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Hum Genomics
Hum Genomics
Human Genomics
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10.1186/s40246-024-00651-3
Research
Integrated multiomics revealed adenosine signaling predict immunotherapy response and regulate tumor ecosystem of melanoma
Xu Yantao 12346
Lau Poyee 12346
Chen Xiang 156
Zhao Shuang 12346
He Yi 12346
Jiang Zixi 12346
Chen Xiang chenxiangck@csu.edu.cn

1234567
Zhang Guanxiong guanxiong_zhang@csu.edu.cn

1234
Liu Hong hongliu1014@csu.edu.cn

1234567
1 grid.216417.7 0000 0001 0379 7164 Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
2 National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China
3 grid.452223.0 0000 0004 1757 7615 Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
4 grid.452223.0 0000 0004 1757 7615 Hunan Engineering Research Center of Skin Health and Disease, Changsha, China
5 https://ror.org/00f1zfq44 grid.216417.7 0000 0001 0379 7164 Xiangya School of Medicine, Central South University, Changsha, China
6 https://ror.org/00f1zfq44 grid.216417.7 0000 0001 0379 7164 Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China
7 grid.452223.0 0000 0004 1757 7615 Research Center of Molecular Metabolomics, Xiangya Hospital, Central South University, Changsha, China
15 9 2024
15 9 2024
2024
18 10122 3 2024
28 7 2024
© The Author(s) 2024
2024
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Extracellular adenosine is extensively involved in regulating the tumor microenvironment. Given the disappointing results of adenosine-targeted therapy trials, personalized treatment might be necessary, tailored to the microenvironment status of individual patients. Here, we introduce the adenosine signaling score (ADO-score) model using non-negative matrix fraction identified patient subtypes using publicly available melanoma dataset, which aimed to profile adenosine signaling-related genes and construct a model to predict prognosis. We analyzed 580 malignant melanoma samples and demonstrated its robust value for prognosis. Further investigation in immune checkpoint inhibitor dataset suggests its potential as a stratified factor of immune checkpoint inhibitor efficacy. We validated the power of the ADO-score at the protein level immunofluorescence in a melanoma cohort from Xiangya Hospital. More importantly, single-cell and spatial transcriptomic data highlighted the cell-specific expression patterns of adenosine signaling-related genes and the existence of adenosine signaling-mediated crosstalk between tumor cells and immune cells in melanoma. Our study reveals a robust connection between adenosine signaling and clinical benefits in melanoma patients and proposes a universally applicable adenosine signaling model, the ADO-score, in gene expression profiles and histological sections. This model enables us to more precisely and conveniently select patients who are likely to benefit from immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-024-00651-3.

Keywords

Adenosine signaling
Melanoma
Immunotherapy
Prognostic biomarkers
Tumor-immune interaction
Development Program of ChinaNo.2019YFA0111600 No.2019YFE0120800 Chen Xiang Liu Hong Key Program of National Natural Science Foundation of China82130090 U22A20329 Chen Xiang Liu Hong Science Found for Creative Research Groups of the National Natural Science Foundation of China82221002 Chen Xiang Central South University Research Program of Advanced Interdisciplinary Studies2023QYJC004 Zhang Guanxiong the Natural Science Foundation of China for outstanding Young ScholarsNo.82022060 Liu Hong The science and technology innovation Program of Hunan Province2022RC3004 Liu Hong issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Aberrant energy metabolism is one of the major hallmarks of cancer [1, 2] and profoundly affects tumor immune escape [3]. Adenosine, a key metabolite in energy metabolism, exerts immunosuppressive effects by interacting with various immune cell types in the tumor microenvironment (TME) [4–13]. It is presumed that high concentrations of adenosine in the tumor microenvironment contribute to tumor progression and are detrimental to patient survival [14, 15]. Upregulation of the key enzyme in adenosine biosynthesis, CD73, has been associated with poorer survival across multiple cancer types, including triple-negative breast cancer (TNBC) [16], prostate cancer [17], renal cell cancer [18] and others [19]. Mechanistically, adenosine can suppress the cytotoxic effector functions of both NK and CD8+ T cells via the adenosine receptor ADORA2A, leading to tumor immune evasion and escape [20]. Additionally, adenosine is essential for maintaining CD73 and programmed cell death protein 1 (PD-1) expression on Treg cells, which inhibits cytotoxic T cells [6]. Consequently, interventions targeting adenosine signaling are considered 'next-generation' therapeutics in immuno-oncology, and more than 200 related clinical trials are being conducted. Indeed, combining adenosine signaling intervention with anti-PD-1 therapy has demonstrated greater clinical efficacy than either treatment alone (Table S1, Fig. 1) [5, 21].Fig. 1 Study workflow

However, several lines of evidence have foreshadowed that adenosine signaling could exert antitumor effects. In endometrial cancer, adenosine produced by CD73 maintains epithelial integrity, while loss of CD73 leads to tumor progression and is associated with a poor prognosis [22]. Similarly, a significant association between CD73 and improved prognosis has been observed in patients with ovarian and breast cancers [19]. These studies also indicate the complex role of adenosine signaling in cancer progression and treatment. Furthermore, the development of several CD73-targeting therapeutic inhibitors has been discontinued due to potential limitations in achieving the expected therapeutic efficacy (e.g., NZV930 [NCT03549000], BMS-986179 [NCT02754141], and LY3475070 [NCT04148937]; see Table S1). Therefore, there is still a significant lack of predictive therapeutic biomarkers, which hampers the integration of adenosine signaling-targeted therapy into standard treatment protocols. In this study, we conducted an integrative analysis of both bulk and single-cell multiomics data, including genomic, transcriptomic, and clinical data, across multiple clinical cohorts with the aim of maximizing the benefits of immunotherapy in melanoma and exploring the potential of adenosine signaling as a biomarker for melanoma immunotherapy. Our findings revealed that adenosine signaling exhibits unique compositional specificity in the tumor microenvironment of metastatic melanoma and mediates aberrant interactions between tumor cells and immune cells.

Materials and methods

Collection of adenosine signaling genes from literature review

Adenosine signaling related signaling genes (n = 22) was generated from a comprehensive literature review (Table S4).

Transcriptomic datasets collection

Gene expression data, mutation data (MAF files), DNA methylation 450 K data, clinical information including overall survival, tumor stage, patient’s age and gender of 33 cancer types were downloaded from The Cancer Genome Atlas, Genomic Data Commons Data Portal (GDC DataPortal, https://portal.gdc.cancer.gov/). As one gene could have multiple methylation probes, we selected the probe which are the most negative correlated with the gene expression for corresponding genes [23]. The richness of T cell receptor/B cell receptor (TCR/BCR) were available at https://gdc.cancer.gov/about-data/publications/panimmune.

To validate the prognostic performance of adenosine signaling score (ADO-score), multiple skin cutaneous melanoma cohorts were acquired, consist of 8 treatment-naive and 3 immunotherapy cohorts (Table S3). These data were collected from GEO (https://www.ncbi.nlm.nih.gov/geo/), SRA database (https://www.ncbi.nlm.nih.gov/bioproject/) and dbGaP database (https://www.ncbi.nlm.nih.gov/gap/). For RNA-seq, we acquired the FPKM normalized data and for microarray we used quantile normalization. The processed melanoma scRNA-seq dataset GSE115978 and GSE72056 were collected from the Tumor Immune Single-cell Hub (TISCH; http://tisch.comp-genomics.org/) [24]. The spatial transcriptome dataset was acquired from R package BayesSpace (Version 4.0.4, http://www.bioconductor.org/packages/release/bioc/html/BayesSpace.html) [25].

Generating adenosine signaling score

The non-negative matrix factorization (NMF) was used to separate clusters in TCGA-SKCM metastasis samples. Cophenetic correlation coefficient maximization was used to select optimal cluster number for subsequent analyses with peaking for k = 3 (Fig. S4A). We performed differential expression analysis on variables (genes) selected though non-negative matrix factorization using R package limma [26] (Version 3.5.3) between cluster 1 (worse survival) and cluster 2 (better survival), cluster 3 (better survival) and cluster 2 merged with cluster 3. We selected genes that were significant in at least one differential analysis to build the model. This two-step strategy allows us to better capture the features of the three clusters. We defined upregulated adenosine signaling genes (diff_UP) as logFC > 0 and FDR < 0.05 in differential expression analysis in all comparison, including AK1, AK2 and NME1. Similarly, we defined down regulated adenosine signaling genes (diff_DN) as logFC < 0 and FDR < 0.05 in all comparison, including ENTPD2, PNP, ADA2, BST1, NT5E, CD38, and ADORA1 (Fig. S4B). We calculated the enrichment score (ES) of diff_UP and diff_DN using gene set variant analysis (GSVA) in the R package GSVA (Version 4.0.4) [27]. The Adenosine Signaling Score (ADO-score) was defined by difference of GSVA score between enrichment score of diff_UP minus diff_DN.ADO_score=ESdiff_UP-ESdiff_DN

Gene set variation analysis (GSVA) and pathways enrichment analysis

Enrichment score of 50 cancer hallmark for each tumor sample or cancer cell line was calculated by using gene set variation analysis (GSVA) [27]. The gene set "h.all.v6.1.symbols", "c2.cp.kegg.v6.2" were retrieved from MSigDB (Molecular Signatures Database, http://software.broadinstitute.org/gsea/msigdb/index.jsp) [28]. Pathway Enrichment analysis was performed by using R Package fgsea (Version 4.0.4) and clusterProfiler (Version 4.0.4) [29, 30].

Evaluation of immune feature

The immune cell abundance measurement was using TIMER [31], Xcell [32], EPIC [33], ESTIMATE [34], MCP-counter [35] and quanTIseq [36] for evaluating tumor infiltration immune cells (TIICs) utilizing deconvolution and geneset scoring. We evaluated the relative abundance from 6 to 64 types of immune and non-immune cells in independent melanoma datasets and pan-cancer level with 33 cancer types, with RNA-seq data normalized and microarray data quantile normalized. GEP level in each sample was computed based on the GEP gene signature from Ayers et al by performing gene set variation analysis (GSVA) [37]. GEP level in each sample was computed based on the geometric mean of the gene expression of two cytolytic markers (GZMA and PRF1) [38].

Inhouse validation cohort collection

For IF staining, Melanoma patients’ paraffin sections that responded or non-responded to PD-1 mAb (Toripalimab) were collected from Xiangya Hospital, Central South University. All tissue samples were obtained in compliance with informed consent policy. The study protocol was approved by the Institutional Review Board and ethics committee of Xiangya Hospital, Central South University. Clinical information is summarized in Table S2.

Fluorescent multiplex immunohistochemical assay and analysis

Paraffin Sects. (4 μm) from formalin-fixed human melanoma samples were baked at 60℃ for 120 min, and then deparaffinized and rehydrated through ethanol. Heat mediated antigen retrieval was performed with Tris–EDTA antigen retrieval buffer (pH 9.0) or citrate antigen retrieval solution (pH 6.0) according to the manufacturer’s instructions of primary antibodies. Multiplex staining was conducted by primary antibodies with subsequent antibody detections using Opal anti-Ms + Rb HRP reagents, and corresponding fluorophores as well as DAPI nuclear counterstain were applied following the manufacturer’s instruction of Opal 6-Plex Manual Detection Kit (NEL811001KT, PerkinElmer). Primary antibodies targeting group one (AK1, AK2, NME1, SOX10), group two (CD3, ADORA1, ENTPD2/CD39L1, CD38), group three (ENTPD2/CD39L1, ADORA1, CD68, CD38), or group four (CD20, ADORA1, ENTPD2/CD39L1, NT5E/CD73) were incubated for 30–60 min at 4℃ in the blocking solution (Table S5). The slides were scanned and the images were captured fluorescent imaging mode by the Phenolmager HT™ Automated Quantitative Pathology Imaging System (PerkinElmer). To identify all target markers in a single image, each multispectral image was then separated into individual components by the spectral library using inForm® tissue analysis software (Version 2.6; Akoya Biosciences, lnc). All multispectral images of sections with spectral unmixing and segmentation were further subjected to create a phenotyping algorithm based on machine active learning of inForm® software. Phenotype quantifications and analyses were conducted blinded to the samples and outcomes by phenoptr and phenoptrReports R packages (Version 0.3.2) [39].

ADO-score was calculated with mIHC (multiplex immunohistochemistry) data based on the same idea that the score should represent the crosstalk between tumor and immune cells. In this situation, mIHC data were limited by the throughput, and GSVA method was not applicable. To address this issue, we calculated the ratio of tumor and immune components. In detail, the colonization between AK1+, AK2+ or NME1+ and SOX10+ cells were enrolled and divided with the count per slide of SOX10+ cells, representing the expression of AK1, AK2 and NME1 in tumor cells. Similarly, CD73+/CD20+, ADORA1+/CD68+, ENTPD2+/CD3+, CD38+/CD3+ were corrected and represented the expression level of each gene in B cells, Macrophages and T cells.

Single cell RNA-seq and spatial transcriptome analysis

We used the R package “Seurat” [40] (Version 4.0.4) to perform single-cell transcriptomic analysis for GSE115978 [41] and GSE72056 [42]. Uniform manifold approximation and projection (UMAP) was used in scRNA-seq for dimension reduction and visualization. The ADO-score in single-cell level was also calculated using gene set variant analysis (GSVA) in the R package “GSVA” (Version 1.50.5) [27]. As for spatial sample, we obtained and preprocessed the melanoma spatial sample using R package “BayeSpace” (Version 1.12.0) [25].

Cellular interaction query

CellChat [43], NATMI [44] and Nichenet [45] were used to estimate the cellular interaction. We calculated the ADO-score of malignant cells in GSE115978 to identify the adenosine signaling strength. We used the median ADO-score to divide the high and low group of adenosine signaling. The differential signaling and L-R pairs of each group were filtrated with P < 0.05 and Log2FC > 0.

Statistical analysis

Wilcoxon rank sum test and Kruskal–Wallis test was used to compare the differences. Spearman correlation was utilized to calculate the correlation coefficient and significance between adenosine signaling genes, ADO-score and caner hallmarks, immune cell infiltration score, immune checkpoint genes expression, GEP/CYP score. Survival analysis was performed by R package “survival” (Version 4.0.4). High and low ADP-score was stratified by median value. HR was calculated by Cox proportional hazards model and 95% confidence interval (CI) was reported, and Kaplan–Meier survival curve was modeled by average ADO-score and expression of adenosine signaling genes. The two-sided long-rank test was used to compare Kaplan–Meier survival curves. All statistic significant levels were p < 0.05.

Results

Adenosine signaling as a prognosis factor of melanoma

To identify effective biomarkers and therapeutic targets that could be used to improve the efficacy of adenosine signaling-targeted treatment, we focused on a set of adenosine signaling targets from ongoing clinical trials to evaluate patient survival (Table S1). In addition to expand the pool of candidate biomarkers, we collected these highly actionable targets and other adenosine signaling-related genes from a literature review (Table S4) [46–49]. To characterize the roles of adenosine signaling in melanoma, we (1) applied nonnegative matrix factorization (NMF) algorithms to classify each patient into an adenosine signaling subtype group, (2) modified and derived an adenosine signaling score (ADO-score) model, and (3) validated the model in 7 independent public and in-house metastasis melanoma and immunotherapy datasets covering multiple omics datasets. We found that decreased adenosine signaling mediates aberrant interactions between tumor immune cells and potentially reshapes the TME (Fig. 1).

Initially, we analyzed the changes in adenosine signaling at the pan-cancer level (see Supplementary Material). Subsequently, we classified 352 metastatic melanoma samples from the TCGA-SKCM cohort into 3 subtypes based on the expression levels of adenosine signaling-related genes (Fig. 2A, Fig. S4A, with optimal k = 3). Patients in the subtype 1 group (n = 157) exhibited worse survival than did those in the subtype 2 (n = 62) and subtype 3 (n = 133) groups, with no significant differences between Clusters 2 and 3 (Fig. 2B, log-rank test, p = 5.7 × 10–7). Interestingly, the genes significantly overexpressed (labeled diff_UP) in subtype 1 were mainly associated with the metabolic enzymes ATP/ADP (e.g., NME1 and AK1), while those in subtype 2 or subtype 3 were involved in the synthesis, degradation and signal transduction of adenosine (labeled diff_DN; e.g., NT5E, ENTPD2, ADORA1, CD38 and ADA2). Based on these distinct expression patterns, we further developed the adenosine signaling score (ADO-score), calculated as the enrichment score (ES) of diff_UP minus that of diff_DN, to quantify the adenosine signaling status of individual samples (Fig. S4B, see Method). The ADO-score accurately reflected the individual characteristics of the NMF subpopulations and the inherent co-expression patterns of adenosine genes (Fig. S4C and S4D). For prognosis, we found that Cluster 1, which had the worst survival, exhibited a significantly greater ADO-score than did Cluster 2 and Cluster 3 (Fig. S5A, Kruskal‒Wallis test p = 2.9 × 10–36).Fig. 2 Clinical relevance of adenosine signaling in metastasis melanoma. A The landscape of adenosine signaling genes in metastasis melanoma between clinical factors in three adenosine metabolism subtypes stratified by NMF. B Kaplan–Meier curves of overall survival in TCGA-SKCM metastasis melanoma in three adenosine metabolism subtypes. C–E Kaplan–Meier curves of overall survival in 3 independent metastasis/stage IV melanoma datasets in high and low adenosine signaling group stratified by median ADO-score (D: GSE54467; E: GSE19234; F: GSE22155). F The ADO-score level various in response status in independent melanoma immunotherapy transcriptomic cohort GSE91061. G Overall survival in independent melanoma immunotherapy transcriptomic cohort GSE91061

The Univariate Cox hazard analysis and Kaplan‒Meier curves from the TCGA cohort showed that the ADO-score could be a prognostic factor for metastatic melanoma (Fig. S5B, hazard ratio (HR) = 1.7, 95% CI [1.2–2.2]; log-rank test, p = 6.7 × 10–4). Multivariate Cox regression analysis further revealed the independent prognostic value of the ADO-score in metastatic melanoma (Fig. S5C). The robustness of the ADO-score was validated using 3 independent metastatic/stage IV melanoma microarray datasets (GSE54467 [50], GSE19234 [51], and GSE22155 [52]) generated by microarray analysis, and corresponding clinical prognostic information were available. Consistently, the high ADO-score group presented worse OS that the low ADO-score group in the univariate Cox analysis (Fig. 2C–E; GSE54467: HR = 1.8, 95% CI [1–3.2], p = 4.7 × 10–2; GSE19234: HR = 3, 95% CI [1.2–7], p = 7 × 10–3; GSE22155: HR = 1.7, 95% CI [0.95–3.2], p = 7.3 × 10–2) and the multivariate Cox analysis (Fig. S5C; GSE54467: HR = 1.3, 95% CI [0.7–2.4], p = 0.37; GSE19234: HR = 3.7, 95% CI [1.4–9.6], p = 6 × 10–3; GSE22155: HR = 2.1, 95% CI [0.9–4.8], p = 7 × 10–2).

Drug therapy (both targeted therapy and immunotherapy) is a critical component of melanoma treatment. To investigate the ability of the ADO-score in immune checkpoint blockade (ICB) efficacy, we further evaluated the value of the ADO-score in stratifying immunotherapy efficacy in cohorts with published immunotherapy response and transcriptomic data (Fig. 2G, H) [53, 54]. The responders treated with nivolumab had significantly lower ADO-score values than the non-responders (GSE91061: Wilcoxon test, p = 0.05; Fig. 2F). Patients with high ADO-score value had worse overall survival (OS) (GSE91061: log-rank test, p = 0.014; Fig. 2G). These results indicate that the ADO-score is a prognostic biomarker for melanoma. To further evaluated the effect of ADO-score in melanoma chemotherapeutic response, we selected common targeted therapy for melanoma (including BRAF and MEK inhibitor) for sensitivity analysis. The results show that lower ADO-score patients exhibited significantly better dabrafenib and trametinib sensitivity (Figs. S5D, S5E). Therefore, dabrafenib and trametinib may be more effective in treating patients with low ADO-scores.

The cell-type specificity of adenosine signaling protein expression is associated with the prognosis of melanoma patients receiving PD-1 mAb therapy

To further validate the phenomenon that we observed in transcriptome and to clarify the ability of adenosine signaling components in clinical biopsy sections to act as biomarkers, we performed immunofluorescence staining on the collected pathological sections. We collected clinical biopsies of 19 melanoma patients treated with PD-1 mAb therapy (toripalimab; Fig. 3A, B and Table S4) from Xiangya Hospital and quantified adenosine signaling-related gene expression using a fluorescent multiplex immunofluorescence (multiplex IF) assay (for example, for ADORA1, ENTPD2/CD39, NT5E/CD73, CD38, AK1, AK2 and NME1). We use CD3, CD20, CD68 and SOX10 to identify the T cells, B cells, Macrophages and Mali Analysis of the immunofluorescence results demonstrated that the expression of AK1 and NME1 was significantly positively correlated with the expression of SOX10 (AK1: R = 0.63, p = 0.0046, NME1: R = 0.52, p = 0.025; Fig. 3C), while the expression of CD38 and ENTPD2 was significantly positively correlated with the expression of CD3 (CD38: R = 0.52, p = 0.024, ENTPD2: R = 0.74, p = 0.00043; Fig. 3D), suggesting potential co-expression pattern. Multiplex IF microscopy analysis indicated that AK1, AK2 and NME1 could be detected in SOX10-positive tumor cells, and NT5E, ENTPD2, and ADORA1 were detected in the surrounding CD20+ B-cell compartment. In addition, cell surface-bound CD38, ENTPD2 and ADORA1 were expressed in both tumor-infiltrating CD68+ macrophages and CD3+ T cells (Fig. 3E). We observed an interesting pattern in which adenosine signaling molecules had a spatial distribution bias in terms of their cell type specificity, and this specificity seems to represent the crosstalk between tumor cells and immune cells in the tumor microenvironment mediated by adenosine signaling.Fig. 3 The immunofluorescence and tissue specificity of adenosine signaling genes is related to the prognosis of melanoma patients with anti-PD1 therapy. A Overview of melanoma patients with anti-PD1 monotherapy IF staining cohorts. All patients were treated with PD1 monoclonal antibody after surgical resection of primary lesions. See also Table S1. B Clinical status statistics of Inhouse melanoma anti-PD1 monotherapy IF staining cohort. C Correlation of adenosine signaling genes AK1 and NME1 with malignant cell marker SOX10 in IF staining slides. D Correlation of adenosine signaling genes CD38 and NNTPD2 with T cell marker CD3 in IF staining slides. E Biopsy specimens obtained from melanoma samples including responders (No.13, No.19) and non-responders (No.4, No.18) were examined and visualized by multiplex IHC. With this staining technique, visible structures include tumor cell panel labeled with DAPI (blue), SOX10 (yellow), AK1 (red), AK2 (green), NME1 (magenta), macrophage panel labeled with DAPI (blue), CD68 (white), CD38 (red), ENTPD2 (magenta), ADORA1 (orange), B cell panel labeled with DAPI (blue), CD20 (cyan), NT5E (red), ENTPD2 (magenta), ADORA1 (orange), and T cell panel labeled with DAPI (blue), CD3 (green), CD38 (red), ENTPD2 (magenta), ADORA1 (orange). Majority of positive cells are located in the yellow dashed box and highlighted with white arrows. DAPI is a nuclear counterstain. Scale bars, 50 μm. F Kaplan–Meier survival curve of melanoma patients’ progression-free survival (PFS) following anti-PD1 therapy grouped by adenosine signaling

After observing the specificity of adenosine-related gene expression in different cells within the cancer immune microenvironment, we further explored the clinical translational value of these genes. Considering the functional relevance of adenosine-related genes in adenosine metabolism, we further assessed whether the ADO-score could represent the celltypes specificity patterns and interaction between tumor and immune cells. (See Materials and Methods). Overall, protein tissue analysis reflected the ADO score derived from gene expression. Meanwhile, we observed that patients receiving PD-1 blockade therapy with low ADO-score values exhibited prolonged progression-free survival (HR = 3.1, 95% CI [1.1–8.8], p = 0.027; Fig. 3F). The above phenomenon suggests that the distinct expression patterns of adenosine-related genes on immune cells and tumor cells can be used to differentiate the response status of patients to immunotherapy. Taken together, our results showed that adenosine signaling might mediate crosstalk between tumor cells and immune cells and could be a prognostic factor in melanoma patients.

Deciphering adenosine signaling at the single-cell and spatial transcriptomics levels

To gain more insight into the utility of the ADO-score model, we investigated in the different adenosine signaling-related genes and ADO-score at the single-cell level. We evaluated the adenosine signaling status of each cell in the melanoma single-cell transcriptomic dataset (GSE115978 and GSE72056) using the same method mentioned above (see Methods). We observed the highest ADO-score value in malignant cells, followed by fibroblasts, and the lowest value was exhibited in immune cells (Fig. 4A–C and D–F). To our surprise, the genes from the diff_UP and diff_DN gene sets were specifically expressed in melanoma and immune cells, respectively (Fig. S6A and S6B). The genes from the diff_UP gene set (AK1, AK2 and NME1) were all generally related to the transformation of adenosine phosphate compounds, such as the ATP-ADP transformation, while the genes from diff_DN mainly participated in adenosine signaling activation and adenosine metabolism through the transformation of adenosine phosphate compounds, including ATP, ADP and AMP, into adenosine in the melanoma microenvironment. In addition, the genes from the diff_DN gene set also exhibited cell type-specific expression (Fig. S6C and S6D). CD38 exhibited the highest expression in macrophages, while NT5E was expressed in B cells and stromal cells, especially in fibroblasts. These results implied that the ADO-score represents the crosstalk between melanoma cells and immune cells centered on energy metabolism.Fig. 4 Distribution and functional association of the ADO-score in single cell and spatial transcriptomics level. A UMAP embedding of single-cell RNA-seq profiles from GSE115978. B UMAP plot show ADO-score profiles of whole tissue cells from GSE115978. C Boxplot and dotplot show the difference and percentage of ADO-score in different cell types in melanoma form GSE115978. D UMAP embedding of single-cell RNA-seq profiles from GSE72056. E UMAP plot show ADO-score profiles of whole tissue cells from GSE72056. F Boxplot and dotplot show the difference and percentage of ADO-score in different cell types in melanoma form GSE72056. G Spatial enhanced-resolution clustering performed by the BayesSpace algorithm identified four clusters corresponding to the original histopathological annotations. H Spatial Feature plot shows the difference of ADO-score profile among four clusters at the enhanced-resolution condition. I Violin plot show the difference of ADO-score in different cell types among four clusters at the enhanced-resolution condition. J, K The biological functions (J cancer hallmarks and K Reactome biological pathways) divergence for malignant cells with different adenosine signaling states stratified by ADO-score

We further examined these phenomena by integrating spatial information. We first acquired spatial transcriptomic data from melanoma [55] and applied the BayesSpace algorithm to obtain higher resolution images of malignant cells (expressing PMEL), T cells (expressing CD2 and CD3D), B cells (expressing CD19), fibroblasts (expressing COL1A1) and macrophages (expressing C1QB). The annotation clustering results revealed that malignant (red region) and T/B cells (blue region) were separated into stromal (orange region) and macrophage (green region) populations (Fig. 4G). Similarly, the distribution of the ADO-score showed a distinct pattern among the different regions. The ADO-score was greater in the red region, which included a significantly higher proportion of malignant cells than stromal cells, immune cells and macrophages (Fig. 4H, I).

We further characterized the biological functions of malignant cells in different adenosine signaling states stratified by the ADO-score. In the high-ADO-score group, we observed significant enrichment of metabolic and proliferation hallmarks, including oxidative phosphorylation, fatty acid metabolism, the G2M checkpoint, spermatogenesis and MYC targets, while in the low-ADO-score group, we observed enrichment of signatures related to antitumor immunity, including TNF-alpha signaling, promotion of the inflammatory response and interferon alpha/gamma response, activation of the complement cascade and malignant cell apoptosis (Fig. 4J and S6E). According to the biological pathway enrichment results from Reactome, malignant cells with low ADO-score values were enriched in immunoregulatory interactions between a lymphoid cell and a non-lymphoid cell and between the complement cascade and interferon gamma signaling, while tumor cells with high ADO-score values were enriched in several metabolic reprogramming and energy metabolism pathways. The citric acid cycle, respiratory electron transport and ATP synthesis were also evaluated (Fig. 4K and S6F). All of these findings indicate the heterogeneity of adenosine signaling in the tumor microenvironment; in particular, malignant cells with different adenosine signaling states exhibit distinct biological functions.

Biological effects of adenosine signaling in melanoma

To investigate the potential effects of adenosine signaling, we performed a comprehensive analysis of oncogenic and immune features, including cancer hallmarks, immune cell infiltration, immune checkpoints, tumor mutation burden (TMB), the T-cell-inflamed gene expression profile (GEP), cytolytic activity (CYT), PD-L1 protein level, B-cell receptor (BCR) enrichment, T-cell receptor (TCR) enrichment, interferon gamma (IFN-gamma) response and the tumor-infiltrating lymphocyte (TIL) regional fraction, in the TCGA-SKCM and 7 independent melanoma datasets (Figs. 5, S7). Generally, we found that the ADO-score was negatively correlated with immune features and positively correlated with the enrichment of oncogenic pathways. Immune cell infiltration was estimated with multiple algorithms (including ssGSEA estimation for immune cell score, EPIC, ESTIMATE, MCP-counter, quanTIseq and Xcell), and the ADO-score was found to be negatively correlated with immune infiltration cells in multiple independent datasets (Fig. 5A, S7-F). We further collected 40 druggable targets or potential biomarker candidates among immune checkpoint genes (Table S6). The ADO-score was negatively correlated with the levels of nearly all immune checkpoint genes, implying the presence of a potentially inhibitory immune microenvironment (Fig. 5B).Fig. 5 Biological effects of ADO-score in metastasis melanoma. A Correlation between ADO-score and immune cell infiltration estimated by TIMER in 8 independent melanoma datasets. B Correlation between ADO-score and immune checkpoints in independent melanoma datasets. C–F The correlation between ADO-score with known immune therapy prognosis factors (C Tumor mutation burden, D CYT scores, E GEP score. F PD-L1 expression). G–J Difference between high and low ADO-score in immune repertoire (G BCR richness, H TCR richness, I IFN-γ response, J TIL region fraction)

Immunotherapy is the latest treatment for melanoma patients, while the efficacy exhibited much heterogeneity. Patient stratify using promising biomarkers is critical for therapeutic approaches selection. Several predictive biomarkers for immunotherapy efficacy, including CYT andGEP, also exhibited rather strong significant negative correlations with the ADO-score but not with PD-L1 and TMB (Fig. 5C–F). In addition, the diversity of the immune repertoire was also significantly greater in the low-ADO-score group than in the high-ADO-score group; the IFN-gamma response was significantly enriched in the low-ADO-score group, but the difference in the proportion of TILs was not significant (Fig. 5G–J). For 50 pathways related to cancer hallmarks, we also revealed that the ADO-score was negatively correlated with the enrichment of immune-related pathways (e.g., interferon-alpha and interferon-gamma response) but positively correlated with the enrichment of oncogenic pathways (e.g., MYC target; Fig. S7A). Together, our results demonstrated the relationship between adenosine signaling and the immune microenvironment and implied that patients with lower ADO-score values are more likely to have a “hot” tumors microenvironment, while patients with higher ADO scores may have a “cold” tumor microenvironment. The ADO-score may be a promising indicator of “cold” tumor immune micro-environment (TIME) because of the strong negative association between ADO-score and the levels of both activating and inhibitory immune checkpoints.

Malignant cells with different adenosine signaling pathways exhibit distinct cell communications in the tumor microenvironment

We further explored the differences in cellular crosstalk mediated by malignant cells with high and low adenosine signaling in GSE115978 with CellChat [43]. Overall, disparate communication signals were observed between malignant cells in the high ADO-score group and those in the low ADO-score group (Fig. 6A: upregulated signaling in the high ADO-score group; Fig. 6B: upregulated signaling in the low ADO-score group). In the high ADO-score group, we observed significant upregulation of FN1, which is a ligand that activates integrin (ITG) superfamily NOTCH signaling among nonmalignant cells, especially fibroblasts (Figs. 6A; S8A and B). Tumor cancer cell migration and invasion are potentially involved, especially in generating lymph angiogenesis and tumor cell colonization and triggering invasive protrusions and pro-invasive EMT signaling [56–59]. The other major high-ADO-score malignant signaling pathways shaping the immunosuppressive microenvironment were MDK and MPZL1 (Fig. 6A). MDK is involved in promoting immunosuppressive macrophage differentiation and endothelial tube formation [60, 61], and MPZL1 has been shown to have an oncogenic function by promoting cell proliferation, migration and invasion but inhibiting cell apoptosis [62, 63]. The relative information flow also revealed several predominant cancer signaling pathways, including the CADM, VEGF and MPZ signaling pathways, in the high ADO-score group (Fig. S8C). For specific ligand–receptor pairs, malignant cells in the high-ADO score group could interact with endothelial cells and fibroblasts via VEGFB–VEGFR1, supporting the use of antiangiogenic drugs (Fig. 6C). The activation of cell‒cell signaling mediated by the COL1A2–ITG family implies a greater risk of tumor invasion [64].Fig. 6 Distinct cellular communications associated with adenosine signaling in malignant cells. A, B Circos plot of the cellular communications associated with high and low ADO-score. Showing only significantly differentiated signaling pathway using Cellchat. C Volcano plot and lollipop plot show activated and top 10 activated LR-pairs associated with high and low ADO-score using NATMI. D The functional enrichment of top targets inferred by Nichenet for CD8T cells affected by different adenosine signaling

Conversely, the upregulated signals in malignant cells with low ADO-score values were associated mainly with type I HLA signaling between malignant cells and Tex and Tprolif cells (Fig. 6B). For malignant cells in the low ADO-score group, we also found stronger interactions with CD8 Tex cells than with those in the high ADO-score group via HLA-A/B/C-CD8A/B pairs, mediating the CD8+ T-cell-dependent killing of cancer cells by efficient presentation of tumor antigens [65] (Figs. 6B and S8D). Moreover, HLA molecules also communicate with KIRK1, which encodes NKG2D and is constitutively expressed on CD8+ T cells, to authenticate the recognition of a stress-induced target and enhance TCR signaling [66]. Conversely, decreased expression of HLA class I molecules on tumors and impaired signal transduction may facilitate tumor immune escape [67], which to some extent explains why patients with high ADO-score values exhibit poorer survival and immunotherapy resistance.

To further confirm the differences in cellular communication between malignant cells with high and low adenosine signaling and to comprehensively characterize their tumor–immune interactions, we further applied NATMI [44] to identify the unique activated ligand‒receptor interactions between malignant cells with high adenosine signaling and those with low adenosine signaling. Similar to the CellChat results, in the malignant cells with high adenosine signaling, the primary ligand‒receptor interactions predominantly occurred between the tumor and stromal cells (fibroblasts and endothelial cells; Fig. 6C, left upper panel), whereas in the low-adenosine signaling group, more interactions between tumor cells and immune cells (including macrophages and T cells; Fig. 6C, left lower panel) occurred. In the high ADO-score group, the activated pathways, which involved pathways such as ANGPT1, EGFR, and integrins, primarily contributed to angiogenesis, cell adhesion, and tumor invasion and metastasis [68, 69]. In contrast, in the low ADO-score group, significant complement activation-mediated interactions were observed, which may exert antitumor effects [70].

To further investigate the potential functional effects of divergent adenosine signaling states on T cells, we identified the target genes of the 20 most differentially expressed receptors on CD8Tex cells and performed functional analysis of using NanoNet [45] (Figs. 6D, S8E). Multiple pathways involved in T-cell activation, chemotaxis, antigen recognition and cytotoxic functions, such as the TNF signaling pathway, T-cell receptor signaling pathway, leukocyte transendothelial migration pathway and chemokine signaling pathway, were enriched (Fig. 6D). Additionally, recent studies have suggested that the IL-17 signaling pathway is associated with CD8+ cytotoxic T-cell infiltration and cytotoxic function via the ICOS-ICOSL interaction [71].

In summary, malignant cells with high ADO-score values may contribute to cancer progression via communication with endothelial cells and fibroblasts, while malignant cells with low ADO-scores prohibit cancer progression by communicating with different adenosine signaling pathways, which results in more frequent communication with immune cells.

Discussion

Accumulated evidence has demonstrated that adenosine can induce a series of biological changes, mainly shifting the cytokine and cellular profile of the tumor microenvironment away from cytotoxic T-cell inflammation toward immune tolerance, contributing to tumor immune escape [13]. Thus, the role of adenosine in cancer has received extensive attention, and the number of clinical trials targeting adenosine signaling pathways has increased exponentially. However, many clinical trials targeting adenosine signaling have not achieved the expected results, possibly due to the significant heterogeneity in the function of adenosine among different cancer types [15]. Thus, further detailed studies of individual cancers should be carried out to determine the functions of adenosine signaling. Despite the critical roles of adenosine in immune regulation, there is a lack of systematic studies on adenosine metabolism in melanoma, the frontier of checkpoint inhibition immunotherapy. In this study, we constructed an ADO-score model to represent the adenosine signaling status in more than 500 melanoma patients. The ADO-score model showed potential prognostic power: the high ADO-score group had worse clinical outcomes in multiple melanoma cohorts. We further validated the power of the ADO-score in a real-world melanoma cohort from Xiangya Hospital. Importantly, we showed that the ADO-score represents cross-talk between tumor cells and immune cells. Overall, our results suggest that adenosine signaling status can be regarded as an adequate predictor of the clinical outcome of melanoma patients and that adenosine signaling plays important roles in the regulation of the tumor microenvironment at multiple omics levels by affecting the crosstalk between tumor cells and immune cells in melanoma.

Previous studies have focused mainly on the latter part of the adenosine signaling pathway, the CD73–CD39 axis, which is closely related to adenosine production and exerts an immunosuppressive effect. Intuitively, high expression of NT5E may be associated with a high level of adenosine, which in turn leads to immune suppression and a worse prognosis. Indeed, numerous clinical trials have attempted to target CD73 for the treatment of solid tumors (i.e., NCT03954704, NCT04262375, NCT03549000, and NCT04262388), but the results have not been satisfactory. However, we found that patients with high expression of NT5E had a better prognosis than patients with low expression of NT5E among patients with melanoma treated with or without immunotherapy (Figs. 6A, B; Fig. S5A, S6B). To resolve this paradox, one key question is which cells contribute to the high expression of adenosine-related genes. By analyzing multiple single-cell and spatial transcriptomic data, we found that this unexpected observation may be due to the cell type-specific expression of adenosine signaling-related genes and to the occurrence of a cascade of bioprocesses involving multiple cell types. According to the single-cell data, AK2 and NME1, which catalyze the mutual conversion of ATP and ADP, were overexpressed in malignant cells, while NT5E, ENTPD-1, CD38 and ADA2, which catalyze the conversion of ATP to adenosine, were overexpressed in immune cells (e.g., T cells and macrophages) and stromal cells (e.g., fibroblasts and endothelial cells). According to previous studies, hypoxia, inflammation or ischemia can promote the release of excessive ATP by malignant cells through direct (including mechanical stress or cell destruction) or indirect approaches (including transporter- or channel-mediated release and active vesicular exocytosis) [6, 14, 72]. Excessive extracellular ATP promotes tumor immune responses and promotes immune cell infiltration in the TME, but the degradation metabolite adenosine has immunosuppressive effects on the TME [4–6, 72, 73]. On the basis of our data, we discovered a new model of adenosine signaling in the melanoma TME, which provides a more comprehensive depiction of the overall landscape of adenosine metabolism compared to other models, and more likely to involve purinergic metabolism dominated by the proinflammatory and antitumor functions of up-streamed metabolite ATP; malignant cells produce ATP to recruit and activate immune cells, while membrane ectonucleotidases on immune cells dephosphorylate ATP and ADP to adenosine to suppress immune cells from overactivation. Similar to this model illustrated in cancer, adenosine and purinergic metabolism were found to regulate the pro- and anti-inflammatory balance in inflammatory diseases by regulating the recruitment and expulsion of immune cells [74, 75]. This model might also explain why adenosine signaling status is a different in immunotherapy. Although excessive ATP in the TME drives the infiltration of abundant immune cells, the secondary generation of adenosine leads to temporary immunosuppression, which can be relieved by immune checkpoint inhibitors such as those targeting PD-1 and PD-L1. Moreover, our findings also indicate that patients with high ADO-score values are good candidates for combined treatment with adenosine signaling inhibitors (such as CD73i) with immune checkpoint inhibitors, which could yield better clinical outcomes than immunotherapy alone.

In addition, we found that several genes may be worthy of further research and drug development by integrating single-cell and multi-omics data. In melanoma, genes associated with ATP/ADP biogenesis (including NME1 and NME2) were specifically expressed in malignant cells. Furthermore, these genes also exhibited consistent expression patterns and clinical relevance across most cancer types (see Supplementary Materials). Moreover, we found that these genes were significantly correlated with cancer hallmarks and immune features (i.e., G2M checkpoint, glycolysis, MYC targets, and oxidative phosphorylation reactive oxygen species pathways), further supporting the important role of these genes. In addition, our study is the first to comprehensively characterize each adenosine-related gene at the multi-omics level, providing clues for further investigation and drug development.

ADO-score has the potential to be applied in clinical settings for patient stratification. Lower ADO-scores may correspond to better patient survival and immunotherapy efficacy. Such patients may benefit from monotherapy or combination immunotherapy, or even neoadjuvant immunotherapy. On the other hand, higher ADO-scores may indicate that the tumor is in a "cold" state. For patients with high ADO-score, approaches to activated the tumor immune microenvironment, such as tumor vaccines or oncolytic virus therapy, may be the first to consider. This study also has its limitations. The in-house cohort we use is a retrospective cohort. Since only a very small number of patients' frozen tissue samples are preserved, we proceeded with further validation of ADO-score only through multicolor immunofluorescence.

In conclusion, the ADO-score signature delineated in this study emerges as a pivotal clinical tool. It not only underscores the adenosine signaling within TME as a critical mediator in orchestrating an efficacious antitumor immune response but also highlights its prognostic capability. This is particularly pertinent in the context of existing immunotherapies, where quantifying the adenosine drive may offer significant predictive value.

Supplementary Information

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Abbreviations

ADO-score Adenosine signaling score

TME Tumor microenvironment

GSVA Gene set variation analysis

NMF Non-negative matrix factorization

ES Enrichment score

UMAP Uniform manifold approximation and projection

mIHC Multiplex immunohistochemistry

HR Hazard ratio

ICB Immune checkpoint blockade

OS Overall survival

PFS Progression-free survival

TMB Tumor mutation burden

GEP Gene expression profile

CYT Cytolytic activity

BCR B-cell receptor

TCR T-cell receptor

IFN-gamma Interferon gamma

TIL Tumor-infiltrating lymphocyte

Acknowledgements

The authors would like to thank TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo/), and GESA (http://www.gsea-msigdb.org/gsea/index.jsp) for data collection and the provision of customizable functions. Especially, the authors would like to express gratitude to all members of department of dermatology and venereology at Central South University and all the patients involved in this study.

Author contributions

Y.X., G.Z. and H.L.: Conceptualization, Methodology, Software. H.L. and S.Z.: Data curation. Y.X.: Writing- Original draft preparation and Visualization. Y.X. and P.L.: Investigation. X.C., H.L. and G.Z.: Supervision. Y.X.: Validation. Y.X., X.C., Y.H. and Z.J.: Writing- Reviewing and Editing. All authors read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (62102455, to Guaxiong Zhang), China Postdoctoral Science Foundation (2020M682587, to Guaxiong Zhang), the science and technology innovation Program of Hunan Province (2023RC3078, to Guaxiong Zhang), National Key Research and Development Program of China (2022YFC2504700, to Xiang Chen, 2022YFC2504702, to Guaxiong Zhang), and the Project of Intelligent Management Software for Multimodal Medical Big Data for New Generation Information Technology, Ministry of Industry and Information Technology of People’s Republic of China (TC210804V, to Xiang Chen).

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/), SRA database (https://www.ncbi.nlm.nih.gov/bioproject/) and dbGaP database (https://www.ncbi.nlm.nih.gov/gap/). The data of the fluorescent multiplex immunohistochemical assay is available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

All tissue samples were obtained in compliance with informed consent policy. The study protocol was approved by the Institutional Review Board and ethics committee of Xiangya Hospital, Central South University. Clinical information is summarized in Table S2. All procedures were performed in compliance with relevant laws and institutional guidelines and have been approved by the appropriate institutional committees. Informed consent was obtained for experimentation with human subjects. The privacy rights of human subjects must always be observed.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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