publications
co-authored publications.
2018
- A New Standard DNA Damage (SDD) Data FormatSchuemann, J., McNamara, A. L., Warmenhoven, J. W., Henthorn, N. T., Kirkby, K., Merchant, M. J., Ingram, S., Paganetti, H., Held, K. D., Ramos-Mendez, J., Faddegon, B., Perl, J., Goodhead, D. T., Plante, I., Rabus, H., Nettelbeck, H., Friedland, W., Kundrát, P., Ottolenghi, A., Baiocco, G., Barbieri, S., Dingfelder, M., Incerti, S., Villagrasa, C., Bueno, M., Bernal, M. A., Guatelli, S., Sakata, D., Brown, J. M. C., Francis, Z., Kyriakou, I., Lampe, N., Ballarini, F., Carante, M. P., Davídková, M., Štěpán, V., Jia, X., Cucinotta, F. A., Schulte, R., Stewart, R. D., Carlson, D. J., Galer, S., Kuncic, Z., Lacombe, S., Milligan, J., Cho, S. H., Sawakuchi, G., Inaniwa, T., Sato, T., Li, W., Solov’yov, A. V., Surdutovich, E., Durante, M., Prise, K. M., and McMahon, S. J.Radiation Research 2018
Our understanding of radiation-induced cellular damage has greatly improved over the past few decades. Despite this progress, there are still many obstacles to fully understand how radiation interacts with biologically relevant cellular components, such as DNA, to cause observable end points such as cell killing. Damage in DNA is identified as a major route of cell killing. One hurdle when modeling biological effects is the difficulty in directly comparing results generated by members of different research groups. Multiple Monte Carlo codes have been developed to simulate damage induction at the DNA scale, while at the same time various groups have developed models that describe DNA repair processes with varying levels of detail. These repair models are intrinsically linked to the damage model employed in their development, making it difficult to disentangle systematic effects in either part of the modeling chain. These modeling chains typically consist of track-structure Monte Carlo simulations of the physical interactions creating direct damages to DNA, followed by simulations of the production and initial reactions of chemical species causing so-called “indirect” damages. After the induction of DNA damage, DNA repair models combine the simulated damage patterns with biological models to determine the biological consequences of the damage. To date, the effect of the environment, such as molecular oxygen (normoxic vs. hypoxic), has been poorly considered. We propose a new standard DNA damage (SDD) data format to unify the interface between the simulation of damage induction in DNA and the biological modeling of DNA repair processes, and introduce the effect of the environment (molecular oxygen or other compounds) as a flexible parameter. Such a standard greatly facilitates inter-model comparisons, providing an ideal environment to tease out model assumptions and identify persistent, underlying mechanisms. Through inter-model comparisons, this unified standard has the potential to greatly advance our understanding of the underlying mechanisms of radiation-induced DNA damage and the resulting observable biological effects when radiation parameters and/or environmental conditions change.
2019
- Clinically relevant nanodosimetric simulation of DNA damage complexity from photons and protonsHenthorn, N. T., Warmenhoven, J. W., Sotiropoulos, M., Aitkenhead, A. H., Smith, E. A. K., Ingram, S. P., Kirkby, N. F., Chadwick, A. L., Burnet, N. G., Mackay, R. I., Kirkby, K. J., and Merchant, M. J.RSC Advances 2019
Relative Biological Effectiveness (RBE), the ratio of doses between radiation modalities to produce the same biological endpoint, is a controversial and important topic in proton therapy. A number of phenomenological models incorporate variable RBE as a function of Linear Energy Transfer (LET), though a lack of mechanistic description limits their applicability. In this work we take a different approach, using a track structure model employing fundamental physics and chemistry to make predictions of proton and photon induced DNA damage, the first step in the mechanism of radiation-induced cell death. We apply this model to a proton therapy clinical case showing, for the first time, predictions of DNA damage on a patient treatment plan. Our model predictions are for an idealised cell and are applied to an ependymoma case, at this stage without any cell specific parameters. By comparing to similar predictions for photons, we present a voxel-wise RBE of DNA damage complexity. This RBE of damage complexity shows similar trends to the expected RBE for cell kill, implying that damage complexity is an important factor in DNA repair and therefore biological effect.
- In Silico Models of DNA Damage and Repair in Proton Treatment Planning: A Proof of ConceptSmith, Edward A. K., Henthorn, N. T., Warmenhoven, J. W., Ingram, S. P., Aitkenhead, A. H., Richardson, J. C., Sitch, P., Chadwick, A. L., Underwood, T. S. A., Merchant, M. J., Burnet, N. G., Kirkby, N. F., Kirkby, K. J., and Mackay, R. I.Scientific Reports 2019
There is strong in vitro cell survival evidence that the relative biological effectiveness (RBE) of protons is variable, with dependence on factors such as linear energy transfer (LET) and dose. This is coupled with the growing in vivo evidence, from post-treatment image change analysis, of a variable RBE. Despite this, a constant RBE of 1.1 is still applied as a standard in proton therapy. However, there is a building clinical interest in incorporating a variable RBE. Recently, correlations summarising Monte Carlo-based mechanistic models of DNA damage and repair with absorbed dose and LET have been published as the Manchester mechanistic (MM) model. These correlations offer an alternative path to variable RBE compared to the more standard phenomenological models. In this proof of concept work, these correlations have been extended to acquire RBE-weighted dose distributions and calculated, along with other RBE models, on a treatment plan. The phenomenological and mechanistic models for RBE have been shown to produce comparable results with some differences in magnitude and relative distribution. The mechanistic model found a large RBE for misrepair, which phenomenological models are unable to do. The potential of the MM model to predict multiple endpoints presents a clear advantage over phenomenological models.
- Mechanistic modelling supports entwined rather than exclusively competitive DNA double-strand break repair pathwayIngram, S. P., Warmenhoven, J. W., Henthorn, N. T., Smith, E. A. K., Chadwick, A. L., Burnet, N. G., Mackay, R. I., Kirkby, N. F., Kirkby, K. J., and Merchant, M. J.Scientific Reports 2019
Following radiation induced DNA damage, several repair pathways are activated to help preserve genome integrity. Double Strand Breaks (DSBs), which are highly toxic, have specified repair pathways to address them. The main repair pathways used to resolve DSBs are Non-Homologous End Joining (NHEJ) and Homologous Recombination (HR). Cell cycle phase determines the availability of HR, but the repair choice between pathways in the G2 phases where both HR and NHEJ can operate is not clearly understood. This study compares several in silico models of repair choice to experimental data published in the literature, each model representing a different possible scenario describing how repair choice takes place. Competitive only scenarios, where initial protein recruitment determines repair choice, are unable to fit the literature data. In contrast, the scenario which uses a more entwined relationship between NHEJ and HR, incorporating protein co-localisation and RNF138-dependent removal of the Ku/DNA-PK complex, is better able to predict levels of repair similar to the experimental data. Furthermore, this study concludes that co-localisation of the Mre11-Rad50-Nbs1 (MRN) complexes, with initial NHEJ proteins must be modeled to accurately depict repair choice.
2020
- Hi-C implementation of genome structure for in silico models of radiation-induced DNA damageIngram, Samuel P., Henthorn, Nicholas T., Warmenhoven, John W., Kirkby, Norman F., Mackay, Ranald I., Kirkby, Karen J., and Merchant, Michael J.PLOS Computational Biology 2020
Developments in the genome organisation field has resulted in the recent methodology to infer spatial conformations of the genome directly from experimentally measured genome contacts (Hi-C data). This provides are detailed description of both intra- and inter-chromosomal arrangements. Chromosomal intermingling is an important driver for radiation-induced DNA mis-repair. Which is a key biological endpoint of relevance to the fields of cancer therapy (radiotherapy), public health (biodosimetry) and space travel. For the first time, we leverage these methods of inferring genome organisation and couple them to nano-dosimetric radiation track structure modelling to predict quantities and distribution of DNA damage within cell-type specific geometries. These nano-dosimetric simulations are highly dependent on geometry and are benefited from the inclusion of experimentally driven chromosome conformations. We show how the changes in Hi-C contract maps impact the inferred geometries resulting in significant differences in chromosomal intermingling. We demonstrate how these differences propagate through to significant changes in the distribution of DNA damage throughout the cell nucleus, suggesting implications for DNA repair fidelity and subsequent cell fate. We suggest that differences in the geometric clustering for the chromosomes between the cell-types are a plausible factor leading to changes in cellular radiosensitivity. Furthermore, we investigate changes in cell shape, such as flattening, and show that this greatly impacts the distribution of DNA damage. This should be considered when comparing in vitro results to in vivo systems. The effect may be especially important when attempting to translate radiosensitivity measurements at the experimental in vitro level to the patient or human level.
- Insights into the non-homologous end joining pathway and double strand break end mobility provided by mechanistic in silico modellingWarmenhoven, John W., Henthorn, Nicholas T., Ingram, Samuel P., Chadwick, Amy L., Sotiropoulos, Marios, Korabel, Nickolay, Fedotov, Sergei, Mackay, Ranald I., Kirkby, Karen J., and Merchant, Michael J.DNA Repair 2020
After radiation exposure, one of the critical processes for cellular survival is the repair of DNA double strand breaks. The pathways involved in this response are complex in nature and involve many individual steps that act across different time scales, all of which combine to produce an overall behaviour. It is therefore experimentally challenging to unambiguously determine the mechanisms involved and how they interact whilst maintaining strict control of all confounding variables. In silico methods can provide further insight into results produced by focused experimental investigations through testing of the hypotheses generated. Such computational testing can asses competing hypotheses by investigating their effects across all time scales concurrently, highlighting areas where further experimental work can have the most significance. We describe the construction of a mechanistic model by combination of several hypothesised mechanisms reported in the literature and supported by experiment. Compatibility of these mechanisms was tested by fitting simulation to results reported in the literature. To avoid over-fitting, we used an approach of sequentially testing individual mechanisms within this pathway. We demonstrate that using this approach the model is capable of reproducing published protein kinetics and overall repair trends. This provides evidence supporting the feasibility of the proposed mechanisms and revealed how they interact to produce an overall behaviour. Furthermore, we show that the assumed motion of individual double strand break ends plays a crucial role in determining overall system behaviour.
2021
- A data science approach for early-stage prediction of Patient’s susceptibility to acute side effects of advanced radiotherapyAldraimli, Mahmoud, Soria, Daniele, Grishchuck, Diana, Ingram, Samuel, Lyon, Robert, Mistry, Anil, Oliveira, Jorge, Samuel, Robert, Shelley, Leila E.A., Osman, Sarah, Dwek, Miriam V., Azria, David, Chang-Claude, Jenny, Gutiérrez-Enríquez, Sara, Santis, Maria Carmen De, Rosenstein, Barry S., Ruysscher, Dirk De, Sperk, Elena, Symonds, R. Paul, Stobart, Hilary, Vega, Ana, Veldeman, Liv, Webb, Adam, Talbot, Christopher J., West, Catharine M., Rattay, Tim, consortium, REQUITE, and Chaussalet, Thierry J.Computers in Biology and Medicine 2021
The prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., disease positive/negative). Similar to statistical inference modelling, ML modelling is subject to the class imbalance problem and is affected by the majority class, increasing the false-negative rate. In this study, seventy-nine ML models were built and evaluated to classify approximately 2000 participants from 26 hospitals in eight different countries into two groups of radiotherapy (RT) side effects incidence based on recorded observations from the international study of RT related toxicity “REQUITE”. We also examined the effect of sampling techniques and cost-sensitive learning methods on the models when dealing with class imbalance. The combinations of such techniques used had a significant impact on the classification. They resulted in an improvement in incidence status prediction by shifting classifiers’ attention to the minority group. The best classification model for RT acute toxicity prediction was identified based on domain experts’ success criteria. The Area Under Receiver Operator Characteristic curve of the models tested with an isolated dataset ranged from 0.50 to 0.77. The scale of improved results is promising and will guide further development of models to predict RT acute toxicities. One model was optimised and found to be beneficial to identify patients who are at risk of developing acute RT early-stage toxicities as a result of undergoing breast RT ensuring relevant treatment interventions can be appropriately targeted. The design of the approach presented in this paper resulted in producing a preclinical-valid prediction model. The study was developed by a multi-disciplinary collaboration of data scientists, medical physicists, oncologists and surgeons in the UK Radiotherapy Machine Learning Network.
- Mechanistic Modelling of Slow and Fast NHEJ DNA Repair Pathways Following Radiation for G0/G1 Normal Tissue CellsQi, Yaping, Warmenhoven, John William, Henthorn, Nicholas Thomas, Ingram, Samuel Peter, Xu, Xie George, Kirkby, Karen Joy, and Merchant, Michael JohnCancers 2021
When cells are irradiated, their DNA can become damaged, this causes different types of repair processes to try and fix the DNA breaks. One of the most lethal types of DNA damage is double-strand breaks (DSBs). This work models the most used DSB repair process called Non-Homologous End Joining (NHEJ) and includes both its resection-independent and resection-dependent pathways. The models produced are benchmarked against experimental normal and deficient cell-types across a wide range of radiation qualities. We compare two approaches of modelling, the first is where the DSBs can repair in parallel and the second is where the DSBs repair is entwined. We find that it is necessary to consider both the resection-independent and resection-dependent pathways as entwined to produce a model which robustly matches experimental work. Through better modelling of NHEJ repair, it can improve our understanding of radiation response which has potential in biological optimisation for radiotherapy. Mechanistic in silico models can provide insight into biological mechanisms and highlight uncertainties for experimental investigation. Radiation-induced double-strand breaks (DSBs) are known to be toxic lesions if not repaired correctly. Non-homologous end joining (NHEJ) is the major DSB-repair pathway available throughout the cell cycle and, recently, has been hypothesised to consist of a fast and slow component in G0/G1. The slow component has been shown to be resection-dependent, requiring the nuclease Artemis to function. However, the pathway is not yet fully understood. This study compares two hypothesised models, simulating the action of individual repair proteins on DSB ends in a step-by-step manner, enabling the modelling of both wild-type and protein-deficient cell systems. Performance is benchmarked against experimental data from 21 cell lines and 18 radiation qualities. A model where resection-dependent and independent pathways are entirely separated can only reproduce experimental repair kinetics with additional restraints on end motion and protein recruitment. However, a model where the pathways are entwined was found to effectively fit without needing additional mechanisms. It has been shown that DaMaRiS is a useful tool when analysing the connections between resection-dependent and independent NHEJ repair pathways and robustly matches with experimental results from several sources.
2022
- Development and optimisation of a machine-learning prediction model for acute desquamation following breast radiotherapy in the multi-centre REQUITE cohortAldraimli, Mahmoud, Osman, Sarah, Grishchuck, Diana, Ingram, Samuel, Lyon, Robert, Mistry, Anil, Oliveira, Jorge, Samuel, Robert, Shelley, Leila E.A., Soria, Daniele, Dwek, Miriam V., Aguado-Barrera, Miguel E., Azria, David, Chang-Claude, Jenny, Dunning, Alison, Giraldo, Alexandra, Green, Sheryl, Gutiérrez-Enríquez, Sara, Herskind, Carsten, Hulle, Hans van, Lambrecht, Maarten, Lozza, Laura, Rancati, Tiziana, Reyes, Victoria, Rosenstein, Barry S., Ruysscher, Dirk de, Santis, Maria C. de, Seibold, Petra, Sperk, Elena, Symonds, R. Paul, Stobart, Hilary, Taboada-Valadares, Begoña, Talbot, Christopher J., Vakaet, Vincent J.L., Vega, Ana, Veldeman, Liv, Veldwijk, Marlon R., Webb, Adam, Weltens, Caroline, West, Catharine M., Chaussalet, Thierry J., Rattay, Tim, and consortium, REQUITEAdvances in Radiation Oncology 2022
Background and purpose Some breast cancer patients treated by surgery and radiotherapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation following whole breast external beam radiotherapy in the prospective multi-centre XXXXXXX cohort study. Materials and methods Using demographic and treatment-related features (m=122) from patients (n=2,058) at 26 centres, we trained eight ML algorithms with 10-fold cross-validation in a 50:50 random-split dataset with class stratification to predict acute breast desquamation. Based on performance in the validation dataset, the Logistic Model Tree, Random Forest and Naïve Bayes models were taken forward to cost-sensitive learning optimisation. Results 192 patients experienced acute desquamation. Re-sampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the ‘hero’ model was the cost-sensitive Random Forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m=114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an AUC of 0.77 in the validation cohort. Conclusion ML algorithms with re-sampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, in order to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan.
- Normal Tissue Complication Probability Modelling for Toxicity Prediction and Patient Selection in Proton Beam Therapy to the Central Nervous System: A Literature ReviewGaito, S., Burnet, N., Aznar, M., Crellin, A., Indelicato, D.J., Ingram, S., Pan, S., Price, G., Hwang, E., France, A., Smith, E., and Whitfield, G.Clinical Oncology 2022
Normal tissue complication probability (NTCP) models can guide clinical decision making in radiotherapy. In recent years, they have been used for patient selection for proton beam therapy (PBT) for some anatomical tumour sites. This review synthesizes the published evidence regarding the use of NTCP models to predict the toxicity of PBT, for different end points in patients with brain tumours. A search of Medline and Embase using the Patients, Intervention, Comparison, Outcome (PICO) criteria was undertaken. In total, 37 articles were deemed relevant and were reviewed in detail. Nineteen articles on NTCP modelling of toxicity end points were included. Of these, 11 were comparative NTCP studies of PBT versus conventional photon radiotherapy (XRT), which evaluated differences in plan dosimetry and then assumed that XRT-derived literature estimates of NTCP would be applicable to both. Seven papers derived NTCP models based on PBT outcome data, two of which provided model parameters. Among analysed end points, the reduced risk of secondary tumours with PBT as compared with XRT is estimated – through modelling studies – to be considerable and was highlighted by most authors. For other analysed end points, the clinical benefit of PBT mainly depends on tumour location in relation to organs at risk as well as prescription doses. NTCP models can be useful tools for treatment plan comparison. However, most published toxicity data were derived from XRT cohorts; this review has highlighted the need for further studies relating dose-volume parameters to observed toxicity in PBT-treated patients. Specifically, there is a need for PBT-specific NTCP models that can be implemented in the clinical practice. NTCP models built on robust clinical data for the most common radiotherapy toxicities in the brain would potentially redefine the current indications for PBT.
- The suitability of micronuclei as markers of relative biological effectHeaven, Charlotte J, Wanstall, Hannah C, Henthorn, Nicholas T, Warmenhoven, John-William, Ingram, Samuel P, Chadwick, Amy L, Santina, Elham, Honeychurch, Jamie, Schmidt, Christine K, Kirkby, Karen J, Kirkby, Norman F, Burnet, Neil G, and Merchant, Michael JMutagenesis 2022
Micronucleus (MN) formation is routinely used as a biodosimeter for radiation exposures and has historically been used as a measure of DNA damage in cells. Strongly correlating with dose, MN are also suggested to indicate radiation quality, differentiating between particle and photon irradiation. The “gold standard” for measuring MN formation is Fenech’s cytokinesis-block micronucleus (CBMN) cytome assay, which uses the cytokinesis blocking agent cytochalasin-B. Here, we present a comprehensive analysis of the literature investigating MN induction trends in vitro, collating 193 publications, with 2476 data points. Data were collected from original studies that used the CBMN assay to quantify MN in response to ionizing radiation in vitro. Overall, the meta-analysis showed that individual studies mostly have a linear increase of MN with dose [85% of MN per cell (MNPC) datasets and 89% of percentage containing MN (PCMN) datasets had an R2 greater than 0.90]. However, there is high variation between studies, resulting in a low R2 when data are combined (0.47 for MNPC datasets and 0.60 for PCMN datasets). Particle type, species, cell type, and cytochalasin-B concentration were suggested to influence MN frequency. However, variation in the data meant that the effects could not be strongly correlated with the experimental parameters investigated. There is less variation between studies when comparing the PCMN rather than the number of MNPC. Deviation from CBMN protocol specified timings did not have a large effect on MN induction. However, further analysis showed less variation between studies following Fenech’s protocol closely, which provided more reliable results. By limiting the cell type and species as well as only selecting studies following the Fenech protocol, R2 was increased to 0.64 for both measures. We therefore determine that due to variation between studies, MN are currently a poor predictor of radiation-induced DNA damage and make recommendations for futures studies assessing MN to improve consistency between datasets.
- Estimating the percentage of patients who might benefit from proton beam therapy instead of X-ray radiotherapyBurnet, Neil G, Mee, Thomas, Gaito, Simona, Kirkby, Norman F, Aitkenhead, Adam H, Anandadas, Carmel N, Aznar, Marianne C, Barraclough, Lisa H, Borst, Gerben, Charlwood, Frances C, Clarke, Matthew, Colaco, Rovel J, Crellin, Adrian M, Defourney, Noemie N, Hague, Christina J, Harris, Margaret, Henthorn, Nicholas T, Hopkins, Kirsten I, Hwang, E, Ingram, Sam P, Kirkby, Karen J, Lee, Lip W, Lines, David, Lingard, Zoe, Lowe, Matthew, Mackay, Ranald I, McBain, Catherine A, Merchant, Michael J, Noble, David J, Pan, Shermaine, Price, James M, Radhakrishna, Ganesh, Reboredo-Gil, David, Salem, Ahmed, Sashidharan, Srijith, Sitch, Peter, Smith, Ed, Smith, Edward AK, Taylor, Michael J, Thomson, David J, Thorp, Nicola J, Underwood, Tracy SA, Warmenhoven, John W, Wylie, James P, and Whitfield, GillianThe British Journal of Radiology 2022
High-energy Proton Beam Therapy (PBT) commenced in England in 2018 and NHS England commissions PBT for 1.5% of patients receiving radical radiotherapy. We sought expert opinion on the level of provision. Invitations were sent to 41 colleagues working in PBT, most at one UK centre, to contribute by completing a spreadsheet. 39 responded: 23 (59%) completed the spreadsheet; 16 (41%) declined, arguing that clinical outcome data are lacking, but joined six additional site-specialist oncologists for two consensus meetings. The spreadsheet was pre-populated with incidence data from Cancer Research UK and radiotherapy use data from the National Cancer Registration and Analysis Service. ‘Mechanisms of Benefit’ of reduced growth impairment, reduced toxicity, dose escalation and reduced second cancer risk were examined. The most reliable figure for percentage of radical radiotherapy patients likely to benefit from PBT was that agreed by 95% of the 23 respondents at 4.3%, slightly larger than current provision. The median was 15% (range 4–92%) and consensus median 13%. The biggest estimated potential benefit was from reducing toxicity, median benefit to 15% (range 4–92%), followed by dose escalation median 3% (range 0 to 47%); consensus values were 12 and 3%. Reduced growth impairment and reduced second cancer risk were calculated to benefit 0.5% and 0.1%. The most secure estimate of percentage benefit was 4.3% but insufficient clinical outcome data exist for confident estimates. The study supports the NHS approach of using the evidence base and developing it through randomised trials, non-randomised studies and outcomes tracking. Less is known about the percentage of patients who may benefit from PBT than is generally acknowledged. Expert opinion varies widely. Insufficient clinical outcome data exist to provide robust estimates. Considerable further work is needed to address this, including international collaboration; much is already underway but will take time to provide mature data.
- Impact of DNA Geometry and Scoring on Monte Carlo Track-Structure Simulations of Initial Radiation-Induced DamageBertolet, Alejandro, Ramos-Méndez, José, McNamara, Aimee, Yoo, Dohyeon, Ingram, Samuel, Henthorn, Nicholas, Warmenhoven, John-William, Faddegon, Bruce, Merchant, Michael, McMahon, Stephen J, Paganetti, Harald, and Schuemann, JanRadiation Research Jun 2022
Track structure Monte Carlo simulations are a useful tool to investigate the damage induced to DNA by ionizing radiation. These simulations usually rely on simplified geometrical representations of the DNA subcomponents. DNA damage is determined by the physical and physico-chemical processes occurring within these volumes. In particular, damage to the DNA backbone is generally assumed to result in strand breaks. DNA damage can be categorized as direct (ionization of an atom part of the DNA molecule) or indirect (damage from reactive chemical species following water radiolysis). We also consider quasi-direct effects, i.e., damage originated by charge transfers after ionization of the hydration shell surrounding the DNA. DNA geometries are needed to account for the damage induced by ionizing radiation, and different geometry models can be used for speed or accuracy reasons. In this work, we use the Monte Carlo track structure tool TOPAS-nBio, built on top of Geant4-DNA, for simulation at the nanometer scale to evaluate differences among three DNA geometrical models in an entire cell nucleus, including a sphere/spheroid model specifically designed for this work. In addition to strand breaks, we explicitly consider the direct, quasi-direct, and indirect damage induced to DNA base moieties. We use results from the literature to determine the best values for the relevant parameters. For example, the proportion of hydroxyl radical reactions between base moieties was 80\%, and between backbone, moieties was 20\%, the proportion of radical attacks leading to a strand break was 11\%, and the expected ratio of base damages and strand breaks was 2.5–3. Our results show that failure to update parameters for new geometric models can lead to significant differences in predicted damage yields.
- A computational approach to quantifying miscounting of radiation-induced double-strand break immunofluorescent fociIngram, Samuel P., Warmenhoven, John-William, Henthorn, Nicholas T., Chadiwck, Amy L., Santina, Elham E., McMahon, Stephen J., Schuemann, Jan, Kirkby, Norman F., Mackay, Ranald I., Kirkby, Karen J., and Merchant, Michael J.Communications Biology Jun 2022
Immunofluorescent tagging of DNA double-strand break (DSB) markers, such as γ-H2AX and other DSB repair proteins, are powerful tools in understanding biological consequences following irradiation. However, whilst the technique is widespread, there are many uncertainties related to its ability to resolve and reliably deduce the number of foci when counting using microscopy. We present a new tool for simulating radiation-induced foci in order to evaluate microscope performance within in silico immunofluorescent images. Simulations of the DSB distributions were generated using Monte Carlo track-structure simulation. For each DSB distribution, a corresponding DNA repair process was modelled and the un-repaired DSBs were recorded at several time points. Corresponding microscopy images for both a DSB and (γ-H2AX) fluorescent marker were generated and compared for different microscopes, radiation types and doses. Statistically significant differences in miscounting were found across most of the tested scenarios. These inconsistencies were propagated through to repair kinetics where there was a perceived change between radiation-types. These changes did not reflect the underlying repair rate and were caused by inconsistencies in foci counting. We conclude that these underlying uncertainties must be considered when analysing images of DNA damage markers to ensure differences observed are real and are not caused by non-systematic miscounting. PyFoci is a tool that simulates distributions of fluorescently labeled DNA double-strand break marker protein foci and allows the estimation of miscounting under different radiation types, doses and microscopy settings.
2023
- Effects of Differing Underlying Assumptions in In Silico Models on Predictions of DNA Damage and RepairWarmenhoven, John W., Henthorn, Nicholas T., McNamara, Aimee L., Ingram, Samuel P., Merchant, Michael J., Kirkby, Karen J., Schuemann, Jan, Paganetti, Harald, Prise, Kevin M., and McMahon, Stephen J.Radiation Research Jun 2023
The induction and repair of DNA double-strand breaks (DSBs) are critical factors in the treatment of cancer by radiotherapy. To investigate the relationship between incident radiation and cell death through DSB induction many in silico models have been developed. These models produce and use custom formats of data, specific to the investigative aims of the researchers, and often focus on particular pairings of damage and repair models. In this work we use a standard format for reporting DNA damage to evaluate combinations of different, independently developed, models. We demonstrate the capacity of such inter-comparison to determine the sensitivity of models to both known and implicit assumptions. Specifically, we report on the impact of differences in assumptions regarding patterns of DNA damage induction on predicted initial DSB yield, and the subsequent effects this has on derived DNA repair models. The observed differences highlight the importance of considering initial DNA damage on the scale of nanometres rather than micrometres. We show that the differences in DNA damage models result in subsequent repair models assuming significantly different rates of random DSB end diffusion to compensate. This in turn leads to disagreement on the mechanisms responsible for different biological endpoints, particularly when different damage and repair models are combined, demonstrating the importance of inter-model comparisons to explore underlying model assumptions.