1
|
Cordoni FG. On the Emergence of the Deviation from a Poisson Law in Stochastic Mathematical Models for Radiation-Induced DNA Damage: A System Size Expansion. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1322. [PMID: 37761621 PMCID: PMC10529388 DOI: 10.3390/e25091322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/02/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023]
Abstract
In this paper, we study the system size expansion of a stochastic model for radiation-induced DNA damage kinetics and repair. In particular, we characterize both the macroscopic deterministic limit and the fluctuation around it. We further show that such fluctuations are Gaussian-distributed. In deriving such results, we provide further insights into the relationship between stochastic and deterministic mathematical models for radiation-induced DNA damage repair. Specifically, we demonstrate how the governing deterministic equations commonly employed in the field arise naturally within the stochastic framework as a macroscopic limit. Additionally, by examining the fluctuations around this macroscopic limit, we uncover deviations from a Poissonian behavior driven by interactions and clustering among DNA damages. Although such behaviors have been empirically observed, our derived results represent the first rigorous derivation that incorporates these deviations from a Poissonian distribution within a mathematical model, eliminating the need for specific ad hoc corrections.
Collapse
Affiliation(s)
- Francesco Giuseppe Cordoni
- Department of Civil, Environmental and Mechanical Engineering, University of Trento, 38123 Trento, Italy
| |
Collapse
|
2
|
Vassiliev ON. Accumulation of sublethal radiation damage and its effect on cell survival. Phys Med Biol 2023; 68:015004. [PMID: 36533628 PMCID: PMC9855632 DOI: 10.1088/1361-6560/aca5e7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 11/24/2022] [Indexed: 11/25/2022]
Abstract
Objective.Determine the extent of sublethal radiation damage (SRD) in a cell population that received a given dose of radiation and the impact of this damage on cell survival.Approach.We developed a novel formalism to account for accumulation of SRD with increasing dose. It is based on a very general formula for cell survival that correctly predicts the basic properties of cell survival curves, such as the transition from the linear-quadratic to a linear dependence at high doses. Using this formalism we analyzed extensive experimental data for photons, protons and heavy ions to evaluate model parameters, quantify the extent of SRD and its impact on cell survival.Main results.Significant accumulation of SRD begins at doses below 1 Gy. As dose increases, so does the number of damaged cells and the amount of SRD in individual cells. SRD buildup in a cell increases the likelihood of complex irrepairable damage. For this reason, during a dose fraction delivery, each dose increment makes cells more radiosensitive. This gradual radosensitization is evidenced by the increasing slope of survival curves observed experimentally. It continues until the fraction is delivered, unless radiosensitivity reaches its maximum first. The maximum radiosensitivity is achieved when SRD accumulated in most cells is the maximum damage they can repair. After this maximum is reached, the slope of a survival curve, logarithm of survival versus dose, becomes constant, dose independent. The survival curve becomes a straight line, as experimental data at high doses show. These processes are random. They cause large cell-to-cell variability in the extent of damage and radiosensitivity of individual cells.Significance.SRD is in effect a radiosensitizer and its accumulation is a significant factor affecting cell survival, especially at high doses. We developed a novel formalism to study this phenomena and reported pertinent data for several particle types.
Collapse
Affiliation(s)
- Oleg N Vassiliev
- Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, TX 77030, United States of America
| |
Collapse
|
3
|
Cordoni FG, Missiaggia M, La Tessa C, Scifoni E. Multiple levels of stochasticity accounted for in different radiation biophysical models: from physics to biology. Int J Radiat Biol 2022; 99:807-822. [PMID: 36448923 DOI: 10.1080/09553002.2023.2146230] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
PURPOSE In the present paper we investigate how some stochastic effects are included in a class of radiobiological models with particular emphasis on how such randomnesses reflect into the predicted cell survival curve. MATERIALS AND METHODS We consider four different models, namely the Generalized Stochastic Microdosimetric Model GSM2, in its original full form, the Dirac GSM2 the Poisson GSM2 and the Repair-Misrepair Model (RMR). While GSM2 and the RMR models are known in literature, the Dirac and the Poisson GSM2 have been newly introduced in this work. We further numerically investigate via Monte Carlo simulation of four different particle beams, how the proposed stochastic approximations reflect into the predicted survival curves. To achieve these results, we consider different ion species at energies of interest for therapeutic applications, also including a mixed field scenario. RESULTS We show how the Dirac GSM2, the Poisson GSM2 and the RMR can be obtained from the GSM2 under suitable approximations on the stochasticity considered. We analytically derive the cell survival curve predicted by the four models, characterizing rigorously the high and low dose limits. We further study how the theoretical findings emerge also using Monte Carlo numerical simulations. CONCLUSIONS We show how different models include different levels of stochasticity in the description of cellular response to radiation. This translates into different cell survival predictions depending on the radiation quality.
Collapse
Affiliation(s)
- Francesco G. Cordoni
- Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy
- TIFPA-INFN, Trento, Italy
| | - Marta Missiaggia
- TIFPA-INFN, Trento, Italy
- Department of Physics, University of Trento, Trento, Italy
- Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Rome, Italy
| | - Chiara La Tessa
- TIFPA-INFN, Trento, Italy
- Department of Physics, University of Trento, Trento, Italy
| | | |
Collapse
|
4
|
Cordoni FG, Missiaggia M, Scifoni E, Tessa CL. Cell Survival Computation via the Generalized Stochastic Microdosimetric Model (GSM2); Part I: The Theoretical Framework. Radiat Res 2021; 197:218-232. [PMID: 34855935 DOI: 10.1667/rade-21-00098.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 10/01/2021] [Indexed: 11/03/2022]
Abstract
The current article presents the first application of the Generalized Stochastic Microdosimetric Model (GSM2) for computing explicitly a cell survival curve. GSM2 is a general probabilistic model that predicts the kinetic evolution of DNA damages taking full advantage of a microdosimetric description of a radiation energy deposition. We show that, despite the high generality and flexibility of GSM2, an explicit form for the survival fraction curve predicted by the GSM2 is achievable. We illustrate how several correction terms typically added a posteriori in existing radiobiological models to improve the prediction accuracy, are naturally included into GSM2. Among the most relevant features of the survival curve derived from GSM2 and presented in this article, is the linear-quadratic behavior at low doses and a purely linear trend for high doses. The study also identifies and discusses the connections between GSM2 and existing cell survival models, such as the Microdosimetric Kinetic Model (MKM) and the Multi-hit model. Several approximations to predict cell survival in different irradiation regimes are also introduced to include intercellular non-Poissonian behaviors.
Collapse
Affiliation(s)
- Francesco G Cordoni
- University of Verona, Department of Computer Science, 37134 Verona, Italy.,Trento Institute for Fundamental Physics and Applications (TIFPA), 38123 Povo, Trento, Italy
| | - Marta Missiaggia
- Trento Institute for Fundamental Physics and Applications (TIFPA), 38123 Povo, Trento, Italy.,University of Trento, Department of Physics, 38123 Povo, Trento, Italy
| | - Emanuele Scifoni
- Trento Institute for Fundamental Physics and Applications (TIFPA), 38123 Povo, Trento, Italy
| | - Chiara La Tessa
- Trento Institute for Fundamental Physics and Applications (TIFPA), 38123 Povo, Trento, Italy.,University of Trento, Department of Physics, 38123 Povo, Trento, Italy
| |
Collapse
|
5
|
Vassiliev ON. On calculation of the average linear energy transfer for radiobiological modelling. Biomed Phys Eng Express 2021; 7. [PMID: 33692907 DOI: 10.1088/2057-1976/abc967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Applying the concept of linear energy transfer (LET) to modeling of biological effects of charged particles usually involves calculation of the average LET. To calculate this, the energy distribution of particles is characterized by either the source spectrum or fluence spectrum. Also, the average can be frequency-or dose-weighted. This makes four methods of calculating the average LET, each producing a different number. The purpose of this note is to describe which of these four methods is best suited for radiobiological modelling. We focused on data for photons (x-rays and gamma radiation) because in this case differences in the four averaging methods are most pronounced. However, our conclusions are equally applicable to photons and hadrons. We based our arguments on recently emerged Monte Carlo data that fully account for transport of electrons down to very low energies comparable to the ionization potential of water. We concluded that the frequency average LET calculated using the fluence spectrum has better predictive power than does that calculated using any of the other three options. This optimal method is not new but is different from those currently dominating research in this area.
Collapse
Affiliation(s)
- Oleg N Vassiliev
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America
| |
Collapse
|
6
|
Holistic View on Cell Survival and DNA Damage: How Model-Based Data Analysis Supports Exploration of Dynamics in Biological Systems. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:5972594. [PMID: 32695215 PMCID: PMC7361897 DOI: 10.1155/2020/5972594] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 04/10/2020] [Accepted: 05/21/2020] [Indexed: 11/18/2022]
Abstract
In this work, a method is established to calibrate a model that describes the basic dynamics of DNA damage and repair. The model can be used to extend planning for radiotherapy and hyperthermia in order to include the biological effects. In contrast to “syntactic” models (e.g., describing molecular kinetics), the model used here describes radiobiological semantics, resulting in a more powerful model but also in a far more challenging calibration. Model calibration is attempted from clonogenic assay data (doses of 0–6 Gy) and from time-resolved comet assay data obtained within 6 h after irradiation with 6 Gy. It is demonstrated that either of those two sources of information alone is insufficient for successful model calibration, and that both sources of information combined in a holistic approach are necessary to find viable model parameters. Approximate Bayesian computation (ABC) with simulated annealing is used for parameter search, revealing two aspects that are beneficial to resolving the calibration problem: (1) assessing posterior parameter distributions instead of point-estimates and (2) combining calibration runs from different assays by joining posterior distributions instead of running a single calibration run with a combined, computationally very expensive objective function.
Collapse
|
7
|
Vassiliev ON, Peterson CB, Grosshans DR, Mohan R. A simple model for calculating relative biological effectiveness of X-rays and gamma radiation in cell survival. Br J Radiol 2020; 93:20190949. [PMID: 32464080 DOI: 10.1259/bjr.20190949] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES The relative biological effectiveness (RBE) of X-rays and γ radiation increases substantially with decreasing beam energy. This trend affects the efficacy of medical applications of this type of radiation. This study was designed to develop a model based on a survey of experimental data that can reliably predict this trend. METHODS In our model, parameters α and β of a cell survival curve are simple functions of the frequency-average linear energy transfer (LF) of delta electrons. The choice of these functions was guided by a microdosimetry-based model. We calculated LF by using an innovative algorithm in which LF is associated with only those electrons that reach a sensitive-to-radiation volume (SV) within the cell. We determined model parameters by fitting the model to 139 measured (α,β) pairs. RESULTS We tested nine versions of the model. The best agreement was achieved with [Formula: see text] and β being linear functions of [Formula: see text] .The estimated SV diameter was 0.1-1 µm. We also found that α, β, and the α/β ratio increased with increasing [Formula: see text] . CONCLUSIONS By combining an innovative method for calculating [Formula: see text] with a microdosimetric model, we developed a model that is consistent with extensive experimental data involving photon energies from 0.27 keV to 1.25 MeV. ADVANCES IN KNOWLEDGE We have developed a photon RBE model applicable to an energy range from ultra-soft X-rays to megaelectron volt γ radiation, including high-dose levels where the RBE cannot be calculated as the ratio of α values. In this model, the ionization density represented by [Formula: see text] determines the RBE for a given photon spectrum.
Collapse
Affiliation(s)
- Oleg N Vassiliev
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Christine B Peterson
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - David R Grosshans
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Radhe Mohan
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| |
Collapse
|
8
|
Zhao L, Chen X, Tian J, Shang Y, Mi D, Sun Y. Generalized Multi-Hit Model of Radiation-Induced Cell Survival with a Closed-Form Solution: An Alternative Method for Determining Isoeffect Doses in Practical Radiotherapy. Radiat Res 2020; 193:359-371. [PMID: 32031917 DOI: 10.1667/rr15505.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
The standard linear-quadratic (LQ) model is currently the preferred model for describing the ionizing radiation-induced cell survival curves and tissue responses. And the LQ model is also widely used to calculate isoeffect doses for comparing different fractionated schemes in clinical radiotherapy. Despite its ubiquity, because the actual dose-response curve may appear linear at high doses in the semilogarithmic plot, the application of the LQ model is greatly challenged in the high-dose region, while the dose employed in stereotactic body radiotherapy (SBRT) is often in this area. Alternatively, the biophysical models of radiation-induced effects with a linear-quadratic-linear (LQL) characteristic can well fit the dose-survival curve of cells in vitro. However, most of these LQL models are phenomenological and have not fully considered the biophysical mechanism of radiation-induced damage and repair, and the fitting quality decreases in some high-dose ranges. In this work, to provide an alternative model to describe the cell survival curves in high-dose ranges and predict the biologically effective dose (BED) for SBRT, we propose a novel generalized multi-hit model with a closed-form solution by considering an upper bound on the number of lethal damages induced by radiation that can be repaired in a cell. This model has a clear biophysical basis and a simple expression, and also has the LQL characteristic under low- and high-dose approximate conditions. The experimental data fitting indicated that compared to the standard LQ model and our previously generalized target model, the current model can better fit the radiation-induced cell survival curves in the high-dose ranges (P < 0.05). The current model parameters and parameter ratios were determined from the fits in different kinds of cell lines irradiated with various dose rates and linear energy transfer (LET), which indicates that the model parameters significantly depend on the dose rate and LET. Based on the current model, we derived two equivalence formulae for the BED calculations in the low- and high-dose ranges, and then calculated the BED for the clinical data of SBRT from 17 selected studies. The correlation analysis showed that there were significant linear correlations between the BED at isocenter and planning target volume (PTV) edge calculated by this model and the LQ model (R > 0.86, P < 0.001). In conclusion, the generalized multi-hit model proposed in this work can be used as an alternative tool to handle in vitro radiation-induced cell survival curves in high-dose ranges, and calculate the in vivo BED for comparing the dose fractionation schemes in clinical radiotherapy.
Collapse
Affiliation(s)
- Lei Zhao
- Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University, Dalian, Liaoning, China
| | - Xinpeng Chen
- Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University, Dalian, Liaoning, China
| | - Jiahuan Tian
- College of Science, Dalian Maritime University, Dalian, Liaoning, China
| | - Yuxuan Shang
- Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University, Dalian, Liaoning, China
| | - Dong Mi
- College of Science, Dalian Maritime University, Dalian, Liaoning, China
| | - Yeqing Sun
- Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University, Dalian, Liaoning, China
| |
Collapse
|
9
|
Vassiliev ON, Peterson CB, Cao W, Grosshans DR, Mohan R. Systematic microdosimetric data for protons of therapeutic energies calculated with Geant4-DNA. Phys Med Biol 2019; 64:215018. [PMID: 31553958 PMCID: PMC7232815 DOI: 10.1088/1361-6560/ab47cc] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The purpose of this study was to generate physical data needed for microdosimetry-based models of proton RBE. Our focus was on the frequency and dose average lineal energies, y F and y D . We report data for proton energies from 0.1 to 100 MeV, for spherical volumes 2-103 nm in diameter. These data were calculated using Geant4-DNA Monte Carlo software. The physics implemented in Geant4-DNA has been extensively tested for this type of calculations but data on y F and y D for protons generated with this code have been very limited. An innovative aspect of our study is that we introduced a straightforward procedure for calculation of y F and y D for polyenergetic beams and presented the data in a format that simplifies these calculations. We compared our data with previous studies that used different Monte Carlo codes and with experimental data.
Collapse
Affiliation(s)
- Oleg N Vassiliev
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America
| | - Christine B Peterson
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America
| | - Wenhua Cao
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America
| | - David R Grosshans
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America
| | - Radhe Mohan
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America
| |
Collapse
|
10
|
Villegas F, Tilly N, Ahnesjö A. Target Size Variation in Microdosimetric Distributions and its Impact on the Linear-Quadratic Parameterization of Cell Survival. Radiat Res 2018; 190:504-512. [PMID: 30106343 DOI: 10.1667/rr15089.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
The linear-quadratic (LQ) parameterization of survival fraction [SF( D)] inherently assumes that all cells in a population receive the same dose ( D), albeit the distribution of specific energy z over the individual cells f( z, D) can be very wide. From these microdosimetric distributions, which are target size dependent, we estimate the size of the cellular sensitive volume by analyzing its influence on the LQ parameterization of cell survival. A Monte Carlo track structure code was used to simulate detailed tracks from a 60Co source as well as proton and carbon ions of various energies. From these tracks, f( z, D) distributions were calculated for spherical targets with diameters ranging from 10 nm to 12 μm. A cell survival function based on f( z, D) was fitted to experimental LQ α values, revealing an intrinsic limitation that target size imposes on the usage of f( z, D) to describe the linear term of the LQ parameterization. The results indicate that such threshold volume arises naturally from the relationship between the particle's probability of no-hit and the probability of cell survival. Further analysis led to the proposal of a radiobiological property [Formula: see text], defined as the mean lineal energy corresponding to the target size that allows equivalence between the mean inactivation dose (MID) and the mean specific energy [Formula: see text]. The fact that [Formula: see text] is an increasing continuous function of target size within the range of biological targets of interest in radiobiology, ensures the uniqueness of [Formula: see text] for any radiation quality, thus, its potential usefulness in modeling. In conclusion, an accurate estimation of such threshold volumes may be useful for improving modeling of cell survival curves.
Collapse
Affiliation(s)
- Fernanda Villegas
- a Medical Radiation Physics, Department of Immunology, Genetics and Pathology, Uppsala University, Akademiska Sjukhuset, Uppsala SE-75185, Sweden
| | - Nina Tilly
- a Medical Radiation Physics, Department of Immunology, Genetics and Pathology, Uppsala University, Akademiska Sjukhuset, Uppsala SE-75185, Sweden.,b Elekta Instrument AB, Stockholm SE-10393, Sweden
| | - Anders Ahnesjö
- a Medical Radiation Physics, Department of Immunology, Genetics and Pathology, Uppsala University, Akademiska Sjukhuset, Uppsala SE-75185, Sweden
| |
Collapse
|
11
|
Vassiliev ON, Grosshans DR, Mohan R. A new formalism for modelling parameters α and β of the linear-quadratic model of cell survival for hadron therapy. Phys Med Biol 2017; 62:8041-8059. [PMID: 28832343 PMCID: PMC5737022 DOI: 10.1088/1361-6560/aa8804] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
We propose a new formalism for calculating parameters α and β of the linear-quadratic model of cell survival. This formalism, primarily intended for calculating relative biological effectiveness (RBE) for treatment planning in hadron therapy, is based on a recently proposed microdosimetric revision of the single-target multi-hit model. The main advantage of our formalism is that it reliably produces α and β that have correct general properties with respect to their dependence on physical properties of the beam, including the asymptotic behavior for very low and high linear energy transfer (LET) beams. For example, in the case of monoenergetic beams, our formalism predicts that, as a function of LET, (a) α has a maximum and (b) the α/β ratio increases monotonically with increasing LET. No prior models reviewed in this study predict both properties (a) and (b) correctly, and therefore, these prior models are valid only within a limited LET range. We first present our formalism in a general form, for polyenergetic beams. A significant new result in this general case is that parameter β is represented as an average over the joint distribution of energies E 1 and E 2 of two particles in the beam. This result is consistent with the role of the quadratic term in the linear-quadratic model. It accounts for the two-track mechanism of cell kill, in which two particles, one after another, damage the same site in the cell nucleus. We then present simplified versions of the formalism, and discuss predicted properties of α and β. Finally, to demonstrate consistency of our formalism with experimental data, we apply it to fit two sets of experimental data: (1) α for heavy ions, covering a broad range of LETs, and (2) β for protons. In both cases, good agreement is achieved.
Collapse
Affiliation(s)
- Oleg N Vassiliev
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America
| | | | | |
Collapse
|
12
|
Zhao L, Mi D, Sun Y. A novel multitarget model of radiation-induced cell killing based on the Gaussian distribution. J Theor Biol 2017; 420:135-143. [PMID: 28284991 DOI: 10.1016/j.jtbi.2017.03.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 02/21/2017] [Accepted: 03/05/2017] [Indexed: 01/26/2023]
Abstract
The multitarget version of the traditional target theory based on the Poisson distribution is still used to describe the dose-survival curves of cells after ionizing radiation in radiobiology and radiotherapy. However, noting that the usual ionizing radiation damage is the result of two sequential stochastic processes, the probability distribution of the damage number per cell should follow a compound Poisson distribution, like e.g. Neyman's distribution of type A (N. A.). In consideration of that the Gaussian distribution can be considered as the approximation of the N. A. in the case of high flux, a multitarget model based on the Gaussian distribution is proposed to describe the cell inactivation effects in low linear energy transfer (LET) radiation with high dose-rate. Theoretical analysis and experimental data fitting indicate that the present theory is superior to the traditional multitarget model and similar to the Linear - Quadratic (LQ) model in describing the biological effects of low-LET radiation with high dose-rate, and the parameter ratio in the present model can be used as an alternative indicator to reflect the radiation damage and radiosensitivity of the cells.
Collapse
Affiliation(s)
- Lei Zhao
- Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University, Dalian, Liaoning, PR China
| | - Dong Mi
- Department of Physics, Dalian Maritime University, Dalian, Liaoning, PR China.
| | - Yeqing Sun
- Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University, Dalian, Liaoning, PR China.
| |
Collapse
|
13
|
Lahiri DK, Maloney B, Bayon BL, Chopra N, White FA, Greig NH, Nurnberger JI. Transgenerational latent early-life associated regulation unites environment and genetics across generations. Epigenomics 2016; 8:373-87. [PMID: 26950428 DOI: 10.2217/epi.15.117] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The origin of idiopathic diseases is still poorly understood. The latent early-life associated regulation (LEARn) model unites environmental exposures and gene expression while providing a mechanistic underpinning for later-occurring disorders. We propose that this process can occur across generations via transgenerational LEARn (tLEARn). In tLEARn, each person is a 'unit' accumulating preclinical or subclinical 'hits' as in the original LEARn model. These changes can then be epigenomically passed along to offspring. Transgenerational accumulation of 'hits' determines a sporadic disease state. Few significant transgenerational hits would accompany conception or gestation of most people, but these may suffice to 'prime' someone to respond to later-life hits. Hits need not produce symptoms or microphenotypes to have a transgenerational effect. Testing tLEARn requires longitudinal approaches. A recently proposed longitudinal epigenome/envirome-wide association study would unite genetic sequence, epigenomic markers, environmental exposures, patient personal history taken at multiple time points and family history.
Collapse
Affiliation(s)
- Debomoy K Lahiri
- Department of Psychiatry, Stark Neurosciences Research Institute, Indiana University School of Medicine, 320 West 15th Street, Indianapolis, IN 46202, USA.,Department of Medical & Molecular Genetics, Indiana University School of Medicine, 320 West 15th Street, Indianapolis, IN 46202, USA
| | - Bryan Maloney
- Department of Psychiatry, Stark Neurosciences Research Institute, Indiana University School of Medicine, 320 West 15th Street, Indianapolis, IN 46202, USA
| | - Baindu L Bayon
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, 320 West 15th Street, Indianapolis, IN 46202, USA
| | - Nipun Chopra
- Department of Psychiatry, Stark Neurosciences Research Institute, Indiana University School of Medicine, 320 West 15th Street, Indianapolis, IN 46202, USA
| | - Fletcher A White
- Department of Anesthesia, Stark Neurosciences Research Institute, Indiana University School of Medicine, 320 West 15th Street, Indianapolis, IN 46202, USA
| | - Nigel H Greig
- Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - John I Nurnberger
- Department of Psychiatry, Stark Neurosciences Research Institute, Indiana University School of Medicine, 320 West 15th Street, Indianapolis, IN 46202, USA.,Department of Medical & Molecular Genetics, Indiana University School of Medicine, 320 West 15th Street, Indianapolis, IN 46202, USA
| |
Collapse
|
14
|
Crezee H, van Leeuwen CM, Oei AL, Stalpers LJA, Bel A, Franken NA, Kok HP. Thermoradiotherapy planning: Integration in routine clinical practice. Int J Hyperthermia 2015; 32:41-9. [PMID: 26670625 DOI: 10.3109/02656736.2015.1110757] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Planning of combined radiotherapy and hyperthermia treatments should be performed taking the synergistic action between the two modalities into account. This work evaluates the available experimental data on cytotoxicity of combined radiotherapy and hyperthermia treatment and the requirements for integration of hyperthermia and radiotherapy treatment planning into a single planning platform. The underlying synergistic mechanisms of hyperthermia include inhibiting DNA repair, selective killing of radioresistant hypoxic tumour tissue and increased radiosensitivity by enhanced tissue perfusion. Each of these mechanisms displays different dose-effect relations, different optimal time intervals and different optimal sequences between radiotherapy and hyperthermia. Radiosensitisation can be modelled using the linear-quadratic (LQ) model to account for DNA repair inhibition by hyperthermia. In a recent study, an LQ model-based thermoradiotherapy planning (TRTP) system was used to demonstrate that dose escalation by hyperthermia is equivalent to ∼10 Gy for prostate cancer patients treated with radiotherapy. The first step for more reliable TRTP is further expansion of the data set of LQ parameters for normally oxygenated normal and tumour tissue valid over the temperature range used clinically and for the relevant time intervals between radiotherapy and hyperthermia. The next step is to model the effect of hyperthermia in hypoxic tumour cells including the physiological response to hyperthermia and the resulting reoxygenation. Thermoradiotherapy planning is feasible and a necessity for an optimal clinical application of hyperthermia combined with radiotherapy in individual patients.
Collapse
Affiliation(s)
- Hans Crezee
- a Department of Radiation Oncology , Academic Medical Centre , Amsterdam and
| | | | - Arlene L Oei
- a Department of Radiation Oncology , Academic Medical Centre , Amsterdam and.,b Laboratory for Experimental Oncology and Radiobiology , Academic Medical Centre , Amsterdam , The Netherlands
| | - Lukas J A Stalpers
- a Department of Radiation Oncology , Academic Medical Centre , Amsterdam and
| | - Arjan Bel
- a Department of Radiation Oncology , Academic Medical Centre , Amsterdam and
| | - Nicolaas A Franken
- a Department of Radiation Oncology , Academic Medical Centre , Amsterdam and.,b Laboratory for Experimental Oncology and Radiobiology , Academic Medical Centre , Amsterdam , The Netherlands
| | - H Petra Kok
- a Department of Radiation Oncology , Academic Medical Centre , Amsterdam and
| |
Collapse
|
15
|
Wang H, Vassiliev O. Microdosimetric characterisation of radiation fields for modelling tissue response in radiotherapy. INTERNATIONAL JOURNAL OF CANCER THERAPY AND ONCOLOGY 2014. [DOI: 10.14319/ijcto.0201.16] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
|
16
|
Scheidegger S, Fuchs HU, Zaugg K, Bodis S, Füchslin RM. Using state variables to model the response of tumour cells to radiation and heat: a novel multi-hit-repair approach. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:587543. [PMID: 24396395 PMCID: PMC3876778 DOI: 10.1155/2013/587543] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Accepted: 10/31/2013] [Indexed: 11/18/2022]
Abstract
In order to overcome the limitations of the linear-quadratic model and include synergistic effects of heat and radiation, a novel radiobiological model is proposed. The model is based on a chain of cell populations which are characterized by the number of radiation induced damages (hits). Cells can shift downward along the chain by collecting hits and upward by a repair process. The repair process is governed by a repair probability which depends upon state variables used for a simplistic description of the impact of heat and radiation upon repair proteins. Based on the parameters used, populations up to 4-5 hits are relevant for the calculation of the survival. The model describes intuitively the mathematical behaviour of apoptotic and nonapoptotic cell death. Linear-quadratic-linear behaviour of the logarithmic cell survival, fractionation, and (with one exception) the dose rate dependencies are described correctly. The model covers the time gap dependence of the synergistic cell killing due to combined application of heat and radiation, but further validation of the proposed approach based on experimental data is needed. However, the model offers a work bench for testing different biological concepts of damage induction, repair, and statistical approaches for calculating the variables of state.
Collapse
Affiliation(s)
- Stephan Scheidegger
- ZHAW School of Engineering, Zurich University of Applied Science, 8401 Winterthur, Switzerland
| | - Hans U. Fuchs
- ZHAW School of Engineering, Zurich University of Applied Science, 8401 Winterthur, Switzerland
| | | | - Stephan Bodis
- Radio-Onkologie-Zentrum KSA-KSB, 5001 Aarau, Switzerland
- Medical Faculty, University of Zurich, 8006 Zurich, Switzerland
| | - Rudolf M. Füchslin
- ZHAW School of Engineering, Zurich University of Applied Science, 8401 Winterthur, Switzerland
- European Centre of Living Technology, 30124 Venice, Italy
| |
Collapse
|