Background: Although the lung is of low biomass, it harbors a diverse and dynamic microbiome that may influence disease and healing. Existing studies have used diverse sampling methods with high propensities for contamination and sampling error, leading to diverse and unclear results. Here, we characterized the lung microbiome via airway and parenchymal samples to determine variation across patients and sampling methods. Methods: We recruited adult patients undergoing lung resection for suspected or confirmed malignancy. After resection and under sterile conditions, a 1 cm cubic piece of non-cancerous lung parenchyma and a swab from the specimen’s bronchus were collected and sent for microbiome analysis via 16S rRNA gene amplicon (V4) sequencing on an Illumina platform. An established bioinformatics pipeline was used to determine taxonomic identification. Baseline clinical and demographic data were compared to microbiome composition. Results: A total of 86 patients were included in the study. Beta diversity (microbial composition) varied significantly by sampling method (biopsy of lung parenchyma versus airway swabs), so all further results were analyzed within sample types. Further analyses revealed significant differences in beta diversity by lobe of the lung, indicating a different microbial composition by anatomic location. Analyses of patient demographics revealed significant differences by age and comorbidities, including chronic obstructive pulmonary disease and atrial fibrillation. Conclusions: The lung harbors a diverse microbiome that differs by anatomic location and patient characteristics. This study provides a framework for more accurate future lung microbiome sampling and characterization.

Background: The lung fosters a diverse microbiome that may have a complex interplay with underlying diseases, such as lung cancer. However, existing literature has revealed diverse results, likely owing to small sample sizes and diverse sampling methods. In this context, we aim to characterize the lung microbiome and its association with malignancy via multiple sampling methods during lung resection. Methods: Patients undergoing lung resection for confirmed malignancy were prospectively included in the study. During the operation, a bronchial swab and a parenchymal biopsy of lung tissue were collected from the resected specimen and sent for analysis via 16S rRNA gene amplicon (V4) sequencing. An established bioinformatics pipeline was used to determine taxonomic identification and compare these results to clinical and tumor data. Results: There were 83 participants included, with 75 (90.4%) patients having primary lung tumors and 8 (9.6%) having metastatic lesions. Resections included lobectomy (69.9%), wedge (25.3%), and segmentectomy (4.8%). Lung microbiome composition differed by primary malignancy site (lung vs metastatic; p=0.014) and tumor mutations (TP53, p=0.040; KRAS, p=0.054). For primary lung cancer, there was no difference in microbiome composition by tumor histology. A near-significant association was noted for patients with prolonged air leaks and alpha diversity of the lung microbiome (p=0.070). Conclusions: In this study, we demonstrate associations between the lung microbiome and underlying malignancy and tumor characteristics. Validation of these results may assist with diagnostic, prognostic, and therapeutic interventions in the operative and peri-operative management of lung cancer.