Lower Airway Dysbiosis Augments Lung Inflammatory Injury in Mild-to-Moderate Chronic Obstructive Pulmonary Disease.

Rationale: Chronic obstructive pulmonary disease (COPD) is associated with high morbidity, mortality, and healthcare costs. Cigarette smoke is a causative factor; however, not all heavy smokers develop COPD. Microbial colonization and infections are contributing factors to disease progression in advanced stages. Objectives: We investigated whether lower airway dysbiosis occurs in mild-to-moderate COPD and analyzed possible mechanistic contributions to COPD pathogenesis. Methods: We recruited 57 patients with a >10 pack-year smoking history: 26 had physiological evidence of COPD, and 31 had normal lung function (smoker control subjects). Bronchoscopy sampled the upper airways, lower airways, and environmental background. Samples were analyzed by 16S rRNA gene sequencing, whole genome, RNA metatranscriptome, and host RNA transcriptome. A preclinical mouse model was used to evaluate the contributions of cigarette smoke and dysbiosis on lower airway inflammatory injury. Measurements and Main Results: Compared with smoker control subjects, microbiome analyses showed that the lower airways of subjects with COPD were enriched with common oral commensals. The lower airway host transcriptomics demonstrated differences in markers of inflammation and tumorigenesis, such as upregulation of IL-17, IL-6, ERK/MAPK, PI3K, MUC1, and MUC4 in mild-to-moderate COPD. Finally, in a preclinical murine model exposed to cigarette smoke, lower airway dysbiosis with common oral commensals augments the inflammatory injury, revealing transcriptomic signatures similar to those observed in human subjects with COPD. Conclusions: Lower airway dysbiosis in the setting of smoke exposure contributes to inflammatory injury early in COPD. Targeting the lower airway microbiome in combination with smoking cessation may be of potential therapeutic relevance.

Evaluating the transcriptional fidelity of cancer models.

BACKGROUND: Cancer researchers use cell lines, patient-derived xenografts, engineered mice, and tumoroids as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derive from the fidelity with which it represents the tumor type under investigation; however, the extent to which this is true is often unclear. The preponderance of models and the ability to readily generate new ones has created a demand for tools that can measure the extent and ways in which cancer models resemble or diverge from native tumors. METHODS: We developed a machine learning-based computational tool, CancerCellNet, that measures the similarity of cancer models to 22 naturally occurring tumor types and 36 subtypes, in a platform and species agnostic manner. We applied this tool to 657 cancer cell lines, 415 patient-derived xenografts, 26 distinct genetically engineered mouse models, and 131 tumoroids. We validated CancerCellNet by application to independent data, and we tested several predictions with immunofluorescence. RESULTS: We have documented the cancer models with the greatest transcriptional fidelity to natural tumors, we have identified cancers underserved by adequate models, and we have found models with annotations that do not match their classification. By comparing models across modalities, we report that, on average, genetically engineered mice and tumoroids have higher transcriptional fidelity than patient-derived xenografts and cell lines in four out of five tumor types. However, several patient-derived xenografts and tumoroids have classification scores that are on par with native tumors, highlighting both their potential as faithful model classes and their heterogeneity. CONCLUSIONS: CancerCellNet enables the rapid assessment of transcriptional fidelity of tumor models. We have made CancerCellNet available as a freely downloadable R package ( https://github.com/pcahan1/cancerCellNet ) and as a web application ( http://www.cahanlab.org/resources/cancerCellNet_web ) that can be applied to new cancer models that allows for direct comparison to the cancer models evaluated here.

Venous Thrombotic Events in ANCA-Associated Vasculitis: Incidence and Risk Factors.

Background: The incidence of venous thromboembolism (VTE) is increased in ANCA-associated vasculitis (AAV). We assessed the frequency of VTE observed among patients with AAV evaluated at our center and identified risk factors. Methods: Patients from the Johns Hopkins Vasculitis Center cohort who were evaluated between 1998 and 2018 and had a diagnosis of granulomatosis with polyangiitis (GPA) or microscopic polyangiitis (MPA) were eligible for analysis. Baseline demographics and clinical and serologic data were extracted. Univariate and multivariate analyses were performed to identify factors associated with VTE in AAV. Results: A total of 162 patients with AAV were identified, 105 (65%) with GPA; 22 (14%) of these patients had a recorded VTE with a median time to VTE of 1 month. The mean (SD) age in the VTE versus non-VTE groups was 54±20 versus 55±17 years (P=0.99), 64% versus 60% female (P=0.93), 82% versus 49% PR3-ANCA positive (P=0.01), with a total mean BMI of 33.3±5.7 versus 28.3±6.1 kg/m2, (P<0.001) respectively. The median Birmingham Vasculitis Activity Score (BVAS version 3) was 19 versus 14 (P=0.02). Univariate analyses identified PR3-ANCA, rapidly progressive GN (RPGN), and hypoalbuminemia. In multivariate analysis, the significant associations with VTE included PR3-ANCA (OR, 4.77; P=0.02), hypoalbuminemia (OR, 4.84; P=0.004), and BMI (OR, 1.18; P<0.001). Conclusions: VTE is a surprisingly common complication of AAV. PR3-ANCA and hypoalbuminemia are risk factors for developing VTEs. Further studies are needed to confirm these findings. Podcast: This article contains a podcast at https://www.asn-online.org/media/podcast/K360/2020_04_30_KID0000572019.mp3.