Faculty Bibliography

BACKGROUND:Risk of early-stage lung adenocarcinoma (LUAD) recurrence after surgical resection is significant, and post-recurrence median survival is approximately two years. Currently there are no commercially available biomarkers that predict recurrence. Here, we investigated whether microbial and host genomic signatures in the lung can predict recurrence. METHODS:In 91 early-stage (Stage IA/IB) LUAD-patients with extensive follow-up, we used 16s rRNA gene sequencing and host RNA-sequencing to map the microbial and host transcriptomic landscape in tumor and adjacent unaffected lung samples. RESULTS:23 out of 91 subjects had tumor recurrence over 5-year period. In tumor samples, LUAD recurrence was associated with enrichment with Dialister, Prevotella, while in unaffected lung, recurrence was associated with enrichment with Sphyngomonas and Alloiococcus. The strengths of the associations between microbial and host genomic signatures with LUAD recurrence were greater in adjacent unaffected lung samples than in the primary tumor. Among microbial-host features in the unaffected lung samples associated with recurrence, enrichment with Stenotrophomonas geniculata and Chryseobacterium were positively correlated with upregulation of IL-2, IL-3, IL-17, EGFR, HIF-1 signaling pathways among the host transcriptome. In tumor samples, enrichment with Veillonellaceae Dialister, Ruminococcacea, Haemophilus Influenza, and Neisseria were positively correlated with upregulation of IL-1, IL-6, IL17, IFN, and Tryptophan metabolism pathways. CONCLUSIONS:Overall, modeling suggested that a combined microbial/transcriptome approach using unaffected lung samples had the best biomarker performance (AUC=0.83). IMPACT/CONCLUSIONS:This study suggests that LUAD recurrence is associated with distinct pathophysiological mechanisms of microbial-host interactions in the unaffected lung rather than those present in the resected tumor.

INTRODUCTION/UNASSIGNED:Mounting evidence indicates that an individual's humoral adaptive immune response plays a critical role in the setting of SARS-CoV-2 infection, and that the efficiency of the response correlates with disease severity. The relationship between the adaptive immune dynamics in the lower airways with those in the systemic circulation, and how these relate to an individual's clinical response to SARS-CoV-2 infection, are less understood and are the focus of this study. MATERIAL AND METHODS/UNASSIGNED:We investigated the adaptive immune response to SARS-CoV-2 in paired samples from the lower airways and blood from 27 critically ill patients during the first wave of the pandemic (median time from symptom onset to intubation 11 days). Measurements included clinical outcomes (mortality), bronchoalveolar lavage fluid (BALF) and blood specimen antibody levels, and BALF viral load. RESULTS/UNASSIGNED:While there was heterogeneity in the levels of the SARS-CoV-2-specific antibodies, we unexpectedly found that some BALF specimens displayed higher levels than the paired concurrent plasma samples, despite the known dilutional effects common in BALF samples. We found that survivors had higher levels of anti-spike, anti-spike-N-terminal domain and anti-spike-receptor-binding domain IgG antibodies in their BALF (p<0.05), while there was no such association with antibody levels in the systemic circulation. DISCUSSION/UNASSIGNED:Our data highlight the critical role of local adaptive immunity in the airways as a key defence mechanism against primary SARS-CoV-2 infection.

Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression.