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Objective. The TOol for PArticle Simulation (TOPAS) is a Geant4-based Monte Carlo software application that has been used for both research and clinical studies in medical physics. So far, most users of TOPAS have focused on radiotherapy-related studies, such as modeling radiation therapy delivery systems or patient dose calculation. Here, we present the first set of TOPAS extensions to make it easier for TOPAS users to model medical imaging systems.Approach. We used the extension system of TOPAS to implement pre-built, user-configurable geometry components such as detectors (e.g. flat-panel and multi-planar detectors) for various imaging modalities and pre-built, user-configurable scorers for medical imaging systems (e.g. digitizer chain).Main results. We developed a flexible set of extensions that can be adapted to solve research questions for a variety of imaging modalities. We then utilized these extensions to model specific examples of cone-beam CT (CBCT), positron emission tomography (PET), and prompt gamma (PG) systems. The first of these new geometry components, the FlatImager, was used to model example CBCT and PG systems. Detected signals were accumulated in each detector pixel to obtain the intensity of x-rays penetrating objects or prompt gammas from proton-nuclear interaction. The second of these new geometry components, the RingImager, was used to model an example PET system. Positron-electron annihilation signals were recorded in crystals of the RingImager and coincidences were detected. The simulated data were processed using corresponding post-processing algorithms for each modality and obtained results in good agreement with the expected true signals or experimental measurement.Significance. The newly developed extension is a first step to making it easier for TOPAS users to build and simulate medical imaging systems. Together with existing TOPAS tools, this extension can help integrate medical imaging systems with radiotherapy simulations for image-guided radiotherapy.

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 physicochemical 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.

Background and Purpose: Radiation-induced lymphopenia (RIL) is a common side effect of radiotherapy (RT) that may negatively impact survival. We aimed to identify RIL predictors in patients with non-small-cell lung cancer (NSCLC) treated intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). Materials and Methods: We retrospectively analysed data of 306 patients who underwent radical RT for NSCLC. Absolute lymphocyte count (ALC) loss was evaluated for each patient by fitting an exponential decay curve to data from first 45 days since treatment start, and percentage ALC loss relative to baseline was calculated based on area under the decay curve and baseline ALC. We compared IMRT and VMAT treatment plans and used linear regression to predict ALC loss. Results: ALC decreased during RT in the whole patient group, while neutrophil counts remained stable and decreased only in those treated with concurrent chemoradiotherapy (CRT). Percentage ALC loss ranged between 11 and 78 % and was more strongly than lymphocyte nadir correlated with dose-volume metrics for relevant normal structures. We found evidence for the association of high radiation dose to the lungs, heart and body with percentage ALC loss, with lung volume exposed to 20–30 Gy being most important predictors in patients treated with IMRT. A multivariable model based on CRT use, baseline ALC and first principal component (PC1) of the dose-volume predictors showed good predictive performance (bias-corrected R2 of 0.40). Conclusion: Percentage lymphocyte loss is a robust measure of RIL that is predicted by baseline ALC, CRT use and dose-volume parameters to the lungs, heart and body.

Neuroinflammation is one of the main mechanisms contributing to the pathogenesis of Alzheimer’s disease (AD), although its key role and the immune cells involved have not yet been identified. Blood monocytes appear to play a role in the clearance of AD-related amyloid-β (Aβ) and tau protein. This retrospective study evaluated a possible correlation between blood monocytes; the concentrations of Aβ, total tau (t-Tau), and phosphorylated tau (p-Tau) in the cerebrospinal fluid (CSF); and cognitive decline assessed according to the Montreal Cognitive Assessment (MoCA). We collected data from 33 patients with AD or mild cognitive impairment (MCI) due to AD (15 men and 18 women) and found, along with a significant reduction in the concentration of blood monocytes in women (p-value = 0.083),significant correlations between the number of blood monocytes and the concentration of t-Tau in CSF (p-value = 0.045) and between blood monocytes and MoCA score (p-value = 0.037). These results confirm the role of blood monocytes in the pathogenesis of AD, provide further evidence of a gender difference in the neuroinflammatory process underlying AD, and show that blood monocyte count may reflect the cognitive impairment of AD patients.

Abstract Neurodegenerative disorders are characterized by the brain deposition of insoluble amyloidogenic proteins, such as α-synuclein or Tau, and the concomitant deterioration of cell functions such as the autophagy-lysosomal pathway (ALP). The ALP is involved in the degradation of intracellular macromolecules including protein aggregates. ALP dysfunction due to inherited defects in lysosomal or non-lysosomal proteins causes a group of diseases called lysosomal storage disorders (LSD) because of abnormal accumulation of lysosomal degradation substrates. Supporting the contribution of ALP defects in neurodegenerative diseases, deposition of amyloidogenic proteins occurs in LSD. Moreover, heterozygous mutations of several ALP genes represent risk factors for Parkinson’s disease. The reciprocal contribution of α-synuclein accumulation and lysosomal dysfunction have been extensively studied. However, whether this adverse crosstalk also embraces Tau pathology needs more investigation. Here, we show in human primary fibroblasts that Tau seeds isolated from the brain of Alzheimer’s disease induce Tau accumulation in acidic degradative organelles and lysosomal stress. Furthermore, inhibition of glucocerebrosidase, a lysosomal enzyme mutated in Gaucher’s disease and a main risk for Parkinson’s disease, causes lysosomal dysfunction in primary fibroblasts and contributes to the accumulation of Tau. Considering the presence of Tau lesions in Parkinson’s disease as well as in multiple neurodegenerative disorders including Alzheimer’s disease, our data call for further studies on strategies to alleviate ALP dysfunction as new therapeutic opportunity for neurodegenerative diseases and LSD.

Abstract Tau (MAPT) is a microtubule-associated protein causing common neurodegenerative diseases or rare inherited frontotemporal lobar degenerations. Emerging evidence for non-canonical functions of Tau in DNA repair and P53 regulation suggests its involvement in cancer. To bring new evidence for a relevant role of Tau in cancer, we carried out an in-silico pan-cancer analysis of MAPT transcriptomic profile in over 10000 clinical samples from 32 cancer types and over 1300 pre-clinical samples from 28 cancer types provided by the TCGA and the DEPMAP datasets respectively. MAPT expression associated with key cancer hallmarks including inflammation, proliferation, and epithelial to mesenchymal transition, showing cancer-specific patterns. In some cancer types, MAPT functional networks were affected by P53 mutational status. We identified new associations of MAPT with clinical outcomes and drug response in a context-specific manner. Overall, our findings indicate that the MAPT gene is a potential major player in multiple types of cancer. Importantly, the impact of Tau on cancer seems to be heavily influenced by the specific cellular environment.

PurposeTo demonstrate the suitability of optically stimulated luminescence detectors (OSLDs) for accurate simultaneous measurement of the absolute point dose and dose-weighted linear energy transfer (LETD) in an anthropomorphic phantom for experimental validation of daily adaptive proton therapy.MethodsA clinically realistic intensity-modulated proton therapy (IMPT) treatment plan was created based on a CT of an anthropomorphic head-and-neck phantom made of tissue-equivalent material. The IMPT plan was optimized with three fields to deliver a uniform dose to the target volume covering the OSLDs. Different scenarios representing inter-fractional anatomical changes were created by modifying the phantom. An online adaptive proton therapy workflow was used to recover the daily dose distribution and account for the applied geometry changes. To validate the adaptive workflow, measurements were performed by irradiating Al2O3:C OSLDs inside the phantom. In addition to the measurements, retrospective Monte Carlo simulations were performed to compare the absolute dose and dose-averaged LET (LETD) delivered to the OSLDs.ResultsThe online adaptive proton therapy workflow was shown to recover significant degradation in dose conformity resulting from large anatomical and positioning deviations from the reference plan. The Monte Carlo simulations were in close agreement with the OSLD measurements, with an average relative error of 1.4% for doses and 3.2% for LETD. The use of OSLDs for LET determination allowed for a correction for the ionization quenched response.ConclusionThe OSLDs appear to be an excellent detector for simultaneously assessing dose and LET distributions in proton irradiation of an anthropomorphic phantom. The OSLDs can be cut to almost any size and shape, making them ideal for in-phantom measurements to probe the radiation quality and dose in a predefined region of interest. Although we have presented the results obtained in the experimental validation of an adaptive proton therapy workflow, the same approach can be generalized and used for a variety of clinical innovations and workflow developments that require accurate assessment of point dose and/or average LET.

The chemical stage of the Monte Carlo track-structure simulation code Geant4-DNA has been revised and validated. The root-mean-square (RMS) empirical parameter that dictates the displacement of water molecules after an ionization and excitation event in Geant4-DNA has been shortened to better fit experimental data. The pre-defined dissociation channels and branching ratios were not modified, but the reaction rate coefficients for simulating the chemical stage of water radiolysis were updated. The evaluation of Geant4-DNA was accomplished with TOPAS-nBio. For that, we compared predicted time-dependentGvalues in pure liquid water for·OH, e-aq, and H2with published experimental data. For H2O2and H·, simulation of added scavengers at different concentrations resulted in better agreement with measurements. In addition, DNA geometry information was integrated with chemistry simulation in TOPAS-nBio to realize reactions between radiolytic chemical species and DNA. This was used in the estimation of the yield of single-strand breaks (SSB) induced by137Csγ-ray radiolysis of supercoiled pUC18 plasmids dissolved in aerated solutions containing DMSO. The efficiency of SSB induction by reaction between radiolytic species and DNA used in the simulation was chosen to provide the best agreement with published measurements. An RMS displacement of 1.24 nm provided agreement with measured data within experimental uncertainties for time-dependentGvalues and under the presence of scavengers. SSB efficiencies of 24% and 0.5% for·OH and H·, respectively, led to an overall agreement of TOPAS-nBio results within experimental uncertainties. The efficiencies obtained agreed with values obtained with published non-homogeneous kinetic model and step-by-step Monte Carlo simulations but disagreed by 12% with published direct measurements. Improvement of the spatial resolution of the DNA damage model might mitigate such disagreement. In conclusion, with these improvements, Geant4-DNA/TOPAS-nBio provides a fast, accurate, and user-friendly tool for simulating DNA damage under low linear energy transfer irradiation.

In radiopharmaceutical treatmentsα-particles are employed to treat tumor cells. However, the mechanism that drives the biological effect induced is not well known. Being ionizing radiation,α-particles can affect biological organisms by producing damage to the DNA, either directly or indirectly. Following the principle that microdosimetry theory accounts for the stochastic way in which radiation deposits energy in sub-cellular sized volumes via physical collisions, we postulate that microdosimetry represents a reasonable framework to characterize the statistical nature of direct damage induction byα-particles to DNA. We used the TOPAS-nBio Monte Carlo package to simulate direct damage produced by monoenergetic alpha particles to different DNA structures. In separate simulations, we obtained the frequency-mean lineal energy (yF) and dose-mean lineal energy (yD) of microdosimetric distributions sampled with spherical sites of different sizes. The total number of DNA strand breaks, double strand breaks (DSBs) and complex strand breaks per track were quantified and presented as a function of eitheryForyD.The probability of interaction between a track and the DNA depends on how the base pairs are compacted. To characterize this variability on compactness, spherical sites of different size were used to match these probabilities of interaction, correlating the size-dependent specific energy (z) with the damage induced. The total number of DNA strand breaks per track was found to linearly correlate withyFandzFwhen using what we defined an effective volume as microdosimetric site, while the yield of DSB per unit dose linearly correlated withyDorzD,being larger for compacted than for unfolded DNA structures. The yield of complex breaks per unit dose exhibited a quadratic behavior with respect toyDand a greater difference among DNA compactness levels. Microdosimetric quantities correlate with the direct damage imparted on DNA.

Abstract Background The mutation in Huntington's disease is a polyglutamine expansion near the N-terminus of huntingtin. Huntingtin expressed in immortalized neurons is cleaved near the N-terminus to form N-terminal polypeptides known as cleavage products A and B (cpA and cpB). CpA and cpB with polyglutamine expansion form inclusions in the nucleus and cytoplasm, respectively. The formation of cpA and cpB in primary neurons has not been established and the proteases involved in the formation of these fragments are unknown. Results Delivery of htt cDNA into the mouse striatum using adeno-associated virus or into primary cortical neurons using lentivirus generated cpA and cpB, indicating that neurons in brain and in vitro can form these fragments. A screen of small molecule protease inhibitors introduced to clonal striatal X57 cells and HeLa cells identified compounds that reduced levels of cpA and are inhibitors of the aspartyl proteases cathepsin D and cathepsin E. The most effective compound, P1-N031, is a transition state mimetic for aspartyl proteases. By western blot analysis, cathepsin D was easily detected in clonal striatal X57 cells, mouse brain and primary neurons, whereas cathepsin E was only detectible in clonal striatal X57 cells. In primary neurons, levels of cleavage product A were not changed by the same compounds that were effective in clonal striatal cells or by mRNA silencing to partially reduce levels of cathepsin D. Instead, treating primary neurons with compounds that are known to inhibit gamma secretase activity either indirectly (Imatinib mesylate, Gleevec) or selectively (LY-411,575 or DAPT) reduced levels of cpA. LY-411,575 or DAPT also increased survival of primary neurons expressing endogenous full-length mutant huntingtin. Conclusion We show that cpA and cpB are produced from a larger huntingtin fragment in vivo in mouse brain and in primary neuron cultures. The aspartyl protease involved in forming cpA has cathepsin-D like properties in immortalized neurons and gamma secretase-like properties in primary neurons, suggesting that cell type may be a critical factor that specifies the aspartyl protease responsible for cpA. Since gamma secretase inhibitors were also protective in primary neurons, further study of the role of gamma-secretase activity in HD neurons is justified.

Abstract Background Huntington's disease (HD) is a progressive neurodegenerative disorder caused by a CAG repeat expansion within the huntingtin gene. Mutant huntingtin protein misfolds and accumulates within neurons where it mediates its toxic effects. Promoting mutant huntingtin clearance by activating macroautophagy is one approach for treating Huntington's disease (HD). In this study, we evaluated the mTOR kinase inhibitor and macroautophagy promoting drug everolimus in the R6/2 mouse model of HD. Results Everolimus decreased phosphorylation of the mTOR target protein S6 kinase indicating brain penetration. However, everolimus did not activate brain macroautophagy as measured by LC3B Western blot analysis. Everolimus protected against early declines in motor performance; however, we found no evidence for neuroprotection as determined by brain pathology. In muscle but not brain, everolimus significantly decreased soluble mutant huntingtin levels. Conclusions Our data suggests that beneficial behavioral effects of everolimus in R6/2 mice result primarily from effects on muscle. Even though everolimus significantly modulated its target brain S6 kinase, this did not decrease mutant huntingtin levels or provide neuroprotection.

Abstract Tau gene mutations cause a progressive dementia and neurotoxic Tau forms deposited in neurofibrillary tangles are hallmarks of neurodegenerative tauopathies. Loss of non-canonical Tau functions may contribute to disease. In fact, Tau depletion affects the cellular response to DNA damage and tauopathies exhibit the accumulation of DNA lesions. Moreover, Tau modulates P53 activity and cell fate. Considering that MDM2 is the main antagonist of P53, we investigated, using orthogonal assays, if Tau interacts with MDM2. We report the existence in cells and brain of a Tau-MDM2 complex that, in vitro, exhibits reduced P53 ubiquitination activity in a manner sensitive to a Tau mutation. The Tau-MDM2 interaction involves the microtubule-binding domain of Tau and the acidic domain of MDM2, reminiscent of the binding of Tau to negatively charged microtubules. Notably, MDM2 accumulates aberrantly in neurofibrillary tangles. Aging-associated insults may expose a novel loss-of-function of Tau in neurodegeneration and cancer.

Abstract Background Expansion of a polyglutamine repeat at the amino-terminus of huntingtin is the probable cause for Huntington's disease, a lethal progressive autosomal-dominant neurodegenerative disorders characterized by impaired motor performance and severe brain atrophy. The expanded polyglutamine repeat changes the conformation of huntingtin and initiates a range of pathogenic mechanisms in neurons including intracellular huntingtin aggregates, transcriptional dysregulation, energy metabolism deficits, synaptic dystrophy and ultimately neurodegeneration. It is unclear how these events relate to each other or if they can be reversed by pharmacological intervention. Here, we describe neuronal cell lines expressing inducible fragments of normal and mutant huntingtin. Results In HN10 cells, the expression of wild type and mutant huntingtin fragments was dependent on the induction time as well as on the concentration of the RheoSwitch® inducing ligand. In order to analyze the effect of mutant huntingtin expression on cellular functions we concentrated on the 72Q exon1 huntingtin expressing cell line and found that upon induction, it was possible to carefully dissect mutant huntingtin-induced phenotypes as they developed over time. Dysregulation of transcription as a result of mutant huntingtin expression showed a transcription signature replicating that reported in animal models and Huntington's disease patients. Crucially, triggering of neuronal differentiation in mutant huntingtin expressing cell resulted in the appearance of additional pathological hallmarks of Huntington's disease including cell death. Conclusion We developed neuronal cell lines with inducible expression of wild type and mutant huntingtin. These new cell lines represent a reliable in vitro system for modeling Huntington's disease and should find wide use for high-throughput screening application and for investigating the biology of mutant huntingtin.

Purpose: The physical properties of protons lower doses to surrounding normal tissues compared with photons, potentially reducing acute and long-term adverse effects, including subsequent cancers. The magnitude of benefit is uncertain, however, and currently based largely on modeling studies. Despite the paucity of directly comparative data, the number of proton centers and patients are expanding exponentially. Direct studies of the potential risks and benefits are needed in children, who have the highest risk of radiation-related subsequent cancers. The Pediatric Proton and Photon Therapy Comparison Cohort aims to meet this need. Methods and Materials: We are developing a record-linkage cohort of 10,000 proton and 10,000 photon therapy patients treated from 2007 to 2022 in the United States and Canada for pediatric central nervous system tumors, sarcomas, Hodgkin lymphoma, or neuroblastoma, the pediatric tumors most frequently treated with protons. Exposure assessment will be based on state-of-the-art dosimetry facilitated by collection of electronic radiation records for all eligible patients. Subsequent cancers and mortality will be ascertained by linkage to state and provincial cancer registries in the United States and Canada, respectively. The primary analysis will examine subsequent cancer risk after proton therapy compared with photon therapy, adjusting for potential confounders and accounting for competing risks. Results: For the primary aim comparing overall subsequent cancer rates between proton and photon therapy, we estimated that with 10,000 patients in each treatment group there would be 80% power to detect a relative risk of 0.8 assuming a cumulative incidence of subsequent cancers of 2.5% by 15 years after diagnosis. To date, 9 institutions have joined the cohort and initiated data collection; additional centers will be added in the coming year(s). Conclusions: Our findings will affect clinical practice for pediatric patients with cancer by providing the first large-scale systematic comparison of the risk of subsequent cancers from proton compared with photon therapy.

Introduction: Progressive Tau deposition in neurofibrillary tangles and neuropil threads is the hallmark of tauopathies, a disorder group that includes Alzheimer’s disease. Since Tau is a microtubule-associated protein, a prevalent concept to explain the pathogenesis of tauopathies is that abnormal Tau modification contributes to dissociation from microtubules, assembly into multimeric β-sheets, proteotoxicity, neuronal dysfunction and cell loss. Tau also localizes in the cell nucleus and evidence supports an emerging function of Tau in DNA stability and epigenetic modulation.Methods: To better characterize the possible role of Tau in regulation of chromatin compaction and subsequent gene expression, we performed a bioinformatics analysis of transcriptome data obtained from Tau-depleted human neuroblastoma cells.Results: Among the transcripts deregulated in a Tau-dependent manner, we found an enrichment of target genes for the polycomb repressive complex 2. We further describe decreased cellular amounts of the core components of the polycomb repressive complex 2 and lower histone 3 trimethylation in Tau deficient cells. Among the de-repressed polycomb repressive complex 2 target gene products, IGFBP3 protein was found to be linked to increased senescence induction in Tau-deficient cells.Discussion: Our findings propose a mechanism for Tau-dependent epigenetic modulation of cell senescence, a key event in pathologic aging.

The cellular response to ionizing radiation continues to be of significant research interest in cancer radiotherapy, and DNA is recognized as the critical target for most of the biologic effects of radiation. Incident particles can cause initial DNA damages through physical and chemical interactions within a short time scale. Initial DNA damages can undergo repair via different pathways available at different stages of the cell cycle. The misrepair of DNA damage results in genomic rearrangement and causes mutations and chromosome aberrations, which are drivers of cell death. This work presents an integrated study of simulating cell response after proton irradiation with energies of 0.5-500 MeV (LET of 60-0.2 keV/µm). A model of a whole nucleus with fractal DNA geometry was implemented in TOPAS-nBio for initial DNA damage simulations. The default physics and chemistry models in TOPAS-nBio were used to describe interactions of primary particles, secondary particles, and radiolysis products within the nucleus. The initial DNA double-strand break (DSB) yield was found to increase from 6.5 DSB/Gy/Gbp at low-linear energy transfer (LET) of 0.2 keV/µm to 21.2 DSB/Gy/Gbp at high LET of 60 keV/µm. A mechanistic repair model was applied to predict the characteristics of DNA damage repair and dose response of chromosome aberrations. It was found that more than 95% of the DSBs are repaired within the first 24 h and the misrepaired DSB fraction increases rapidly with LET and reaches 15.8% at 60 keV/µm with an estimated chromosome aberration detection threshold of 3 Mbp. The dicentric and acentric fragment yields and the dose response of micronuclei formation after proton irradiation were calculated and compared with experimental results.

Monte Carlo (MC) track structure simulation tools are commonly used for predicting radiation induced DNA damage by modeling the physical and chemical reactions at the nanometer scale. However, the outcome of these MC simulations is particularly sensitive to the adopted parameters which vary significantly across studies. In this study, a previously developed full model of nuclear DNA was used to describe the DNA geometry. The TOPAS-nBio MC toolkit was used to investigate the impact of physics and chemistry models as well as three key parameters (the energy threshold for direct damage, the chemical stage time length, and the probability of damage between hydroxyl radical reactions with DNA) on the induction of DNA damage. Our results show that the difference in physics and chemistry models alone can cause differences up to 34% and 16% in the DNA double strand break (DSB) yield, respectively. Additionally, changing the direct damage threshold, chemical stage length, and hydroxyl damage probability can cause differences of up to 28%, 51%, and 71% in predicted DSB yields, respectively, for the configurations in this study.

PurposeThis paper covers recent developments and applications of the TOPAS TOol for PArticle Simulation and presents the approaches used to disseminate TOPAS.Materials and methodsFundamental understanding of radiotherapy and imaging is greatly facilitated through accurate and detailed simulation of the passage of ionizing radiation through apparatus and into a patient using Monte Carlo (MC). TOPAS brings Geant4, a reliable, experimentally validated MC tool mainly developed for high energy physics, within easy reach of medical physicists, radiobiologists and clinicians. Requiring no programming knowledge, TOPAS provides all of the flexibility of Geant4.ResultsAfter 5 years of development followed by its initial release, TOPAS was subsequently expanded from its focus on proton therapy physics to incorporate radiobiology modeling. Next, in 2018, the developers expanded their user support and code maintenance as well as the scope of TOPAS towards supporting X-ray and electron therapy and medical imaging. Improvements have been achieved in user enhancement through software engineering and a graphical user interface, calculational efficiency, validation through experimental benchmarks and QA measurements, and either newly available or recently published applications. A large and rapidly increasing user base demonstrates success in our approach to dissemination of this uniquely accessible and flexible MC research tool.ConclusionsThe TOPAS developers continue to make strides in addressing the needs of the medical community in applications of ionizing radiation to medicine, creating the only fully integrated platform for four-dimensional simulation of all forms of radiotherapy and imaging with ionizing radiation, with a design that promotes inter-institutional collaboration.

It is well known that radiation therapy causes lymphopenia in patients and that this is correlated with a negative outcome. The mechanism is not well understood because radiation can have both immunostimulatory and immunosuppressive effects. How tumor dose conformation, dose fractionation, and selective lymph node irradiation in radiation therapy does affect lymphopenia and immune response is an active area of research. In addition, understanding the impact of radiation on the immune system is important for the design and interpretation of clinical trials combining radiation with immune checkpoint inhibitors, both in terms of radiation dose and treatment schedules. Although only a few percent of the total lymphocyte population are circulating, it has been speculated that their increased radiosensitivity may contribute to, or even be the primary cause of, lymphopenia. This review summarizes published data on lymphocyte radiosensitivity based on human, small animal, and in vitro studies. The data indicate differences in radiosensitivity among lymphocyte subpopulations that affect their relative contribution and thus the dynamics of the immune response. In general, B cells appear to be more radiosensitive than T cells and NK cells appear to be the most resistant. However, the reported dose-response data suggest that in the context of lymphopenia in patients, aspects other than cell death must also be considered. Not only absolute lymphocyte counts, but also lymphocyte diversity and activity are likely to be affected by radiation. Taken together, the reviewed data suggest that it is unlikely that radiation-induced cell death in lymphocytes is the sole factor in radiation-induced lymphopenia.

Amongst the scientific frameworks powered by the Monte Carlo (MC) toolkit Geant4 (Agostinelli et al., 2003), the TOPAS (Tool for Particle Simulation) (Perl et al., 2012) is one. TOPAS focuses on providing ease of use, and has significant implementation in the radiation oncology space at present. TOPAS functionality extends across the full capacity of Geant4, is freely available to non-profit users, and is being extended into radiobiology via TOPAS-nBIO (Ramos-Mendez et al., 2018). A current "grand problem" in cancer therapy is to convert the dose of treatment from physical dose to biological dose, optimized ultimately to the individual context of administration of treatment. Biology MC calculations are some of the most complex and require significant computational resources. In order to enhance TOPAS's ability to become a critical tool to explore the definition and application of biological dose in radiation therapy, we chose to explore the use of Field Programmable Gate Array (FPGA) chips to speedup the Geant4 calculations at the heart of TOPAS, because this approach called "Reconfigurable Computing" (RC), has proven able to produce significant (around 90x) (Sajish et al., 2012) speed increases in scientific computing. Here, we describe initial steps to port Geant4 and TOPAS to be used on FPGA. We provide performance analysis of the current TOPAS/Geant4 code from an RC implementation perspective. Baseline benchmarks are presented. Achievable performance figures of the subsections of the code on optimal hardware are presented; Aspects of practical implementation of "Monte Carlo on a chip" are also discussed.

Microdosimetric energy depositions have been suggested as a key variable for the modeling of the relative biological effectiveness (RBE) in proton and ion radiation therapy. However, microdosimetry has been underutilized in radiation therapy. Recent advances in detector technology allow the design of new mico- and nano-dosimeters. At the same time Monte Carlo (MC) simulations have become more widely used in radiation therapy. In order to address the growing interest in the field, a microdosimetric extension was developed in TOPAS. The extension provides users with the functionality to simulate microdosimetric spectra as well as the contribution of secondary particles to the spectra, calculate microdosimetric parameters, and determine RBE with a biological weighting function approach or with the microdosimetric kinetic (MK) model. Simulations were conducted with the extension and the results were compared with published experimental data and other simulation results for three types of microdosimeters, a spherical tissue equivalent proportional counter (TEPC), a cylindrical TEPC and a solid state microdosimeter. The corresponding microdosimetric spectra obtained with TOPAS from the plateau region to the distal tail of the Bragg curve generally show good agreement with the published data.

The biological effectiveness of proton beams relative to photon beams in radiation therapy has been taken to be 1.1 throughout the history of proton therapy. While potentially appropriate as an average value, actual relative biological effectiveness (RBE) values may differ. This Task Group report outlines the basic concepts of RBE as well as the biophysical interpretation and mathematical concepts. The current knowledge on RBE variations is reviewed and discussed in the context of the current clinical use of RBE and the clinical relevance of RBE variations (with respect to physical as well as biological parameters). The following task group aims were designed to guide the current clinical practice: Assess whether the current clinical practice of using a constant RBE for protons should be revised or maintained. Identifying sites and treatment strategies where variable RBE might be utilized for a clinical benefit. Assess the potential clinical consequences of delivering biologically weighted proton doses based on variable RBE and/or LET models implemented in treatment planning systems. Recommend experiments needed to improve our current understanding of the relationships among in vitro, in vivo, and clinical RBE, and the research required to develop models. Develop recommendations to minimize the effects of uncertainties associated with proton RBE for well-defined tumor types and critical structures.

The TOPAS Monte Carlo (MC) system is used in radiation therapy and medical imaging research, having played a significant role in making Monte Carlo simulations widely available for proton therapy related research. While TOPAS provides detailed simulations of patient scale properties, the fundamental unit of the biological response to radiation is a cell. Thus, our goal was to develop TOPAS-nBio, an extension of TOPAS dedicated to advance understanding of radiobiological effects at the (sub-)cellular, (i.e., the cellular and sub-cellular) scale. TOPAS-nBio was designed as a set of open source classes that extends TOPAS to model radiobiological experiments. TOPAS-nBio is based on and extends Geant4-DNA, which extends the Geant4 toolkit, the basis of TOPAS, to include very low-energy interactions of particles down to vibrational energies, explicitly simulates every particle interaction (i.e., without using condensed histories) and propagates radiolysis products. To further facilitate the use of TOPAS-nBio, a graphical user interface was developed. TOPAS-nBio offers full track-structure Monte Carlo simulations, integration of chemical reactions within the first millisecond, an extensive catalogue of specialized cell geometries as well as sub-cellular structures such as DNA and mitochondria, and interfaces to mechanistic models of DNA repair kinetics. We compared TOPAS-nBio simulations to measured and published data of energy deposition patterns and chemical reaction rates (G values). Our simulations agreed well within the experimental uncertainties. Additionally, we expanded the chemical reactions and species provided in Geant4-DNA and developed a new method based on independent reaction times (IRT), including a total of 72 reactions classified into 6 types between neutral and charged species. Chemical stage simulations using IRT were a factor of 145 faster than with step-by-step tracking. Finally, we applied the geometric/chemical modeling to obtain initial yields of double-strand breaks (DSBs) in DNA fibers for proton irradiations of 3 and 50 MeV and compared the effect of including chemical reactions on the number and complexity of DSB induction. Over half of the DSBs were found to include chemical reactions with approximately 5% of DSBs caused only by chemical reactions. In conclusion, the TOPAS-nBio extension to the TOPAS MC application offers access to accurate and detailed multiscale simulations, from a macroscopic description of the radiation field to microscopic description of biological outcome for selected cells. TOPAS-nBio offers detailed physics and chemistry simulations of radiobiological experiments on cells simulating the initially induced damage and links to models of DNA repair kinetics.

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.

IntroductionDNA damage is the main predictor of response to radiation therapy for cancer. Its Q8 quantification and characterization are paramount for treatment optimization, particularly in advanced modalities such as proton and alpha-targeted therapy.MethodsWe present a novel approach called the Microdosimetric Gamma Model (MGM) to address this important issue. The MGM uses the theory of microdosimetry, specifically the mean energy imparted to small sites, as a predictor of DNA damage properties. MGM provides the number of DNA damage sites and their complexity, which were determined using Monte Carlo simulations with the TOPAS-nBio toolkit for monoenergetic protons and alpha particles. Complexity was used together with a illustrative and simplistic repair model to depict the differences between high and low LET radiations.ResultsDNA damage complexity distributions were were found to follow a Gamma distribution for all monoenergetic particles studied. The MGM functions allowed to predict number of DNA damage sites and their complexity for particles not simulated with microdosimetric measurements (yF) in the range of those studied.DiscussionCompared to current methods, MGM allows for the characterization of DNA damage induced by beams composed of multi-energy components distributed over any time configuration and spatial distribution. The output can be plugged into ad hoc repair models that can predict cell killing, protein recruitment at repair sites, chromosome aberrations, and other biological effects, as opposed to current models solely focusing on cell survival. These features are particularly important in targeted alpha-therapy, for which biological effects remain largely uncertain. The MGM provides a flexible framework to study the energy, time, and spatial aspects of ionizing radiation and offers an excellent tool for studying and optimizing the biological effects of these radiotherapy modalities.

Purpose: This work evaluates an online adaptive (OA) workflow for head-and-neck (H&N) intensity-modulated proton therapy (IMPT) and compares it with full offline replanning (FOR) in patients with large anatomical changes. Methods: IMPT treatment plans are created retrospectively for a cohort of eight H&N cancer patients that previously required replanning during the course of treatment due to large anatomical changes. Daily cone-beam CTs (CBCT) are acquired and corrected for scatter, resulting in 253 analyzed fractions. To simulate the FOR workflow, nominal plans are created on the planning-CT and delivered until a repeated-CT is acquired; at this point, a new plan is created on the repeated-CT. To simulate the OA workflow, nominal plans are created on the planning-CT and adapted at each fraction using a simple beamlet weight-tuning technique. Dose distributions are calculated on the CBCTs with Monte Carlo for both delivery methods. The total treatment dose is accumulated on the planning-CT. Results: Daily OA improved target coverage compared to FOR despite using smaller target margins. In the high-risk CTV, the median D98 degradation was 1.1 % and 2.1 % for OA and FOR, respectively. In the low-risk CTV, the same metrics yield 1.3 % and 5.2 % for OA and FOR, respectively. Smaller setup margins of OA reduced the dose to all OARs, which was most relevant for the parotid glands. Conclusion: Daily OA can maintain prescription doses and constraints over the course of fractionated treatment, even in cases of large anatomical changes, reducing the necessity for manual replanning in H&N IMPT.

The use of adjuvant Immune Checkpoint Inhibitors (ICI) after concurrent chemo-radiation therapy (CCRT) has become the standard of care for locally advanced non-small cell lung cancer (LA-NSCLC). However, prolonged radiotherapy regimens are known to cause radiation-induced lymphopenia (RIL), a long-neglected toxicity that has been shown to correlate with response to ICIs and survival of patients treated with adjuvant ICI after CCRT.In this study, we aim to develop a novel neural network (NN) approach that integrates patient characteristics, treatment related variables, and differential dose volume histograms (dDVH) of lung and heart to predict the incidence of RIL at the end of treatment. Multi-institutional data of 139 LA-NSCLC patients from two hospitals were collected for training and validation of our suggested model. Ensemble learning was combined with a bootstrap strategy to stabilize the model, which was evaluated internally using repeated cross validation.The performance of our proposed model was compared to conventional models using the same input features, such as Logistic Regression (LR) and Random Forests (RF), using the Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC) curves. Our suggested model (AUC=0.77) outperformed the comparison models (AUC=0.72, 0.74) in terms of absolute performance, indicating that the convolutional structure of the network successfully abstracts additional information from the differential DVHs, which we studied using Gradient-weighted Class Activation Map.This study shows that clinical factors combined with dDVHs can be used to predict the risk of RIL for an individual patient and shows a path toward preventing lymphopenia using patient-specific modifications of the radiotherapy plan.

Abstract Purpose To develop a model of the internal vasculature of the adult liver and demonstrate its application to the differentiation of radiopharmaceutical decay sites within liver parenchyma from those within organ blood. Method Computer-generated models of hepatic arterial (HA), hepatic venous (HV), and hepatic portal venous (HPV) vascular trees were algorithmically created within individual lobes of the ICRP adult female and male livers (AFL/AML). For each iteration of the algorithm, pressure, blood flow, and vessel radii within each tree were updated as each new vessel was created and connected to a viable bifurcation site. The vascular networks created inside the AFL/AML were then tetrahedralized for coupling to the PHITS radiation transport code. Specific absorbed fractions (SAF) were computed for monoenergetic alpha particles, electrons, positrons, and photons. Dual-region liver models of the AFL/AML were proposed, and particle-specific SAF values were computed assuming radionuclide decays in blood within two locations: (1) sites within explicitly modeled hepatic vessels, and (2) sites within the hepatic blood pool residing outside these vessels to include the capillaries and blood sinuses. S values for 22 and 10 radionuclides commonly used in radiopharmaceutical therapy and imaging, respectively, were computed using the dual-region liver models and compared to those obtained in the existing single-region liver model. Results Liver models with virtual vasculatures of ~ 6000 non-intersecting straight cylinders representing the HA, HPV, and HV circulations were created for the ICRP reference. For alpha emitters and for beta and auger-electron emitters, S values using the single-region models were approximately 11% (AML) to 14% (AFL) and 11% (AML) to 13% (AFL) higher than the S values obtained using the dual-region models, respectively. Conclusions The methodology employed in this study has shown improvements in organ parenchymal dosimetry through explicit consideration of blood self-dose for alpha particles (all energies) and for electrons at energies below ~ 100 keV.

Abstract Many efforts targeting amyloid‐β (Aβ) plaques for the treatment of Alzheimer's Disease thus far have resulted in failures during clinical trials. Regional and temporal heterogeneity of efficacy and dependence on plaque maturity may have contributed to these disappointing outcomes. In this study, we mapped the regional and temporal specificity of various anti‐Aβ treatments through high‐resolution light‐sheet imaging of electrophoretically cleared brains. We assessed the effect on amyloid plaque formation and growth in Thy1‐APP/PS1 mice subjected to β‐secretase inhibitors, polythiophenes, or anti‐Aβ antibodies. Each treatment showed unique spatiotemporal Aβ clearance, with polythiophenes emerging as a potent anti‐Aβ compound. Furthermore, aligning with a spatial‐transcriptomic atlas revealed transcripts that correlate with the efficacy of each Aβ therapy. As observed in this study, there is a striking dependence of specific treatments on the location and maturity of Aβ plaques. This may also contribute to the clinical trial failures of Aβ‐therapies, suggesting that combinatorial regimens may be significantly more effective in clearing amyloid deposition.

Computational simulations, such as Monte Carlo track structure simulations, offer a powerful tool for quantitatively investigating radiation interactions within cells. The modelling of the spatial distribution of energy deposition events as well as diffusion of chemical free radical species, within realistic biological geometries, can help provide a comprehensive understanding of the effects of radiation on cells. Track structure simulations, however, generally require advanced computing skills to implement. The TOPAS-nBio toolkit, an extension to TOPAS (TOol for PArticle Simulation), aims to provide users with a comprehensive framework for radiobiology simulations, without the need for advanced computing skills. This includes providing users with an extensive library of advanced, realistic, biological geometries ranging from the micrometer scale (e.g. cells and organelles) down to the nanometer scale (e.g. DNA molecules and proteins). Here we present the geometries available in TOPAS-nBio.

PURPOSE: Proton therapy can allow for superior avoidance of normal tissues. A widespread consensus has been reached that proton therapy should be used for patients with curable pediatric brain tumor to avoid critical central nervous system structures. Brainstem necrosis is a potentially devastating, but rare, complication of radiation. Recent reports of brainstem necrosis after proton therapy have raised concerns over the potential biological differences among radiation modalities. We have summarized findings from the National Cancer Institute Workshop on Proton Therapy for Children convened in May 2016 to examine brainstem injury. METHODS AND MATERIALS: Twenty-seven physicians, physicists, and researchers from 17 institutions with expertise met to discuss this issue. The definition of brainstem injury, imaging of this entity, clinical experience with photons and photons, and potential biological differences among these radiation modalities were thoroughly discussed and reviewed. The 3 largest US pediatric proton therapy centers collectively summarized the incidence of symptomatic brainstem injury and physics details (planning, dosimetry, delivery) for 671 children with focal posterior fossa tumors treated with protons from 2006 to 2016. RESULTS: The average rate of symptomatic brainstem toxicity from the 3 largest US pediatric proton centers was 2.38%. The actuarial rate of grade ≥2 brainstem toxicity was successfully reduced from 12.7% to 0% at 1 center after adopting modified radiation guidelines. Guidelines for treatment planning and current consensus brainstem constraints for proton therapy are presented. The current knowledge regarding linear energy transfer (LET) and its relationship to relative biological effectiveness (RBE) are defined. We review the current state of LET-based planning. CONCLUSIONS: Brainstem injury is a rare complication of radiation therapy for both photons and protons. Substantial dosimetric data have been collected for brainstem injury after proton therapy, and established guidelines to allow for safe delivery of proton radiation have been defined. Increased capability exists to incorporate LET optimization; however, further research is needed to fully explore the capabilities of LET- and RBE-based planning.

Simulation of water radiolysis and the subsequent chemistry provides important information on the effect of ionizing radiation on biological material. The Geant4 Monte Carlo toolkit has added chemical processes via the Geant4-DNA project. The TOPAS tool simplifies the modeling of complex radiotherapy applications with Geant4 without requiring advanced computational skills, extending the pool of users. Thus, a new extension to TOPAS, TOPAS-nBio, is under development to facilitate the configuration of track-structure simulations as well as water radiolysis simulations with Geant4-DNA for radiobiological studies. In this work, radiolysis simulations were implemented in TOPAS-nBio. Users may now easily add chemical species and their reactions, and set parameters including branching ratios, dissociation schemes, diffusion coefficients, and reaction rates. In addition, parameters for the chemical stage were re-evaluated and updated from those used by default in Geant4-DNA to improve the accuracy of chemical yields. Simulation results of time-dependent and LET-dependent primary yields Gx (chemical species per 100 eV deposited) produced at neutral pH and 25 °C by short track-segments of charged particles were compared to published measurements. The LET range was 0.05-230 keV µm-1. The calculated Gx values for electrons satisfied the material balance equation within 0.3%, similar for protons albeit with long calculation time. A smaller geometry was used to speed up proton and alpha simulations, with an acceptable difference in the balance equation of 1.3%. Available experimental data of time-dependent G-values for [Formula: see text] agreed with simulated results within 7%  ±  8% over the entire time range; for [Formula: see text] over the full time range within 3%  ±  4%; for H2O2 from 49%  ±  7% at earliest stages and 3%  ±  12% at saturation. For the LET-dependent Gx, the mean ratios to the experimental data were 1.11  ±  0.98, 1.21  ±  1.11, 1.05  ±  0.52, 1.23  ±  0.59 and 1.49  ±  0.63 (1 standard deviation) for [Formula: see text], [Formula: see text], H2, H2O2 and [Formula: see text], respectively. In conclusion, radiolysis and subsequent chemistry with Geant4-DNA has been successfully incorporated in TOPAS-nBio. Results are in reasonable agreement with published measured and simulated data.

Flagged uniform particle splitting was implemented with two methods to improve the computational efficiency of Monte Carlo track structure simulations with TOPAS-nBio by enhancing the production of secondary electrons in ionization events. In method 1 the Geant4 kernel was modified. In method 2 Geant4 was not modified. In both methods a unique flag number assigned to each new split electron was inherited by its progeny, permitting reclassification of the split events as if produced by independent histories. Computational efficiency and accuracy were evaluated for simulations of 0.5-20 MeV protons and 1-20 MeV u-1 carbon ions for three endpoints: (1) mean of the ionization cluster size distribution, (2) mean number of DNA single-strand breaks (SSBs) and double-strand breaks (DSBs) classified with DBSCAN, and (3) mean number of SSBs and DSBs classified with a geometry-based algorithm. For endpoint (1), simulation efficiency was 3 times lower when splitting electrons generated by direct ionization events of primary particles than when splitting electrons generated by the first ionization events of secondary electrons. The latter technique was selected for further investigation. The following results are for method 2, with relative efficiencies about 4.5 times lower for method 1. For endpoint (1), relative efficiency at 128 split electrons approached maximum, increasing with energy from 47.2  ±  0.2 to 66.9  ±  0.2 for protons, decreasing with energy from 51.3  ±  0.4 to 41.7  ±  0.2 for carbon. For endpoint (2), relative efficiency increased with energy, from 20.7  ±  0.1 to 50.2  ±  0.3 for protons, 15.6  ±  0.1 to 20.2  ±  0.1 for carbon. For endpoint (3) relative efficiency increased with energy, from 31.0  ±  0.2 to 58.2  ±  0.4 for protons, 23.9  ±  0.1 to 26.2  ±  0.2 for carbon. Simulation results with and without splitting agreed within 1% (2 standard deviations) for endpoints (1) and (2), within 2% (1 standard deviation) for endpoint (3). In conclusion, standard particle splitting variance reduction techniques can be successfully implemented in Monte Carlo track structure codes.

Computational simulations offer a powerful tool for quantitatively investigating radiation interactions with biological tissue and can help bridge the gap between physics, chemistry and biology. The TOPAS collaboration is tackling this challenge by extending the current Monte Carlo tool to allow for sub-cellular in silico simulations in a new extension, TOPAS-nBio. TOPAS wraps and extends the Geant4 Monte Carlo simulation toolkit and the new extension allows the modeling of particles down to vibrational energies (∼2eV) within realistic biological geometries. Here we present a validation of biological geometries available in TOPAS-nBio, by comparing our results to two previously published studies. We compare the prediction of strand breaks in a simple linear DNA strand from TOPAS-nBio to a published Monte Carlo track structure simulation study. While TOPAS-nBio confirms the trend in strand break generation, it predicts a higher frequency of events below an energy of 17.5eV compared to the alternative Monte Carlo track structure study. This is due to differences in the physics models used by each code. We also compare the experimental measurement of strand breaks from incident protons in DNA plasmids to TOPAS-nBio simulations. Our results show good agreement of single and double strand breaks predicting a similar increase in the strand break yield with increasing LET.

Head and neck squamous cell carcinomas (HNSCCs) often present as local-regionally advanced disease at diagnosis, for which a current standard of care is x-ray–based radiation therapy, with or without chemotherapy. This approach provides effective local regional tumor control, but at the cost of acute and late toxicity that can worsen quality of life and contribute to mortality. For patients with human papillomavirus (HPV)–associated oropharyngeal squamous cell carcinoma (SCC) in particular, for whom the prognosis is generally favorable, de-escalation of the radiation dose to surrounding normal tissues without diminishing the radiation dose to tumors is desired to mitigate radiation-related toxic effects. Proton radiation therapy (PRT) may be an excellent de-escalation strategy because of its physical properties (that eliminate unnecessary radiation to surrounding tissues) and because of its biological properties (including tumor-specific variations in relative biological effectiveness [RBE] and linear energy transfer [LET]), in combination with concurrent systemic therapy. Early clinical evidence has shown that compared with x-ray–based radiation therapy, PRT offers comparable disease control with fewer and less severe treatment-related toxicities that can worsen the quality of life for patients with HNSCC. Herein, we review aspects of the biological basis of enhanced HNSCC cell response to proton versus x-ray irradiation in terms of radiation-induced gene and protein expression, DNA damage and repair, cell death, tumor immune responses, and radiosensitization of tumors.

Abstract Huntington’s disease (HD) is a devastating neurodegenerative disorder, caused by a CAG/polyglutamine repeat expansion, that results in the aggregation of the huntingtin protein, culminating in the deposition of inclusion bodies in HD patient brains. We have previously shown that the heat shock response becomes impaired with disease progression in mouse models of HD. The disruption of this inducible arm of the proteostasis network is likely to exacerbate the pathogenesis of this protein-folding disease. To allow a rapid and more comprehensive analysis of the heat shock response, we have developed, and validated, a 16-plex QuantiGene assay that allows the expression of Hsf1 and nine heat shock genes, to be measured directly, and simultaneously, from mouse tissue. We used this QuantiGene assay to show that, following pharmacological activation in vivo, the heat shock response impairment in tibialis anterior, brain hemispheres and striatum was comparable between zQ175 and R6/2 mice. In contrast, although a heat shock impairment could be detected in R6/2 cortex, this was not apparent in the cortex from zQ175 mice. Whilst the mechanism underlying this impairment remains unknown, our data indicated that it is not caused by a reduction in HSF1 levels, as had been reported.