Lung cancer is the leading cause of cancer deaths globally (Bray et al., 2018). Though the most common cause of lung cancer is tobacco smoking, studies estimate that approximately 25% of lung cancers worldwide occur in individuals who have never smoked (Parkin et al., 2005). Recently, lung cancer in never smokers (LCINS) were molecular profiled and new genomic features were revealed (Chen et al., 2020b; Govindan et al., 2012; Chen et al., 2020a; Zhang et al., 2021a; Zhang et al., 2019; Xu et al., 2020; Gillette et al., 2020). For now, further stratification of LCINS based on different risk factors would be helpful to reveal the oncogenic mechanisms and develop more targeted therapies. Air pollutants, which can directly affect the pulmonary airway, play crucial roles in promoting lung adenocarcinoma (Hill et al., 2023; Turner, 2020; Fajersztajn et al., 2013). More than 20 environmental and occupational agents are lung carcinogens (Cogliano et al., 2011; IARC Working Group on the Evaluation of Carcinogenic Risks to Humans, 2010) and amount of studies have been made to investigate molecular mechanisms in tumor progression of air pollution chemicals or components using cell lines or mouse/rat models (Shi et al., 2017; Saravanakumar et al., 2022; Abd El-Fattah and Abdelhamid, 2021). However, a comprehensive molecular characterization of clinical lung cancer samples associated with air pollution is still lacking.

The Xuanwei area has the highest rate of lung cancer in China, and extensive research has established a strong link between lung cancer and exposure to domestic coal smoke (Mumford et al., 1987; Barone-Adesi et al., 2012; Chapman et al., 2005; Lin et al., 2015; Zhang et al., 2016; Lan et al., 2002). Specifically, the etiologic link between smoky coal burning and cancer was epidemiologically established Mumford et al., 1987; Barone-Adesi et al., 2012, and an association between household stove improvement and lower risk of lung cancer was observed (Lan et al., 2002). Moreover, genomic evidence of lung carcinogenesis associated with coal smoke in Xuanwei area, China was provided in our previous study (Zhang et al., 2016). Thus, lung cancer in Xuanwei areas exemplifies the ideal disease to study characteristics of lung cancers associated with air pollution. In recent years, the molecular features of Xuanwei lung cancer have been gradually revealed (Hosgood et al., 2013; Wang et al., 2018; Wang et al., 2017a; Zhang et al., 2021b). A large sample size with multi-comic molecular profiling is urgently needed to explicit the air pollution chemicals and furthermore propose more targeted therapies.

To better understand the molecular mechanisms and heterogeneity of XWLC and to advance precision medicine, we expanded the sample size of our next-generation sequencing dataset to 169 sample size and performed proteomic and phosphoproteomic profiling on 112 samples. Furthermore, we integrated 107 radiomic features derived from X-ray computed tomography (CT) scans into 115 samples to non-invasively distinguish molecular subtypes. This allowed us to identify potential major risk factors, distinguish the genomic features, and establish clinically relevant molecular subtypes. Our study provides an exceptional resource for future biological, diagnostic, and drug discovery efforts in the study of lung cancer related to air pollution.

To investigate unique biological features of LUAD associated with air pollution, three previous LUAD datasets related to different carcinogens were used for comparison (Figure 1a). CNLC is the subset of lung adenocarcinoma from non-smoking patients in Chinese Human Proteome Project (CNHPP project)(Xu et al., 2020) (n=77). TSLC is the subset of lung adenocarcinoma from smoking females in the TCGA-LUAD project (Cancer Genome Atlas Research, 2014) (n=168). TNLC is the subset of lung adenocarcinoma from non-smoking females in TCGA-LUAD project (Cancer Genome Atlas Research, 2014) (n=102). The clinicopathological characteristics of patients from CNLC, TSLC, and TNLC cohorts were supplied in Supplementary file 1a and b.

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The air pollutant Benzo[a]pyrene (BaP) primarily contributes to the mutation landscape of XWLC

To infer the primary risk factor responsible for the progression of XWLC, we used SomaticSignatures Gehring et al., 2015 to identify mutational signatures from single nucleotide variants. Mutational signatures were identified in each cohort and a cosine similarity analysis was performed against mutational signatures in COSMIC mutational signatures Alexandrov et al., 2013b; Alexandrov et al., 2013a and environmental agents mutational sigantures (Kucab et al., 2019) allowing for inference of the underlying causes (Figure 1f–i and Figure 1—figure supplement 2). Generally, exposure to tobacco smoking carcinogens (COSMIC signature 4) and chemicals such as BaP (Kucab signatures 49 and 20) were identified as the most significant contributing factors in both the XWLC and TSLC cohorts (Figure 1f and g). In contrast, defective DNA mismatch repair (COSMIC signature ID: SBS6) was identified as the major contributor in both the TNLC and CNLC cohorts (Figure 1h and i), with no potential chemicals identified based on signature similarities. Therefore, the XWLC and TSLC cohorts appear to be more explicitly associated with environmental carcinogens, while the TNLC and CNLC cohorts may be more associated with defective DNA mismatch repair processes. BaP, a representative compound of polycyclic aromatic hydrocarbons (PAHs), is found in both cigarette smoke and coal smoke and is recognized as a major environmental risk factor for lung cancer (Petit et al., 2019; Widziewicz et al., 2017; Mangal et al., 2009). Upon metabolism, BaP forms the carcinogenic metabolite 7β,8α-dihydroxy-9α,10α-epoxy-7,8,9,10-tetrahydrobenzo[a]pyrene (BPDE), which creates DNA adducts leading to mutations and malignant transformations. This process involves two key regulators: CYP1A1 and AhR. CYP1A1 plays a crucial role in BaP epoxidation at the 7,8 positions, which is the most critical step in BPDE formation (Chung et al., 2007). AhR is a ligand-activated transcription factor that responds to various chemicals, including chemical carcinogens, and is activated by BaP (Hidaka et al., 2019). Accordingly, our results demonstrated significantly higher protein expression of CYP1A1 in tumor samples compared to normal samples (Figure 1j). AhR showed higher mRNA expression in tumor samples, with no difference in protein level expression (Figure 1k). All these results suggested the involvement of BaP and its metabolite in the development of lung cancer.

Though coal-smoke-related lung cancer (XWLC cohort) and cigarette-smoke-related lung cancer (TSLC cohort) showed similar environmental carcinogens, we found that downstream pathway activation and therapeutic targeted potential showed distinctive features. First, the correlation of genomic mutations between XWLC and TSLC was found to be low (Figure 2a). Second, there was a remarkable difference in the fraction of samples affected by pathway mutations between the two cohorts (Figure 2b). Notably, the TSLC cohort exhibited a higher fraction of samples affected by oncogenic pathways comparing to XWLC cohort. Third, mutation frequencies of top mutated genes (Zhang et al., 2021b), such as EGFR, TP53, RBM10, and KRAS (Figure 2c), as well as the distribution of amino acid changes in EGFR and TP53, showed noticeable differences between the XWLC and other cohorts (Figure 2d). Specifically, the XWLC cohort exhibited a higher mutation rate in G719C/A/D/S within the EGFR gene compared to the other three cohorts. For the TP53 gene, frame shift mutations including D9Gfs*3 (n=1), S15Ifs*28 (n=1), D22Efs*61 (n=1), and V34Wfs*49 (n=2) were exclusively detected in the XWLC cohort, whereas tetramer domain mutations were only found in the other three cohorts (Figure 2d). Finally, there was a noticeable disparity in the percentage of samples with actionable targets among the cohorts. (Figure 2e). Actionable targets in the XWLC cohort were mainly focused on EGFR mutations including pG719S, pG719C, pG719A, and pL858R, whereas the TSLC cohort had more actionable targets in CHEK2 p.K373E, KRAS p.G12V/D/C/A (Figure 2e).

Figure 2

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Taken together, we found that the XWLC and TSLC cohorts, which are smoke-related lung adenoma groups, demonstrated distinct etiology compared to the TNLC and CNLC cohorts which may be influenced by endogenous risk factors to a greater extent. Additionally, significant disparities were observed between XWLC and TSLC in terms of downstream pathway activations and specific oncogene loci. Consequently, we conclude that air pollution-associated lung cancer represents a distinct subtype within LUAD.

The EGFR-G719X mutation, which is a hotspot associated with BaP exposure, possesses distinctive biological features

Notably, the XWLC cohort displayed a distinguishable mutation pattern in specific EGFR mutation sites compared to the other cohorts (Figure 3a and Figure 2d). In particular, the G719C/A/D/S (G719X) mutation was the most prevalent EGFR mutation in the XWLC cohort (20%), while it was rarely found in the other three cohorts (CNLC: 1.9%; TSLC: 1.9%; TNLC: 0) (Figure 3b). Notably, we found it was a hot spot associated with BaP exposure (Figure 3c and d). Specifically, GGC is the 719 codon, the first G can be converted to T (pG719C, n=13) or A (pG719S, n=5), the second G can be converted to A (pG719D, n=1) or C (pG719C, n=8). Thus, pG719C was the most detected mutation type (Figure 3c). G>T/C>A transversion can be induced by several compounds such as BaP or dibenz(a,h)anthracene (DBA), unlike other compounds, the tallest peak induced by BaP occurs at GpGpG, reflecting how their DNA adducts are formed principally at N²-guanine (Kucab et al., 2019). Our result showed that the most frequently detected pG719C AAchange corresponded to GGGC>GTGC transversion (Figure 3d). Thus, pG719C is a hot spot associated with BaP exposure.

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We conducted further investigations into the biological characteristics of samples carrying the G719X mutations. Notably, we observed a moderate to high expression of MAPK signaling components, MAP2K2 (MEK), and MAPK3 (ERK1), in tumors harboring the EGFR-G719X mutation compared to other EGFR statuses (Figure 3e–h). Utilizing hallmark capability analysis and RNA-seq-based estimation of immune cell infiltration, we found that tumors with G719X mutations exhibited similarities to those with L858R mutations (Figure 3—figure supplement 1a–b). However, patients with G719X mutations were notably younger than those with L858R mutations, indicating a higher occurrence rate of G719X in younger female patients (Figure 3i). Analysis of overall survival and progression-free interval (PFI) revealed that patients with the G719X mutation had worse outcomes compared to other EGFR mutation subtypes (Figure 3j and k) which was consistent with a previous study (Watanabe et al., 2014). Furthermore, there were no significant differences in mutation burden or the number of neoantigens between tumors with G719X mutations and tumors with other EGFR mutation statuses (Figure 3—figure supplement 1c).

To explore the heterogeneity of signaling pathways activated by different EGFR mutation statuses, we conducted a Kinase-Substrate Enrichment Analysis (KSEA) Wiredja et al., 2017; Casado et al., 2013 based on the XWLC phosphoproteomics dataset. Our analysis of the phosphoproteome across various EGFR mutation types revealed distinct activation patterns of kinases. Specifically, the G719X mutation was associated with the activation of PRKCZ, CDK2, AURKB, CSNK1A1, CDK4, and HIPK2. The L858R mutation showed activation of PRKCZ, MAPK7, MAPK12, HIPK2, and CSNK2A1. The Exon19del mutation exhibited activation of CHUK, TTK, PRKCZ, PLK1, NEK2, MAP2K2, CDK2, PRKDC, and MAP2K6. Other EGFR mutations were associated with the activation of AURKB, NEK2, TTK, PLK1, PRKACB, and PRKACG. EGFR-WT mutations showed activation of CSNK1E, PRKCZ, AURKB, CDK2, AURKC, CDK1, CSNK1A1, PRKDC, and CSNK2A1 (Figure 3l). In Figure 3—figure supplement 1d, we provide a list of FDA-approved drugs that target the activated kinases in tumors harboring the G719X mutation. Currently, afatinib is widely regarded as a first-line therapy for patients with the G719X mutation (Ettinger et al., 2022; Janning et al., 2022; Yang et al., 2020; Cho et al., 2020). However, reports indicate that 80% of patients with this mutation may develop resistance to afatinib, even in the absence of T790M (Harada et al., 2019), underscoring the need for a deeper understanding of the downstream pathways associated with the G719X mutation. Therefore, a promising approach to overcome resistance in tumors with this mutation could involve combining afatinib, which targets activated EGFR, with FDA-approved drugs that specifically target the activated kinases associated with G719X. Therefore, we propose a potential approach to overcoming resistance in tumors with this mutation, which could involve combining afatinib, targeting activated EGFR, with FDA-approved drugs that specifically target the activated kinases associated with G719X.

Clinically relevant subtyping in XWLC

To uncover the inherent subgroups within air-pollution-associated tumors, we employed unsupervised Consensus Clustering Wilkerson and Hayes, 2010 on integrated RNA, protein, and phosphoprotein profiles of XWLC tumor samples. This analysis led to the identification of four distinct intrinsic clusters, denoted as MC-I, II, III, and IV (Figure 4a, Figure 4—figure supplement 1a and Methods). Further survival analysis demonstrated that patients belonging to the MC-IV group exhibited the poorest overall survival compared to the other three subgroups, thus indicating the prognostic potential of multi-omic clustering (Figure 4b). Notably, there were no significant differences in clinical features such as age and stage observed among the four subgroups (Figure 4a). As CYP1A1 is a key regulator involved in BaP metabolism and has been proven to be highly expressed in tumor samples (Figure 1j), we next examined the expression of CYP1A1 among the four subgroups to evaluate their associations with air pollution. Our findings revealed that the MC-II subtype exhibited higher expression of CYP1A1 (Figure 4c). Moreover, the MC-II possessed more G719X mutations (MC-1:0.39, MC-II:0.42, MC-III: 0.20, MC-IV: 0.08). Notably, there was a significant correlation between CYP1A1 and EGFR expression (Figure 4e), with EGFR being more highly expressed in the MC-II subtype (Figure 4e). Collectively, these results indicated that MC-II was more associated with air-pollution.

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Through KSEA of the phosphoproteome in tumor samples compared to normal adjacent tissues (NATs), we identified specific kinase activations within the four subgroups. In MC-I samples, kinase activations included PRKDC, PRKCZ, CSNK1A1, NEK2, GSK3A, and ROCK1. MC-II samples showed activations of CDK2, CDK1, AURKA, TTK, CDK6, and CHUK. MC-III samples exhibited activations of AKT1, AKT3, RPS6KB1, CSNK2A1, and PAK2. Finally, CDK2 and ROCK1 were activated in MC-IV samples (Figure 4f). Particularly noteworthy is the enrichment of CDK1/2/6 kinases, which regulate cell cycle checkpoints, in the MC-II subtype, indicating its high proliferation capabilities. These findings imply that distinct kinase pathways are activated within each subgroup, suggesting the presence of specific therapeutic targets for each subgroup. Consequently, we proceeded to explore therapeutic strategies for each subgroup as outlined below:

The MC-IV subtype exhibited the poorest overall survival compared to the other three subtypes (Figure 4b). Given the crucial role of epithelial-mesenchymal transition (EMT) in malignant progression, our first evaluation focused on the EMT process across the four subtypes. We observed higher expression levels of mesenchymal markers such as VIM, FN1, TWIST2, SNAI2, ZEB1, ZEB2, and others in the MC-IV subtype (Figure 5a). To comprehensively assess the EMT capability of the MC-IV subtype, we calculated EMT scores using the ssGSEA enrichment method based on protein levels and GSEA hallmark gene set (M5930) (Liberzon et al., 2015). The results confirmed the elevated EMT capability of the MC-IV subtype at the protein level (Figure 5b). Furthermore, Fibronectin (FN1), an EMT marker that promotes the dissociation, migration, and invasion of epithelial cells, was found to be highly expressed in the MC-IV subtype at the protein level (Figure 5c). Additionally, β-Catenin, a key regulator in initiating EMT, was highly expressed in the MC-IV subtype at the protein level (Figure 5d). Collectively, our findings demonstrate that the MC-IV subtype is associated with enhanced EMT capability, which may contribute to the high malignancy observed in this subtype.

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The MC-II subtype demonstrated the second-worst outcome and was found to be more strongly associated with air pollution (Figure 4). This subtype exhibited dysregulation of cell cycle processes, including cell division, glycolysis, and cell cycle biological processes (Figure 4a). The KSEA analysis revealed that the CDK1 and CDK2 pathways, which are closely linked to cell cycle regulation, were predominantly activated in the MC-II subtype (Figure 5e). Consistently, we observed higher expression levels of CDK1 and CDK2 at both the protein and phosphoprotein levels in the MC-II subtype, indicating specific elevation of the G2M phase in the cell cycle (Figure 5e). The cell cycle and glycolysis processes are tightly coordinated, allowing cells to synchronize their metabolic state and energy requirements with cell cycle progression to ensure proper cell growth and division (Vander Heiden et al., 2009; DeBerardinis and Thompson, 2012; Cairns et al., 2011). In line with this, we found that key enzymes involved in glycolysis regulation, such as Hexokinase 1 (HK1), Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and Glyceraldehyde-3-phosphate dehydrogenase-like protein (GPL), were highly expressed in the MC-II subtype (Figure 5f). Additionally, the MC-II subtype was enriched with EGFR mutations (MC-II vs. others: 18/24 vs. 51/110; Fisher’s exact p=0.013) and TP53 mutations (MC-II vs. others: 14/24 vs. 35/110; Fisher’s exact p=0.019), consistent with the characteristic loss of control over cell proliferation. In summary, the MC-II subtype exhibited dysregulated cell cycle processes accompanied by an elevated glycolysis capability, indicating a distinct metabolic and proliferative phenotype.

The MC-I subtype exhibited enrichment in various biological processes including angiogenesis, the cAMP signaling pathway, complement and coagulation cascades, PDL1 expression, the PD-1 checkpoint pathway, leukocyte transendothelial migration, and actin cytoskeleton processes (Figure 4a). In-depth exploration of key components involved in angiogenesis revealed that vascular endothelial growth factor A (VEGFA), a growth factor crucial for both physiological and pathological angiogenesis, was highly expressed in the MC-I subtype (Figure 5g). Additionally, phosphorylation of vascular endothelial growth factor receptor 1 (VEGFR1), a receptor tyrosine kinase essential for angiogenesis and vasculogenesis, was also highly expressed in the MC-I subtype (Figure 5h). The angiogenesis scores, calculated using the ssGSEA method based on protein levels and the hallmark gene set (M5944), were relatively high in the MC-I and MC-IV subtypes (Figure 5i). Furthermore, the relationship between the Notch signaling pathway and angiogenesis is well-established (Carmeliet, 2005). Notch signaling plays a role in multiple aspects of angiogenesis, including endothelial cell sprouting, vessel branching, and vessel maturation (Pitulescu et al., 2017; Gridley, 2007). In the MC-I subtype, the expression of Notch receptors (Notch1-4) and ligands (DLL1, DLL4, JAG1, and JAG2) was highly elevated, indicating increased activation of Notch signaling (Figure 5j). KEGG path view analysis demonstrated that key regulators of the VEGF signaling pathway were highly expressed in the MC-I subtype (Figure 5—figure supplement 1a). Therefore, manipulating Notch signaling could potentially serve as a strategy to regulate angiogenesis and control pathological angiogenesis in the MC-I subtype.

The MC-III subtype is characterized by the upregulation of various metabolic processes, including oxidative phosphorylation, peroxisome function, adipogenesis, fatty acid metabolism, and xenobiotic metabolism-related processes (Figure 5k). Additionally, we conducted further investigations into the immune features across the subtypes. Interestingly, we observed higher expression of genes associated with PD-1 signaling (GSEA, SYSTEMATIC_NAME M18810) in the MC-III subtype (Figure 5l). Since PD-1 is primarily expressed on the surface of certain immune cells, particularly activated T cells, we inferred the immune cell infiltration using the ssGSEA method based on immune cell-specific gene sets. We found that activated CD8 + T cells exhibited higher infiltration levels in the MC-III subtype compared to the other three subtypes (Figure 5m and Supplementary file 7), which may explain the elevated PD-1 signaling in the MC-III subtype. Furthermore, we examined the expression of receptor-ligand pairs involved in both anti-tumor and pro-tumor lymphocyte recruitment. Remarkably, the MC-III subtype exhibited specific high expression of anti-tumor lymphocyte receptors and ligands, while the expression of pro-tumor lymphocyte receptors and ligands was relatively lower (Figure 5n). In general, the MC-I subtype showed the reverse expression trend in terms of anti-tumor and pro-tumor receptor-ligand pairs (Figure 5n).

In conclusion, our classification of lung adenocarcinoma associated with air pollution resulted in the identification of four subtypes, each exhibiting distinct biological pathway activation and immune features. The MC-I subtype demonstrated elevated angiogenesis processes, while the MC-II subtype showed a high capacity for cell division and glycolysis. The MC-III subtype exhibited a notable infiltration of CD8 + cells, and the MC-IV subtype was characterized by high EMT capability, which may contribute to its poor outcome. These findings have significant implications for the development of precision treatments for XWLC (Figure 5—figure supplement 1b).

Radiomic features across subtypes

Furthermore, we built a noninvasive method to distinguish MC subtypes with radiomics which entails the extensive quantification of tumor phenotypes by utilizing numerous quantitative image features. In the initial step, we defined 107 quantitative image features that describe various characteristics of tumor phenotypes, including tumor image intensity, size, shape, and texture. These features were derived from X-ray computed tomography (CT) scans of 155 patients with XWLC (Methods). The baseline characteristics of this cohort can be found in Supplementary file 8. Firstly, all features were compared among the four subtypes, and notably, eight features showed significant differences between the MC-II subtype and the other three subtypes (Figure 6a). Features such as median and mean reflect the average gray level intensity and Idmn and Gray Level Non-Uniformity measure the variability of gray-level intensity values in the image, with a higher value indicating greater heterogeneity in intensity values. These results suggest a denser and more heterogeneous image in the MC-II subtype. We further established a signature using a multivariate linear regression model with five image features to distinguish MC-II from the other three subgroups (Figure 6—figure supplement 1). The performance of the five-feature radiomic signature was validated using the AUC value, which is a generation of the area under the ROC curve. The radiomic signature had an AUC value of 0.94 in the training set and 0.83 in the validation set (Figure 6b). The confusion matrix revealed an overall accuracy of 0.875 for sample classification using the signature, indicating proficient performance. However, it exhibited suboptimal performance in terms of false-negative classification (Figure 6c). Taken together, we found that MC-II showed a dense image phenotype, which can be noninvasively distinguished using radiomic features.

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Identification of novel targets based on mutation-informed protein-protein interface (PPI) analysis

The integration of genomics and interactomics has enabled the discovery of functional and biological consequences of disease mutations (Sahni et al., 2015; Cheng et al., 2021). To explore novel targets with the concept, we created PPI networks with structural resolution using missense mutations from the XWLC, CNLC, TSLC, and TNLC cohorts (Figure 7a and Methods). OncoPPIs, defined as a significant enrichment of interface mutations in either of the two protein-binding partners across individuals, were identified in each cohort and were provided in Supplementary file 9. The OncoPPIs from the four cohorts are named XWLC_oncoPPIs, CNLC_oncoPPIs, TSLC_oncoPPIs, and TNLC_oncoPPIs, respectively (Figure 7b and Figure 7—figure supplement 1a–c). Initially, the nodes from these four OncoPPIs were subjected to biological process enrichment analysis (Figure 7—figure supplement 1d). The analysis revealed that biological processes such as regulation of the mitotic cell cycle, TGF-beta signaling pathway, and immune system were predominantly enriched in the genes related to OncoPPIs. Moreover, the processes disrupted by interface mutations showed a relatively higher similarity between the XWLC and TSLC cohorts (Figure 7—figure supplement 1d) suggesting convergent targets or pathways affected by smoke-induced mutations.

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To refine the novel targets from XWLC_oncoPPs, we performed molecular dynamics simulations to predict the binding affinity change by the interface-located mutations (Methods). Mitotic Arrest Deficient 1 Like 1 (MAD1), a crucial component of the mitotic spindle-assembly checkpoint (Wang et al., 2017b; Tsukasaki et al., 2001), forms a tight core complex with MAD2, facilitating the binding of MAD2 to CDC20 (Yang et al., 2008), which plays a critical role in sister chromatid separation during the metaphase-anaphase transition (Sironi et al., 2002). Specifically, MAD1 Arg558His has been identified as a susceptibility factor for lung cancer Guo et al., 2010 and colorectal cancer (Zhong et al., 2015). Here, we found that MAD1 allele carrying a p.Arg558His substitution may disrupt the interaction between MAD1 and MAD2 (Figure 7c). To assess this, we performed molecular dynamics simulations and found that the binding affinity between Arg558His MAD1 and MAD2 was –195.091 kJ/mol and that of the wild-type was –442.712 kJ/mol (Figure 7c). Furthermore, on a per-residue basis, the predicted binding affinity (ΔΔG) of Arg558His (Figure 7—figure supplement 1e) was projected to increase by 118.319 kJ/mol relative to the wild-type (Figure 7—figure supplement 1f), indicating that the substitution of Arg558His in MAD1 perturbs the binding affinity. Thus, our findings suggest that the MAD1 Arg558His attributed to lung cancer progression by disrupting of the interaction between MAD1 and MAD2, which showed potential to be explored as a target.

Notably, we identified TPRN as a novel significantly mutated gene in the XWLC cohort (Figure 7—figure supplement 1g and h) whose status was also associated with patients’ outcomes (Figure 7e). Previous studies have reported that TPRN interacts with PPP1CA (Ferrar et al., 2012). Thus, we assessed the binding affinity of the TPRN-PPP1CA complex affected by the mutant variant His550Gln. Our result showed that the binding affinities of the complex were –694.372 kJ/mol and –877.570 kJ/mol in mutant and WT cases, respectively (Figure 7d). On a per-residue basis, the predicted ΔΔG of His550Gln compared to the wild-type exhibited an increase of 96.774 kJ/mol (Figure 7—figure supplement 1i–j). All these results indicated that TPRN His550Gln increase the binding affinity of the TPRN-PPP1CA complex. To investigate the effect of TPRN His550Gln mutation on tumor progression, we examined proliferation and migration capabilities in both A549 and H1299 lung adenocarcinoma cell lines. CCK-8 assay showed significantly enhanced cell growth after transfection of the TPRN mutant allele in both A549 (Figure 7f) and H1299 cells (Figure 7—figure supplement 2a). Moreover, the wound-healing assay showed that the TPRN mutant cell had achieved enhanced migration capacity (Figure 7g–h and Figure 7—figure supplement 2b–c). Finally, more cell clones in TPRN His550Gln mutation cells were observed in both TPRN-mutant A549 and H1299 cells (Figure 7i). All these results supported that TPRN His550Gln could be explored as a target in XWLC.

Taken together, our integrated analysis of oncoPPIs and molecular dynamics simulations showed the potential to explore novel therapeutic vulnerabilities.

In this study, we conducted proteogenomic and characterized air-pollution-related lung cancers. We found that Benzo[a]pyrene (BaP) influenced the mutation landscape, particularly the EGFR-G719X hotspot found in 20% of cases. This mutation correlated with elevated MAPK pathway activation, worse clinical outcomes, and younger patients. Multi-omics clustering identified four subtypes with unique biological pathways and immune cell patterns. Moreover, our analysis of protein-protein interfaces unveiled novel therapeutic targets. These findings have significant implications for preventing and developing precise treatments for air-pollution-associated lung cancers.

Previously considered uncommon, the EGFR-G719X mutation was detected in only 1–2% of CNLC or TCGA-LUAD cohort samples. Limited knowledge exists from G719X, mostly based on isolated case reports or small series studies (Chiu et al., 2015; Kunishige et al., 2023; Yang et al., 2015; Massarelli et al., 2013; Han et al., 2005). In vitro experiments using G719X mutant cell lines and patient-derived xenografts (PDX) demonstrated that osimertinib effectively inhibits signaling pathways and cellular growth, leading to sustained tumor growth inhibition (Floc’h et al., 2020). However, in silico protein structure analysis suggests that G719 alterations may confer osimertinib resistance due to reduced EGFR binding (Yang et al., 2018). Presently, afatinib is proposed as the first-line therapy for G719X mutation patients (Ettinger et al., 2022; Janning et al., 2022; Yang et al., 2020; Cho et al., 2020). Unfortunately, 80% of G719X patients develop acquired resistance to afatinib without detecting the T790M mutation40. Hence, further mechanistic studies are warranted for G719X. Our study reveals that the G719X mutation is prevalent in the XWLC cohort, significantly impacting treatment selection. Additionally, the large number of G719X samples allowed us to uncover variations in biology and pathway activation, which may facilitate the development of more precise targeted therapies for these patients.

There is substantial evidence linking lung cancer in the Xuanwei area to coal smoke (Mumford et al., 1987; Barone-Adesi et al., 2012; Lin et al., 2015; Zhang et al., 2021b; Wong et al., 2019; Vermeulen et al., 2019). In addition, we conducted a rat model study that demonstrated the induction of lung cancer by local coal smoke exposure (Zhang et al., 2021b). However, the specific chemical compound in coal smoke responsible for causing lung cancer remains largely unknown. Previous research has mainly focused on studying indoor concentrations of airborne particles and BaP (Mumford et al., 1987; Barone-Adesi et al., 2012; Lin et al., 2015; Lan et al., 2002; Kim et al., 2014). For instance, studies have shown an association between the concentration of BaP and lung cancer rates across counties (Mumford et al., 1987). Moreover, improvements in household stoves have led to reduced exposure to benzopyrene and particulate matter, benefiting people’s health (Lan et al., 2002; Kim et al., 2014). However, these studies primarily relied on epidemiological data, which may be influenced by confounding factors. Mechanistically, Qing Wang showed that BaP induces lung carcinogenesis, characterized by increased inflammatory cytokines, and cell proliferative markers, while decreasing antioxidant levels, and apoptotic protein expression (Wang et al., 2020). In our study, we used clinical samples and linked the mutational signatures of XWLC to the chemical compound BaP, which advanced the etiology and mechanism of air-pollution-induced lung cancer. In our study, several limitations must be acknowledged. First, although our multi-omics approach provided a comprehensive analysis of the subtypes and their unique biological pathways, the sample size for each subtype was relatively small. This limitation may affect the robustness of the clustering results and the identified subtype-specific pathways. Larger cohort studies are necessary to confirm these findings and refine the subtype classifications. Second, although our study advanced the understanding of air-pollution-induced lung cancer by using clinical samples, the reliance on epidemiological data in previous studies introduces potential confounding factors. Our findings should be interpreted with caution, and further mechanistic studies are warranted to establish causal relationships more definitively. Third, our in silico analysis suggested a potential approach to drug resistance in G719X mutations. However, these predictions need to be validated through extensive in vitro and in vivo experiments. The reliance on computational models without experimental confirmation may limit the clinical applicability of these findings.

In summary, our proteogenomic analysis of clinical tumor samples provides insights into air-pollution-associated lung cancers, especially those induced by coal smoke, and offers an opportunity to expedite the translation of basic research to more precise diagnosis and treatment in the clinic.

Raw sequencing data have been deposited in the Genome Sequence Archive for Human (GSA-Human, https://ngdc.cncb.ac.cn/gsa-human/) under accession codes HRA000124, HRA001481 and HRA001482. Proteomics and phosphoproteomics data have been deposited in the Open Archive for Miscellaneous Data (OMIX, https://ngdc.cncb.ac.cn/omix/) under accession codes OMIX001292. The raw lung CT images used in this paper are available from OMIX under accession codes OMIX002491.

1. 1. Zhang H
   2. Liu C
   3. Wang S

   (2021) NGDC GSA for Human

   ID HRA000124. XWFA.

   https://ngdc.cncb.ac.cn/gsa-human/browse/HRA000124
2. 1. Zhang H
   2. Liu C
   3. Wang S

   (2021) NGDC GSA for Human

   ID HRA001481. XWFA_2nd_batch_WES.

   https://ngdc.cncb.ac.cn/gsa-human/browse/HRA001481
3. 1. Zhang H
   2. Liu C
   3. Wang S

   (2021) NGDC GSA for Human

   ID HRA001482. XWFA_2nd_batch_RNAseq.

   https://ngdc.cncb.ac.cn/gsa-human/browse/HRA001482
4. 1. Zhang H
   2. Liu C
   3. Zhang H

   (2022) NGDC OMIX

   ID OMIX001292. LCCS proteomics.

   https://ngdc.cncb.ac.cn/omix/release/OMIX001292
5. 1. Zhang H
   2. Liu C
   3. Wang S

   (2022) NGDC OMIX

   ID OMIX002491. lung cancer imaging.

   https://ngdc.cncb.ac.cn/omix/release/OMIX002491

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Author details

1. Honglei Zhang

   Center for Scientific Research, Yunnan University of Chinese Medicine, Kunming, China

   Contribution

   Conceptualization, Data curation, Supervision, Investigation, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Chao Liu, Shuting Wang and Qing Wang

   For correspondence

   hlzhang2014@163.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5522-8120

2. Chao Liu

   Department of Nuclear Medicine, Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China

   Contribution

   Resources

   Contributed equally with

   Honglei Zhang, Shuting Wang and Qing Wang

   Competing interests

   No competing interests declared

3. Shuting Wang

   Department of Thoracic Surgery II, Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China

   Contribution

   Investigation

   Contributed equally with

   Honglei Zhang, Chao Liu and Qing Wang

   Competing interests

   No competing interests declared

4. Qing Wang

   Department of Oncology, Qujing First People’s Hospital, Kuming, China

   Contribution

   Data curation

   Contributed equally with

   Honglei Zhang, Chao Liu and Shuting Wang

   Competing interests

   No competing interests declared

5. Xu Feng

   Center for Scientific Research, Yunnan University of Chinese Medicine, Kunming, China

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

6. Huawei Jiang

   Department of Ophthalmology, Second People's Hospital of Yunnan Province, Kunming, China

   Contribution

   Data curation

   Competing interests

   No competing interests declared

7. Li Xiao

   Department of Oncology, Qujing First People’s Hospital, Kuming, China

   Contribution

   Resources, Data curation, Investigation

   Competing interests

   No competing interests declared

8. Chao Luo

   Department of Thoracic Surgery II, Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China

   Contribution

   Data curation, Formal analysis, Funding acquisition

   Competing interests

   No competing interests declared

9. Lu Zhang

   Department of Nuclear Medicine, Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China

   Contribution

   Resources, Formal analysis, Funding acquisition

   Competing interests

   No competing interests declared

10. Fei Hou

    Department of Nuclear Medicine, Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China

    Contribution

    Resources, Funding acquisition, Investigation

    Competing interests

    No competing interests declared

11. Minjun Zhou

    Department of Family Medicine, Community Health Service Center, Kunming, China

    Contribution

    Investigation

    Competing interests

    No competing interests declared

12. Zhiyong Deng

    Department of Nuclear Medicine, Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China

    Contribution

    Resources, Data curation, Formal analysis

    Competing interests

    No competing interests declared

13. Heng Li

    Department of Thoracic Surgery II, Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China

    Contribution

    Formal analysis, Investigation

    Competing interests

    No competing interests declared

14. Yong Zhang

    Department of Nephrology, Institutes for Systems Genetics, Frontiers Science Center for Disease Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China

    Contribution

    Data curation, Funding acquisition, Investigation

    For correspondence

    nankai1989@foxmail.com

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0513-8941

15. Xiaosan Su

    Center for Scientific Research, Yunnan University of Chinese Medicine, Kunming, China

    Contribution

    Resources, Funding acquisition, Investigation

    For correspondence

    suxs163@163.com

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6163-2429

16. Gaofeng Li

    Department of Thoracic Surgery II, Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China

    Contribution

    Resources, Funding acquisition, Investigation

    For correspondence

    ligaofeng@kmmu.edu.cn

    Competing interests

    No competing interests declared

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