Endocrine-Related
Cancer
Z-B Lautaro et al.
Notch inhibition and pituitary
tumor growth
26:1
13–29
RESEARCH
Inhibition of Notch signaling attenuates
pituitary adenoma growth in Nude mice
Lautaro Zubeldía-Brenner1, Catalina De Winne1, Sofía Perrone2, Santiago A Rodríguez-Seguí3,4, Christophe Willems5,
Ana María Ornstein1, Isabel Lacau-Mengido1, Hugo Vankelecom5, Carolina Cristina2* and Damasia Becu-Villalobos1*
1Instituto
de Biología y Medicina Experimental, IBYME-CONICET, Buenos Aires, Argentina
de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires, CITNOBA (UNNOBA-CONICET), Universidad Nacional del
Noroeste de la Provincia de Buenos Aires, Buenos Aires, Argentina
3Departamento de Fisiología y Biología Molecular y Celular, Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Buenos
Aires, Argentina
4CONICET-Universidad de Buenos Aires, Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE), Buenos Aires, Argentina
5Department of Development and Regeneration, Cluster Stem Cell and Developmental Biology, Unit of Stem Cell Research, KU Leuven (University of
Leuven), Leuven, Belgium
2Centro
Correspondence should be addressed to D Becu-Villalobos: dbecu@dna.uba.ar
*(C Cristina and D Becu-Villalobos contributed equally)
Abstract
Preclinical and clinical studies support that Notch signaling may play an important
oncogenic role in cancer, but there is scarce information for pituitary tumors.
We therefore undertook a functional study to evaluate Notch participation in
pituitary adenoma growth. Tumors generated in Nude mice by subcutaneous GH3
somatolactotrope cell injection were treated in vivo with DAPT, a γ-secretase inhibitor,
thus inactivating Notch signaling. This treatment led to pituitary tumor reduction, lower
prolactin and GH tumor content and a decrease in angiogenesis. Furthermore, in silico
transcriptomic and epigenomic analyses uncovered several tumor suppressor genes
related to Notch signaling in pituitary tissue, namely Btg2, Nr4a1, Men1, Zfp36 and Cnot1.
Gene evaluation suggested that Btg2, Nr4a1 and Cnot1 may be possible players in GH3
xenograft growth. Btg2 mRNA expression was lower in GH3 tumors compared to the
parental line, and DAPT increased its expression levels in the tumor in parallel with the
inhibition of its volume. Cnot1 mRNA levels were also increased in the pituitary xenografts
by DAPT treatment. And the Nr4a1 gene was lower in tumors compared to the parental
line, though not modified by DAPT. Finally, because DAPT in vivo may also be acting on
tumor microenvironment, we determined the direct effect of DAPT on GH3 cells in vitro.
We found that DAPT decreases the proliferative, secretory and migration potential of
GH3 cells. These results position selective interruption of Notch signaling as a potential
therapeutic tool in adjuvant treatments for aggressive or resistant pituitary tumors.
Key Words
f DAPT
f pituitary
f angiogenesis
f prolactin
f GH
Endocrine-Related Cancer
(2019) 26, 13–29
Introduction
Pituitary adenomas are mostly benign intracranial
tumors, which do not metastasize but may recur after
surgical removal, compress nearby structures or produce
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considerable morbidity related to hormonal dysfunction.
A subset of these tumors may be aggressive, atypical or
recurrent, and presently, there is a paucity of molecular
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Z-B Lautaro et al.
markers that could improve diagnosis, treatment and
prognosis. Stem-like cell activation of different components
of the Notch pathway have been consistently detected in
pituitary tumors (Mertens et al. 2015) suggesting potential
therapeutic benefit for targeting Notch in tumoral
pituitaries. Nevertheless, functional studies linking the
Notch pathway with pituitary tumorigenesis are lacking.
Notch signaling regulates numerous cellular
processes, including stem cell maintenance, proliferation,
cellular differentiation and apoptosis (Artavanis-Tsakonas
& Muskavitch 2010). It maintains precursor cells by
balancing cellular proliferation, cell fate decisions and
differentiation in several tissues such as brain, muscle,
intestine and the hematopoietic system. It is therefore not
surprising that Notch pathway dysfunction is implicated
in the pathogenesis of adult human disease, including
cancer (Ranganathan et al. 2011).
The mammalian Notch receptor family consists of
four type 1 transmembrane receptors (termed NOTCH
1–4), which are synthesized as precursor forms and
cleaved by a furin-like convertase to generate the mature
receptor, composed of two subunits: an extracelluar and
an intracelluar domain (NICD) held together by noncovalent interactions. Notch signaling is initiated by
cell-to-cell contact of the receptor with the neighboringcell Notch ligands Jagged1 and 2 (JAG1 and JAG2) and
Delta-like 1,3 and 4 (DLL1,3,4). Ligand binding initiates
a series of cleavages and a final cleavage mediated by
the γ-secretase complex, which releases NICD from the
plasma membrane so that it can translocate into the
nucleus where it recruits a transcriptional activation
complex activating and repressing genes. Classical target
genes are the transcriptional factors of the Hairy Enhancer
of Split (HES) family (HES 1,5,6, and 7), the Hairy-Related
Transcription factor family (HRT1,2 and 5; also known as
HEY), Notch receptors, Notch ligands, cyclin D1 and MYC
(Bray 2006, Gordon et al. 2008), among others.
Substantial evidence derived from preclinical and
clinical studies support that Notch signaling may play an
important oncogenic role in several types of cancer. In
particular, most patients with T cell acute lymphoblastic
leukaemia (T-ALL) harbor activating mutations in the
NOTCH1 gene, which result in ligand-independent
proteolytic cleavage of the receptor and increased stability
of the NICD (Ellisen et al. 1991). This leads to constitutive
activation of the Notch pathway and neoplastic
transformation of T cells. Nevertheless, in solid tumors,
there is little evidence for genetic alterations in Notch
genes, even though Notch signaling seems to be crucial in
the generation and progression of breast, colon, pancreas
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Notch inhibition and pituitary
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26:1
14
and prostate cancer (Radtke & Raj 2003). Intriguingly,
Notch signaling may also have a tumor suppressor role
as it was described in mouse keratinocytes, pancreatic
and hepatocellular carcinoma (Koch & Radtke 2010,
Ranganathan et al. 2011).
The versatility and pleiotropic effects, which result
from aberrant Notch activity may be interpreted based
on contextual and developmental cues. Moreover, each
tissue and even every cellular component within a tissue
express different proportions of Notch paralogs and target
genes, which may ultimately determine cell fate during
Notch dysfunction. Activity and outcome of increased
Notch signaling may therefore depend on the specific
paralog involved as found in medulloblastoma tumors
(Castro et al. 2003) and in breast (Harrison et al. 2010) and
pancreatic carcinomas (Avila & Kissil 2013). Complexity
is increased when target genes are considered, leading
to the concept that Notch activity outcome depends on
cellular context.
In the search for targets for pituitary adenoma
combinatorial treatment, elucidation of relevant
Notch signaling components within each adenoma
type would be highly valuable. Knowledge on the
participation of the Notch system in pituitary tumor
generation and progression is scarce. In general, links
between pituitary adenomas and Notch have been
revealed by the description of expression levels of
Notch pathway elements, but to our knowledge, no
functional study has been performed so far. Notch 3
was increased in prolactinomas and non-functioning
adenomas (Moreno et al. 2005, Evans et al. 2008,
Miao et al. 2012, Lu et al. 2013) and decreased in
somatotropinomas (Lu et al. 2013). Furthermore, HES1
expression was decreased in prolactinomas and nonfunctioning adenomas (Evans et al. 2008), and levels
of Jagged1 were increased (Lu et al. 2013). Importantly,
in pituitary adenomas, the side population with stem
cell characteristics showed increased levels of HES1,
JAGGED1 as well and NOTCH 1,2 and 4 (Mertens et al.
2015). Furthermore, pituitary adenoma-derived stemlike cells express higher levels of NOTCH4, JAG2 and
DLL1 and are more resistant to chemotherapeutics than
their differentiated daughter cells (Xu et al. 2009).
We recently found that all four Notch receptors are
expressed in the pituitary gland and also demonstrated
enhanced gene expression of the Notch ligands Jag1 and
Dll1, and the target gene Hey1, as well as activated Notch2
intracellular domain N2ICD in the somatolactotrope cell
line GH3 compared to normal rat pituitaries (Perrone et al.
2017). Furthermore, in prolactinomas harbored by
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Z-B Lautaro et al.
lacDrd2KO female mice an activated Notch signaling
pathway was found (Perrone et al. 2017). Therefore, in
the present study, we undertook a functional approach
to evaluate Notch participation in pituitary adenoma
growth. GH3 somatolactotrope tumors generated in Nude
mice were treated with a γ-secretase inhibitor, N-[N-(3,5difluorophenacetyl)- L-alanyl]-S-phenylglycine t-butyl
ester (DAPT), thus inactivating Notch signaling. Tumor
development, Notch signaling component expression
and angiogenic and proliferative markers were assessed.
Furthermore, because regulation of Notch signaling
pathways is specific for each tumor type, we undertook a
bioinformatic approach based on a combined epigenomic
and transcriptomic analysis to identify Notch target genes
with a potential role in tumor suppression, which may
be relevant to pituitary tumors. We next determined their
expression in treated and untreated GH3 xenografts.
Finally, because DAPT in vivo may be acting not only
on pituitary tumor cells, but also on endothelial cells
or modifying the extracellular matrix of the tumor, we
determined the direct effect of DAPT on GH3 cells in vitro.
Our results demonstrate that inhibiting Notch signaling
in vivo leads to pituitary tumor reduction and a decrease
in tumor angiogenesis. In addition, DAPT acts directly
on GH3 cells decreasing their proliferation, secretory
and migration potential. These results position selective
interruption of Notch signaling as a potential therapeutic
tool in the search for adjuvant treatments in aggressive or
resistant pituitary tumors.
Materials and methods
Notch inhibition and pituitary
tumor growth
26:1
15
was inactivated with excess (20 mL) F12K medium
(supplemented with 15% (v/v) horse serum, 2.5% (v/v)
bovine fetal serum). Cells were centrifuged for 10 min at
950 rpm, 23°C, the pellet resuspended in 1 mL PBS or F12K
medium and cells were counted.
Experiments with athymic Nude mice
Nude mice BALB/c NU/NU were housed at the Animal
House Facility of the Instituto de Biología y Medicina
Experimental. Experimental tumors were induced by sc
injection of 700,000 GH3 cells suspended in 100 μL PBS
in one flank of adult female Nude mice. DAPT treatment
was started when the tumor volume had reached about
70 mm3 in size (approximately 21 days after GH3
injection). DAPT was dissolved in 0.5 μM DMSO–PBS,
and 8 mg/kg BW per mouse was administered i.p., thrice
a week. Vehicle-treated animals served as controls. The
tumor volume was regularly determined with a caliper
until the animals were killed after 3 weeks of treatment.
Tumors were excised, weighed and frozen at −70°C for
mRNA and protein studies and a portion was embedded
in paraffin for immunohistochemical studies.
All experimental procedures were carried according
to guidelines of the Institutional Animal Care and Use
Committee of the Instituto de Biología y Medicina
Experimental, Buenos Aires (in accordance with the
Animal Welfare Assurance for the Instituto de Biología
y Medicina Experimental, Office of Laboratory Animal
Welfare, NIH, A#5072-01). Study #07/2016 was approved
by IBYME IACUC.
Cell line and culture conditions
RNA extraction and cDNA synthesis
GH3 rat somato-prolactinoma cell line (ATCC, CCL-82.1)
was cultured in adhesion as reported (Vela et al. 2007) in
DMEM/F12K medium, supplemented by 2.5% (v/v) fetal
bovine serum, 15% (v/v) horse serum, 1% glutamine and
1% (w/v) penicillin/streptomycin and fungizone, pH 7.3
and maintained at 37°C and 5% CO2. After incubation
in serum-free medium for 18–24 h cells were treated with
DAPT 1, 5 and 10 μM (Calbiochem Cat No: 565770) or
vehicle. Medium was refreshed every 24 h with the
appropriate stimuli. Aliquots of supernatant were collected
for GH and prolactin measurements at 24 and 48 h. To
analyze gene and protein expression, cells were detached
and dissociated using trypsin (0.05%) with EDTA (0.02%;
Life Technologies).
For GH3 sc injections in Nude mice, GH3 cells
were cultured and detached as indicated, and trypsin
Xenotransplant tissue or GH3 cells cultured in vitro were
processed for recovery of total RNA using TRIzol reagent
(Invitrogen). Reverse transcription was performed as
previously described (Perrone et al. 2017).
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Real-time PCR
Measurements were performed as previously described
(Garcia-Tornadu et al. 2009, Perrone et al. 2017). Sense
and antisense oligonucleotide primers were designed
on the basis of the published cDNA or by the use of
PrimerBlast (http://www.ncbi.nlm.nih.gov/tools/primerblast/). Oligonucleotides were obtained from Invitrogen.
The sequences are described in Supplementary Table 1
(see section on supplementary data given at the end of
this article).
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Western blot
Xenotransplant and cell lysates were homogenized in
a motor microtissue mixer in 80–300 μL of lysis buffer
(50 mM HEPES (pH 7.4), 140 mM NaCl, 10% (v/v) glycerol,
1 mM EDTA, 1 mM sodium orthovanadate, 10 mM sodium
pyrophosphate, 100 mM sodium fluoride, 1% Triton
X-100), and 1 mM phenymethylsulfonyflouride and
protease cocktail inhibitor (Roche Diagnostic) were added
to the buffer just before use. The homogenate was then
centrifuged at 12,000 rpm for 30 min at 4°C. An aliquot
of the supernatant was taken to quantify proteins by the
Qubit Quant-it protein assay kit (Invitrogen).
Thirty to forty micrograms of proteins in 20 µL of
homogenization buffer were mixed with 5 µL of 5× sample
buffer (312 mM Tris-HCl, 10% SDS, 25% glycerol, 0.002%
bromophenol blue and 1% Beta-mercaptoethanol, pH
6.8). Samples were heated 5 min at 95°C and separated
by 10% SDS-PAGE and electrotransferred to nitrocellulose
membranes (G&E, Little Chalfont, UK). After blocking
with 3% nonfat dry milk solution in phosphate saline
buffer – Tween (PBST) (10 mM sodium phosphate, 2 mM
potassium phosphate pH 7.4, 140 mM NaCl, 3 mM KCl,
and 0.1% Tween 20) blots were incubated overnight at
4°C with primary antibodies. Antibodies used were rabbit
polyclonal anti-Notch 1 (1/1000, EMD-Millipore, Cat
#07-1232), anti-Notch2 (1:1000, Merck Millipore): antiHes1 (1:1000, EMD-Millipore, Cat. #AB5702).
Membranes were washed with PBST and incubated
with the corresponding horse radish peroxidase (HRP)conjugated secondary antibody, and protein bands were
detected in a G:box chemi HR16 (Syngene, Frederick, MD,
USA). The monoclonal beta-Tubulin (1:7000, Sigma-Aldrich,
Cat #T0198) was used to validate equal amount of protein
loaded and transferred. For repeated immunoblotting,
membranes were incubated in stripping buffer (62.5 mM Tris,
2% sodium dodecyl sulfate and 100 mm mercaptoethanol,
pH 6.7) for 40 min at 55°C and reprobed. Band intensities
were quantified using ImageJ software (National Institutes
of Health, Bethesda, MD, USA).
NOTCH1-2 expression levels were evaluated by the
semi-quantification of two bands, the active intracellular
domain (NICD) of 80 kDa and the membrane domain
plus the NICD of the receptor of 110 kDa.
Prolactin and GH RIAs
Serum
Aliquots (10 µL) of serum obtained from Nude mice were
used to assay serum prolactin and GH by RIA.
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Tissue
Xenotransplanted GH3 tumoral samples (1–5 mg)
were homogenized in ice-cold PBS and centrifuged
at 3000 rpm for 5 min. Supernatant protein contents
were measured with the QUBIT Fluorometer and the
QUANT-IT protein Assay Kit (Invitrogen). Aliquots of
equal quantity of protein were used to assay GH and
prolactin content.
In vitro supernatants from cultured GH3 cells, 10 µL
diluted 1/20–1/40, were kept at −20°C for GH and
prolactin RIA assays.
RIA assays were performed using kits provided by the
National Institute of Diabetes and Digestive and Kidney
Diseases (NIDDK; Dr. A.F. Parlow, National Hormone
and Pituitary Program (NHPP), Torrance, CA). Results are
expressed as ng/mL for in vitro studies and serum, and
ng/µg protein for xenograft content, in terms of rat
prolactin standard RP3 and GH standard AFP-10783B.
Intra- and inter-assay coefficients of variation were 7.2
and 12.8% and 8.4 and 13.2%, for prolactin and GH,
respectively. Sensitivity threshold was 0.02 and 0.04 ng
for prolactin and GH, respectively.
Quantification of cell proliferation
MTS proliferation assay
Proliferation of GH3 cells was colorimetrically determined
at 490 nm using a commercial proliferation assay kit
CellTiter 96 (AQueous Non-Radioactive Cell Proliferation
Assay, Promega Corp.) following the manufacturer’s
instructions. Cell cultures were repeated four times and
each had duplicate samples.
Cell motility assay
Cell motility was evaluated using the ‘scratch assay’. After
reaching 90% confluence, GH3 cells were serum-starved
for 24 h and then treated with mitomycin C (10 μg/mL;
Calbiochem 475820) to inhibit cell proliferation. A
straight scratch was created, and cells were further kept
in DMEM/F12K (2% horse serum, 1% fetal bovine serum),
together with DAPT (5 and 10 μM) or vehicle. Medium was
changed every 24 h. The migration of cells into the scratch
was evaluated by light microscopy, and live pictures were
taken with an Olympus CKX 41 microscope at different
time points. The open area was calculated using the image
processing and analysis software ImageJ http://rsbweb.
nih.gov/ij/.
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Tumor microvessel density, vascular area and vessel
size assessment
Immunohistochemistry
Xenotransplants were deparaffinized and dehydrated in
graded ethanols. A microwave pre-treatment for antigen
retrieval was performed in 10 mM sodium citrate buffer,
pH 6. Endogenous peroxidase activity and nonspecific
binding sites were blocked. Primary antibody (goat
polyclonal antibody PECAM for CD31 endothelial cell
detection (1:200, sc-1506 Santa Cruz Biotechnologies
Inc.) or rabbit polyclonal SMA antibody (1:200; α-SMA
ab15734; Abcam) for vessel mural cell detection) was
incubated overnight at 4°C. After incubation with biotinconjugated secondary antibody for 1 h, the reaction
was developed using an avidin-biotin kit coupled to
peroxidase (Vector Laboratories, Burlingame, CA, USA)
and diaminobenzidine as a chromogen substrate.
Samples were counterstained with hematoxylin and
mounted with permanent mounting medium. Each
immunohistochemical run included negative controls
replacing the primary antibody with PBS. As a measure
of angiogenesis, we determined the microvascular density
(MVD) by counting the number of CD31+ or ɑSMA+ vessels
per square millimetre, the vascular area determined by
the cumulative area of the tumor occupied by CD31+ or
ɑSMA+ vessels and expressed as % vessel area/total area
and the average vessel size. Images of randomly selected
fields were recorded using 40× or 100× objective, using a
Zeiss Axiostar Plus microscope and a Canon PowerShot
G6 digital camera. Three slides per tumor (4 tumors per
group) were analyzed and at least five images per slide at
400× of total magnification were counted by the image
processing and analysis software: Image J, http://rsbweb.
nih.gov/ij/.
Bioinformatic analysis
Publicly available raw RNA-seq datasets were obtained
from the Sequence Read Archive (SRA) database as listed
in Supplementary Table 2. Human normal pituitary
datasets used for the analyses described in this manuscript
were obtained from dbGaP at http://www.ncbi.nlm.nih.
gov/gap through dbGaP accession number phs000424.
v6.p1. Raw read sequence alignment and gene expression
quantification was performed with the Tuxedo suite
(Langmead et al. 2009, Trapnell et al. 2009, 2012). In brief,
raw reads were first aligned to the human genome (hg19
version) using TopHat v2.0.12 (Trapnell et al. 2009) with
default parameters, and Cufflinks (Trapnell et al. 2012)
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Notch inhibition and pituitary
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was used with default settings to quantify the expression
levels as fragments per kilobase of exon per million
fragments mapped (FPKM). Further analysis included
normalization of transcript levels and differential
expression analysis using Cuffnorm and Cuffdiff tools
(Trapnell et al. 2012).
Publicly available raw ChIP-seq datasets were
obtained from the Sequence Read Archive (SRA) database
as listed in Supplementary Table 3, and aligned and
processed as follows to infer putative Notch1-bound
regulatory regions in pituitary GC cells. Sequence reads
were aligned to the rat genome (version rn4) using Bowtie
1.1.1 (Langmead et al. 2009). Only sequences uniquely
aligned with ≤1 mismatch were retained. Post-alignment
processing of sequence reads included in silico extension
and signal normalization based on the number of million
mapped reads. Reads were extended to a final length equal
to MACS fragment size estimation (Zhang et al. 2008), and
only unique reads were retained. For signal normalization,
the number of reads mapping to each base in the genome
was counted using the genomeCoverageBed command
from BedTools(Quinlan & Hall 2010). Processed files were
visualized in the UCSC genome browser (Kent et al. 2002).
ChIP-seq enrichment sites were detected with MACS
v1.4.0beta (Zhang et al. 2008) using default parameters
and a P value of 1e-5. A control dataset derived by
sequencing input DNA samples was used to define a
background model.
Next, active regulatory regions in GC cells were
defined as H3K27ac-enriched genomic sites that
overlapped with H3K4me1 signal in rat GC cells. To infer
putative Notch1 binding, we lifted over the Notch1 peaks
as published by the group of Dr Pear (Zhang et al. 2008)
to the rat genome (rn4) and searched for overlap among
these sites and the active regulatory regions profiled
in GC cells. For gene ontology analysis, the putatively
Notch1-bound active regulatory regions in GC cells
were lifted over to the mouse (mm9) genome, regions
were associated to genes and gene ontology analysis
was performed using GREAT with default settings. To
gain further insights into the tissue specificity of the
regulatory regions of interest for this work, we also
downloaded, re-aligned and analyzed a Pit1 ChIP-seq
dataset profiled in GC cells.
Combination of in silico transcriptomic and epigenomic
analyses allowed us to choose several tumor suppressors
associated with enhancers potentially bound by Notch1
(and thus putative downstream targets of Notch signaling)
to be evaluated in our experiment, namely Btg2, Nr4a1,
Men1, Zfp36 and Cnot1.
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Notch inhibition and pituitary
tumor growth
Statistical analysis
DAPT treatment decreased Notch 2 intracellular
domain, the target protein HES1 and target
gene Hey2
By Western blot, we identified NOTCH 1 and 2 active
intracellular domain (NICD 80 kDa) and the membrane
domain (110 kDa). N2ICD significantly decreased
after inhibition of γ-secretase by DAPT (Fig. 2A), while
membrane domain levels were not modified. Furthermore,
DAPT treatment decreased HES1, a Notch target protein
(Fig. 2B). These results indicate that i.p. DAPT effectively
reduced Notch activation and signaling in xenografts.
Messenger RNA levels of Notch2 receptor, Notch
ligands (Jagged 1 and Dll1) and several Notch target genes
(Hey1 and 2, Hes1 and 5, Cyclin D1 and D3 and Tgfb1)
were measured in ex vivo xenotransplants by RTqPCR
at the end of the treatment. No differences in Notch2
mRNA levels (which measures both active and membrane
domains) or Notch ligands were found in response to
DAPT treatment in vivo (Fig. 3A). The target gene Hey2 was
markedly decreased (P = 0.020), while no differences were
encountered for Hey1, Hes1, Cyclin D1, Cyclin D3 or Tgfb1
mRNA expression (Fig. 3B and C). On the other hand, Hes5
and Dlk1 could not be detected in the xenotransplants
(not shown).
Results
Notch signaling inhibition decreased xenograft
tumor growth and prolactin and GH content in
GH3-inoculated Nude mice
Mice s.c. inoculated with 700,000 GH3 cells developed
visible tumors 21 days after inoculation (Volume
61.0 ± 7.1 mm3); at this point, i.p. DAPT treatment was
started (day 0). Xenograft volumes in DAPT-treated mice
were consistently smaller at all time points, and statistical
significance was achieved beginning on day 16 after the
initiation of treatment (Fig. 1A). Body weight remained
unaltered during treatment (Fig. 1B). Average tumor
volume was 42% lower in DAPT-treated mice at killing
(Fig. 1A), and prolactin and GH tumor content and serum
B
Xenogra tumor volume
Control
600
*
DAPT
Body weight
25
*
500
Control
DAPT
30
18
prolactin levels were significantly decreased in the DAPT
group at the end of the treatment period (Fig. 1C).
Results are expressed as means ± S.E.M. The differences
between means were analyzed by the unpaired Student’s
t-test in the case of only two groups. Two-way ANOVA
with repeated-measures design was used to analyze
tumor volume in vivo, prolactin and GH secretion
in vitro, protein and gene expression in vitro, motility and
proliferation assays, for the effects of drug and time. Post
hoc Tukey’s test was employed when necessary. Parametric
or nonparametric comparisons were used as dictated by
data distribution. P < 0.05 was considered significant.
A
26:1
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15
g
mm3
400
300
200
10
100
5
0
0
4
8
12
16
0
20
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Days of treatment
4
7
10
14
17
Days of treatment
3
*
2
1
0
10
8
*
6
4
2
0
CTRL
DAPT
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CTRL
DAPT
200
400
150
*
100
50
Serum GH (ng/ml)
4
12
Serum prolac n ng/ml
5
ng GH/ug protein
ng prolac n/ug protein
C
300
200
100
0
0
Control
DAPT
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CTRL
DAPT
Figure 1
Notch signaling inhibition decreased xenograft
volume and prolactin and GH tumor content in
GH3 inoculated Nude mice. (A) Tumor volume (in
mm3) in DAPT and vehicle-treated GH3 inoculated
Nude mice. DAPT treatment (8 mg/kg, three times
a week) was begun on day indicated as 0.
*P ≤ 0.05. N = 11 control, and 12 DAPT. (B) Body
weight was not modified by the treatment. (C)
Prolactin and GH content in the excised tumor (ng
prolactin/µg protein), and serum prolactin and GH
on day 17 (ng/mL). N = 11 and 12 for hormone
content, and 10 and 10 for serum hormones,
control and DAPT, respectively. *P ≤ 0.05, DAPT vs
control.
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NOTCH/TUB (% of Control)
140
120
100
100
80
60
60
40
40
20
20
0
0
NOTCH 1 NOTCH 2
100
*
80
60
40
20
0
NOTCH 1 NOTCH 2
Dapt Con Dapt Con N2 Dapt Con Dapt Con
CON
DAPT
Dapt Con Dapt Con
HES1
Actin
Tubulin
Bioinformatic analyses identified specific Notch
targets in pituitary
The results prompted us to search for additional Notch
target genes in pituitary tissue. To that end we undertook
a bioinformatic approach which consisted in analyzing
the information of existing ChIP-seq data to infer putative
Notch1-bound regulatory regions in pituitary cells. For
this purpose, we first defined 53,695 active regulatory
regions in pituitary GC cells as those genomic regions
co-enriched for H3K4me1 and H3K27ac signals, as
previously reported (Heintzman et al. 2007, Pasquali et al.
2014). In these regions, 2699 putative Notch1-bound
sites were identified, that resulted from the intersection
of regulatory regions in GC cells with Notch1-binding
sites (lifted over from T-ALL, see Materials and methods).
The rationale for this choice was that, given that Notch
ChIP-seq in pituitary samples was not available, some
of the Notch-binding sites in other tissue samples could
overlap with those of pituitary cells, as long as both
cell types have accessible chromatin (as in the case for
the active regulatory regions profiled in GC cells). We
are aware that this approach may not detect all tissuerelevant Notch1-binding sites in pituitary cells. Rather,
it was useful in the context in which it was applied, to
infer a subset of regions that might have a shared relevant
role in tumorigenesis not only in T-ALL and pituitary,
but potentially in other tissues. And, furthermore, in
order to assess the relevance of the genes analyzed for the
function of pituitary cells, we checked for the presence of
Pit1-binding sites, a pituitary-specific transcription factor,
at their nearby regulatory regions.
Then, the genes associated with the putative Notch1bound regulatory regions in GC cells (see Materials and
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19
120
100kD
80 kD
26:1
Control
DAPT
HES1
140
120
*
80
N1
B
Notch membrane domain (110
KDa)
Notch acve domain (80 KDa)
140
Notch inhibition and pituitary
tumor growth
Hes-1 /TUB (% of Control)
A
Z-B Lautaro et al.
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Figure 2
DAPT treatment decreased Notch 2 intracellular
domain and the target gene Hes1. (A) Effect of
DAPT treatment on NOTCH 1 and 2 intracellular
domains (80 kDa, Western blot), and NOTCH 1
and 2 membrane domain (110 kDa, Western blot)
in tumors excised after 17 days of DAPT
treatment. *P ≤ 0.05, DAPT vs control, N = 11 and
12 for NOTCH 1, and 7 and 8 for NOTCH 2. (B)
Effect of DAPT treatment on the target HES1
(Western blot), N = 11 and 12 , P = 0.033. Below
representative blots for Notch1 (N1, left) Notch 2
(N2, middle) and HES1 (right); Con, Control.
methods) were functionally annotated. Our results
revealed, among others, significant enrichment for
categories related to ‘Genes involved in positive regulation
of mRNA catabolic process’ (P = 1.1E-7) and ‘histone lysine
methylation’ (P = 5.8E-6). Noteworthy, these analyses
revealed putative Notch1-target genes Btg2, Cnot1, Men1,
Nr4a1 and Zfp36 with previously reported or suspected
tumor suppressor functions (Rouault et al. 1996, FarioliVecchioli et al. 2007, Hafner et al. 2011, Wenzl et al. 2015,
Montorsi et al. 2016).
We next compared the list of genes associated with
putative Notch1-bound regulatory regions in GC cells
with transcriptome information obtained by comparing
human control pituitaries (six samples) and three pituitary
adenomas (a PRL/GH adenoma and two GH adenomas)
(Table 1 and Supplementary Table 4). We quantified gene
expression from RNA-seq datasets, and by performing
comparisons between Control and PRL/GH+GH
adenomas, we found 1778 differentially expressed genes
(Supplementary Table 4). These included BTG2, ZFP36
and NR4A1, which were significantly downregulated in
human pituitary somato/somatolactotrope adenomas
when contrasted to the control human pituitaries (Table
1). Noteworthy, NR4A2 and NR4A3 expression was also
highly downregulated in the adenomas (Supplementary
Table 4), further supporting a relevant role for the NR4A
genes in suppressing adenoma development.
Figure 4 shows the epigenomic profiles for the loci
containing the five putative Notch target genes in pituitary
cells, which emerged from our strategy: Men1, Zpf36, Btg2,
Cnot1 and Nr4a1. These five genes had active regulatory
regions nearby (i.e. co-enrichment of H3K4me1, brown signal
and H3K27ac, yellow signal, in the plots), and putatively
bound by Notch1 (grey boxes). The presence of binding sites
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Z-B Lautaro et al.
Notch inhibition and pituitary
tumor growth
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for Pit-1 (blue signal) validates pituitary specificity of the
analysis). Noteworthy, active regulatory regions that could
bind Notch1 were found especially in Btg2 and Nr4a1, which
had 3 and 4 regions, respectively; Cnot1 and Men1 had 2
each and Zfp36 had only one (Table 1).
Taken together, these in silico transcriptomic and
epigenetic analyses prompted us to evaluate the tumor
suppressors Btg2, Nr4a1, Men1, Zfp36 and Cnot1 as targets
of Notch signaling in our model.
DAPT treatment increased mRNA levels of the tumor
suppressors Btg2 and Cnot1 in xenografts
A direct interrogation of gene expression in GH3 cells
compared to pituitary tumors originated by xenograft
transplants of GH3 cells showed that all suppressor genes
presented a downregulation trend in the xenografts, which
indeed achieved significance for Btg2 and Nr4a1 mRNA
expression levels (Fig. 5A). Conversely, in vivo inhibition
of Notch (DAPT treatment) significantly increased the
expression of the tumor repressor genes Btg2 and Cnot1
in the xenografts (Fig. 5B), advancing them as potential
mediators of the DAPT-induced pituitary tumor growth
inhibition and suggesting new putative therapeutic
targets for pituitary adenoma treatment. mRNA but not
protein levels were evaluated in the absence of adequate
commercial antibodies for all transcription factors,
therefore, results should be interpreted with caution.
DAPT treatment decreased in vivo angiogenesis
in xenotransplants
Figure 3
Effect of DAPT treatment on Notch receptors, ligands and target genes.
(A) mRNA levels of Notch 2 receptor, and the ligands Jagged1 and Delta
like 1N in xenografts from control and DAPT treated mice; (B) mRNA of
Notch target genes and (C) mRNA levels of Notch target genes involved in
proliferation or epithelial-to-mesenchymal transition. *P = 0.020, N
between 8 and 12.
Table 1
Immunohistochemical analysis of xenotransplants
at the end of treatment showed that microvascular
CD31 + relative area was reduced by DAPT (Fig. 6A), with
no significant differences in vessel size or density (Fig. 6B
and C). Moreover, ɑSMA+ vascular area and vessel size but
Transcriptomic analysis of selected genes based on a combined differential gene expression (control pituitaries and a
somatolactotrope/somatotrope adenomas) and ChIP-seq analysis.
Gene
Signaling
Control
Ad-PRL-GH/GH
q_value
#of Active Regulatory Regions bound by
Notch 1
MEN1
ZFP36
BTG2
CNOT1
NR4A1
Suppressor
Suppressor
Suppressor
Suppressor
Suppressor
22.5
258.8
276.5
30.1
1046.1
17.5
22.0
18.4
29.8
12.0
N.S.
0.011
0.002
N.S.
0.002
2
1
3
2
4
q values indicate significant differences between transcriptomes as evaluated by Trapnell et al. (2012). The number of Active Regulatory Regions
putatively bound by Notch 1 (determined by ChIP-seq analyses as described in Materials and methods) is also shown.
Ad-PRL-GH/GH, somatolactotrope and somatotrope adenomas.
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Notch inhibition and pituitary
tumor growth
26:1
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10 kb
chr 1
Notch1 T-ALL
25
H3K27ac
1
25
H3K4me1
1
32
Pit1
1
Map4k2
Men1
10 kb
chr 7
Notch1 T-ALL
25
H3K27ac
1
25
H3K4me1
1
32
Pit1
1
Plekhg2
Zfp36
Med29
20 kb
chr 13
13
chr
Notch1 T-ALL
T-ALL
Notch1
25
25
H3K27ac
H3K27ac
11
25
25
H3K4me1
H3K4me1
1
1
32
32
Pit1
Pit1
1
1
Fmod
Chit1
Btg2
50 kb
chr 19
Notch1 T-ALL
25
H3K27ac
1
25
H3K4me1
1
32
Pit1
1
Slc38a7
Setd6
Cnot1
20 kb
chr 7
Notch1 T-ALL
25
H3K27ac
1
25
H3K4me1
1
32
Pit1
1
LOC103692984
Nr4a1
Atg101
Grasp
Figure 4
Genome Browser screenshots showing the epigenomic profile in the vicinity of selected genes. The enrichment profiles for H3K4me1, H3K27ac and Pit1
in GC cells, as well as the putative binding sites for Notch1 (gray boxes on top) are presented. A full colour version of this figure is available at https://doi.
org/10.1530/ERC-18-0337.
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Relative mRNA (target/Gapdh)
%GH3)
Endocrine-Related
Cancer
A
Z-B Lautaro et al.
GH3
Notch inhibition and pituitary
tumor growth
26:1
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Tumor
160
120
80
*
40
*
0
Men1
Zfp36
Btg2
Cnot1
Nr4a1
Target/Gapdh (%Control)
B
350
300
250
200
150
100
50
0
Zfp 36
Men1
CTRL
DAPT
CTRL
Btg2 *
CTRL
DAPT
#
Cnot1
DAPT
CTRL
DAPT
not microvessel density were decreased in xenotransplants
of DAPT-treated mice (Fig. 6D and E). These findings
suggest an anti-angiogenic effect of Notch inhibition in
pituitary xenotransplants.
Effect of in vitro DAPT treatment on Notch
system components
We next tested a direct effect of DAPT on Notch signaling
in GH3 cells. After a 48-h incubation period, DAPT (10 μM)
decreased N2ICD but not HES1 protein or NOTCH2
B
5
3
*
2
1
400
200
DAPT
D
*
2
SMA vessel size
4
DAPT
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40
Control
1200
120
1000
100
800
600
#
400
DAPT
80
60
40
20
0
0
CONTROL
60
F
200
0
We evaluated prolactin and GH secretion after in vitro
DAPT treatment for 24 and 48 h of GH3 cell cultures.
80
DAPT
E
6
DAPT decreased hormone secretion in cultured
GH3 cells
0
Control
8
membrane domain (Fig. 7A). mRNA levels of the target
gene Hes1 was also decreased in vitro by DAPT treatment
(at 1 and 5 μM: Fig. 7B) while no significant differences
were found for Jag1, and the target genes Hey1 and 2,
Cyclin 3 and Tgf b1 (Fig. 7B and not shown).
20
0
Control
SMA+ Area/ Total Area (%)
600
Vessels/mm2
0
DAPT
100
Vessels/mm2
4
CTRL
Figure 5
DAPT treatment increased the tumor suppressors
Btg2 and Cnot1. (A) Comparative mRNA levels of
rat Men1, Zfp36, Btg2, Cnot1 and Nr4a1 in GH3
cells and xenografts resulting from GH3
inoculation (tumor).*P ≤ 0.020, N 3 and 3. (B)
Effect of DAPT treatment on mRNA levels of Notch
targets in excised tumors at the end of the
treatment. *P ≤ 0.01, and #P = 0.06; N 7 and 8,
control and DAPT, respectively.
C
800
Vessel size (um2)
CD31+ Area/ Total Area (%)
A
Nr4a1
CONTROL
DAPT
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CONTROL
DAPT
Figure 6
DAPT treatment decreased angiogenesis in GH3
xenografts. Effect of DAPT treatment on (A) CD31+
vessel area/total area %, (B) average vessel size
(µm2) and (C) microvessel density (number of
vessels per mm2 in immunohistochemical
evaluation of excised tumors at the end of the
treatment. *P = 0.046; N 4 and 4, control and
DAPT, respectively. (D, E and F) ɑSMA vessel area/
total area %, average vessel size (µm2), and
microvessel density, respectively. *P = 0.015;
#P = 0.052; N 3 and 3, control and DAPT,
respectively.
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Control
Z-B Lautaro et al.
DAPT 1uM
Notch inhibition and pituitary
tumor growth
DAPT 5 uM
26:1
23
DAPT 10uM
*
160
140
120
100
80
60
40
20
0
HES1
160
140
120
100
80
60
40
20
0
NOTCH2 (100 kDa)
N2-ICD (80kDa)
A
160
140
120
100
80
60
40
20
0
B
120
100
80
60
40
20
0
Hes1/Gapdh (% Control)
Notch2/Gapdh (% Control)
140
140
120
100
80
60
40
20
*
*
Cyclin d3/Gapdh (% Control)
160
160
160
140
120
100
80
60
40
20
0
Prolactin secretion was decreased at 24 and 48 h by DAPT
incubation in a concentration-related manner (Fig. 8A).
On the other hand, no significant differences were
observed for GH secretion (Fig. 8B).
DAPT decreased proliferation and prevented cell
motility in GH3 cells in vitro
DAPT (10 μM) decreased cellular proliferation, as measured
by MTS assay, at 24 but not at 48 h of incubation (Fig. 9A).
Finally, DAPT (10 μM) prevented cell motility or wound
healing in GH3 cells at 24, 48 and 72 h of incubation as
evaluated by a scratch assay (Fig. 9B and C). Both results
indicate an active participation of Notch signaling in GH3
cell proliferation and migration.
Discussion
Various components of the Notch pathway are expressed
during pituitary development, including Notch2 and 3
receptors, the ligand Jagged1 and the downstream effector
Hes1 (Raetzman et al. 2006). NOTCH2 is expressed in the
periluminal cells of Rathke’s pouch that are undergoing
rapid proliferation but not in the differentiated cells that
are able to secrete glycoprotein hormones (Raetzman et al.
2006), and its expression, as well as that of several Notch
family members, decrease as pituitary development
proceeds, indicating an inverse correlation with cell
differentiation (Raetzman et al. 2004). Nevertheless, in the
adult pituitary gland, components of the Notch signaling
pathway persist, particularly in locations containing
progenitor/stem cells, both in hormone-producing and
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Figure 7
In vitro DAPT treatment decreased NOTCH2
intracellular domain and Hes1 mRNA levels in
GH3 cells. (A) Effect of 48-h treatment with DAPT
(1, 5 and 10 µM) on active and membrane Notch 2
receptor (80 and 100 kDa respectively), and Hes-1
measured by Western blot analysis. N = 4
independent cultures, of duplicate samples. (B)
Effect of DAPT (1, 5 and 10 µM) on mRNA levels of
Notch signaling components. N = 4 independent
cultures of duplicate samples. *P ≤ 0.05 vs control
group.
hormone-null cells (Chen et al. 2005, 2006, Kelberman et al.
2009, Tando et al. 2013, Mertens et al. 2015, Perrone et al.
2017). This aspect is in line with the role of Notch in
maintaining progenitor cells in an undifferentiated state
(as documented in the brain and intestine) and may be
important in pituitary plasticity.
Insights into pituitary tumorigenesis may be
gained from studies on pituitary development and
cell differentiation. Genes that are important during
development or differentiation often contribute to tumor
promotion, survival or resistance when they become
uncontrolled. Indeed, cancer may be considered a
developmental disease, and pathways such as Notch that
can affect cell fate, and the balance between differentiation,
apoptosis and proliferation, are known to be involved in
tumorigenesis. Therefore, the Notch pathway is evolving
into an actively pursued drug target in cancer.
In numerous types of experimental models of cancer
blocking Notch activation by γ-secretase inhibitors, like
DAPT and others, was effective in reducing proliferation or
resistance to chemotherapeutics (Espinoza & Miele 2013).
For example, in colon cancer cells, (Akiyoshi et al. 2008),
in ER-negative breast cancer (Lee et al. 2008), glioma stem
cells (Wang et al. 2010), prostate cancer (Wang et al. 2011),
renal cell carcinoma (Sjolund et al. 2008) or experimental
brain tumors (Gilbert et al. 2010). Nevertheless, it has
become apparent that altered Notch status may be
associated with both pro- and anti-tumor-suppressive roles.
For example, it had a suppressive role in the formation
of vascular tumors in the liver, in mouse keratinocytes,
pancreatic and hepatocellular carcinoma (Koch &
Radtke 2010, Liu et al. 2011, Ranganathan et al. 2011),
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Cancer
Control
DAPT 1 uM
A
Z-B Lautaro et al.
Notch inhibition and pituitary
tumor growth
DAPT 5 uM
DAPT 10 uM
Prolacn (ng/ml)
8000
*
6000
4000
2000
* *
0
GH (ng/ml)
B
24
48
h
24
48
h
1800
1600
1400
1200
1000
800
600
400
200
0
Figure 8
In vitro DAPT treatment decreased prolactin secretion by GH3 cells. Effect
of DAPT (1, 5 and 10 µM) on (A) prolactin and (B) GH secretion by GH3
cells cultured in vitro for 24 or 48 h. N = 3 independent cultures, of
duplicate samples. *P ≤ 0.05 vs control group.
among others. Therefore, it is important to determine the
functional direction of Notch activation in each tumor
type as its effects are dependent on the cellular context
and the interaction with other signal transduction
pathways. In this context, the role of Notch signaling in
pituitary adenoma development and growth has not yet
been addressed. Recent evidence revealed a differential
sensitivity to Notch activation within and between
pituitary endocrine cell lineages during development
(Cheung et al. 2018), which further stresses the necessity
to establish its role within each pituitary adenoma
histotype.
Descriptive but not functional data of Notch pathway
in pituitary tumors suggest that Notch may be implicated
in the pathogenesis of human pituitary adenomas. By
microarray analysis, Evans et al. identified increased
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NOTCH3, DLK1 and decreased HES1 in prolactinomas
compared to normal pituitaries (Evans et al. 2008).
Data from Runchun et al. indicated non-significant
increases in NOTCH3 and JAGGED1 expression
in prolactinomas compared to normal pituitaries;
however, only 4 prolactinomas were used in the study
(Lu et al. 2013). Functional studies of pituitary tumor
generation or maintenance using Notch inhibition
are lacking in prolactinomas, somatotropinomas,
somatolactotropinomas or corticotropinomas. One
in vitro study was performed by Tando et al. (2013) who
described that DAPT treatment of anterior pituitary cells
in culture decreased Hes1 mRNA levels, and proliferation
but only in the non-hormone-producing S100 cells of
the S100b-GFP rat (Tando et al. 2013). Nevertheless, the
fact that many pituitary cell types co-exist in the normal
pituitary is an important caveat that should be kept in
mind when interpreting results presented in this work,
as in the other mentioned studies that compare tumoral
and normal pituitaries. To address this limitation, we
performed a combined transcriptomic and epigenomic
approach, which allowed focusing our functional analysis
and ultimately validate part of our findings.
In a previous study, we showed that in prolactinomas
which develop in lacDrd2KO female mice, Notch1 and
Notch3 mRNA levels and also NOTCH 2–3 membrane and
N1ICD were highly expressed compared to pituitaries
of control animals (Perrone et al. 2017). We also
determined that all four Notch receptors were expressed
in somatolactotrope GH3 cells, and that N2ICD, and
Jagged1, Dll1 and Hey1 were upregulated in the cell line
compared to rat pituitary cells. We therefore sought
to determine if inhibition of Notch signaling would
modify GH3 xenotransplant growth and angiogenesis in
Nude mice. We used DAPT a γ-secretase inhibitor, which
prevents cleavage of intracellular Notch domains, and
therefore, modifies target-specific transcription factors
in the nucleus.
Our results show that inhibition of γ-secretase lowered
tumor burden by 42% and decreased tumor angiogenesis
by 26% in somatolactotrope xenotransplants. It effectively
decreased active N2ICD formation, expression of the target
protein HES1 and the Hey2 gene indicating a blockade
of Notch signaling, and suggesting a novel strategy in
the treatment of aggressive or resistant prolactinomas.
Nevertheless, the lack of specificity of targeting γ-secretase
may constitute a significant limitation (Lamy et al. 2017),
and therefore, specific tumor-related targets activated by
Notch signaling in different tumor types are under the
spotlight. The classical Notch targets, such as HES and
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Notch inhibition and pituitary
tumor growth
Prolifera on Assay
A
110
MTS %
100
**
Control
90
DAPT 5
80
DAPT 10
70
60
24 h
B
48 h
Scratch Assay
1.0
*
*
*
Open area (%)
0.8
Control
DAPT5
DAPT10
0.6
0.4
0.2
0.0
24
48
72
C
DAPT 10 uM
Control
Figure 9
In vitro DAPT treatment decreased proliferation and migration in GH3
cells. (A) Effect of 24 and 48-h DAPT treatment (1, 5 and 10 µM) on cell
proliferation assessed by MTS assay. *P ≤ 0.05 vs control group, N = 4
independent cultures of duplicate samples; (B) Effect of 24, 48 and 72-h
treatment with DAPT (1, 5 and 10 µM) on remaining open area in culture
in a scratch assay (expressed as % of initial open area). *P ≤ 0.05 vs
control group, N = 4 independent cultures of duplicate samples.
(C) Representative images.
HEY families, are recurrently found in many tissues,
while others seem to be tissue specific. In this context,
the inventory of Notch targets has begun to expand
(Hurlbut et al. 2009). Recent gene expression studies
combined with chromatin immunoprecipitation arrays
revealed the existence of a large number of genes that can
directly be regulated by Notch in different solid tumors
(Koch & Radtke 2010). The challenge will be to identify
and distinguish driver target genes from passenger ones
in each cancer type. In this context, our bioinformatic
approach combining epigenomic and transcriptomic
information obtained from public databases uncovers
potential genes activated by Notch, which may be specific
to pituitary adenoma development and growth.
By epigenomic profiling of active regulatory regions
(enhancers and promoters, which might also present
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mild enrichments in H3K4me1 Heintzman et al. 2007)
in a pituitary somatotrope cell line we were able to
infer putative Notch-bound regions and novel target
genes. Combining this information with the differential
gene expression profiles obtained from human normal
and somatotrope + somatolactotrope pituitary samples
allowed us to focus on some interesting putative Notch
targets genes, whose regulation was next validated
experimentally. Epigenetic analysis revealed that the
genes Btg2, Nr4a1, Men1, Zfp36 and Cnot1, presented
active regulatory regions associated to Notch-binding
sites. Particularly, the Notch-related tumor suppressor
genes selected by epigenetic analysis, Btg2, Zpf36 and
Nr4a1 were downregulated in all somatotrope and
somatolactotrope adenomas when compared to normal
pituitaries. Therefore, these in silico transcriptomic and
epigenomic analyses allowed us to select several tumor
suppressors of Notch signaling in pituitary tissue to be
evaluated in our study, namely Btg2, Nr4a1, Men1, Zfp36
and Cnot1.
Our results point to Btg2, Nr4a1 and Cnot1 as
possible players in GH3 xenograft development and
growth. In particular, Btg2 mRNA expression was lower in
xenografted GH3 cells compared to the parental line, and
DAPT increased its expression in the xenograft in parallel
with the inhibition of tumor volume. Nr4a1 was also
decreased in xenotransplants compared to the parental
line, similar to results uncovered in RNA-seq analysis
comparing human somatolactotrope adenomas and
normal pituitaries, but its expression was not modified by
DAPT treatment. Finally, Cnot1, also a suppressor gene was
increased by DAPT treatment in the pituitary xenografts.
B-cell translocation gene 2, BTG2, is a tumor
suppressor gene whose overexpression leads to decreased
proliferation and arrest of cells at the G1 phase of the
cell cycle (Rouault et al. 1996). It is downregulated in
preneoplastic and neoplastic lesions in various cancers
(Farioli-Vecchioli et al. 2007, Mao et al. 2015), and it
intersects with the Notch pathway (Farioli-Vecchioli et al.
2014). It is found in the embryonic and adult anterior
pituitary (Terra et al. 2008), and, using the NCBI database
(http://www.ncbi.nlm.nih.gov/Genbank/) to analyze the
differentially expressed genes in plurihormonal and GH
pituitary adenomas compared with healthy pituitaries,
BTG2 was found downregulated (Jiang et al. 2010, 2012).
In accordance, our experimental and bioinformatic results
support its role as a tumor suppressor in the pituitary and
suggest its regulation by Notch.
Nuclear receptor (NR) subfamily 4 group A (NR4A)
is a family of three highly homologous orphan nuclear
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Endocrine-Related
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Z-B Lautaro et al.
receptors that have multiple physiological and pathological
roles. These NRs are reportedly dysregulated in multiple
cancer types, with many studies demonstrating prooncogenic roles for NR4A1 (Nur77) and NR4A2 (Nurr1),
while tumor suppression roles have been suggested for
NR4A1 and NR4A3 (Nor-1) in leukemia (Wenzl et al.
2015). In the pituitary, NR participates in CRH-induced
proopiomelanocortin
expression
in
corticotrophs
(Kovalovsky et al. 2002), and Nur77 gene expression
levels may be critical in the different autonomy of ACTH
production between Cushing’s syndrome and subclinical
Cushing’s syndrome (Tabuchi et al. 2016). Our results
demonstrate that Nr4a1 is decreased in GH3 xenografts,
and our re-analysis of public RNA-seq datasets showed that
expression of all NR4A genes (NR4A1, NR4A2 and NR4A3)
is severely downregulated in somatoprolactinomas and
somatotropinomas when compared to normal human
pituitaries, indicating a possible suppressive role for this
gene in the pituitary.
CNOT1 is a scaffold protein of the CCR4–NOT
complex. This complex participates in various
physiological functions, including cell proliferation,
apoptosis, mitotic progression, fertility, bone formation,
heart function, energy metabolism (Zukeran et al. 2016)
and miRNA-mediated mRNA repression (Hafner et al.
2011). Furthermore, CCR4-NOT deadenylase activity
contributes to induction of pluripotent stem cells
(Zukeran et al. 2016). No relation has been yet established
with pituitary regulation, and our results show that it can
be modulated by Notch inhibition to potentially activate
a putative tumor-suppressive role.
Menin is a putative tumor suppressor associated
with multiple endocrine neoplasia type 1 (MEN-1
syndrome), and the development of tumors in target
neuroendocrine tissues. Even though epigenetic analysis
suggested that it may be regulated by Notch signaling,
no difference for this gene was found in DAPT-treated
tumors. Finally, the mRNA-destabilizing protein ZFP36,
which had been previously described as a tumor
suppressor and impairs the epithelial-to-mesenchymal
transition (Montorsi et al. 2016) was not modified in the
present experimental model.
Among the signaling pathways involved in tumor
angiogenesis, Notch signaling stands as a crucial player.
This pathway does not just participate in physiological
angiogenesis during development, wound healing
or pregnancy, but is also involved in pathological
vascularization, such as in tumor angiogenesis. Importantly,
experimental evidence revealed that Notch may be involved
in anticancer drug resistance, indicating that targeting
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Notch inhibition and pituitary
tumor growth
26:1
26
this pathway could be a novel therapeutic approach to
the treatment for cancer by overcoming drug resistance.
Notch receptors and ligands are widely expressed in the
vasculature, but as described for tumor proliferation, it
has been reported that Notch has angiogenic properties,
but may also act in anti-angiogenesis in vascular tumors
(Liu et al. 2011). It is therefore paramount to validate its
angiogenic action in each tumor type.
We show that DAPT treatment decreased microvascular
area determined by CD31+ and ɑSMA + cells, indicating
that in pituitary tumors Notch increases angiogenesis,
as described for neck squamous cell or breast carcinoma
models (Zeng et al. 2005, Funahashi et al. 2008), among
others. This is a novel finding for pituitary tumors and
should be highlighted in the context of anti-angiogenic
therapies, which have been successful in experimental
prolactinomas (Luque et al. 2011), as well as in a particularly
aggressive Cushing tumors (Ortiz et al. 2012, Touma et al.
2017). Furthermore, the fact that DAPT reduced the
expression of the smooth muscle cell marker αSMA is
an indication that the Notch system may participate in
vasculature remodeling and vessel maturation through
interaction of mural and endothelial cells, as described for
NOTCH3 (Liu et al. 2009).
We have previously shown that expression of
different components of the Notch system vary when
comparing GH3 in vivo tumors generated by GH3
inoculation, and GH3 cells. GH3 tumors showed higher
activation of NOTCH1 and lower of NOTCH2 receptor
than isolated GH3 somatolactotropic cells (Perrone et al.
2017). Differences in Dll1 ligand expression were also
observed, suggesting that tumor vasculature and/or
extracellular matrix components, which are absent in cell
lines may be important modulators of Notch signaling
in xenografted somatoprolactinomas. The extracellular
matrix plays a critical role in tumor development
in various cancers, and its importance in xenograft
growth cannot be disregarded. Therefore, in order to
ascertain whether the Notch system cell-autonomously
participated in GH3 tumor development, we performed
in vitro studies inhibiting γ-secretase directly in cultured
GH3 cells. Our results clearly indicate that Notch
signaling in GH3 cells is positively involved in cellular
proliferation and migration. Similarly, results using DAPT
treatment of pituitary explants in vitro or postnatal mice
in vivo suggested that Notch signaling allows pituitary cell
proliferation during postnatal development, even though
a direct effect on dispersed cells was not verified in this
study (Nantie et al. 2014). Furthermore, we show that
inhibition of Notch activation led to decreased prolactin
Downloaded from Bioscientifica.com at 06/25/2024 06:14:00PM
via free access
Endocrine-Related
Cancer
Z-B Lautaro et al.
but not GH secretion, suggesting a differential activity in
the production of both hormones. In human GeneChip
microarrays and proteomics analyses, increased expression
of NOTCH3 was found in prolactin and non-functioning
secreting adenomas while in somatotropinomas, a
significantly reduced expression of NOTCH3 was found
(Moreno et al. 2005, Evans et al. 2008). Furthermore, in
GH3 cells, it was described that the non-canonical Notch
ligand Dlk1 is expressed in some clones, in which it
represses GH expression and secretion but does not affect
prolactin production (Ansell et al. 2007). Therefore, it
may be hypothesized that Notch manipulation may have
a differential outcome for prolactin and GH-secreting
tumors.
Personalized molecular treatments based on specific
genetic markers may improve diagnosis, treatment and
outcome in resistant and aggressive somatotropinomas
and somatoprolactinomas. In this context, salient features
identify Notch as a candidate diagnostic and prognostic
biomarker and a promising target for cancer therapy
(Espinoza & Miele 2013). Currently, most Notch-directed
therapies involve the use of γ-secretase inhibitors, but
the lack of substrate specificity and associated toxicity
found in clinical studies constitute limitations to their
therapeutic use (Lamy et al. 2017). Antibodies have
emerged as powerful biological therapeutics due to
their specificity and efficacy; and soluble decoys which
compete with natural ligands of Notch signaling but lack
the transmembrane domain are being tested (Espinoza &
Miele 2013). Our results suggest that interruption of Notchselective pituitary targets might be a novel strategy when
designing combinatorial treatment regimens in aggressive
or atypical prolactin and GH-secreting adenomas.
Supplementary data
This is linked to the online version of the paper at https://doi.org/10.1530/
ERC-18-0337.
Declaration of interest
The authors declare that there is no conflict of interest that could be
perceived as prejudicing the impartiality of the research reported.
Funding
This work was supported by grants from Agencia Nacional de Promoción
Científica y Tecnológica, Argentina: PICT 330-2013; PICT 901-2013; PICT
1343-2015; PICT 526-2016, Fundación Rene Barón, Fundación Williams,
Consejo Nacional de Investigaciones Científicas y Técnicas and Universidad
Nacional del Noroeste de la Provincia de Buenos Aires: PIO CONICETUNNOBA 2015-2016 and SIB UNNOBA 2015-3160 and collaboration
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https://doi.org/10.1530/ERC-18-0337
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Published by Bioscientifica Ltd.
Printed in Great Britain
Notch inhibition and pituitary
tumor growth
26:1
27
grant between MINCYT and Fund for Scientific Research (FWO) – Flanders
(Belgium).
Acknowledgements
The authors thank the National Institute of Diabetes and Digestive and
Kidney Diseases’ National Hormone and Pituitary Program and Dr A F
Parlow for prolactin and GH RIA kit. C Cristina and D Becu-Villalobos
contributed equally.
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Received in final form 24 July 2018
Accepted 8 August 2018
Accepted Preprint published online 18 August 2018
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