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.

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.

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.

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.

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.

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.

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.

Phase I with expansion clinical trial of seleno-l-methionine (SLM) in combination with axitinib in patients with relapsed clear cell renal cell carcinoma (ccRCC): Bench to bedside

322

Background: Hypoxia induced factor 1α/2α (HIFs) and vascular endothelial growth factor (VEGF) are overexpressed in ccRCC and associated with increased tumor angiogenesis, vascular instability and drug resistance. In xenografts, high dose SLM resulted in sustained down regulation of HIFs, normalization of tumor vasculature that resulted in increased drug delivery, and therapeutic synergy with anti-VEGF therapeutic. These effects were highly SLM dose, sequence, and schedule dependent. Methods: After completion of the escalating phase I (3+3) trial, the non-toxic SLM dose of 4000μg was administered orally twice daily (BID) for 14 days, followed by once daily in combination with axitinib 5 mg BID. The primary endpoint is safety and the secondary end point include overall response rate (ORR), progression free survival (PFS), and overall survival (OS). To assess the potential effects of SLM on tumor vascular function, dual energy computed tomography (DECT) was conducted at baseline, day 14 of SLM, and at 3 months of SLM + axitinib. Results: Thirty subjects are screened. Of whom 25 have efficacy data (3 subjects screen failure, 1 withdrew, 1 taken off study prior to first scan due to progression with brain lesions likely present prior to study entry). 13/25 (52%) have confirmed response (CR/PR). Two subjects had CR lasting for at least 35 and 44 months, 6 subjects achieved stable disease (SD) lasting at least 6 months accounting for disease control rate (DCR) of 76%, mPFS is 9.5 months. 5/30 patients have sarcomatoid features, all of which are evaluable for efficacy with 1 PR achieved (20%). In the 20 patients without sarcomatoid features, 12/20 achieved PR (60%). The most prevalent adverse events (AE) included fatigue, diarrhea, anorexia, nausea, hoarseness, weight loss, and hypertension. No deaths nor grade 4 toxicities observed. The blood selenium concentrations achieved in the 4000μg SLM dose cohorts are similar to those determined molecularly and therapeutically synergistic with axitinib in xenografts and are being achieved clinically without significant toxicity. DECT data demonstrated increased iodine uptake by tumor lesions, but not in normal tissues, on day 14 SLM treatment and decreased in these same lesions at 3 months of SLM/axitinib. Conclusions: Blood selenium concentrations and duration of treatment seem to be critical determinants of response. Pre and concurrent treatment with SLM enhance the ORR and PFS of axitinib, with no additional toxicity. Currently documented responses are similar across IMDC risk groups. Clinical trial information: NCT02535533 .

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.

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.

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.

Differentiation of non-small cell lung cancer and histoplasmosis pulmonary nodules: insights from radiomics model performance compared with clinician observers

Background

Histoplasmosis pulmonary nodules often present in computed tomography (CT) imaging with characteristics suspicious for lung cancer. This presents a work-up decision issue for clinicians in regions where histoplasmosis is an endemic fungal infection, when a nodule suspicious for lung cancer is detected. We hypothesize the application of radiomic features extracted from pulmonary nodules and perinodular parenchyma could accurately distinguish between suspicious histoplasmosis lung nodules and non-small cell lung cancer (NSCLC).

Methods

A retrospective clinical cohort of pulmonary nodules with a confirmed diagnosis of histoplasmosis or NSCLC was collected from the University of Iowa Hospitals and Clincs. Radiomic features were extracted describing characteristics of the nodule and perinodular parenchyma regions and used to build a machine learning tool. These cases were assessed by four expert clinicians who gave a blinded risk prediction for NSCLC. Tool and observer performance were assessed by calculating the area under the curve for the receiver operating characteristic (AUC-ROC) and interclass correlation coefficient (ICC).

Results

A cohort of 71 subjects with confirmed histopathology (40 NSCLC, 31 histoplasmosis) were case-matched based on age, sex, and smoking history. Superior performance (AUC-ROC =0.89) was demonstrated using leave-one-subject out validation in the tool that incorporated radiomics from the nodule and perinodular parenchyma region extended to 100% nodule diameter. Observers had perfect intra-repeatability (ICC =1.0) and demonstrated fair inter-reader variability (ICC =0.52).

Conclusions

Radiomics have potential utility in the challenging task of differentiation between lung cancer and histoplasmosis. Expert clinician readers have high intra-repeatability but demonstrated inter-reader variability which could provide context for a supplemental radiomics-based tool.

COPDGene® 2019: Redefining the Diagnosis of Chronic Obstructive Pulmonary Disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) remains a major cause of morbidity and mortality. Present-day diagnostic criteria are largely based solely on spirometric criteria. Accumulating evidence has identified a substantial number of individuals without spirometric evidence of COPD who suffer from respiratory symptoms and/or increased morbidity and mortality. There is a clear need for an expanded definition of COPD that is linked to physiologic, structural (computed tomography [CT]) and clinical evidence of disease. Using data from the COPD Genetic Epidemiology study (COPDGene®), we hypothesized that an integrated approach that includes environmental exposure, clinical symptoms, chest CT imaging and spirometry better defines disease and captures the likelihood of progression of respiratory obstruction and mortality.

METHODS

Four key disease characteristics - environmental exposure (cigarette smoking), clinical symptoms (dyspnea and/or chronic bronchitis), chest CT imaging abnormalities (emphysema, gas trapping and/or airway wall thickening), and abnormal spirometry - were evaluated in a group of 8784 current and former smokers who were participants in COPDGene® Phase 1. Using these 4 disease characteristics, 8 categories of participants were identified and evaluated for odds of spirometric disease progression (FEV1 > 350 ml loss over 5 years), and the hazard ratio for all-cause mortality was examined.

RESULTS

Using smokers without symptoms, CT imaging abnormalities or airflow obstruction as the reference population, individuals were classified as Possible COPD, Probable COPD and Definite COPD. Current Global initiative for obstructive Lung Disease (GOLD) criteria would diagnose 4062 (46%) of the 8784 study participants with COPD. The proposed COPDGene® 2019 diagnostic criteria would add an additional 3144 participants. Under the new criteria, 82% of the 8784 study participants would be diagnosed with Possible, Probable or Definite COPD. These COPD groups showed increased risk of disease progression and mortality. Mortality increased in patients as the number of their COPD characteristics increased, with a maximum hazard ratio for all cause-mortality of 5.18 (95% confidence interval [CI]: 4.15-6.48) in those with all 4 disease characteristics.

CONCLUSIONS

A substantial portion of smokers with respiratory symptoms and imaging abnormalities do not manifest spirometric obstruction as defined by population normals. These individuals are at significant risk of death and spirometric disease progression. We propose to redefine the diagnosis of COPD through an integrated approach using environmental exposure, clinical symptoms, CT imaging and spirometric criteria. These expanded criteria offer the potential to stimulate both current and future interventions that could slow or halt disease progression in patients before disability or irreversible lung structural changes develop.

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.

Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT

PURPOSE

Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment.

METHODS

Lung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k-medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign).

RESULTS

The inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features (P < 0.01). The best performing ML tool included features derived from nodule diameter-based surrounding parenchyma tissue quartile bands. We demonstrate similar high-performance values on the independent validation cohort (AUC-ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow-up guidelines demonstrated a theoretical reduction in recommended follow-up imaging and procedures.

CONCLUSIONS

Radiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.

Post-imaging pulmonary nodule mathematical prediction models: are they clinically relevant?

OBJECTIVES

Post-imaging mathematical prediction models (MPMs) provide guidance for the management of solid pulmonary nodules by providing a lung cancer risk score from demographic and radiologists-indicated imaging characteristics. We hypothesized calibrating the MPM risk score threshold to a local study cohort would result in improved performance over the original recommended MPM thresholds. We compared the pre- and post-calibration performance of four MPM models and determined if improvement in MPM prediction occurs as nodules are imaged longitudinally.

MATERIALS AND METHODS

A common cohort of 317 individuals with computed tomography-detected, solid nodules (80 malignant, 237 benign) were used to evaluate the MPM performance. We created a web-based application for this study that allows others to easily calibrate thresholds and analyze the performance of MPMs on their local cohort. Thirty patients with repeated imaging were tested for improved performance longitudinally.

RESULTS

Using calibrated thresholds, Mayo Clinic and Brock University (BU) MPMs performed the best (AUC = 0.63, 0.61) compared to the Veteran's Affairs (0.51) and Peking University (0.55). Only BU had consensus with the original MPM threshold; the other calibrated thresholds improved MPM accuracy. No significant improvements in accuracy were found longitudinally between time points.

CONCLUSIONS

Calibration to a common cohort can select the best-performing MPM for your institution. Without calibration, BU has the most stable performance in solid nodules ≥ 8 mm but has only moderate potential to refine subjects into appropriate workup. Application of MPM is recommended only at initial evaluation as no increase in accuracy was achieved over time.

KEY POINTS

• Post-imaging lung cancer risk mathematical predication models (MPMs) perform poorly on local populations without calibration. • An application is provided to facilitate calibration to new study cohorts: the Mayo Clinic model, the U.S. Department of Veteran's Affairs model, the Brock University model, and the Peking University model. • No significant improvement in risk prediction occurred in nodules with repeated imaging sessions, indicating the potential value of risk prediction application is limited to the initial evaluation.

Quantitative Imaging Markers of Lung Function in a Smoking Population Distinguish COPD Subgroups with Differential Lung Cancer Risk

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition with respect to onset, progression, and response to therapy. Incorporating clinical- and imaging-based features to refine COPD phenotypes provides valuable information beyond that obtained from traditional clinical evaluations. We characterized the spectrum of COPD-related phenotypes in a sample of former and current smokers and evaluated how these subgroups differ with respect to sociodemographic characteristics, COPD-related comorbidities, and subsequent risk of lung cancer.

METHODS

White (N = 659) and African American (N = 520) male and female participants without lung cancer (controls) in the INHALE study who completed a chest CT scan, interview, and spirometry test were used to define distinct COPD-related subgroups based on hierarchical clustering. Seven variables were used to define clusters: pack years, quit years, FEV₁/FVC, % predicted FEV₁, and from quantitative CT (qCT) imaging, % emphysema, % air trapping, and mean lung density ratio. Cluster definitions were then applied to INHALE lung cancer cases (N = 576) to evaluate lung cancer risk.

RESULTS

Five clusters were identified that differed significantly with respect to sociodemographic (e.g., race, age) and clinical (e.g., BMI, limitations due to breathing difficulties) characteristics. Increased risk of lung cancer was associated with increasingly detrimental lung function clusters (when ordered from most detrimental to least detrimental).

CONCLUSIONS

Measures of lung function vary considerably among smokers and are not fully explained by smoking intensity.

IMPACT

Combining clinical (spirometry) and radiologic (qCT) measures of COPD defines a spectrum of lung disease that predicts lung cancer risk differentially among patient clusters.

Radiomic biomarkers informative of cancerous transformation in neurofibromatosis-1 plexiform tumors

BACKGROUND

This study explores whether objective, quantitative radiomic biomarkers derived from magnetic resonance (MR), positron emission tomography (PET), and computed tomography (CT) may be useful in reliably distinguishing malignant peripheral nerve sheath tumors (MPNST) from benign plexiform neurofibromas (PN).

METHODS

A registration and segmentation pipeline was established using a cohort of NF1 patients with histopathological diagnosis of PN or MPNST, and medical imaging of the PN including MR and PET-CT. The corrected MR datasets were registered to the corresponding PET-CT via landmark-based registration. PET standard-uptake value (SUV) thresholds were used to guide segmentation of volumes of interest: MPNST-associated PET-hot regions (SUV≥3.5) and PN-associated PET-elevated regions (2.0<SUV<3.5). Quantitative imaging features were extracted from the MR, PET, and CT data and compared for statistical differences. Intensity histogram features included (mean, media, maximum, variance, full width at half maximum, entropy, kurtosis, and skewness), while image texture was quantified using Law's texture energy measures, grey-level co-occurrence matrices, and neighborhood grey-tone difference matrices.

RESULTS

For each of the 20 NF1 subjects, a total of 320 features were extracted from the image data. Feature reduction and statistical testing identified 9 independent radiomic biomarkers from the MR data (4 intensity and 5 texture) and 4 PET (2 intensity and 2 texture) were different between the PET-hot versus PET-elevated volumes of interest.

CONCLUSIONS

Our data suggests imaging features can be used to distinguish malignancy in NF1-realted tumors, which could improve MPNST risk assessment and positively impact clinical management of NF1 patients.

A porcine model of neurofibromatosis type 1 that mimics the human disease

Loss of the NF1 tumor suppressor gene causes the autosomal dominant condition, neurofibromatosis type 1 (NF1). Children and adults with NF1 suffer from pathologies including benign and malignant tumors to cognitive deficits, seizures, growth abnormalities, and peripheral neuropathies. NF1 encodes neurofibromin, a Ras-GTPase activating protein, and NF1 mutations result in hyperactivated Ras signaling in patients. Existing NF1 mutant mice mimic individual aspects of NF1, but none comprehensively models the disease. We describe a potentially novel Yucatan miniswine model bearing a heterozygotic mutation in NF1 (exon 42 deletion) orthologous to a mutation found in NF1 patients. NF1+/ex42del miniswine phenocopy the wide range of manifestations seen in NF1 patients, including café au lait spots, neurofibromas, axillary freckling, and neurological defects in learning and memory. Molecular analyses verified reduced neurofibromin expression in swine NF1+/ex42del fibroblasts, as well as hyperactivation of Ras, as measured by increased expression of its downstream effectors, phosphorylated ERK1/2, SIAH, and the checkpoint regulators p53 and p21. Consistent with altered pain signaling in NF1, dysregulation of calcium and sodium channels was observed in dorsal root ganglia expressing mutant NF1. Thus, these NF1+/ex42del miniswine recapitulate the disease and provide a unique, much-needed tool to advance the study and treatment of NF1.

Features of COPD as Predictors of Lung Cancer

BACKGROUND

Lung cancer is a leading cause of death and hospitalization for patients with COPD. A detailed understanding of which clinical features of COPD increase risk is needed.

METHODS

We performed a nested case-control study of Genetic Epidemiology of COPD (COPDGene) Study subjects with and without lung cancer, age 45 to 80 years, who smoked at least 10-pack years to identify clinical and imaging features of smokers, with and without COPD, that are associated with an increased risk of lung cancer. The baseline evaluation included spirometry, high-resolution chest CT scanning, and respiratory questionnaires. New lung cancer diagnoses were identified over 8 years of longitudinal follow-up. Cases of lung cancer were matched 1:4 with control subjects for age, race, sex, and smoking history. Multiple logistic regression analyses were used to determine features predictive of lung cancer.

RESULTS

Features associated with a future risk of lung cancer included decreased FEV₁/FVC (OR, 1.28 per 10% decrease [95% CI, 1.12-1.46]), visual severity of emphysema (OR, 2.31, none-trace vs mild-advanced [95% CI, 1.41-3.86]), and respiratory exacerbations prior to study entry (OR, 1.39 per increased events [0, 1, and ≥ 2] [95% CI, 1.04-1.85]). Respiratory exacerbations were also associated with small-cell lung cancer histology (OR, 3.57 [95% CI, 1.47-10]).

CONCLUSIONS

The degree of COPD severity, including airflow obstruction, visual emphysema, and respiratory exacerbations, was independently predictive of lung cancer. These risk factors should be further studied as inclusion and exclusion criteria for the survival benefit of lung cancer screening. Studies are needed to determine if reduction in respiratory exacerbations among smokers can reduce the risk of lung cancer.

Computed Tomography Measure of Lung at Risk and Lung Function Decline in Chronic Obstructive Pulmonary Disease

RATIONALE

The rate of decline of lung function is greater than age-related change in a substantial proportion of patients with chronic obstructive pulmonary disease, even after smoking cessation. Regions of the lung adjacent to emphysematous areas are subject to abnormal stretch during respiration, and this biomechanical stress likely influences emphysema initiation and progression.

OBJECTIVES

To assess whether quantifying this penumbra of lung at risk would predict FEV₁ decline.

METHODS

We analyzed paired inspiratory-expiratory computed tomography images at baseline of 680 subjects participating in a large multicenter study (COPDGene) over approximately 5 years. By matching inspiratory and expiratory images voxel by voxel using image registration, we calculated the Jacobian determinant, a measure of local lung expansion and contraction with respiration. We measured the distance between each normal voxel to the nearest emphysematous voxel, and quantified the percentage of normal voxels within each millimeter distance from emphysematous voxels as mechanically affected lung (MAL). Multivariable regression analyses were performed to assess the relationship between the Jacobian determinant, MAL, and FEV₁ decline.

MEASUREMENTS AND MAIN RESULTS

The mean (SD) rate of decline in FEV₁ was 39.0 (58.6) ml/yr. There was a progressive decrease in the mean Jacobian determinant of both emphysematous and normal voxels with increasing disease stage (P < 0.001). On multivariable analyses, the mean Jacobian determinant of normal voxels within 2 mm of emphysematous voxels (MAL₂) was significantly associated with FEV₁ decline. In mild-moderate disease, for participants at or above the median MAL₂ (threshold, 36.9%), the mean decline in FEV₁ was 56.4 (68.0) ml/yr versus 43.2 (59.9) ml/yr for those below the median (P = 0.044).

CONCLUSIONS

Areas of normal-appearing lung are mechanically influenced by emphysematous areas and this lung at risk is associated with lung function decline. Clinical trial registered with www.clinicaltrials.gov (NCT00608764).

Immunohistochemical Markers for Prospective Studies in Neurofibromatosis-1 Porcine Models

Neurofibromatosis type 1 (NF1) is a common, cancer-predisposing disease caused by mutations in the NF1 tumor gene. Patients with NF1 have an increased risk for benign and malignant tumors of the nervous system (e.g., neurofibromas, malignant peripheral nerve sheath tumors, gliomas) and other tissues (e.g., leukemias, rhabdomyosarcoma, etc.) as well as increased susceptibility to learning disabilities, chronic pain/migraines, hypertension, pigmentary changes, and developmental lesions (e.g., tibial pseudoarthrosis). Pigs are an attractive and upcoming animal model for future NF1 studies, but a potential limitation to porcine model research has been the lack of validated reagents for direct translational study to humans. To address that issue, we used formalin-fixed tissues (human and pigs) to evaluate select immunohistochemical markers (activated caspase-3, allograft inflammatory factor-1, beta-tubulin III, calbindin D, CD13, CD20, desmin, epithelial membrane antigen, glial fibrillary acidic protein, glucose transporter-1, laminin, myelin basic protein, myoglobin, proliferating cell nuclear antigen, S100, vimentin, and von Willebrand factor). The markers were validated by comparing known expression and localization in human and pig tissues. Validation of these markers on fixed tissues will facilitate prospective immunohistochemical studies of NF1 pigs, as well as other pig models, in a more efficient, reproducible, and translationally relevant manner.

Approaches to Evaluate Lung Inflammation in Translational Research

Inflammation is a common feature in several types of lung disease and is a frequent end point to validate lung disease models, evaluate genetic or environmental impact on disease severity, or test the efficacy of new therapies. Questions relevant to a study should be defined during experimental design and techniques selected to specifically address these scientific queries. In this review, the authors focus primarily on the breadth of techniques to evaluate lung inflammation that have both clinical and preclinical applications. Stratification of approaches to assess lung inflammation can diminish weaknesses inherent to each technique, provide data validation, and increase the reproducibility of a study. Specialized techniques (eg, imaging, pathology) often require experienced personnel to collect, evaluate, and interpret the data; these experts should be active contributors to the research team through reporting of the data. Scoring of tissue lesions is a useful method to transform observational pathologic data into semiquantitative or quantitative data for statistical analysis and enhanced rigor. Each technique to evaluate lung inflammation has advantages and limitations; understanding these parameters can help identify approaches that best complement one another to increase the rigor and translational significance of data.

Comparison of low- and ultralow-dose computed tomography protocols for quantitative lung and airway assessment

PURPOSE

Quantitative computed tomography (CT) measures are increasingly being developed and used to characterize lung disease. With recent advances in CT technologies, we sought to evaluate the quantitative accuracy of lung imaging at low- and ultralow-radiation doses with the use of iterative reconstruction (IR), tube current modulation (TCM), and spectral shaping.

METHODS

We investigated the effect of five independent CT protocols reconstructed with IR on quantitative airway measures and global lung measures using an in vivo large animal model as a human subject surrogate. A control protocol was chosen (NIH-SPIROMICS + TCM) and five independent protocols investigating TCM, low- and ultralow-radiation dose, and spectral shaping. For all scans, quantitative global parenchymal measurements (mean, median and standard deviation of the parenchymal HU, along with measures of emphysema) and global airway measurements (number of segmented airways and pi10) were generated. In addition, selected individual airway measurements (minor and major inner diameter, wall thickness, inner and outer area, inner and outer perimeter, wall area fraction, and inner equivalent circle diameter) were evaluated. Comparisons were made between control and target protocols using difference and repeatability measures.

RESULTS

Estimated CT volume dose index (CTDIvol) across all protocols ranged from 7.32 mGy to 0.32 mGy. Low- and ultralow-dose protocols required more manual editing and resolved fewer airway branches; yet, comparable pi10 whole lung measures were observed across all protocols. Similar trends in acquired parenchymal and airway measurements were observed across all protocols, with increased measurement differences using the ultralow-dose protocols. However, for small airways (1.9 ± 0.2 mm) and medium airways (5.7 ± 0.4 mm), the measurement differences across all protocols were comparable to the control protocol repeatability across breath holds. Diameters, wall thickness, wall area fraction, and equivalent diameter had smaller measurement differences than area and perimeter measurements.

CONCLUSIONS

In conclusion, the use of IR with low- and ultralow-dose CT protocols with CT volume dose indices down to 0.32 mGy maintains selected quantitative parenchymal and airway measurements relevant to pulmonary disease characterization.

Protein Kinase Cζ Inhibitor Promotes Resolution of Bleomycin-Induced Acute Lung Injury

Protein kinase Cζ (PKCζ) is highly expressed in the lung, where it plays several regulating roles in the pathogenesis of acute lung injury (ALI). Proliferation and differentiation of integrin β4⁺ distal lung epithelial progenitor cells seem to play a key role in proper lung regeneration. We investigated the effects of a myristoylated PKCζ inhibitor (PKCζi) in a murine model of bleomycin-induced ALI. After intratracheal injury, we treated mice three times a week with PKCζi or its vehicle, DMSO. We found that mice injured with bleomycin and then treated with PKCζi for one week showed decreased activation of PKCζ, improved lung compliance, and decreased lung protein permeability compared to injured mice treated with DMSO. Mice treated continuously with PKCζi for 6 weeks showed reduced evidence of lung fibrosis by computed tomographic images, decreased lung collagen deposition, and decreased active transforming growth factor-β in the bronchoalveolar lavage fluid. In addition, we found an increased number of lung β4⁺ cells compared to DMSO at Week 6. Therefore, we grew isolated integrin β4⁺ lung progenitor cells in the presence of PKCζi or DMSO and found that β4⁺ cells treated with PKCζi proliferated more in vitro compared to DMSO. We conclude that the use of a PKCζi promotes resolution of lung fibrosis in a bleomycin ALI model and increases the number of β4⁺ progenitor cells with regenerative potential in the lung.

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features

Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic “feature” sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy.

CT-derived Biomechanical Metrics Improve Agreement Between Spirometry and Emphysema

RATIONALE AND OBJECTIVES

Many patients with chronic obstructive pulmonary disease (COPD) have marked discordance between forced expiratory volume in 1 second (FEV1) and degree of emphysema on computed tomography (CT). Biomechanical differences between these patients have not been studied. We aimed to identify reasons for the discordance between CT and spirometry in some patients with COPD.

MATERIALS AND METHODS

Subjects with Global initiative for chronic Obstructive Lung Disease stages I-IV from a large multicenter study (The Genetic Epidemiology of COPD) were arranged by percentiles of %predicted FEV1 and emphysema on CT. Three categories were created using differences in percentiles: Catspir with predominant airflow obstruction/minimal emphysema, CatCT with predominant emphysema/minimal airflow obstruction, and Catmatched with matched FEV1 and emphysema. Image registration was used to derive Jacobian determinants, a measure of lung elasticity, anisotropy, and strain tensors, to assess biomechanical differences between groups. Regression models were created with the previously mentioned categories as outcome variable, adjusting for demographics, scanner type, quantitative CT-derived emphysema, gas trapping, and airway thickness (model 1), and after adding biomechanical CT metrics (model 2).

RESULTS

Jacobian determinants, anisotropy, and strain tensors were strongly associated with FEV1. With Catmatched as control, model 2 predicted Catspir and CatCT better than model 1 (Akaike information criterion 255.8 vs. 320.8). In addition to demographics, the strongest independent predictors of FEV1 were Jacobian mean (β = 1.60,95%confidence intervals [CI] = 1.16 to 1.98; P < 0.001), coefficient of variation (CV) of Jacobian (β = 1.45,95%CI = 0.86 to 2.03; P < 0.001), and CV of strain (β = 1.82,95%CI = 0.68 to 2.95; P = 0.001). CVs of Jacobian and strain are both potential markers of biomechanical lung heterogeneity.

CONCLUSIONS

CT-derived measures of lung mechanics improve the link between quantitative CT and spirometry, offering the potential for new insights into the linkage between regional parenchymal destruction and global decrement in lung function in patients with COPD.

Computed Tomography and Magnetic Resonance Imaging for Longitudinal Characterization of Lung Structure Changes in a Yucatan Miniature Pig Silicosis Model

Medical imaging is a rapidly advancing field enabling the repeated, noninvasive assessment of physiological structure and function. These beneficial characteristics can supplement studies in swine by mirroring the clinical functions of detection, diagnosis, and monitoring in humans. In addition, swine may serve as a human surrogate, facilitating the development and comparison of new imaging protocols for translation to humans. This study presents methods for pulmonary imaging developed for monitoring pulmonary disease initiation and progression in a pig exposure model with computed tomography and magnetic resonance imaging. In particular, a focus was placed on systematic processes, including positioning, image acquisition, and structured reporting to monitor longitudinal change. The image-based monitoring procedure was applied to 6 Yucatan miniature pigs. A subset of animals (n= 3) were injected with crystalline silica into the apical bronchial tree to induce silicosis. The methodology provided longitudinal monitoring and evidence of progressive lung disease while simultaneously allowing for a cross-modality comparative study highlighting the practical application of medical image data collection in swine. The integration of multimodality imaging with structured reporting allows for cross comparison of modalities, refinement of CT and MRI protocols, and consistently monitors potential areas of interest for guided biopsy and/or necropsy.

Improved pulmonary nodule classification utilizing quantitative lung parenchyma features

Current computer-aided diagnosis (CAD) models for determining pulmonary nodule malignancy characterize nodule shape, density, and border in computed tomography (CT) data. Analyzing the lung parenchyma surrounding the nodule has been minimally explored. We hypothesize that improved nodule classification is achievable by including features quantified from the surrounding lung tissue. To explore this hypothesis, we have developed expanded quantitative CT feature extraction techniques, including volumetric Laws texture energy measures for the parenchyma and nodule, border descriptors using ray-casting and rubber-band straightening, histogram features characterizing densities, and global lung measurements. Using stepwise forward selection and leave-one-case-out cross-validation, a neural network was used for classification. When applied to 50 nodules (22 malignant and 28 benign) from high-resolution CT scans, 52 features (8 nodule, 39 parenchymal, and 5 global) were statistically significant. Nodule-only features yielded an area under the ROC curve of 0.918 (including nodule size) and 0.872 (excluding nodule size). Performance was improved through inclusion of parenchymal (0.938) and global features (0.932). These results show a trend toward increased performance when the parenchyma is included, coupled with the large number of significant parenchymal features that support our hypothesis: the pulmonary parenchyma is influenced differentially by malignant versus benign nodules, assisting CAD-based nodule characterizations.

Porcine cancer models for translational oncology

Large-animal cancer models are needed to advance the development of innovative and clinically applicable tumor diagnostic, therapeutic, and monitoring technologies. We developed a genetically modified porcine model of cancer based on a TP53 mutation, and established its utility for tracking tumorigenesis in vivo through non-invasive clinical imaging approaches.

Abstract LB-162: Translational imaging of tumorigenesis in a TP53 porcine cancer model

Cancer remains the second deadliest disease in the United States, necessitating improvements in tumor diagnosis and treatment. Current model systems of cancer have been informative but also present challenges to translating promising imaging approaches and therapies to the clinic. The lack of a large animal model that accurately mimics human cancer has been a major barrier to the development of effective diagnostic tools and interventions (surgical and therapeutic). This study sought to 1) develop a genetically-modified porcine model of cancer expressing a TP53 mutation commonly found in humans (R175H in people, R167H in pigs), 2) validate the cellular p53 mutation and 3) evaluate tumor development in the animals using medical imaging, histopathology and molecular approaches. TP53R167H/R167H mutant pigs primarily developed lymphomas and osteogenic tumors, mimicking the tumor types observed in mice and humans expressing the orthologous TP53 mutant allele. CT and MRI imaging data effectively detected developing tumors, which were validated by histopathologic evaluation following necropsy. Molecular genetic analyses confirmed mutant p53 expression and characteristic chromosomal instability within the tumors. In conclusion, TP53R167H/R167H pigs provide a novel large animal tumor model that replicates the human condition and is uniquely suited to developing clinically-relevant, non-invasive imaging approaches to facilitate earlier detection, diagnosis and treatment of human cancers.

Citation Format: Jessica C. Sieren, Xiao-Jun Wang, Bryan Davis, John D. Newell, Emily Hammond, Judy Rohret, Frank Rohret, Jason Struzynski, Adam Goeken, Paul Naumann, Mariah Leidinger, Jussara Hagen, Richard Van Rheeden, Benjamin W. Darbro, Dawn E. Quelle, David K. Meyerholz, Christopher S. Rogers. Translational imaging of tumorigenesis in a TP53 porcine cancer model. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-162. doi:10.1158/1538-7445.AM2014-LB-162

Development and translational imaging of a TP53 porcine tumorigenesis model

Cancer is the second deadliest disease in the United States, necessitating improvements in tumor diagnosis and treatment. Current model systems of cancer are informative, but translating promising imaging approaches and therapies to clinical practice has been challenging. In particular, the lack of a large-animal model that accurately mimics human cancer has been a major barrier to the development of effective diagnostic tools along with surgical and therapeutic interventions. Here, we developed a genetically modified porcine model of cancer in which animals express a mutation in TP53 (which encodes p53) that is orthologous to one commonly found in humans (R175H in people, R167H in pigs). TP53(R167H/R167H) mutant pigs primarily developed lymphomas and osteogenic tumors, recapitulating the tumor types observed in mice and humans expressing orthologous TP53 mutant alleles. CT and MRI imaging data effectively detected developing tumors, which were validated by histopathological evaluation after necropsy. Molecular genetic analyses confirmed that these animals expressed the R167H mutant p53, and evaluation of tumors revealed characteristic chromosomal instability. Together, these results demonstrated that TP53(R167H/R167H) pigs represent a large-animal tumor model that replicates the human condition. Our data further suggest that this model will be uniquely suited for developing clinically relevant, noninvasive imaging approaches to facilitate earlier detection, diagnosis, and treatment of human cancers.

Disproportionate contribution of right middle lobe to emphysema and gas trapping on computed tomography

RATIONALE

Given that the diagnosis of chronic obstructive pulmonary disease (COPD) relies on demonstrating airflow limitation by spirometry, which is known to be poorly sensitive to early disease, and to regional differences in emphysema, we sought to evaluate individual lobar contributions to global spirometric measures.

METHODS

Subjects with COPD were compared with smokers without airflow obstruction, and non-smokers. Emphysema (% low attenuation area, LAAinsp<-950 HU, at end-inspiration) and gas trapping (%LAAexp<-856 HU at end-expiration) on CT were quantified using density mask analyses for the whole lung and for individual lobes, and distribution across lobes and strength of correlation with spirometry were compared.

RESULTS

The right middle lobe had the highest %LAAinsp<-950 HU in smokers and controls, and the highest %LAAexp<-856 HU in all three groups. While RML contributed to emphysema and gas trapping disproportionately to its relatively small size, it also showed the least correlation with spirometry. There was no change in correlation of whole lung CT metrics with spirometry when the middle lobe was excluded from analyses. Similarly, RML had the highest %LAAexp<-856 HU while having the least correlation with spirometry.

CONCLUSIONS

Because of the right middle lobe's disproportionate contribution to CT-based emphysema measurements, and low contribution to spirometry, longitudinal studies of emphysema progression may benefit from independent analysis of the middle lobe in whole lung quantitative CT assessments. Our findings may also have implications for heterogeneity assessments and target lobe selection for lung volume reduction.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov NCT00608764.

Comparison of spirometric thresholds in diagnosing smoking-related airflow obstruction

BACKGROUND

Diagnosis of chronic obstructive pulmonary disease is based on detection of airflow obstruction on spirometry. There is no consensus regarding using a fixed threshold to define airflow obstruction versus using the lower limit of normal (LLN) adjusted for age. We compared the accuracy and discrimination of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommended fixed ratio of forced expiratory volume in the first second/forced vital capacity<0.70 with LLN in diagnosing smoking-related airflow obstruction using CT-defined emphysema and gas trapping as the disease gold standard.

METHODS

Data from a large multicentre study (COPDGene), which included current and former smokers (age range 45-80 years) with and without airflow obstruction, were analysed. Concordance between spirometric thresholds was measured. The accuracy of the thresholds in diagnosing emphysema and gas trapping was assessed using quantitative CT as gold standard.

RESULTS

7743 subjects were included. There was very good agreement between the two spirometric cutoffs (κ=0.85; 95% CI 0.83 to 0.86, p<0.001). 7.3% were discordant. Subjects with airflow obstruction by fixed ratio only had a greater degree of emphysema (4.1% versus 1.2%, p<0.001) and gas trapping (19.8% vs 7.5%, p<0.001) than those positive by LLN only, and also smoking controls without airflow obstruction (4.1% vs 1.9% and 19.8% vs 10.9%, respectively, p<0.001). On follow-up, the fixed ratio only group had more exacerbations than smoking controls.

CONCLUSIONS

Compared with the fixed ratio, the use of LLN fails to identify a number of patients with significant pulmonary pathology and respiratory morbidity.

Sinus hypoplasia precedes sinus infection in a porcine model of cystic fibrosis

OBJECTIVES/HYPOTHESIS

Chronic sinusitis is nearly universal in humans with cystic fibrosis (CF) and is accompanied by sinus hypoplasia (small sinuses). However, whether impaired sinus development is a primary feature of loss of the cystic fibrosis transmembrane conductance regulator (CFTR) or a secondary consequence of chronic infection remains unknown. Our objective was to study the early pathogenesis of sinus disease in CF.

STUDY DESIGN

Animal/basic science research.

METHODS

Sinus development was studied in a porcine CF model.

RESULTS

Porcine sinus epithelia expressed CFTR and exhibited transepithelial anion transport. Disruption of the CFTR gene eliminated both. Sinuses of newborn CF pigs were not infected and showed no evidence of inflammation, yet were hypoplastic at birth. Older CF pigs spontaneously developed sinus disease similar to that seen in humans with CF.

CONCLUSIONS

These results define a role for CFTR in sinus development and suggest the potential of the CF pig as a genetic model of CF-sinus disease in which to test therapeutic strategies to minimize sinus-related CF morbidity.

Mitochondrial Rac1 GTPase import and electron transfer from cytochrome c are required for pulmonary fibrosis

The generation of reactive oxygen species, particularly H(2)O(2), from alveolar macrophages is causally related to the development of pulmonary fibrosis. Rac1, a small GTPase, is known to increase mitochondrial H(2)O(2) generation in macrophages; however, the mechanism by which this occurs is not known. This study shows that Rac1 is localized in the mitochondria of alveolar macrophages from asbestosis patients, and mitochondrial import requires the C-terminal cysteine of Rac1 (Cys-189), which is post-translationally modified by geranylgeranylation. Furthermore, H(2)O(2) generation mediated by mitochondrial Rac1 requires electron transfer from cytochrome c to a cysteine residue on Rac1 (Cys-178). Asbestos-exposed mice harboring a conditional deletion of Rac1 in macrophages demonstrated decreased oxidative stress and were significantly protected from developing pulmonary fibrosis. These observations demonstrate that mitochondrial import and direct electron transfer from cytochrome c to Rac1 modulates mitochondrial H(2)O(2) production in alveolar macrophages pulmonary fibrosis.

Recent technological and application developments in computed tomography and magnetic resonance imaging for improved pulmonary nodule detection and lung cancer staging

This review compares the emerging technologies and approaches in the application of magnetic resonance (MR) and computed tomography (CT) imaging for the assessment of pulmonary nodules and staging of malignant findings. Included in this review is a brief definition of pulmonary nodules and an introduction to the challenges faced. We have highlighted the current status of both MR and CT for the early detection of lung nodules. Developments are detailed in this review for the management of pulmonary nodules using advanced imaging, including: dynamic imaging studies, dual energy CT, computer aided detection and diagnosis, and imaging assisted nodule biopsy approaches which have improved lung nodule detection and diagnosis rates. Recent advancements linking in vivo imaging to corresponding histological pathology are also highlighted. In vivo imaging plays a pivotal role in the clinical staging of pulmonary nodules through TNM assessment. While CT and positron emission tomography (PET)/CT are currently the most commonly clinically employed modalities for pulmonary nodule staging, studies are presented that highlight the augmentative potential of MR.

Exploration of the volumetric composition of human lung cancer nodules in correlated histopathology and computed tomography

Gaining a complete and comprehensive understanding of lung cancer nodule histological compositions and how these tissues are represented in radiological data is important not only for expanding the current knowledge base of cancer growth and development but also has potential implications for classification standards, radiological diagnosis methods and for the evaluation of treatment response. In this study we generate large scale histological segmentations of the cancerous and non-cancerous tissues within resected lung nodules. We have implemented a processing pipeline which allows for the direct correlation between histological data and spatially corresponding computed tomography data. Utilizing these correlated datasets we evaluated the statistical separation between Hounsfield Unit (HU) histogram values for each tissue type. The findings of this study revealed that lung cancer nodules contain a complex intermixing of cellular tissue types and that trends exist in the relationship between these tissue types. It was found that the mean Hounsfield Unit values for isolated lung cancer nodules imaged with computed tomography, had statistically significantly different values for non-solid bronchoalveolar carcinoma, solid cancerous tumor, blood, and inactive fibrotic stromal tissue.