Abstract
Pancreatic neuroendocrine tumors (PanNETs) have considerable malignant potential. Frequent somatic mutations and loss of DAXX protein expression have been found in PanNETs. DAXX is known as a transcriptional repressor; however, molecular functions underlying DAXX loss remain unclear in PanNETs. We evaluated DAXX expression by immunohistochemistry in 44 PanNETs. DAXX-knockdown (KD) and -knockout (KO) PanNET cells were analyzed for in vitro and vivo. The target genes were screened by microarray and chromatin immunoprecipitation (ChIP) assays for DAXX, histone H3.3 and H3K9me3 complex. In clinicopathological features, low DAXX expression was significantly correlated with nonfunctional tumors, higher Ki-67 index and WHO grade. Microarray and ChIP assays of DAXX-KD/KO identified 12 genes as the direct targets of DAXX transcriptional repressor. Among them, expression of five genes including STC2 was suppressed by DAXX/H3.3/H3K9me3 pathway. DAXX-KD/KO cells enhanced sphere forming activity, but its effect was suppressed by knockdown of STC2. In xenograft models, tumorigenicity and tumor vessel density were significantly increased in DAXX-KO cells with high expression of STC2. Clinically, higher recurrence rate was recognized in PanNETs with low expression of DAXX and high expression of STC2 than others (P = 0.018). Our data suggest that DAXX plays as a tumor suppressor and DAXX/H3.3 complex suppresses target genes by promoting H3K9me3 in PanNETs. Combination of DAXX loss and its target gene STC2 overexpression might be effective biomarkers and therapeutic candidates.
Introduction
Pancreatic neuroendocrine tumors (PanNETs) comprise 1–3% of all pancreatic neoplasms and are the second common primary malignancy of the pancreas (Yao et al. 2008). The incidence of PanNETs in the United States was 5.25 per 100,000 people in 2004, and the number of PanNETs diagnosed has increased over the past several decades (Halfdanarson et al. 2008, Yao et al. 2008, Zhang et al. 2013, Tanaka 2016). Although PanNETs have been considered as indolent tumors with their slow progression, metastases were observed in 64% of patients with PanNETs, and the median overall survival (OS) for all patients with PanNETs was 28 months (Halfdanarson et al. 2008, Yao et al. 2008). Survival was improved by surgery, the 5- and 10-year survivals following curative resection were only ~65% and 45%, respectively (Halfdanarson et al. 2008, Yao et al. 2008), suggesting that PanNETs have considerable malignant potential. To date, the low incidence of PanNETs and their heterogeneous manifestations make it difficult to clarify this disease in clinical research (de Wilde et al. 2012). Moreover, few PanNETs cell lines and genetically engineered mouse models also remain serious obstacles in basic research (de Wilde et al. 2012). Several drugs including a multitarget kinase inhibitor sunitinib (Raymond et al. 2011) and an mTOR inhibitor everolimus (Yao et al. 2011) have been approved for use in PanNETs; however, the proof-of-concept for the treatment is still unsatisfactory, mainly due to limited understanding of the molecular mechanisms and predictive biomarkers (Berardi et al. 2016). Further mechanistic and biological investigations should be required.
PanNETs demonstrated frequent somatic mutations in the MEN1, DAXX, ATRX and PTEN by whole exome sequencing analysis, resulting in loss of their functions (Jiao et al. 2011). Since recurrent mutations of MEN1- and PTEN-related genes are detected in pulmonary and intestinal types of NET (Banck et al. 2013, Fernandez-Cuesta et al. 2014), DAXX/ATRX mutations might be specific for the pancreas type of NET (Jiao et al. 2011, Marinoni et al. 2014, Kim et al. 2017, Singhi et al. 2017). DAXX is a death domain-associated protein that regulates cell cycle, apoptosis and gene transcription (Salomoni & Khelifi 2006). DAXX cooperates with ATRX in the replication-independent chromatin assembly at telomeres, of which complex suppresses alternative lengthening of telomeres (ALT) pathway that is a telomerase-independent telomere maintenance mechanism (Lewis et al. 2010, Clynes et al. 2015). Dysfunction of DAXX or ATRX is associated with ALT activation in general (Heaphy et al. 2011, Marinoni et al. 2014, Kim et al. 2017, Singhi et al. 2017), though not necessarily (Lovejoy et al. 2012, O’Sullivan et al. 2014, Flynn et al. 2015, Napier et al. 2015, Pickett & Reddel 2015, Hu et al. 2016). Additional tumor suppressor functions might remain to be elucidated.
DAXX is an H3.3-specific histone chaperone and facilitates H3.3 deposition at heterochromatin loci in cooperation with ATRX (Drané et al. 2010, Lewis et al. 2010, Schwartzentruber et al. 2012). Recently, DAXX/ATRX/H3.3 complex is reported to be essential for transcriptional repression of repetitive elements and endogenous retroviral elements in mouse ES cells by promoting H3K9 trimethylation (me3) that is a hallmark of heterochromatin (Elsässer et al. 2015, He et al. 2015), suggesting that DAXX plays a role in embryonic development. Although frequent somatic mutations and loss of DAXX protein expression have been frequently found in PanNETs (Heaphy et al. 2011, Jiao et al. 2011, Marinoni et al. 2014, Kim et al. 2017, Singhi et al. 2017), molecular mechanisms underlying loss of DAXX remain unclear in PanNETs.
By using RNA interference and CRISPR/Cas9 system, we assessed how DAXX alterations contribute to development of PanNETs in this study. We found that DAXX loss increased malignant potential and tumorigenicity, and impaired transcriptional repression through H3.3/H3K9me3 pathway in PanNETs, resulting in upregulation of DAXX target genes including stanniocalcin 2 (STC2). STC2 is a glycoprotein hormone that regulates calcium and phosphate homeostasis (Wagner et al. 1998, Yeung et al. 2012), which is frequently overexpressed in human cancers (Yeung et al. 2012). Our data strongly indicate that DAXX act as a tumor suppressor, and combination of DAXX loss and STC2 overexpression might be effective biomarkers and therapeutic candidates in PanNETs.
Materials and methods
Patients and tissue samples
A total of 44 PanNETs were collected by pancreatic resection at the Tokyo Medical and Dental University Hospital between 2004 and 2014. Patients with neuroendocrine carcinoma or genetic syndrome associated with PanNETs were excluded in this study. Written informed consent was obtained from the patients, and our institutional review board approved this study (permission number 1080).
Cell culture and animal experiments
Two human NET cell lines, QGP1 (PanNET) and NCI-H727 (pulmonary NET) were obtained from the Health Science Research Resources Bank (Osaka, Japan) and the American Type Culture Collection, respectively, and authenticated by STRS analysis. These two NET cell lines are known to have both DAXX- and ATRX-wild type genes (Boora et al. 2015). In addition, one murine PanNET/insulinoma cell line MIN6 was used in this study, which was provided by Professor Jun-ichi Miyazaki (Osaka University, Japan). QGP1 and MIN6 cells were cultured in RPMI-1640 (Wako) and DMEM (Wako), respectively, supplemented with 10% fetal bovine serum, 100 U/mL penicillin and 100 μg/mL streptomycin (Invitrogen). NCI-H727 cells were grown in RPMI-1640 with high concentration of glucose. These cell lines were maintained in a humidified incubator at 37°C in 5% CO2.
NOD/SCID (NOD.CB17-Prkdcscid/J) mice were purchased from Charles River Laboratories. All mouse procedures were approved by the Institutional Animal Care and Use Committee of Tokyo Medical and Dental University (permission number 0170135A).
Establishment of DAXX-knockout cells by using CRISPR/Cas9 system
Two CRISPR targeting sequences of DAXX (5′-CTATAACCGGCAGCAACGT-3′ and 5′-ATTCCTCTATAACCGGCAG-3′) were designed on the basis of the Optimized CRISPR Design web tool (http://crispr.mit.edu/). Oligos were cloned into the PX459 vector (plasmid #62988, Addgene, Cambridge, MA, USA) according to the Protocol (Ran et al. 2013) by using Neon Transfection System (Invitrogen). Transfected cells were selected in RPMI-1640 media containing 3 μg/mL Puromycin (Invitrogen) for two days, and then isolated single cells by a limited dilution method. Genomic DNA was extracted from subclones and direct sequencing was conducted with a pair of primers (5′-GTCCTTTCTTTCCCACTAACCCC-3′ and 5′-TTTGGCTGAGTGGGCCTTGAGAA-3′). Finally, two subclones from different targeting sequences (KO1 /KO2) were obtained.
Immunohistochemical analysis
Immunohistochemistry was performed on formalin-fixed, paraffin-embedded (FFPE) sections of PanNETs using an automated immunostainer (DISCOVERY XT; Ventana Medical Systems, Tucson, AZ, USA) according to the manufacturer’s instructions. Primary antibodies used in this study were anti-DAXX antibody (HPA008736; rabbit polyclonal, Sigma Aldrich), anti-ATRX antibody (HPA001906; rabbit polyclonal, Sigma Aldrich) and anti-STC2 antibody (10314-1-AP; rabbit polyclonal, Proteintech, Rosemont, IL, USA) at 1:100–1:200 dilution with PBS. Anti-CD31 antibody (1:100, PECAM-1/D8V9E, rabbit polyclonal, Cell Signaling Technology) was used for immunohistochemistry to evaluate the vessel density within mice xenograft tumors (Ohata et al. 2017). All tissue sections were counterstained with hematoxylin.
Immunolabeling for DAXX was defined as only nuclear staining within tumor cells (Singhi et al. 2017). Stromal cells, islet of Langerhans and endothelial cells were used as a positive internal control of DAXX. In this study, DAXX expression in tumor tissues was determined as ‘high’ when the expression intensity was as same as internal control. The tumor tissues with negative nuclear labeling and weak expression of DAXX in tumor cells compared to internal control cells were determined as DAXX low. Likewise, ATRX immunoreactivity was assessed in PanNETs. Cytoplasmic STC2 expression in PanNETs was defined as ‘high’ and ‘low’, according to the previous report (Zhou et al. 2014).
Genomic alteration analysis
Tumor DNA was extracted form FFPE tissue samples by using the phenol/chloroform method. Direct sequencing of the PCR products was performed after PCR amplification of each exon. The primer sequences of DAXX and their PCR conditions were shown in Supplementary Table 1 (see section on supplementary data given at the end of this article). Restriction fragment-length polymorphism analysis (RFLP) using Tsp45I was utilized to screen for loss of heterozygosity (LOH) in the promoter region of DAXX. The digested fragments were separated using 3% agarose gel electrophoresis. The primer sequences and their PCR conditions of genomic DAXX were available upon request.
Reverse transcription (RT)-PCR analysis
Total RNA was isolated with an RNeasy Mini kit (Qiagen) and then cDNA was prepared from RNA using a SuperScript III kit (Invitrogen). After PCR amplification, their expression levels were semi-quantitatively determined in 2–3% agarose gels. Quantitative RT-PCR was conducted with a StepOne real-time PCR system (Thermo Fisher Scientific) using aTB Green Premix Ex Taq (TaKaRa Bio) according to the manufacturer’s instructions. The delta (Ct) method was used for quantification. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal control for conventional and real-time RT-PCR. The primer sequences and their PCR conditions were shown in Supplementary Table 1.
Western blot analysis
Total protein was extracted using RIPA buffer (Thermo Fisher Scientific) and then electrophoresed on sodium dodecyl sulfate (SDS)-polyacrylamide gels. Western blot was conducted using anti-DAXX (1:1000, sc7152; rabbit polyclonal, Santa Cruz Biotechnology) and anti-α-tubulin (1:200, sc-8035, mouse monoclonal, Santa Cruz) antibodies.
Knockdown analysis using small interfering RNAs
siRNA-based knockdown of DAXX, SPX, PAX6, SYCP2 and STC2 were performed using an electroporation (Neon transfection system, Invitrogen) according to the manufacturer’s instructions. We transfected with 50 nM siRNA of DAXX (human; SASI_Hs01_00138407, mouse; SASI_Mm01_00200483, Sigma Aldrich Japan, Ishikari, Japan), SPX (also known as C12orf39, SASI_Hs02_00358140), PAX6 (SASI_Hs02_00302237), SYCP2 (SASI_Hs01_00202137) and STC2 (SASI_Hs01_00025657) or negative control siRNA (Mission siRNA Universal Negative Control, Sigma). After 72 h culturing, cells were harvested for RT-PCR, Western blot, proliferation, migration, invasion and sphere-forming assay.
Proliferation, migration, invasion and sphere-forming assays
Cell proliferation was examined using the Cell Counting Kit-8 (Dojindo, Kumamoto, Japan). Cells (2.5 × 103 cells/well) were seeded onto 96-well plates, and their cell proliferation rates were then determined on days 1, 3 and 5. The double-chamber migration and Matrigel-invasion assays were performed using transwells (24-well plate, 8-μm pores; BD Biosciences, Franklin Lakes, NJ, USA). The lower chambers were filled with 0.8 mL culture media without antibiotics, and cells (5 × 105 in 0.5 mL serum-free media) were seeded onto the upper chambers and incubated at 37°C for 48 h. After removing cells on the upper surface of the filters, the remaining cells were fixed with 100% methanol and stained with Giemsa solution, and the number of cells migrating or infiltrating into the lower surface was counted in three randomly selected high-magnification fields (100×) for each sample.
For sphere-forming assays, cells (1.0 × 103) were plated separately in low attachment plates (24-well Ultra Low Cluster Plate, Corning, Corning, NY, USA) and incubated in serum-free Dulbecco’s modified Eagle medium/F12 media (Wako) with epidermal growth factor (EGF), hydrocortisone and insulin for 5 days. To quantify the sphere forming efficiency, the total number of tumor spheres (>100 µm) was counted under microscopic examination.
Microarray analysis
Total RNA was extracted from cells transfected with siRNA of DAXX or negative control, and the integrity of the obtained RNA was confirmed by using a 2100 Bioanalyzer (Agilent Technologies). Contaminating DNA was removed by digestion with RNase-Free DNase Set (Qiagen). Complementary RNA was prepared from 100 ng of total RNA from each sample with 3′ IVT Express Kit (Affymetrix). The hybridization and signal detection of the GeneChip Human Genome U133 Plus 2.0 Array (Affymetrix) were performed in accordance with the manufacturer’s instructions. The two microarray datasets of cells transfected with siRNA of DAXX or negative control were normalized by using the robust multiarray average method in the R statistical software (version 3.0.3) and the Affy Bioconductor package (Ohata et al. 2017). Ratio of signal intensity of DAXX-KD (QGP1_KD) to DAXX-NC (QGP1_NC) was determined as a fold change (fc). The microarray data have been deposited in the Gene Expression Omnibus (GEO) database under accession number GSE93050.
Chromatin immunoprecipitation (ChIP)
We prepared sonicated DNA samples from DAXX-WT and DAXX-KO cells using a ChIP-IT Express (No. 39163, Active Motif, Carlsbad, CA, USA), and then incubated with 1–2 μg of three antibodies; DAXX (1 μg, sc7152; rabbit polyclonal, Santa Cruz Biotechnology), Histone H3.3 (2 μg, ab176840; rabbit monoclonal, Abcam) and Histone H3K9me3 (1 μg, 39765, Active Motif). Normal rabbit IgG (Cell Signaling Technology) was used as a negative control for each assay. Input DNA samples were used as internal controls. The primer sequences and their conditions are shown in Supplementary Table 1.
Tumor seeding
DAXX-WT and DAXX-KO1 cells were suspended in 100 μL Matrigel (BD Biosciences) and subcutaneously injected into NOD/SCID mice. The volume of the growing tumors was monitored every three days and calculated by the formula; volume = length × width2 × 0.5.
Statistics analysis
All statistical analyses were performed using SPSS software, version 23.0 (IBM). Two-sided Student’s t test was used to analyze for differences between continuous values of two independent groups. The χ 2 test or Fisher’s exact test was applied to analyze the categorical variables. Cumulative recurrence rate was constructed by using the Kaplan–Meier method and compared with the log-rank test. P < 0.05 was considered to denote a statistically significant difference.
Results
Reduced DAXX protein expression and clinicopathological features of PanNETs
DAXX expression was observed at nuclei in tumor cells and surrounding non-tumor cells including acinar, stromal cells, islet of Langerhans and endothelial cells by immunohistochemistry. However, low expression of DAXX was found in 12 of 44 PanNETs (27.3%, Table 1). Representative data on immunostaining of DAXX in PanNETs and pancreatic tissues are shown in Fig. 1A. In addition, eight PanNETs exhibited low expression of ATRX (18.2%, Table 1 and Supplementary Fig. 1), four of which also had DAXX-low expression.
DAXX expression and genetic alterations in human clinical samples of PanNETs. (A) Representative photographs of DAXX immunostaining in PanNET tissues. DAXX was expressed in pancreatic tissues (i). High (ii) and low (iii) expressions of DAXX were detected in PanNET tissues. Original magnification ×100. (B) Direct sequencing of the DAXX gene in PanNETs with low DAXX expression. Case 1: nonsense mutation (TCA to TAA) at codon 593; case 2, 1-bp deletion (CGG to CG) at codon 365; case 3, 6-bp deletion (TGCAAC) at codons 106–107 within helical bundle domain that binds to tumor suppressor RASSF1 (Escobar-Cabrera et al. 2010).
Citation: Endocrine-Related Cancer 25, 6; 10.1530/ERC-17-0328
Correlation between DAXX and ATRX expression and clinicopathological factors in 44 PanNETs.
| DAXX | ATRX | |||||
|---|---|---|---|---|---|---|
| Low (n = 12) | High (n = 32) | P value | Low (n = 8) | High (n = 36) | P value | |
| Age (years) | 48 (37–80)a | 55 (31–79) | 0.145 | 53 (38–73) | 53.5 (31–80) | 0.52 |
| Gender male/female | 8/4 | 15/17 | 0.242 | 6/2 | 17/19 | 0.155 |
| Functional yes/no | 0/12 | 14/18 | 0.006* | 1/7 | 13/23 | 0.195 |
| Tumor size (mm) | 14 (8–150) | 13 (3–120) | 0.145 | 17.5 (5–70) | 13 (3–150) | 0.58 |
| Ki-67 index | 2.095 (0.4–13.1) | 1.15 (0.5–12) | 0.049* | 1.45 (1.2–13.1) | 1.05 (0.5–12) | 0.142 |
| WHO grade G1/G2 | 7/5 | 4/28 | 0.033* | 6/2 | 7/29 | 0.725 |
| Invasion | ||||||
| Vascular invasion | 6 | 12 | 0.453 | 4 | 14 | 0.563 |
| Perineural invasion | 3 | 7 | 0.826 | 3 | 7 | 0.27 |
| Lymphatic invasion | 2 | 4 | 0.72 | 1 | 5 | 0.918 |
| Adjacent organ invasion | 3 | 8 | 1 | 2 | 9 | 1 |
| Metastasis | ||||||
| Liver metastasis | 3 | 2 | 0.081 | 1 | 4 | 0.911 |
| Lymph node metastasis | 2 | 4 | 0.72 | 0 | 6 | 0.214 |
| AJCC stage | ||||||
| Stage I | 9 | 27 | 0.354 | 6 | 30 | 0.912 |
| Stage II | 0 | 3 | 1 | 2 | ||
| Stage III | 0 | 0 | 0 | 0 | ||
| Stage IV | 3 | 2 | 1 | 4 | ||
aMedian values (range) are shown in age, tumor size and Ki-67 index; *Statistically significant difference at P < 0.05.
We further evaluated the relationship between DAXX and ATRX expression and clinicopathological features in 44 PanNETs. Low expression of DAXX was significantly correlated with nonfunctional tumors, higher Ki-67 index and WHO grade G2 (P < 0.05, Table 1). In low DAXX expression group, progression-free survival (PFS) was worse (P = 0.03, data not shown), and synchronous liver metastasis tended to be observed more frequently (P = 0.081, Table 1). In contrast, no significant relationship was found between ATRX expression and clinicopathological features (Table 1) and PFS (P = 0.827, data not shown). Thus, our data indicate that PanNETs with DAXX-low expression may have worse malignant potential.
Genetic alterations of DAXX in PanNETs
Direct sequencing analysis of the DAXX gene was performed in the 12 PanNETs with low DAXX expression. We detected a nonsense mutation (TCA to TAA) at codon 593 in case 1, 1-bp deletion (CGG to CG) at codon 365 in case 2 and 6-bp deletion (TGCAAC) at codons 106–107 within helical bundle domain in case 3 (Fig. 1B). The DNA helix bundle domain of DAXX is reported to bind to tumor suppressor RASSF1C (Escobar-Cabrera et al. 2010). In addition, we observed a newly single-nucleotide polymorphism site (SNP) at the promoter region (42 bp upstream from the transcription start site) of DAXX. LOH was detected in one case by RFLP analysis using Tsp45I (case 5; Supplementary Fig. 2). Totally, four (33.3%) of 12 PanNETs with low DAXX expression exhibited genetic alterations of DAXX.
Effects of DAXX knockdown in human PanNET cells
To investigate the loss of functions of DAXX in PanNETs, we conducted transient knockdown of DAXX in QGP1 human PanNET cells using DAXX siRNA (Figs 2A and 3A, Top panel, Blue bar). There was no difference of the cell proliferation rates between QGP1 cells with DAXX knockdown (KD) and negative control (NC) siRNA transfection (Fig. 2B), and cell migration and invasion (Supplementary Fig. 3A and B). We observed that sphere-forming activity was significantly increased in DAXX-KD cells compared with that in NC cells (P < 0.01, Fig. 2C and D), which implies the self-renewal potential of cancer stemness leading to in vivo tumor initiation (Adikrisna et al. 2012). According to the recent report by Marinoni et al., QGP1 was insusceptible to ALT activation induced by DAXX knockdown (Marinoni et al. 2017). We confirmed that obvious changes of telomere length were not seen in DAXX-KD QGP1 cells (data not shown), and these findings are also consistent with other cell lines including HeLa and HCT116 cells (Lovejoy et al. 2012, O’Sullivan et al. 2014, Napier et al. 2015, Pickett & Reddel 2015, Hu et al. 2016).
Effects of DAXX knockdown in QGP1 human PanNET cells. (A) RT-PCR (left) and Western blot (right) analysis of DAXX expression in QGP1 cells. DAXX siRNA (siDAXX) and negative control siRNA (siNC) were transfected into QGP1 cells. GAPDH and α-tubulin were used as an internal control of RT-PCR and Western blot, respectively. (B) WST-8 cell proliferation assay of QGP1 cells transfected with siNC and siDAXX. (C) Representative microscopic images of spheres in QGP1 cells. (D) Sphere-forming assay of QGP1 at day 5 of incubation. The number of spheres in QGP1 cells with siDAXX transfection was compared to those with siNC. The average (column) ± s.d. (bar) of three independent experiments is indicated (*P < 0.01).
Citation: Endocrine-Related Cancer 25, 6; 10.1530/ERC-17-0328
Establishment of DAXX-knockout QGP1 cells by CRISPR/Cas9 system and in vitro analysis. (A) Top: Schematics of the protein structure of DAXX. Red and green triangles indicate nonsense and missense mutation, respectively, as described in human PanNET tissues (Heaphy et al. 2011, Marinoni et al. 2014, Scarpa et al. 2017). The location of DAXX siRNA is shown as blue bar. The arrows indicate the site that a single guide RNA targets for knockout by CRISPR/Cas9 system. Bottom: Direct sequencing of DAXX gene in two DAXX knockout cells (KO1 and KO2). DAXX-unknockout QGP1 cells were indicated as DAXX-WT. (B) Western blot of two DAXX-knockout (DAXX-KO1 and KO2) cells established from QGP1 cells by using CRISPR/Cas9 system. α-Tubulin was used as an internal control. (C) Sphere-forming assay of DAXX-KO QGP1 cells at day 5 of incubation. The average (column) ± s.d. (bar) of three independent experiments is indicated (*P < 0.01). (D) Real-time RT-PCR and ChIP analyses of 12 DAXX target genes in DAXX-KO cells. Left panel: Real-time RT-PCR analysis. Relative expression was calculated using GAPDH expression as an internal control. Gray and white bars indicate DAXX-KO and -WT, respectively. The average (column) ± s.d. (bar) is indicated. Expression levels of all the 12 genes were significantly elevated in DAXX-KO cells compared to DAXX-WT cells (P < 0.05). Right: ChIP assays of DAXX binding. Sonicated chromatin from DAXX-KO1 and -WT cells was immunoprecipitated using anti-DAXX antibody, and then the promoter regions of target genes were amplified by PCR. Input DNA amplified from sonicated chromatin of the cells was used as an internal control. Normal rabbit IgG was used as a negative control of ChIP. The PCR products were loaded onto 2% agarose gels. (E) ChIP assays of the DAXX target genes. Anti-H3.3 (left panel) and anti-H3K9me3 (right panel) antibodies were used for immunoprecipitation. (F) Sphere-forming assay. Knockdown of four DAXX target genes was conducted using each siRNA (siSPX, siPAX6, siSYCP2 and siSTC2) in DAXX-KO1 cells. The average (column) ± s.d. (bar) of three independent experiments is indicated (*P < 0.01). (G) RT-PCR analysis of STC2 expression in MIN6 (murine PanNET/insulinoma) and NCI-H727 (human bronchial NET) cells after knockdown of DAXX. GAPDH was used as an internal control.
Citation: Endocrine-Related Cancer 25, 6; 10.1530/ERC-17-0328
We next performed microarray to compare gene expression patterns between DAXX-KD and DAXX-NC QGP1 cells. After uncharacterized genes were excluded, 81 and 96 genes were upregulated and downregulated at >2-fold changes in DAXX-KD QGP1 cells. We focused on upregulated genes in DAXX-KD cells, since DAXX acts as a transcriptional repressor (Salomoni & Khelifi 2006). Top fifteen genes of which expression levels were upregulated were DDIT4, CHAC1, SPX, C6orf58, INHBE, KCNA2, PAX6, SYCP2, LILRA2, TRIB3, STC2, NALCN, LHX8, NIPSNAP3B and SESN2 (Supplementary Table 2). The expression levels of all fifteen genes were elevated in DAXX-knockdown cells by conventional RT-PCR analysis (Supplementary Fig. 3C).
DAXX/H3.3 complex suppress target genes by promoting H3K9me3
According to the DAXX mutation spectrum in human PanNET tissues (Heaphy et al. 2011, Marinoni et al. 2014, Scarpa et al. 2017), we designed two small guide RNA targeting exon 3 of DAXX for CRISPR/Cas9 system (Fig. 3A), and established two DAXX-knockout (DAXX-KO1 and KO2) cells, both of which completely lacked DAXX protein expression compared to DAXX-WT QGP1 cells (Fig. 3B). The numbers of spheres were increased in both DAXX-KO1 and -KO2 cells compared with those in DAXX-WT cells by sphere-forming assays (Fig. 3C).
Among the 15 upregulated genes detected by microarray in DAXX-KD cells (Supplementary Table 2), 12 genes showed elevated expression in DAXX-KO cells by real-time RT-PCR (Fig. 3D, P < 0.05, left) and conventional RT-PCR (Supplementary Fig. 4A). Thus, phenotypes and gene expression patterns in DAXX-KO cells were similar to those in DAXX knockdown cells. We next investigated whether or not DAXX acts a chaperone of H3.3 for transcriptional repression of the gene. ChIP assay demonstrated that the DAXX bound at the promoter regions of all the 12 genes in DAXX-WT cells, whereas the binding was lost in DAXX-KO1 cells (Fig. 3D, right). H3.3 levels at the promoter regions of seven genes were enriched in DAXX-WT cells relative to those in DAXX-KO1 cells, indicating that DAXX deposits H3.3 at these seven gene promoters. Five of the seven genes (SPX, PAX6, SYCP2, STC2 and NIPSNAP3B) showed enrichments of H3K9me3 levels at the regions in DAXX-WT cells (Fig. 3E). In contrast, total H3 or H3K4me3 level at the promoter region of the seven genes was not changed in DAXX-KO1 cells (Supplementary Fig. 4B), which are consistent with previous findings (Feng et al. 2017). Thus, these five genes may be transcriptionally direct targets of DAXX/H3.3 complex by promoting H3K9me3.
In vitro significance of DAXX downregulation and STC2 expression
We examined which DAXX target genes are responsible for increase of sphere formation in QGP1 cells lacking DAXX (KD and KO, Figs 2D and 3F). The number of spheres was significantly decreased in DAXX-KO QGP1 cells with STC2 knockdown (P = 0.01) (Fig. 3F and Supplementary Fig. 5), supporting the previous finding that STC2 maintains stem cell survival (Kim et al. 2015). However, knockdown of SPX, PAX6 or SYCP2 did not show any effects on sphere-forming activity in these DAXX-KO cells.
To evaluate the relationship between DAXX and STC2 expression, we examined STC2 expression in murine PanNET cell line MIN6 and human pulmonary NET cell line NCI-H727, with DAXX knockdown by conventional RT-PCR analysis. DAXX-KD MIN6 cells but not DAXX-KD NCI-H727 cells showed significant increase of STC2 expression, when DAXX was knockdown using its siRNA transfection (Fig. 3G).
Increased tumorigenicity of DAXX-deficient QGP1 cells
According to in vivo studies of xenografts, tumor volume of DAXX-KO1 was much greater than that of DAXX-WT (P < 0.01, Fig. 4A and B). Tumor weight derived from DAXX-KO1 cells was heavier than those from DAXX-WT cells (P < 0.01, Fig. 4C and D), suggesting that loss of DAXX in QGP1 cell lines prominently increased tumorigenicity. To confirm the correlation between DAXX and STC2 in vivo, we evaluated STC2 expression in the xenograft tumors. Engraft tumors derived from DAXX-WT cells weakly expressed STC2 protein, whereas tumors lacking DAXX protein strongly expressed STC2 (Fig. 4E). Thus, inverse relationship between DAXX and STC2 was found in our engrafted tumor model. We next investigated the vessel density within xenograft tumors by immunohistochemistry with anti-CD31 antibody, an endothelial cell marker (Fig. 4F). The area occupied by CD31-positive endothelial cells in DAXX-KO tumors was larger than that in DAXX-WT tumors.
In vivo tumorigenicity of the DAXX-knockout QGP1 cells. (A) Photo images of transplanted tumors. DAXX-KO1 (right side) and DAXX-WT cells (left side) were inoculated in each NOD/SCID mouse. (B) Growth curves of the tumors. Tumor volumes were measured at an interval of 6 days after subcutaneous injection in NOD-SCID mice (n = 4). (C) Tumors derived from the DAXX-KO1 and DAXX-WT cells 60 days after subcutaneous injection. (D) Tumor weight histogram. The average (column) ± s.d. (bar) of tumor weight in 4 mice is indicated. (E) Immunohistochemistry of DAXX and STC2 in DAXX-WT and DAXX-KO1-derived xenografts. Inverse expression patterns of DAXX and STC2 expression were shown in the xenograft tumors. Original magnification ×200. (F) CD31 positivity and vessel density in xenograft tumors. Left panel: Immunohistochemistry of CD31 in DAXX-WT- and DAXX-KO1-derived xenografts. Original magnification ×200. Right panel: CD31-positive area within tumors was quantitatively calculated as a vessel density by Image J (*P < 0.05).
Citation: Endocrine-Related Cancer 25, 6; 10.1530/ERC-17-0328
Clinical significance of low expression of DAXX and high expression of STC2 in PanNET patients
Among the 44 human PanNET tissues, 32 cases (72.7%) exhibited higher expression of STC2 protein by immunohistochemistry. Although there was no significant relationship between DAXX and STC2 in our cases (P = 0.162), almost PanNETs with low DAXX expression exhibited highly STC2 expression (11/12, 91.6%). Nine of the 32 cases with high DAXX expression had weakly STC2 expression (P = 0.162, Fig. 5A). It is noteworthy that significantly higher recurrence rate was recognized in low expression of DAXX and high expression of STC2 than others (P = 0.018, Fig. 5B). The univariate analysis indicated that WHO grade G1/G2, perineural invasion, lymphatic invasion, adjacent organ invasion, DAXX-low tumors and the combined expression signature of DAXX-low and STC2-high were significantly associated with poor prognosis, while ATRX-low expression and STC2 overexpression were not (Supplementary Table 3). Finally, perineural invasion (P = 0.004) and the combined signature of DAXX-low and STC2-high expression (P = 0.008) were independent prognostic markers for PFS by multivariate analysis (Supplementary Table 3).
Clinical analysis of DAXX and STC2 expression in PanNET patients. (A) Immunohistochemistry of DAXX and STC2 expression in clinical samples. Representative cases with inverse correlation between these two protein expression patterns were shown. Original magnification ×200. (B) Cumulative incidence rate of PanNET recurrence. Difference in recurrence rates was evaluated by the Kaplan–Meier method and the log-rank test. Higher recurrence was recognized in PanNETs with low DAXX and high STC2 expression than others (P = 0.018).
Citation: Endocrine-Related Cancer 25, 6; 10.1530/ERC-17-0328
Discussion
In this study, we investigated the clinicopathological and biological functions of DAXX in PanNETs. Low expression of DAXX was detected in 27.3% of our clinical samples, which is consistent with the previous reports that DAXX loss was found in 17–25% of PanNETs (Jiao et al. 2011, Marinoni et al. 2014, Singhi et al. 2017). Our results revealed that DAXX loss correlated with nonfunctional tumors, higher Ki-67 index, WHO grade G2 and worse PFS, supporting that DAXX loss is associated with malignant progression of PanNETs (Marinoni et al. 2014, Kim et al. 2017, Singhi et al. 2017). DAXX is a multifunctional protein that modulates cell death, gene regulation and telomere regulation (Salomoni & Khelifi 2006, Clynes et al. 2015). Here, we observed that DAXX loss by genome editing in QGP1 cells led to an increase of sphere-forming activity and tumorigenic and angiogenic properties, strongly indicating tumor suppressor functions of DAXX in PanNETs.
Although DAXX acts as a transcriptional repressor of genes, such as c-met, CCAAT/enhancer-binding protein β and MME, in cancer cell lines (Morozov et al. 2008, Wethkamp & Klempnauer 2009, Feng et al. 2017), DAXX target genes are still unknown in PanNETs. Here, we provide that newly 12 genes are transcriptionally silenced by DAXX through direct interaction to their promoter region in QGP1 cells by ChIP assay. These 12 genes included hormone (SPX, STC2), differentiation (INHBE, TRIB3) and homeobox- (PAX6, LHX8) related genes, which supports the previous findings that DAXX is a multifunctional protein (Salomoni & Khelifi 2006). Several DAXX target genes detected in this study have been reported to show oncogenic activities in cancer cells. For example, STC2 overexpression accelerates tumorigenicity of colorectal cancer in mouse xenograft model (Chen et al. 2016) and promotes angiogenic sprouting in human umbilical vascular endothelial cells through VEGF/VEGFR2 and angiopoietin signaling pathways (Law & Wong 2013). TRIB3 overexpression positively correlates with VEGF-A expression and microvessel density in gastric cancers (Dong et al. 2016). Therefore, upregulation of these genes by DAXX inactivation may contribute to tumorigenicity and angiogenesis in our xenograft tumors of DAXX-KO cells.
Most studies on epigenetic machinery through DAXX and H3.3 complex have focused on the heterochromatic region including telomeres and pericentric heterochromatin (Drané et al. 2010, Lewis et al. 2010, He et al. 2015, Voon & Wong 2016), although H3.3 is deposited into both euchromatin (promoter and coding region of gene and regulatory element) and heterochromatin (Schwartz & Ahmad 2005, Jin et al. 2009, Goldberg et al. 2010). H3.3 deficiency reduces H3K9me3 levels and causes loss of chromatin repression at telomeres in mouse ES cells (Udugama et al. 2015). Among the 12 target genes, we demonstrated that the expression of five genes (SPX, PAX6, SYCP2, STC2 and NIPSNAP3B) was suppressed through DAXX/H3.3 complex by promoting H3K9me3 at their promoter regions in QGP1 cells. Furthermore, our data provide evidence that DAXX-histone modification pathway plays a role in transcriptional silencing of coding genes located outside telomeres and pericentric heterochromatin.
On the contrary, transcriptionally upregulated genes in DAXX-knockdown pulmonary NET cell line, NCI-H727, were different from those in QGP1 cells by microarray (Supplementary Table 4). It has been reported that pulmonary NETs have frequent mutations of histone modifier (40%), such as MEN1 and PSIP1 and SWI/SNF complex (22%) including ARID1A (Fernandez-Cuesta et al. 2014). Except for MEN1, these mutation spectrums of pulmonary NETs were different from those of PanNETs (43% of tumors, DAXX and ATRX) as reported previously (Jiao et al. 2011). Thus, our data on microarray give the possibility that molecular pathogenesis of PanNETs may be distinct from pulmonary NETs.
STC2 is frequently overexpressed in multiple cancer types and is associated with poor prognosis (Ito et al. 2004, Tamura et al. 2009, Volland et al. 2009, Yeung et al. 2012, Na et al. 2015). We observed that DAXX inactivation resulted in elevated STC2 expression levels in three PanNET cell lines, QGP1, MIN6 and BON1 (gift from Professor Brigitte Lankat-Buttgereit, Germany, data not shown), but not in pulmonary NET cells, indicating a strong correlation between DAXX and STC2 in PanNETs. STC2 overexpression increases mesenchymal stem cell survival by suppressing oxidative stress (Kim et al. 2015), and STC2 knockdown sensitizes drug-resistant colorectal cancer cells to oxaliplatin (Yuan et al. 2016). In our sphere-forming assay, DAXX-KO QGP1 cells increased stemness phenotype, and the effect was attenuated by STC2 knockdown in this DAXX-KO cells. These data suggest that STC2 plays a potential role in cancer stemness (Kim et al. 2015), and hence, PanNET cells with DAXX loss may acquire stemness by STC2 overexpression. However, DAXX-WT QGP1 cells with transient STC2 overexpression did not enhance the sphere forming activity, whereas transient STC2-overexpressing DAXX-KO QGP1 cells significantly increased the activity (Supplementary Fig. 6), suggesting that STC2 overexpression alone may be insufficient to activate stemness in DAXX-WT QGP1 cells. Several interacting proteins, such as Ran-binding protein M and heme oxygenase 1, of STC2 have been identified (Jiang et al. 2012, Shin & Sohn 2014). It is possible that STC2 could require its binding partner(s) to increase stemness activities in QGP1 cells, which may also be regulated by DAXX. Our data on in vitro sphere formation could lead that concurrent DAXX loss and STC2 overexpression may contribute to in vivo tumor initiation of PanNETs. Further studies are required to clarify the molecular mechanism underlying regulation of stemness through DAXX/STC2 pathway in cancer cells. Clinically, we found that primary PanNETs with low expression of DAXX and high expression of STC2 indicated higher recurrence rate than others (Fig. 5B). Consistent with other studies indicating the rapid recurrence of PanNETs with DAXX downregulation (Marinoni et al. 2014, Kim et al. 2017, Singhi et al. 2017), combination of DAXX loss and STC2 overexpression might be useful biomarkers and therapeutic targets for PanNETs.
Supplementary data
This is linked to the online version of the paper at https://doi.org/10.1530/ERC-17-0328.
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 Grant-in-Aid for Scientific Research (A) 16H02670, Scientific Research on Innovative Areas 15H01484, Challenging Exploratory Research 15K15491 from the Ministry of Education, Culture, Sports, Science and Technology of Japan; Research Grant from the Princess Takamatsu Cancer Research Fund 14-24619 and P-CREATE JP17cm0106518 from AMED (Japan Agency for Medical Research and Development).
Author contribution statement
H U, Y A and M S performed the experiments; H U and S S analyzed the data; S S and K M performed bioinformatics; H I, S M, Y M, A A, D B, T O, A K and M T obtained and prepared surgical tissue samples of PanNETs; H U and Y A wrote the manuscript with comments from all authors and S T conceptualized, designed and supervised the study.
Acknowledgements
The authors thank Dr Masayuki Imamura (Neuroendocrine Tumor Center, Kansai Electric Power Hospital), Dr Tetsuhide Ito (Neuroendocrine Tumor Center, International University of Health and Welfare) and Dr Hironori Koga (Department of Medicine, Kurume University School of Medicine) for advice on PanNET cell lines. They also thank Ms. Hiromi Nagasaki for technical assistance.
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