Airway Microbiota Is Associated with Upregulation of the PI3K Pathway in Lung Cancer

Abstract

Rationale: In lung cancer, upregulation of the PI3K (phosphoinositide 3-kinase) pathway is an early event that contributes to cell proliferation, survival, and tissue invasion. Upregulation of this pathway was recently described as associated with enrichment of the lower airways with bacteria identified as oral commensals.

Objectives: We hypothesize that host-microbe interactions in the lower airways of subjects with lung cancer affect known cancer pathways.

Methods: Airway brushings were collected prospectively from subjects with lung nodules at time of diagnostic bronchoscopy, including 39 subjects with final lung cancer diagnoses and 36 subjects with noncancer diagnoses. In addition, samples from 10 healthy control subjects were included. 16S ribosomal RNA gene amplicon sequencing and paired transcriptome sequencing were performed on all airway samples. In addition, an in vitro model with airway epithelial cells exposed to bacteria/bacterial products was performed.

Measurements and main results: The composition of the lower airway transcriptome in the patients with cancer was significantly different from the control subjects, which included up-regulation of ERK (extracellular signal-regulated kinase) and PI3K signaling pathways. The lower airways of patients with lung cancer were enriched for oral taxa (Streptococcus and Veillonella), which was associated with up-regulation of the ERK and PI3K signaling pathways. In vitro exposure of airway epithelial cells to Veillonella, Prevotella, and Streptococcus led to upregulation of these same signaling pathways.

Conclusions: The data presented here show that several transcriptomic signatures previously identified as relevant to lung cancer pathogenesis are associated with enrichment of the lower airway microbiota with oral commensals.

Keywords: bronchoscopy; lung cancer; microbiome.

Figure 1.

Differences in airway transcriptome between cancer, disease control, and healthy control. (A) Transcriptomic differences between involved airway of subjects with cancer (red, LC), disease control (dark green, DC), and healthy control (light green, H) were explored using principal coordinate analysis on the basis of Bray-Curtis dissimilarity index. Disease control samples were defined as airway samples from segments without disease from subjects with benign lung nodules. Significant differences were identified in β-diversity of transcriptome data in involved airways of subjects with cancer compared with both control subjects (permutational multivariate analysis of variance P value < 0.05). (B) Linear discriminant analysis (LDA > 2) detected significant differences in transcriptome (summarized L3 data) between involved cancer airway and both control groups. (C) Ingenuity pathway analysis was used to identify canonical pathways dysregulated in lung cancer when compared with the two control groups (differential gene FDR < 0.15). ERK and PI3K pathways were among those identified as upregulated in lung cancer airways when compared with control groups. (D) Gene set enrichment analysis of transcriptomic differences identified between lung cancer versus disease control (false discovery rate [FDR] < 0.15) was compared with transcriptomic differences previously published in three studies (–36). Histogram on the left shows the number of transcriptomic signature genes identified in each study (–36), with the genes in common with our current investigation highlighted in red. Histogram on the right shows signaling pathways shared between the current investigation and the other three prior studies. AKT = protein kinase B; CCKR = cholecystokinin receptor; CML = chronic myeloid leukemia; EGFR = epidermal growth factor receptor; ERK = extracellular signal–regulated kinase; FGF = fibroblast growth factor; HCM = hypertrophic cardiomyopathy; HER-2 = human epidermal growth factor receptor 2; GnRH = gonadotropin-releasing hormone; GSEA = gene set enrichment analysis; Inv. = involved; JAK/Stat = Janus kinase/signal transducer and activator of transcription protein; MAPK = mitogen-activated protein kinase; mTOR = mammalian target of rapamycin; NF-κB = nuclear factor-κB; NRF2 = nuclear factor (erythroid-derived-2)–like 2; NSCLC = non–small-cell lung cancer; PC = principal component; PDGF = platelet-derived growth factor; PI3K = phosphoinositide 3-kinase; PTEN = phosphatase and tensin homolog; STAT3 = signal transducer and activator of transcription 3; TGF-β = transforming growth factor-β; TNF = tumor necrosis factor; VEGF = vascular endothelial growth factor; Wnt = wingless/integrated.

Figure 2.

Differences in airway microbiota between cancer, disease control, and healthy control. (A) β-diversity comparisons of microbiota composition were explored using principal coordinate analysis on the basis of Bray-Curtis dissimilarity index between cancer (involved lung segments) and noncancer of airways. The microbiome of involved airways of subjects with cancer (red, LC) was significantly different when compared with disease control (dark green, DC) and healthy control (light green, H) (permutational multivariate analysis of variance P value < 0.05 for all comparisons). (B) Linear discriminant analysis (LDA > 2) detected differential taxonomic enrichment in involved airways from subjects with cancer when compared with both control groups. PC = principal component; u.g. = undetermined genus.

Figure 3.

Association network between microbiome and transcriptome. Compositionally robust sparse partial least squares (compPLS) was used to evaluate for associations between airway microbiota and transcriptome considering samples from lung cancer, disease control, and healthy control. Analysis was adjusted by smoking status and for paired samples obtained in subjects. Edge colors are representative of negative associations in red and positive associations in blue, and edge weight is scaled to represent the degree of the confidence for the associations. Only taxa identified as having significant correlations are displayed, and size of nodes denotes median relative abundance of taxa at a genus level. The most abundant taxon were: Veillonella, which was positively correlated with ERK/MAPK, T-cell receptor, p53, and cancer pathways; Megasphaera, which was positively correlated with ERK/MAPK, PI3K/Akt, IL-17, and TGF-β; and Bulleidia, which was negatively correlated with similar pathways. ERK = extracellular signal–regulated kinase; FoxO = forkhead box O; MAPK = mitogen-activated protein kinase; NF-κB = nuclear factor-κB; TGF-β = transforming growth factor-β; VEGF = vascular endothelial growth factor; Wnt = wingless/integrated.

Figure 4.

In vitro transcript activation of epithelial cells by exposure to bacterial products. (A) Schematic experimental design for three in vitro experiments. For all conditions, A549 cells were exposed for 4 hours and then harvested for RNA isolation. Experiment 1: exposure to whole BAL from a subject whose lower airways were enriched with background-predominant taxa (BPT), supraglottic-predominant taxa (SPT) with high bacterial load (SPT_high), or SPT with low bacterial load (SPT_low). Experiment 2: exposure to media alone, LPS, or bacteria: Veillonella parvula, Prevotella melaninogenica, and Streptococcus mitis (both culture supernatant, heat-killed bacteria). Experiment 3: exposure to cigarette smoke condensate (CSC) and V. parvula (heat-killed and supernatant). Each experiment was done in triplicate or quadruplicates. (B) Ingenuity Pathway Analysis (IPA) was used to identify canonical pathways dysregulated in each in vitro experiment (false discovery rate [FDR] < 0.15). (C–E) IPA network analysis for transcriptomic changes (FDR < 0.15) annotated to ERK (extracellular signal–regulated kinase) or PI3K (phosphoinositide 3-kinase) pathways from one representative condition from each in vitro experiment. (F) Gene set enrichment analysis (GSEA) of transcriptomic signatures (FDR < 0.15) comparing in vitro experiment to in vivo human data. Bar chart compares transcriptomic changes for conditions identified in vitro with transcriptomic changes identified as associated with lung cancer on the basis of our human data. Overlapping signature genes are shown in red. PBS = phosphate-buffered saline.