UNIVERSITA’ DEGLI STUDI DI PISA
Facoltà di Medicina e Chirurgia
Dottorato di ricerca in scienze cliniche e traslazionali
Tesi di dottorato:
Evaluation of Placental growth factor-Neuropilin and Slit-Robo expression and
signaling in neuroendocrine pancreatic tumors and their prognostic role.
Relatore:
Prof. Fausto Bogazzi
Candidata:
Martina Lombardi
INDEX
1. SUMMARY
2. INTRODUCTION
2.1 Epidemiology
2.1.2 Epidemiology: pancreatic neuroendocrine tumors (pNETs) 2.2 Classification
2.3 Diagnosis 2.4 Treatment
2.5 Neuroendocrine pancreatic tumors prognosis
2.6 Vascular endothelial growth factor (VEGF) and placental growth factors (PlGF) and its receptors
2.6.1 VEGF and PlGF pathway in cancer
2.6.2 PlGF pathways and Neuropilin in neuroendocrine tumors 2.7 Slit-Robo signaling
2.7.1 Slit-Robo signaling in cancer 3. AIMS OF THE STUDY
4. PATIENTS, MATERIALS AND METHODS 4.1 Study population
4.2 Neuropiling 2 expression analysis
4.2.1 Primary anti-neuropilin 2 antibody selection analysis
4.2.2 Immunohistochemistry for neuropilin 2 in neuroendocrine pancreatic tumors 4.3 PlGF expresssion analysis
4.3.1 Immunohistochemistry for PlGF in neuroendocrine pancreatic tumors 4.4 Slit2 and Robo1 expression analysis
4.4.1 Immunohistochemistry for Robo 1 in neuroendocrine pancreatic tumors 4.4.2 qPCR for Slit2 and Robo1
5 In vitro Slit-Robo signaling evaluation 4.5.1 Growth assays
4.5.3 Colony formation 6 Statistical analysis
5. RESULTS
5.1 Neuropilin 2-PlGF prognostic role
5.2 Slit2-Robo1 expression and their prognostic role 5.3 Slit1-Robo2 signaling and its biological role 6. DISCUSSION
7. CONCLUSIONS 8. BIBLIOGRAPHY
1. SUMMARY
Background: pancreatic neuroendocrine tumors (pNETs) prognosis is difficult to predict. In
other tumor models Placental growth factor (PlGF)-Neuropilin (NP) and Slit-Robo signaling have been involved in angiogenesis and tumor spread, but their function in pNETs and their potential prognostic role need to be clarified.
Methods: a clinical evaluation was initially performed into two series of pNET patients,
correlating PlGF, Neuropilin 2 (NP2), Slit2 and Robo1 tumoral expression to overall survival, progression free survival (PFS) and main clinical features.
An Italian cohort of 55 surgically treated pNETs was retrospectively investigated, evaluating PlGF and NP2 expression by immunohistochemistry (IHC) technique. Moreover, PlGF-NP2 IHC expression was evaluated in a smaller series of 12 advanced pNETs treated with Everolimus, and its expression was retrospectively correlated to clinical response to that target therapy.
In a German series of 45 pNET, Slit2 expression was assessed by qPCR, while Robo1 expression was evaluated by qPCR and IHC, and compared with the one observed in 27 normal pancreatic tissue samples.
On the basis of the clinical results, Slit2-Robo1 pathway was further investigated in vitro. Migration, cell proliferation and colony formation (CF) were evaluated in BON and QGP cell lines with and without Slit2 and Robo1 expression.
Results: NP2 and PlGF expression was not significantly associated with main clinical and
biological features, survival or PFS, in the evaluated pNET series.
On the contrary, a reduced Robo1 expression was associated with a shorter time to tumor progression (TTP) (p=0.025). Slit2mRNA, although not significantly correlated with survival or TTP, appeared significantly reduced in pNETs, as compared to normal pancreas (p=0.0019).
From the in vitro studies, Slit2 expression in BON cells revealed to inhibit proliferation, migration and CF, while Robo1 knockdown or sequestration of Slit2 in QGP, stimulated migration and CF.
On the opposite, Slit2-Robo1 showed a tumor-suppressor function in vitro and Robo1 loss was related to a worse clinical prognosis, suggesting its potential role as prognostic marker in pNETs.
2. INTRODUCTION
Neuroendocrine cells are distinguished by the ability to produce neurotransmitters, neuromodulators or neuropeptide hormones that are stored in granules and secretory vesicles from whom they are released through exocitosis; and by the absence of axons and synapsis (1).
Different neuroendocrine cell types share many specific properties and express different commons proteins, often used as histological markers, although the expression of these proteins is not an absolute criteria to define a cell as neuroendocrine (2)
Neuroendocrine cells can give origin to neuroendocrine glands or be spread in different organs and apparatuses such as thyroid, pancreas, skin or respiratory, gastrointestinal or urinary tracts (3). From this latter kind of cells comes neoplasms traditionally defined as neuroendocrines tumors (NETs) (3).
NETs show heterogeneity in morphological, functional and clinical features (4), determining large differences in survival rates. Moreover, among NETs, neuroendocrine pancreatic tumors (pNETs) have a special biology and natural history with a specific clinical management (5-7).
2.1 Epidemiology
Recent epidemiological studies show an annual NETs incidence of 2.5-5.25 cases for 100,000 inhabitants per year, depending on different series (8-15).
This incidence seems in great increase in the recent years, with a 4-5 folds increased incidence in comparison with the one reported in seventies (10,14,16-17).
Therefore, although a general increase of human cancer diagnosis has been documented, the increase of NETs diagnosis has been more relevant (8).
NETs incidence is not substantially different between sexes, in the majority of series (8, 9), while a significant higher incidence of NETs has been reported in black population, especially for small intestine and rectal NETs (8).
Mean age at diagnosis, although variable in different series, is generally reported around 50-60 year-old, although patients with appendix NETs are on average younger than patients with NETs of other primary sites (10,12). This is probably due to the frequent incidental
diagnosis of appendix NETs during an appendix resection, that is more frequently performed in young patients.
As already reported in other kinds of cancer, patients with familiar NETs, diagnosed in known genetic syndromes (such as multiple endocrine syndrome type 1), are on average younger than the ones with sporadic NETs (13,18).
The anatomical distribution of NETs is very different in the various studies, according to the geographic origin of the series. In Asian population, colorectal NETs and pancreatic ones are the most frequent (13), while in American registries lung NETs have the higher incidence, followed by rectal ones (8).
On the opposite in European series, the majority of NETs comes from small intestine, lung and pancreas, while there is a lower incidence of colorectal NETs (9,12,14).
In particular, a recent multi-centric Italian study has shown as pancreatic and lung NETs are the most common primary NETs, corresponding respectively to 31 and 29% of reported overall NETs (17).
2.1.2 Epidemiology: pancreatic neuroendocrine tumors (pNETs)
Pancreatic neuroendocrine tumors represent around 2% of all pancreatic neoplasms, showing a definitely better prognosis than exocrine tumors (18). Although the annual incidence is around 1 case every 100000 inhabitants (15,19), autopsy series suggest a much higher incidence, showing a prevalence of 0.3-1.6% in general non selected series and a prevalence up to 10% in those series in whom the whole pancreas was systematically examined (20).
The intrapancreatic distribution seems almost homogeneous, with a similar distribution of the primary pNETs between head-body and pancreatic tale (13, 21).
Pancreatic neuroendocrine tumors are classified as functioning and non functioning-ones, where the latter represent around 60-80% of the whole pNETs cases (13,20)
Non functioning pNETs can show an immunohistochemistry positivity for general (such as chromogranina A, neuron-specific enolase or synaptophysin) and specific neuroendocrine markers (such as insulin, glucagone, gastrin etc). However, pNETs are considered as non functioning whenever a clear and typical clinical hypersecreting hormonal syndrome can't
be documented, even though one or more neuroendocrine hormones could be over their serum normal values. Actually, non functioning pNETs can determine some hormonal hypersecretion such as pancreatic polypeptide, neuron-specific enolase, neurotensine, ghrelin and others, without any consequent specific clinical symptoms.
On the contrary, around 30-40% of pNETs are functioning, causing specific pathognomonic symptoms and signs; among the functioning pNETs, insulinomas are the most frequent (70-60% of functioning pNETs), followed by gastrinomas (10-20%); while glucagone, VIP or somatostatine hypersecretion are much more rare. (13,18,21).
Over 90% of pNETs has a sporadic presentation and the most frequent somatic mutations, in order of frequency, are mutations of MEN1 (44%), DAXX (25%), ARTX (18%), and genes of the mTOR pathway (16%) (22). However, almost 10% of pNETs is associated to genetic syndrome (13,17,19) such as, multiple endocrine neoplasia type 1 syndrome (MEN1), von Hippel-Lindau syndrome (VHL), neurofibromatosis type 1(NF-1) and tuberous sclerosis. Multiple endocrine neoplasia type 1 syndrome is the most relevant one, and in an Italian epidemiological study, around 10% of pNETs where associated with MEN-1 syndrome (17). MEN-1 syndrome is generally associated with a germinal mutation of menin gene; patients develop a clinical pNETs in 30-75% of cases, according to the different series, and that incidence increases up to 80% in autopsy series data (23). In MEN 1 syndrome pNETs are generally multicentric, without a significantly prevalent primary intrapancreatic site, and show various histological and biological grade of aggressiveness (23). Malignant pNETs are rare in younger than 30 year-old MEN1 patients, but at least an occult pNETs is present in around half of 50 year-old patients belonging to these syndromic families (24). In MEN1 patients, pNETs are non functioning in 50-60% of cases, more rarely showing an hormonal hypersecreting syndrome (20% gastrinomas, 5-6% insulinomas) (23). Recently, it has become clear that 5-10% of patients, who clinically fit the criteria for MEN-1, do not have mutations in the menin gene, however some of these patients (1.5%) have mutations in the cyclin-dependent kinase inhibitor gene, CDK1B, which encodes for p27kip1 (p27), a cyclin dependent-kinase inhibitor that regulates the transition of cells from G1 to S phase, and are now classified as MEN-4. Others have germline mutations of the cyclin-dependent kinase inhibitors p15, p18 and p21, which are a probable cause of MEN1 in approximately
1, 0.5 and 0.5% of patients (25,26). Patients with VHL develop pNETs in a lower percentage of cases (10-17%), mainly showing non functioning pNETs; in NF-1 subjects this kind of tumors develop in around 10% of patients, with an unusual higher frequency of somatostatinomas; while in tuberous sclerosis only 1% of patients present pNETs, generally a non functioning one (24).
2.2 Classification
From the first NETs classification of Williams and Sandlers in 1963 (27), many different classifications have been proposed and used.
The World Health Organization (WHO) classification of 2000, considering previous papers (28), suggested to distinguish mixed endocrine-exocrine tumors (3) from pure NETs. Among NETs, independently from the tumor primary site, three different forms of NETs were defined (28, 29):
1. Well differentiated endocrine tumors with benign behaviour (a) or with uncertain behaviour (b)
2. Well differentiated endocrine carcinomas 3. Poorly differentiated endocrine carcinomas
This classification stratify the different NETs, suggesting their prognosis and giving an initial useful tool to select specific treatment. Subsequent studies have then used this classification to guide specific treatments (30,31).
Although the WHO classification was an important step in defining NETs biology, many other efforts were made to optimize prognostic stratification of NETs, introducing TNM (tumor-node-metastasis) site-specific staging (32).
Moreover, following evidences that primary mitotic index and Ki67 index correlate with long term prognosis of NETs patients (33), a grading classification was added to the histological diagnosis of these tumors (Table 1).
Table 1. Pancreatic NETs grading
Grade Mitosis count for 10 HPF 1 Ki67 index (%) 2
G1 <2 ≤2
G2 2 - 20 3 - 20
G3 >20 >20
110 HPF: high power field=2 mm2 , at least 40 fields (at 40×magnification) evaluated in areas of highest mitotic density 2 MIB1 antibody; % of 2,000 tumor cells in areas of highest nuclear labeling
This classification, with staging and grading of the primary tumor at diagnosis, seems, although in same cases with some modifications introduced by single authors, well correlated to patients prognosis in terms of overall survival and progression free survival (PFS) (33-38).
For this reason, in 2010 the WHO has introduced a new classification (40), that included the grading concept, dividing the whole NETs in (40):
1. Well differentiated neuroendocrine tumors subdivided in: grading 1 NETs (G1: less than 2 mitosis/10HPF and/ or Ki67 ≤ 2%) and grading 2 NETs (G2: mitotic index 2-20/ 10HPF and/or ki67 between 3 and 20%)
2. Poorly differentiated neuroendocrine carcinomas (NEC) with small or large cells (grading 3): ( G3: Mitotic index> 20 mitosis/10HPF and/or ki67 >20%).
This classification was accepted by the European Neuroendocrine Tumor Society (ENETS), and confirmed in its latest guidelines (41). Recent reports from studies in pNETs suggest that a Ki-67 proliferation index threshold of 5% could better distinguish G1 from G2 pNETs, but this should be further explored (34,42).
2.3 Diagnosis
Pancreatic neuroendocrine tumor diagnosis can be due to a hormonal hypersecretion syndrome. Non functioning pNETs (NF-pNETs) usually become clinically apparent when they reach a size that causes compression or invasion of adjacent organs, or when they metastasize. In this context, NF-pNETs are usually diagnosed late in the course of the disease with primary tumor of significant dimensions and advanced stages; according to different series, between 33 and 55% of patients with NF-pNETs has already distant metastasis at diagnosis (8,12, 39). However, the mean tumor diameter is decreasing in the last decades and this is mainly due to the widespread use of cross-sectional imaging technique (43). When symptomatic, the most common presenting symptoms are abdominal pain (35–78%), weight loss (20–35%), anorexia and nausea (45%). Less frequent signs are intra-abdominal hemorrhage (4–20%), jaundice (17–50%) or a palpable mass (7–40%) (44-49).
A good biomarker to diagnose NETs is serum chromogranin A (CgA), that is a glycoprotein, which is present in the secretory granules throughout the neuroendocrine system. CgA is the best circulating neuroendocrine marker available for the management of differentiated NETs, showing a diagnostic sensibility between 71 and 84% and a specificity between 71 and 85%, depending on the dosage methods (50). Moreover, its determination is useful to evaluate the response to therapy and to follow-up patients with liver metastases (51).
In functioning pNETs other specific serum markers can be used (such as gastrin, insulin, VIP etc) as basal level and after proper stimulating tests.
Pancreatic neuroendocrine tumors diagnosis often needs traditional radiology such as ultrasound, CT or MRI. Ultrasound has generally a sensitivity of less than 40%, although it can be improved by the use of ultrasound contrast enhancement (52). On the other side, CT sensibility and specificity are respectively around 75% and 95% if performed with multislices with slices of 1mm or less and obtaining imaging at the pick arterious phase enhancement (52). This results are comparable with the one obtained with MRI that should be used as interchangeable or complementary to CT, according to the local radiological experience (52).
Somatostatin receptor scintigraphy with SPECT remains useful in staging pNETs, although numerous studies have demonstrated that imaging with positron emission tomography (PET/CT) with 68Ga labeled somatostatin analogues has the highest sensitivity for localizing p-NETs (41, 52).
Moreover, in pancreatic head pNETs, the use of endoscopic ultrasound can become a useful diagnostic tool, showing a high spatial resolution and giving the opportunity, in selected cases, to perform fine needle aspiration of pancreatic lesions, confirming the neuroendocrine origin before surgery (53, 54).
2.4 Treatment
Surgical treatment with a curative resection intent, is the first line therapy in all patients with functioning NETs with or without MEN1, although specific surgical management is necessary in MEN1 patients (41). Surgery is a mainstay of treatment of NF-pNETs,
although several studies explored the safety and feasibility of a non-operative management approach for asymptomatic sporadic NF-p-NET smaller than 2 cm (41).
Albeit NF-pNETs smaller than 2 cm do not have an invariable benign behaviour and larger NF-pNETs are associated with a worse prognosis (55), a conservative approach seems to be safe, as the majority of the observed tumors did not show any significant changes during follow-up (56, 57). However follow-up data are needed to guarantee the safety of this policy (41).
In advanced and metastatic pNETs many different therapeutic options are available and they can be summarized as follows:
1. medical therapies: somatostatin analogues (SSA), interferon–alpha (IFN), chemiotherapy (CT);
2. medical target therapies: everolimus, sunitinib and other experimental treatments
3. Peptide receptor radionuclide therapy (PRRT) with radiolabeled somatostatin
analogues (i.e. 177Lu-DOTA-TATE and 90Y-DOTA-TATE )
4. loco-regional hepatic therapies: radiofrequency, embolization, chemio or radioembolization
5. hepatic transplant
Somatostatin analogs are first-line therapy in functionally active pNETs (58) and the commercially available agents octreotide and lanreotide are considered equally effective for symptoms control, while IFN is a second-line therapy in functionally active pNETs (58). Pasireotide is a novel universal somatostatin ligand that binds to four of five somatostatin receptors and that is approved for functional pNETs with not controlled symptoms during traditional SSA, IFN or loco-regional treatment (59).
In well differentiated NF-pNETs there is consensus that SSA can be used as first-line systemic therapy, and although the anti-proliferative effects of SSA are considered a drug class effect, based on the CLARINET study (60), lanreotide autogel should preferably be used in pNETs, since prospective data on the use of octreotide LAR in pNETs are lacking (58). When considering SSA as first-line therapy in pNETs, the more appropriate Ki-67 cut-off threshold to select patients to be treated, is not completely defined, with experts opinions suggesting to use 5 or 10% cut-off (58). Moreover, it remains controversial if SSA should be
started at initial diagnosis of advanced pNETs or after observation of the spontaneous tumor progression, although there is consensus that SSA should be started at diagnosis in cases of high liver tumor burden (58).
Clinical studies show that different molecular targets are expressed in pNETs, among which angiogenic growth factors and their receptors (e.g. VEGF receptor, PDGF receptor), peptide receptors (e.g. SS receptors 1–5, EGF receptor, IGF1R) and intracellular molecules (mTOR) seem to have a great clinical impact. The intracellular mTOR pathway is particularly expressed in pNETs, where somatic mutations were identified in 15% of cases (22). A variety of targeted agents have been studied in pNETs, such as angiogenesis inhibitors (e.g. bevacizumab, thalidomide), tyrosine kinase inhibitors (e.g. imatinib, gefitinib, sorafenib, sunitinib, pazopanib), mTOR inhibitors (e.g. temsirolimus and everolimus) and IGF1 receptor antibodies (61).
Everolimus and Sunitinib have been recently approved for pNETs based on the results of two placebo controlled trials in progressive pNETs (62, 63). Median PFS is around 11 months with either of the drugs, while tumor remission occurs in 5% and less than 10% of the patients treated with Everolimus and Sunitinib, respectively. Use of either Everolimus or Sunitinib is recommended in progressive G1/G2 pNETs, irrespective of Ki-67 and tumor burden (58). Targeted drugs, Everolimus or Sunitinib, may be used as 1st-line or 2nd line options with respect to chemotherapy or subsequent to SSA therapy. Although there is no evidence on the exact sequencing of different treatment options in pNETs (58), a possible algorithm can be suggested (Figure 1).
Moreover, some critical issues concerning their use in clinical practice remain unresolved. In particular, for Sunitinib treatment, it is necessary to evaluate: the risk of bleeding in a sequential surgical approach and the mechanism involved in tumor progression in case of resistance. On the other hand, for Everolimus, it is mandatory to clarify the mechanisms of resistance (e.g. the reactivation of PI3K and Akt via MAPK or the over-expression of some other growth factors), the synergic therapeutic role of combination with SSA, the risk of immunosuppression drug-related and the increased incidence of infections or interstitial pneumonias (61).
Figure 1. Management of pNETs with advanced loco-regional disease and/ or distant metastases
Legend : PD progressive disease, SD stable disease, SSTR somatostatin receptor, SSA somatostatin analogs, CTX chemotherapy, PRRT peptide receptor radionuclide therapy, LM liver metastases, TEM/CAP temozolomide/capecitabine *cisplatin may be replaced by carboplatin.
Systemic chemotherapy is indicated in progressive or bulky well differentiated pNETs and in poorly differentiated NEC. Cytotoxic therapy combinations for well differentiated pNETs include: streptozotocin with 5-fluorouracil (STZ/5FU) ; doxorubicin with streptozotocin or temozolomide/capecitabine, although data on the latter combination are still limited (58). Systemic chemotherapy may be considered without prior progression in patients with high tumor burden. There is no established Ki-67 cut-off value for recommendation of chemotherapy, that has been successfully used in pNETs with Ki67 of 5-20% (58).
In NEC G3 cisplatin based chemotherapy (e.g. cisplatin/ etoposide) is standard therapy and it is recommended as first-line therapy (58).
PRRT is a therapeutic option in progressive pNETs with a positive expression of somatostatin receptors (SSTR). Radionuclide therapy with either 90Y and/or 177Lu-labeled SSA is most frequently used, although 177Lu-labelled SSA is increasingly used due to its
lower kidney toxicity. PPRT is generally recommended after failure of medical therapy, even though the optimal sequencing with targeted drugs and/or chemotherapy needs to be defined with the prospective randomized trials on going at the moment (58).
Moreover, if the disease is limited to the liver, loco-regional therapies may be an alternative option to systemic therapies in patients with functioning and NF-pNETs (58). In the absence of any large comparative trials of different loco-regional or ablative therapies (bland embolization, chemo-embolisation, radio-embolisation, radiofrequency ablation or microwave destruction) or systemic treatment, the choice of treatment is based on individual patient features (e.g. size, distribution, number of liver lesions, vascularization,
proliferative index) and local physicians expertise (64). Anyway, a clinical algorithm of liver metastasis treatment could be suggested, as the one subsequently reported (58) (Figure 2).
Figure 2. Management of liver metastases without extra-hepatic disease in NEN G1/G2
* SIRT, selective internal radiotherapy is still an investigational method
LM liver metastases, TAE transarterial embolisation, TACE transarterial chemoembolisation, RFA Radiofrquency ablation, RPVE right portal vein embolisation, RPVL right portal vein ligation, LiTT laser induced thermotherapy
2.5 Neuroendocrine pancreatic tumors prognosis:
Neuroendocrine pancreatic tumors has a better prognosis and a more indolent course in comparison to the other pancreatic neoplasia, with a 5-years overall survival after diagnosis between 65 and 40%, according to different series (37, 65-67).
Overall survival is firstly related to the diagnosis staging with distant metastasis associated with a significant worse prognosis, in the various series reported (33-36, 39, 68-72).
Grading is an another main predictive prognostic factors and this has been confirmed in many previous study (33, 36, 68,70, 73), although different Ki-67 cut-offs have been proposed to better stratify pNETs patients prognosis, at diagnosis (36, 37, 72).
In other series higher mitosis, neuroinvasion, necrosis and tumoral margins invasion have been proposed as negative prognosis factors (67, 73-74).
Albeit, these clinical and pathological parameters can be useful tools to predict prognosis of pNETs, the single pNETs prognosis, especially of the well differentiated ones, is still difficult to be foretold.
For these reason, new significant biological prognostic markers would be extremely helpful to guide clinical management of these tumors and to select a personalized medical treatment, especially now that the therapeutic armamentarium has been enriched of many different options and in particular of some effective molecular target therapies.
In these direction, and considering the promising responses to the new anti-angiogenic therapies, the evaluation of some specific proteins, involved in angiogenetic pathways becomes particularly relevant.
2.6 Vascular endothelial growth factor (VEGF) and placental growth factors (PlGF) and its receptors
The vascular endothelial growth factor receptor (VEGFR) family consists of three receptors: VEGFR1, VEGFR2 and VEGFR3 (also known as FLT1, FLK1 and FLT4) (75). VEGFR2 binds VEGFA and VEGFC, and is the primary receptor that drives angiogenesis in healthy conditions and during disease. By contrast, VEGFR1 binds VEGFA,VEGFB and placental growth factor (PlGF), while VEGFR3 binds VEGFC and VEGFD (76).
Placental growth factor directly binds VEGFR1 and its co-receptors neuropilin 1 and 2 (NP1 and NP2) but not VEGFR2 (77, 78).
Neuropilin-1 and NP2 form a small family of conserved and widely expressed
transmembrane proteins, originally implicated in the regulation of axon guidance and vascular development (1, 2). The extracellular portion of neuropilins (NPs) interact with secreted
class 3 semaphorins and VEGFs. Neuropilins are found in complex with VEGFRs
enhancing affinity of VEGFs for VEGFRs and significantly increasing their post-receptor signaling and biological effects. Neuropilins seem extensively implicated in endothelial cell proliferation and in angiogenesis, either during embryonal development or adult life,
showing an important up-regulation and over-functioning during abnormal metabolic condition ( such as hypoxia or nutrient deprivation) (78). Furthermore, NPs are involved in the recruitment of bone marrow cells to sites of neo-angiogenesis in response to VEGFs, where these cells mediate vessel maturation and arteriogenesis (78).
PlGF through its receptor and co-receptors, induces various biological effects in vitro and in vivo by affecting a wide range of different cell types. PlGF can stimulate vessel growth and maturation directly by affecting endothelial and mural cells, as well as indirectly by recruiting pro-angiogenic cell types (76). Indeed, PlGF stimulates the growth, migration and survival of endothelial cells (79,80), increases the proliferation of fibroblasts and smooth-muscle cells, induces vasodilatation and stimulates collateral vessel growth (81,82). It also promotes the recruitment and maturation of angiogenesis-competent myeloid progenitors to growing sprouts and collateral vessels (83-85). PlGF also activates and attracts macrophages that release angiogenic and lymphangiogenic molecules (86). Several studies have analyzed the molecular mechanisms of PlGF that seems to stimulate angiogenesis, in part by
enhancing VEGF signaling. In fact, it displaces VEGFA from VEGR1, allowing VEGFA to activate VEGFR2 and enhance VEGF-driven angiogenesis (87). In addition,
mass-spectrometry studies have shown that PlGF and VEGFA induce phosphorylation of distinct tyrosine residues in VEGFR1 (88), further highlighting the fact that PlGF and VEGFA transmit distinct angiogenic signals through VEGFR1.
However, some PlGF studies do not support the findings discussed above. For example, PlGF fails to stimulate endothelial-cell growth and migration in vitro, and induces only a weak tyrosine-kinase signal in VEGFR1 (87). This lack of endothelial responsiveness
to exogenous PlGF might be due, at least in part, to the abundant endogenous production of PlGF by cultured endothelial cells, which may saturate VEGFR1 and preclude a response to exogenous PlGF (76). Moreover it has also been proposed that PlGF could inhibit angiogenesis when it is overexpress at supra-physiological levels and it could also produce VEGFA-PlGF heterodimers which could deplete the intracellular pool of VEGFA, reducing angiogenesis (76).
A possible explanation for the redundancy of PlGF and VEGFA is that PlGF expression, unlike that of VEGFA, is undetectable in most organs in healthy conditions, but is highly up-regulated in pathological conditions (89). Evidence that VEGFR1 expression is induced in pathological conditions, is consistent with this explanation (89).
2.6.1 VEGF and PlGF pathway in cancer
The key role of VEGFA and its receptor VEGFR2 in tumour angiogenesis is firmly established, and many anti-angiogenetic therapies are targeting its pathway. However, some patients are resistant to VEGF-based therapies or show an escape to these treatments. Various mechanisms contribute to the resistance, and escape from anti-VEGF therapies (90), including the increased expression of other pro-angiogenic factors, a process that is termed angiogenic rescue (91, 92). In addition, tumour infiltrating inflammatory cells contribute to angiogenic rescue by secreting angiogenic factors, such as VEGFA or granulocyte-colony stimulating factor (G-CSF) (76).
Moreover, agents targeting VEGF–VEGFR cause a number of adverse effects significantly impairing quality of life, and treatment compliance. There is in fact, emerging evidences indicating that VEGF–VEGFR inhibitors not only target proliferating tumour endothelial cells, but also affect quiescent endothelial cells (93). In healthy conditions, endothelial cells survive for prolonged periods, owing to the low level of VEGFA expression by the vasculature. When quiescent endothelial cells are deprived of this trophic VEGFA signal, they become dysfunctional or disappear (94). Some of the adverse effects of anti-VEGF–
VEGFR agents have therefore been attributed to the deprivation of quiescent endothelial cells from VEGFA-maintenance signals (95).
PlGF seems selectively related to pathological angiogenesis, and it promotes tumor growth, progression and dissemination (92, 96).
Conversely, blocking PlGF using anti-PlGF antibodies emerged as therapeutic strategy to inhibit growth and metastasis in various preclinical tumor models (92, 97-100). As it does not have a primary role in angiogenesis of healthy tissues, therapies targeting PlGF could be free of the classical adverse events of anti-VEGF treatments.
PlGF mRNA and protein levels are increased in some kinds of cancer (such as gastric and colorectal cancer, NSCLC and breast cancer) (76). Moreover, PlGF levels have been described to correlate with prognosis and survival in some other cancer models. In particular PlGF levels were associated to serosal invasion, lymph-node metastasis, tumour stage and survival in gastric cancer (101); with disease progression and survival in colorectal cancer (102); with tumour stage in non–small lung cancer (103); with recurrence, metastasis and mortality in breast cancer (104); with post-surgical early recurrence of hepatocellular carcinoma (105); and with tumour grade and survival in patients with renal-cell carcinoma (106).
Furthermore, PlGF induction occurred as a result of antiangiogenic therapies in human cancer patients and in mouse models, and (in the latter situation) constituted a functionally relevant mechanism of resistance development (76, 92, 107, 108). Finally, changes in circulating PlGF following treatment initiation are emerging as predictors of therapy response for selected antiangiogenic treatment modalities in clinical trials (109).
Likewise, NPs levels are often significantly increased in cancer cells and tumor biopsies of various origin, compared with normal counterparts (78). High levels of NP1 were significantly associated with poor outcome in patients with colon cancer, breast cancer, and NSCLC and correlated with invasive behavior and metastatic potential in gastrointestinal carcinoma, glioma, and prostate carcinoma (78).
Equally, knocking down NP1 expression in carcinoma cells inhibits proliferation, cell survival, and extracellular matrix invasion in vitro (110); consistently, other studies indicated that NP1 over-expression can inhibit cancer cell apoptosis (111, 112). These
effects have been often explained by the role of NP1 in supporting VEGF-PlGF signaling (111-113).
In contrast to these findings, in other studies, an elevated expression of NP1 was associated with more favorable prognosis of patients with colon cancer (114) and reduced tumor growth in experimental models in mice (115).
These discrepancies are currently unresolved and they might reflect cell-specific responses and/or the involvement of different signaling pathways. Antibodies and short peptides interfering with NP1 function have been shown to inhibit tumor angiogenesis and tumor growth in vivo in mice (116,117); validating NPs as possible tumor markers and significant targets for antiangiogenic and antitumor agents.
2.6.2 PlGF pathways and neuropilin in neuroendocrine tumors:
Angiogenic growth factors, such as VEGF-A (118) or angiopoietins (119), are known to be present in NETs and characteristic neuroendocrine secretion products, including chromogranin A fragments (120) and serotonin (121), affect angiogenesis and vascular permeability. In addition, targeted therapies (i.e. Sunitinib and Everolimus), acting through angiogenesis inhibition, prolong survival and PFS in NETs.
Thus, the angiogenetic pathway seems candidate to be a very relevant signalling in NETs biology.
In a previous study PlGF was determined in NETs patients’ sera collected retrospectively and prospectively (122). Circulating PlGF was elevated in patients with pNETs compared with control sera. In the retrospective cohort, median PlGF levels progressively increased from grade 1 to grade 3 and PlGF over the 50th percentile correlated with reduced tumor-related survival in pNETs. In a prospective cohort of grade 1 and grade 2 pNETs, circulating PlGF levels over a cut-off statistically selected to discriminate normal serum from serum of patients with pNETs, were associated with better tumor-related survival. Moreover, circulating PlGF levels allowed to separate prognostic subgroups within the group of G2 pNETs. In a prospective cohort of in intestinal NETs, circulating PlGF above median emerged as an independent prognostic factor for shorter time-to-progression in multivariate analyses and median PlGF levels further stratified the clinically heterogeneous group of G2
intestinal NETs into prognostic subgroups (122).An immunohistochemistry for PlGF was performed in pNETs showing strong PlGF expression in stromal cells, such as endothelial and inflammatory cells, and occasionally weak immunoreactivity for PlGF in the tumor cell compartment. In contrast, no staining was observed in non-transformed endocrine or exocrine pancreatic tissues, including their stroma indicating de novo expression of PlGF in pNETs (122).
Indeed, recombinant PlGF enhanced NET proliferation and migration in vitro (in BON, QGP, KRJ-I and H727 cell lines). Conversely, growing BON orthotopic pNETs xenografts in nude mice and treating them with either isotype-matched control IgG1 or a combination of neutralizing antibodies to mouse and human PlGF, showed a significant reduction of tumor weight in mice treated with neutralizing antibodies, indicating that PlGF supports NET growth in vivo (122).
This first report proposes PlGF as an important factor of pNETs progression, and as a potential biomarker of pNETs prognosis.
Moreover, preliminary data obtained from biomarker determinations in the RADIANT-3 trial, revealed a small transient reduction of circulating PlGF in patients receiving Everolimus (123). Though preliminary, this observation could suggest PlGF as predictor of response to antiangiogenetic treatment in pNETs
These above considerations induce the demand for a systematic study of PlGF in NETs. Moving to the role of neuropilins, it is known that NP2 is often overexpressed in melanoma, a kind of tumor that shares many biological features with NETs. Furthermore, in melanoma, increased immunohistochemical expression of NP2 has been found in metastases compared with primary lesion, highlighting the potential utility of NP2 as a prognostic marker for metastatic melanoma (124). In addition, elevated levels of NP2 transcript have been found to be significantly related to an increased malignant progression and a positive correlation between NP2 expression and depth invasion of melanoma has been reported (125).
Thus, although the role of NP2 seems significant in melanoma biology and progression, limited data are available in NETs.
small and large intestines and stomach (126). In particular, NP2 staining was specific to neuroendocrine cells and was not seen in other mucosal epithelial or stromal cells (126). Expression of NP2 was also detected in a subset of gastrointestinal carcinoid tumours (126) and there is only one paper that evaluated NP expression in pNETs (127). In this previous study, NP2 stained a subset of islet cells situated primarily at the islet periphery apparently co-localing with glucagon-expressing cells. Moderate to strong NP2 staining was present in 27 of 30 pNETs (127), however the role of NP2 as a tumoral marker in pNETs remains to be elucidated.
2.7 Slit- Robo signaling
The Roundabout (Robo) gene encodes a transmembrane receptor that was firstly identified in Drosophila (128), thereafter four different Robos (Robo1/DUTT1, Robo2, Robo3/
RIG-1, Robo4/Magic Roundabout) members have been described in human (129,130). Slit protein was then identified as the ligand for Robo receptor through which it mediates its functions. The most commonly studied member is Slit2; it is known to regulate many aspects of tissue morphogenesis and cell function, including cell migration, proliferation, adhesion and death (131). Although the genes overlap, their expression patterns and functions are distinct. Slit1 is specific to the brain, and Slit2 and Slit3 are expressed in the brain as well as other tissues (132). Its receptor Robo, is a single-pass, transmembrane receptor belonging to the immunoglobulin superfamily. In mammals, Robo1-3 is expressed in many tissues during development and particularly in the nervous system (133, 134). The latest member of this family Robo4, was originally thought to be expressed mainly by endothelial cells (135, 136). Robo1 and Robo4 are two receptors of the secretory protein Slit2. Slit-Robo signaling was first established as an extracellular signature to guide axon path finding, promote axon branching and control neuronal migration. More recently, the interaction of Slit and Robo proteins have also been documented to be crucially involved in the development processes of various vital organs such as breast, lung, liver, kidney, eye and reproductive systems (137). Slit-Robo signaling also has important roles in regulating both non-pathological and pathological angiogenesis. Slit2, Slit3, Robo1 and Robo4 are
expressed by endothelial cells. Robo4 in particular is exclusively expressed by endothelial cells (136, 138) and maintains the vascular integrity via either Slit2 dependent or Slit2 independent pathways. Slit-Robo signaling can regulate other cellular processes involved in cell growth. It can inhibit hepatocyte growth factor (HGF), stromal derived factor-1 (SDF-1) and β-catenin activity (139-141). Apart from physiological functions, Slit-Robo interactions are involved in many pathological cellular processes, including cell cycle, apoptosis, cell adhesion, motility and invasion, which are important for tumorigenesis (142).
2.7.1 Slit-Robo signaling in cancer
The first link between Slit-Robo signaling and cancer was reported by Sundaresan (143). Since then, the activation or suppression of the Slit-Robo pathway have been correlated to several oncogenic signaling pathways that are associated with the development and progression of cancer (144, 145). In 2003, Wang et al. demonstrated the angiogenesis promoting function of Slit-Robo signaling and evaluated the significance of this pathway in the pathogenesis of cancers (146). They examined human samples of different carcinomas and found out that in gastric squamous carcinoma samples, Slit2 was expressed in the cancerous tissues, but not in the nearby regions of apparently normal tissues. Their results showed Slit2 can attract vascular endothelial cells and promote tumor-induced angiogenesis (146). Furthermore, they demonstrated the neutralization of Robo1 reduced the micro-vessel density and the tumor mass of human malignant melanoma A375 cells in vivo (147). Slit2/ Robo1 network was then documented to play an essential role in development of gastric cancer metastasis (147).
On the contrary in 2013, Zhang et al. described the expression pattern of Slit2 in gastric cancer. Immunohistochemistry (IHC) staining revealed that Slit2 was found decreasingly expressed in advanced-stage gastric cancer tissues when compared with early-stage gastric cancer (148). They subsequently generated a Slit2-knockdown gastric cell model and found Slit2 knockdown promoted gastric cell growth and metastasis in vitro and in vivo. Slit2-knockdown cells formed larger tumors and produced more peritoneal metastatic nodules in nude mice. Subsequently, they suggested that knocking down of Slit2 activates Akt, enhances anti-apoptotic ability and activates β-catenin oncogenic signaling. They suggested
a tumor-suppressor role of Slit2 in gastric cancer (149). Meanwhile, Robo1 or Robo2 expression has been demonstrated to be frequently lost in many other cancers, including head/neck, breast, lung, kidney and uterine cervix cancers, as well as methylated on (150-152). Moreover, some reports suggested that Slit2-Robo1 inhibits metastasis formation in different tumor model such as breast cancer (153), fibrosarcoma or squamous cell carcinoma (154). In most cancers, Slit-Robo seems mainly involved in regulating chemockine that modulates metastases, and tumor growth such as stromal-derived factor 1 (SDF1) and monocyte chemotatic protein 1 (MCP-1) (153, 154).
These data suggest that Slit-Robo signaling could have a potential tumor suppressor role, that has been recently confirmed even in pancreatic ductal adenocarcinoma (PDAC), where Slit2 mRNA appeared distinctly reduced in PDAC specimens in comparison to normal pancreas (155). Moreover, ectopic expression of Slit2, in Slit2-deficient PDAC cell lines, inhibited directed migration and invasion, but not their random movement or proliferation (155). Furthermore, an exome sequencing in a prospectively cohort of PDAC revealed Slit2 and Robo2 mutations were present in 5% of patients, with focal copy-number losses of Robo1 and Slit2, potentially having an impact on a further 15% of the cohort, suggesting that aberrant Slit/Robo signaling is potentially a common feature of PDAC (145).
3. AIMS OF THE STUDY
1. Evaluation of possible clinical prognostic markers in pNETs:
a) correlation of NP2 and PlGF tumoral expression to clinical and biological characteristics, overall and progression free survival (PFS) in a pNETs series of patients, surgically treated and subsequently followed up at the University of Pisa (Department of Clinical and Experimental Medicine-Endocrinology section).
Further correlation between NP2 and PlGF tumoral expression and
therapeutic response to Everolimus, in a smaller cohort of advanced pNETs, submitted to this target therapy and followed up in the above Department. b) correlation of Slit2 and Robo1 tumoral expression to clinical and biological
characteristics and time to tumor progression in a pNETs series of patients, surgically treated and subsequently followed up at Charitè Hospital in Berlin (Department of Clinical Hepatology and Gastroenterology).
4. PATIENTS, MATERIALS AND METHODS 4.1 Study population
Clinical records of all patients, referred to the Endocrinology section (Department of Clinical and Experimental Medicine) of Pisa University and submitted to pancreatic surgery at the Surgery Unit for a pNETs between January 2000 and December 2009, were retrospectively evaluated (n=99). Patients with a concomitant diagnosis (n=1) of exocrine pancreatic cancer were excluded from the study, as well as all patients for whom a radiological post-surgical follow-up was not available (n=4) or relevant clinical data were missing (n=9). Patients with MEN1 syndrome (n=8), diagnosed by family history and confirmed by a genetic mutation, were also excluded, as well as all the ones for whom the pathological pancreatic tissue was not any more retrievable (n=22). Thus, data of 55 patients were retrospectively obtained from clinical records and included in the study to evaluate the role of NP2 and PlGF expression in pNETs prognosis. The pNETs histopathological diagnosis was reviewed according to TNM and WHO 2010 classification.
These patients were regularly followed, undergoing a standardized post-operative clinical and radiological follow-up, which included a radiological examination 6 months after surgery. Subsequent controls were based on clinical and pathological findings, and were more frequent in patients with progressive disease or more aggressive histology. A radiological examination was performed by CT scan in all patients, whereas MRI, ultrasound or In-111-labeled octreotide scintigraphy or Ga-PET were carried out on selected patients, as clinically appropriate.
Progressive disease was defined as any new lesion suggestive of a neuroendocrine neoplasm on the basis of imaging, or a radiologically measurable increase of the known un-resected lesions, according to the RECIST criteria 1.1 (156).
Progression free survival (PFS) or time to tumor progression (TTP) was calculated as the months between surgery and the 31st December 2014 or the first documented disease progression or relapse. Survival was evaluated at the 31st December 2014.
Patients with advanced tumors, persistent after surgery, were submitted to adjuvant therapy. In particular patients with poorly differentiated carcinoma were treated with systemic chemotherapy; whereas patients with well-differentiated neoplasms were mainly treated
with somatostatin analogues until progression, and in selected cases, with hepatic loco-regional treatment or systemic target therapies.
Moreover, 12 non functioning metastatic (ENETS stage IV) pNETs patients were treated with Everolimus at the Oncology or Endocrinoloy sections of Pisa University, for an advanced neoplasm; their primary tumors were stained for NP2 and PlGF and its expression was correlated to the drug response. Patients treated with Everolimus were all submitted to a first morphological evaluation with total body CT scan after 6 and 12 months of treatment. In some cases, due to clinical suspected progression, the radiological evaluation was performed earlier than 6 months. Subsequently, they performed regular radiological evaluations with total body CT scan or other appropriate imaging, according to the different clinical settings, and therapy response was evaluated and classified according to RECIST criteria 1.1 (156).
Response to treatment was then retrospectively analyzed as better response to drug during the whole treatment (classified as: progressive disease, stable disease or partial or complete response, according to RECIST criteria 1.1). Patients that did not reached 3 month-course therapy due to drug toxicity, were considered not evaluable for drug response.
At the same time tissues of 45 pNETs surgically treated at Charite´-University Hospital of Berlin, Department of Gastroenterology, from 1995 to 2014 were included in the study to evaluate the role of Slit-Robo signaling in pNETs clinical prognosis. Tumor staging with TNM classification and histopathological diagnosis and grading, were established and updated to WHO 2010 classification, as suggested by ENETS guidelines (41). These patients were followed up and treated, similarly to what has already been described above, for the Italian cohort. Clinical parameters were obtained from systematic retrospective review of the medical records, evaluating disease progression at last available follow up at the December 2014.
4.2 Neuropiling 2 expression analysis
Before to proceed to the evaluation of NP2 tumoral expression by immunohistochemistry (IHC), some experiments have been conducted to validate a reliable immunohistochemistry technique, mainly referring to the staining specificity.
Paraffin-embedded sections were obtained from cell cultures previously evaluated for NP2 expression through qPCR analysis. In particular, IHC expression was evaluated in sections of PANC1, DANG o ASPC1 cell cultures, known to express high levels of NP2. The staining was then compared to the one obtained from sections of MiaPaca cell cultures, known to have a nearly absent expression of NP2.
Two different primary antibodies anti-Neuropilin 2 (a goat polyclonal anti-NP2 antibody, produced by R&D system and a murine polyclonal anti-NP2 antibody (C-9), produced by Santa Cruz Biotechnology) were analyzed, using different concentrations and different antigen retrieval techniques.
The goat polyclonal primary anti-NP2 antibody, produced by R&D system resulted to be able to optimally differentiate the two cell lines with known high NP2 expression (PANC1 e DANG ) from the one not expressing NP2 (MiaPaca).
Thus, we proceeded to use that primary antibody on the pNETs tissues defining the most suitable concentration, able to better contrast the stained tissues from the background.
4.2.2 Immunohistochemistry for neuropilin 2 in neuroendocrine pancreatic tumors
Paraffin-embedded tissues were cut in 3µm sections and set up on Superfrost Plus (Menzel-Glasser, Germany) slides. Sections were heated at 60°C for one hour, then de-paraffinated and rehydrated with xylene and ethanol at decreasing concentrations. Antigen retrieval was obtained placing sections in pH 6, citrate 100mM buffer, boiling them in a pressure cooker for 4 minutes. After adequate washing, we proceed to block endogenous peroxidase, incubating sections with hydrogen peroxide at 0.3% for five minutes and then with a 5% rabbit anti-goat serum for twenty minutes. Sections were then incubated overnight with the primay anti-NP2 antibody (goat polyclonal, produced by R&D system) diluted 1:50 at 4°C. After proper washing we incubated sections with an anti-goat secondary antibody diluted 1:300 for thirty minutes, completing the staining using the Vectastain Elite ABC-kit (Vector
Laboratories, Wertheim-Bettingen, Germany) and AEC as substrate chromogen (DAKO, Hamburg, Germany). Sections were counterstained with Mayer's hematoxylin, dehydrated with increasing ethanol concentration and xylene solutions and then mounted with coverslip by means of Canada balsam (Diapath, Mortinengo (BG), Italy), and evaluated with microscope (Nikon E600, Shinjiuku, Japan).
On the whole series of sections a semi-quantitative expression score was defined, evaluating the intensity of the protein expression (intensity score, on four levels: zero: absent; 1: light; 2: moderate; 3: high expression) as well as the percentage of tumoral cells expressing NP2. We then calculated a total score, multiplying the intensity score for the percentage of cell expressing the evaluated protein (intensity score x % of expression). As staining controls we used the surrounding normal pancreatic tissues and we added two slices of normal pancreas for each set of staining, one with and one without the addition of the primary antibody.
4.3 PlGF expresssion analysis
4.3.1 Immunohistochemistry for PlGF in neuroendocrine pancreatic tumors
Immunohistochemistry for PlGF was performed as above described for NP2 staining. However, in this case antigen retrieval was performed using microwave at 600 Watt for 15 minutes in pH 6 citrate 100mM buffer. A primary goat policlonal antibody anti-PlGF, produced by Abcam, was used diluted 1:50, as previously reported (122). A semi-quantitative score was again defined to analyze the obtained results, using normal pancreas slices as control, as described for NP2 IHC.
4.4 Slit2 and Robo1 expression analysis
4.4.1 Immunohistochemistry for Robo 1 in neuroendocrine pancreatic tumors
Immunohistochemistry for Robo 1 was performed in the 45 pNETs of the German series, as previously described (155). In particular a primary anti-Robo1 polyclonal rabbit antibody produced by Bethyl Laboratories was used at a concentration of 1: 50.
4.4.2 qPCR for Slit2 and Robo1
RNA was purified using the RNeasy Mini Kit (Qiagen) from the 45 pNETs of the German series, and from 27 normal pancreatic tissues and quantified on Agilent's 2100 Bioanalyzer using the RNA 6000 Nano Kit (Agilent). Quantitative real-time PCR (qRT-PCR) was conducted in triplicate using Slit2 TaqMan primer/probes (Hs00191193_m1; Applied Biosystems) and Robo1 (Hs 00268049_m1; Applied Biosystems ) with the One-Step RTPCR. Relative quantification was calculated by the Livak method.
4.5 In vitro Slit-Robo signaling evaluation
Slit-Robo signaling was evaluated in BON and QGP-1 cells.
BON cells were a generous gift from C M Townsend (Department of Surgery, University of Texas Medical Branch, Galveston), while QGP-1 cells were from the Health Science Research Resources Bank (Osaka, Japan).
Constructs for expression of hSL2myc and RoboN cDNAs in mammalian cells were a kind gift from Jane Wu (Northwestern University, Feinberg School of Medicine containing, Chicago Illinois). Full-length Slit2-cDNA was transfected into BON cells using Effectene transfection reagent. Stably transfected cell clones were obtained by selection with 1 mg/ml Geneticin (G-418 sulfate; Invitrogen) over a time period of 4 weeks. Clone mixtures were used for the subsequent biological characterization.
For assessment of Robo1 function in QGP-1 pancreatic NET cells, QGP-1 cells were incubated with MISSION® lentiviral-transduction particles (Sigma-Aldrich) for shRNA-mediated Robo1 knockdown or non-target control particles at a MOI of 10. Stable clones were selected in puromycin-containing medium (0.6µg/ml) and successful knockdown was confirmed via Robo1 immunoblot.
At the same time, to evaluate Slit2 function in NET cells, a soluble Robo1-decoy receptor (Recombinant rat Robo1-Fc chimera, produced by R&D Systems) was used to sequester Slit2. In particular it was used in cell culture assays at a concentration of 5-10 µg/ml.
To evaluate the role of Slit2-Robo1 signaling in NET cell lines, growth, migration and colony formation assays were used.
4.5.1 Growth assays:
In 24-well dishes, 105 cells/well were plated and cultured in DMEM Hams/F12 and glucose free, FBS supplemented medium, allowed to attach for 6 h. Following stimulation with fetal calf serum (FCS) at 1% for 72 hours, cell numbers were counted using a Neubauer
hemocytometer. Growth assays were performed in BON cells with and without the induced re-expression of Slit2.
4.5.2 Migration assays:
Polycarbonate membranes pore size 8.0 µm (Corning Life Sciences, Lowell, MA) were used as cell culture inserts according to the manufacturer’s instructions. Membranes were coated with Collagen (200 µg/mL in PBS) to facilitate cell adhesion. BON or QGP-1 cells (2x105 cells/insert) were placed in the upper chamber of the cell culture insert; serum free culture medium was added to the lower chamber. After an adhesion period of 24 hours, serum free culture media were exchanged in the upper and lower chamber, and FCS at 1% was added to the lower chamber. After further incubation for 24 hours, inserts were removed and all cells on the upper surface of the membrane were carefully removed with a cotton swab. The cells on the lower side of the membrane were fixed in 4% formaldehyde for 30 minutes and stained with crystal violet solution. Migrated cells were counted under the microscope at 200 X magnification in 12 standardized fields. Migration assays were performed in BON cells with and without the induced re-expression of Slit2. Migration assay was also evaluated in QGP1 cell with and without Robo 1 knockdown, as well as in QGP cells with Slit2 re-expression with and without the addiction of soluble Robo1-decoy receptor.
4.5.3 Colony formation:
To determine effects on anchorage-independent growth of NET cells, colony formation in agar-suspension was evaluated. Briefly, 3x104 cells were resuspended in 300µl culture medium and added to a mixture of 2,7 ml Hyclone FCS, 0.8 ml Iscove’s modified DMEM, 3.6 ml of 2,1 % (w/v) Methylcellulose in Iscove’s, 1.6 ml agar solution (10 ml 3% agar and 20 ml DMEM) and 0.06 ml beta-mercaptoethanol to obtain an agar suspension. Aliquots
(1ml, 3000 cells) of this suspension were then dispensed in 35 mm dishes and colony formation was assessed under an inverted microscope by manual counting after a 10 day incubation period. A threshold of 20 cells was arbitrarily set to score cell accumulations as colonies. Colony formation assay was then evaluated in BON cells with Slit2 re-expression with and without the addiction of soluble Robo1-decoy receptor. Colony formation was also evaluated in QGP1 cell with and without Robo 1 knockdown.
4.6 Statistical analysis
Data were expressed as mean ± SD for continuous variables and as percentage for
categorical variables. The comparison between two independent groups was performed by the unpaired Student's t-test and by the Chi-square test for categorical data. Survival and PFS were estimated according to the Kaplan-Meier method. The correlation between two continuous variables was estimated by the Pearson correlation coefficient. The log-rank test was used to compare survival and PFS of different subgroups. Cox regression analysis was used to study determinants of survival and PFS present at the time of the diagnosis. A 2-sided p-value <0.05 was considered statistically significant. Statistical analyses were performed in SPSS (SPSS Inc., Chicago, IL) for Windows (version 21).
5. RESULTS
5.1 Neuropilin 2-PlGF prognostic role
Clinical main parameters, of the series of patients studied to evaluate the role of NP2-PlGF, are reported in Table 2.
PlGF appeared well expressed in normal pancreatic islet (Figure 3), localizing mainly at citoplasmatic levels; a weak expression was also present in the stromal and exocrine tissues. PlGF was differently expressed in neoplasms, maintaining in almost all cases a
citoplasmatic expression (Figure 3).
However, PlGF expression, evaluated as intensity score, percentage expression or overall score, was not significantly associated with the main clinical parameters such as: age at diagnosis, sex, primary tumor dimension, lymph-nodal metastasis, distant metastasis, angioinvasion, grading, Ki67, mitotic count or ENETS staging (Table 3).
Neuropilin 2 was well expressed in normal pancreatic islet (Figure 4), with a predominant citoplasmatic expression; a strong expression was documented in intrapancreatic nerves with a weak staining in stroma and exocrine pancreas.
However, NP2 expression, evaluated as intensity score, percentage expression or overall score, was not significantly associated with any of the main clinical and biological parameters (Table 3).
Moreover NP2 and PlGF expression, evaluated as intensity score, percentage expression or overall score, were not correlated neither with overall survival or progression free survival (Table 3, Figure 5).
Table2: Clinical findings of patients evaluated for PlGF-NP2 expression
Clinical Parameters at diagnosis Mean±SD
Age 59.3 ± 15.5
Sex (M/F) 29/26
pNETs Functionality 9/55 (7 insulinomas; 2 gastrinomas) Grading 1 2 3 27 24 4 Ki67 (%) 5.6 ± 8.7 Angioinasion (Yes) 29 (53%) Staging 1 2 3 4 16 15 (12a/3b) 10 ( All 3b) 14
Adiuvant therapy 21 (38%) (14 SSA; 3 CT; 4 other therapy) Overall survival (months) 86.9±37.8
PFS (months) 63.0±42.6
Figure 3.
A B
C D
E
A PlGF stained normal islet; B PlGF highly expressed in pNETs, C PlGF moderately expressed in pNETs; D PlGF weakly expressed in pNETs and a concomitant staining of normal pancreatic islet; E PlGF negative pNETs and positive normal islet.
Figure 4
A B
C
Table 3. PlGF-Neuropilin 2 expression and their association with clinical parameters
Survival
(p) PFS(p) Grading(p) Ki67(p) Angioinvasion(p) (p)T (p)N (p)M Staging(p) NP2 overall score 0,886 0,900 0,270 0,065 0,344 0,111 0,337 0,609 0,172 NP2 intensity score 0,960 0,605 0,525 0,051 0,051 0,247 0,631 0,824 0,172 NP2 % cells 0,840 0,618 0,245 0,113 0,281 0,050 0,247 0,696 0,299 PLGF overall score 0,524 0,715 0,522 0,531 0,185 0,757 0,876 0,577 0,629 PLGF intensity score 0,505 0,377 0,523 0,522 0,228 0,653 0,617 0,539 0,495 PLGF % cells 0,516 0,917 0,352 0,342 0,200 0,713 0,755 0,738 0,917
Figure 5.
Clinical main parameters of patients treated with Everolimus and furtherly evaluated for NP2-PlGF expression in primary tumor, are reported in Table 4.
Table 4. Clinical findings of patients treated with Everolimus
Clinical Parameters at diagnosis Mean±SD
Age 64.4 ± 15.9
Sex (M/F) 6/6
Grading 1
2 5 (41.7%)7 (58.3%)
Ki67 (%) 5.7 ± 5.7
Previous therapy 1/12 (8.3%) (only SSA) 1/12 (8.3%) (SSA, PRRT) 5/12 (41.7%) (SSA and CT) 4/12 (33.3) (SSA, PRRT and CT) Concomitant therapy 2/12 Lanreotide
9/12 Octeotide
1/12 Lanreotide+Zolendronic Acid Better response to Everolimus SD 7 (58.3%)
PD 2 (16.6%) NE 3 (25%)
PFS (months) 15.9±25.2
Mucous toxicity (Yes) 6/12 (50%)
SSA: somatostatin analogues; PRRT:peptide receptor radionuclide therapy; CT: chemotherapy; SD: stable disease; PD: progressive disease; NE: not evaluable
NP2 and PlGF expression, evaluated as intensity score, percentage expression or overall score, was not correlated with progression free survival or with therapeutic response to Everolimus. (Table 5, Figure 6)
NP2 and PlGF expression, either as intensity score, percentage expression or overall score, were not even significantly related to Everolimus muco-cutaneous toxicity (Table 5)
Table 5. PlGF-Neuropilin 2 expression and response to Everolimus treatment
Better therapeutic response
(p) PFS (p) Toxicity (p) Grading (p) Ki67 (p)
NP2 overall score 0,795 0,504 0,659 0,576 NP2 intensity score 0,816 0,565 0,516 0,074 0,966 NP2 %cells 0,649 0,747 0,427 0,506 0,703 PLGF overall score 0,275 0,693 0,331 0,249 0,338 PLGF intensity score 0,101 0,075 0,470 0,091 0,278 PLGF % cells 0,301 0,689 0,536 0,536 0,589
Figure 6. PlGF-Neuropilin 2 expression and response to Everolimus treatment
PFS in Everolimus (months)
5.2 Slit2-Robo1 expression and their prognostic role
Clinical main parameters, of the series of patients studied to evaluate the role of Slit2-Robo1, are reported in Table 6.
Table 6. Clinical findings of patients evaluated for Slit2-Robo1 expression
Clinical Parameters at diagnosis Age (Median)
(Range)
55 (19 -73)
Sex (M/F) 23/22
pNET Functionality 4 insulinomas 1 gastrinoma 2 other functionality
Type of analyzed tissues 17 metastases, 1 local recurrence, 27 primary tumor tissues Grading 1 2 3 10 12 5
Ki67 (%) (Median 5; Mean 11.44±18.33)
Follow up PFS (months)
Median = 40.3 months Median= 12.5 months
Slit2 mRNA expression was reduced in pNETs tissues as compared to healthy pancreatic tisseus, p=0.0019. Robo1 immunoreactivity localized to epithelial tumor cells of pNETs, and reduction of Robo1 mRNA levels significantly correlated to shorter time-to-progression in pNETs patients, p=0.0251. On the contrary the expression of Slit2 did not significantly correlate with TTP (Figure 7).
Figure 7. Correlation of Slit2 and Robo 1 expression and TTS
E
A-B: Relative Slit2 mRNA levels in human pNETs (n=45) and non transformed pancreatic tisseus (n=27) using qRT-PCR, normalized to GAPDH. Median with interquartile range; p= 0.0019. C-D: IHC staining for Robo 1 in pNET sections. E: Kaplan-Meier curves depicting time to tumor progression (TTP) in pNET patients stratified by Robo1 or Slit2 mRNA expression.
5.3 Slit1-Robo2 signaling and its biological role
BON cells with an induced re-expression of Slit2 showed a significantly reduced migration and colony formation. Conversely, in QGP cell lines, disruption of Slit2-Robo1 signaling via Robo1 knockdown or sequestration of endogenous Slit2 by the addition of a soluble Robo1 receptor stimulated directed migration and colony formation (Figure 8).
Figure 8
A Confirmation of Slit2 expression and secretion in Slit2 transfected BON cells (Slit2) and vector transfected controls (Mock). Forced Slit2 re-expression inhibited proliferation (B), directed migration of BON cells in transwell assay (C) and colony formation in soft agar (D). Inhibition of colony formation was abrogated by addition of chimeric Robo1-FC fragment with sequesters Slit2 (D). (E) Confirmation of lentiviral shRNA mediated knock-down of Robo 1 in QGP-1 cells (Robo 1 KD). Scr denotes controls transduced with scramble
sh RNA sequences. Reduced Robo1 expression enhanced proliferation (F), colony formation (G) and directed migration (H). Data are mean ±SD; *=p<0.05
