Faculty Bibliography

Mortality among patients with COVID-19 and respiratory failure is high and there are no known lower airway biomarkers that predict clinical outcome. We investigated whether bacterial respiratory infections and viral load were associated with poor clinical outcome and host immune tone. We obtained bacterial and fungal culture data from 589 critically ill subjects with COVID-19 requiring mechanical ventilation. On a subset of the subjects that underwent bronchoscopy, we also quantified SARS-CoV-2 viral load, analyzed the microbiome of the lower airways by metagenome and metatranscriptome analyses and profiled the host immune response. We found that isolation of a hospital-acquired respiratory pathogen was not associated with fatal outcome. However, poor clinical outcome was associated with enrichment of the lower airway microbiota with an oral commensal ( Mycoplasma salivarium ), while high SARS-CoV-2 viral burden, poor anti-SARS-CoV-2 antibody response, together with a unique host transcriptome profile of the lower airways were most predictive of mortality. Collectively, these data support the hypothesis that 1) the extent of viral infectivity drives mortality in severe COVID-19, and therefore 2) clinical management strategies targeting viral replication and host responses to SARS-CoV-2 should be prioritized.

In lung cancer, enrichment of the lower airway microbiota with oral commensals commonly occurs and ex vivo models support that some of these bacteria can trigger host transcriptomic signatures associated with carcinogenesis. Here, we show that this lower airway dysbiotic signature was more prevalent in group IIIB-IV TNM stage lung cancer and is associated with poor prognosis, as shown by decreased survival among subjects with early stage disease (I-IIIA) and worse tumor progression as measured by RECIST scores among subjects with IIIB-IV stage disease. In addition, this lower airway microbiota signature was associated with upregulation of IL-17, PI3K, MAPK and ERK pathways in airway transcriptome, and we identified Veillonella parvula as the most abundant taxon driving this association. In a KP lung cancer model, lower airway dysbiosis with V. parvula led to decreased survival, increased tumor burden, IL-17 inflammatory phenotype and activation of checkpoint inhibitor markers.

BACKGROUND:Emphysema in asymptomatic heavy smokers can be detected during CT-scan screening for lung cancer. Metalloproteinases (MMPs) have been found to play a role in the pathogenesis of chronic obstructive pulmonary disease and to possibly serve as biomarkers for emphysema. METHODS:The NYU Lung Cancer Biomarker Center enrolled study subjects over 50 years of age with lung cancer risk factors from January 1, 2010, to December 31, 2015. These subjects received chest multi-detector computed tomography, spirometry, and provided serum for immunoassays for metalloproteinases (MMP) -1, -2, -7, -9, -10 and tissue inhibitor of metalloproteinases (TIMP) -1 and -2. RESULTS:/FVC percent compared to smokers without emphysema (68 ± 11 (mean ± sd) versus 75 ± 8; p < 0.0001). Increased age and pack-years of smoking were associated with increased odds of emphysema. None of the metalloproteinases or tissue inhibitors of metalloproteinases were useful to predict the presence of emphysema in smokers. CONCLUSION/CONCLUSIONS:/FVC ratio).

SESSION TITLE: Monday Fellow Case Report Posters SESSION TYPE: Fellow Case Report Posters PRESENTED ON: 10/21/2019 02:30

OBJECTIVES/OBJECTIVE:Tumor draining lymph nodes (TDLN) are key sites of early immunoediting in patients with non-small cell lung cancer (NSCLC) and play an important role in generating anti-tumor immunity. Immune suppression in the tumor microenvironment has prognostic implications and may predict therapeutic response. T cell composition of draining lymph nodes may reflect an immunophenotype with similar prognostic potential which could be measured during standard-of-care bronchoscopic assessment. In this study, we compared the immunophenotype from different sites within individuals to primary tumor characteristics in patients with NSCLC to see whether there were tumor-regional differences in immunophenotype which could be evaluated from transbronchial needle aspirates. MATERIALS AND METHODS/METHODS:Twenty patients were enrolled in this study and had tissue (lymph node aspirates and/or peripheral blood) obtained during standard of care bronchoscopy with endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) for diagnosis or staging of known or suspected NSCLC. Aspirates and blood underwent flow-assisted cell sorting and a subset of sorted effector T cells underwent RNA quantitation to determine feasibility of this approach. Immunophenotypic patterns from twelve patients with paired data from tumor-draining and non-tumor draining lymph nodes (NDLN) were compared relative to one another and based on PD-L1 immunohistochemistry and primary tumor histology. RESULTS: T cell depletion compared to patients with PD-L1 expression <50% (-35.98% vs -1.89%, p = 0.0357; negative values represent absolute difference between paired TDLN and NDLN). CONCLUSIONS:In patients with NSCLC, TDLN have a suppressive immunophenotype correlating with tumor PD-L1 status and can be assessed during routine EBUS-TBNA.

: Low-dose CT (LDCT) is widely accepted as the preferred method for detecting pulmonary nodules. However, the determination of whether a nodule is benign or malignant involves either repeated scans or invasive procedures that sample the lung tissue. Noninvasive methods to assess these nodules are needed to reduce unnecessary invasive tests. In this study, we have developed a pulmonary nodule classifier (PNC) using RNA from whole blood collected in RNA-stabilizing PAXgene tubes that addresses this need. Samples were prospectively collected from high-risk and incidental subjects with a positive lung CT scan. A total of 821 samples from 5 clinical sites were analyzed. Malignant samples were predominantly stage 1 by pathologic diagnosis and 97% of the benign samples were confirmed by 4 years of follow-up. A panel of diagnostic biomarkers was selected from a subset of the samples assayed on Illumina microarrays that achieved a ROC-AUC of 0.847 on independent validation. The microarray data were then used to design a biomarker panel of 559 gene probes to be validated on the clinically tested NanoString nCounter platform. RNA from 583 patients was used to assess and refine the NanoString PNC (nPNC), which was then validated on 158 independent samples (ROC-AUC = 0.825). The nPNC outperformed three clinical algorithms in discriminating malignant from benign pulmonary nodules ranging from 6-20 mm using just 41 diagnostic biomarkers. Overall, this platform provides an accurate, noninvasive method for the diagnosis of pulmonary nodules in patients with non-small cell lung cancer. SIGNIFICANCE: These findings describe a minimally invasive and clinically practical pulmonary nodule classifier that has good diagnostic ability at distinguishing benign from malignant pulmonary nodules.