Re: Melanoma—Time to fast or time to feast? An interplay between PPARs, met

[문형철]

beyond reason

면역항암제는 20-30% 흑색종을 포함한 말기암환자를 완치시킨다.

놀랍게도 beta-hydroxybutyrate는 면역항암제 CTLA-4, PD-1항체와 마찬가지의 기전을 가지고 있다.... 정리해야...

The connections between melanoma biology and behaviour, and the systemic physiology, particularly the energy balance and the activity of immune system, including inflammatory response and immunosuppression.

On the systemic level (left), deregulated food intake may lead to obesity and increased risk of cancer development.

On the cellular level (right), dietary factors including PPAR agonists or ketogenic diet affect melanoma cell physiology and anti‐tumor immunity, as described in the text.

Abbreviations: CTLA‐4, cytotoxic T lymphocyte‐associated protein 4, an immune checkpoint receptor; MDSC, myeloid‐derived suppressor cells; NLRP3, inflammasome; PD‐1, programmed death 1, an immune checkpoint receptor

Melanoma—Time to fast or time to feast? An interplay between PPARs, metabolism and immunity

Maja Grabacka 

Przemyslaw M. Plonka 

Krzysztof Reiss

First published: 20 January 2020

Abstract

Development and progression of melanoma can be accelerated by intensification of particular metabolic pathways, such as aerobic glycolysis and avid amino acid catabolism, and is accompanied by aberrant immune responses within the tumor microenvironment. Contrary to other cancer types, melanoma reveals some unique tissue‐specific features, such as melanogenesis, which is intertwined with metabolism.

Nuclear peroxisome proliferator‐activated receptors (PPARs) take part in regulation of systemic and cellular metabolism, inflammation and melanogenesis. They appear as a focal regulatory point for these three distinct processes by occupying the intersection among AMP‐dependent protein kinase (AMPK), mammalian target of rapamycin (mTOR) and PPAR gamma coactivator 1‐alpha (PGC‐1α) signalling pathways. When deregulated, they may accelerate melanoma malignant growth. Presenting the contribution of PPARα and PPARγ in melanoma biology, we attempt to ask how two contrasting metabolic states: obesity and fasting, can change progression of the disease and possible outcome of the treatment. This short essay is aimed to provoke a discussion about some practical implications for melanoma prevention and treatment, especially: how metabolic manipulation may be exploited to overcome immunosuppression and support immune checkpoint blockade efficacy.

Abbreviations

• AA - acetoacetate
• Akt-human analog of viral oncogene from thymoma line derived in Ak mouse bred by Jacob Furth
• AMPK - AMP‐dependent protein kinase
• bHB - beta‐hydroxybutyrate
• BMI - body mass index
• BRAF V600E
• B‐isoform of Rapidly Accelerating Fibrosarcoma (mutation valine glutamate at position 600)
• CAA - cancer‐associated adipocytes
• CAR - chimeric antigen receptor
• CTLA‐4 : cytotoxic T lymphocyte‐associated protein 4
• CTLs - cytotoxic T lymphocytes
• DAMPs - damage‐associated molecular patterns
• DCB - durable clinical benefit
• DCT - dopachrome tautomerase
• EPR - electron paramagnetic resonance
• Erk - extracellular signal‐regulated kinase
• FAO - fatty acid oxidation
• GSK - glycogen synthase kinase
• HCAR2 - ydroxycarboxylic acid receptor 2
• HIF‐1α : hypoxia‐inducible factor 1 α
• MAPK : mitogen‐activated protein kinase
• MDSCs - myeloid‐derived suppressor cells
• MEK - MAPK/ErK kinase
• MITF - microphthalmia‐associated transcription factor
• mTORC1 - mammalian target of rapamycin (mTOR) complex 1
• NF‐κB : nuclear factor of kappa chains in B lymphocytes
• NLRP
• nucleotide‐binding oligomerization domain, leucine rich repeat and pyrin domain containing
• NOS - nitric oxide synthase
• NRAS Q61R
• Q61K, Neuroblastoma RAS viral oncogene homolog (RAS—rat sarcoma), mutation glutamine
• OXPHOS - oxidative phosphorylation
• PAMPs : pathogen‐associated molecular patterns
• PD‐1 : programmed death protein 1
• PD‐L1 : ligand for PD receptors type 1
• PGC‐1α : peroxisome proliferator‐associated receptor gamma coactivator 1‐alpha
• PPARs : peroxisome proliferator‐associated receptors
• PRMT : protein arginine methyltransferase
• PTEN : phosphatase and tensin homolog deleted on chromosome ten
• ROS : reactive oxygen species
• STAT : signal transducer and activator of transcription
• TAMs : tumor‐associated macrophages
• TCA : tricarboxylic acid cycle (Krebs cycle)
• TCR : T‐cell receptor
• TRP1 : tyrosinase‐ related protein 1, DHICA (5,6‐dihydroxyindole‐2‐carboxylic acid) oxidase
• VEGF : vascular endothelial growth factor

1 INTRODUCTION ‐ ENERGY METABOLISM IN TUMORS IS ATYPICAL BUT PPARS AFFECT IT

Cancer is a systemic disease that has a strong and destructive impact on the body metabolic homeostasis. Metabolic reprogramming on the cellular level is an integral part of neoplastic transformation and has far reached consequences for the host organism energy balance.

Majority of cancer cells rely mostly on glycolysis and glutaminolysis for energy production and consume glucose and glutamine at much higher extent than non‐transformed cells, showing no deceleration of glycolysis rate in aerobic conditions (the so‐called Warburg effect).1 In melanoma, the presence of oncogenic mutations determines the metabolic landscape and clinical behaviour, which provided the basis for assignment into four subtypes: tumors with BRAF V600E mutation, tumors with NRAS Q61R or Q61K mutations, tumors with neurofibromatosis 1 (NF1) loss of function mutations and triple wt (ie with neither of these mutations) tumors.2 The glycolytic phenotype is particularly distinct in the tumors with BRAF V600E, present in approximately 50% of cutaneous melanoma cases. Such tumors show hyperactive Erk1/2 signalling, a high expression‎ of hypoxia‐inducible factor 1α (HIF‐1α) and glucose transporter GLUT‐1, which enables avid glucose consumption (reviewed in3).

Recently Najera and co‐authors have analysed bio energetic cellular (BEC) index (β‐F1‐ATPase/HSP60/glyceraldehyde dehydrogenase expression‎ ratio) of large cohorts of melanoma samples from various progression stages (63 melanocytic nevi, 55 primary lesions, 35 metastases), as well as a set of benign and malignant cell lines. Their study revealed that enhanced expression‎ of glycolytic enzymes and mitochondrial chaperonin HSP60 together with decreased mitochondrial β‐F1‐ATPase (low values of BEC index) were associated with aggressive, metastatic disease and correlated with worse disease‐free and overall survival.4

Importantly, apart from glucose and glutamine addiction, cancer cells are usually incapable of using ketone bodies (ie beta‐hydroxybutyrate—bHB, and acetoacetate—AA) for energetic purposes due to the lack of proper enzymatic machinery.5, 6 This fact and cancer‐directed cytotoxicity of bHB7 were rationales to apply a ketogenic therapy to treat malignant brain tumors. Gliomas and neuroblastomas have been proved to be particularly prone to this therapy in certain situations.8-11 Melanoma cells utilize large quantities of glucose producing high amounts of lactate, but it is not clear if they can perform ketolysis. The Krebs cycle (TCA) in these cells is not sustained by glycolysis‐derived pyruvate flux, but by glutamine via reductive carboxylation reactions catalysed by isocitrate dehydrogenase 2 (IDH2) in the reverse direction.12 Such a direction of TCA reactions is important for anaplerosis.13

Peroxisome proliferator‐activated receptors (PPARα, PPAR β/δ and PPARγ) are nuclear receptor superfamily members that work as ligand‐activated transcription factors and govern lipid, amino acid and glucose metabolism in both non‐transformed and malignant cells.14, 15 The fact that free fatty acids and their derivatives are PPARs’ endogenous ligands defines PPARs as nutritional sensors. PPARα activates fatty acid transport, uptake and oxidation, as well as ketone body biosynthesis; therefore, it is the main regulator of physiological response to fasting.16 PPARγ induces lipid synthesis and storage, as well as adipocyte differentiation, so it coordinates the physiological response to fed state.16

A long‐accepted notion indicated that ketogenesis triggered by fasting and subsequent elevation of blood ketone bodies creates cancer‐hostile environment. However, the oncology patients frequently struggle with cancer‐induced cachexia, so caloric restriction or fasting could dramatically worsen their state. On the other hand, the opposite condition, such as obesity or type II diabetes, is well‐established risk factors for the development of numerous cancers, including melanoma.17, 18

2 HYPOTHESIS: PPARS‐AFFECTABLE KETOSIS IS AN UNDERAPPRECIATED PROGNOSTIC FACTOR IN MELANOMA

Peroxisome proliferator‐activated receptors occupy the central point within the signalling network, at the intersection of axes driven by mTOR, AMPK, PGC‐1α and STATs, and therefore regulate ketosis and inflammation. Obesity, chronic, low‐grade inflammation and associated immunosuppression in the tumor microenvironment all create a favourable condition for melanoma progression and correlate with elevated risk of melanoma.17 Dietary prophylactic programs aiming at overweight/obesity reduction should, consequently, supplement melanoma prevention, beside responsibly dosed sun exposure.

In the present viewpoint, we attempt to support the hypothesis outlined above through following steps:

• Showing the unexpected importance of ketogenic enzymes in melanoma
• Describing how metabolic sensors that control intracellular energy status affect melanogenesis
• Presenting an experimental evidence that PPARα expression‎ level affects some of these metabolic sensors (AMPK, mTOR)
• Pointing out to an opposite activity of ketone bodies (acetoacetate vs beta‐hydroxybutyrate) in inflammation and melanoma progression
• Illustrating how melanoma tumors take advantage of the local inflammation
• Showing an indirect correlation between ketotic metabolism and immunological aspects of melanoma growth control, such as restriction of myeloid‐derived suppressor cells by PPARγ, engagement of ketosis in inflammation and immunosuppression, as well as arginine metabolism in melanoma
• Presenting how PPARα‐driven metabolism affects T‐cell functions important for immunotherapy

These steps are set out in greater detail below, and they track down to identification of the so‐called “obesity paradox” in melanoma and to potential elucidation of this phenomenon. Finally, we come to the conclusion that to provide ultimate clear‐cut clinical perspectives, that is to answer the question of “to fast, or not to fast?” each case must be analysed individually. We believe that such an approach is crucial to succeed in therapeutic attempts.

3 MELANOMA TUMORS TAKE ADVANTAGE OF EXPRESSION‎ OF KETOGENIC ENZYMES

Ketogenesis is characteristic only for particular tissues and cell types, such as hepatocytes, believed not to exist in tumors. Nevertheless, melanoma and glioma cells are able to synthesize beta‐hydroxybutyrate (bHB) in response to fenofibrate, a factitious PPARα ligand, although in a PPARα‐independent fashion.19 Recent findings indicate melanoma being the only known tumor that benefits from ketogenic gene expression‎: the presence of a ketogenic enzyme: 3‐hydroxy‐3‐methylglutaryl‐CoA lyase (HMGCL) is necessary for proliferation of melanoma cells with mutated (V600E) BRAF oncogene.20 HMGCL activity allows intracellular AA accumulation, which leads to activation of oncogenic MEK1/Erk signalling pathway and tumor progression.20 bHB does not show such an effect.20

4 METABOLIC SENSORS mTOR, PGC‐1α AND PPARα REGULATE MELANOGENESIS

Recent studies on melanocytes and melanoma cells revealed that metabolic circuits intriguingly intertwine with pigmentation (Figure 1). In patients with tuberous sclerosis syndrome (inactivating mutations of mTORC1 inhibitors TSC1 or TSC2), mTORC1 activation leads to hypopigmentation through decreased expression‎ of MITF and melanogenic enzymes.21 Some other reports also show the negative regulation of melanogenesis by mTORC1 in both melanocytes and melanoma cells.22-24 Meanwhile, PGC‐1α (PPAR gamma coactivator 1α) stimulates melanogenesis through the cooperation with MITF in response to UV.25 Mechanistically, MITF and PGC‐1α seem to reciprocally activate each other transcription.25, 26

Figure 1

Panel A, PPARα expression‎ level in murine melanoma B16 F10 cells affects mTOR phosphorylation status at Ser 2448 and Ser 2481 residues, as well as mTOR expression‎. Ctrl shRNA—the cells stably expressing vector that generates irrelevant shRNA molecules (Control Plasmid A, Santa Cruz Biotechnology), represent regular PPARα level; PPARa shRNA—the cells stably expressing vector (Santa Cruz Biotechnology) that generates shRNA molecules that specifically knock‐down PPARα expression‎ (for more technical details, including the cell line characterization and PPARα protein expression‎ data, please see29). The cells were treated with a PPARα pharmacological ligand fenofibrate (FF, 50 μmol/L) for the indicated time periods. The protein signal intensity ratios are shown over the respective bands. Grb2 probing served as a loading control. Panel B, The scheme illustrating the interplay among the kinases and other signalling molecules that affect melanoma progression through the action on metabolism, inflammation and melanogenesis. The dashed arrows represent the indirect action pf PPARα on pigmentation29 and mTOR phosphorylation. Phosphorylation sites cited after.95 mTORC1 components: Deptor, DEP domain TOR‐binding protein; mLST8, mammalian lethal with Sec13 protein 8; PRAS40, proline rich Akt substrate 40 kDa; Raptor, non‐enzymatic subunit of mTORC1. Other proteins: p70S6K, kinase phosphorylating ribosomal S6 protein, 70 kDa; Rheb, Ras homolog enriched in brain; TSC1/2, tuberous sclerosis complex protein 1/2

In the literature, there exists a long discussion on the role of melanogenesis in melanoma induction and progression, and at least some aspects of the problem indicate that melanogenesis badly reflexes itself in the prognosis.27, 28 This situates the “melanogenesis—PPARs‐metabolism—melanoma progression” axis in a special position. Recently, we have observed that in B16 F10 murine melanoma cells, PPARα expression‎ is inversely correlated with melanization. Reduced PPARα leads to hyperpigmentation, whereas PPARα overexpression‎ blocks melanogenesis.29 The cells with shRNA‐mediated PPARα knock‐down revealed significantly higher MITF, tyrosinase, TRP1 and DCT protein levels, as well as tyrosinase enzymatic activity and melanin content, comparing to the cells expressing control shRNA molecules or to the wt cells.29 The assessment of PGC‐1α acetylation status showed that PPARα‐deficient cells had much higher proportion of deacetylated (active) PGC‐1α, which indicated a possible cooperation with MITF.

5 PPARα EXPRESSION‎ LEVEL AFFECTS mTOR ACTIVITY IN MELANOMA

These observations prompted us to investigate the changes in phosphorylation of mTOR, AMPK and other kinases that might be involved in metabolic reprogramming, using a PathScan Intracellular Signaling antibody array (Cell Signaling Technology). The relative changes in phosphorylation are shown in Table 1, with statistically significant differences marked in grey. PPARα knock‐down increased phosphorylation of AMPK, Akt Thr308, STAT1 and STAT3, as well as decreased mTOR Ser 2448 phosphorylation. Surprisingly, there is no difference in glycogen synthase kinase 3β (GSK‐3β) phosphorylation, which is another important player in metabolism and melanogenesis.30 GSK‐3β is a negative regulator of β‐catenin, a marker of melanoma progression[.31, 32]

Table 1. PPARα expression‎ level affects activity of certain signal transduction pathways in B16 F10 melanoma cells

Signalling protein phosphorylation/modificationCtrl shRNA (mean intensity±SD, n = 4)PPARα shRNA (mean intensity±SD, n = 4)The effect of PPARα knock‐down)SignalStatistically higher over backgroundSignalStatistically higher over backgroundCtrl shRNA vs PPARa shRNA

Erk1/2 Thr202/Tyr204	4.4 ± 2.9	ns	20.3 ± 8.9	P < .05	ns
Stat1 Tyr701	4.9 ± 1.3	ns	10.9 ± 3.7	P < .05	P < .05↑
Stat3 Tyr705	64.6 ± 5.2	P < .0001	88.9 ± 3.6	P < .0001	P < .001↑
Akt Thr308	21.4 ± 4.3	P < .001	67.7 ± 6.5	P < .0001	P < .001↑
Akt Ser473	95.6 ± 12.7	P < .001	98.2 ± 1.7	P < .0001	ns
AMPK Thr172	34.4 ± 4.6	P < .0001	57.2 ± 9.0	P < .001	p<0.01↑
S6 Ribosomal protein Ser235/236	10.0 ± 2.1	P < .05	43.4 ± 9.1	P < .01	ns
mTOR Ser2448	60.2 ± 4.4	P < .0001	48.8 ± 6.4	P < .0001	P < .01↓
HSP27 Ser78	8.7 ± 2.2	P < .05	23.0 ± 7.5	P < .01	ns
Bad Ser112	70.7 ± 6.4	P < .0001	74.8 ± 3.1	P < .0001	ns
p70 S6K Thr389	6.1 ± 3.0	ns	16.5 ± 5.0	P < .01	ns
PRAS40 Thr246	96.6 ± 12.8	P < .001	95.2 ± 1.2	P < .0001	ns
p53 Ser15	5.4 ± 1.3	ns	11.7 ± 4.1	P < .01	ns
p38 Thr180/Tyr182	0.4 ± 0.3	ns	4.2 ± 2.4	ns	ns
SAPK/JNK Thr183/Tyr185	25.1 ± 8.8	P < .05	36.6 ± 10.6	P < .01	ns
PARP Asp214 cleavage	2.6 ± 1.1	ns	9.0 ± 5.2	ns	ns
Caspase‐3 Asp175 cleavage	0.8 ± 1.1	ns	3.4 ± 2.8	ns	ns
GSK‐3β Ser9	99.8 ± 9.9	P < .0001	97.7 ± 2.9	P < .0001	ns

Note

• The data obtained from the densitometric analysis of the PathScan® Intracellular Signaling Array Kit (Cell Signaling Technology; #7323).
• Ctrl shRNA—the cells stably expressing vector that generates irrelevant shRNA molecules (Control Plasmid A, Santa Cruz Biotechnology), represent regular PPARα level; PPARa shRNA—the cells stably expressing vector (Santa Cruz Biotechnology) that generates shRNA molecules that specifically knock‐down PPARα expression‎ (for more technical details, including the cell line characterization and PPARα protein expression‎ data, please see29).
• Values represent intensities normalized to the internal positive control of the kit (intensity value 100).
• The negative control (no signal) possesses 0 value.
• Statistically significant differences between Ctrl shRNA and PPARα shRNA marked in grey, the arrows denote the direction of changes induced PPARα knock‐down.

Mammalian target of rapamycin (mTOR) is phosphorylated on two residues: Ser2448 by Akt and Ser2481 by autophosphorylation, which are both the signs of activation, so we checked their phosphorylation status in the cells with different PPARα level (PPARα shRNA and Ctrl shRNA), which were stimulated with fenofibrate in order to find out if PPARα activation also contributes to mTOR phosphorylation. Figure 1A shows the results of this experiment: PPARα shRNA cells express mTOR with much lower basal level of Ser2448 phosphorylation comparing to Ctrl shRNA cells (time 0, phospho‐Ser2448 to total mTOR ratio 0.09 vs 3.31), while the autophosphorylation at Ser2481 seems to be fairly constant. This suggests that the mode of mTOR phosphorylation indirectly depends on the PPARα expression‎ level (as marked in Figure 1, panel B, dashed arrow). The mutual interactions among the chosen signalling molecules from PathScan array are summarized in this panel, too.

The cell lines that differ in PPARα expression‎ respond to fenofibrate in the opposite way: phosphorylation of Ser2448 decreases in Ctrl shRNA and increases in PPARα shRNA cells in a time‐dependent manner (Figure 1), which is quite unexpected. It points out to the possibility that fenofibrate acts in a PPARα‐independent fashion in the context of mTOR activity and that it works differently in the cell lines varying in the PPARα level. This notion is further supported by our previous reports of a strong PPARα‐independent impact of fenofibrate on mitochondrial function and intracellular ATP balance in melanoma and glioblastoma.19, 33 In the PPARα shRNA cells, lower basal mTOR activity is accompanied by increased AMPK phosphorylation, which goes in line with the general tendency that mTOR and AMPK signalling pathways proceed in opposite directions. Up‐regulation of STAT1 and STAT3 phosphorylation in PPARα knock‐down cells (Table 1) might be attributed to the absence of anti‐inflammatory activities exerted normally by PPARα.34 Natural and synthetic PPARα ligands, including fenofibrate, were shown to inhibit STAT1 proinflammatory signalling in rat glial cells challenged with LPS.35 Antagonistic effects between STAT1, 3 and 5b, and PPARα were reported in various experimental settings,36-38 which is clinically relevant, since STAT3 is necessary for the melanoma and glioma tumor initiating cell maintenance.39, 40 Taken together, it seems that PPARα occupies the central point within the signalling network, at the intersection of axes driven by metabolic sensors mTOR, AMPK and inflammatory STATs.

6 ACETOACETATE AND BETA‐HYDROXYBUTYRATE EXERT OPPOSITE ACTIONS IN MELANOMA PROGRESSION AND INFLAMMATION

Ketosis—elevated blood concentration of ketone bodies—may result from three conditions: prolonged fasting, ketogenic diet (low carbohydrate, adequate protein, high fat) or severe diabetes. However, particular ketone bodies reveal distinct activities. A recent study by Xia et al41 reported that elevated acetoacetate (AA), but, interestingly, not beta‐hydroxybutyrate (bHB), leads to enhanced tumor growth and melanoma cell proliferation rate. This is puzzling, because AA is quite rapidly converted to bHB, which is the main ketone body in the circulation and tissues.42 Xia et al41 concluded that high fat ketogenic diet as potentially dangerous for melanoma patients is certainly not a recommended dietary option for them. This observation is important since ketogenic diet or its calorie‐restricted version was proved to be effective in numerous neurological conditions, such as paediatric epilepsy, as well as brain malignancies, particularly glioblastoma,43, 44 and is a common dietary support to the pharmacological treatment. Jain and collaborators observed the differences in the AA and bHB actions in the context of inflammation, and again, AA, but not bHB, induced various NF‐κB–dependent proinflammatory responses, including release of cytokines by monocytes and expression‎ of adhesion molecules in endothelial cells.45-49 In contrast, bHB inhibited the inflammasome NLRP3 activation in response to various stimuli, such as damage‐ or pathogen‐associated molecular patterns (DAMPs and PAMPs).50 Concluding, physiological ketosis characterized by high plasma levels of bHB seems to be much more favourable than high acetoacetate levels.

7 MELANOMA CELLS BENEFIT FROM LOCAL INFLAMMATION

The inflammasome should be regarded as a nodal point among ketogenesis, obesity and melanoma. NLRP3 is a cytoplasmic protein complex responsible for mobilization and release of IL‐1β and IL‐18 in response to pathogen‐ and damage‐associated molecular patterns (PAMPs and DAMPs). Recent studies revealed that the activity of NLRP3 was a driving force of melanoma metastatic behaviour and progression through IL‐1β secretion and autocrine activation loop.51-54 STAT3 phosphorylation is critical for IL‐1β production during inflammation and STAT1 contributes to invasive capacity in late‐stage melanomas.55, 56 NLRP3 up‐regulates IL‐1β and IL‐18 in adipose tissue and contributes to obesity and insulin resistance in mice fed with high fat diet, through maintaining of low‐grade chronic inflammation and promoting macrophage infiltration in adipose tissue.57, 58 In this context, NLRP3 inhibition and block of IL‐1β production by bHB encourage one to apply the dietary induction of ketosis or use this ketone body both in prevention and treatment of obesity and melanoma. The supportive use of fenofibrate as a well‐known anti‐hyperlipidemic PPARα agonist59 is well‐justified in these conditions, too.

8 EVASION FROM IMMUNOSURVEILLANCE IS A CHALLENGE FOR MELANOMA IMMUNOTHERAPY

Melanoma is considered as immunogenic tumor, and the number of well‐documented incidents of a spontaneous regression of the tumor is by 1.5 orders of magnitude higher than of other types of tumors and only comparable with lymphoma and hypernephroma.60 It must be, consequently, found surprising that melanomas have turned up resistant to typical immunotherapy approaches, tested so far.61 This is because of the phenomenon of immunosuppression, which often develops in tumor microenvironment, and counteracts host immune surveillance against cancer. No wonder that the Nobel Prize in 201862 was conferred for the therapeutically applicable modes to break the tolerance via targeting the suppressor receptors CTLA‐4 and PD‐1/PD‐2, which lead to the suppression of T‐cell‐mediated cytotoxic activity against melanoma cells, via weakening the cytotoxic response or inducing apoptosis of the effector T cells.

In advanced melanoma, expression‎ of PD ligand (PD‐L1) is potently up‐regulated by IFNγ, which is associated with the decreased relapse‐free survival.63 One of the approaches developed to overcome the problems of anti‐melanoma immunotherapy is the use of chimeric antigen receptor (CAR) T‐cell constructs. The CAR‐T designed to target melanoma cells contains a single‐chain variable fragment derived from a monoclonal antibody against a specific antigen (such as CD70, c‐Met, VEGF receptor 2 or ganglioside GD2), an intracellular component from T‐cell receptor (TCR) signalling domain and co‐stimulatory motifs.64 The anti‐melanoma efficacy of CAR‐T therapy is currently tested and seems promising, despite some difficulties that include limited penetration of transfected T cells in solid tumors, immunosuppressive tumor microenvironment and optimal choice of target antigens.64 Of equal importance is, however, the problem—why melanoma induces so effective an immunosuppression in the host.

9 PPARγ RESTRAINS THE EXPANSION OF IMMUNOSUPPRESSIVE MYELOID‐DERIVED SUPPRESSOR CELLS

Immunosuppression engages several cellular components including cancer‐associated fibroblasts, myeloid‐derived suppressor cells (MDSCs), regulatory T cells (T‐regs) and tumor‐associated macrophages (TAMs).65 A possible reason for immunosuppression in melanoma is the infiltration of MDSCs, which impair melanoma‐specific cytotoxic DC8+ and CD4 + T cells, and activate immunosuppressive T‐regs cells.61 An important link between the atypical metabolism and this immunological phenomenon in melanoma may be the anti‐inflammatory action of PPARγ which can control MDSCs‐mediated immunosuppression.66 In particular, attenuation of PPARγ, either by knocking down the lysosomal acid lipase (LAL−/−), which generates PPAR ligands, including 9‐hydroxyoctadecadienoic acid (9‐HODE),66 or by dominant negative PPARγ,67 elevated overall non‐specific inflammatory responses, infiltration of MDSCs, and MDSCs‐mediated immunosuppression towards T cells. These important observations indicate that, indeed, PPARγ activation may serve as a new strategy to counteract immunosuppression in melanoma tumor microenvironment.

10 KETOSIS SUPPRESSES INFLAMMATION BUT DOES NOT CONTRIBUTE TO IMMUNOSUPPRESSION

A similarly interesting link between the immune response of myeloid cells, which keep the tumor immunity suppressed and modifies the metabolism, involves hydroxycarboxylic acid receptor 2 (HCAR2), known also as GPR109A, which is highly expressed in myeloid cells from monocyte and granulocyte lineage. bHB is a prominent ligand of GPR109A,68, 69 and the activation of this receptor, on the one hand, suppresses inflammation in the gut, and on the other hand, its deficiency accelerates colon carcinogenesis.70 Ketogenic diet and bHB, specifically, have been shown to regulate macrophage M1/M2 phenotype switch, promoting alternatively activated, healing‐associated M2 macrophages that infiltrate adipose tissue and contribute to maintain the so‐called "metabolically healthy obesity," which means adiposity without inflammatory, insulin resistance and glucose intolerance traits.71 It is important to note that anti‐inflammatory actions do not necessarily imply immunosuppression, since the competence of lymphocytes involved in adaptive immunity is not affected by ketosis.72

11 METABOLISM OF ARGININE MAY BE ANOTHER CHECKPOINT IN METABOLIC CONTROL OF MACROPHAGE‐RELATED IMMUNITY

The balance between the Th1 and Th2 immunity, and, consequently, the M1 and M2 profile of TAMs is, in turn, an important aspect of the L‐arginine metabolism and therefore general effector responsivity of the immune system.73 In the case of the M1 reactivity, the macrophages use L‐arginine mainly to produce NO by the inducible NO synthase (NOS‐2), and in the M2 type—L‐arginine is used up by arginase to produce L‐ornithine, which delivers the carbon skeletons to produce biogenic amines (spermin and spermidin) necessary to synthesize DNA in proliferating cells.74 Macrophage‐produced NO is the important cytotoxic effector, which may lead to extensive necrosis of tumor tissue, and explains the extraordinary high EPR signal of nitroso‐iron complexes of haemorrhagic necrosis, typical of some murine melanomas and lymphomas growing in the M1‐type hosts (C57BL/6 but not DBA/2 mice).75 As the M2 phenotype favours proliferation, it may hypothetically affect also melanoma cells or at least tumor‐neoangiogenesis‐associated endothelium.76 This exemplifies tumor as “a wound that does not heal”77, 78 and provides a rationale for brand new therapy strategies based on L‐arginine deprivation,79 linking together the growth, immunity and atypical metabolism of melanoma. A prompt towards exploration of this direction is engagement of PPAR in the metabolism of amino acids.78

Another, still hypothetical, arginine‐related problem tempting to be verified is the engagement of PPARs, in particular PPARγ in the regulation of the expression‎ and activity of protein transmethylases of arginine (PRMT). This aspect of arginine metabolism concerns methylation of arginyl groups in proteins and is one of the most important ways of post‐translational modification of proteins. It has been shown that, for example PRMT 1 affects macrophage differentiation via modulation of PPARγ activity.80 Meanwhile, little is known about the vice versa effects of PPARs on the expression‎ and activity of PRMTs, all the more that there are implementations of free arginine methylation in the production of endogenous NOS inhibitors and PPARs activity.81, 82 This line of investigations—whether there are any PPAR‐related aspects of arginine methylation—is by all means worth a deeper exploration in the context of melanoma, but the scope of this paper is too narrow to discuss it in‐depth here.

12 PPARα/PGC‐1α‐DRIVEN METABOLISM IMPROVES T‐CELL FUNCTIONS—IMPLICATIONS FOR IMMUNOTHERAPY

Bidirectional “mitochondria—nucleus” signalling is important for both innate and adaptive immunity.83 The observations made on various types of immune cells, particularly T cells, demonstrated the quite obvious principle that quiescent cells (such as naïve T cells) rely on catabolic pathways (such as fatty acid oxidation—FAO) and respiration, whereas activation switches on anabolic pathways and intense glycolysis. However, the mitochondria, through the retrograde signalling to the nucleus, can influence the direction of immune cell differentiation and stability of their phenotype. The ROS production resulting from the respiratory chain activity and the key Krebs cycle (TCA) metabolites provides the means for such an influence.83 ROS are necessary for proximal TCR signalling and optimal NF‐κB function, the proper T‐cell activation and subsequent production of IL‐2 and IL‐4.84, 85 The TCA intensity indirectly controls protein post‐translational modifications through alterations in the metabolite levels: acetyl‐CoA for acetylation, succinate for succinylation, alpha‐ketoglutarate for demethylation (by Jumonji domain‐containing histone demethylases), proline hydroxylation (protein hydroxylases) and the above‐mentioned arginine transmethylation. Acetylation and demethylation, responsible for epigenetic regulation, are the most important histone modifications that control T‐cell fate.86

Awareness about these facts led to the discovery that chemical uncouplers and activators of mitochondrial ROS generation significantly improve the effectiveness of PD‐1 blockade immunotherapy in tumor‐bearing mice.87 The proliferative CD8 + effector T cells with the highest tumoricidal activity were characterized with increased intracellular ROS level, mitochondrial membrane potential and overall mitochondrial mass. Interestingly, both AMPK and mTOR signalling pathways were activated in these cells and contributed to enhanced anti‐PD‐1 efficacy.87 The importance of the mitochondrial biogenesis, oxidative metabolism and FAO in CTLs for the improvement of PD‐1 blockade was further confirmed in the experiments with bezafibrate, a pan‐PPAR agonist. The activation of PGC‐1α/PPAR pathway during the PD‐1 blockade reprograms the effector T‐cell metabolism in favour of OXPHOS and FAO, which helps them to retain proliferative capacity and prolongs their survival.88 In consequence, the higher numbers of tumor‐reactive CTLs accumulate in the draining lymph nodes and the tumor cell clearance is much more effective.88 Importance of the ROS role was also demonstrated in the study by Kim et al,89 who used phenformin, biguanide AMPK activator to improve immune checkpoint blockade treatment against BRAFV600E/PTENnull melanoma by a selective inhibition of myeloid‐derived suppressor cells (MDSCs). This serves as an example that pharmacological intervention in PPAR and AMPK pathways may be a valuable fortification of nivolumab (anti‐PD‐1 mAb)‐ or ipilimumab (anti‐CTLA‐4 mAb)‐based melanoma immunotherapy, with the chance of therapeutic success in the so far refractory patients. This is a promising direction for future clinical trials.

13 “OBESITY PARADOX” IN MELANOMA—TO FAST, OR NOT TO FAST?

The hypothesis that obesity affects clinical outcomes for melanoma patients was tested by the multicohort analysis of association of body mass index (BMI) and outcomes in patients with advanced metastatic melanoma, subjected to various treatment modes.90 Quite unexpectedly, the study revealed the association between obesity and improved overall survival and progression‐free survival in male patients in the cohort that received targeted combined therapy (BRAF/MEK inhibitors: dabrafenib plus trametinib), as well as in the cohort treated with immunotherapy (ipilimumab plus dacarbazine), as compared to patients with normal body weight. No such correlation was observed for patients treated with chemotherapy (dacarbazine). Interestingly, the survival benefit associated with high BMI strongly depended on sex and was completely absent in female patients.90 The authors of the analysis brought the term of “obesity paradox” to describe this phenomenon, explaining that higher BMI is associated with the increased risk of melanoma development but simultaneously confers a survival advantage in advanced disease in certain clinical circumstances.90

The further attempt to explain the phenomena underlying “obesity paradox” came from Naik et al,91 who correlated survival outcomes with markers of body mass composition (which is not revealed by BMI) in patients with advanced melanoma who received immunotherapy. On the basis of the facts that low lean body mass and high adiposity are associated with poor outcome in cancer,92 the authors correlated durable clinical benefit (DCB) with serum creatinine levels, which is a surrogate marker of skeletal muscle content. Low creatinine levels indicate sarcopenia, which is a strong predictor for mortality, because it impairs immune functions and demonstrates limited reserves of proteins and microelements.93 Indeed, a correlation of serum creatinine levels with survival in melanoma patients eliminates the “obesity paradox” in obese patients (BMI > 35).91 This explains also the gender bias in favour of males, as they usually have higher skeletal muscle content as compared to females. In the cohort of obese patients (BMI > 35), females had much lower serum creatinine levels and chances for DCB.91

14 CONCLUSIONS AND TRANSLATIONAL PERSPECTIVES—OBESITY AND MELANOMA

In melanoma, both cellular pathophysiology and aetiology of the systemic disease are connected with metabolism and energy homeostasis (this idea is outlined in Figure 2). On the cellular level, metabolism is tightly linked to pigmentation and melanin biosynthesis with PPARα being engaged in this process, too. Recent progress in understanding the onset of melanoma points out to obesity as one of the risk factors.94 Clement et al94 have gathered and adduced pieces of evidence from both human epidemiological and murine studies indicating that obesity creates favourable environment for melanoma development and progression in the following aspects: (a) increased Breslow thickness of primary lesions, (b) in situ tumor growth enhancement due to cytokine release from hypertrophic adipose tissue and cancer‐associated adipocytes (CAA), (c) release of fatty acids through lipolysis of CAA fat stores, (d) fact that serum from obese mice promotes melanoma invasiveness through epithelial‐to‐mesenchymal transition, (e) facilitated colonization of distant organs after intravenous injection of melanoma cells, (f) remodelling of extracellular matrix by adipocytes leading to facilitated angiogenesis and local invasion. All these special attributes of melanoma have to be considered in the context of immune system physiology and the fragile balance between pro‐ and anti‐inflammatory activities of its components, which provide the means of cancer cell eradication, but also create the cytokine‐rich environment that can be exploited by a developing tumor.

Figure 2

The connections between melanoma biology and behaviour, and the systemic physiology, particularly the energy balance and the activity of immune system, including inflammatory response and immunosuppression. On the systemic level (left), deregulated food intake may lead to obesity and increased risk of cancer development. On the cellular level (right), dietary factors including PPAR agonists or ketogenic diet affect melanoma cell physiology and anti‐tumor immunity, as described in the text. Abbreviations: CTLA‐4, cytotoxic T lymphocyte‐associated protein 4, an immune checkpoint receptor; MDSC, myeloid‐derived suppressor cells; NLRP3, inflammasome; PD‐1, programmed death 1, an immune checkpoint receptor

We need to be aware that, similarly to the case of ketogenic diet, the reduction of overweight per se is no longer an obvious target for a dietary intervention. Instead, a reasonable physical activity plan should be considered to prevent a muscle cell loss or to improve lean mass to total body mass ratio. Nevertheless, a careful analysis of individual needs is necessary, so as to provide the optimal support for melanoma patients.

ACKNOWLEDGEMENTS

The Faculty of Biochemistry, Biophysics and Biotechnology of the Jagiellonian University was a partner of the Leading National Research Center (KNOW) supported by the Polish Ministry of Science and Higher Education. The paper was partially supported from this grant (KNOW 35p/10/2015 to PMP). KR is supported by the grant 1P20 GM121288‐02.

CONFLICT OF INTEREST

The authors have declared no conflicting interests.

AUTHOR CONTRIBUTIONS

MG performed the experiments shown in Figure 1 and Table 1, prepared the displayed items and wrote the first draft of the text. All the authors (MG, PMP, KR) equally contributed to the design of the concept of the paper, preparation of the text and figures, literature scan and the final manuscript edition.