Smoker and non-smoker lung adenocarcinoma is characterized by distinct tumor immune microenvironments

Abstract

Tobacco smoking causes DNA damages in epithelial cells and immune dysfunction in the lung, which collectively contribute to lung carcinogenesis and progression. However, potential mechanisms by which tumor-infiltrating immune cells contribute to lung cancer survival and their differential contributions in ever-smokers and never-smokers are not well studied. Here, we performed integrative analysis of 11 lung cancer gene-expression datasets, including 1,111 lung adenocarcinomas and 200 adjacent normal lung samples. Distinct pathways were altered in lung carcinogenesis in ever-smokers and never-smokers. Never-smoker patients had a better outcome than ever-smoker patients. We characterized compositional patterns of 21 types of immune cells in lung adenocarcinomas and revealed the complex association between immune cell composition and clinical outcomes. Interestingly, we found two subsets of immune cells, mast cells and CD4⁺ memory T cells, which had completely opposite associations with outcomes in resting and activated status. We further discovered that several chemokines and their associated receptors (e.g., CXCL11-CX3CR1 axis) were selectively altered in lung tumors in response to cigarette smoking and their abundances showed stronger correlation with fractions of these immune subsets in ever-smokers than never-smokers. The status switched from the resting to activated forms in mast cells and CD4⁺ memory T cells might manifest some important processes induced by cigarette smoking during tumor development and progression. Our findings suggested that aberrant activation of mast cells and CD4+ memory T cells plays crucial roles in cigarette smoking-induced immune dysfunction in the lung, which contributes to tumor development and progression.

Keywords: Cancer survival; gene expression; immune; lung cancer; microenvironments; tobacco smoking.

Figure 1.

Dysregulated genes and their associated altered pathways in lung adenocarcinoma. (A) Heatmaps for dysregulated genes across four datasets. There are distinct gene expression patterns between tumor and normal tissue samples. (B) Pathway enrichment by dysregulated genes across four datasets. P-values of Fisher’s exact test for pathway enrichments were calculated by Ingenuity Pathway Analysis (IPA).

Figure 2.

Survival analyses and pathway enrichments for lung cancer patients in ever- and never-smokers. (A) Overall survival of lung adenocarcinoma patients in ever- and never-smokers. (B) Recurrence-free survival of lung adenocarcinoma patients in ever- and never-smokers. (C) Pathways enriched by genes significantly downregulated (left) and upregulated (right) in tumor samples from smokers as compared with those from never-smokers. (D) Pathways enriched by DEGs in the tumor-normal comparison in never-smokers (left) and in ever-smokers (right).

Figure 3.

Robustness and accuracy of inferring cell compositions using the refined new signature gene matrix, and the comparability of immune cell compositions among 11 datasets. (A) The accuracy of estimating immune cell fractions in simulated mixture gene expression profiles using the refined new signature gene matrix. Heatmap displayed accuracy for each type of immune cells in 1,000 simulations, and barplot showed the median accuracy for each cell subtype in the simulations. (B) Mean tumor immune cell compositions estimated by CIBERSORT across 11 datasets. These datasets were comparable and had similar tumor immune cell infiltration levels.

Figure 4.

Immune cell compositions and their association with clinical outcome. (A) Immune cell infiltration score in tumor and adjacent normal tissues. (B) Compositional differences in immune cells between tumor and normal samples (left) and associations of tumor immune cell fractions with survival (right). (C) Compositional differences in immune cells between tumors from ever- and never-smokers (left) and associations of tumor immune cell fractions with survival (right). Meta-z-scores were used to measure the overall association of each type of immune cell with survival across all datasets.

Figure 5.

Chemokine-receptor networks differ between ever- and never-smokers. Nodes in green are chemokine ligands and nodes in red are corresponding chemokine receptors; edges indicate existing molecular interactions between a chemokine and its receptor. For each study, the association between the gene expression of the chemokine/receptor and tumor-infiltrating immune cell fractions was calculated using a linear regression analysis for mast cells (A and B) and CD4⁺ memory T cells (C and D) separately, under smoking status (left) and nonsmoking status (right). If and only if both the chemokine ligand and receptor in a pair were significantly associated with the infiltrating immune cell levels, a colored dot representing the study was placed on the edge connecting the chemokine and receptor.