A novel insight into the anticancer mechanism of metformin in pancreatic neuroendocrine tumor cells

E. Vitalia,∗, I. Boemia, S. Piccinic,h, G. Tarantolaa,h, V. Smiroldob, E. Lavezzic, T. Brambillad, A. Zerbie, C. Carnaghif, G. Mantovanig, A. Spadag, A.G. Laniaa,c,h

Abstract

The antidiabetic drug metformin displays anticancer properties in several neoplasms. In pituitary NETs, aryl hydrocarbon receptor-interacting protein (AIP) is up-regulated by the somatostatin analog octreotide.
Metformin inhibited QGP-1 cell proliferation in a dose- and time-dependent manner, at concentrations similar to those achievable in treated patients (−31 ± 12%, p < 0.05 vs basal at 100 μM). Moreover, metformin decreased pancreatic neuroendocrine tumors (PAN-NETs) cell proliferation (−62 ± 15%, p < 0.0001 vs basal at 10 mM), without any additive effect when combined with octreotide. Both octreotide and metformin induced AIP up-regulation. AIP silencing abolished the reduction of mTOR phosphorylation induced by metformin and octreotide. Moreover, metformin decreased HSP70, increased Zac1 and AhR expression; these effects were abolished in AIP silenced QGP-1 cells.
In conclusion, metformin acts as an anticancer agent in PAN-NET cells, its activity is mediated by AIP and its interacting proteins. These findings provide a novel insight into the antitumorigenic mechanism of metformin.

Keywords:
Metformin
Octreotide
Pancreatic neuroendocrine tumors AIP
mTOR

1. Introduction

Pancreatic neuroendocrine tumors (PAN-NETs) are a heterogeneous group of pancreatic neoplasms with neuroendocrine differentiation, whose incidence and prevalence are increasing worldwide (Hallet et al., 2015). Since PAN-NETs are usually asymptomatic at their onset, about 50% of the patients are diagnosed when the tumor has already metastasized (Halfdanarson et al., 2008; Yao et al., 2013) and surgical resection is not curative. Treatment options for these patients include somatostatin analogs (SSAs), biologically targeted agents (i.e. Everolimus, Sunitinib) or cytotoxic chemotherapy (Auernhammer et al., 2018; Pavel et al., 2016). Nevertheless, the prognosis for patients with metastatic PAN-NETs remains poor: the median overall survival of G1/ G2 PAN-NETs patients is 5 years, while that of G3 tumors is 1 year (Dasari et al., 2017).
Metformin (1.1-dimethylbiguanide hydrochloride), a widely used antidiabetic drug, has been reported to have potent anticancer properties in different types of cancers, including neuroendocrine tumors (Bao et al., 2012; Kato et al., 2012; Vlotides et al., 2014; Zhuang and Miskimins, 2011). At cellular level, metformin acts via 5′-AMP-activated protein kinase (AMPK) dependent or independent pathways, leading to mTORC1 complex inhibition (Pernicova and Korbonits, 2014; Quinn et al., 2013). In fact, several studies demonstrated that metformin suppresses mammalian target of rapamycin (mTOR), a catalytic subunit of mTORC1, implicated in protein synthesis and tumor cell proliferation (Nair et al., 2014; Vlotides et al., 2014).
Recently, a potential relationship between diabetes mellitus and the prognosis of pancreatic neuroendocrine tumors (PAN-NETs) has been proposed (Pusceddu et al., 2016). In particular, diabetic PAN-NET patients treated with everolimus, the somatostatin analog (SSA) octreotide and metformin showed a prolonged progression free survival compared with those who had not taken metformin (Pusceddu et al., 2016), suggesting a potential synergistic activity between metformin, everolimus and SSAs.
In pituitary tumors, SSAs have been shown to increase AIP (Aryl hydrocarbon receptor-interacting protein) expression levels (Chahal et al., 2012; Jaffrain-Rea et al., 2013). AIP acts as a tumor suppressor gene at pituitary level (Igreja et al., 2010; Leontiou et al., 2008; Soares et al., 2005; Vierimaa et al., 2006), and its mutations confer a predisposition to the development of pituitary tumors. AIP interacts with several proteins (Trivellin and Korbonits, 2011) including the Aryl Hydrocarbon Receptor (AhR) (Carver and Bradfield, 1997; Jaffrain-Rea et al., 2009; Ma and Whitlock, 1997; Meyer et al., 1998), heat shock proteins (HSP90) (Carver and Lindner, 1998; Meyer and Perdew, 1999; Morgan et al., 2012), phosphodiesterase 4A5 and PDE2A3, G proteins and ZAC1, a putative tumor suppressor gene that exerts an antiproliferative effect through induction of apoptosis and G1 cell cycle arrest (Morgan et al., 2012). AIP is able to create a complex with AhR and HSP90, a molecular chaperone, thus enhancing the transcriptional activity of AhR (Carver and Bradfield, 1997; Ma and Whitlock, 1997; Meyer et al., 1998).
Although metformin has been demonstrated to exert antiproliferative effects in different neuroendocrine tumor cell lines (Vlotides et al., 2014), the involved mechanism remains elusive. Aims of the present study were to evaluate the role of metformin in PAN-NET cells in relation to proliferation rate, apoptosis and colony formation and the possible additive effects between metformin and octreotide, focusing on the potential involvement of AIP and its interacting proteins.

2. Materials and methods

2.1. Pancreatic neuroendocrine tumor cell cultures

The study was approved by the Independent Ethics Commitee of Istituto Clinico Humanitas – IRCCS, Rozzano (Milan). Informed consent was obtained from all subjects involved in the study. Human neuroendocrine cells were obtained from 11 PAN-NETs (Table 1) that were enzymatically dissociated in DMEM containing 2 mg/mL collagenase (Sigma–Aldrich Corporate Headquarters St. Louis, MO) at 37 °C for 2 h, as previously reported (Vitali et al., 2016, 2015). After, we used cell strainer, a strong nylon mesh with 100 μm micron pores designed for isolating primary cells to consistently obtain a uniform single cell suspension from tissues. Dispersed cells were cultured in DMEM supplemented with 10% fetal bovine serum, 2 mM glutamine and antibiotics. Characterization of the neuroendocrine origin of the cells by flow cytometry is provided in the supplementary file S1. Briefly, after the lysis of red blood cells within the cell suspension, dead cells were discriminated using the Zombie Aqua™ Fixable Viability Kit (BioLegend). Subsequently, cells were fixed with PFA 1% for 20 min at 4 °C and then, incubated with blocking buffer to block non-specific binding. As PANNETs endogenously express SST2 receptor (Vitali et al., 2016), this receptor was used as a “marker” to characterize PAN-NET cells in the cell suspension. Cells were incubated with primary unconjugated SST2 antibody for 30 min at 4 °C and then with a secondary antibody (Alexa Fluor 488 goat anti-rabbit IgG, #A11008, Life Technologies) for 30 min at 4 °C. Finally, cells were incubated with a mixture of hCD90, hCD146 and hCD45 antibodies for 15’ at 4 °C. The list of antibodies is provided in Table S2. Stained cell were analyzed on a FACSymphony flow cytometer (BD). The data were quantified using the DIVA software (BD).
Human pancreatic endocrine QGP-1 cell line (Carcinoembryonic antigen (CEA)-producing human pancreatic islet cell carcinoma, JCRB0183, Japanese Homo Sapiens) was grown in RPMI 1640 medium supplemented with 10% FBS, 2 mM glutamine and antibiotics. QGP1 cell line was cultured at 37 °C in 5% CO2 atmosphere. QGP-1 cell line was authenticated by BMR Genomics srl (Padova, Italy), according to Cell ID TM System (Promega) protocol and using Genemapper ID Ver 3.2.1, to identify DNA STR profile.

2.2. Proliferation assay

Cell proliferation was assessed by colorimetric measurement of 5bromo-2′-deoxyuridine (BrdU) incorporation during DNA synthesis in proliferating cells, according to the manufacturer’s instructions, as previously reported (Lania et al., 2004).
Briefly, QGP-1 cells or PAN-NETs cells were seeded into 96-well plates in triplicate at a density of 20000 cells or 50000 cells, respectively. After 24 h serum starvation, cells were incubated with increasing concentrations of metformin (100 μM, 1 mM, 10 mM) (#1396309, Sigma Aldrich Corporate Headquarters St. Louis, MO, soluble in water) for 16 h, or treated with octreotide 10 nM for 72 h, or with the combination of octreotide 10 nM and metformin 1 mM or 10 mM for the same time as with the single drugs. When mentioned, QGP1 were treated with low doses of metformin (50 μM, 100 μM and 400 μM) and incubated for four and five days (the maximum time suggested by manufacturer’s instructions).
Afterwards, cells were incubated with BrdU for 2 h (cell lines) and 24 h (primary cultures) to allow BrdU incorporation in newly synthesized cellular DNA. All experiments were repeated at least three times and each determination was done in triplicate.

2.3. Colony formation

QGP-1 cells were seeded into 6-well plates and 96-well plates in triplicate at a density of 1000 cells or 250 cells, with metformin at 50 μM, 100 μM and 400 μM concentrations in a 37 °C for 7 days. Cells were incubated with the CyQUANT® GR dye/cell-lysis buffer, according to the manufacturer’s protocol (#C7026, Invitrogen). Number of cells was then quantified by using a fluorescence microplate reader. Images of QGP-1 clones were acquired using brightfield microscopy with a 4X objective lens (Widefield IX53 Inverted, Olympus). Average colony area was measured using the NIH software ImageJ.

2.4. Apoptosis analysis by flow cytometry

Detection of apoptosis was performed using two different kits: the Cell Apoptosis Kit (#V13244, Invitrogen) or the Annexin V/PI kit (#640932, Biolegend), according to the respective manufacturer’s protocol. After 48 h of metformin (1 mM, 10 mM or 400 μM) incubation alone or in combination with octreotide (10 nM), cells were doublestained with the blue-fluorescent Hoechst 33342 dye or Fluorochromelabeled Annexin V and the red-fluorescent propidium iodide dye. The percentage of apoptotic cells was determined by flow cytometry using FACS LSRFortessa (BD Biosciences). The results were analyzed using FACSDiva software (BD Biosciences).

2.5. AIP silencing in pancreatic neuroendocrine cells

Preliminary experiments to determine the optimal concentration of siRNAs and the kinetics of AIP silencing were performed. A negative control siRNA (C- siRNA, # AM4611 Thermo Fisher Scientific), a nontargeting sequence without significant homology to the sequence of human, mouse or rat transcripts, was used in each experiment. Western blotting was performed in each experiment to verify the inhibition of AIP expression after silencing. AIP gene silencing was performed in QGP-1 cells using two species-specific human AIP pre-designed siRNA (#4392420, Life Technologies and ON-TARGETplus Human AIP (9049) siRNA, Dharmacon) and Lipofectamine 2000 Transfection Reagent (#11668019, Life Technologies) according to the manufacturer’s instructions. Briefly, 400,000 cells and 20,000 cells were seeded into 6well plates and 96-well plates respectively. Lipofectamine 2000 (5 μl or 0.5 μL) was diluted in Opti–MEM (Invitrogen) and AIP siRNA, used at a concentration 45 nM, was also diluted in Opti-MEM, then added to diluted Lipofectamine 2000 Reagent (1:1 ratio) and incubated for 5 min at room temperature. Subsequently, QGP-1 cells were incubated with the mixture for 48 h.

2.6. Caspase-3 activity in QGP-1 cell silenced for AIP

Caspase-3 enzymatic activity was measured using Apo-ONE Homogenous Caspase-3/7 assay (#G7790, Promega Italia) as previously described (Ferrante et al., 2006). QGP-1 cells were seeded into 96 wells plate and transfected with c- SIRNA or AIP siRNA for 48 h and then incubated with or without metformin 10 mM for 48 h. Experiments were repeated three times and each determination was done in triplicate.

2.7. Co-immunoprecipitation of AIP and AhR

QGP-1 cells were lysed in non-denaturing lysis buffer (cod #9803, Cell Signaling Technology, Danvers, MA) in the presence of protease inhibitors. The homogenates were centrifuged for 10 min at 14,000 rpm at 4 °C and the supernatant was incubated with 2 μg of AIP or AhR antibodies (#LS-C200107 LifeSpan BioSciences, Inc.) and mixed overnight on a rotating wheel at 4 °C.
Then 20 μl of resuspended volume of Protein A/G PLUS-Agarose beads (#sc-2003, Santa Cruz) were added to the antibody-lysate mix and incubated at 4 °C on a rotating device for 3 h. Immunoprecipitates were collected by centrifugation at 2500 rpm for 5 min at 4 °C. The pellet was washed 5 times with non-denaturing lysis buffer, each time repeating the centrifugation step above. After the final wash, pellet was resuspended in SDS sample buffer containing reducing agents and analyzed by Western blotting to visualize AIP partners Zac1 (LifeSpan BioSciences, Inc), HSP70 (Elabscience, Bethesda,MD), and AhR. The presence of equal amounts of receptor in the immunoprecipitates was confirmed by stripping and reprobing with anti-AIP or AhR antibodies. As a negative control no antibody (Ab) was used for the co-immunoprecipitation (co-IP).

2.8. Western immunoblots of AIP, cyclin D1, HSP70, Zac1, AhR

Preliminary experiments to determine the optimal concentration of metformin and octreotide and the kinetics of protein expression were performed. QGP-1 cells were incubated at 37 °C with or without metformin (10 mM) for 16 h or with octreotide (10 nM) for 24 h, to analyse AIP expression levels. To study Cyclin D1, AhR, HSP70, and Zac1, we stimulated QGP-1 cells with metformin for 16 h and for 24 h to detect p27 (#sc1641, Santa Cruz Biotechnology, Santa Cruz, CA). Antibodies raised against epitopes specific for Cyclin D1 (ENT1173, Elabscience, Bethesda,MD), AhR, HSP70, Zac1, and AIP and then antirabbit horseradish peroxidase-linked antibodies (#7074, Cell signaling Technology, 1:2000) were used. For p27, 1:100 dilution of p27 antibody and then anti-mouse HRP-linked antibody were used (1:2000 dilution, Cell Signaling Technology). Monoclonal anti-GAPDH (AM4300, Ambion, 1:4000) was used as housekeeping, developed using an anti-mouse HRP-linked antibody (#7076, Cell Signaling Technology 1:2000).

2.9. Immunoblot detection of phospho-mTOR and total mTOR

To detect mTOR phosphorylation, QGP-1 cells were transfected with c-siRNA or AIP siRNA for 48 h and they were treated for 16 h with or without 10 mM metformin and 10 nM octreotide, alone or in combination. Immunoblotting analysis was performed with 1:1000 dilution of phospho-mTOR antibody and 1:1000 dilution of total mTOR antibody (#2971 and #2972, Cell Signaling Technology, Danvers, MA).

2.10. Statistical analysis

The results were expressed as the mean ± SD. A paired two-tailed Student’s test was used to detect the significance between two series of data. One-way ANOVA analysis was used to compare three or more groups, followed by Bonferroni post-hoc test. Dunnett’st’s method was used to test two or more experimental groups against a single control group. Calculations were performed by GraphPad Prism 7.0 software (GraphPad Software, Inc., La Jolla, CA). P < 0.05 was accepted as statistically significant.

3. Results

3.1. Metformin decreases cell proliferation and colony formation in PAN-NET cells

First, we evaluated the antiproliferative effects of metformin on cultured cells obtained from surgically removed human PAN-NETs by cell proliferation assay, after incubation with metformin (10 mM). As shown in Fig. 1A, metformin reduces cell proliferation in primary PANNET cells (−47 ± 9%, p < 0.000001 vs basal). Considering the low availability of PAN-NET tumor samples and the low yield in terms of viable cells from sample dispersion, we used the QGP-1 cell line as a model for PAN-NETs to further elucidate the antiproliferative action of metformin. We assessed cell proliferation after incubation with different concentrations of metformin (100 μM, 1 mM, 10 mM). As shown in Fig. 1B, metformin inhibited QGP-1 cell proliferation with the greatest effect at 10 mM (−62 ± 15% p < 0.0001 vs basal). The inhibitory effect of metformim was further confirmed by the decrease in Cyclin D1 expression (−46 ± 7%, p < 0.001 vs basal) and the increase in p27 expression levels (+68 ± 30%, p < 0.05 vs basal) (Fig. 1C–D).
In order to determine whether lower concentrations of metformin, compatible with the concentrations achievable in treated patients, are still able to affect QGP-1 cell proliferation, we treated cells with metformin 50 μM, 100 μM or 400 μM, for four or five days (the maximum time suggested by manufacturer’s instructions) and we performed BrdU incorporation assay. As shown in Fig. 2A, concentration of 400 μM is sufficient to decrease cell proliferation, after 4 days (−33 ± 22% p < 0.05 vs basal). Moreover, after 5 days both 400 μM and 100 μM metformin significantly inhibited QGP1 cell proliferation (−53 ± 18% p < 0.001 vs basal and −31 ± 12% p < 0.05 vs basal, respectively). Finally, to determine the possible involvement of metformin in the ability of QGP-1 cells to grow in vitro and to form colonies, cells were incubated with or without metformin (50 μM, 100 μM or 400 μM) for 7 days. Metformin significantly decreased quantity of colonies (Fig. 2B), number of QGP-1 cells (−33% ± 6%, p < 0.01 at 100 μM; −44% ± 12%, p < 0.001 at 400 μM) and colony size (2.9 fold, p < 0.01 at 100 μM; 5.2 fold, p < 0.01 at 400 μM) with respect to untreated cells (Fig. 2C–D).

3.2. Metformin increases apoptosis in QGP-1 cells

To investigate the ability of metformin to induce apoptosis, QGP1 cells were treated with or without 1 mM or 10 mM of metformin and analyzed by flow cytometry. Metformin significantly increased QGP1 cell apoptosis with Hoechst/PI assay (Fig. 3A–B) (+389 ± 170%, p < 0.001 vs basal) and with annexin V/PI assay (+233 ± 40%, p < 0.0001 vs basal, data not shown).

3.3. The effect of octreotide and metformin on cell proliferation and apoptosis

In order to investigate the possible additive effect between metformin and octreotide, we tested the two compounds alone or in combination on PAN-NET and QGP-1 cell proliferation. The optimal concentration of each compound and the kinetics of BrdU incorporation were assessed (data not shown) and subsequently used in cell proliferation assays. As previously shown, metformin inhibited cell proliferation in PAN-NET cells (−43 ± 13%, p < 0.05 vs basal) and in QGP-1 cells (−76 ± 9%, p < 0.01 vs basal). Similarly, octreotide reduced cell proliferation in both primary cells and cell line (−45 ± 6%, p < 0.05 vs basal and −33 ± 2%, p < 0.01 vs basal, respectively) (Fig. 4 A&B). When octreotide 10 nM was used in combination with metformin 10 mM (Fig. 4 A&B) no additive effect was observed. The lack of additive antiproliferative effect on QGP1 cells was confirmed even at low concentrations of metformin (50 μM, 100 μM or 400 μM) after long-term incubation (5 days) (Fig. 4 C). Subsequently, we performed apoptosis experiments using octreotide alone or in combination with metformin in QGP1 cells. As shown in Fig. 4D, metformin (400 μM) and octreotide alone (10 nM) significantly increased QGP-1 cell apoptosis with annexin V/PI assay (+43 ± 2%, p < 0.01 vs basal and +24 ± 10%, p < 0.5 vs basal, respectively). When the combination of metformin and octreotide was used, no additive effect on apoptosis was observed.

3.4. Effect of metformin and octreotide on AIP expression and p-mTOR phosphorylation

We incubated QGP-1 cells with or without metformin (10 mM) or octreotide (10 nM) and we subsequently evaluated AIP expression levels by Western blot. Octreotide significantly increased AIP expression levels (+111 ± 32% p < 0.05 vs basal). A similar increase in AIP expression was also induced by metformin (+140 ± 20% p < 0.01 vs basal) (Fig. 5 A). The combination of metformin and octreotide did not result in additive effects, confirming the data observed in cell proliferation experiments.
Finally, we analyzed the effects of metformin and octreotide on mTOR phosphorylation, a key player in the regulation of cell growth, and the possible involvement of AIP. Metformin and octreotide significantly decreased mTOR phosphorylation in control cells (−53 ± 3% and −49 ± 8%, p < 0.05 vs untreated cells, respectively) and, when octreotide was used in combination with metformin, no additive effect was observed.
The reduction of mTOR phosphorylation was totally abolished in AIP silenced cells (Fig. 5 B) (confirmed by a second siRNA AIP, data not shown). These data suggest that metformin and octreotide act via AIP to inhibit mTOR phosphorylation in QGP-1 cells.

3.5. Effect of AIP knock-down on cell proliferation and cell apoptosis

We silenced QGP-1 cells with AIP siRNA (about 95% of AIP silencing, data not shown) and examined the effects of AIP knock-down on cell proliferation after incubation with metformin. As shown, the antiproliferative effects of metformin observed in QGP-1 cells (−37 ± 15%, p < 0.01 vs untreated cells) were completely lost in AIP-silenced cells (Fig. 6 A) (confirmed by a second siRNA AIP, data not shown).
Interestingly, AIP was also involved in mediating the proapoptotic effects of metformin. In fact, the stimulatory effect of metformin on cell apoptosis in QGP-1 cells (+38 ± 8% P < 0.05 vs basal) was completely abolished after AIP silencing (Fig. 6 B). These data suggest that AIP is crucial to mediate the antiproliferative and the proapoptotic effects of metformin in QGP-1 cells.

3.6. AIP interacts with AhR, Zac1 and HSP70

In order to explore the AIP pathway in QGP-1 cells, we studied the possible interactions between AIP and its potential partners by immunoprecipitation. As shown in Fig. 7 A, AIP interacts with AhR, Zac1 and HSP70. The possible formation of a complex was further supported by anti-AhR co-IP, in which AhR bound to the same AIP-interacting proteins (Fig. 7 B).

3.7. Metformin modulation of AIP partners’ expression is AIP-mediated

To determine the possible modulation of AIP partners’ expression by metformin, PAN-NET cells were treated with or without metformin and then analyzed by Western blot. As shown in Fig. 8, metformin not only increased AIP expression levels, thus confirming the results on QGP1 cells, but also increased AhR and Zac1 levels, and decreased HSP70 expression levels.
Next, we evaluated the role of AIP in the modulation of its interacting proteins induced by metformin. To this purpose, the expression levels of AIP partners were evaluated in QGP-1 cells silenced for AIP after incubation with metformin. AIP silencing abolished the increase in AhR and Zac1 (+117 ± 83%, p < 0.05 vs C- siRNA and +153 ± 79%, p < 0.01 vs C- siRNA, respectively) (Fig. 9A–B), and the decrease in HSP70 expression levels triggered by metformin (−29 ± 7%, p < 0.05 vs C- siRNA) (Fig. 9 C). These data suggest that AIP itself is responsible for mediating the effect of metformin on AIP partners.

4. Discussion

Metformin is a widely used, highly effective and well-tolerated treatment for type 2 diabetes mellitus. It has recently been studied for its anticancer activity in a wide spectrum of neoplasms. To date, the anticancer efficacy of metformin and its mechanisms of action in pancreatic neuroendocrine tumors (PAN-NETs) remain incompletely elucidated.
We first focused on primary PAN-NET cells, and, considering their low viability, we used metformin at high concentration (10 mM) for a relatively short incubation time (16 h). We demonstrated that metformin significantly decreased PAN-NET cell proliferation.
Thereafter, considering the scarce availability of PAN-NET tumor samples, we focused on the effects of metformin on the corresponding QGP-1 cell line. Similarly to the results obtained in primary cells, after 16 h of incubation metformin inhibited QGP1 cell proliferation in a dose-dependent manner, with the greatest effect at 10 mM.
Accordingly, we showed that metformin decreases Cyclin D1 expression and increases p27 expression levels in QGP-1 cells, in agreement with different studies on several cancer cell lines, in which Cyclin D1 and the CDK inhibitors, such as p21 and p27, have been shown to play an important role in mediating the inhibitory effects of metformin (Rocha et al., 2011; Wang et al., 2008; Zhuang and Miskimins, 2008). The antineoplastic effect of metformin was mediated by the induction of QGP1 cell apoptosis, as already demonstrated in pancreatic cancer (Wang et al., 2008) and melanoma cells (Janjetovic et al., 2011).
It is worth noting that metformin has been reported to exert its effects in vitro at doses in the millimolar range (Vlotides et al., 2014), that are much higher than those reached in the plasma of patients taking conventional metformin doses (500–3000 mg per day), which are in the micromolar range (Luo et al., 2012). Interestingly, Gritti et al. demonstrated that the antiproliferative effects of metformin on human glioblastoma stem cells are not only dependent on metformin concentrations but also on the duration of treatment (Gritti et al., 2014). Accordingly, we tested lower concentrations of metformin for a prolonged incubation time: we showed that metformin 100 μM and 400 μM was significantly able to reduce QGP1 cell proliferation and colony formation. As a matter of fact, Vacante et al., considered metformin 400 μM to be comparable to the human therapeutic dose in hepatocellular carcinoma cells (Vacante et al., 2019). These data demonstrate that metformin’s efficacy as an antiproliferative agent in PAN-NETs is also time-dependent, since lower metformin concentrations elicit anticancer effects only after a longer exposure to the drug. These findings further support the potential clinical application of metformin in PANNETs: a chronic treatment at therapeutic doses of metformin is likely to block neuroendocrine tumors’ growth, given their indolent features, without major toxic effects, as the drug is generally well-tolerated. It should also be noted that previous studies that found a significant association between metformin use (at the usual dosages used in type 2 diabetes mellitus, i.e.: 1000–3000 mg/day) and longer PFS (progression-free survival) in patients affected by advanced PAN-NETs (Pusceddu et al., 2016) have been recently confirmed (Pusceddu et al., 2018).
Somatostatin analogs (SSAs), including octreotide, are routinely used for the treatment of advanced NETs (14). In this respect, QGP-1 and PAN-NETs endogenously express somatostatin receptor type 2 (SSTR2) and SSAs have antiproliferative and proapoptotic effects in QGP-1 cells (Cambiaghi et al., 2016; Vitali et al., 2016). Metformin decreases BON-1 (well-differentiated pancreatic NET cell line) cell counts and cell viability (Vlotides et al., 2014), but no in vitro data are available regarding the effects of drug combinations in PAN-NETs.
Interestingly, octreotide inhibits the autocrine/paracrine secretion of insulin-like growth factor (IGF-1) (Msaouel et al., 2009), potentially contributing to the blockade of the IGF-1/PI3K/Akt/mTOR pathway, which is also inhibited by metformin (Pernicova and Korbonits, 2014) (Fig. 10). Thus, we decided to evaluate the possible additive effects between metformin and octreotide on PAN-NET cell growth. We demonstrated that each drug inhibited PAN-NET and QGP-1 cell proliferation when used alone. In contrast, when used in combination, no additive effects were observed, independently from metformin concentration.
Given that somatostatin analogs up-regulate Aryl hydrocarbon receptor-interacting protein (AIP) expression levels (Chahal et al., 2012; Ferrante et al., 2006; Hubina et al., 2006; Jaffrain-Rea et al., 2013), which acts as tumor suppressor gene in pituitary neuroendocrine tumors (Igreja et al., 2010; Leontiou et al., 2008; Soares et al., 2005; Vierimaa et al., 2006) and the fact that metformin and octreotide could affect the same IGF-1/PI3K/Akt/mTOR pathway, we decided to evaluate the effect of octreotide and metformin on AIP expression. We have demonstrated that both single drugs increased AIP expression levels and no additive effect was observed with the combination.
The most potent anticancer effect of metformin is the suppression of the mammalian target of rapamycin (mTOR) pathway, a key player in the regulation of cell-growth, metabolism and survival (Pernicova and Korbonits, 2014). Kasajima and colleagues demonstrated a significant correlation between mTOR expression and primary tumor location and metastatic status in neuroendocrine tumors (Kasajima et al., 2011).
Consistently with these observations, metformin and octreotide decreased mTOR phosphorylation in QGP-1 cells and this reduction was totally abolished in AIP silenced cells. These data suggest that metformin and octreotide act via AIP to inhibit mTOR phosphorylation in QGP-1 cells. We speculated that this overlap might explain, at least in part, the lack of additive effect between the two drugs seen in our model (Fig. 10).
In addition, the key role of AIP in mediating metformin action was supported by the finding that AIP silencing abolished the antiproliferative and the proapoptotic effects of metformin. We concluded that the AIP-mediated decrease in mTOR phosphorylation is reasonably relevant for the antiproliferative effect of metformin.
Next, we focused on the potential interactions involved in AIP signaling pathways that could be affected by metformin. In this study, we evaluated possible AIP interactions, focusing on: 1) AhR, the best characterized AIP partner (Jaffrain-Rea et al., 2009); 2) Zac1, AIP’s zinc finger effector with putative tumor suppressor properties (Chahal et al., 2012; Theodoropoulou et al., 2010) and 3) HSP70, a heat shock protein able to inhibit apoptosis.
Concerning heat shock proteins, AIP has been shown to interact with HSP90, while the possibility of an interaction with HSP70 is controversial (Morgan et al., 2012; Schülke et al., 2010). Our results showed for the first time that AIP interacts with HSP70, Zac1, and AhR in PAN-NET cells and suggested the formation of a multiprotein complex.
Interestingly, metformin not only increased AIP expression levels, but also AhR’s and Zac1′s expression. In this respect, a concordant AIPAhR expression, consistent with the strong interaction between the two proteins, has been reported in GH-secreting adenomas, in which AIP downregulation is accompanied by a decrease in AhR expression (Jaffrain-Rea et al., 2009). The observed increase in Zac1 expression triggered by AIP is in line with previous data indicating that in somatotroph adenomas AIP mediates the action of somatostatin analogs via Zac1 (Chahal et al., 2012).
In various types of malignancies, including PAN-NETs (Zitzmann et al., 2013), increased HSPs expression has been observed. In particular, HSP70 plays a pivotal role in inhibiting the apoptotic processes at mitochondrial level (Jackson, 2012; Lianos et al., 2015; Morgan et al., 2012; Prodromou, 2012). Consistent with its proapoptotic action, metformin decreased HSP70 levels in our model, as previously shown in adrenocortical carcinomas (Poli et al., 2016).
The finding that AIP silencing abrogated metformin’s modulation of AIP partners’ expression provides a plausible mechanistic role of AIP as a regulator of the AIP-AHR-Zac1-HSP70 complex in PAN-NETs.
In summary, we showed Guanidine for the first time that metformin exerts its anticancer effects through AIP complex modulation. This novel finding brings new clues to the understanding of metformin action on PAN-NET cells. Finally, the drastic effects exerted by metformin on PAN-NET cell growth and apoptosis and its anticancer effect even at “therapeutic concentrations“ provide the molecular rationale for its use in the clinical setting for the treatment of PAN-NET patients.

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