CDK4/6-MEK Inhibition in MPNSTs Causes Plasma Cell Infiltration, Sensitization to PD-L1 Blockade, and Tumor Regression.

PURPOSE: Malignant peripheral nerve sheath tumors (MPNST) are lethal, Ras-driven sarcomas that lack effective therapies. We investigated effects of targeting cyclin-dependent kinases 4 and 6 (CDK4/6), MEK, and/or programmed death-ligand 1 (PD-L1) in preclinical MPNST models. EXPERIMENTAL DESIGN: Patient-matched MPNSTs and precursor lesions were examined by FISH, RNA sequencing, IHC, and Connectivity-Map analyses. Antitumor activity of CDK4/6 and MEK inhibitors was measured in MPNST cell lines, patient-derived xenografts (PDX), and de novo mouse MPNSTs, with the latter used to determine anti-PD-L1 response. RESULTS: Patient tumor analyses identified CDK4/6 and MEK as actionable targets for MPNST therapy. Low-dose combinations of CDK4/6 and MEK inhibitors synergistically reactivated the retinoblastoma (RB1) tumor suppressor, induced cell death, and decreased clonogenic survival of MPNST cells. In immune-deficient mice, dual CDK4/6-MEK inhibition slowed tumor growth in 4 of 5 MPNST PDXs. In immunocompetent mice, combination therapy of de novo MPNSTs caused tumor regression, delayed resistant tumor outgrowth, and improved survival relative to monotherapies. Drug-sensitive tumors that regressed contained plasma cells and increased cytotoxic T cells, whereas drug-resistant tumors adopted an immunosuppressive microenvironment with elevated MHC II-low macrophages and increased tumor cell PD-L1 expression. Excitingly, CDK4/6-MEK inhibition sensitized MPNSTs to anti-PD-L1 immune checkpoint blockade (ICB) with some mice showing complete tumor regression. CONCLUSIONS: CDK4/6-MEK inhibition induces a novel plasma cell-associated immune response and extended antitumor activity in MPNSTs, which dramatically enhances anti-PD-L1 therapy. These preclinical findings provide strong rationale for clinical translation of CDK4/6-MEK-ICB targeted therapies in MPNST as they may yield sustained antitumor responses and improved patient outcomes.

Radiomic biomarkers from chest computed tomography are assistive in immunotherapy response prediction for non-small cell lung cancer.

Background: Immunotherapies, such as programmed death 1/programmed death ligand 1 (PD-1/PD-L1) antibodies have been shown to improve overall and progression-free survival (PFS) in patients with locally advanced or metastatic non-small cell lung cancer (NSCLC). However, not all patients derive a meaningful clinical benefit. Additionally, patients receiving anti-PD-1/PD-L1 therapy can experience immune-related adverse events (irAEs). Clinically significant irAEs may require temporary pause or discontinuation of treatment. Having a tool to identify patients who may not benefit and/or are at risk for developing severe irAEs from immunotherapy will aid in an informed decision-making process for the patients and their physicians. Methods: Computed tomography (CT) scans and clinical data were retrospectively collected for this study to develop three prediction models using (I) radiomic features, (II) clinical features, and (III) radiomic and clinical features combined. Each subject had 6 clinical features and 849 radiomic features extracted. Selected features were run through an artificial neural network (NN) trained on 70% of the cohort, maintaining the case and control ratio. The NN was assessed by calculating the area-under-the-receiver-operating-characteristic curve (AUC-ROC), area-under-the-precision-recall curve (AUC-PR), sensitivity, and specificity. Results: A cohort of 132 subjects, of which 43 (33%) had a PFS ≤90 days and 89 (67%) of which had a PFS >90 days was used to develop the prediction models. The radiomic model was able to predict progression-free survival with a training AUC-ROC of 87% and testing AUC-ROC, sensitivity, and specificity of 83%, 75%, and 81%, respectively. In this cohort, the clinical and radiomic combined features did add a slight increase in the specificity (85%) but with a decrease in sensitivity (75%) and AUC-ROC (81%). Conclusions: Whole lung segmentation and feature extraction can identify those that would see a benefit from anti-PD-1/PD-L1 therapy.

Magnetic resonance brain volumetry biomarkers of CLN2 Batten disease identified with miniswine model.

Late-infantile neuronal ceroid lipofuscinosis type 2 (CLN2) disease (Batten disease) is a rare pediatric disease, with symptom development leading to clinical diagnosis. Early diagnosis and effective tracking of disease progression are required for treatment. We hypothesize that brain volumetry is valuable in identifying CLN2 disease at an early stage and tracking disease progression in a genetically modified miniswine model. CLN2R208X/R208X miniswine and wild type controls were evaluated at 12- and 17-months of age, correlating to early and late stages of disease progression. Magnetic resonance imaging (MRI) T1- and T2-weighted data were acquired. Total intercranial, gray matter, cerebrospinal fluid, white matter, caudate, putamen, and ventricle volumes were calculated and expressed as proportions of the intracranial volume. The brain regions were compared between timepoints and cohorts using Gardner-Altman plots, mean differences, and confidence intervals. At an early stage of disease, the total intracranial volume (- 9.06 cm3), gray matter (- 4.37% 95 CI - 7.41; - 1.83), caudate (- 0.16%, 95 CI - 0.24; - 0.08) and putamen (- 0.11% 95 CI - 0.23; - 0.02) were all notably smaller in CLN2R208X/R208X miniswines versus WT, while cerebrospinal fluid was larger (+ 3.42%, 95 CI 2.54; 6.18). As the disease progressed to a later stage, the difference between the gray matter (- 8.27%, 95 CI - 10.1; - 5.56) and cerebrospinal fluid (+ 6.88%, 95 CI 4.31; 8.51) continued to become more pronounced, while others remained stable. MRI brain volumetry in this miniswine model of CLN2 disease is sensitive to early disease detection and longitudinal change monitoring, providing a valuable tool for pre-clinical treatment development and evaluation.

A Novel Porcine Model of CLN2 Batten Disease that Recapitulates Patient Phenotypes.

CLN2 Batten disease is a lysosomal disorder in which pathogenic variants in CLN2 lead to reduced activity in the enzyme tripeptidyl peptidase 1. The disease typically manifests around 2 to 4 years of age with developmental delay, ataxia, seizures, inability to speak and walk, and fatality between 6 and 12 years of age. Multiple Cln2 mouse models exist to better understand the etiology of the disease; however, these models are unable to adequately recapitulate the disease due to differences in anatomy and physiology, limiting their utility for therapeutic testing. Here, we describe a new CLN2R208X/R208X porcine model of CLN2 disease. We present comprehensive characterization showing behavioral, pathological, and visual phenotypes that recapitulate those seen in CLN2 patients. CLN2R208X/R208X miniswine present with gait abnormalities at 6 months of age, ERG waveform declines at 6-9 months, vision loss at 11 months, cognitive declines at 12 months, seizures by 15 months, and early death at 18 months due to failure to thrive. CLN2R208X/R208X miniswine also showed classic storage material accumulation and glial activation in the brain at 6 months, and cortical atrophy at 12 months. Thus, the CLN2R208X/R208X miniswine model is a valuable resource for biomarker discovery and therapeutic development in CLN2 disease.

Task-based validation and application of a scanner-specific CT simulator using an anthropomorphic phantom.

BACKGROUND: Quantitative analysis of computed tomography (CT) images traditionally utilizes real patient data that can pose challenges with replicability, efficiency, and radiation exposure. Instead, virtual imaging trials (VITs) can overcome these hurdles through computer simulations of models of patients and imaging systems. DukeSim is a scanner-specific CT imaging simulator that has previously been validated with simple cylindrical phantoms, but not with anthropomorphic conditions and clinically relevant measurements. PURPOSE: To validate a scanner-specific CT simulator (DukeSim) for the assessment of lung imaging biomarkers under clinically relevant conditions across multiple scanners using an anthropomorphic chest phantom, and to demonstrate the utility of virtual trials by studying the effects or radiation dose and reconstruction kernels on the lung imaging quantifications. METHODS: An anthropomorphic chest phantom with customized tube inserts was imaged with two commercial scanners (Siemens Force and Siemens Flash) at 28 dose and reconstruction conditions. A computational version of the chest phantom was used with a scanner-specific CT simulator (DukeSim) to simulate virtual images corresponding to the settings of the real acquisitions. Lung imaging biomarkers were computed from both real and simulated CT images and quantitatively compared across all imaging conditions. The VIT framework was further utilized to investigate the effects of radiation dose (20-300 mAs) and reconstruction settings (Qr32f, Qr40f, and Qr69f reconstruction kernels using ADMIRE strength 3) on the accuracy of lung imaging biomarkers, compared against the ground-truth values modeled in the computational chest phantom. RESULTS: The simulated CT images matched closely the real images for both scanners and all imaging conditions qualitatively and quantitatively, with the average biomarker percent error of 3.51% (range 0.002%-18.91%). The VIT study showed that sharper reconstruction kernels had lower accuracy with errors in mean lung HU of 84-94 HU, lung volume of 797-3785 cm3 , and lung mass of -800 to 1751 g. Lower tube currents had the lower accuracy with errors in mean lung HU of 6-84 HU, lung volume of 66-3785 cm3 , and lung mass of 170-1751 g. Other imaging biomarkers were consistent under the studied reconstruction settings and tube currents. CONCLUSION: We comprehensively evaluated the realism of DukeSim in an anthropomorphic setup across a diverse range of imaging conditions. This study paves the way toward utilizing VITs more reliably for conducting medical imaging experiments that are not practical using actual patient images.

The improved success rate and reduced complications of a novel localization device vs. hookwire for thoracoscopic resection of small pulmonary nodules: a single-center, open-label, randomized clinical trial.

Background: In our previous study, we developed a 4-hook claw-suture localization device for pulmonary nodule resection, which acheived satifisfactory results. Following this, we conducted this single-center, open-label, randomized clinical trial to compare the success rate and complication rate of this novel localization device and currently widely-used hookwire. Methods: Patients with small pulmonary nodules (0.4-1 cm) who received preoperative localization and thoracoscopic resection at Shanghai Chest Hospital were randomly assigned (1:2 ratio, via computer-generated randomized numbers) to undergo localization using either a novel claw-suture system (claw group) or classical (hookwire group) localization device. The primary endpoint of this study was localization success rate, and the secondary endpoints included complications, localization-related time, and pain. Results: A total of 411 patients were randomly assigned to the claw group (n=136) or the hookwire group (n=275) before thoracoscopic resection of small pulmonary nodules and analyzed. Compared with the hookwire group, the claw group had a significantly higher success rate (133/136, 97.8% vs. 254/275, 92.4%, P=0.027), less asymptomatic hemorrhage (16.9% vs. 37.5%, P=0.003) and pleural reaction (0% vs. 5.1%, P=0.017), as well as better pain alleviation 10 min after localization (measured using the difference between two visual analog scale scores, 0.84±0.98 vs. 0.35±0.79, P<0.001). In contrast, the hookwire group was associated with a shorter localization procedure duration than the claw group (7.2±2.9 vs. 14.4±6.6 min, P<0.001). In the multiple localization subgroup, the claw group compared to the hookwire group also achieved higher success (32/33, 97.0% vs. 70/86, 81.4%) and less pleural reaction (0% vs. 16.3%). Conclusions: The new claw-suture localization device is superior to traditional hookwire, with a higher success rate, fewer complications, and better patient tolerance for preoperative localization of small pulmonary nodules. Trial Registration: Chinese Clinical Trial Registry ChiCTR1900027346.

Using Cg05575921 methylation to predict lung cancer risk: a potentially bias-free precision epigenetics approach.

The decision to engage in lung cancer screening (LCS) necessitates weighing benefits versus harms. Previously, clinicians in the United States have used the PLCOM2012 algorithm to guide LCS decision-making. However, that formula contains race and gender-based variables. Previously, using data from a European study, Bojesen and colleagues have suggested that cg05575921 methylation could guide decision-making. To test this hypothesis in a more diverse American population, we examined DNA and clinical data from 3081 subjects from the National Lung Screening Trial (NLST) study. Using survival analysis, we found a simple linear predictor consisting of age, pack-year consumption and cg05575921, to have the best predictive power among several alternatives (AUC = 0.66). Results showed that the highest quartile of risk was more than 2-fold more likely to develop lung cancer than those in the lowest quartile. Race, ethnicity, and gender had no effect on prediction with both cg05575921 and pack years contributing equally (both p < 0.003) to risk prediction. Current smokers had considerably lower methylation than former smokers (46% vs 67%; p < 0.001) with the average methylation of those who quit approaching 80% after 25 years of cessation. Finally, current male smokers had lower mean cg05575921 percentage than female smokers (46% vs 49%; p < 0.001). We conclude that cg05575921 (along with age and pack years) can be used to guide LCS decision-making, and additional studies might focus on how best to use methylation to inform decision-making.

Scanner-specific validation of a CT simulator using a COPD-emulated anthropomorphic phantom.

Traditional methods of quantitative analysis of CT images typically involve working with patient data, which is often expensive and limited in terms of ground truth. To counter these restrictions, quantitative assessments can instead be made through Virtual Imaging Trials (VITs) which simulate the CT imaging process. This study sought to validate DukeSim (a scanner-specific CT simulator) utilizing clinically relevant biomarkers for a customized anthropomorphic chest phantom. The physical phantom was imaged utilizing two commercial CT scanners (Siemens Somatom Force and Definition Flash) with varying imaging parameters. A computational version of the phantom was simulated utilizing DukeSim for each corresponding real acquisition. Biomarkers were computed and compared between the real and virtually acquired CT images to assess the validity of DukeSim. The simulated images closely matched the real images both qualitatively and quantitatively, with the average biomarker percent difference of 3.84% (range 0.19% to 18.27%). Results showed that DukeSim is reasonably well validated across various patient imaging conditions and scanners, which indicates the utility of DukeSim for further VIT studies where real patient data may not be feasible.

Inter- and intra-scan variability for lung imaging quantifications via CT.

CT imaging provides physicians valuable insights when diagnosing disease in a clinical setting. In order to provide an accurate diagnosis, is it important to have a high accuracy with controlled variability across CT scans from different scanners and imaging parameters. The purpose of this study was to analyze variability of lung imaging biomarkers across various scanners and parameters using a customized version of a commercially available anthropomorphic chest Phantom (Kyoto Kagaku) with several experimental sample inserts. The phantom was across 10 different CT scanners with a total of 209 imaging conditions. An algorithm was developed to compute different imaging biomarkers. Variability across images from the same scanner and from different scanners was analyzed by computing coefficients of variation (CV) and standard deviations of HU values. LAA -950 and LAA -856 biomarkers had the highest levels of variability, while the majority of other biomarkers had variability less than 10 HU or 10% CV in both inter and intra-scan measurements. There was no clear trend present between the biomarker measurements and CTDIvol. The results of this study demonstrates the existing variability in CT quantifications for lung imaging, which prompt further studies on how to reduce such variation.

Computed Tomography Features of Lung Structure Have Utility for Differentiating Malignant and Benign Pulmonary Nodules.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a known comorbidity for lung cancer independent of smoking history. Quantitative computed tomography (qCT) imaging features related to COPD have shown promise in the assessment of lung cancer risk. We hypothesize that qCT features from the lung, lobe, and airway tree related to the location of the pulmonary nodule can be used to provide informative malignancy risk assessment. METHODS: A total of 183 qCT features were extracted from 278 individuals with a solitary pulmonary nodule of known diagnosis (71 malignant, 207 benign). These included histogram and airway characteristics of the lungs, lobe, and segmental paths. Performances of the least absolute shrinkage and selection operator (LASSO) regression analysis and an ensemble of neural networks (ENN) were compared for feature set selection and classification on a testing cohort of 49 additional individuals (15 malignant, 34 benign). RESULTS: The LASSO and ENN methods produced different feature sets for classification with LASSO selecting fewer qCT features (7) than the ENN (17). The LASSO model with the highest performing training area under the curve (AUC) (0.80) incorporated automatically extracted features and reader-measured nodule diameter with a testing AUC of 0.62. The ENN model with the highest performing AUC (0.77) also incorporated qCT and reader diameter but maintained higher testing performance AUC (0.79). CONCLUSIONS: Automatically extracted qCT imaging features of the lung can be informative of the differentiation between individuals with malignant pulmonary nodules and those with benign pulmonary nodules, without requiring nodule segmentation and analysis.

Menstrual cycle impacts lung structure measures derived from quantitative computed tomography.

OBJECTIVE: Quantitative computed tomography (qCT) is being increasingly incorporated in research studies and clinical trials aimed at understanding lung disease risk, progression, exacerbations, and intervention response. Menstrual cycle-based changes in lung function are recognized; however, the impact on qCT measures is currently unknown. We hypothesize that the menstrual cycle impacts qCT-derived measures of lung structure in healthy women and that the degree of measurement change may be mitigated in subjects on cyclic hormonal birth control. METHODS: Thirty-one non-smoking, healthy women with regular menstrual cycles (16 of which were on cyclic hormonal birth control) underwent pulmonary function testing and qCT imaging at both menses and early luteal phase time points. Data were evaluated to identify lung measurements which changed significantly across the two key time points and to compare degree of change across metrics for the sub-cohort with versus without birth control. RESULTS: The segmental airway measurements were larger and mean lung density was higher at menses compared to the early luteal phase. The sub-cohort with cyclic hormonal birth control did not have less evidence of measurement difference over the menstrual cycle compared to the sub-cohort without hormonal birth control. CONCLUSIONS: This study provides evidence that qCT-derived measures from the lung are impacted by the female menstrual cycle. This indicates studies seeking to use qCT as a more sensitive measure of cross-sectional differences or longitudinal changes in these derived lung measurements should consider acquiring data at a consistent time in the menstrual cycle for pre-menopausal women and warrants further exploration. KEY POINTS: • Lung measurements from chest computed tomography are used in multicenter studies exploring lung disease progression and treatment response. • The menstrual cycle impacts lung structure measurements. • Cyclic variability should be considered when evaluating longitudinal change with CT in menstruating women.

A CT-Based Automated Algorithm for Airway Segmentation Using Freeze-and-Grow Propagation and Deep Learning.

Chronic obstructive pulmonary disease (COPD) is a common lung disease, and quantitative CT-based bronchial phenotypes are of increasing interest as a means of exploring COPD sub-phenotypes, establishing disease progression, and evaluating intervention outcomes. Reliable, fully automated, and accurate segmentation of pulmonary airway trees is critical to such exploration. We present a novel approach of multi-parametric freeze-and-grow (FG) propagation which starts with a conservative segmentation parameter and captures finer details through iterative parameter relaxation. First, a CT intensity-based FG algorithm is developed and applied for airway tree segmentation. A more efficient version is produced using deep learning methods generating airway lumen likelihood maps from CT images, which are input to the FG algorithm. Both CT intensity- and deep learning-based algorithms are fully automated, and their performance, in terms of repeat scan reproducibility, accuracy, and leakages, is evaluated and compared with results from several state-of-the-art methods including an industry-standard one, where segmentation results were manually reviewed and corrected. Both new algorithms show a reproducibility of 95% or higher for total lung capacity (TLC) repeat CT scans. Experiments on TLC CT scans from different imaging sites at standard and low radiation dosages show that both new algorithms outperform the other methods in terms of leakages and branch-level accuracy. Considering the performance and execution times, the deep learning-based FG algorithm is a fully automated option for large multi-site studies.

Factors Affecting Radiation Dose in Computed Tomography Angiograms for Pulmonary Embolism: A Retrospective Cohort Study.

OBJECTIVES: Computed tomography pulmonary angiogram (CTPA) is one of the most commonly ordered and frequently overused tests. The purpose of this study was to evaluate the mean radiation dose to patients getting CTPA and to identify factors that are associated with higher dose. MATERIAL AND METHODS: This institutionally approved retrospective study included all patients who had a CTPA to rule out acute pulmonary embolism between 2016 and 2018 in a tertiary care center. Patient data (age, sex, body mass index [BMI], and patient location), CT scanner type, image reconstruction methodology, and radiation dose parameters (dose-length product [DLP]) were recorded. Effective dose estimates were obtained by multiplying DLP by conversion coefficient (0.014 mSv•mGy-1•cm-1). Multivariate logistic regression analysis was performed to determine the factors affecting the radiation dose. RESULTS: There were 2342 patients (1099 men and 1243 women) with a mean age of 58.1 years (range 0.2-104.4 years) and BMI of 31.3 kg/m2 (range 12-91.5 kg/m2). The mean effective radiation dose was 5.512 mSv (median - 4.27 mSv; range 0.1-43.0 mSv). Patient factors, including BMI >25 kg/m2, male sex, age >18 years, and intensive care unit (ICU) location, were associated with significantly higher dose (P < 0.05). CT scanning using third generation dual-source scanner with model-based iterative reconstruction (IR) had significantly lower dose (mean: 4.90 mSv) versus single-source (64-slice) scanner with filtered back projection (mean: 9.29 mSv, P < 0.001). CONCLUSION: Patients with high BMI and ICU referrals are associated with high CT radiation dose. They are most likely to benefit by scanning on newer generation scanner using advance model-based IR techniques.

A Risk Prediction Model for Mortality Among Smokers in the COPDGene® Study.

BACKGROUND: Risk factor identification is a proven strategy in advancing treatments and preventive therapy for many chronic conditions. Quantifying the impact of those risk factors on health outcomes can consolidate and focus efforts on individuals with specific high-risk profiles. Using multiple risk factors and longitudinal outcomes in 2 independent cohorts, we developed and validated a risk score model to predict mortality in current and former cigarette smokers. METHODS: We obtained extensive data on current and former smokers from the COPD Genetic Epidemiology (COPDGene®) study at enrollment. Based on physician input and model goodness-of-fit measures, a subset of variables was selected to fit final Weibull survival models separately for men and women. Coefficients and predictors were translated into a point system, allowing for easy computation of mortality risk scores and probabilities. We then used the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) cohort for external validation of our model. RESULTS: Of 9867 COPDGene participants with standard baseline data, 17.6% died over 10 years of follow-up, and 9074 of these participants had the full set of baseline predictors (standard plus 6-minute walk distance and computed tomography variables) available for full model fits. The average age of participants in the cohort was 60 for both men and women, and the average predicted 10-year mortality risk was 18% for women and 25% for men. Model time-integrated area under the receiver operating characteristic curve statistics demonstrated good predictive model accuracy (0.797 average), validated in the external cohort (0.756 average). Risk of mortality was impacted most by 6-minute walk distance, forced expiratory volume in 1 second and age, for both men and women. CONCLUSIONS: Current and former smokers exhibited a wide range of mortality risk over a 10- year period. Our models can identify higher risk individuals who can be targeted for interventions to reduce risk of mortality, for participants with or without chronic obstructive pulmonary disease (COPD) using current Global initiative for obstructive Lung Disease (GOLD) criteria.

Understanding the Redox Biology of Selenium in the Search of Targeted Cancer Therapies.

Selenium (Se) is an essential trace nutrient required for optimal human health. It has long been suggested that selenium has anti-cancer properties. However, clinical trials have shown inconclusive results on the potential of Se to prevent cancer. The suggested role of Se in the prevention of cancer is centered around its role as an antioxidant. Recently, the potential of selenium as a drug rather than a supplement has been uncovered. Selenium compounds can generate reactive oxygen species that could enhance the treatment of cancer. Transformed cells have high oxidative distress. As normal cells have a greater capacity to meet oxidative challenges than tumor cells, increasing the flux of oxidants with high dose selenium treatment could result in cancer-specific cell killing. If the availability of Se is limited, supplementation of Se can increase the expression and activities of Se-dependent proteins and enzymes. In cell culture, selenium deficiency is often overlooked. We review the importance of achieving normal selenium biology and how Se deficiency can lead to adverse effects. We examine the vital role of selenium in the prevention and treatment of cancer. Finally, we examine the properties of Se-compounds to better understand how each can be used to address different research questions.

Longitudinal phenotype development in a minipig model of neurofibromatosis type 1.

Neurofibromatosis type 1 (NF1) is a rare, autosomal dominant disease with variable clinical presentations. Large animal models are useful to help dissect molecular mechanisms, determine relevant biomarkers, and develop effective therapeutics. Here, we studied a NF1 minipig model (NF1+/ex42del) for the first 12 months of life to evaluate phenotype development, track disease progression, and provide a comparison to human subjects. Through systematic evaluation, we have shown that compared to littermate controls, the NF1 model develops phenotypic characteristics of human NF1: [1] café-au-lait macules, [2] axillary/inguinal freckling, [3] shortened stature, [4] tibial bone curvature, and [5] neurofibroma. At 4 months, full body computed tomography imaging detected significantly smaller long bones in NF1+/ex42del minipigs compared to controls, indicative of shorter stature. We found quantitative evidence of tibial bowing in a subpopulation of NF1 minipigs. By 8 months, an NF1+/ex42del boar developed a large diffuse shoulder neurofibroma, visualized on magnetic resonance imaging, which subsequently grew in size and depth as the animal aged up to 20 months. The NF1+/ex42del minipig model progressively demonstrates signature attributes that parallel clinical manifestations seen in humans and provides a viable tool for future translational NF1 research.

Validating indicators of CNS disorders in a swine model of neurological disease.

Genetically modified swine disease models are becoming increasingly important for studying molecular, physiological and pathological characteristics of human disorders. Given the limited history of these model systems, there remains a great need for proven molecular reagents in swine tissue. Here, to provide a resource for neurological models of disease, we validated antibodies by immunohistochemistry for use in examining central nervous system (CNS) markers in a recently developed miniswine model of neurofibromatosis type 1 (NF1). NF1 is an autosomal dominant tumor predisposition disorder stemming from mutations in NF1, a gene that encodes the Ras-GTPase activating protein neurofibromin. Patients classically present with benign neurofibromas throughout their bodies and can also present with neurological associated symptoms such as chronic pain, cognitive impairment, and behavioral abnormalities. As validated antibodies for immunohistochemistry applications are particularly difficult to find for swine models of neurological disease, we present immunostaining validation of antibodies implicated in glial inflammation (CD68), oligodendrocyte development (NG2, O4 and Olig2), and neuron differentiation and neurotransmission (doublecortin, GAD67, and tyrosine hydroxylase) by examining cellular localization and brain region specificity. Additionally, we confirm the utility of anti-GFAP, anti-Iba1, and anti-MBP antibodies, previously validated in swine, by testing their immunoreactivity across multiple brain regions in mutant NF1 samples. These immunostaining protocols for CNS markers provide a useful resource to the scientific community, furthering the utility of genetically modified miniswine for translational and clinical applications.

RABL6A Is an Essential Driver of MPNSTs that Negatively Regulates the RB1 Pathway and Sensitizes Tumor Cells to CDK4/6 Inhibitors.

PURPOSE: Malignant peripheral nerve sheath tumors (MPNST) are deadly sarcomas that lack effective therapies. In most MPNSTs, the retinoblastoma (RB1) tumor suppressor is disabled by hyperactivation of cyclin-dependent kinases (CDK), commonly through loss of CDK-inhibitory proteins such as p27(Kip1). RABL6A is an inhibitor of RB1 whose role in MPNSTs is unknown. To gain insight into MPNST development and establish new treatment options, we investigated RABL6A-RB1 signaling and CDK inhibitor-based therapy in MPNSTs. EXPERIMENTAL DESIGN: We examined patient-matched MPNSTs and precursor lesions by RNA sequencing (RNA-Seq) and IHC. Molecular and biological effects of silencing RABL6A and/or p27 in MPNST lines and normal human Schwann cells were determined. Tumor-suppressive effects of CDK inhibitors were measured in MPNST cells and orthotopic tumors. RESULTS: RABL6A was dramatically upregulated in human MPNSTs compared with precursor lesions, which correlated inversely with p27 levels. Silencing RABL6A caused MPNST cell death and G1 arrest that coincided with p27 upregulation, CDK downregulation, and RB1 activation. The growth-suppressive effects of RABL6A loss, and its regulation of RB1, were largely rescued by p27 depletion. Importantly, reactivation of RB1 using a CDK4/6 inhibitor (palbociclib) killed MPNST cells in vitro in an RABL6A-dependent manner and suppressed MPNST growth in vivo. Low-dose combination of drugs targeting multiple RB1 kinases (CDK4/6, CDK2) had enhanced antitumorigenic activity associated with potential MPNST cell redifferentiation. CONCLUSIONS: RABL6A is a new driver of MPNST pathogenesis that acts in part through p27-RB1 inactivation. Our results suggest RB1 targeted therapy with multiple pathway drugs may effectively treat MPNSTs.

High-energy external defibrillation and transcutaneous pacing during MRI: feasibility and safety.

BACKGROUND: Rapid application of external defibrillation, a crucial first-line therapy for ventricular fibrillation and cardiac arrest, is currently unavailable in the setting of magnetic resonance imaging (MRI), raising concerns about patient safety during MRI tests and MRI-guided procedures, particularly in patients with cardiovascular diseases. The objective of this study was to examine the feasibility and safety of defibrillation/pacing for the entire range of clinically useful shock energies inside the MRI bore and during scans, using defibrillation/pacing outside the magnet as a control. METHODS: Experiments were conducted using a commercial defibrillator (LIFEPAK 20, Physio-Control, Redmond, Washington, USA) with a custom high-voltage, twisted-pair cable with two mounted resonant floating radiofrequency traps to reduce emission from the defibrillator and the MRI scanner. A total of 18 high-energy (200-360 J) defibrillation experiments were conducted in six swine on a 1.5 T MRI scanner outside the magnet bore, inside the bore, and during scanning, using adult and pediatric defibrillation pads. Defibrillation was followed by cardiac pacing (with capture) in a subset of two animals. Monitored signals included: high-fidelity temperature (0.01 °C, 10 samples/sec) under the pads and 12-lead electrocardiogram (ECG) using an MRI-compatible ECG system. RESULTS: Defibrillation/pacing was successful in all experiments. Temperature was higher during defibrillation inside the bore and during scanning compared with outside the bore, but the differences were small (ΔT: 0.5 and 0.7 °C, p = 0.01 and 0.04, respectively). During scans, temperature after defibrillation tended to be higher for pediatric vs. adult pads (p = 0.08). MR-image quality (signal-to-noise ratio) decreased by ~ 10% when the defibrillator was turned on. CONCLUSIONS: Our study demonstrates the feasibility and safety of in-bore defibrillation for the full range of defibrillation energies used in clinical practice, as well as of transcutaneous cardiac pacing inside the MRI bore. Methods for Improving MR-image quality in the presence of a working defibrillator require further study.