Subcellular functions of tau mediates repair response and synaptic homeostasis in injury.

Injury responses in terminally differentiated cells such as neurons is tightly regulated by pathways aiding homeostatic maintenance. Cancer patients subjected to neuronal injury in brain radiation experience cognitive declines similar to those seen in primary neurodegenerative diseases. Numerous studies have investigated the effect of radiation in proliferating cells of the brain, yet the impact in differentiated, post-mitotic neurons, especially the structural and functional alterations remain largely elusive. We identified that microtubule-associated tau is a critical player in neuronal injury response via compartmentalized functions in both repair-centric and synaptic regulatory pathways. Ionizing radiation-induced injury acutely induces increase in phosphorylated tau in the nucleus and directly interacts with histone 2AX (H2AX), a DNA damage repair (DDR) marker. Loss of tau significantly reduced H2AX after irradiation, indicating that tau may play an important role in neuronal DDR response. We also observed that loss of tau increases eukaryotic elongation factor levels after irradiation, the latter being a positive regulator of protein translation. This cascades into a significant increase in synaptic proteins, resulting in disrupted homeostasis. Consequently, novel object recognition test showed decrease in learning and memory in tau-knockout mice after irradiation, and electroencephalographic activity showed increase in delta and theta band oscillations, often seen in dementia patients. Our findings demonstrate tau's previously undefined, multifunctional role in acute responses to injury, ranging from DDR response in the nucleus to synaptic function within a neuron. Such knowledge is vital to develop therapeutic strategies targeting neuronal injury in cognitive decline for at risk and vulnerable populations.

Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: A multidimensional perspective.

Neoadjuvant chemoradiotherapy (NCRT) followed by surgery has been established as the standard treatment strategy for operable locally advanced esophageal cancer (EC). However, achieving pathologic complete response (pCR) or near pCR to NCRT is significantly associated with a considerable improvement in survival outcomes, while pCR patients may help organ preservation for patients by active surveillance to avoid planned surgery. Thus, there is an urgent need for improved biomarkers to predict EC chemoradiation response in research and clinical settings. Advances in multiple high-throughput technologies such as next-generation sequencing have facilitated the discovery of novel predictive biomarkers, specifically based on multi-omics data, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra. The application of multi-omics data has shown the benefits in improving the understanding of underlying mechanisms of NCRT sensitivity/resistance in EC. Particularly, the prominent development of artificial intelligence (AI) has introduced a new direction in cancer research. The integration of multi-omics data has significantly advanced our knowledge of the disease and enabled the identification of valuable biomarkers for predicting treatment response from diverse dimension levels, especially with rapid advances in biotechnological and AI methodologies. Herein, we summarize the current status of research on the use of multi-omics technologies in predicting NCRT response for EC patients. Current limitations, challenges, and future perspectives of these multi-omics platforms will be addressed to assist in experimental designs and clinical use for further integrated analysis.

Looking on the horizon; potential and unique approaches to developing radiation countermeasures for deep space travel.

Future lunar missions and beyond will require new and innovative approaches to radiation countermeasures. The Translational Research Institute for Space Health (TRISH) is focused on identifying and supporting unique approaches to reduce risks to human health and performance on future missions beyond low Earth orbit. This paper will describe three funded and complementary avenues for reducing the risk to humans from radiation exposure experienced in deep space. The first focus is on identifying new therapeutic targets to reduce the damaging effects of radiation by focusing on high throughput genetic screens in accessible, sometimes called lower, organism models. The second focus is to design innovative approaches for countermeasure development with special attention to nucleotide-based methodologies that may constitute a more agile way to design therapeutics. The final focus is to develop new and innovative ways to test radiation countermeasures in a human model system. While animal studies continue to be beneficial in the study of space radiation, they can have imperfect translation to humans. The use of three-dimensional (3D) complex in vitro models is a promising approach to aid the development of new countermeasures and personalized assessments of radiation risks. These three distinct and unique approaches complement traditional space radiation efforts and should provide future space explorers with more options to safeguard their short and long-term health.

Targeting hippocampal neurogenesis to protect astronauts' cognition and mood from decline due to space radiation effects.

Neurogenesis is an essential, lifelong process during which neural stem cells generate new neurons within the hippocampus, a center for learning, memory, and mood control. Neural stem cells are vulnerable to environmental insults spanning from chronic stress to radiation. These insults reduce their numbers and diminish neurogenesis, leading to memory decline, anxiety, and depression. Preserving neural stem cells could thus help prevent these neurogenesis-associated pathologies, an outcome particularly important for long-term space missions where environmental exposure to radiation is significantly higher than on Earth. Multiple developments, from mechanistic discoveries of radiation injury on hippocampal neurogenesis to new platforms for the development of selective, specific, effective, and safe small molecules as neurogenesis-protective agents hold great promise to minimize radiation damage on neurogenesis. In this review, we summarize the effects of space-like radiation on hippocampal neurogenesis. We then focus on current advances in drug discovery and development and discuss the nuclear receptor TLX/NR2E1 (oleic acid receptor) as an example of a neurogenic target that might rescue neurogenesis following radiation.

Cognitive and Imaging Differences After Proton and Photon Whole Brain Irradiation in a Preclinical Model.

PURPOSE: Compared with photon cranial radiation therapy (X-CRT), proton cranial radiation therapy (P-CRT) offers potential advantages in limiting radiation-induced sequalae in the treatment of pediatric brain tumors. This study aims to identify cognitive, functional magnetic resonance and positron emission tomography imaging markers and molecular differences between the radiation modalities. METHODS AND MATERIALS: Juvenile rats received a single faction of 10 Gy (relative biological effectiveness-weighted dose) delivered with 6 MV X-CRT or at the midspread out Bragg peak of a 100 MeV P-CRT beam. At 3, 6, and 12 months post-CRT, executive function was measured using 5-choice serial reaction time task. At ∼12 months post-CRT, animals were imaged with 18F-Flurodeoxy-glucose positron emission tomography imaging followed by functional ex vivo magnetic resonance imaging and stained for markers of neuroinflammation. RESULTS: Irradiated animals had cognitive impairment with a higher number of omissions and lower incorrect and premature responses compared with sham (P ≤ .05). The accuracy of the animals' X-CRT was less than that of sham (P ≤ .001). No significant difference in rates of cognitive change were found between the radiation modalities. At 12 months post-CRT, glucose metabolism was significantly higher than sham in X-CRT (P = .04) but not P-CRT. Using diffusion tensor imaging, P-CRT brains were found to have higher white matter volume and fiber lengths compared with sham (P < .03). Only X-CRT animals had higher apparent diffusion coefficient values compared with sham (P = .04). P-CRT animals had more connectomic changes compared with X-CRT. Correlative analysis identified several imaging features with cognitive performance. Furthermore, microgliosis (P < .05), astrogliosis (P < .01), and myelin thinning (P <.05) were observed in both radiation modalities, with X-CRT showing slightly more inflammation. CONCLUSIONS: Both P-CRT and X-CRT lead to neurocognitive changes compared with sham. Although no significant difference was observed in neuroinflammation between the irradiated groups, differences were found in late-term glucose metabolism and brain connectome. Our results indicate that despite relative biological effectiveness weighting of the proton dose there are still differential effects which warrants further investigation.

Mapping the Relative Biological Effectiveness of Proton, Helium and Carbon Ions with High-Throughput Techniques.

Large amounts of high quality biophysical data are needed to improve current biological effects models but such data are lacking and difficult to obtain. The present study aimed to more efficiently measure the spatial distribution of relative biological effectiveness (RBE) of charged particle beams using a novel high-accuracy and high-throughput experimental platform. Clonogenic survival was selected as the biological endpoint for two lung cancer cell lines, H460 and H1437, irradiated with protons, carbon, and helium ions. Ion-specific multi-step microplate holders were fabricated such that each column of a 96-well microplate is spatially situated at a different location along a particle beam path. Dose, dose-averaged linear energy transfer (LETd), and dose-mean lineal energy (yd) were calculated using an experimentally validated Geant4-based Monte Carlo system. Cells were irradiated at the Heidelberg Ion Beam Therapy Center (HIT). The experimental results showed that the clonogenic survival curves of all tested ions were yd-dependent. Both helium and carbon ions achieved maximum RBEs within specific yd ranges before biological efficacy declined, indicating an overkill effect. For protons, no overkill was observed, but RBE increased distal to the Bragg peak. Measured RBE profiles strongly depend on the physical characteristics such as yd and are ion specific.

Author Correction: Exploring the advantages of intensity-modulated proton therapy: experimental validation of biological effects using two different beam intensity-modulation patterns.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Exploring the advantages of intensity-modulated proton therapy: experimental validation of biological effects using two different beam intensity-modulation patterns.

In current treatment plans of intensity-modulated proton therapy, high-energy beams are usually assigned larger weights than low-energy beams. Using this form of beam delivery strategy cannot effectively use the biological advantages of low-energy and high-linear energy transfer (LET) protons present within the Bragg peak. However, the planning optimizer can be adjusted to alter the intensity of each beamlet, thus maintaining an identical target dose while increasing the weights of low-energy beams to elevate the LET therein. The objective of this study was to experimentally validate the enhanced biological effects using a novel beam delivery strategy with elevated LET. We used Monte Carlo and optimization algorithms to generate two different intensity-modulation patterns, namely to form a downslope and a flat dose field in the target. We spatially mapped the biological effects using high-content automated assays by employing an upgraded biophysical system with improved accuracy and precision of collected data. In vitro results in cancer cells show that using two opposed downslope fields results in a more biologically effective dose, which may have the clinical potential to increase the therapeutic index of proton therapy.

Evaluation of the accuracy of deformable image registration on MRI with a physical phantom.

BACKGROUND AND PURPOSE: Magnetic resonance imaging (MRI) has gained popularity in radiation therapy simulation because it provides superior soft tissue contrast, which facilitates more accurate target delineation compared with computed tomography (CT) and does not expose the patient to ionizing radiation. However, image registration errors in commercial software have not been widely reported. Here we evaluated the accuracy of deformable image registration (DIR) by using a physical phantom for MRI. METHODS AND MATERIALS: We used the "Wuphantom" for end-to-end testing of DIR accuracy for MRI. This acrylic phantom is filled with water and includes several fillable inserts to simulate various tissue shapes and properties. Deformations and changes in anatomic locations are simulated by changing the rotations of the phantom and inserts. We used Varian Velocity DIR software (v4.0) and CT (head and neck protocol) and MR (T1- and T2-weighted head protocol) images to test DIR accuracy between image modalities (MRI vs CT) and within the same image modality (MRI vs MRI) in 11 rotation deformation scenarios. Large inserts filled with Mobil DTE oil were used to simulate fatty tissue, and small inserts filled with agarose gel were used to simulate tissues slightly denser than water (e.g., prostate). Contours of all inserts were generated before DIR to provide a baseline for contour size and shape. DIR was done with the MR Correctable Deformable DIR method, and all deformed contours were compared with the original contours. The Dice similarity coefficient (DSC) and mean distance to agreement (MDA) were used to quantitatively validate DIR accuracy. We also used large and small regions of interest (ROIs) during between-modality DIR tests to simulate validation of DIR accuracy for organs at risk (OARs) and propagation of individual clinical target volume (CTV) contours. RESULTS: No significant differences in DIR accuracy were found for T1:T1 and T2:T2 comparisons (P > 0.05). DIR was less accurate for between-modality comparisons than for same-modality comparisons, and was less accurate for T1 vs CT than for T2 vs CT (P < 0.001). For between-modality comparisons, use of a small ROI improved DIR accuracy for both T1 and T2 images. CONCLUSION: The simple design of the Wuphantom allows seamless testing of DIR; here we validated the accuracy of MRI DIR in end-to-end testing. T2 images had superior DIR accuracy compared with T1 images. Use of small ROIs improves DIR accuracy for target contour propagation.

Nonhomologous End Joining Is More Important Than Proton Linear Energy Transfer in Dictating Cell Death.

PURPOSE: This study seeks to identify biological factors that may yield a therapeutic advantage of proton therapy versus photon therapy. Specifically, we address the role of nonhomologous end-joining (NHEJ) and homologous recombination (HR) in the survival of cells in response to clinical photon and proton beams. METHODS AND MATERIALS: We irradiated HT1080, M059K (DNA-PKcs+/+), and HCC1937 human cancer cell lines and their isogenic counterparts HT1080-shDNA-PKcs, HT1080-shRAD51IND, M059J (DNA-PKcs-/-), and HCC1937-BRCA1 (BRCA1 complemented) to assess cell clonogenic survival and γ-H2AX radiation-induced foci. Cells were irradiated with either clinically relevant photons or 1 of 3 proton linear energy transfer (LET) values. RESULTS: Our results indicate that NHEJ deficiency is more important in dictating cell survival than proton LET. Cells with disrupted HR through BRCA1 mutation showed increased radiosensitivity only for high-LET protons whereas RAD51 depletion showed increased radiosensitivity for both photons and protons. DNA double strand breaks, assessed by γ-H2AX radiation-induced foci, showed greater numbers after 24 hours in cells exposed to higher LET protons. We also observed that NHEJ-deficient cells were unable to repair the vast majority of double strand breaks after 24 hours. CONCLUSIONS: BRCA1 mutation significantly sensitizes cells to protons, but not photons. Loss of NHEJ renders cells hypersensitive to radiation, whereas the relative importance of HR increases with LET across several cell lines. This may be attributable to the more clustered damage induced by higher LET protons, which are harder to repair through NHEJ. This highlights the importance of tumor biology in dictating treatment modality and suggests BRCA1 as a potential biomarker for proton therapy response. Our data also support the use of pharmacologic inhibitors of DNA repair to enhance the sensitivity to different radiation types, although this raises issues for normal tissue toxicity.

Using the Proton Energy Spectrum and Microdosimetry to Model Proton Relative Biological Effectiveness.

PURPOSE: We introduce a methodology to calculate the microdosimetric quantity dose-mean lineal energy for input into the microdosimetric kinetic model (MKM) to model the relative biological effectiveness (RBE) of proton irradiation experiments. METHODS AND MATERIALS: The data from 7 individual proton RBE experiments were included in this study. In each experiment, the RBE at several points along the Bragg curve was measured. Monte Carlo simulations to calculate the lineal energy probability density function of 172 different proton energies were carried out with use of Geant4 DNA. We calculated the fluence-weighted lineal energy probability density function (fw(y)), based on the proton energy spectra calculated through Monte Carlo at each experimental depth, calculated the dose-mean lineal energy yD¯ for input into the MKM, and then computed the RBE. The radius of the domain (rd) was varied to reach the best agreement between the MKM-predicted RBE and experimental RBE. A generic RBE model as a function of dose-averaged linear energy transfer (LETD) with 1 fitting parameter was presented and fit to the experimental RBE data as well to facilitate a comparison to the MKM. RESULTS: Both the MKM and LETD-based models modeled the RBE from experiments well. Values for rd were similar to those of other cell lines under proton irradiation that were modeled with the MKM. Analysis of the performance of each model revealed that neither model was clearly superior to the other. CONCLUSIONS: Our 3 key accomplishments include the following: (1) We developed a method that uses the proton energy spectra and lineal energy distributions of those protons to calculate dose-mean lineal energy. (2) We demonstrated that our application of the MKM provides theoretical validation of proton irradiation experiments that show that RBE is significantly greater than 1.1. (3) We showed that there is no clear evidence that the MKM is better than LETD-based RBE models.

A mechanistic relative biological effectiveness model-based biological dose optimization for charged particle radiobiology studies.

In charged particle therapy, the objective is to exploit both the physical and radiobiological advantages of charged particles to improve the therapeutic index. Use of the beam scanning technique provides the flexibility to implement biological dose optimized intensity-modulated ion therapy (IMIT). An easy-to-implement algorithm was developed in the current study to rapidly generate a uniform biological dose distribution, namely the product of physical dose and the relative biological effectiveness (RBE), within the target volume using scanned ion beams for charged particle radiobiological studies. Protons, helium ions and carbon ions were selected to demonstrate the feasibility and flexibility of our method. The general-purpose Monte Carlo simulation toolkit Geant4 was used for particle tracking and generation of physical and radiobiological data needed for later dose optimizations. The dose optimization algorithm was developed using the Python (version 3) programming language. A constant RBE-weighted dose (RWD) spread-out Bragg peak (SOBP) in a water phantom was selected as the desired target dose distribution to demonstrate the applicability of the optimization algorithm. The mechanistic repair-misrepair-fixation (RMF) model was incorporated into the Monte Carlo particle tracking to generate radiobiological parameters and was used to predict the RBE of cell survival in the iterative process of the biological dose optimization for the three selected ions. The post-optimization generated beam delivery strategy can be used in radiation biology experiments to obtain radiobiological data to further validate and improve the accuracy of the RBE model. This biological dose optimization algorithm developed for radiobiology studies could potentially be extended to implement biologically optimized IMIT plans for patients.

Physical parameter optimization scheme for radiobiological studies of charged particle therapy.

We have developed an easy-to-implement method to optimize the spatial distribution of a desired physical quantity for charged particle therapy. The basic methodology requires finding the optimal solutions for the weights of the constituent particle beams that together form the desired spatial distribution of the specified physical quantity, e.g., dose or dose-averaged linear energy transfer (LETd), within the target region. We selected proton, 4He ion, and 12C ion beams to demonstrate the feasibility and flexibility of our method. The pristine dose Bragg curves in water for all ion beams and the LETd for proton beams were generated from Geant4 Monte Carlo simulations. The optimization algorithms were implemented using the Python programming language. High-accuracy optimization results of the spatial distribution of the desired physical quantity were then obtained for different cases. The relative difference between the real value and the expected value of a given quantity was approximately within ±1.0% in the whole target region. The optimization examples include a flat dose spread-out Bragg peak (SOBP) for the three selected ions, an upslope dose SOBP for protons, and a downslope dose SOBP for protons. The relative difference was approximately within ±2.0% for the case with a flat LETd (target value = 4 keV/µm) distribution for protons. These one-dimensional optimization algorithms can be extended to two or three dimensions if the corresponding physical data are available. In addition, this physical quantity optimization strategy can be conveniently extended to encompass biological dose optimization if appropriate biophysical models are invoked.

RBE Model-Based Biological Dose Optimization for Proton Radiobiology Studies.

PURPOSE: The purpose of the current study was (1) to develop a straightforward and rapid method to incorporate a dose-averaged linear energy transfer (LET d )-based biological effect model into a dose optimization algorithm for scanned protons; and (2) to apply a novel beam delivery strategy with increased LET d within the target, thereby enhancing the biological effect predicted using the selected relative biological effectiveness (RBE) model. MATERIALS AND METHODS: We first generated pristine dose Bragg curves in water and their corresponding LET d distributions for 94 groups of proton beams, using experimentally validated Geant4 Monte Carlo simulations. Next, we developed 1-dimensional dose optimization algorithms by using the Python programming language. To calculate the RBE of protons for biological dose optimization, we invoked the McNamara RBE model and applied the radiobiological parameters of the lung cancer H460 cell line with 137Cs reference photons. RESULTS: High-accuracy optimization results were obtained. The relative difference between the delivered dose and the prescribed dose was approximately within ±1.0% in the target. In addition, we obtained the RBE enhancement within the target by applying the LET-painting technique. For example, considering a simple case in which 2 opposed downslope dose fields were superimposed to form a uniform dose in the 5- to 10-cm target region, the center RBE was 1.23 ± 0.01, which was greater than the center RBE of 1.16 ± 0.01 found when using the traditional method of delivering 2 opposed flat dose fields. CONCLUSION: We have successfully developed an easy-to-implement method to perform the biological dose optimization procedure by invoking the McNamara RBE model in the iteration process using the Python programming language. According to the RBE model predictions, we conclude that the increased target LET d enhances the RBE. The accuracy of the RBE model predictions needs to be validated in radiobiological experiments.

Radiation induces age-dependent deficits in cortical synaptic plasticity.

Background: Radiation-induced cognitive dysfunction is a significant side effect of cranial irradiation for brain tumors. Clinically, pediatric patients are more vulnerable than adults. However, the underlying mechanisms of dysfunction, including reasons for age dependence, are still largely unknown. Previous studies have focused on the loss of hippocampal neuronal precursor cells and deficits in memory. However, survivors may also experience deficits in attention, executive function, or other non-hippocampal-dependent cognitive domains. We hypothesized that brain irradiation induces age-dependent deficits in cortical synaptic plasticity. Methods: In vivo recordings were used to test neuronal plasticity along the direct pathway from the cornu ammonis 1 (CA1)/subicular region to the prefrontal cortex (PFC). Specifically, long-term potentiation (LTP) in the CA1/subicular-PFC pathway was assessed after cranial irradiation of juvenile and adult Sprague Dawley rats. We further assessed a potential role for glutamate toxicity by evaluating the potential neuroprotective effects of memantine. Results: LTP was greatly inhibited in both adult and juvenile animals at 3 days after radiation but returned to near-normal levels by 8 weeks-only in adult rats. Memantine given before, but not after, irradiation partially prevented LTP inhibition in juvenile and adult rats. Conclusion: Cranial radiation impairs neuroplasticity along the hippocampal-PFC pathway; however, its effects vary by age. Pretreatment with memantine offered protection to both juvenile and adult animals. Deficits in cortical plasticity may contribute to radiation-induced cognitive dysfunction, including deficits in attention and age-dependent sensitivity of such pathways, which may underlie differences in clinical outcomes between juveniles and adults after cranial irradiation.

Optimization of Monte Carlo particle transport parameters and validation of a novel high throughput experimental setup to measure the biological effects of particle beams.

PURPOSE: Accurate modeling of the relative biological effectiveness (RBE) of particle beams requires increased systematic in vitro studies with human cell lines with care towards minimizing uncertainties in biologic assays as well as physical parameters. In this study, we describe a novel high-throughput experimental setup and an optimized parameterization of the Monte Carlo (MC) simulation technique that is universally applicable for accurate determination of RBE of clinical ion beams. Clonogenic cell-survival measurements on a human lung cancer cell line (H460) are presented using proton irradiation. METHODS: Experiments were performed at the Heidelberg Ion Therapy Center (HIT) with support from the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg, Germany using a mono-energetic horizontal proton beam. A custom-made variable range selector was designed for the horizontal beam line using the Geant4 MC toolkit. This unique setup enabled a high-throughput clonogenic assay investigation of multiple, well defined dose and linear energy transfer (LETs) per irradiation for human lung cancer cells (H460) cultured in a 96-well plate. Sensitivity studies based on application of different physics lists in conjunction with different electromagnetic constructors and production threshold values to the MC simulations were undertaken for accurate assessment of the calculated dose and the dose-averaged LET (LETd ). These studies were extended to helium and carbon ion beams. RESULTS: Sensitivity analysis of the MC parameterization revealed substantial dependence of the dose and LETd values on both the choice of physics list and the production threshold values. While the dose and LETd calculations using FTFP_BERT_LIV, FTFP_BERT_EMZ, FTFP_BERT_PEN and QGSP_BIC_EMY physics lists agree well with each other for all three ions, they show large differences when compared to the FTFP_BERT physics list with the default electromagnetic constructor. For carbon ions, the dose corresponding to the largest LETd value is observed to differ by as much as 78% between FTFP_BERT and FTFP_BERT_LIV. Furthermore, between the production threshold of 700 µm and 5 µm, proton dose varies by as much as 19% corresponding to the largest LETd value sampled in the current investigation. Based on the sensitivity studies, the FTFP_BERT physics list with the low energy Livermore electromagnetic constructor and a production threshold of 5 µm was employed for determining accurate dose and LETd . The optimized MC parameterization results in a different LETd dependence of the RBE curve for 10% SF of the H460 cell line irradiated with proton beam when compared with the results from a previous study using the same cell line. When the MC parameters are kept consistent between the studies, the proton RBE results agree well with each other within the experimental uncertainties. CONCLUSIONS: A custom high-throughput, high-accuracy experimental design for accurate in vitro cell survival measurements was employed at a horizontal beam line. High sensitivity of the physics-based optimization establishes the importance of accurate MC parameterization and hence the conditioning of the MC system on a case-by-case basis. The proton RBE results from current investigations are observed to agree with a previous measurement made under different experimental conditions. This establishes the consistency of our experimental findings across different experiments and institutions.

A model for relative biological effectiveness of therapeutic proton beams based on a global fit of cell survival data.

We introduce an approach for global fitting of the recently published high-throughput and high accuracy clonogenic cell-survival data for therapeutic scanned proton beams. Our fitting procedure accounts for the correlation between the cell-survival, the absorbed (physical) dose and the proton linear energy transfer (LET). The fitting polynomials and constraints have been constructed upon generalization of the microdosimetric kinetic model (gMKM) adapted to account for the low energy and high lineal-energy spectrum of the beam where the current radiobiological models may underestimate the reported relative biological effectiveness (RBE). The parameters (α, ß) of the linear-quadratic (LQ) model calculated by the presented method reveal a smooth transition from low to high LETs which is an advantage of the current method over methods previously employed to fit the same clonogenic data. Finally, the presented approach provides insight into underlying microscopic mechanisms which, with future study, may help to elucidate radiobiological responses along the Bragg curve and resolve discrepancies between experimental data and current RBE models.

Radiobiological issues in proton therapy.

BACKGROUND: The relative biological effectiveness (RBE) for particle therapy is a complex function of particle type, radiation dose, linear energy transfer (LET), cell type, endpoint, etc. In the clinical practice of proton therapy, the RBE is assumed to have a fixed value of 1.1. This assumption, along with the effects of physical uncertainties, may mean that the biologically effective dose distributions received by the patient may be significantly different from what is seen on treatment plans. This may contribute to unforeseen toxicities and/or failure to control the disease. Variability of Proton RBE: It has been shown experimentally that proton RBE varies significantly along the beam path, especially near the end of the particle range. While there is now an increasing acceptance that proton RBE is variable, there is an ongoing debate about whether to change the current clinical practice. Clinical Evidence: A rationale against the change is the uncertainty in the models of variable RBE. Secondly, so far there is no clear clinical evidence of the harm of assuming proton RBE to be 1.1. It is conceivable, however, that the evidence is masked partially by physical uncertainties. It is, therefore, plausible that reduction in uncertainties and their incorporation in the estimation of dose actually delivered may isolate and reveal the variability of RBE in clinical practice. Nevertheless, clinical evidence of RBE variability is slowly emerging as more patients are treated with protons and their response data are analyzed. Modelling and Incorporation of RBE in the Optimization of Proton Therapy: The improvement in the knowledge of RBE could lead to better understanding of outcomes of proton therapy and in the improvement of models to predict RBE. Prospectively, the incorporation of such models in the optimization of intensity-modulated proton therapy could lead to improvements in the therapeutic ratio of proton therapy.