Abstract
Decades of published research support a role for growth hormone (GH) in cancer. Accordingly, there is increasing interest in targeting GH in oncology, with GH antagonists exhibiting efficacy in xenograft studies as single agents and in combination with anticancer therapy or radiation. Here we discuss challenges associated with using growth hormone receptor (GHR) antagonists in preclinical models and considerations for translation, such as the identification of predictive biomarkers for selecting patients and for monitoring drug efficacy. Ongoing research will determine whether suppressing GH signalling pharmacologically will also reduce the risk of developing cancer. An increase in GH-targeted drugs in preclinical development will ultimately provide new tools to test anticancer efficacy of blocking the GH signalling pathway.
Introduction
Observations in humans and animals with elevated or disrupted growth hormone (GH) signalling clearly support a role for GH in cancer development (Guevara-Aguirre et al. 2020, Werner et al. 2020, Duran-Ortiz et al. 2021). Clinically, increased GH levels have been identified in the sera of cancer patients and in numerous tumour types, with GH and growth hormone receptor (GHR) expression levels correlating with poorer clinical and histopathological parameters (Emerman et al. 1985, Mazzoccoli et al. 1999, Wu et al. 2011, Kong et al. 2016, Perry et al. 2017, Arumugam et al. 2019, Kaseb et al. 2022). There is also extensive preclinical evidence implicating GH signalling in multiple aspects of cancer progression (Chhabra et al. 2011, Brittain et al. 2017, Perry et al. 2017, Chesnokova & Melmed 2019, Kopchick et al. 2022) and treatment resistance (Basu & Kopchick 2019, Cheng et al. 2021). Such observations highlight the potential of this pathway as a therapeutic target. However, this has not yet translated into clinical trials. Encouragingly though, two studies have reported anticancer efficacy for a GHR antagonist in individual patients with liver cancer and male breast cancer in a patient with acromegaly (Leporati et al. 2015, Kaseb et al. 2022).
Systemic release of GH from the anterior pituitary regulates hepatic release of insulin-like growth factor-1 (IGF1), which like GH has been implicated in cancer (Cao & Yee 2021) (Fig. 1). GH also has autocrine and paracrine effects within the tumour (Perry et al. 2006, 2017, Harvey et al. 2015). Local GH expression is observed in cancer cells and cells within the tumour microenvironment, with autocrine GH having demonstrably more potent oncogenic effects on cancer cells compared to exogenous GH treatment (Kaulsay et al. 1999, Zhu et al. 2005, Perry et al. 2006). GH actions are initiated by interaction with a cell surface GHR homodimer, which is a class 1 cytokine receptor, through two asymmetric binding sites on GH. Binding causes a rotational change in the GHR transmembrane domain, facilitating phosphorylation of two JAK2 molecules that are associated with the cytoplasmic domain of the receptor (Brooks et al. 2014, Waters & Brooks 2015). In addition to activating the cell surface GHR, GH also activates the related prolactin receptor (PRLR) at the cell membrane (Bartke & Kopchick 2015) and crosstalks with other hormone and growth factor receptors, resulting in a complex multi-component signal transduction network (Liu et al. 2016, Lu et al. 2019, Frank 2020). Therapeutic strategies targeting GH will therefore need to consider receptor crosstalk and systemic and local functions.
Regulation of GH and therapeutic blockade. GH and IGF1 affect tumour growth through endocrine, autocrine and paracrine mechanisms. GH is secreted from the anterior pituitary with release regulated by the hypothalamic hormones, growth hormone releasing hormone (GHRH) and somatostatin (SST), and ghrelin, which is predominantly produced by the stomach. Increased circulating GH promotes release of IGF1 from the liver and other target tissues. Additional regulation is provided by a feedback loop mechanism (+ and −), with high circulating GH and IGF1 inhibiting GH secretion. GH and IGF1 can also be expressed in extra-pituitary tissues and tumours, where they have local autocrine/paracrine actions on GHR- and IGF1R-expressing tumour cells, as well as cells in the tumour microenvironment. Inhibition of GH actions can be achieved through direct targeting of the GHR and by inhibition of GHR protein production. GH actions can also be blocked indirectly by inhibitors/agonists of hormones that regulate GH release from the pituitary. *GHRH, antagonists, ghrelin antagonists, and SST analogues also directly target tumour cells expressing their respective receptors. GHBP, growth hormone-binding protein; GHR, growth hormone receptor; GHRH, growth hormone releasing hormone; IGF1, insulin-like growth factor 1; SST, somatostatin. Created with BioRender.com.
Citation: Endocrine-Related Cancer 30, 9; 10.1530/ERC-23-0033
The many reported mechanisms by which GH promotes cancer (promoting cell proliferation and survival, epithelial to mesenchymal transition, cell migration and invasion, cell senescence, tumour growth, angiogenesis and metastasis, and drug and radiation resistance) have been reviewed previously (Chhabra et al. 2011, Brittain et al. 2017, Perry et al. 2017, Basu & Kopchick 2019, Chesnokova & Melmed 2019, Kopchick et al. 2022) and elsewhere in this special issue. The purpose of this review is to discuss recent advances and some of the considerations when translating GH inhibition into the clinical oncology setting.
Clinical studies
Recent years have seen a rapid increase in the number of clinical studies supporting the role for GH in cancer, with higher GH and GHR levels tending to be associated with poorer clinical outcome, although this varies by cancer. Good clinical correlations are observed for cancers such as breast, endometrial, melanoma and hepatocellular carcinoma (HCC) (Wu et al. 2011, Sustarsic et al. 2013, Kong et al. 2016, Perry et al. 2017, Kaseb et al. 2022) (Tables 1 and 2). In HCC, increased GH is associated with poorer outcome, whereas the converse is seen for GHR (as described later on). However, study designs and the methodology used to assess GH and GHR levels can vary greatly, making it difficult to compare across studies.
Clinical assessment of GH and GHR levels in mammary carcinoma.
| Breast cancer | ||
|---|---|---|
| References | Clinical outcome | Study methodology |
| (Emerman et al. 1985) | Among the serum of 42 patients with breast cancer, 40% had increased GH and 17% had elevated PRL; total lactogens/(GH+PRL) ratio was greater in patients with breast cancer compared to controls a | Radioimmunoassay; bioassay |
| (Love et al. 1991) | Increasing age was associated with decreased GH. No differences in prolactin or GH levels among premenopausal women with breast cancer (18), healthy women with a strong family history of breast cancer (n = 23) and healthy women (n = 39) | Radioimmunoassay; bioassay |
| (Mol et al. 1995) | GH was expressed in normal and neoplastic mammary gland tissue | RT-PCR; IHC |
| (Mertani et al. 1998) | Co-expression of GHR and PRLR in all breast cancer samples, with similar expression levels | ISH; IHC |
| (Gebre-Medhin et al. 2001) | GHR expression was increased in patients with breast cancer a | RT-PCR; WB; IHC |
| (Raccurt et al. 2002) | GH expression was increased in metastatic mammary carcinoma with de novo stromal expression in proliferative disorders of the mammary gland a | ISH; RT-PCR |
| (Menashe et al. 2010) | GH-induced intracellular signalling pathway was the third most highly associated with breast cancer susceptibility among 421 pathways containing 3962 genesa | GWAS/pathway analysis |
| (Wu et al. 2011) | Tumoral expression of GH mRNA was significantly associated with lymph node metastasis, tumour stage, epidermal growth factor receptor 2 and proliferative index in breast cancer. GH and PRL expression correlated with worse relapse-free survival and overall survival in patients with breast cancer a | ISH; IHC; Kaplan–Meier and Cox regression |
| (Chiesa et al. 2011) | GH mRNA expression was present in 52% of clinically derived primary mammary cancer cell lines and GHR mRNA was expressed in all primary mammary cancer cell lines. In EpCAM-positive breast cancer, GH mRNA was not significantly associated with cancer grade, stage, lymph node metastasis or patient age | RT-PCR; immunofluorescence |
| (Lombardi et al. 2014) | GHR was expressed in the normal mammary epithelium. Expansion of a GHR-positive cell population in 72% of ductal carcinoma in situ lesions. There was a positive correlation between GHR and ER expressiona | Immunofluorescence; tissue microarrays |
| (Leporati et al. 2015) | Case report: a 72-year-old male with acromegaly and breast cancer had normalisation of IGF1 with pegvisomant therapy. The patient stopped pegvisomant and started on anastrozole due to metastases 4 years after breast cancer surgery, but the metastatic lesion increased, and the patient was shifted to tamoxifen and restarted on pegvisomant. The size of the metastatic lesion reduced, and a further reduction was observed in the subsequent 24-month follow-up | CT scan |
| (Hamann et al. 2019) | GH concentration was significantly lower in early breast cancer | N/A |
| (Arumugam et al. 2019) | IHC demonstrated increased GHR expression was associated with lower ER expression (n = 72). In ER− tumours (n = 671), high GHR mRNA was associated with poorer survival a | IHC; KM plotter; Oncomine |
| (Dabrosin & Dabrosin 2020) | Extracellular GH and IGFBPs higher in ER+ breast cancer than normal adjacent breast tissue (n = 10) a | Multiplex PEA |
| (Zhu et al. 2020) | GHR mRNA was highly expressed in breast tumour (n = 12) compared to adjacent normal tissues (n = 12)a | qPCR |
aIndicates studies that show an increase in GHR or GH expression in tumour samples or serum; upregulation of GHR signalling in tumour samples; or a positive association of GH or GHR with poorer prognosis.
AR V7, androgen receptor splice variant 7; CT scan, computerised tomography scan; EpCAM, epithelial cell adhesion molecule; ER, oestrogen receptor; GEM, gemcitabine; GWAS, genome-wide association study; Hep C, hepatitis C; HCC, hepatocellular carcinoma; IHC, immunohistochemistry; IGF1, insulin-like growth factor-1; IGFBP, IGF-binding protein; ISH, in situ hybridisation; KM, Kaplan–Meier; mAb, monoclonal antibody; MRI, magnetic resonance imaging; PEG, pegvisomant; PRL, prolactin; PRLR, prolactin receptor; PEA, proximity extension assay; qPCR, quantitative real-time PCR; STAT5, signal transducer and activator of transcription 5; TCGA, The Cancer Genome Atlas; WB, western blot.
Clinical assessment of GH and GHR levels in hepatocellular carcinoma.
| Hepatocellular carcinoma (HCC) | ||
|---|---|---|
| References | Clinical outcome | Study methodology |
| (Chang et al. 1990) | GHR not detected in HCC or cirrhotic tissue, all control liver tissues had high-affinity and low-affinity GHR | Radioreceptor assays |
| (Liu et al. 2003) | The receptor-binding capacity of the GHR in HCC was lower than in normal liver, and it was negatively associated with tumour size and disease stage | Radioreceptor assays |
| (García-Caballero et al. 2000) | The expression of GHR and PRLR was increased in HCC samples compared to normal liver a | ISH; IHC |
| (Kong et al. 2016) | Both GH and PRL were increased in HCC compared with non-neoplastic liver tissues on gene and protein levels. GH expression significantly associated with tumour size and grade. Expression of GH and PRL was associated with worse relapse-free and overall survival in HCC patientsa | ISH; PCR; IHC |
| (Lin et al. 2021) | 200 patients with chronic Hep C and HCC; GHR mRNA was significantly lower in Hep C virus-related HCC | qPCR |
| (Mai et al. 2021) | GHRmRNA was downregulated in HCC patients | MustSeq |
| (Cai et al. 2022b ) | 11 differentially expressed ageing-related genes (including GHR) correlated with HCC patient prognosis | TCGA and GEO databases |
| (Basu et al. 2022) | GHR and IGF1 mRNA levels were strongly correlated in HCC patients (n = 371). IGF1R was correlated with poorer survival, and GH-regulated ABCB1 and IGF1-regulated ABCC1 were negatively correlated with survival for HCC | Bioinformatic analyses of TCGA datasets |
| (Kaseb et al. 2022) | High plasma GH associated with poor clinical outcome. Two HCC patients with high plasma GH had stabilised disease after treatment with pegvisomant combined with sorafenib following progression on sorafenib alone a | ELISA; MRI |
| (Abu El-Makarem et al. 2022) | Cirrhotic patients with HCC had considerably lower hepatic expression of the GHR, STAT5 and IGF1 proteins compared to cirrhotic patients without HCC and healthy controls | IHC |
| (Mohamed et al. 2022) | HCC patients in GH-low group (n = 18) had significantly superior 1-year survival rate and overall survival compared to patients in GH-high group (n = 19) a | ELISA; Kaplan–Meier survival analysis |
| (Kodama et al. 2022) | Significant inverse association between GHR gene expression with patient survival | qPCR and RNA-Seq |
| (Guo et al. 2022) | High GHR mRNA expression in tumours from the low-risk group | Gene expression, TCGA |
| (Zhang et al. 2022) | An eight-gene signature including GHR was used in predicting the clinical outcome of HCC patients | Transcriptome analysis |
| The GHR gene was downregulated in high-risk group | KM analysis; qPCR | |
| (He et al. 2022) | HCC patients with high expression of the GHRmRNA had longer survival time | Gene expression |
aIndicates studies that show an increase in GHR or GH expression in tumour samples or serum; upregulation of GHR signalling in tumour samples; or a positive association of GH or GHR with poorer prognosis.
CT scan, computerised tomography scan; EpCAM, epithelial cell adhesion molecule; ER, oestrogen receptor; GWAS, genome-wide association study; Hep C, hepatitis C; HCC, hepatocellular carcinoma; IHC, immunohistochemistry; IGF1, insulin-like growth factor-1; IGFBP, IGF-binding protein; ISH, in situ hybridisation; KM, Kaplan–Meier; MRI, magnetic resonance imaging; PRL, prolactin; PRLR, prolactin receptor; PEA, proximity extension assay; qPCR, quantitative real-time PCR; STAT5, signal transducer and activator of transcription 5; TCGA, The Cancer Genome Atlas.
Because of the endocrine, autocrine and paracrine functions of GH, measurements of GH mRNA or protein levels can be made in tumour tissues and circulating GH in serum or plasma (Perry et al. 2013, 2017). Gene expression profiling of GH and/or GHR from large clinical cohorts is available to researchers through online databases such as The Cancer Genome Atlas, but the data lacks protein expression and circulating GH levels. In addition, while low levels of GH expression can have a profound effect on cancer cell phenotype, low mRNA levels are not easily detected using methods such as RNA sequencing and require other techniques such as in situ hybridisation. Furthermore, GH is a secreted protein, so measurements of protein expression in tissues can be problematic but can be achieved using immunohistochemistry. In the circulation, GH is released in pulses from the pituitary so measurements in serum or plasma may be inaccurate depending on the time the sample is taken. Despite this, correlations between serum GH and outcome have been observed in HCC, breast, gastric, lung and colon cancer and this approach may warrant further investigation (Emerman et al. 1985, Mazzoccoli et al. 1999, Triantafillidis et al. 2003, Kaseb et al. 2022).
Test specificity is also an issue. The GH gene locus is composed of five genes that code for evolutionarily related hormones (GH(GH1), placental GH (GH2) and the placental lactogens (CSH1, CSH2, CSHL1)) that share a high degree of sequence similarity at the DNA and amino acid level (Liao et al. 2018). Differentiating between these five genes can be difficult when using quantitative PCR, and antibodies to GH often cross-react with placental GH and placental lactogen. GH-related hormones may also be deregulated in cancer cells so ELISAs and antibodies used in immunohistochemistry and western blotting should be rigorously validated to ensure specificity.
The increasing number of publications in this area is encouraging but there is still an acute need for large-scale clinical studies profiling GH and GHR expression, circulating GH, clinicopathological features and survival outcome in patients to inform clinical trials.
Cancer variants
GH and GHR are encoded respectively by the GH1 and GHR genes. Perhaps surprisingly given the described role for GH in cancer, cancer-associated driver mutations in the GH1 and GHR gene are not common. Notably, the GHR-P495T mutation is the only GHR variant for which a role in cancer promotion is supported by functional data (Chhabra et al. 2018). The single nucleotide polymorphisms (SNP) contributing to GHR-P495T (rs6183) was initially identified from a lung cancer genome wide association study (GWAS) (minor allele frequency = 0.001, odds ratio = 12.98, P = 0.0019) (Rudd et al. 2006). Cells harbouring the GHR-P495T mutation have prolonged GHR signalling due to reduced SOCS2 binding at amino acid position 495 which leads to impaired cellular degradation and turnover of the mutant receptor (Chhabra et al. 2018). Cancer-associated variants have been identified in genes that regulate GH expression and release, such as the somatostatin gene promoter (Wagner et al. 2006). A small number of cancer variants linked to GH/IGF1 genes have also been identified. For example, SNPs in the highly polymorphic proximal promoter of the GH1 gene have been linked to breast cancer risk (Wagner et al. 2007), while a SNP in intron 4 of GH1 was associated with lower circulating GH and IGF1, and reduced risk of colorectal cancer (Hasegawa et al. 2000, le Marchand et al. 2002, Millar et al. 2010, Shi et al. 2014).
However, caution needs to be taken when assigning gene targets to non-coding SNPs. Although a disease-associated SNP may be in close proximity to a gene of interest (for example in the promoter, intron or 3’-UTR), proximity should not be mistaken for association. Intragenic SNPs residing in regulatory regions such as enhancers can affect the expression of other neighbouring genes, or even genes located much further away on the same or different chromosomes (Sanyal et al. 2012, Pudjihartono et al. 2022). An example of this is a breast cancer-associated SNP, rs6171, in the GH1 promoter. The GH locus control region and promoter act as a complex regulatory hub that coordinates expression of the five genes within the GH locus through chromosome looping (Tsai et al. 2016) but may also regulate the expression of numerous other genes proximal and distal to the locus (Jain et al. 2020). Indeed, the rs6171 SNP is routinely linked to GH due to its location in the promoter and is associated with changes in expression of the GH locus gene, CSH2. But it is also associated with changes in expression of several genes flanking the locus (CD79B, DDX42, FTSJ3 and TCAM1P) (Jain et al. 2020, supplementary data; GTEx), two of which (FTSJ3 and TCAM1P) have been linked to cancer (Manning et al. 2020, Zhu et al. 2022). Thus, GH1 SNPs such as rs6171 may impact on cancer risk through other genes in addition to GH1 or besides.
Expression changes in individual genes or effect sizes of single SNPs may be small in isolation, but when combined these may have more impact on cancer risk. Body mass index, exercise and ethnicity may modify the association between some GH-related SNPs and cancer risk (le Marchand et al. 2002, Khoury-Shakour et al. 2008, Shi et al. 2014, Gao et al. 2015). This highlights the importance of pathway analyses rather than focussing on single SNPs. For example, GWAS are useful for identifying common-low penetrance risk alleles but tend to identify relatively few highly significant loci. But combining pathway analysis with existing GWAS data can discover new candidate genes. Such an approach identified the GH intracellular signalling pathway as one of three pathways (out of 421 investigated) that were highly associated with breast cancer susceptibility (Menashe et al. 2010).
Confirming functionality for non-coding SNPs is a complex process but necessary in order to avoid mis-assigning SNPs to a proximal gene. Finally, it should be noted that these types of analyses also have the potential to identify new non-coding SNPs that can act independently or in combination to change GH1 transcript levels, thus potentiating GH effects in cancer cells.
Challenges using GHR antagonists in animal models
Decades of published research strongly support a role for GH in cancer; however, only a few preclinical oncology studies involving pharmacological inhibition of GHR have been carried out in animals. This can be attributed to a lack of access to suitable long-acting antagonists. Of the studies that have been performed, results have been largely promising, with tumour growth inhibition observed with monotherapy GHR antagonist treatment and in combination with chemotherapy or radiation. Tumour models include breast, colorectal, meningeal, prostate, endometrial gastric, pancreatic and hepatocellular cancer (McCutcheon et al. 2001, Dagnaes-Hansen et al. 2004, Divisova et al. 2006, Lombardi et al. 2014, Evans et al. 2016, Brody et al. 2021, Kaseb et al. 2022, Meng et al. 2022, Unterberger et al. 2022) (Table 3 and reviewed in this issue).
Pharmacological inhibition of GH signalling in xenograft studies.
| Cancer type | Cell line | Dose | Outcome | References |
|---|---|---|---|---|
| Meningioma | Primary meningiomas (n = 15) (↓) | Pegvisomant, 45 mg/kg, daily | Serum IGF1: 19% reduction | (McCutcheon et al. 2001) |
| Monotherapy inhibition of tumour growth | ||||
| Colon | COLO 205 (↓) | Pegvisomant, 60 mg/kg, every second day | Serum IGF1: 56–64% reduction | (Dagnaes-Hansen et al. 2004) |
| HT-29 (-) | Reduced COLO 205 volume with monotherapy, not HT-29 | |||
| Breast | MCF-7 (↓) | Pegvisomant, 100 or 250 mg/kg, daily | Serum IGF1: 75% reduction (injection daily), 62% reduction (every other day), 54% (every 3 days) | (Divisova et al. 2006) |
| MDA-MD-231 (-) | Reduced MCF-7 tumour volume at 100 & 250 mg/kg monotherapy; no effect on MDA-MB-231 or MDA-MB-435 | |||
| MDA-MB-435 (-) | ||||
| Breast | Patient-derived breast cancer (↓) | Pegvisomant, 40 mg/kg, daily, for 2 weeks | IGF-1: not described | (Lombardi et al. 2014) |
| Monotherapy with pegvisomant-inhibited tumour growth | ||||
| Endometrial | RL95-2 (↓) | Pegvisomant, 100 mg/kg, every second day | Serum IGF1: 45–76% reduction | (Evans et al. 2016) |
| Radiation + PEG significantly delayed tumour growth; no monotherapy effect | ||||
| Prostate | 22Rv1 (-) | Pegvisomant, 100 mg/kg, 3 times weekly | IGF1: liver and tumour expression reduced (WB) | (Recouvreux et al. 2017) |
| No effect on tumour growth with monotherapy or combination with enzalutamide; decreased expression of AR V7 | ||||
| Prostate | Pten-mutant mouse prostate cancer cell (↓) | Pegvisomant, 100 mg/kg, 3 times per week | IGF1: 18% reduction (3 days after the final injection) | (Unterberger et al. 2022) |
| Monotherapy-reduced Pten-mutant prostate tumour growth | ||||
| Hepatocellular | HepG2 (↓) | Pegvisomant, 100 mg/kg, daily | IGF1: ~80% reduction | (Kaseb et al. 2022) |
| Monotherapy-reduced tumour growth; reduction was more pronounced when combined with sorafenib | ||||
| Gastric | SUN-16 (↓) | Pegvisomant, 250 mg/kg, daily | IGF-1: not described | (Meng et al. 2022) |
| Monotherapy: inhibition of tumour growth | ||||
| Pancreatic | PANC1 (↓) | Pegvisomant, 10 mg/kg/day; Compound G, 10 mg/kg/day | IGF1: not described | (Brody et al. 2021) |
| Monotherapy: PEG and compound G decreased tumour growth | ||||
| GEM combined with PEG or compound G was more efficacious | ||||
| Breast | MCF-7 (↓) | GHR/PRLR neutralising mAb; 15 or 30 mg/kg, twice weekly | IGF1: not described | (Chen et al. 2020) |
| T-47D (↓) | Monotherapy: inhibited the growth of T-47D and MCF-7 | |||
| Breast | MDA-MB-231 (↓) | Small-molecule BM 001–003, 1 and 5 mg/kg, 3 times per week | IGF1: 73% reduction | (van der Velden et al. 2022) |
| Monotherapy: all three reduced tumour growth; BM001 was most effective |
(↓) indicates cell lines for which efficacy with GHR antagonism was observed either as a single agent or in combination therapy, while (–) indicates no effect.
AR V7, androgen receptor splice variant 7; GEM, gemcitabine; IGF1, insulin-like growth factor-1; mAb, monoclonal antibody; PEG, pegvisomant; PRLR, prolactin receptor; WB, western blot.
A major obstacle to preclinical studies in animal models which also limits access to suitable antagonists is the species specificity of the drug. Pegvisomant is the only clinically available GHR antagonist. It is approved by the FDA for the treatment of acromegaly, a disease associated with elevated circulating GH and IGF1 (Neggers et al. 2016). Pegvisomant is a PEGylated derivative of B2036, a mutant GH protein containing a G120K mutation at binding site 2 which abrogates activation of the GHR, as well as eight additional mutations in binding site 1 (Kopchick et al. 2002, 2014, van der Lely & Kopchick 2006). Both GH and B2036 have a very short circulating half-life (15–20 min). To improve half-life, B2036 is conjugated with 5 kDa polyethylene glycol (PEG) via amine residues resulting in a heterogenous mix of conjugates with 4–6 PEG moieties. PEGylation reduces renal filtration and extends the serum half-life of pegvisomant in humans to 72 h, but it comes at the cost of a significantly lower binding affinity for the mouse Ghr (Kopchick et al. 2002, 2014, van der Lely & Kopchick 2006). Consequently, very high doses of pegvisomant are required to achieve effective Ghr inhibition and reduce circulating IGF1 in rodents. For example, the dose used in humans to normalise IGF1 levels is 10–30 mg daily subcutaneous injections (~0.1–0.4 mg/kg for an average person) (Jen et al. 2013). However, the equivalent dose used in rodents is several hundred-fold higher with doses of 45–250 mg/kg/day (Table 3). Furthermore, as for many biotherapeutics, the circulating half-life is shorter in rodents vs humans so more frequent dosing is required (Mordenti et al. 1991).
Cancer studies that have used pegvisomant are summarised in Table 3. Combined, with the challenges that receptor species specificity poses to xenograft studies, there is a clearly a need for the development of GHR antagonists that have better efficacy against the mouse Ghr. Arguably, by using antagonists that have poor efficacy in rodents and as a consequence reduced anticancer efficacy, potentially effective therapies may be discontinued before they even have a chance of being evaluated in humans.
Another approach to investigate mechanisms of GHR antagonism in cancer progression is to use genetic models as an in vivo preclinical platform (Swanson & Unterman 2002, Kopchick et al. 2014, Qian et al. 2020, Lantvit et al. 2021, Basu et al. 2022, Haque et al. 2022). For example, the Kopchick lab used Ghr knockout mice or mice transgenic for a Ghr antagonist to assess the efficiency of combining chemotherapy with Ghr antagonism in mouse melanoma and HCC. Their results indicated that Ghr antagonism sensitised tumours to anticancer therapy (e.g. cisplatin and sorafenib) by downregulation of ATP-binding cassette multidrug efflux transporters (Qian et al. 2020, Basu et al. 2022). Swanson and Unterman found that GH-deficient spontaneous dwarf rats were resistant to the development of carcinogen-induced mammary tumours (Swanson & Unterman 2002). Another recent study incorporated mice with global or liver-specific disruption of the Ghr gene and induced HCC using diethylnitrosamine. Only 5.6% of mice with global Ghr deletion (Ghr−/−) developed tumours, compared with 93.5% of wild-type mice. Mice with liver-specific Ghr disruption also had significantly less tumours in the liver (Haque et al. 2022). While results from genetic studies are persuasive, deficiency in GH signalling, for example, through Gh1/Ghr gene disruption or constitutive expression of a GHR/Ghr antagonist, would be expected to affect initial tumour establishment and growth prior to treatment when compared with wild-type animals which are used as a control. A comparison with a treatment setting in patients is therefore limited. Alternatively, inducible deletion of the Ghr gene or expression of an antagonist in transgenic animals can be used. However, leaky expression of the transgene is common in inducible models which is likely to still affect tumour establishment compared to wild-type animals. These types of studies are still extremely useful, particularly in the absence of accessible long-acting inhibitors and clearly support a role for GH in tumour development and progression. But pharmacological inhibition is more likely to yield relevant data on translation, particularly if the drug effectively targets both mouse and human GH signalling.
Another consideration for preclinical studies is the mode of GH action. GH has endocrine, autocrine and paracrine effects (Fig. 1), with both systemic and tumour-derived GH accelerating cancer progression (Harvey et al. 2015, Perry et al. 2017, Lu et al. 2019). In addition, systemic release of GH from the anterior pituitary increases circulating IGF1 which is also implicated in many malignancies, particularly breast cancer (Cao & Yee 2021). Inhibiting GHR signalling would therefore be expected to have additional benefits via suppressing systemic IGF1. Clinical trials with agents that target the IGF1 receptor (IGF1R) have not been encouraging but this is attributed to multiple reasons including interaction with insulin receptor signalling and recent evidence that the role of the IGF1R in tumour development and phenotype may be context-dependent (Cao & Yee 2021, Bulatowicz & Wood 2022).
Taking endocrine, autocrine and paracrine actions into account when investigating GHR antagonism is therefore important, particularly in the context of rodent xenograft models where tumours are derived from human cancer cell lines and cells of murine origin in the tumour microenvironment. Due to species specificity, mouse Gh does not activate the human GHR (Souza et al. 1995). However, systemic Gh is the key regulator of Igf1 release from the liver into the circulation. Mouse Igf1 exhibits cross-species activity so can affect human cancer cells expressing human IGF1R and mouse cells expressing Igfr1 in the tumour microenvironment (Perry et al. 2013). But the endocrine system is not the only secretion pathway for GH. GH is also secreted locally from cancer and microenvironment cells, such as endothelial, immune and fibroblast cells, and affects tumour progression through autocrine/paracrine effects on neighbouring cells (Chesnokova & Melmed 2019, Lu et al. 2019).
Studies from in vivo xenograft experiments across different cancer models demonstrate that GHR antagonism can inhibit the growth of certain tumour types (Table 3). It is likely that this is due to the combined effects of supressing endocrine Gh/Igf1 and autocrine/paracrine effects of GH/Gh/IGF1/Igf1. This is harder to achieve in xenograft models due to the species specificity of GH and the antagonists (as described above) but is an important consideration as it more closely mimics the human clinical setting. Indeed, for preclinical models to effectively predict clinical activity, therapies will need to block GHR and Ghr to a similar degree. However, from a mechanistic point, the contribution that systemic vs autocrine/paracrine GH secretion plays in tumour progression is certainly of interest. Species-specific inhibitors will help dissect out the relative contributions of systemic and autocrine GH secretion in xenograft experiments.
The need for new drugs: current strategies targeting GH signalling and secretion
Strategies targeting GHR signalling
While pegvisomant is an extremely effective human GHR inhibitor, there has been no indication that the drug is being repurposed for oncology applications. The only cancer-related clinical study reported was a phase 1 dose-escalation trial conducted in 2007 to assess the clinical efficacy of pegvisomant in individuals with normal GH/IGF1 levels to guide dosing regimen for further evaluation in cancer patients. In this trial pegvisomant was well tolerated at high doses over 14 days (Yin et al. 2007). Access to pegvisomant for research purposes is also limited which has hampered the types of in vivo preclinical studies that can be conducted. The lack of access is partially driving a growing interest among researchers and clinicians in the development of novel inhibitors. Encouragingly, there has been a lot of recent activity in preclinical drug development, which should eventually expand the options available for clinical and research applications (Lu et al. 2019). We anticipate this will intensify, particularly given the interest in GHR signalling as a target for extending healthy lifespan (Longo et al. 2015, van der Spoel et al. 2016, Duran-Ortiz et al. 2021).
Generation of long-acting biological therapeutics such as pegvisomant has been the preferred and most effective approach to ensure selectivity (pegvisomant has no off-target effects against the related PRLR (Goffin et al. 1999)). Recently, variants of pegvisomant have been reported with alteration in the amino acid sequence and/or PEGylation at specific sites on the protein with larger PEG moieties (Brody et al. 2020, Wang et al. 2020, 2021). These include compound G, which is site-specifically PEGylated at 2 sites on the protein core (Brody et al. 2020, 2021). Compound G and pegvisomant exhibited single-agent efficacy against pancreatic cancer xenografts and reduced tumour growth in combination with gemcitabine, compared with monotherapy (Brody et al. 2021). Ongoing development of PEGylated protein GHR antagonists will likely utilise site-specific PEGylation as this yields a more homogenous drug during manufacture compared with the amine-conjugated approach used for pegvisomant. Another approach for generating long-acting therapeutics is by making large fusion proteins. For example, fusion of a GHR antagonist to a variant of GH-binding protein (GHBP) generated a potent antagonist with extended serum half-life (Wilkinson et al. 2016).
Another inhibitor that is under development is the antisense oligonucleotide, ATL1103 (Antisense Therapeutics). ATL1103 significantly reduced average serum IGF1 levels in patients with acromegaly in a phase II clinical trial (Trainer et al. 2018). Additional approaches include neutralising anti-GHR antibodies (Jiang et al. 2011, Siebel et al. 2014, Sun et al. 2015, Chen et al. 2020). An antibody reported recently by Chen et al. is a dual-function antagonist targeting both the GHR and PRLR that blocks signalling from both receptors and attenuates T-47D and MCF-7 tumour growth in vivo (Chen et al. 2020). Peptide antagonists are also an attractive approach due to more convenient manufacturing processes. A novel 16 amino acid helical peptide antagonist of the GHR (S1H) was recently reported that inhibits GH-dependent STAT5 phosphorylation in cell lines (Basu et al. 2021). A second bicyclic 16 amino acid peptide GHR antagonist (AZP-3813, Amolyt Pharma) is being developed as a potential treatment for acromegaly for use in combination with somatostatin analogues.
The GHR has not been considered a particularly good druggable target for small-molecule development. Targeting protein–protein interactions (PPIs), such as the GH–GHR interface, is difficult, as the contact surfaces tend to be large, flat and featureless which can negatively impact on the binding affinity of small molecules and their selectivity for the target. Despite challenges, recent high-profile successes targeting PPIs (Nero et al. 2014, Lu et al. 2020) and small-molecule inhibitors of the structurally related PRLR have been published which bind near the prolactin receptor PPIs (Borcherding et al. 2021). Thus, the likelihood of identifying GHR-inhibiting small molecules is more encouraging. An earlier report of a small-molecule GHR inhibitor most likely acts downstream of the receptor (Rosengren et al. 2007). But another approach by van der Velden et al. identified molecules that may inhibit GHR protein synthesis in the endoplasmic reticulum (van der Velden et al. 2022). This may be a better approach for cancer therapy as it avoids potential challenges when targeting autocrine GH; it has been suggested that receptor antagonists such as pegvisomant may not be as effective at inhibiting autocrine GH, as autocrine GH interacts with the GHR and initiates signalling in the endoplasmic reticulum and Golgi before it reaches the cell surface (van den Eijnden & Strous 2007, Strous et al. 2020). Given earlier studies from Peter Lobie’s group demonstrating that autocrine GH promotes cancer progression more effectively than exogenous GH treatment (Kaulsay et al. 1999, Mukhina et al. 2004, Zhu et al. 2005), effective targeting of autocrine GH will be an important clinical consideration. However, inhibiting intracellular autocrine GH alone may not be sufficient. B2036/pegvisomant can inhibit autocrine GH that is secreted into the media by breast cancer cells (Kaulsay et al. 2001). In our hands, stimulation with exogenous GH still activates GHR signalling in cell lines expressing endogenous autocrine GH (unpublished). Similarly, primary human mammary carcinoma cells expressing endogenous GH proliferate in response to exogenous GH treatment, and this can be blocked with a GHR antagonist (Chiesa et al. 2011). It is therefore feasible that both GH-induced signalling via cell surface receptor activation and intracellular GHR activation are necessary for full autocrine action by GH (Chhabra et al. 2011). Certainly, clarification regarding the mechanism by which autocrine GH has a greater role in cancer promotion than endocrine/systemic GH will be important to inform which inhibition strategies are more appropriate for oncology applications.
In addition to human antagonists, a mouse Ghr antagonist should be considered for studies in rodents. Given the promising results obtained with pegvisomant in tumour studies, it is likely that a mouse Ghr antagonist will be more effective due to more potent GHR inhibition/IGF1 suppression. Alternately, animal models humanised for the GH and GHR genes can be considered. A humanised GH1 mouse has been reported previously (Ariyasu et al. 2019). As observed with the human GH signalling system, human GH in humanised mice will activate the mouse Prlr (Bartke & Kopchick 2015) and therefore provides a means of investigating combinations of GHR and PRLR inhibitors in cancer.
Strategies targeting GH secretion
Strategies that regulate endocrine GH secretion may also be beneficial in cancer therapy. Hormones that control GH release from the anterior pituitary include hypothalamic hormones, GH-releasing hormone (stimulatory; GHRH) and somatostatin (inhibitory), and the predominantly gastric hormone, ghrelin (stimulatory) (Fig. 1). GH secretion can be inhibited by somatostatin analogues and dopamine agonists that are used clinically, as well as GHRH and ghrelin antagonists that are in development (Costantini et al. 2011, Melmed et al. 2018, Lu et al. 2019).
Somatostatin analogues were developed for acromegaly and are recommended as the first-line medical treatment for patients with uncontrolled disease following surgery or for those who do not undergo surgery (Melmed et al. 2018). Since then, these drugs have demonstrated benefit in the clinical management of neuroendocrine tumours that overexpress somatostatin receptors (SSTRs) and may also have application in other types of cancer (Rai et al. 2015, Kawasaki et al. 2023). Somatostatin inhibits hormone and growth factor secretion, apoptosis, cell proliferation and angiogenesis. The receptors for somatostatin are a family of five G protein-coupled receptors (GPCRs). First-generation somatostatin analogues, octreotide and lanreotide, are standard-of-care options for patients with advanced unresectable neuroendocrine tumours (Kawasaki et al. 2023). Anticancer efficacy is mediated by direct interaction with SSTRs on tumour cells, with potential additional mechanisms through reduced systemic GH/IGF1 and secretion of other hormones (Wolin 2012). Octreotide and lanreotide have higher affinity for SSTR2 than the other four receptors and inhibit GH secretion and, to a lesser extent, thyroid-stimulating hormone, adrenocorticotrophin and prolactin secretion (Labadzhyan & Melmed 2022).
GHRH and its receptor are also expressed across many different cancer types (Schally et al. 2008). For example, high levels of the GHRH receptor are associated with reduced 10-year overall survival rate in patients with locally advanced rectal cancer (Fodor et al. 2023). Several generations of well-characterised GHRH antagonists have been described that exhibit anticancer activity in vitro and in preclinical tumour models (Schally & Varga 2006, Schally et al. 2008, Zarandi et al. 2017, Cai et al. 2022a ). As with somatostatin analogues, mechanisms of action include direct inhibition of autocrine/paracrine effects on tumours, as well as indirect actions through reduced endocrine release of GH/IGF1 (Schally et al. 2008).
Another key hormone that controls GH secretion is ghrelin which regulates energy homeostasis and is a potent stimulator of GH release. Ghrelin acts through interaction with GH secretagogue receptor (GHSR), a GPCR which is expressed in the hypothalamus and pituitary (Yanagi et al. 2018). Small-molecule and peptide GHSR antagonists have been described. However, some exhibit unexpected behaviour with both antagonistic and agonist properties (Price et al. 2021). For example, GSK1614343 and BIM-28131 inhibit GH secretion in vivo but stimulate other ghrelin functions such as food intake (Costantini et al. 2011, Hassouna et al. 2013). GHSR signalling is very complex; there are many ligands, and receptor activity and pathway activation can be modified by heterodimerisation and crosstalk with numerous other transmembrane proteins (Price et al. 2021, Gross et al. 2023). As with GHRH and somatostatin, GHSR and its ligands are associated with tumour development and progression in multiple tumour types. Whether ghrelin antagonists will be beneficial as anticancer agents is still under investigation (Soleyman-Jahi et al. 2019, Kotta et al. 2022).
Looking forward: translational considerations
While there is now considerable evidence that GHR signalling plays an important role in cancer, these observations have not yet translated into clinical trials of GHR antagonists in patients. However, there have been anecdotal reports of tumour stabilisation and/or regression in individual cases treated with pegvisomant: two HCC patients and a male breast cancer patient with acromegaly (Leporati et al. 2015, Kaseb et al. 2022).
There are important factors to consider as the field turns towards the translation of GHR antagonists into clinical trials, particularly considering 94% of oncology drugs tested in clinical trials never obtain FDA approval (Haslam et al. 2023). Leading causes for failure in clinical trials include a lack of clinical efficacy, toxicities, trial design and a lack of biomarkers (Wong et al. 2019, Sun et al. 2022). In terms of clinical efficacy and toxicity, using pegvisomant or derivative drugs may be an advantage as pegvisomant has already been demonstrated to be a highly potent antagonist in the clinical setting that is selective for GHR and is well tolerated in normal individuals when given at high doses for short periods (Goffin et al. 1999, Yin et al. 2007). Efficacy studies in cancer clinical trials will be of interest to evaluate not only if pegvisomant has anticancer efficacy, but also whether its low toxicity profile is maintained over the longer treatment periods that might be needed in an oncology setting.
Crucially important is also the study design and selection of the tumour type and treatment modality, Monotherapy efficacy has been observed for some tumour types but not all (Table 3). Ultimately combination therapy is likely to be more successful. GH blockade may have application in a wide range of GH- and IGF1-responsive cancers (Kopchick et al. 2022). However, early trials should focus on a tumour type for which there is strong supporting evidence, such as breast, melanoma or HCC (Tables 1 and 2) (Perry et al. 2008, Wu et al. 2011, Sustarsic et al. 2013, Kong et al. 2016, Kaseb et al. 2022). HCC is an aggressive and difficult-to-treat cancer with limited systemic treatment options for advanced disease (Tümen et al. 2022) that might be a good candidate for clinical evaluation of GHR antagonists. Increased GH in the serum and tumours of HCC patients has been shown to correlate with poor prognosis (Tables 1 and 2), while GHR inhibition reduces HCC tumour growth in mice (Kong et al. 2016, Basu et al. 2022, Haque et al. 2022, Kaseb et al. 2022). However, confusing the issue, low GHR expression in HCC correlates with better prognosis in several recent reports (Tables 1 and 2). This highlights the need for further studies clarifying the role of GHR signalling prior to clinical applications. High serum GH in HCC may be explained by loss of negative feedback that regulates GH secretion. Kaseb et al. hypothesised that liver damage might be expected to decrease hepatic IGF1 release, which in turn leads to increased GH release from the pituitary due to the loss of negative feedback from IGF1 (Kaseb et al. 2022).
The utility of biomarkers to select patient populations for trials, to monitor toxicity and to serve as surrogate clinical efficacy endpoints will also be an important consideration. Clinical trials that use predictive biomarkers to enrich patient selection have much higher overall success rates (Wong et al. 2019). At present there is a lack of diagnostic biomarkers capable of predicting GH response in tumours and it is clear from in vitro and in vivo studies that GHR expression alone will not predict responsiveness to GHR antagonism (Divisova et al. 2006, Chiesa et al. 2011). In xenograft experiments, pegvisomant does not inhibit all tumour types (Table 3), despite expression of the GHR. Reduction in serum IGF1 measurements work very well as a surrogate biomarker for confirming inhibition of systemic GH in patients, but this does not measure tumoural GHR inhibition so other variables may need to be considered for predictive biomarkers.
Will suppression of GH signalling reduce cancer incidence in humans?
Life expectancy has increased over the last two centuries, but this has not been accompanied by a comparable increase in healthspan (the period of life free from disease) due to the increase in non-communicable diseases afflicting a growing older population (Garmany et al. 2021, Le Couteur & Barzilai 2022). Inhibition of the GH/IGF1 axis has been highlighted as a key strategy to improve human healthspan by reducing the incidence of chronic age-related diseases such as cancer and diabetes (Berryman et al. 2008, Longo et al. 2015, van der Spoel et al. 2016, Aguiar-Oliveira & Bartke 2019, Lu et al. 2019, Duran-Ortiz et al. 2021, Bartke 2022, Brown-Borg 2022). Supporting this, humans born with Laron syndrome have remarkably reduced incidence of cancer, increased insulin sensitivity and youthful cognitive and motor functions (Laron et al. 2017, Guevara-Aguirre et al. 2020, Werner et al. 2020). Animal models of Laron syndrome or GH insensitivity also have reduced cancer rates and are protected from experimentally induced tumours (Bartke et al. 2016, Duran-Ortiz et al. 2021). Mice with postnatal deletion of the Ghr gene from 6 weeks also appear to have many of the beneficial effects observed in long-lived Ghr knockout mice (Junnila et al. 2016). Beneficial effects are also seen with Ghr gene disruption in mature animals, with sexually dimorphic effects observed on lifespan and cancer (Duran-Ortiz et al. 2021). While pharmacological suppression of GH signalling has been proposed as a means of protecting against cancer, this field is still in its infancy and the potential treatment window critical for extended healthspan is unclear. Ongoing investigations in this area will be of interest.
Conclusion
There is considerable evidence that GH signalling through the GHR can promote cancer progression; however, although several GHR antagonists have been developed, there is little known about how to optimise their use in cancer patients in the clinic. Identifying biomarkers for patient selection and efficacy endpoint monitoring will be critical, with additional preclinical studies required. For preclinical models to effectively predict clinical activity, therapies will need to block both human and mouse GH signalling. Development of a surrogate rodent-specific Ghr inhibitor is therefore recommended. Ultimately, an increased interest in preclinical drug development will expand the options available for clinical and research applications and can inform large-scale clinical studies where GH and GHR expression in tumours, circulating GH, clinicopathological features and survival outcome are profiled in patients to identify optimal use of these agents.
Declaration of interest
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of this review.
Funding
This work did not receive any specific grant from any funding agency in the public, commercial or not-for-profit sector.
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