Metabolic heterogeneity in tumor cells impacts immunology in lung squamous cell carcinoma
Metabolic reprogramming is a hallmark of cancer, leading to heterogeneous and complex metabolic profiles that complicate metabolic-targeted therapies. These alterations are closely linked to immune cell functions within the tumor microenvironment (TME). Recent research highlights the intricate interplay between metabolic processes and immune functions, with dysregulation of these pathways associated with chronic inflammatory disorders and cancers. Metabolic pathways unexpectedly regulate T-cell fate, function, and differentiation, but the complexity of immunometabolic reprogramming in diseases remains poorly understood due to disease heterogeneity and diverse phenotypes. The TME’s unique immune and metabolic characteristics reprogram cell-cell communication and alter antitumor responses. Given that tumors rely on metabolites for growth and T cells must substantially increase nutrient uptake to mount a proper immune response, investigating the association between metabolic heterogeneity and immune functions could uncover potential crosstalk or competition within the TME, further revealing novel strategies to inhibit tumor growth while enhancing anti-tumor immunity. Tumor heterogeneity is a significant challenge in cancer diagnosis and treatment, manifesting as genetic, metabolic, TME, and other heterogeneity. Studies across various cancer types indicate that intratumoral heterogeneity drives tumor progression and fosters drug resistance, including in lung cancer. Lung cancer, the leading cause of cancer-related death globally, is predominantly non-small-cell lung cancer (NSCLC), with about 30% being lung squamous cell carcinoma (LUSC). , Studies in NSCLC patients revealed that intratumoral heterogeneity varies with mutational burden, and low mutational burden was correlated with better overall survival. , Single-cell RNA sequencing further showed that LUSC has greater inter- and intratumor heterogeneity than lung adenocarcinoma. These findings underscore the importance of studying factors like metabolic heterogeneity in lung cancer. Immunometabolism bridges metabolism with immunology at both intracellular and whole-body levels. Studying tumor immunometabolism requires techniques that capture metabolic heterogeneity in TME. Mass spectrometry imaging (MSI) is a key method, enabling direct, systematic quantification of metabolism in intact cell populations. MSI has provided insights into the metabolic underpinnings of lung cancer, aiding molecular classification strategies. , Matrix-assisted laser desorption/ionization (MALDI) is the most common approach in spatial metabolomics, allowing in situ screening of molecular classes within their histopathological context. High cellular specificity of MSI has proven effective in identifying predictive biomarkers. , , Identifying molecular alterations within tumor tissues and underlying immunometabolism offers insights into biochemical heterogeneity and the role of metabolic reprogramming in cancer. Immunohistochemistry (IHC) complements this by identifying tumor-infiltrating T lymphocytes (TILs) and other immune markers, which can be predictive and prognostic. Exploring the spatial distribution of immune cells and their interactions with cancer and stromal cells may enhance our understanding of cancer progression. In this study, we integrated spatial metabolomics with unsupervised k-means clustering and Simpson’s diversity to investigate tumor-specific metabolic heterogeneity and interactions in the LUSC immune microenvironment. Using MALDI-MSI, we mapped spatially resolved metabolites across tumor regions in clinically annotated samples. We assessed metabolic heterogeneity and identified distinct tumor subpopulations with unique metabolic activities and chemotherapy responses. We found significant associations between TILs and both metabolic heterogeneity and tumor subpopulations. Combining these findings with genomics data, we identified genes correlated with TILs, specific metabolites, and patient survival. This study offers new insights into the cross-talk between immune cells and tumor metabolic heterogeneity in LUSC. 

Patient cohort and tissue samples Tumor samples were obtained from 106 LUSC patients  diagnosed at the Institute of Pathology, University of Bern, excluding cases with prior or concurrent LUSC in other organs. A tissue microarray (TMA) was constructed from formalin-fixed and paraffin-embedded (FFPE) tissue blocks. 

Tumor samples were obtained from 106 LUSC patients  diagnosed at the Institute of Pathology, University of Bern, excluding cases with prior or concurrent LUSC in other organs. A tissue microarray (TMA) was constructed from formalin-fixed and paraffin-embedded (FFPE) tissue blocks. 

Briefly, the FFPE section (4 μm) was mounted onto indium tin oxide-coated glass slides. The air-dried tissue section was spray-coated with 10 mg/mL 9-aminoacridine (9-AA) hydrochloride monohydrate matrix in 70% methanol. Prior to matrix application, the FFPE tissue section was deparaffinized in xylene. Metabolite detection was performed in negative ion mode. Mass spectra were acquired in the range of m/z 50–1,100 with a lateral resolution of 50 µm. 

Using the segmentation tool in Bruker SCiLS Lab, unsupervised k -means clustering was performed pixel-wisely containing overall resolved MS peaks within only tumor regions with k range from 2 to 15. For further statistical analysis, we linked the survival data of patients to the presence of specific clusters and consequently determined the metabolic tumor subpopulations (MTSs). The fitness of the model at each k was assessed using the Akaike information criterion (AIC) involving patients’ overall survival; the model with the lowest AIC value was presumed to most closely fit the data. Applying the k value and threshold of pixels fraction corresponding to the lowest AIC value, patients were assigned to certain clusters, so-called MTSs. Specifically, each MTS stands for a LUSC patient cluster with certain tumor-specific metabolic profiling and features. 

The calculation formular Simpson diversity index measures in our study is defined as follows: D = 1 − ∑ i = 1 k p i 2 , where p i is the share of pixels in cluster i and k is the number of clusters. The formular is built considering the spatial locations of pixels for each patient in each cluster. Each pixel is located by x and y coordinates which were extracted from SCiLS Lab software. The index can have values between 0 and 1. A value of 0 means that all the pixels from one patient are in one cluster. A higher value of the index, therefore, indicates higher diversity of the pixels in the different clusters for a patient. As the heterogeneity score for each patient, the Simpson index to measure the metabolic heterogeneity of the patients is calculated using the within sum of squares as entries p_i. A higher heterogeneity score stands for a higher metabolic heterogeneity level. More details of materials and methods can be found in the Supplementary file. 

Identification of MTSs in LUSC The study design and pipeline are shown in . Supplementary Fig. S1A represents the high-resolution IHC images of immunological markers analyzed in this study. Supplementary Fig. S1B displayed the distinguished distributions of three typical metabolites in all patient tissues. Via the comparison of the minimum AIC values at each k , k = 7 with a threshold of 35% showed the lowest AIC value across all k values and thresholds, and was thus defined as the optimal value for the number of clusters and the threshold . One patient could be segmented into more than one cluster , which was due to the high metabolic heterogeneity of the patients. The spatial pixel distribution of overall peaks from 106 LUSC patients in 7 clusters were quantified as percentages and visualized in . In order to determine the clinical importance of each metabolomics cluster, the results of the metabolite segmentation had to be linked to the clinical data of the patients. By comparing with the AIC-guided threshold of 35% pixel presences, 106 LUSC patients were assigned to MTS1 ( n = 30), MTS2( n = 1), MTS3 ( n = 22), MTS4 ( n = 59), MTS5 ( n = 15) and MTS7 ( n = 6); as each of patients in MTS6 held < 35% of the pixels, there was no patient were assigned . To achieve robust and substantial results, MTS2 ( n = 1) was also excluded from the further analyses in the consideration of statistical principle. 

The study design and pipeline are shown in . Supplementary Fig. S1A represents the high-resolution IHC images of immunological markers analyzed in this study. Supplementary Fig. S1B displayed the distinguished distributions of three typical metabolites in all patient tissues. Via the comparison of the minimum AIC values at each k , k = 7 with a threshold of 35% showed the lowest AIC value across all k values and thresholds, and was thus defined as the optimal value for the number of clusters and the threshold . One patient could be segmented into more than one cluster , which was due to the high metabolic heterogeneity of the patients. The spatial pixel distribution of overall peaks from 106 LUSC patients in 7 clusters were quantified as percentages and visualized in . In order to determine the clinical importance of each metabolomics cluster, the results of the metabolite segmentation had to be linked to the clinical data of the patients. By comparing with the AIC-guided threshold of 35% pixel presences, 106 LUSC patients were assigned to MTS1 ( n = 30), MTS2( n = 1), MTS3 ( n = 22), MTS4 ( n = 59), MTS5 ( n = 15) and MTS7 ( n = 6); as each of patients in MTS6 held < 35% of the pixels, there was no patient were assigned . To achieve robust and substantial results, MTS2 ( n = 1) was also excluded from the further analyses in the consideration of statistical principle. 

To further compare the heterogeneity status of individual patients, Simpson’s diversity index was applied to quantify each patient with an index value. The index distribution shown in indicated a diversity among patients’ heterogeneity. As metabolic heterogeneity increased, the correlation of heterogeneity with the metabolic pathways of nicotinate and nicotinamide metabolism, pyrimidine metabolism as well as tryptophan metabolism enhanced, while the correlation with the metabolic pathways of steroid hormone biosynthesis, amino sugar and nucleotide sugar metabolism, pantothenate and CoA biosynthesis as well as cysteine and methionine metabolism weakened . By correlating Simpson index to the expression of immunological markers, we found that the patients with higher Simpson index tended to have lower CD3+ and CD8+ TILs, while the expression of PD-L1 didn’t show a significant correlation . Metabolites usually interact with genes to regulate immune responses through a complex network of interactions. Integrative analyses, such as genomics, transcriptomics, proteomics, and metabolomics, help understand such kind of complex interactions. For that, by integrating the genomics data from a validation LUSC cohort ( n = 501) into our analysis, we identified 27 genes that may have functions in human by interacting with heterogeneity-correlated metabolites and meanwhile positively correlated with CD8+ TIL infiltration level in LUSC patients ( & Supplementary Fig. S2). Those heterogeneity-correlated metabolites include five negatively correlated ones and three positively correlated ones. Among the 27 infiltration-related genes, the LUSC patients with high expression of endoplasmic reticulum auxin binding protein 1 ( ABP1 ), phospholipase A2 group IB ( PLA2G1B ) and snail family zinc finger 1 ( SNAI1 ) led to a worse overall survival . Phosphatidylcholines (PC) as a metabolite that negatively associated with heterogeneity score were found to have an unfavorable impact on the PFS of LUSC patients in our study . Based on Simpson’s diversity index, 30 patients were stratified as high heterogeneity and 76 patients were as low heterogeneity . The Kaplan-Meier curves revealed that LUSC patients with high heterogeneity had significantly worse overall survival  and progression-free survival  compared to LUSC patients with low heterogeneity. Meanwhile, the patients with high CD3+ or CD8+ TIL were found to have better progression-free survival . Importantly, by multivariate Cox analysis, we found that the impact of metabolic heterogeneity on survival was stronger than the impact of TILs and independent . Given the negative correlation between metabolic heterogeneity with TILs, the metabolites co-abundant with CD3+ and CD8+ TILs in LUSC patients were identified to explore their roles in immune processes. The metabolite-immune network displayed that, the co- abundant metabolites in high heterogeneity mainly take part in the immune process of neutrophil and memory B cell; while in low heterogeneity mainly the immune process of erythrocyte, granulocyte, and microglia . Basophil activation was a shared correlated immune process in both high- and low-heterogeneity . The correlations of metabolic heterogeneity levels with clinicopathological features were investigated as shown in . To rule out potential confounding interaction between metabolic heterogeneity levels and clinicopathological features, we performed multivariate Cox analysis for PFS and OS . High metabolic heterogeneity, high UICC stages, and age equal or above 70 years old had a significantly unfavorable impact on OS and PFS. Other co-factors (sex, tumor size, distant metastases, and primary resection) did not affect PFS or OS . 

Followed by linking the clinical data of LUSC patients to the AIC-threshold MTSs, statistically significant differences in OS ( p = 0.019) were found between MTSs , but not in PFS ( p = 0.250) . In the pairwise comparisons for OS, statistically significant differences were observed between MTS1 versus MTS3 ( p = 0.042), MTS4 versus MTS5 ( p = 0.021), and MTS3 versus MTS5 ( p < 0.001), respectively . In the pairwise comparisons for PFS, statistically significant differences were observed between MTS3 versus MTS5 ( p = 0.027) . We next assessed the distributions of Simpson index and heterogeneity levels among MTSs, which indicated diversity of their heterogeneity . Correlation analysis then was used to investigate the immunological and clinical features of each MTS . MTS1 was positively correlated with CD3+ TIL, CD8+ TIL and tumor size. MTS3 was negatively correlated with age and primary resection status. MTS4 was positively correlated with CD3+ TIL and CD8+ TIL while negatively correlated with tumor size. MTS7 was positively correlated with PD-L1 and UICC stages while negatively correlated with primary resection status. No statistically significant correlation was observed for MTS5. Distribution of the certain TILs-correlated metabolites were compared between each MTS to investigate the further relationship between TILs and MTSs (Supplementary Fig. S3). Multivariate Cox analysis among 5 MTSs with clinical factors for PFS and OS  revealed that, MTS1 (CD3+CD8+) and MTS5 had significantly unfavorable impact on OS, while MTS1 (CD3+CD8+) and MTS4 (CD3+CD8+) had significantly unfavorable impact on PFS. MTS3 and MTS7 (PD-L1+) did not show independent prognostic potential. 

To compare the altered metabolic processes in MTSs, the metabolites closely associated with each MTS were subjected to metabolic pathway analysis, which in the meantime evaluated heterogeneity at the level of pathway. summarizes the major categories of identified pathways for five MTSs on the left and illustrated the detailed metabolic pathways and their correlations with each of five MTSs on the right. It is noteworthy that as independently prognostic factors, MTS1 (CD3+CD8+) exhibited positive association with carbohydrate metabolism and lipid metabolism; MTS4 (CD3+CD8+) and MTS5 exhibited negative association with carbohydrate metabolism and nucleotide metabolism, as well as positive correlation with amino acid metabolism and lipid metabolism. Next, we mapped the metabolites closely correlated with each MTS to the SMPDB database for getting an insight of association between metabolites and chemotherapy. As demonstrated in the MTS-metabolite-drug network , among all correlated metabolites, adenosine 5’-diphosphate (5’-ADP), adenosine 3‘,5’-diphosphate (3’,5’-ADP), uridine diphosphate (UDP), glutathione were observed to play roles in several anti-cancer drug pathways. In details, MTS3 was positively correlated with the pathways of sorafenib, tamoxifen, doxorubicm, docetaxel, erlotinib, vinblastine, paclitaxel, vindesine, vincristine and vinorelbine. MTS4 (CD3+CD8+) and MTS5 were negatively correlated with the pathways of sorafenib, tamoxifen, doxorubicm, docetaxel, erlotinib, vinblastine, paclitaxel, vindesine, vincristine, and vinorelbine. MTS7 (PD-L1+) was positively correlated with the pathways of cyclophosphamide . 

In this study, we explored tumor heterogeneity and immunometabolism in LUSC using high-resolution spatial metabolomics. We uncovered significant metabolic heterogeneity in LUSC tissues, finding that higher heterogeneity correlates with T cell suppression and poorer patient survival. By integrating genomics with metabolomics, we identified genes linked to metabolites and TILs, forming a metabolism-immune-gene connection. We defined five distinct metabolic tumor subpopulations, three of which were independently associated with poor prognosis. This is the first study to investigate the relationship between metabolic heterogeneity and the immunological landscape in LUSC. It is known that the tumor metabolic microenvironment can suppress T cells, as those isolated from tumors often show signs of exhaustion and distinct metabolic signatures. , In LUSC tissues, we observed a negative correlation between CD3+ and CD8+ TILs and metabolic heterogeneity. This suggests competition for nutrients in the tumor microenvironment, impairing TILs and aiding tumor progression. , , It could be a possible explanation for our result that LUSC patients with higher heterogeneity were found to contain less TILs and had worse survival. What’s more, metabolic heterogeneity showed an independent and stronger unfavorable impact on survival compared to TILs. Disruption of T cell activation due to altered tumor cell metabolism and other metabolic features in the TME indicates that this is an important mechanism for immunosuppression. On this basis, we can also reconsider that the constitutes of metabolic heterogeneity and immunity in subpopulations displaying distinct TIL features may enable the sensitivity or resistance to the specific therapy. It also implies that TILs are not distributed at random manner but closely associated with the metabolites constitution. Our comprehensive characterization of immunometabolism may provide new insights for further exploration of resistance mechanisms and optimization of immunotherapy strategies. The tumor immune microenvironment as characterized by infiltration of CD8+ TILs and PD-L1 expression has been associated with the efficacy of immune checkpoint inhibitors (ICIs). , A thorough interrogation of the immunological landscape is crucial for immunotherapy strategy selection and prediction of clinical responses in LUSC patients. We identified a metabolic tumor subpopulation, MTS4, which was positively associated with CD3+ and CD8+ TILs and showed independent prognostic value in LUSC. Our analysis suggests that ATP-ADP mediated catalysis and the effectiveness of erlotinib may be reduced in MTS4 (CD3+CD8+). Antibodies targeting PD1 or PD-L1 have achieved substantial overall survival improvements in advanced NSCLC, although major challenges still remain, including low response rate in unselected patients, lack of reliable predictive biomarkers, and identification of more immunotherapeutic targets. Additionally, our findings indicate that MTS7, a subpopulation positively correlated with PD-L1 expression, may show enhanced sensitivity to cyclophosphamide. A previous study concluded that the LUSC patients with high tumor PD-L1 expression tend to resistant to ICIs therapy. Taken together, for patients in MTS7 (PD-L1+), chemotherapy with cyclophosphamide could potentially be more effective than immunotherapy. Those findings may bring more insights for explaining the chemotherapy resistance mechanism of LUSC patients and improving the therapeutic strategies targeting different subpopulations. Recent studies have linked cancer heterogeneity to tumor growth and treatment response. , In our spatial metabolic profiling of LUSC, we identified distinct metabolic pathways associated with different heterogeneity levels and subpopulations. Increased metabolism of nucleotides including purine and pyrimidine is acknowledged a hallmark of cancer, supporting uncontrolled growth of tumors. , Notably, pyrimidine metabolism was enhanced in patients with higher heterogeneity, correlating with worse survival and T cell suppression. Nucleotide metabolism was positively correlated with MTS3 and MTS7 (PD-L1+) but negatively with MTS4 (CD3+CD8+). Interestingly, tryptophan metabolism pathway was observed a positive correlation with metabolic heterogeneity, which means that the LUSC patients with higher heterogeneity might have enhanced tryptophan metabolism. Increased tryptophan catabolism in TME can mediate immunosuppressive effect on T cells, while decreased tryptophan catabolism was associated with a marked increase in the tumor infiltration and proliferation of polyfunctional CD8+ lymphocytes. , As an independent prognostic factor, the subpopulation MTS5 in our study was found major abundant activities of amino acid metabolism and potentially resist to most chemotherapy drugs. Previously, activated amino acid metabolism was demonstrated to generate apatinib resistance by reprograming glutamine metabolism in NSCLC. Furthermore, a growing body of evidence now supports the concept that targeting nucleotide metabolism can increase the antitumor immune response and enhance cancer immunotherapy. While, MTS5 showed decreased activities of nucleotides metabolism and had no significant immune features observed. Taken above, the patients in MTS5 might face more unfavorable prognosis compared to other LUSC patients, due to a high possibility of resistance to chemotherapy and immunotherapy. Besides, we suppose that LUSC patients in MTS3 and MTS7 (PD-L1+) might show better response to the chemotherapy treatment that targets nucleotides metabolism, while the patients in the MTS4 (CD3+CD8+) with prognostic value and MTS5 might tend to be resistant to such treatment. In clinics, agents inhibiting synthesis and incorporation of nucleotides in DNA are widely used as chemotherapeutics to reduce tumor growth, cause DNA damage, and induce cell death. Therefore, considering the suppression effect of tryptophan catabolism on immune immunity, blocking tryptophan catabolism with indoleamine 2,3-dioxygenase (IDO) inhibitors could be considered a prioritized treatment strategy for the LUSC patients with high metabolic heterogeneity, or those patients in subpopulation MTS7 (PD-L1+) that was also positively correlated to tryptophan metabolism. Chemotherapy, radiotherapy, and targeted therapy alter tumor metabolism, and metabolic changes are often implicated in treatment resistance. , Cancer patients elicit very individualized responses to different treatments. This situation demands better characterization of the whole tumor ecosystem. That is to say, the identification and effective targeting of molecular alterations in more specific subtypes of LUSC patients could further improve the treatment efficiency. CD8+ TILs are key mediators of antitumor immunity and are strongly linked to survival in solid tumors. We identified several genes interacting with metabolites and CD8+ TILs by integrating metabolic profiling with genomics in LUSC. High expressions of PLA2G1B, SNAI1, and ABP1 were associated with high CD8+ TIL levels and worse survival in a validation cohort of 501 LUSC patients. PLA2G1B, a phospholipase, may modulate lung inflammation. , SNAI1, an EMT transcription factor, is linked to chemotherapy resistance. , ABP1, less studied in human diseases, was found a potential role in our study in LUSC involving metabolic tumor heterogeneity and immune infiltrating landscapes. An emerging view regarding cancer metabolism is that it is heterogeneous and context-specific. In our study, “metabolic heterogeneity” refers to the variation in metabolic activity that occurs within different regions of a tumor or between different cells within the same tumor, in particular lung tumor. The metabolic heterogeneity was reflected by the clustering among patient samples of lung cancer, and the complexity was further verified at the metabolic profiling level. This study highlights the existence of distinct metabolic tumor subpopulations within lung squamous cell carcinoma and each patient has a certain level of the metabolic profiling variation. These subpopulations or certain patients may exhibit different metabolic profiles, contributing to variations in immune cell infiltration, treatment response, and patient survival. This concept is central to understanding how different areas within a tumor may respond differently to therapies, especially those targeting metabolic pathways. This study has some limitations inherent to the methodologies used. Specifically, the IHC antibodies are incompatible with the MALDI-MSI matrix, making it impossible to perform the two experiments on consecutive tissue sections. As a result, precise co-registration between IHC and spatial metabolomics data could not be achieved. Furthermore, the lack of direct co-localization between tumor-infiltrating lymphocytes (TILs) and metabolite distributions limits the ability to draw definitive spatial conclusions. Despite these challenges, the combined use of IHC and spatial metabolomics provides valuable insights into the interplay between immune cells and metabolic pathways. Future studies leveraging advanced imaging techniques or alternative co-registration methodologies could further refine this analysis and overcome these limitations. Together, we provide a novel perspective for unraveling the metabolic heterogeneity and its association with immunology in LUSC. Our findings demonstrated that higher metabolic tumor heterogeneity foreshadows worse survival and revealed a strong connection between metabolic heterogeneity with TILs. The spatial metabolomics has opened the door to describe the complexity of lung cancer immunometabolism. Our comprehensive depiction of the tumor heterogeneity-related immune landscapes and definition of distinct metabolic subpopulations could help design personalized treatment for LUSC patients in clinical practice. 

Supplemental Material 

