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Proton beam therapy (PBT) combined with chemotherapy, such as cis-diamminedichloroplatinum (II) (CDDP) and 5-fluorouracil (5-FU), has been employed as an alternative approach to improve clinical outcomes. PBT has been reported to be effective against esophageal cancer. However, apart from 5-FU and CDDP, almost no other drug has been tested in combined chemotherapy with PBT. Therefore, we investigated the effects of a poly (ADP-ribose) polymerase inhibitor on enhancing proton beam effects using esophageal cancer cell lines that exhibit resistance to radiation and CDDP. Esophageal squamous cell carcinoma cell lines OE-21 and KYSE-450 were exposed to the drugs for 1 h prior to irradiation. The cell survival curve was obtained using a clonogenic assay and the sensitizing effect ratio (SER) was calculated. The clonogenic assay was used to compare the effect of multi-fractioned irradiation between 8 Gy/1 fraction (fr) and 8 Gy/4 fr. γH2AX, Rad51, BRCA1, BRCA2 and 53BP1 foci were detected via immunofluorescence. Olaparib exhibited an SER of 1.5–1.7 on PBT. The same sensitizing effect was exhibited in multi-fractioned irradiation, and the combined use increased the expression of double-strand breaks and homologous recombination-related genes in an additive manner. Such additive effects were not observed on non-homologous end joining-related genes. We demonstrated that olaparib has a high sensitizing effect on PBT in platinum- and radiation-resistant esophageal cancer cells. Our results suggest a potential clinical application of olaparib-proton irradiation (PT) against platinum- and radiation-resistant esophageal cancer.

Background High-dose hypofractionated radiotherapy should theoretically result in a deviation from the typical linear-quadratic shape of the cell survival curve beyond a certain threshold dose, yet no evidence for this hypothesis has so far been found in clinical data of stereotactic body radiotherapy treatment (SBRT) for early-stage non-small cell lung cancer (NSCLC). A pragmatic explanation is a larger α/β ratio than the conventionally assumed 10 Gy. We here attempted an estimation of the α/β ratio for NSCLC treated with SBRT using individual patient data. Materials and methods We combined two large retrospective datasets, yielding 1294 SBRTs (≤10 fractions) of early stage NSCLC. Cox proportional hazards regression, a logistic tumor control probability model and a biologically motivated Bayesian cure rate model were used to estimate the α/β ratio based on the observed number of local recurrences and accounting for tumor size. Results A total of 109 local progressions were observed after a median of 17.7 months (range 0.6–76.3 months). Cox regression, logistic regression of 3 year tumor control probability and the cure rate model yielded best-fit estimates of α/β = 12.8 Gy, 14.9 Gy and 12–16 Gy (depending on the prior for α/β), respectively, although with large uncertainties that did not rule out the conventional α/β = 10 Gy. Conclusions Clinicians can continue to use the simple LQ formalism to compare different SBRT treatment schedules for NSCLC. While α/β = 10 Gy is not ruled out by our data, larger values in the range 12–16 Gy are more probable, consistent with recent meta-regression analyses.

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.

The strategy of pH-responsive aggregation in tumor micro-environment (TME) provides an intriguing platform for enhancing tumor retention and exerting therapeutic effects sufficiently. In this work, we have designed an intelligent dual pH-responsive self-aggregating nano gold system (Au@PAH-Pt/DMMA) for the combined chemo-radiotherapy, in which a “charge-reversal like” strategy was utilized to realize irreversible stable aggregation and pH-specific release of cisplatin prodrug in TME. Responsive aggregation increases the cellular uptake of Au@PAH-Pt/DMMA by 55%–60%, and the cellular uptake of Pt after X-ray irradiation can be further enhanced by 80%. Additionally, responsive aggregation greatly slows down the rate of efflux from tumor in vivo. This system not only promotes B16 cell apoptosis as a chemotherapeutic agent (30.4%), it also enhances the effect of chemo-radiotheray (CRT) by promoting apoptosis as a radiosensitizer (55.3%). The colony formation assay results were fitted to cell survival curve of B16 cells and the sensitization enhancement ratio (SER) was calculated to be 1.29, which shows a good radiosensitizing ability. When exposed to X-ray, this nanoplatform reached the ideal therapeutic effect, and the tumor inhibition rate of Au@PAH-Pt/DMMA reached 91.6% with low drug administration frequency and dose of X-ray. Overall, the dual pH-responsive nanoparticles Au@PAH-Pt/DMMA could effectively enhance tumor therapeutic efficiency by combined chemo-radiotherapy, which provides a potential method for clinical transformation of cancer treatment.

PurposeTo develop an empirical model to predict radiosensitivity and relative biological effectiveness (RBE) after helium (He) and carbon (C) ion irradiation with or without DNA repair inhibitors.MethodsWe characterized survival in eight human cancer cell lines exposed to 6 MV photons and to He- and C-ions with linear energy transfer (LET) values of 2.2-60.5 keV/μm to verify that the radiosensitivity parameters (D5%, D10%, D20%, D37%, D50% and SF2Gy) correlate linearly between photon and ion radiation with or without DNA-PKcs or ATR inhibitors. Then, we parameterized the LET response of the parameters governing these linear correlations up to LET values of 225 keV/μm using the data in the Particle Irradiation Data Ensemble (PIDE) v3.2 database, creating a model that predicts a cell’s ion radiosensitivity, RBE and ion survival curve for a given LET on the basis of the cell’s photon radiosensitivity. We then trained this model using the PIDE database as a training dataset, and validated it by predicting the radiosensitivity of the cell lines we exposed to He- and C- ions with LET ranging from 2.2-60.5 keV/μm.ResultsRadiosensitivity to ions depended linearly with radiosensitivity of photons in the range of investigated LET values and the slopes and intercepts of these linear relationships within the PIDE database vary exponentially and linearly, respectively. Our model predicted ion radiosensitivity (e.g., D10%) within 5.1–21.3%, RBED10% within 5.0-17.1%, and ion mean inactivation dose within 6.7-25.1% for He- and C-ion LET ranging from 2.2-60.5 keV/μm.ConclusionsRadiosensitivity to He- and C-ions depend linearly with radiosensitivity to photons and can be used to predict ion radiosensitivity, RBE and cell survival curves for clinically relevant LET values from 2.2–60.5 keV/μm, with or without drug treatment.SUMMARYWe present a new empirical model capable of predicting clonogenic cell survival of cell lines exposed to helium and carbon ion beams. Our model is based on an observed linear correlation between radiosensitivity to ions and photons across a wide range of LET values. This linear correlation can be used to predict ion RBE, radiosensitivity, and the cell survival curve for a given LET all based on a cell’s photon survival curve.

There is an increasing number of radiobiological experiments being conducted with low energy protons (less than 5 MeV) for radiobiological studies due to availability of sub-millimetre focused beam. However, low energy proton has broad microdosimetric spectra which can introduce dosimetric uncertainty. In this work, we quantify the impact of this dosimetric uncertainties on the cell survival curve and how it affects the estimation of the alpha and beta parameters in the LQ formalism. Monte Carlo simulation is used to generate the microdosimetric spectra in a micrometer-sized water sphere under proton irradiation. This is modelled using radiobiological experiment set-up at the Centre of Ion Beam Application (CIBA) in National University of Singapore. Our results show that the microdosimetric spectra can introduce both systematic and random shifts in dose and cell survival; this effect is most pronounced with low energy protons. The alpha and beta uncertainties can be up to 10% and above 30%, respectively for low energy protons passing through thin cell target (about 10 microns). These uncertainties are non-negligible and show that care must be taken in using the cell survival curve and its derived parameters for radiobiological models.

Exponential survival curves of early-passage human fibroblasts challenge classic biophysical models of cell inactivation. Thus, X-ray doses of 2-4 Gy inactivate normal, human skin fibroblasts in spite of negligible residual double-strand breaks. By contrast, radioresistant p53-mutant U251 glioblastoma cells proliferate in spite of residual damage. Similarly, p53 wildtype TK6 lymphoblastoid cells show exponential survival curves while the related p53-mutant WTK1 cell line continued to proliferate and showed a shouldered survival curve. Here, we propose a model in which the radioresistant shoulder region is due to tolerance to certain types or amounts of residual damage that would otherwise inactivate normal cells. Thus, the steeper initial slope and absence of a shoulder in the survival curve of normal cells may not imply a higher number of residual lesions but rather non-tolerance to these lesions.

The relationship between the radiation dose delivered to a tumor and its effect is not completely predictable. Uncertainty in the estimation of the boron concentration in a tumor, variation in the radiation sensitivity of the tumor cells, and the complexity of the interactions between the four types of radiation comprising the boron neutron capture therapy (BNCT) dose contribute to this uncertainty. We reanalyzed the data of our previous papers to investigate the variation in radiosensitivity of tumor cells to the 10B(n,α)7Li dose: the dose generated by the reaction of thermal neutrons and 10B, hereafter the ‘boron-neutron dose’. The radiosensitivities of five tumors (EL4, SAS/neo, SAS/mp53, SCCVII and B16-BL6 melanoma) were examined. For the combination of p-boron-L-phenylalanine (BPA: C9H12BNO4) with neutron irradiation, D0, the cell survival curve for the boron-neutron dose was the smallest for the SAS/neo, followed by the EL4, SAS/mp53, SCCVII and B16-BL6 melanoma, in that order. For the combination of mercaptoundecahydrododecaborate (BSH: Na2B12H11SH) with neutron irradiation, D0 was the smallest for the EL4, followed by the SAS/neo, B16–BL6melanoma, SAS/mp53 and SCCVII, in that order. The relationships between these D0 values and the nucleocytoplasmic ratios (Xncs) or cell size indices (Xcs) obtained by histopathological microslide image were as follows: (D0 = 0.1341Xnc–1.586, R2 = 0.9721) for all tumor types with BPA-BNCT, and D0 = 0.0122Xcs–0.1319 (R2 = 0.9795) for four tumor types (all except the B16-BL6 melanoma) with BSH-BNCT. Based on these results, we proposed a new biologically equivalent effectiveness factor: the absolute biological effectiveness (ABE) factor. The ABE factor is Gy/D0. Thus, the ABE dose is the physical dose multiplied by the ABE factor, and refers to the dose needed to decrease the cell survival rate to e–ABE dose/Gy.

Gold nanoparticles (AuNPs) have been experimentally proven to induce radiosensitization effects particularly for kilovoltage photon beam. Theoretically, AuNPs bombarded with kilovoltage photon beam would increase the photoelectric interactions cross section and hence increase cell death. Despite the intriguing finding, this type of beam is not common in clinical radiotherapy and possesses limited application compare to megavoltage beams. In this study, quantification of the radiosensitization effects acquired from clinical megavoltage photon, electron, and high-dose rate (HDR) gamma rays from Ir-192 source were conducted. In addition, radiobiological characterization of the cell survival curves that are commonly described by linear-quadratic (LQ) model was also evaluated using multi-target (MT) and repairable conditionally repairable (RCR) model. Significant sensitization enhancement ratios (SER) were obtained particularly for 15 MeV electron beams in which the amplification of radiation effects by AuNPs is up to the factor of 1.78. The results also demonstrate validity of experimental models to describe the cell survival curve and determine the radiosensitization effects. However, slight variation was observed for SER values computed from each model which indicate the importance to specify the quantification method. Adoption of radiobiological models and their parameters could also be employed as indicator to explain the mechanistic events in the AuNPs’ radiosensitization effects. As conclusion effective radiosensitization by megavoltage clinical radiotherapy beams could be achieved and precise radiobiological characterization drawn from radiobiological models may provide insight towards radiobiological impacts induced by AuNPs.

Purpose High-dose-rate irradiation with 6 MV linac X-rays is a wide-spread means to treat cancer tissue in radiotherapy. The treatment planning relies on a mathematical description of surviving fraction (SF), such as the linear-quadratic model (LQM) formula. However, even in the case of high-dose-rate treatment, the repair kinetics of DNA damage during dose-delivery time plays a function in predicting the dose-SF relation. This may call the SF model selection into question when considering the dose-delivery time or dose-rate effects (DREs) in radiotherapy and in vitro cell experiments. In this study, we demonstrate the importance of dose-delivery time at high-dose-rate irradiations used in radiotherapy by means of Bayesian estimation. Methods To evaluate the model selection for SF, three types of models, the LQM and two microdosimetric-kinetic models with and without DREs (MKMDR and MKM) were applied to describe in vitro SF data (our work and references). The parameters in each model were evaluated by a Markov chain Monte Carlo (MCMC) simulation. Results The MCMC analysis shows that the cell survival curve by the MKMDR fits the experimental data the best in terms of the deviance information criterion (DIC). In the fractionated regimen with 30 fractions to a total dose of 60 Gy, the final cell survival estimated by the MKMDR was higher than that by the LQM. This suggests that additional fractions are required for attaining the total dose equivalent to yield the same effect as the conventional regimen using the LQM in fractionated radiotherapy. Conclusions Damage repair during dose-delivery time plays a key role in precisely estimating cell survival even at a high dose rate in radiotherapy. Consequently, it was suggested that the cell-killing model without repair factor during a short dose-delivery time may overestimate actual cell killing in fractionated radiotherapy. This article is protected by copyright. All rights reserved.

Understanding cell cycles is very important in knowing radiobiology behind radiosensitivity and cell death. Cell death for non-proliferating cells is defined as the loss of specific function and for proliferating cells it is defined as the loss of reproductive integrity. This chapter will describe the basic cell survival curve and the relevant mathematical models to explain the cell survival curve (photons) at both conventional and high dose per fractions.

The current study evaluates dosimetric and spectral effects when platinum (Pt)-based chemotherapeutics and less toxic tungstophosphoric-acid (TPA) organometallics are present during x-ray radiotherapy. We hypothesize that the use of high energy photon beams (i.e. 18 MV) will increase absorbed dose due to increased pair production from high-Z elements and thus result in additional tumor cell kill. EGSnrc code was used to examine the contribution of pair production to dose in the presence of the high-Z material (TPA, Pt mixtures and tungsten, W) as a function of beam energy. Variables included different concentrations (100 µmolar, 1 mmolar), depths (5 mm, 10 cm), thicknesses (5 mm, 5 cm) and energies (6, 18 MV). Overall, for the deeper depth, the 511 keV photon fluence increase was up 31% (18 MV-1 mmolar) while at 6 MV it was between 10%-11% depending on the concentration. For the shallower depth, 18 MV fluence increase was up 14.6% (1 mmolar) and 18.6% (1 mmolar) for the 6 MV. The dose enhancement effect due to pair production was up 25%-30% and a total 33%-58% depending on the depth. The benefit related to pair production was more for 18 MV and under conditions that simulated a realistic clinical setup. While part of the effect could be attributed to photoabsorption, a significant contribution of dose could result from pair production. Experimental clonogenic survival assay was consistent with the theory in that the low dose shoulder region of a cell survival curve was reduced using TPA and 18 MV compared with TPA and 6 MV or compared with no TPA and 18 MV; RBE was approximately 2 at the dose commonly used in conventional fractionated clinical radiotherapy. This suggests a potential new strategy for dose enhancement based on pair production using higher energy beamlines.

This paper outlines a phenomenological approach towards cell survival curve at low dose using tools of extensive Statistical Mechanics and nonextensive Statistical Mechanics. An Ising chain model is developed for the cell survival curve and the canonical ensemble formalism based on Boltzmann Gibbs statistic and Tsallis statistic is presented. The resulting cell survival curve shows excellent agreement with the experimental data and the physical parameters from our Tsallis model (N’, q) can be shown to provide clear classification between healthy and cancerous cells. In this paper, we also provides possible biophysical interpretation to the (N’, q) parameters where N’ is representative of the amount of repairable DNA content in the nucleus and q represents the degree of correlation in DNA damage. Overall, this is the first time a Statistical Mechanics approach is used in Radiobiology, and could present a new perspective.

Aim: This study is designed to investigate the superiority and applicability of the model among the linear-quadratic (LQ), linear-quadratic-linear (LQ-L) and universal-survival-curve (USC) models by fitting published radiation cell survival data of lung cancer cell lines. Materials and Method: The radiation cell survival data for small cell (SC) and non-small cell (NSC) lung cancer cell lines were obtained from published reports, and were used to determine the LQ and cell survival curve parameters, which ultimately were used in the curve fitting of the LQ, LQ-L and USC models. Results: The results of this study demonstrate that the LQ-L(Dt-mt) model, compared with the LQ and USC models, provides best fit with smooth and gradual transition to the linear portion of the curve at transition dose Dt-mt, where the LQ model loses its validity, and the LQ-L(Dt-2α/β) and USC(Dt-mt) models do not transition smoothly to the linear portion of the survival curve. Conclusion: The LQ-L(Dt-mt) model is able to fit wide variety of cell survival data over a very wide dose range, and retains the strength of the LQ model in the low-dose range.

ObjectiveTo observe the anti-proliferation and radiosensitization effect of chitooligosaccharides (COS) on human lung cancer cell line HepG2.MethodsCCK-8 assay was employed to obtain the inhibition ratio of COS on HepG2 cells at 24 h after treatment. The clonogenic assay was used to analyze the cell viability of RAY group and RAY + COS group with X-ray of 0, 1, 2, 4, 6 and 8 Gy, and the cell survival curve was used to analyze the sensitization ratio of COS. Flow cytometry was employed to detect cell cycle and apoptosis rate in control group, RAY group and RAY + COS group after 24 h treatment.ResultsCOS inhibited the proliferation of HepG2 cells, and the inhibition rate positively correlated with the concentration of COS. The cell viability decreased with increasing exposure dose in RAY group and RAY + COS group. The cell viabilities of RAY + COS group were lower than those of RAY group at the dose of 4, 6 and 8 Gy (P

Radiobiological responses of a highly clonogenic fish cell line, eelB, to low-LET ionizing radiation and effects of dose rates were studied. In acute exposure to 0.1–12 Gy of gamma rays, eelB’s cell survival curve displayed a linear–quadratic (LQ) relationship. In the LQ model, α, β, and α/β ratio were 0.0024, 0.037, and 0.065, respectively; for the first time that these values were reported for fish cells. In the multi-target model, n, D o, and D q values were determined to be 4.42, 2.16, and 3.21 Gy, respectively, and were the smallest among fish cell lines being examined to date. The mitochondrial potential response to gamma radiation in eelB cells was at least biphasic: mitochondria hyperpolarized 2 h and then depolarized 5 h post-irradiation. Upon receiving gamma rays with a total dose of 5 Gy, dose rates (ranging between 83 and 1366 mGy/min) had different effects on the clonogenic survival but not the mitochondrial potential. The clonogenic survival was significantly higher at the lowest dose rate of 83 mGy/min than at the other higher dose rates. Upon continuous irradiation with beta particles from tritium at 0.5, 5, 50, and 500 mGy/day for 7 days, mitochondria significantly depolarized at the three higher dose rates. Clearly, dose rates had differential effects on the clonogenic survival of and mitochondrial membrane potential in fish cells.

This research was performed to evaluate the superparamagnetic i ron oxide nanoparticles’(SPIONs) cell toxicity and to measure the radiation cell survival curve chang es of SPIONs-uptake glioblastoma multiforme cells. The results could be practically used as the fundamental data to ameliorate proton beam cancer therapy, for example, providing necessary GBM treatment dose in the prot on beam therapy when the therapy takes advantage of SPIONs. The assessment of the toxicological evalua tion of synthesized SPIONs was accomplished by MTT assay as an in vitro experiment. The results showed no m eaningful differences in the cell survival rate at the 1-100μg/ml SPIONs concentrations, but the cell toxicity was shown as the cell survival rate decreased up to 74.2% at the 200μg/ml SPIONs concentration. Then, we measured each radiation cel l survival curve for U373MG cells and SPIONs-uptake U373MG cells with 0~5 Gy of proton beam irradiations. It is learned from the analysis of the experimental results that the SPION-uptake cells’ radiation survival rate was more rapidly decreased as the irradiation dose increased. In c onclusion we confirmed that SPIONs-uptake in U373MG cells induces cell death at the much less dose than the lethal dose of SPION-non-uptake cell. This research shows that the therapeutic efficacy of glioblastoma mu ltiforme treatment in proton beam therapy can be improved by SPIONs targeting to the GBM cells.

A comparative study of the effects of different radiation modalities on cell death was performed.Radiation modalities included γ-rays, fast neutrons, a mixed energy neutron beam called the modified enhanced thermal neutron beam and the mixed beam including Auger electron irradiation by gadolinium neutron capture. U87 (human brain tumor cells) cell survival curve data were modeled to predict how cells died. Transmission electron microscopy (TEM) images were assembled into a morphology of cell death (MCD) database and used to determine the fraction of necrotic or autophagic cells.Linear energy transfer (LET) differences for the different radiation modalities were revealed by modeling. All radiation modalities induced autophagy but only fast neutrons induced significant levels of necrosis. No necrosis, above control levels, was found in cells irradiated with mixed beam irradiation including Auger electrons. The number of autophagosomes increased with increasing time after exposure to all radiation modalities indicating progression of autophagy but only cells irradiated with the mixed beam plus Auger electrons exhibited extreme autophagy.Mixed neutron beam irradiation plus Auger electron irradiation from gadolinium neutron capture is a moderately high LET modality that kills U87 cells without the induction of necrosis and with progression of autophagy to an extreme state.

The main purpose of this paper is to provide a brief overview of the response of cells to ionising radiation. The molecular and cellular response of normal and neoplastic cells to radiation has important clinical implications and is also an active area of research. Accumulating detailed knowledge of cellular biology may also allow more effective ways of delivering radiation therapy or combining it with systemic therapies in ways which may improve the therapeutic index.

Theoretical modelling of tumour control probability (TCP) with respect to non-uniformity in the dose to the tumour, alternate fractionation schemes and tumour kinetics is a very useful tool for assessment of the influence of changes in dosimetric or radiobiological factors on the outcome of the treatment. Various attempts have been made to also include effects from non-uniform dose to the tumour volume, but the problem has not been fully solved and many factors were totally neglected or not accurately taken into account. This paper presents derivations of analytical expressions of TCP for macroscopic inter-cell dose variations and for random inter-fractional variations in average tumour dose, based on binomial statistics for the TCP and the well-known linear quadratic model for the cell survival. Numerical calculations have been performed to validate the analytical expressions. An analysis of the influence of the deterministic and stochastic heterogeneity in dose delivery on the TCP was performed. The precision requirements in dose delivery are discussed briefly with the support of the presented results. The main finding of this paper is that it is primarily the shape of the cell survival curve that governs how the response is affected by macroscopic dose variations. The analytical expressions for TCP accounting for heterogeneity in dose can quite well describe the TCP for varying dose from cell to cell and random dose in each fraction. An increased TCP is seen when a large number of fractions are used and the variations in dose to the cells are rather high for tissues with low α/β.

The Hug-Kellerer (H-K) equation is one of the earliest proposed radiation cell survival curves. We examine this equation in view of the recent perceived need for a universal cell survival curve which would be applicable to single radiation fractions at high doses. We derive relationships between the three parameters of the H-K equation and the parameters alpha and beta of the linear-quadratic equation. Using these relationships we show how the H-K equation can be used to determine single-fraction doses which are equivalent in theory to the dose in a conventional multi-fraction course of radiation therapy.

Intercellular communication after ionizing radiation exposure, so-called non-targeted effects (NTEs), reduces cell survival. Here we describe an integrated cell-killing model considering NTEs and DNA damage along radiation particle tracks, known as DNA-targeted effects (TEs) based on repair kinetics of DNA damage. The proposed model was applied to a series of experimental data, i.e., signal concentration, DNA damage kinetics, cell survival curve and medium transfer bystander effects (MTBEs). To reproduce the experimental data, the model considers the following assumptions: (i) the linear-quadratic (LQ) function as absorbed dose to express the hit probability to emit cell-killing signals, (ii) the potentially repair of DNA lesions induced by NTEs, and (iii) lower efficiency of repair for the damage in NTEs than that in TEs. By comparing the model results with experimental data, we found that signal-induced DNA damage and lower repair efficiency in non-hit cells are responsible for NTE-related repair kinetics of DNA damage, cell survival curve with low-dose hyper-radiosensitivity (HRS) and MTBEs. From the standpoint of modelling, the integrated cell-killing model with the LQ relation and a different repair function for NTEs provide a reasonable signal-emission probability and a new estimation of low-dose HRS linked to DNA repair efficiency.

During the last 20 years, the field of cellular and not least molecular radiation biology has been developed substantially and can today describe the response of heterogeneous tumors and organized normal tissues to radiation therapy quite well. An increased understanding of the sub-cellular and molecular response is leading to a more general systems biological approach to radiation therapy and treatment optimization. It is interesting that most of the characteristics of the tissue infrastructure, such as the vascular system and the degree of hypoxia, have to be considered to get an accurate description of tumor and normal tissue responses to ionizing radiation. In the limited space available, only a brief description of some of the most important concepts and processes is possible, starting from the key functional genomics pathways of the cell that are not only responsible for tumor development but also responsible for the response of the cells to radiation therapy. The key mechanisms for cellular damage and damage repair are described. It is further more discussed how these processes can be brought to inactivate the tumor without severely damaging surrounding normal tissues using suitable radiation modalities like intensity-modulated radiation therapy (IMRT) or light ions. The use of such methods may lead to a truly scientific approach to radiation therapy optimization, particularly when invivo predictive assays of radiation responsiveness becomes clinically available at a larger scale. Brief examples of the efficiency of IMRT are also given showing how sensitive normal tissues can be spared at the same time as highly curative doses are delivered to a tumor that is often radiation resistant and located near organs at risk. This new approach maximizes the probability to eradicate the tumor, while at the same time, adverse reactions in sensitive normal tissues are as far as possible minimized using IMRT with photons and light ions.

The local effect model predicts the relative biological effectiveness (RBE) for different ions and cell lines starting from the corresponding experimental photon data and an amorphous track structure model. Here we present an extension of the model that takes cluster effects of single-strand breaks (SSBs) at the nanometer scale into account. In line with the main idea of the local effect model, we take the yields of SSBs and double-strand breaks (DSBs) from experimental photon data and use a Monte Carlo method to distribute them onto the DNA. We score clusters of SSBs where individual SSBs are separated by less than 25 bp as additional DSBs. Assuming that the number of DSBs is a measure of cell lethality, we derive a modified cell survival curve for photons that takes these cluster effects into account. In combination with an improved radial dose distribution, we find that the extended local effect model including cluster effects reproduces most experimental data better than the original local effect model and thus enhances the accuracy of the local effect model.

Purpose: The introduction of stereotactic radiotherapy has raised concerns regarding the use of the linear-quadratic (LQ) model for predicting radiation response for large fractional doses. To partly address this issue, a transition dose D* below which the LQ model retains its predictive strength has been proposed. Estimates of D* which depends on the a, β, and D0 parameters are much lower than fractional doses typically encountered in stereotactic radiotherapy. D0, often referred to as the final slope of the cell survival curve, is thought to be constant. In vitro cell survival curves generally extend over the first few logs of cell killing, where D0-values derived from the multi-target formalism may be overestimated and can lead to low transition doses. Methods: D0-values were calculated from first principles for each decade of cell killing, using experimentally-determined a and β parameters for 17 human glioblastoma, neuroblastoma, and prostate cell lines, and corresponding transition doses were derived.Results: D0 was found to decrease exponentially with cell killing. Using D0-values at cell surviving fractions of the order of 10-10 yielded transition doses ~3-fold higher than those obtained from D0-values obtained from conventional approaches. D* was found to increase from 7.84 ± 0.56, 8.91 ± 1.20, and 6.55 ± 0.91 Gy to 26.84 ± 2.83, 23.95 ± 2.03, and 22.49 ± 2.31 Gy for the glioblastoma, neuroblastoma, and prostate cell lines, respectively. Conclusion: These findings suggest that the linear-quadratic formalism might be valid for estimating the effect of stereotactic radiotherapy with fractional doses in excess of 20 Gy.

In this introductory lecture of the Symposium, essential knowledge concerning biological mechanisms of post highradiation dose will be present. Emerging evidence suggests that distinct mechanisms of cell death, likely including a major contribution from vascular epithelial cell apoptosis (devascularizing effect), might play an important role in the success of SBRT in controlling tumors. Also “5Rs” of conventional fractionated RT will be discussed in aspect of SBRT, including new pathways of tumor clonogen repopulation, i.e. “phoenix rising”, as well as constructing SBRT cell survival curve attempts. However, studies taken toward maximizing this effect should also be designed with the cautionary awareness that tissue regenerative processes might also be triggered as a result.

The preliminary framework of a combined radiobiological model is developed and calibrated in the current work. The model simulates the production of individual cells forming a tumour, the spatial distribution of individual ionization events (using Geant4-DNA) and the stochastic biochemical repair of DNA double strand breaks (DSBs) leading to the prediction of survival or death of individual cells. In the current work, we expand upon a previously developed tumour generation and irradiation model to include a stochastic ionization damage clustering and DNA lesion repair model. The Geant4 code enabled the positions of each ionization event in the cells to be simulated and recorded for analysis. An algorithm was developed to cluster the ionization events in each cell into simple and complex double strand breaks. The two lesion kinetic (TLK) model was then adapted to predict DSB repair kinetics and the resultant cell survival curve. The parameters in the cell survival model were then calibrated using experimental cell survival data of V79 cells after low energy proton irradiation. A monolayer of V79 cells was simulated using the tumour generation code developed previously. The cells were then irradiated by protons with mean energies of 0.76 MeV and 1.9 MeV using a customized version of Geant4. By replicating the experimental parameters of a low energy proton irradiation experiment and calibrating the model with two sets of data, the model is now capable of predicting V79 cell survival after low energy (

For short range high linear energy transfer (LET) radiation therapy the biological effects are strongly affected by the heterogeneity of the specific energy (z) distribution delivered to tumour cells. Three-dimensional (3-D) dosimetry information at the cellular level is required for this study. An ideal approach would be the reconstruction of the cell and the radiation source microdistribution from sequential autoradiographic sections, which is, however, not a practical solution. In this paper, a novel microdosimetry analysis method, which obtains the specific energy (z) distribution directly from the morphological information in individual autoradiographic sections, is applied to human glioblastoma multifore (GBM) and normal brain tissue specimens in boron neutron capture therapy. The results are consistent with Monte Carlo simulation and demonstrate a uniform radiation source distribution in both GBM and normal brain tissues. We also hypothesise a biophysical model based on specific energy for survival analysis. The specific energy distributions to cell nuclei were calculated with a uniform radiation source distribution. By combining this microdosimetric analysis with measured cell survival data at the low dose region, a cell survival curve at high doses is predicted, which is consistent with the commonly used simple exponential curve model for high LET radiation.

Clinically, there is growing interest in strategies for intensifying radiation therapy by escalating the dose per fraction. This paper considers the steepness of the dose-response curve in this case. The steepness of a radiation dose-response curve is most conveniently quantified by the normalized dose-response gradient, gamma. Under the assumption of a linear-quadratic dose-effect model, a simple analytical relationship is derived between the gamma-value for a dose-response curve generated by varying the total dose while keeping the number of fractions constant, i.e. escalating the dose per fraction, and the gamma-value for a dose-response curve generated by varying the total dose while keeping the dose per fraction constant. This formulation is compared with clinical dose-response data from the literature and shown to be in good agreement with the observations. Some implications of this formulation for non-uniform dose distributions delivered using 3D conformal radiotherapy or intensity modulated radiotherapy (IMRT) are briefly discussed.

Purpose: To demonstrate the influence of pH on the cytotoxicity and radiosensitization by COX (cyclooxygenase) -1 and -2 inhibitors using established human cancer cells in culture. Methods and Materials: Nonselective COX inhibitor, ibuprofen (IB), and selective COX-2 inhibitor, SC-236, were used to determine the cytotoxicity and radiosensitization at varying pH of culture media. Human colon carcinoma cell line (HT-29) was exposed to the drug alone and in combination with radiation at different pH of the cell culture media. The end point was clonogenic ability of the single-plated cells after the treatment. Results: Cytotoxicity and radiosensitization of IB increased with higher drug concentration and longer exposure time. The most significant radiosensitization was seen with IB (1.5 mM) for 2-h treatment at pH 6.7 before irradiation. The dose-modifying factor as defined by the ratio of radiation doses required to achieve the same effect on cell survival was 1.8 at 10% survival level. In contrast, SC-236 (50 μM for 2–8 h) showed no pH-dependent cytotoxicity. There was modest increase in the cell killing at lower doses of radiation. Conclusion: An acidic pH was an important factor affecting the increased cytotoxicity and radiosensitization by ibuprofen. Radiation response was enhanced at shoulder portion of the cell survival curve by selective COX-2 inhibitor.

It has been established that hyperthermia can enhance cytotoxicity of some chemotherapeutic agents. This has led to various clinical trials of thermochemotherapy, although many questions remain unanswered. The effects of various agents have been studied on animal tumours with different histopathology at elevated temperatures. These studies indicated that alkylating agents were most effective to all tumours at a moderately elevated temperature. Cisplatin was also effective to all tumours, but its effectiveness at 41.5 degrees C was less than that of alkylating agents. To quantitatively study these findings, the magnitude of thermal enhancement of melphalan, an alkylating agent, and that of oxaliplatin, a new platinum compound, were studied at 37-44.5 degrees C by the colony formation assay. The dose of each agent was kept constant, and cell survival was determined as a function of treatment time. The cell survival curve was exponentially related with treatment time at all test temperatures, and the T(0) (the time to reduce survival from 1 to 0.37) decreased with an increasing temperature. These results suggested that the cytotoxic effect of these agents occurred with a constant rate at 37 degrees C, and the rate was facilitated with an increasing temperature. This suggests that heat can accelerate the cytotoxic chemical reaction, leading to substantial thermal enhancement. The thermal enhancement ratio (TER, the ratio of the T(0) at 37 degrees C to the T(0) at an elevated temperature) increased with an increase in the temperature. The activation energy for melphalan at moderately elevated temperatures was largest among the agents tested in the laboratory and that for oxaliplatin was approximately half of the melphalan activation energy. This suggests that the thermal enhancement for the cytotoxicity of melphalan or alkylating agents might be the greatest. Potential mechanisms of thermal enhancement of cytotoxicity were discussed.