cell cycle checkpoint functions upon ionizing radiation (IR)-induced DNA damage and gene expression signatures were analyzed in the current study to test for differential responses in human lymphoblastoid cell lines with different ATM genotypes. While both dose- and time-dependent G1 and G2 checkpoint functions were highly attenuated in ATM−/− cell lines, these functions were preserved in ATM+/− cell lines equivalent to ATM+/+ cell lines. However, gene expression signatures at both baseline (consisting of 203 probes) and post-IR treatment (consisting of 126 probes) were able to distinguish ATM+/− cell lines from ATM+/+ and ATM−/− cell lines. Gene ontology (GO) and pathway analysis of the genes in the baseline signature indicate that ATM function-related categories, DNA metabolism, cell cycle, cell death control, and the p53 signaling pathway, were overrepresented. The same analyses of the genes in the IR-responsive signature revealed that biological categories including response to DNA damage stimulus, p53 signaling, and cell cycle pathways were overrepresented, which again confirmed involvement of ATM functions. The results indicate that AT carriers who have unaffected G1 and G2 checkpoint functions can be distinguished from normal individuals and AT patients by expression signatures of genes related to ATM functions.

scholarly journals A Novel Analysis Method for Evaluating the Interplay of Oxygen and Ionizing Radiation at the Gene Level

2021 ◽  
Vol 12 ◽  
Author(s):  
Jeannette Jansen ◽  
Patricia Vieten ◽  
Francesca Pagliari ◽  
Rachel Hanley ◽  
Maria Grazia Marafioti ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cell Line ◽  
Cell Lines ◽  
Radiation Treatment ◽  
Hierarchical Cluster ◽  
Specific Gene ◽  
Analysis Method ◽  
Whole Genome Analysis ◽  
Gene Level ◽  
The Impact

Whilst the impact of hypoxia and ionizing radiations on gene expression is well-understood, the interplay of these two effects is not. To better investigate this aspect at the gene level human bladder, brain, lung and prostate cancer cell lines were irradiated with photons (6 Gy, 6 MV LINAC) in hypoxic and normoxic conditions and prepared for the whole genome analysis at 72 h post-irradiation. The analysis was performed on the obtained 20,000 genes per cell line using PCA and hierarchical cluster algorithms to extract the most dominant genes altered by radiation and hypoxia. With the help of the introduced novel radiation-in-hypoxia and oxygen-impact profiles, it was possible to overcome cell line specific gene regulation patterns. Based on that, 37 genes were found to be consistently regulated over all studied cell lines. All DNA-repair related genes were down-regulated after irradiation, independently of the oxygen state. Cell cycle-dependent genes showed up-regulation consistent with an observed change in cell population in the S and G2/M phases of the cell cycle after irradiation. Genes behaving oppositely in their regulation behavior when changing the oxygen concentration and being irradiated, were immunoresponse and inflammation related genes. The novel analysis method, and by consequence, the results presented here have shown how it is important to consider the two effects together (oxygen and radiation) when analyzing gene response upon cancer radiation treatment. This approach might help to unrevel new gene patterns responsible for cancer radioresistance in patients.

Sign in / Sign up

Export Citation Format

Share Document