The use of radiotherapy, surgery and chemotherapy in the curative treatment of cancer: results from the FORTY (Favourable Outcomes from RadioTherapY) project.

OBJECTIVES: Radiotherapy, surgery and chemotherapy play key roles in the curative treatment of cancer, alone and in combination. Quantifying their roles is essential for equipment provision and workforce planning. The estimate that 40% of cancer patients are cured by RT has been used extensively to inform and influence policy but is relatively old and warrants review. METHODS: Patient, tumour and treatment event data was obtained for the 5 year period from 2009 to 2013, allowing a further 5 years for survival outcomes to be known. We analysed patient-level data on utilisation of surgery, radiotherapy, and chemotherapy in cancer patients in England. Data were sourced from Public Health England, using National Cancer Registrations, the National Radiotherapy Dataset (RTDS) and the Systemic Anti-Cancer Therapy Dataset (SACT). All tumour sites (excluding C44) and ages were included. We analysed three cohorts: all patients [n = 1,029,569], patients who survived 5 years or more [n = 537,970] and patients who survived <5 years [n = 491,599]. RESULTS: Overall cancer-specific 5-year survival was 52%, and in those patients, surgery was the most common curative treatment, with 80% receiving surgery, alone or in combination; radiotherapy was delivered to 39% and chemotherapy to 29%; 45% received two and 13% all three modalities. CONCLUSIONS: The high proportion receiving multi-modality treatment emphasises the importance of integrated, resourced, multidisciplinary cancer care. Radiotherapy was delivered to almost 40% of patients who survived 5 years which underlines its importance in cancer management. ADVANCES IN KNOWLEDGE: The results are essential in planning cancer services. They also inform the public health narrative.

A computational approach to quantifying miscounting of radiation-induced double-strand break immunofluorescent foci.

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.

The suitability of micronuclei as markers of relative biological effect.

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 radiotherapy.

OBJECTIVES: 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. METHODS: 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. RESULTS: 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%. CONCLUSIONS: 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. ADVANCES IN KNOWLEDGE: 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.

A Monte Carlo study of different LET definitions and calculation parameters for proton beam therapy.

The strongin vitroevidence that proton Relative Biological Effectiveness (RBE) varies with Linear Energy Transfer (LET) has led to an interest in applying LET within treatment planning. However, there is a lack of consensus on LET definition, Monte Carlo (MC) parameters or clinical methodology. This work aims to investigate how common variations of LET definition may affect potential clinical applications. MC simulations (GATE/GEANT4) were used to calculate absorbed dose and different types of LET for a simple Spread Out Bragg Peak (SOBP) and for four clinical PBT plans covering a range of tumour sites. Variations in the following LET calculation methods were considered: (i) averaging (dose-averaged LET (LETd) & track-averaged LET); (ii) scoring (LETdto water, to medium and to mass density); (iii) particle inclusion (LETdto all protons, to primary protons and to particles); (iv) MC settings (hit type and Maximum Step Size (MSS)). LET distributions were compared using: qualitative comparison, LET Volume Histograms (LVHs), single value criteria (maximum and mean values) and optimised LET-weighted dose models. Substantial differences were found between LET values in averaging, scoring and particle type. These differences depended on the methodology, but for one patient a difference of ∼100% was observed between the maximum LETdfor all particles and maximum LETdfor all protons within the brainstem in the high isodose region (4 keVµm-1and 8 keVµm-1respectively). An RBE model using LETdincluding heavier ions was found to predict substantially different LET-weighted dose compared to those using other LET definitions. In conclusion, the selection of LET definition may affect the results of clinical metrics considered in treatment planning and the results of an RBE model. The authors' advocate for the scoring of dose-averaged LET to water for primary and secondary protons using a random hit type and automated MSS.

Hi-C implementation of genome structure for in silico models of radiation-induced DNA damage.

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 a 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.

Radiobiologically derived biphasic fractionation schemes to overcome the effects of tumour hypoxia.

OBJECTIVE: As a fractionated course of radiotherapy proceeds tumour shrinkage leads to resolution of hypoxia and the initiation of accelerated proliferation of radioresistant cancer cells with better repair capacity. We hypothesise that, in tumours with significant hypoxia, improved tumour control could be achieved with biphasic fractionation schedules that either use acceleration after 3-4 weeks of conventional radiotherapy or deliver a higher proportional dose towards the end of a course of treatment. We conducted a modelling study based on the concept of biological effective dose (BED) comparing such novel regimens with conventional fractionation. METHODS: The comparator conventional fractionation schedule 70 Gy in 35 fractions delivered over 7 weeks was tested against the following novel regimens, both of which were designed to be isoeffective in terms of late normal tissue toxicity.40 Gy in 20 fractions over 4 weeks followed by 22.32 Gy in 6 consecutive daily fractions (delayed acceleration)30.4 Gy in 27 fractions over 4 weeks followed by 40 Gy in 15 fractions over 3 weeks (temporal dose redistribution)The delayed acceleration regimen is exactly identical to that of the comparator schedule over the first 28 days and the BED gains with the novel schedule are achieved during the second phase of treatment when reoxygenation is complete. For the temporal redistribution regimen, it was assumed that the reoxygenation fraction progressively increases during the first 4 weeks of treatment and an iterative approach was used to calculate the final tumour BED for varying hypoxic fractions. RESULTS: Novel fractionation with delayed acceleration or temporal fractionation results in tumour BED gains equivalent to 3.5-8 Gy when delivered in 2 Gy fractions. CONCLUSION: In hypoxic tumours, novel fractionation strategies result in significantly higher tumour BED in comparison to conventional fractionation. ADVANCES IN KNOWLEDGE: We demonstrate that novel biphasic fractionation regimens could overcome the effects of tumour hypoxia resulting in biological dose escalation.

Proton beam therapy: perspectives on the National Health Service England clinical service and research programme.

The UK has an important role in the evaluation of proton beam therapy (PBT) and takes its place on the world stage with the opening of the first National Health Service (NHS) PBT centre in Manchester in 2018, and the second in London coming in 2020. Systematic evaluation of the role of PBT is a key objective. By September 2019, 108 patients had started treatment, 60 paediatric, 19 teenagers and young adults and 29 adults. Obtaining robust outcome data is vital, if we are to understand the strengths and weaknesses of current treatment approaches. This is important in demonstrating when PBT will provide an advantage and when it will not, and in quantifying the magnitude of benefit.The UK also has an important part to play in translational PBT research, and building a research capability has always been the vision. We are perfectly placed to perform translational pre-clinical biological and physical experiments in the dedicated research room in Manchester. The nature of DNA damage from proton irradiation is considerably different from X-rays and this needs to be more fully explored. A better understanding is needed of the relative biological effectiveness (RBE) of protons, especially at the end of the Bragg peak, and of the effects on tumour and normal tissue of PBT combined with conventional chemotherapy, targeted drugs and immunomodulatory agents. These experiments can be enhanced by deterministic mathematical models of the molecular and cellular processes of DNA damage response. The fashion of ultra-high dose rate FLASH irradiation also needs to be explored.

Modelling and Bayesian adaptive prediction of individual patients' tumour volume change during radiotherapy.

The aim of this study is to develop a mathematical modelling method that can predict individual patients' response to radiotherapy, in terms of tumour volume change during the treatment. The main concept is to start from a population-average model, which is subsequently updated from an individual's tumour volume measurement. The model becomes increasingly personalized and so too does the prediction it produces. This idea of adaptive prediction was realised by using a Bayesian approach for updating the model parameters. The feasibility of the developed method was demonstrated on the data from 25 non-small cell lung cancer patients treated with helical tomotherapy, during which tumour volume was measured from daily imaging as part of the image-guided radiotherapy. The method could provide useful information for adaptive treatment planning and dose scheduling based on the patient's personalised response.

Mathematical modelling of tumour volume dynamics in response to stereotactic ablative radiotherapy for non-small cell lung cancer.

This paper reports a modelling study of tumour volume dynamics in response to stereotactic ablative radiotherapy (SABR). The main objective was to develop a model that is adequate to describe tumour volume change measured during SABR, and at the same time is not excessively complex as lacking support from clinical data. To this end, various modelling options were explored, and a rigorous statistical method, the Akaike information criterion, was used to help determine a trade-off between model accuracy and complexity. The models were calibrated to the data from 11 non-small cell lung cancer patients treated with SABR. The results showed that it is feasible to model the tumour volume dynamics during SABR, opening up the potential for using such models in a clinical environment in the future.

The effect of the G1-S transition checkpoint on an age structured cell cycle model.

Knowledge of how a population of cancerous cells progress through the cell cycle is vital if the population is to be treated effectively, as treatment outcome is dependent on the phase distributions of the population. Estimates on the phase distribution may be obtained experimentally however the errors present in these estimates may effect treatment efficacy and planning. If mathematical models are to be used to make accurate, quantitative predictions concerning treatments, whose efficacy is phase dependent, knowledge of the phase distribution is crucial. In this paper it is shown that two different transition rates at the G1-S checkpoint provide a good fit to a growth curve obtained experimentally. However, the different transition functions predict a different phase distribution for the population, but both lying within the bounds of experimental error. Since treatment outcome is effected by the phase distribution of the population this difference may be critical in treatment planning. Using an age-structured population balance approach the cell cycle is modelled with particular emphasis on the G1-S checkpoint. By considering the probability of cells transitioning at the G1-S checkpoint, different transition functions are obtained. A suitable finite difference scheme for the numerical simulation of the model is derived and shown to be stable. The model is then fitted using the different probability transition functions to experimental data and the effects of the different probability transition functions on the model's results are discussed.

Evaluation of poly (ADP-ribose) polymerase inhibitor ABT-888 combined with radiotherapy and temozolomide in glioblastoma.

BACKGROUND: The cytotoxicity of radiotherapy and chemotherapy can be enhanced by modulating DNA repair. PARP is a family of enzymes required for an efficient base-excision repair of DNA single-strand breaks and inhibition of PARP can prevent the repair of these lesions. The current study investigates the trimodal combination of ABT-888, a potent inhibitor of PARP1-2, ionizing radiation and temozolomide(TMZ)-based chemotherapy in glioblastoma (GBM) cells. METHODS: Four human GBM cell lines were treated for 5 h with 5 µM ABT-888 before being exposed to X-rays concurrently with TMZ at doses of 5 or 10 µM for 2 h. ABT-888's PARP inhibition was measured using immunodetection of poly(ADP-ribose) (pADPr). Cell survival and the different cell death pathways were examined via clonogenic assay and morphological characterization of the cell and cell nucleus. RESULTS: Combining ABT-888 with radiation yielded enhanced cell killing in all four cell lines, as demonstrated by a sensitizer enhancement ratio at 50% survival (SER50) ranging between 1.12 and 1.37. Radio- and chemo-sensitization was further enhanced when ABT-888 was combined with both X-rays and TMZ in the O6-methylguanine-DNA-methyltransferase (MGMT)-methylated cell lines with a SER50 up to 1.44. This effect was also measured in one of the MGMT-unmethylated cell lines with a SER50 value of 1.30. Apoptosis induction by ABT-888, TMZ and X-rays was also considered and the effect of ABT-888 on the number of apoptotic cells was noticeable at later time points. In addition, this work showed that ABT-888 mediated sensitization is replication dependent, thus demonstrating that this effect might be more pronounced in tumour cells in which endogenous replication lesions are present in a larger proportion than in normal cells. CONCLUSIONS: This study suggests that ABT-888 has the clinical potential to enhance the current standard treatment for GBM, in combination with conventional chemo-radiotherapy. Interestingly, our results suggest that the use of PARP inhibitors might be clinically significant in those patients whose tumour is MGMT-unmethylated and currently derive less benefit from TMZ.

Optimal dosing of cancer chemotherapy using model predictive control and moving horizon state/parameter estimation.

Model predictive control (MPC), originally developed in the community of industrial process control, is a potentially effective approach to optimal scheduling of cancer therapy. The basis of MPC is usually a state-space model (a system of ordinary differential equations), whereby existing studies usually assume that the entire states can be directly measured. This paper aims to demonstrate that when the system states are not fully measurable, in conjunction with model parameter discrepancy, MPC is still a useful method for cancer treatment. This aim is achieved through the application of moving horizon estimation (MHE), an optimisation-based method to jointly estimate the system states and parameters. The effectiveness of the MPC-MHE scheme is illustrated through scheduling the dose of tamoxifen for simulated tumour-bearing patients, and the impact of estimation horizon and magnitude of parameter discrepancy is also investigated.

In vitro evaluation of combined temozolomide and radiotherapy using X rays and high-linear energy transfer radiation for glioblastoma.

High-linear energy transfer radiation offers superior biophysical properties over conventional radiotherapy and may have a great potential for treating radioresistant tumors, such as glioblastoma. However, very little pre-clinical data exists on the effects of high-LET radiation on glioblastoma cell lines and on the concomitant application of chemotherapy. This study investigates the in vitro effects of temozolomide in combination with low-energy protons and α particles. Cell survival, DNA damage and repair, and cell growth were examined in four human glioblastoma cell lines (LN18, T98G, U87 and U373) after treatment with either X rays, protons (LET 12.91 keV/µm), or α particles (LET 99.26 keV/µm) with or without concurrent temozolomide at clinically-relevant doses of 25 and 50 µM. The relative biological effectiveness at 10% survival (RBE(10)) increased as LET increased: 1.17 and 1.06 for protons, and 1.84 and 1.68 for α particles in the LN18 and U87 cell lines, respectively. Temozolomide administration increased cell killing in the O(6)-methylguanine DNA methyltransferase-methylated U87 and U373 cell lines. In contrast, temozolomide provided no therapeutic enhancement in the methylguanine DNA methyltransferase-unmethylated LN18 and T98G cell lines. In addition, the residual number of γ-H2AX foci at 24 h after treatment with radiation and concomitant temozolomide was found to be lower than or equal to that expected by DNA damage with either of the individual treatments. Kinetics of foci disappearance after X-ray and proton irradiation followed similar time courses; whereas, loss of γ-H2AX foci after α particle irradiation occurred at a slower rate than that by low-LET radiation (half-life 12.51-16.87 h). The combination of temozolomide with different radiation types causes additive rather than synergistic cytotoxicity. Nevertheless, particle therapy combined with chemotherapy may offer a promising alternative with the additional benefit of superior biophysical properties. It is also possible that new fractionation schedules could be designed to exploit the change in DNA repair kinetics when MGMT-methylated cells respond to high-LET radiation.

A mathematical model of brain tumour response to radiotherapy and chemotherapy considering radiobiological aspects.

Glioblastoma is the most frequent and malignant brain tumour. For many years, the conventional treatment has been maximal surgical resection followed by radiotherapy (RT), with a median survival time of less than 10 months. Previously, the use of adjuvant chemotherapy (given after RT) has failed to demonstrate a statistically significant survival advantage. Recently, a randomized phase III trial has confirmed the benefit of temozolomide (TMZ) and has defined a new standard of care for the treatment of patients with high-grade brain tumours. The results showed an increase of 2.5 months in median survival, and 16.1% in 2 year survival, for patients receiving RT with TMZ compared with RT alone. It is not clear whether the major benefit of TMZ comes from either concomitant administration of TMZ with RT, or from six cycles of adjuvant TMZ, or both. The objectives were to develop our original model, which addressed survival after RT, to construct a new module to assess the potential role of TMZ from clinical data, and to explore its synergistic contribution in addition to radiation. The model has been extended to include radiobiological parameters. The addition of the linear quadratic equation to describe cellular response to treatment has enabled us to quantify the effects of radiation and TMZ in radiobiological terms. The results indicate that the model achieves an excellent fit to the clinical data, with the assumption that TMZ given concomitantly with RT synergistically increases radiosensitivity. The alternative, that the effect of TMZ is due only to direct cell killing, does not fit the clinical data so well. The addition of concomitant TMZ appears to change the radiobiological parameters. This aspect of our results suggests possible treatment developments. Our observations need further evaluations in real clinical trials, may suggest treatment strategies for new trials, and inform their design.

A mathematical model of the treatment and survival of patients with high-grade brain tumours.

More years of life per patient are lost as the result of primary brain tumours than any other form of cancer. The most aggressive of these is known as glioblastoma (GBM). The median survival time of patients with GBM is under 10 months and the outlook has hardly improved over the past 20 years. Generally, these tumours are remarkably resistant to radiotherapy and yet about 2-3% of all GBMs appear to be cured. The objectives of this study were to formulate a mathematical and phenomenological model of tumour growth in a population of patients with GBM to predict survival, and to use the model to extract biological information from clinical data. The model describes the growth of the tumour and the resulting damage to the normal brain using simple concepts borrowed from chemical reaction engineering. Death is assumed to result when the amount of surviving normal brain falls to a critical level. Radiotherapy is assumed to destroy tumour but not healthy brain. Simple rules are included to represent approximately the clinician's decisions about what type of treatment to offer each patient. A population of patients is constructed by assuming that key parameters can be sampled from statistical distributions. Following Monte Carlo simulation, the model can be fitted to data from clinical trials. The model reproduces clinical data extremely accurately. This suggests that the long-term survivors are not a separate sub-population but are the 'lucky tail' of a unimodal distribution. The estimated values of radiation sensitivity (represented as SF2, the survival fraction after 2Gy) suggest the presence of severe hypoxia, which renders cells less sensitive to radiation. The model can predict the probable age distribution of tumours at presentation. The model shows the complicated effects of waiting times for treatment on the survival outcomes, and is used to predict the effects of escalation of radiotherapy dose. The model may aid the design of clinical trials using radiotherapy for patients with GBM, especially in helping to estimate the size of trial required. It is also designed in a generic form, and might be applicable to other tumour types.