Exploring GPCR signaling pathway networks as cancer therapeutic targets

Abstract

GPCR signaling can contribute to establishing the tumor microenvironment and influence the progression and metabolism of tumors. Arora et al.¹ describe a systems-level approach to investigate the patterns of co-expression of GPCR signaling pathway networks across diverse tumors and identify network components that correlate with patient-survival data across different cancer types.

GPCR signaling can contribute to establishing the tumor microenvironment and influence the progression and metabolism of tumors. Arora et al. describe a systems-level approach to investigate the patterns of co-expression of GPCR signaling pathway networks across diverse tumors and identify network components that correlate with patient-survival data across different cancer types.

Main text

G-protein-coupled receptors (GPCRs) are a family of membrane proteins that function in many physiological processes and play critical roles in health and disease.^2,3 More than 800 genes encode GPCRs in the human genome, and these receptors respond to an extensive repertoire of extracellular ligands, which includes a vast array of metabolites and peptides, functioning as hormones, neurotransmitters, and paracrine signaling molecules.^4,5,6 Indeed, GPCRs are the target of about 35% of all FDA-approved drugs.⁷ Changes in the expression of GPCR-encoding genes, genes involved in ligand production, or genes encoding downstream effector proteins can alter or rewire GPCR signaling pathway networks (GSPNs). Some rewiring events have been linked to cancer initiation, progression, invasion, and metastasis.^3,8,9 Furthermore, in a multicellular context, the GSPNs also contribute to establishing and maintaining a tumor microenvironment (TME) that supports tumor growth, immune evasion, and resistance,^9,10 thus revealing that GSPNs could potentially be exploited for cancer treatment.

Uncovering GSPNs that contribute to cancer could enable the discovery of new therapeutic targets and, possibly, the repurposing of FDA-approved drugs, including those targeting GPCRs, that are approved for applications other than cancer. However, a comprehensive investigation and understanding of the GSPNs in cancers and the associated TME is crucial for identifying or developing such targeted therapeutics. A systems-level approach that evaluates whether alterations in gene expression contribute to cancer-relevant GSPNs is needed. Identifying such GSPNs could yield actionable insights and verifiable therapeutic targets.

A recent paper by Arora et al.¹ provides an essential step toward a systems-level analysis for identifying GSPNs in cancer. The authors considered networks formed by GPCRs, their ligands, and the enzymes that participate in the biogenesis of the ligand molecules, investigating their coordinated changes in gene expression across cancer types (Figure 1). To this end, they first reconstructed the GPCR-ligand-enzyme networks by connecting ligands to their cognate receptors and their synthesizing enzymes by incorporating information from two public databases: (1) the IUPHAR database, which contains data on associations between known endogenous ligands and GPCRs (https://www.guidetopharmacology.org), and (2) the Rhea database, a reaction knowledgebase, (https://www.rhea-db.org) that connects metabolic ligands to enzymes involved in their synthesis (Figure 1). The authors then integrated transcriptomics data from more than 10,000 patients from The Cancer Genome Atlas (TCGA), which inherently encompasses the TME, with the GSPNs. This allowed them to detect correlated gene expression patterns in individual GPCR-ligand-enzyme networks across cancers (Figure 1) compared with appropriate healthy tissue samples (i.e., coordinated upregulation or downregulation).

Figure 1.

Understanding GPCR-ligand-enzyme systems in cancers from a transcriptomics perspective

The GPCR-ligand-enzyme networks were assigned using the IUPHAR and Rhea databases. For GPCRs that bind peptide ligands, the IUPHAR database was used to designate the mapping between GPCRs and their cognate ligands. For GPCRs that bind metabolite ligands, mapping GPCRs to ligands was accomplished through the enzyme that synthesizes the ligand, assigned using the Rhea database. These mappings (GPCR-ligand-enzyme) were integrated with transcriptomics data from TCGA and GTEx (healthy tissue) databases to evaluate the coordinated up- or downregulation within a GPCR-ligand-enzyme network. Red (upward) and blue (downward) arrows indicate significant up- and downregulation of gene expression patterns. The GPCR-peptide ligand or GPCR-enzyme (ligand) pairs with the arrows in the same direction represent coordinated expression; pairs with arrows in the opposite direction represent discordant expression. Created with BioRender.com.

The resulting networks exhibited coordinated or discordant gene expression changes specific to cancer. The authors found over 100 instances of GPCR-ligand-enzyme networks featuring either coordinated increases or decreases in the expression of network components. Coordinated changes in GPCR-ligand-enzyme networks could yield synergistic effects on the network by amplifying an increase or decrease in the GSPN compared to an effect due to a change in only the receptor or ligand abundance. Such coordinated changes in gene expression within the tumor and the TME may reveal likely actionable GPCR targets for cancer treatment. Conversely, the effect of discordant changes in gene expression among GSPN components is more difficult to predict due to functional overlap of GPCRs and ligand promiscuity (Figure 1). Although the study provides GSPNs that might be involved in cancer, the promiscuity in GPCR-ligand interactions means additional strategies are needed to investigate the role of promiscuity and functional redundancy of GPCRs in rewiring GSPNs across cancers.

Arora et al.¹ revealed that some of the coordinated expression changes in GPCR-ligand-enzyme networks occurred across multiple cancers, whereas others were confined to a few cancer types. The immune-signaling GSPNs elicited by CC chemokine receptors (CCR1, CCR3, CCR4, CCR5, CCR6, and CCR8) and their cognate ligands, which are genetically encoded peptides, were prominently upregulated across multiple cancer types, as was the CXCR3 GSPN. Thus, these GSPNs potentially represent targets that may be broadly relevant to different cancer types. Similarly, muscarinic receptors and the enzyme synthesizing acetylcholine, the endogenous ligand for those receptors, were coordinately upregulated across numerous cancers. While these GSPNs are likely to be important across a broad range of cancers, these interesting correlations warrant further cellular and mechanistic investigations to be actionable.

Arora et al.¹ found a significant association between the coordinated upregulation of various GPCR-ligand-enzyme networks and mutations in certain tumor suppressor genes such as APC, FBXW7, ZFHX3, and SMAD4. Mutations in other tumor suppressors, such as PIK3R1 and ATRX, were associated with coordinated downregulation of some GPCR-ligand-enzyme networks. While this is an interesting link, the mechanistic details of the interplay between tumor suppressor genes and GPCR-ligand-enzyme networks remain to be investigated.

Whether the dysregulation of these GPCR networks causally contributes to cancer is unclear, and future studies will be required to dissect those relationships fully. Nevertheless, Arora et al.¹ provide evidence for the clinical and translational relevance of the coordinated regulation of GPCR-ligand-enzyme networks in cancers. Using patient-survival data, the authors stratified patients into high- or low-expression groups based on expression of the GPCR-ligand-enzyme networks. Dysregulation of the GPCR-ligand-enzyme network was more predictive of patient survival than that of individual components in many instances. In addition, they analyzed the data on drug screening across cancer cell lines. Several small molecules inhibit cellular proliferation and are antagonists to a broad spectrum of GPCR family members, including adrenergic, CC chemokine, glutamate, histamine, opioid, acetylcholine, and dopamine receptors, indicating the potential for GPCR-targeted drug development. However, the cellular mechanisms by which these antagonist drugs result in antiproliferative effects in cancer cells through their GPCR targets and whether they also affect the proliferation of other healthy cells remain to be studied.

Understanding cancer from the perspective of the TME is essential for deriving deeper insights and identifying more precise therapeutic targets. The computational framework developed to study tissue-level transcriptomic data can be applied to single-cell transcriptomic data in the future. Such single-cell analyses are critical for understanding the evolution and maintenance of cancerous cells and the TME. Incorporating single-cell transcriptomics data would enable researchers to investigate what cell types are responsible for specific dysregulation of GPCR network components and to understand the mechanistic basis for the coordinated and discordant changes within the tumor and the TME. Such knowledge would allow target refinement for translational studies. Analyzing GPCR networks at the single-cell level using data for untreated vs. drug-treated conditions can further illuminate how the TME influences GPCR-ligand interactions and, consequently, may provide explanations for the observed patient outcomes. Such research may also uncover how immune responses and signaling pathways interact within the TME under diverse conditions. Arora et al.¹ present an exciting work that opens many interesting questions for future research.