Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled receptors and tumor microenvironment in melanoma

Melanoma is the primary cause of skin tumor-related death with increasing annual cases of morbidity and mortality [1, 2]. While patients with melanoma undergo a combination of surgery, chemotherapeutic drugs, and molecular-targeted therapeutic drugs, the effectiveness of current therapies varies among individuals [3, 4]. The emergence of immunotherapy has offered hope to patients with advanced melanoma, but its benefits are limited [5‐7]. Due to the inconsistent treatment outcomes and varying response rates, considerable effort has been devoted to identifying prognostic and therapeutic biomarkers for melanoma, particularly those capable of predicting the efficacy of immunotherapy [8‐10].

G protein-coupled receptors (GPCRs) are membrane protein receptors that bind chemicals in the cellular environment and activate a series of intracellular signaling pathways that ultimately lead to changes in cellular state [11‐13]. GPCRs have also been implicated in carcinogenesis and metastasis [14‐17] and have emerged as important targets for drug therapy due to their wide distribution in the body [18]. In addition, they play a pivotal role in shaping the tumor microenvironment (TME). Santagata et al. found that two GPCRs, CXCR4 and CXCR7, orchestrate the recruitment of immune and stromal cells [19]. In the context of melanoma, Ridky et al. demonstrated that a combination of anti-PD-1 and G-1, a G protein-coupled estrogen receptor-selective agonist, could effectively inhibit tumor growth [20]. Nevertheless, there is a noticeable absence of comprehensive and sophisticated studies delving into the role of GPCRs in melanoma. Furthermore, the precise mechanism through which GPCRs influence the TME and the response to immunotherapy in melanomas remain unknown.

The emergence of high-throughput sequencing techniques, particularly single-cell RNA sequencing (scRNA-seq), has propelled tumor research into the era of precision. The integration of scRNA-seq and bulk sequencing (bulk-seq) allows researchers to dissect the contribution of individual genes in tumorigenesis and progression, both at the tissue and single-cell levels [21‐23]. In the current study, we have harnessed the power of GPCRs in conjunction with the intricate cellular landscape of the TME to construct a GPCR–TME classifier for better clinical classification and therapeutic strategies. This innovative approach enhances clinical classification and informs the development of more effective therapeutic strategies. To a significant extent, our findings address the limitations of current clinical staging methodologies and offer valuable insights for the precise management of melanoma.

In recent years, the increased number of studies dedicated to GPCRs and their interaction with the TME has significantly enhanced our understanding of their critical roles in the prognosis and therapeutic approaches for cancer patients [33‐36]. For instance, Zhang et al. observed a substantial down-regulation of GPRASP1 in head and neck cancers, which was notably associated with the infiltration of CD8 T cells [37]. In a separate study, Yu et al. identified GNAI2 as a risk factor for gastric cancer as it appeared to promote the accumulation of Tregs [38]. In addition, Yu et al. found that P2RY12 was downregulated in lung adenocarcinoma and exhibited a significant correlation with M2 macrophage and dendritic cell infiltration [39]. Nevertheless, these investigations primarily focused on individual GPCR for other cancer types. Studies utilizing multi-omics data, coupled with GPCRs and the TME, to predict immunotherapy response rate and OS remain relatively scarce. In our comprehensive study, we systematically integrated extensive melanoma data sets, enabling us to thoroughly explore the crosstalk between the GPCRs and the TME. The outcomes of this effort, the GPCR–TME classifier, has proven to be an exceptionally effective predictor for both the OS and immunotherapy response rate of melanoma.

Using a variety of machine learning algorithms, we identified 12 prognostic GPCRs and constructed the GPCR score. Notably, a high GPCR score signifies a poor OS for melanoma; however, it may serve as a protective factor in other cancers. This discrepancy could be attributed to the distinct roles played by these 12 GPCRs in the tumorigenesis of various cancers. Furthermore, the widespread distribution of GPCRs in vivo may account for our inability to detect differential expression levels between HaCaT and A375 cells. Considering prior research highlighting the varying impact of GPCRs on TME [40‐42], we delved into the immune microenvironment of the high- and low-GPCR score groups using CIBERSORT. Our findings revealed that the low-GPCR group exhibits heightened infiltration levels of immunologic effector cells, including CD8 T cells and M1 macrophages, thus providing some insight into their improved OS.

Given that bulk-seq analysis treats all cells in the TME as homogeneous and may result in the loss of crucial information, we took a closer look at the GPCR score at the single-cell level. In the TME of melanoma, we identified seven distinct cell types, including T cells, melanoma, fibroblasts, macrophages, endothelial cells, B cells, and plasma cells. Notably, immune cells have exhibited significantly higher GPCR scores when compared to tumor and stroma cells, further substantiating the intricate relationship between GPCR and TME. Consequently, we proceeded to construct the TME score to provide a quantitative assessment of the TME in patients with melanoma. This score integrated the impact of five immune cell types that were found to be prognostically relevant in melanoma. As expected, a higher TME score demonstrated a favorable OS, and we combined the GPCR and TME scores to develop a GPCR–TME classifier. The GPCR^low/TME^high subgroup had the best OS, whereas the GPCR^high/TME^low subgroup had the worst OS. To further underly reasons for diverse prognoses among these subgroups, we conducted various enrichment analyses. The results shed light on the underlying mechanism of superior OS in the GPCR^low/TME^high subgroup, which included the activation and recruitment of immune effector cells and the positive regulation of the immune response.

Subsequently, we sought to unravel the GPCR–TME classifier at the genomic level. Since TMB has gained widespread acceptance as a fundamental biomarker influencing responses to immunotherapy [43‐45], we conducted a comparative analysis of TMB across the various GPCR–TME subgroups. Intriguingly, the GPCR^low/TME^high subgroup, which had the best OS, had the highest TMB, whereas the GPCR^high/TME^low subgroup displayed a significantly lower TMB. This result indicated that different GPCR–TME subgroups may have different immunotherapy response rates. In addition, we found that the mutation rate of BRAF was higher in the GPCR^low/TME^high subgroup, implying the potential efficacy of BRAF inhibitors, such as vemurafenib and dabrafenib, for these patients [46, 47].

Having established that GPCRs exhibit crosstalk with the TME and that the GPCR^low/TME^high subgroup exhibited higher TMB, we speculated that the GPCR–TME classifier might serve as a predictor of immunotherapy response for melanoma. Initially, we compared the expression levels of antigen presentation genes among the GPCR–TME subgroups, which all displayed upregulation in the GPCR^low/TME^high subgroup. This suggests that dendritic cells may more effectively recognize tumor cells and initiate tumor eradication via CD8 T cell activation [48]. Subsequently, we investigated the expression levels of classical immune checkpoints and observed elevated levels of CTLA-4, CD274, PDCD1, TIGIT, CD86, CD209, IDO1, and LAG3 in the GPCR^low/TME^high subgroup. Pul et al. found that the local delivery of anti-CTLA-4 could reduce the systemic Treg populations and activate effector T cells in melanoma [49]. CD274, also known as PD-L1, has been implicated in inducing immune evasion by tumor cells. Research by Ribas et al. demonstrated that a combination of anti-PD-L1 and dabrafenib can enhance immune infiltration and elicit a durable response in advanced melanoma [50]. Anti-PDCD1 (PD-1) therapy is a well-established immunotherapy approach for melanoma. Tjulandin et al. found that the novel PD-1 inhibitor prolgolimab could mediate significant anti-tumor effects and an endurable safety profile in advanced melanoma [51]. TIGIT is an inhibitory receptor expressed by Tregs [52]; research by Shusuke et al. suggests that the TIGIT/CD155 axis mediates resistance to ICIs in melanoma [53]. CD86 is expressed on the cell membrane of melanoma and activates the T cells, which enhances the anti-tumor effect [54]. CD209 can regulate dendritic cell trafficking and transient T-cell binding [55]; however, its role in melanoma remains unexplored. IDO1, was initially observed in plasmacytoid-shaped cells within melanoma. Kevin et al. found that IDO1 was correlated with intra-tumoral CD8 T cells and Th1-related genes, suggesting that IDO1 could act as a biomarker of immunologic tumor control [56]. LAG3 is expressed on the surface of activated CD4 and CD8 T cells [57], and Nicolas et al. found that the combination of anti-LAG-3 and anti-PD-1 enhances the cytotoxic capacity of CD8 T cells and leads to the anti-tumor effect [58]. Combining this information above, we constructed a GPCR–TME classifier for three melanoma immunotherapy cohorts. The outcomes demonstrated that the GPCR^low/TME^high subgroup exhibited significantly higher immunotherapy response rate compared to others in the GSE91061 cohort. In the GSE145996 and GSE35640 cohorts, these differences did not reach statistical significance, potentially due to the bias introduced by the limited sample sizes under stringent filtering criteria. Nevertheless, it was noteworthy that we still observed a higher immune therapy response rate in GPCR^low/TME^high subgroup compared to others in these two cohorts.

In a clinical application, the GPCR–TME classifier holds the potential to enhance the refinement of molecular subtyping and treatment strategies for melanoma. Specifically, following the surgical removal of a patient’s melanoma specimen, bulk-seq can be employed. Based on the gene expression data, calculations can be made to determine both the GPCR score and TME score. These scores aid in categorizing the patient into a specific GPCR–TME subgroup, enabling the prediction of the patient’s OS and their potential response rate to immunotherapy.

We acknowledge several limitations in our study. First, due to constraints related to tumor specimens, our research primarily relies on bioinformatics. We anticipate that future experiments, such as flow cytometry and immunohistochemistry, will help validate our findings. Second, to enhance the robustness of our conclusions, we recommend utilizing an internal cohort that includes gene expression data, survival data, and immunotherapy response data to further assess the performance of the GPCR–TME classifier.

In summary, the multi-omics validation supports the GPCR–TME classifier as a highly effective predictor for melanoma OS and immunotherapy response rate.