Patterns of Carbon-Bound Exogenous Compounds in Patients with Lung Cancer and Association with Disease Pathophysiology

Asymptomatic anthracosis is the macroscopically and histologically visible black discoloration resulting from the deposition of black carbon particles in various anatomical locations of human lungs. Associated with age, environmental pollution, and smoking load, anthracosis can serve as an index of lifetime exposure to exogenous factors (1). Studies have shown lungs of heavy smokers to exhibit more pronounced anthracosis (2) and an association with lung carcinogenesis or cancer progression (3, 4). Others have shown cigarette smoke not to be a risk factor for anthracosis and found no epidemiologic or etiologic link with lung cancer (5).

The highly complex and heterogeneous chemical composition of black carbon particles comprises numerous organic and inorganic compounds, including carbon, silica, aluminum, and iron oxide (5, 6). Black carbon particles can bind potentially toxic or carcinogenic compounds present in air pollutants, soot, dust, or tobacco smoke (2, 7, 8). Furthermore, black carbon particles are carriers of toxic chemicals, such as polycyclic aromatic hydrocarbons (PAH) and nicotine-derived nitrosamine ketones, to the lung, immune cells, and systemic blood circulation (9). The toxins that were originally thought to be chemically inert can be retained, released, or metabolized over a long period of time (10). Although extensively researched, the effects and interplay of carbon particles in anthracosis and exogenous compounds within their natural cellular and extracellular context of human lung tissue are unexplored and challenging due to the complex histologic interrelationships.

Mass spectrometry imaging (MSI) has gained significant relevance in biomedical research and reveals the discrete distribution of compounds and their related metabolites. MSI has high molecular specificity and allows comprehensive, multiplexed detection and localization of thousands of endogenous metabolites directly in tissues (11). In a very recent study, MSI was applied on mice to characterize the in situ organ distribution of intratracheal-instilled and intravenously injected carbon particles, revealing surface-adsorbed aromatic hydrocarbons (12). The toxicologic and pathologic findings based on studies of the molecular and cellular processes induced by toxins are important to achieve a mechanistic understanding. One of the strengths of MSI is its ability to directly overlay molecular information with tissue sections to correlatively compare molecular and histologic information. Therefore, MSI can provide novel insights into the effects and interactions of anthracosis, compounds, and endogenous metabolites within their natural cellular and extracellular context in human lung tissue.

Using in situ high mass resolution matrix-assisted laser desorption/ionization (MALDI) fourier-transform ion cyclotron resonance (FT-ICR) MSI analysis, we report carbon-bound exogenous compounds in a series of 330 patients with lung cancer. The spatial distribution of compounds like PAHs, tobacco-specific nitrosamines, or aromatic amines, as well as their impact on patient outcome, tumor progression, composition of intratumoral immune cells, programmed death-ligand 1 (PD-L1) expression, and DNA damage is examined. Furthermore, we investigate metabolic differences between tumor cells and the tumor microenvironment and illuminate the relationship of concentration and composition of black carbon pigments in patients with lung cancer.

We retrospectively analyzed 330 consecutive patients with primary resected squamous cell carcinoma (SCC), diagnosed at the Institute of Pathology, University of Bern without previous or concomitant diagnosis of SCC of other organs, to reliably exclude metastatic lung disease, as previously described (13). The study was done in accordance with the Declaration of Helsinki, and the local Ethics Committee of the Canton of Bern approved the study and waived the requirement for written informed consent (KEK 200/14). The cohort was assembled according to pathology files and validated according to clinical files. The histology of all cases was reassessed according to current World Health Organization (WHO) guidelines for diagnosis of SCC (14). All tumors were restaged according to the Union for International Cancer Control (UICC) 2017, 8th edition tumor–node–metastasis (TNM) classification (15). Overall survival (OS) was defined as the time from the resection to death of any cause. For baseline characteristics, see Supplementary Table S1. A tissue microarray was constructed from formalin-fixed, paraffin-embedded (FFPE) tissue blocks as described before (13). In short, slides were scanned and digitally annotated with subsequent automatic transferal of the punches to a tissue microarray (TMA) receptor block, which was used for further analysis. Additionally, full tissue sections were used for comparison between tissue microarray cores and full tissue sections.

Idiopathic pulmonary fibrosis (IPF) tissues were collected at the Institute of Pathology, Hannover Medical School, Germany (FFPE), as previously described (16). All patients provided written informed consent, and the study was done in accordance with the Declaration of Helsinki. All experiments were performed in accordance with relevant guidelines and regulations (ethical votes #1691–2013 or #3381–2016, Hannover Medical School). In brief, the specimens for primary surgical resection were obtained from patients diagnosed with lung IPF (n = 10) and preserved as FFPE material.

In addition, explanted lung tissue from patients with IPF (n = 4) and healthy organ donors (n = 4) were inflated with air to a transpulmonary pressure of 30 cm H₂O, then deflated to 10 cm H₂O while freezing in liquid nitrogen vapor; frozen samples were stored at −80°C. This study was approved by the hospital ethics and university biosafety committees in Leuven, Belgium (ML6385). IPF tissues and healthy lung tissues were collected at KU Leuven, as previously described (17). All patients provided written informed consent, and the study was done in accordance with the Declaration of Helsinki. For baseline characteristics, see Supplementary Table S2.

Tissues were counterstained with nuclear red stain (Fluka, 60700, 0.1%). Stained tissue sections were scanned using an AxioScan.Z1 digital slide scanner (Zeiss) equipped with a 20x magnification objective. Quantification of the amount of anthracotic pigments was determined by digital image analysis using the software Definiens Developer XD2 (Definiens AG), following a previously published procedure (18). The calculated parameter was the ratio of pigment area respective to total tissue area for each core.

High mass resolution MALDI FT-ICR MSI was performed as previously described (19, 20). In brief, FFPE sections (4 μm) or fresh frozen sections (12 μm) were mounted onto indium–tin–oxide (ITO)–coated glass slides (Bruker Daltonik). The air-dried tissue sections were spray-coated with 10 mg/mL of 9-aminoacridine hydrochloride monohydrate matrix (Sigma–Aldrich) in methanol (70%) using the SunCollect sprayer (Sunchrom). Prior to matrix application, FFPE tissue sections were incubated additionally for 1 hour at 70°C and deparaffinized in xylene (2 × 8 minutes). Spray-coating of the matrix was conducted in 8 passes (ascending flow rates 10 μL/minute, 20 μL/minute, and 30 μL/minute for layers 1–3 and for layers 4–8 with 40 μL/minute), utilizing 2-mm line distance and a spray velocity of 900 mm/minute.

Metabolites were detected in negative-ion mode on a 7 T Solarix XR FT-ICR mass spectrometer (Bruker Daltonik) equipped with a dual electrospray ionization MALDI (ESI-MALDI) source and a SmartBeam-II Nd: YAG (355 nm) laser. Mass spectra were acquired within m/z 50 to 1,100 and a lateral resolution of 50 μm. L-Arginine was used for external calibration in the electrospray ionization (ESI) mode. The SCiLS lab software 2020b was used to export the picked peaks of the mass spectra as processed and root mean square normalized imzML files.

The SPACiAL workflow was used as previously described to automatically annotate tumor and stroma regions in SCC tissues (21). In short, after MALDI-MSI analysis, the 9-aminoacridine matrix was removed with ethanol (70%) for 5 minutes from tissue sections, followed by IHC staining. Double staining of the TMA was performed using pan-cytokeratin [monoclonal mouse pan-cytokeratin plus (AE1/AE3+8/18), 1:75, catalog no. CM162; Biocare Medical, RRID: AB_10582491) and vimentin (Abcam, clone ab92547, 1:500, RRID: AB_10562134). Regions positive for pan-cytokeratin were defined as tumor. Regions negative for pan-cytokeratin but positive for vimentin were defined as stroma.

In order to discover and visualize exogenous and endogenous compounds, mass spectra in and near anthracotic pigments were extracted using the SCiLS lab software 2020b. Annotations were performed using Kyoto Encyclopedia of Genes and Genomes (KEGG, RRID: SCR_012773; ref. 22), The Human Metabolome Database (HMDB; RRID: SCR_007712; ref. 23), and Hoffmann analytes (24).

We performed a stringent annotation of molecules using the following inclusion criteria: (i) The molecular mass of endogenous and exogenous compounds must be between 50 Da and 1100 Da; (ii) the signal to noise ratio must be above 2; (iii) for exogenous compounds, literature evidence must exist for their presence in tobacco smoke. Exclusion criteria were: (i) Signals that were annotated as isotopes were excluded; (ii) as previously published, substances with HMDB descriptions containing a reference to drugs, pesticides, or other implausible descriptions were excluded (21). M-H, M-H₂O-H, and M+Cl as negative adducts with a mass tolerance of 4 ppm were prioritized.

Benzo[a]pyrene was purchased from Sigma Aldrich and diluted in xylene. One microliter benzo[a]pyrene solution was spotted onto human fresh frozen lung tissue sections between the absolute amounts of 60 nmol–0.6 nmol benzo[a]pyrene. Matrix application and high mass resolution MALDI FT-ICR MSI was performed as described before. Stack plot was created by flexImaging (v. 5.0, Bruker), and overlayed peak spectra were illustrated in mMass (v. 5.5.0). Curve fitting was performed with GraphPad Prism (v. 9.2.0).

IHC staining for cluster of differentiation 3 (CD3), cluster of differentiation 8 (CD8), and PD-L1 was performed as previously described (13) on consecutive sections. In brief, an automated immunostainer (Bond III, Leica Bio-systems) with anti-CD3 (Abcam Cambridge; clone SP7, 1:400, RRID: AB_443425), anti-CD8 (Dako, clone C8/144B, 1:100, RRID: AB_2075537), and anti-PD-L1 (Cell Signaling Technology, clone E1L3N, 1:400, RRID: AB_2687655) was used. The numbers of CD8⁺ and CD3⁺ tumor infiltrating lymphocytes were determined using image analysis (Aperio Image Scope) and adjusted for core completeness. PD-L1 expression was assessed as the intensity of membranous staining by a pathologist (S. Berezowska).

Immunofluorescence analysis of γH2AX expression was achieved using primary antibodies against pH2A.X (Cell Signaling Technology; catalog no. 2577, 1:400, RRID: AB_2118010) and pan-cytokeratin [monoclonal mouse pan-cytokeratin plus (AE1/AE3+8/18), 1:75, catalog no. CM162; Biocare Medical, RRID: AB_10582491) on consecutive sections. Slides were digitized at 20× objective magnification using an Axio Scan.Z1 (Zeiss). Quantification was performed by digital image analysis in Definiens Developer XD2, following a previously published procedure (18). The quantified parameter was the ratio of γH2AX and pan-cytokeratin-positive cells to the total number of pan-cytokeratin-positive cells.

Correlations were calculated using pairwise Spearman rank-order correlation (Python 3.7, SciPy 1.2.0, RRID: SCR_008058). Spearman P values were adjusted with Benjamini–Hochberg correction (Python 3.7, StatsModels 0.9.0). To determine significant differences between UICC TNM stages, Kruskal–Wallis test by ranks (Python 3.7, SciPy 1.2.0) and posthoc Dunn multiple comparison test (Python 3.7, scikit-posthocs 0.6.1) were used in conjunction with Benjamini–Hochberg correction. Cutoff-optimized survival analyses were performed using a Kaplan–Meier Fitter and log–rank test (Python 3.7, lifelines 0.24.8). Cutoff-optimized in this context means that the threshold for low and high abundance of a compound was chosen such that the P value in the resulting Kaplan–Meier curve is minimal, while ensuring robust results for similar cutoffs.

We investigated the association between the survival time of patients and several predictor variables. The Cox proportional hazards model is a regression model commonly used in medical research for this purpose. The multivariate analysis was performed using the Cox proportional hazards model (Python 3.8, lifelines 0.25.7) using the same cutoffs as for Kaplan–Meier Fitter. Categorical data were used for the Cox proportional hazards model. Compounds that passed the nonproportional test were included in the model (Python 3.8, lifelines 0.25.7). All survival calculations were based on OS.

Correlation networks were created with Cytoscape (v. 3.8.0, RRID: SCR_003032; ref. 25). All networks were visualized using the edge weighted spring embedded layout and the absolute value of the correlation coefficient calculated as described above. Compounds with at least one significant correlation are shown (P < 0.05).

Circular plots were generated using Circos (v.0.69.8, RRID: SCR_011798; ref. 26). The metabolites of interest and correlations with the exogenous compounds were extracted from the spatial correlation networks. Pathway information for each metabolite was extracted from KEGG (22). If available, common compound name abbreviations were retrieved from HMDB (23) or KEGG databases.

Carbon deposits can be seen macroscopically (Fig. 1A), often found in the center of the tumor (27), beyond parenchymal (Fig. 1B) and pleural anthracosis (Fig. 1C). They are found intratumorally dispersed with varying degrees (Fig. 1D and E). Using digital image analysis, anthracotic pigment was quantified in SCC tissues of 313 patients (Fig. 1F–H) for subsequent statistical analyses (e.g., correlations with clinical parameters).

There is no significant association between the amount of carbon pigment and smoking behavior (pack-years, P = 0.91), DNA damage (γH2AX expression, P = 0.61), lymphocyte number (CD3, P = 0.42; and CD8, P = 0.67), PD-L1 expression (P = 0.07), and patient OS (P = 0.23, cut-off = 0.005%; Fig. 1I–K). There are no significant correlations for pigment quantity. Next, the molecular composition was further investigated.

Using high mass resolution MALDI FT-ICR MSI, 11 exogenous compounds were detected at different abundances throughout the tissues. Importantly, the abundance is highest in and nearby the anthracotic pigment (Fig. 2). The exogenous compounds can be grouped into four classes: PAHs, tobacco-specific nitrosamines, aromatic amines, and organohalogens (Fig. 2A).

Five PAHs, benzo[a]pyrene (m/z: 287.0639), dibenz(a,h)anthracene (m/z: 313.0800), dibenzo[a,l]pyrene (m/z: 337.0775), benzo[b]pyridine (m/z: 128.0504), and 7-OH-12-methylbenz[a]anthracene sulfate (m/z: 351.0692) are particularly rich in carbon pigment (Fig. 2). Within pigment interspersed tissue, 3 tobacco-specific nitrosamines were detected: 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone [nicotine-derived nitrosamine ketone (NNK), m/z: 242.0702], 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL, m/z: 208.1091), and NNAL-N-glucuronide (m/z: 367.1373; Fig. 2B and C). N-hydroxy-4-aminobiphenyl (m/z: 220.0526) and N-hydroxy-MeIQx (m/z: 264.0650) are aromatic amines and dichloroethane (m/z: 96.9617) is an organohalogen. To determine the quantity of benzo[a]pyrene as an example, we performed a spicking experiment. The minimum amount for detecting benzo[a]pyrene in lung tissues is 2 nmol (Supplementary Fig. S1).

In total, the concentration of exogenous compounds correlates with the amount of anthracotic pigment for benzo[a]pyrene (P = 0.0009), dibenz(a,h)anthracene (P = 0.0056), dibenzo[a,l]pyrene (P = 0.0405), NNK (P = 0.0316), NNAL (P = 0.0338), and NNAL-N-glucuronide (P = 0.0257). The correlations between carbon pigment and exogenous compounds are all positive. In the next, we analyzed the patterns of the exogenous compounds in the carbon pigment within and between the individual patients.

The chemical composition of carbon pigment is heterogeneous in terms of the pattern and abundance of the compounds within the different areas of lung tissue (Fig. 3; Fig. 3A; Supplementary Fig. S2 and S3). The variability of the chemical composition is also visible at the microscopic scale: Fig. 3B shows SCC regions from two patients with unique compositions of carbon-bound compounds. Although the carbon pigments of both regions exhibit a high amount of dibenz(a,h)anthracene, other compounds are present at very different abundances. A multicolor visualization of NNK, NNAL, and NNAL-N-glucuronide also shows an entirely different chemical signature (Fig. 3C; Supplementary Fig. S4). The chemical composition was shown to be unique and heterogenous within and between patients. Next, we investigated the differences within the tissue compartments stratified to tumor cells and tumor stroma.

We used our recently published SPACiAL method for immunophenotype-guided separation of tumor and stromal tissue compartments (21) to investigate the associated carbon pigment (Supplementary Figs. S5 and S6). The amount and prevalence of exogenous compounds differ between tumor and stromal regions (Fig. 4A; Supplementary Fig. S7). Most exogenous molecules were more frequently measured in tumor pigments. However, PAHs and tobacco-specific nitrosamines, if present in stroma, are more abundant there.

The abundance of carbon-bound compounds was next correlated to tumor features and patient characteristics. Figure 4A is a comparative representation of the abundance of compounds correlating with smoking behavior, DNA damage, and immunologic features.

Several carbon-bound compounds correlate with smoking behavior, DNA damage, lymphocyte number, PD-L1 expression, and tumor progression (Fig. 4). In tumor, NNK and dichloroethane are significantly associated with smoking behavior (Fig. 4B, P = 0.0114 and P = 0.0215, respectively).

A total of 89.7% of the patients have γH2AX-positive tumor cells, which is indicative of DNA damage. The PAHs benzo[a]pyrene (P = 0.0020), dibenz(a,h)anthracene (P = 0.0262), and dibenzo[a,l]pyrene (P = 0.0432) correlate positively with γH2AX (Fig. 4C).

In tumor regions, dibenz(a,h)anthracene abundance correlates negatively with both the overall number of tumor infiltrating T cells (CD3, P = 0.0429) and CD8 (P = 0.0450; Fig. 4D). In stromal areas, dibenzo[a,l]pyrene correlates positively with CD3 and CD8 (P = 0.0292 and P = 0.0156, respectively). The intensity of tumoral PD-L1 expression correlates negatively with NNAL abundance (P = 0.0489).

Benzo[a]pyrene in stroma regions (P = 0.0364) and dibenz(a,h)anthracene in tumor and stroma regions (P = 0.0400, P = 0.0439, respectively) are associated with UICC tumor stages (Fig. 4E). In contrast to carbon-bound PAHs, the amount of carbon pigment shows no association with tumor stage (P = 0.1729).

Two PAHs, benzo[b]pyridine (tumor, P = 0.0103) and dibenz(a,h)anthracene (stroma, P = 0.0270) correlate significantly with OS (Fig. 5A, see also Supplementary Figs. S8 and S9). Additionally, two tobacco-specific nitrosamines, NNK (P = 0.0071) and NNAL-N-glucuronide (P = 0.0298), are significantly correlated with survival (Fig. 5B). However, a higher concentration of NNK is associated with poor OS, while its detoxified form, NNAL-N-glucuronide, is significantly linked with better OS. In contrast, higher amounts of the two aromatic amines, N-hydroxy-MeIQx and N-hydroxy-4-aminobiphenyl, are significantly correlated with poor patient outcome, both in tumor and in stroma (Fig. 5C).

We also tested the correlation of CD3, CD8, PD-L1, and pack-years with patient survival. High expression of CD3 (P = 0.0139) and CD8 (P = 0.0275) in tumor stroma regions, low expression of PD-L1 in tumor cells (P = 0.0021), and low pack-years (P = 0.0003) are associated with good survival (Supplementary Fig. S10).

We investigated the association between the survival time of patients and several predictor variables and used the Cox proportional hazards model. Benzo[b]pyridine (P = 0.0019), dibenz(a,h)anthracene (P = 0.0150), NNK (P = 0.0043), and N-hydroxy-MeIQx (P = 0.0008) are independent factors for OS (Fig. 5D). The highest HR was determined for NNK in stroma (HR = 5.0263) and N-hydroxy-MeIQx in tumor (HR = 3.0943), indicating that a higher amount of these compounds is deleterious.

After finding significant correlations of individual carbon-bound compounds with tumor features and patient characteristics, we investigated spatial correlations of exogenous and endogenous compounds with a focus on pathway and network analyses.

To investigate the metabolic changes of tumor cells associated with exogenous compound quantities, we evaluated the spatial correlation networks of metabolites in 330 patient tissues. Dense clusters within the networks indicate stronger spatial correlation, and therefore, dependencies between quantities of exogenous and endogenous compounds. Pixel-wise spatial correlations within and between metabolites and eleven exogenous compounds were calculated and filtered (P < 0.05). In the two resulting networks, the spatial correlation of 133 metabolites within tumor cells and 159 metabolites in the stroma are visualized (Fig. 6).

The spatial correlation network in tumor reveals no distinct cluster of exogenous compounds (Fig. 6A). N-Hydroxy-MeIQx, which has the most striking effect on patient survival, was detected in a dense cluster of metabolites (maximum r_S = 0.79), featuring a higher spatial positive correlation with glutathione (GSH, r_S = 0.408).

Most endogenous metabolites within the cluster of interest can be associated with lipid metabolism (41.2%), whereby the strongest, albeit not very pronounced positive correlations to N-hydroxy-MeIQx were found for 9 (10)-EpOME (9,10-EOA, r_S = 0.166), sn-glycero-3-phosphoethanolamine (NGPE, r_S = 0.173), and sn-glycerol 3-phosphate (G3P, r_S = 0.185). The second most represented pathway is nucleotide metabolism (17.6%). Higher quantities of the purine metabolite deoxyinosine-phosphate (dIMP, r_S = 0.159) and the pyrimidine metabolites cytidine (Cyd, r_S = 0.127) and deoxycytidine diphosphate (dCDP, r_S = 0.151) are associated with an increased N-hydroxy-MeIQx concentration (Fig. 6A).

The spatial correlation network for the stroma region differs substantially from the network for the tumor region (Fig. 6B). Six exogenous molecules are part of a dense cluster together with endogenous metabolites. The highest spatial correlation was found between the two exogenous compounds dibenzo[a,l]pyrene and dibenz(a,h)anthracene (r_S = 0.679). Unlike the network for the tumor region, most of the correlating metabolites in the main cluster take part in amino acid or nucleotide metabolism.

Four metabolites with a role in amino acid metabolism are related to tryptophan metabolism. A high abundance of tryptophan metabolites is associated with high PAH and tobacco-specific nitrosamine concentrations. Deoxyadenosine (dA), deoxyinosine (D-Ino), deoxycytidine (dC), and deoxyuridine (dU) are two purine and two pyrimidine metabolites from the nucleotide metabolism pathway that correlate positively with the exogenous compounds (Fig. 6B).

In addition to lung cancer, other respiratory pathophysiologic conditions, such as interstitial lung diseases, have been linked to environmental pollutants, e.g., due to epigenetic modification (28). With the analysis of IPF tissue, we aim to highlight, albeit not in depth, the presence and possible significance of anthracosis on other respiratory pathophysiologic conditions. Similarly to SCC we found inter- and intrapatient heterogeneity of carbon-bound exogenous compounds in both normal lung parenchyma and IPF. In contrast to the tumor and tumor stroma, spatial correlation networks for IPF tissues reveal two clusters of exogenous compounds and endogenous metabolites. One cluster comprises several PAHs including dibenzo[a,l]pyrene, dibenz(a,h)anthracene, and benzo[a]pyrene, while the other is a mixture of two PAHs - benzo[b]pyrene and 7-hydroxymethyl-12-methylbenz[a]anthracene sulfate, one tobacco-specific nitrosamine and one aromatic amine. Both the spatially correlating endogenous and exogenous compounds within the clusters and the biological pathways they are related to show only minor similarity to the tumor metabolic networks and pathway analysis. See supplementary information for details (Supplementary Fig. S11–S14).

We have discovered a biologically active pool of carbon-bound exogenous compounds in lung cancer tissue. High amounts of these exogenous compounds in various and patient-unique chemical combinations were found in and near anthracotic pigment. Although the detected exogenous compounds are known carcinogens, we show here for the first time that these exogenous compounds also have a strong impact on tumor pathophysiology and survival outcome of patients with lung cancer.

Carbon particles accumulate in human lungs and exhibit a large surface area as well as specific surface characteristics for the adsorption of inorganic and organic exogenous compounds (29–31). In mouse lungs, carbon particles were shown to persist indefinitely (32). The long-term persistence and bioavailability of carbon-bound exogenous compounds is supported by further animal studies showing that the detection of benzo[a]pyrene was possible up to 5.6 months after incubation (33).

We used in situ high mass resolution MALDI FT-ICR MSI to show that the chemical composition of carbon particles in human lung cancer tissue is much more complex than expected. Each patient showed a unique chemical signature of carbon particles (Fig. 3A). Even within 1 patient, carbon particles exhibit high chemical heterogeneity (Fig. 3B). Both extrinsic factors, such as environmental conditions, and intrinsic factors, such as metabolism of exogenous compounds, may play a role for the diverse chemical patterns of carbon pigments. However, our cohort mainly comprises patients with a history of smoking and SCC is a clear smoker-associated type of cancer (Supplementary Table S1). Hence, the particles analyzed in this study are likely smoking related. Certain PAHs accumulate in smokers' lungs (34, 35), however, their localization and pathophysiologic impact remains unclear. Nevertheless, Tomingas and colleagues demonstrated a large increase of benzo[a]pyrene in human bronchial carcinoma in contrast to adjacent tissue (36). In contrast, our study localized exogenous compounds in human lungs and illustrates the significance of these compounds on SCC depending on their spatial localization. In our patient cohort, NNK, as the most abundant systemic lung carcinogen in cigarette smoke (37), showed the strongest correlation with pack-years (Fig. 4B). Indeed, NNK is derived mainly from tobacco smoke. In contrast, PAHs can be derived from numerous environmental sources (38), explaining the lack of a significant correlation.

Unique differences in the metabolism of exogenous compounds are an intrinsic factor for the chemical signature of carbon particles. This is known in carcinogenesis research, and crucial pathways in PAH metabolism arguably differ between patients (39, 40). We found that carbon-bound exogenous compounds are present and bioactive in tumor tissue beyond carcinogenesis (Figs. 2, 4 and 5), and their abundance may strongly depend on the metabolic activity in individual patients. Our observations revealed a high variation in NNK, its reduced form NNAL, and its detoxification product NNAL-N-glucuronide (41), suggesting unique metabolic activities in patients (Fig. 3A).

In our study, an increased concentration of PAHs was associated with increased DNA damage in tumor cells (Fig. 4C). Another study focused on carbon particles in mouse lungs and confirmed our finding that anthracosis is associated with DNA damage (32). Alexandrov and colleagues showed that tobacco smoking and PAH exposure cause specific mutations representing the leading mutation signature of lung cancer (42). High mutational burden is associated with an improved objective response to anti–Programmed cell death protein 1 (PD-1) therapy, patient survival, and durable clinical benefit in non–small cell lung cancers (43). Tumor mutational burden and PD-L1 are used as predictive markers for immunotherapies. Thus, our data suggests that these exogenous molecules may influence SCC therapies.

While we found exogenous compounds as toxic molecules of tobacco smoke and environmental factors, a recent study found the presence of an exogenous molecule as a drug in anthracosis. Cisplatin can be accurately detected in tissues using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) imaging. Greenhalgh and colleagues (44) applied LA-ICP-MS imaging for 3D ex vivo human explant model and demonstrated for the first time a spatial correlation between platinum and anthracosis in lung tissue. The carbon deposits found in lung tissue may affect the movement and thus the efficacy of cisplatin treatment. The authors conclude that cisplatin penetration can be predicted and monitored by LA-ICP-MS imaging as a screening tool (44). This underlines the potential therapeutic implications of anthracosis in non–small cell lung cancer. Because LA-ICP-MS imaging is capable of measuring metals, it would be interesting to apply this MSI technology to measure metals in the anthracotic pigment, which are also present in cigarette smoke (45).

Lymphocytes are known to be associated with harmful compounds in lung cancer tissues. In our study, the infiltration of CD3⁺ and CD8⁺ lymphocytes was correlated with the concentrations of dibenz(a,h)anthracene and dibenzo[a,l]pyrene (Fig. 4D). Many PAHs influence patient immunity, as they are important ligands of the aryl hydrocarbon receptor (AhR) on several immune cells (46, 47). Systemic AhR activation by an exogenous compound triggers the suppression of the CD8⁺ T-cell response in infected lungs (48). In our study, a suppression of T cells was observed with a higher concentration of dibenz(a,h)anthracene in tumor regions. Another study showed that tobacco smoke and benzo[a]pyrene lead to CD8⁺ lymphocyte enrichment in mouse lungs, possibly induced by AhR (49). In this study, a similar effect of lymphocyte enrichment was seen in the stroma regions for high concentrations of dibenzo[a,l]pyrene.

We have shown for the first time that exogenous compounds have a strong impact on patient survival (Fig. 5). The presence of NNK, NNAL-N-glucuronide, and N-hydroxy-4-aminobiphenyl in SCC tissues was associated with patient outcomes. A study on 770 resected lung cancers revealed poorer prognosis for smokers (50). We observed the same effect of smoking on survival (Supplementary Fig. S10). All substances with a significant effect on survival can be associated with tobacco smoke. Interestingly, the nonmetabolized primary substance from tobacco smoke, NNK, was associated with poor OS. In contrast, the detoxified variant of the metabolite, NNAL-N-glucuronide, was an indicator of a favorable prognosis (Fig. 5B). Hence, glucuronidation may be worth investigating in more detail for detoxification of exogenous compounds. This is underlined by another study, which revealed that smokers with an increased urinary level of glucuronidated nitrosamine N'-nitrosonornicotine experienced a significantly reduced risk of esophageal cancer (51). Whether and to what extent glucuronidation in lungs of human individuals can be specifically influenced still remains open.

Correlations between endogenous metabolites and exogenous molecules are fundamentally different between tumor and stroma regions. Notably, in tumor regions, N-hydroxy-MeIQx alters lipid and glutathione metabolism. A higher abundance of the metabolite N-hydroxy-MeIQx is associated with increased lipid species and a higher concentration of glutathione (Fig. 6A). The effect of MeIQx on lipid profile, potentially caused by dysregulated maturation of autolysosomes, has been described in hepatocytes (52). Otherwise, glutathione has been shown to increase the perceptivity to oxidative stress (53). The formation of 8-hydroxy-2′-deoxyguanosine, an oxidative DNA damage marker, increases in the liver with a specific dose of MeIQx (54). Oxidative stress caused by N-hydroxy-MeIQx may be contained by an increase in glutathione in tumor cells.

In tumor stroma, PAHs and tobacco-specific nitrosamines revealed a strong impact on amino acid and nucleotide metabolism (Fig. 6B). Notably, the majority of the changed amino acid metabolites can be associated with tryptophan metabolism, which is of crucial importance to the immune system as its metabolites orchestrate local and systemic responses to control inflammation (55). The indole ring of the critical regulatory molecule tryptophan is cleaved by indoleamine 2,3-dioxygenase (IDO; ref. 56). The activation of AhR increases IDO expression (57). Therefore, PAHs may increase tryptophan metabolism through the activation of AhR. Our second finding that nucleotide metabolism is enhanced is most likely associated with DNA damage and repair caused by these substances.

The effects of carbon-bound exogenous compounds might also be of pathophysiologic significance for other lung diseases. Anthracosis has been described to be associated with nonneoplastic diseases such as emphysema (32). We have selected IPF as an example for an exploratory analysis of anthracotic pigment in the context of nonneoplastic diseases. IPF is a lung disease of unknown etiology and is characterized by progressive scarring. The underlying pathomechanisms of IPF, with its complex immunologic and inflammatory processes and external impacts, have been the focus of recent research. Lifestyle and environmental influences are held responsible for much of its natural history. Because smoking, pneumotoxic medications, and inhalation of dust are known risk factors of IPF (58), we analyzed the presence and constitution of exogenous compounds within anthracotic tissue of patients with IPF to uncover differences to the smoking-related SCC. Indeed, we also found carbon-bound exogenous molecules in IPF anthracotic pigment. Similarly to SCC we found inter and intrapatient heterogeneity of carbon-bound exogenous compounds in both, normal-lung parenchyma and IPF. Furthermore, the network analysis revealed differences in the affected metabolic pathways compared with SCC tissues. We conclude that exogenous compounds could be an unrecognized factor in the development and progression of IPF. These preliminary findings warrant further investigation.

When the amount of anthracotic pigment is considered over the total volume of both lungs, there is a large and persistent pool of carbon-bound exogenous compounds with possible systemic effects beyond the lungs. Tobacco smoking is also the leading risk factor for bladder cancer (41). As a representative of aromatic amines, 4‐aminobiphenyl has been extensively studied to understand the mechanism of bladder carcinogenesis (59). We discovered that N-hydroxy-4‐aminobiphenyl, a carbon-bound exogenous compound, was highly abundant in human lung tissue. Given the potentially large amount of anthracotic pigment in both lungs, it is possible that 4-aminobiphenyl is stored in the pigment and released continuously over the long term and thus may contribute to the development of bladder carcinoma. Similarly, other carcinogens could be stored and continuously released via the persistent carbon pool and thus also play a role in the development of tumors outside the lung.

Since carbon particles and carbon-bound exogenous compounds are known to be persistent, and the subsequent removal of the carbon particles from the lung is as of yet not feasible, the most reasonable and implementable courses of action right now are risk assessment and prevention. Apart from smoking cessation, a change in smoking behavior may influence concentrations of biomarkers of exposure. For example, in a large scale study including 5,105 participants, e-cigarette users showed a 10% to 98% lower concentration of PAHs compared with exclusive cigarette smokers– albeit it was still significantly increased compared to the levels in never smokers (60). Based on another study, the most significant determinants of PAH exposure beyond smoking are diet and indoor exposures like coal- or wood-heaters, cooking, diverse leisure activities, and passive tobacco smoke - and most of these exposures can be deemed preventable (61). In the case of PAHs, there are several physical and chemical remediation strategies to remove PAHs from polluted environments including membrane filtration, soil washing, adsorption, electrokinetic, thermal, oxidation, and photocatalytic treatments (62). Given that we here show that several exogenous compounds are present directly within anthracotic pigment and that they are an unrecognized factor with strong impact on tumor pathophysiology underlines the importance of risk assessment and prevention.

In conclusion, the bioactive pool of exogenous compounds in and nearby the anthracotic pigment is associated with several changes in tumor pathophysiology and has adverse effects on patient outcome. Genome integrity, immune factors, and tumor progression are associated with specific chemical signatures in the anthracotic pigment. The exact localization of exogenous compounds influences patient outcome by altering amino acid, nucleotide, and lipid metabolism. In lung IPF, exogenous substances can also be found in and nearby anthracotic pigment, however, these molecules affect other pathways (Supplementary Fig. S11). The exogenous compounds may contribute to the formation and influence the progression of diseases of the lung and other organs. Because all healthy lung tissues contained exogenous compounds (Supplementary Fig. S14), a deeper understanding of the unique composition and pathophysiologic relevance of anthracosis is needed.

W. Wuyts reports grants from Roche, Boehringer Ingelheim and grants from Galapagos outside the submitted work. O. Eickelberg reports personal fees from Blade Therapeutics and personal fees from Pieris outside the submitted work. A. Walch reports grants from Ministry of Education and Research of the Federal Republic of Germany, the Deutsche Forschungsgemeinschaft, and the Deutsche Krebshilfe outside the submitted work. No disclosures were reported by the other authors.

T. Kunzke: Conceptualization, visualization, methodology, writing–original draft, writing–review and editing. V.M. Prade: Conceptualization, visualization, methodology, writing–original draft, writing–review and editing. A. Buck: Methodology, writing–review and editing. N. Sun: Visualization, methodology, writing–review and editing. A. Feuchtinger: Methodology, writing–review and editing. M. Matzka: Methodology, writing–review and editing. I.E. Fernandez: Resources, writing–review and editing. W. Wuyts: Resources, writing–review and editing. M. Ackermann: Resources, writing–review and editing. D. Jonigk: Resources, writing–review and editing. M. Aichler: Methodology, writing–review and editing. R.A. Schmid: Resources, writing–review and editing. O. Eickelberg: Resources, writing–review and editing. S. Berezowska: Conceptualization, resources, supervision, writing–original draft, writing–review and editing. A. Walch: Conceptualization, resources, supervision, writing–review and editing.

The authors thank Ulrike Buchholz, Claudia-Mareike Pflüger, Andreas Voss, Cristina Hübner Freitas, and Elenore Samson for excellent technical assistance.

The study was funded by the Ministry of Education and Research of the Federal Republic of Germany (BMBF; Grant nos. 01ZX1610B and 01KT1615), the Deutsche Forschungsgemeinschaft (Grant nos. SFB 824TP C04, CRC/TRR 205 S01), and the Deutsche Krebshilfe (Grant no. 70112617) to A. Walch. Funding was provided through the Impulse and Networking Fund of the Helmholtz Association and the Helmholtz Zentrum München (Helmholtz Enterprise-2018-6) to A. Buck.

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