Skip to main content
Download PDF

PTTG1 expression is associated with hyperproliferative disease and poor prognosis in multiple myeloma

Journal of Hematology & Oncology volume 8, Article number: 106 (2015) Cite this article

Abstract

Background

Multiple myeloma (MM) is an incurable haematological malignancy characterised by the clonal proliferation of malignant plasma cells within the bone marrow. We have previously identified pituitary tumour transforming gene 1 (Pttg1) as a gene that is significantly upregulated in the haematopoietic compartment of the myeloma-susceptible C57BL/KaLwRij mouse strain, when compared with the myeloma-resistant C57BL/6 mouse. Over-expression of PTTG1 has previously been associated with malignant progression and an enhanced proliferative capacity in solid tumours.

Methods

In this study, we investigated PTTG1 gene and protein expression in MM plasma cells from newly diagnosed MM patients. Gene expression profiling was used to identify gene signatures associated with high PTTG1 expression in MM patients. Additionally, we investigated the effect of short hairpin ribonucleic acid (shRNA)-mediated PTTG1 knockdown on the proliferation of the murine myeloma plasma cell line 5TGM1 in vitro and in vivo.

Results

PTTG1 was found to be over-expressed in 36–70 % of MM patients, relative to normal controls, with high PTTG1 expression being associated with poor patient outcomes (hazard ratio 2.49; 95 % CI 1.28 to 4.86; p = 0.0075; log-rank test). In addition, patients with high PTTG1 expression exhibited increased expression of cell proliferation-associated genes including CCNB1, CCNB2, CDK1, AURKA, BIRC5 and DEPDC1. Knockdown of Pttg1 in 5TGM1 cells decreased cellular proliferation, without affecting cell cycle distribution or viability, and decreased expression of Ccnb1, Birc5 and Depdc1 in vitro. Notably, Pttg1 knockdown significantly reduced MM tumour development in vivo, with an 83.2 % reduction in tumour burden at 4 weeks (p < 0.0001, two-way ANOVA).

Conclusions

This study supports a role for increased PTTG1 expression in augmenting tumour development in a subset of MM patients.

Background

Multiple myeloma (MM) is characterised by the clonal proliferation of malignant plasma cells within the bone marrow (BM) and is the second most common haematological malignancy. The key clinical manifestations of MM include the development of painful osteolytic bone lesions, renal insufficiency, suppressed haematopoietic function and increased BM angiogenesis [1]. There are an array of clinical variants of the disease, ranging from the asymptomatic monoclonal gammopathy of undetermined significance (MGUS) and smouldering MM, to the more aggressive active MM and plasma cell leukaemia. Numerous genes, pathways and miRNAs have been identified in MM that function as predictive biomarkers of highly proliferative disease and likelihood of response to treatment [2–6]. Although the introduction of novel therapies has seen a significant improvement in the median survival of some groups of MM patients, the survival for some subgroups of patients, particularly those with highly proliferative disease, remains poor [7]. This highlights the need to identify new genes and pathways that may be involved in the pathophysiology of MM to aid in both prognosis and the development of novel therapeutics.

The C57BL/KaLwRij (KaLwRij) mouse strain, a closely related derivative of the C57BL/6 strain, is one of the best-studied pre-clinical animal models of MM. The KaLwRij strain is susceptible to developing benign monoclonal gammopathy and, in a small proportion of mice, MM at >2 years of age [8, 9]. Additionally, KaLwRij mice exhibit an inherent ability to support the growth of exogenous malignant plasma cells. The intravenous injection of murine myeloma cell lines, such as the KaLwRij-derived lines 5T33MM, 5T2MM and 5TGM1, into KaLwRij mice results in a myeloma-like disease that closely resembles human MM [10–14]. The mechanisms responsible for this susceptibility to the development of myeloma in this strain of mice remain largely unknown. Importantly, we [15, 16] and others [17] have previously utilised this model to identify key genes whose expression may play a role in the development of MM disease in these mice.

We have previously identified pituitary tumour transforming gene 1 (Pttg1) (also known as securin, EAP1 and TUTR1) as a gene that displays significantly increased expression in KaLwRij mice compared with C57BL/6 controls [15]. Notably, PTTG1 is over-expressed in a vast array of malignancies including pituitary [18, 19], colorectal [20], thyroid [21] and lung [22] cancer, and high levels of PTTG1 are commonly associated with an enhanced proliferative capacity, increased tumour grade and high invasive potential [23]. PTTG1 is a key regulator of sister chromatid segregation during mitosis and, additionally, is involved in DNA damage repair [23]. An increase in PTTG1 expression has previously been described in up to 63 % of MM patients [24, 25]; however, the role played by PTTG1 in MM disease development has not been determined. In the present study, we confirm up-regulation of PTTG1 in MM plasma cells from a subset of MM patients compared with both MGUS and healthy controls and show that elevated PTTG1 expression is associated with an increase in cell cycle-related gene expression and is associated with poor survival. Furthermore, knockdown of Pttg1 decreases cellular proliferation in vitro and reduces myeloma tumour burden in vivo in the KaLwRij model of MM. Collectively, these data support a role for PTTG1 in promoting MM disease pathogenesis, likely through cell cycle- and proliferation-related pathways.

Results

PTTG1 is over-expressed in the C57BL/KaLwRij mouse model of myeloma

In order to identify genes that may play a role in the development of myeloma, we previously compared the transcriptome of the bone/BM of KaLwRij mice to that of the genetically related C57BL/6 strain using microarray [15]. Using this approach, we identified Pttg1 as a gene with significantly increased expression (2.9-fold; p = 0.00037, LIMMA) within the bone/BM of KaLwRij mice compared with C57BL/6 controls [15]. Quantitative real-time PCR (qRT-PCR) was subsequently used to assess the relative messenger ribonucleic acid (mRNA) expression levels of Pttg1 in a range of tissues derived from the C57BL/6 and KaLwRij mice (n = 3/group). As seen in Fig. 1a, Pttg1 mRNA levels were significantly increased in the bone, BM, peripheral blood and spleen of KaLwRij mice, when compared with those of C57BL/6 controls (p < 0.05, t test). Although an increase in Pttg1 expression was also noted in the thymus, this did not reach significance (p = 0.11, t test). Furthermore, there was a significant increase in Pttg1 expression in CD138+ plasma cells derived from KaLwRij mice compared with those from C57BL/6 controls (Fig. 1b; p = 0.045, t test). Together, these data confirm that Pttg1 expression levels are up-regulated in the bone, haematopoietic tissues and plasma cells of KaLwRij mice.

Fig. 1
figure 1

Pttg1 expression is increased in haematopoietic tissues of C57BL/KaLwRij mice. a Bone, bone marrow (BM), peripheral blood (PB), spleen and thymus and combined popliteal, inguinal and axillary lymph node tissues were isolated from C57BL/6 and C57BL/KaLwRij mice (n = 3/group) and total RNA extracted. Pttg1 mRNA expression was determined by qRT-PCR analysis. *p < 0.05, t test. b CD138+ plasma cells (PC) were isolated from the BM of C57BL/6 and C57BL/KaLwRij mice (n = 3/group). Pttg1 mRNA was significantly increased in KaLwRij-derived plasma cells compared with C57BL/6 controls. Graphs depict mean + SEM; *p < 0.05, t test

Full size image

PTTG1 is over-expressed in MM patients and is associated with poor survival

To examine whether MM patients display increased PTTG1 expression, we performed in silico analyses in three independent publically available microarray datasets (E-GEOD-6477; E-GEOD-16122; E-MTAB-363) comparing the gene expression profiles of CD138+-isolated plasma cells from newly diagnosed MGUS and MM patients and normal controls. In E-GEOD-6477 and E-MTAB-363, PTTG1 expression was significantly increased in the MM patient cohort compared with the normal controls (p < 0.05) and MGUS patients (p < 0.05; Fig. 2a, b). In E-GEOD-16122, PTTG1 expression was significantly increased in MM patients compared with MGUS patients (p < 0.05; Fig. 2c). Approximately 38–70 % (38.4 % [28/73], E-GEOD-6477; 70.3 % [109/155], E-MTAB-363; 68.4 % [66/133], E-GEOD-16122) of MM patients expressed PTTG1 at levels higher than the normal range (mean + 2SD of the normal cohort expression). We subsequently isolated CD138+ plasma cells from diagnostic MM patient BM (n = 11) using CD138-MACS, total RNA was isolated and PTTG1 mRNA expression examined by qRT-PCR. Four of the 11 patients (36.4 %) were found to express PTTG1 in the purified plasma cells (Additional file 1: Figure S1). Using dual-colour immunohistochemistry, we also confirmed PTTG1 protein expression within CD138+ plasma cells in BM trephines from two PTTG1-expressing MM patients (Fig. 2d). Consistent with previous reports [24, 26], PTTG1 protein was predominantly cytoplasmic.

Fig. 2
figure 2

PTTG1 is over-expressed in MM patients. In silico analysis was performed on publically available gene expression datasets from CD138+ plasma cells isolated by MACS from MM (n = 73) and MGUS (n = 22) patients and healthy controls (n = 15) (E-GEOD-6477) (a), MM (n = 155) and MGUS (n = 5) patients and healthy controls (n = 5) (E-MTAB-363) (b) and MM (n = 133) and MGUS (n = 11) patients and healthy controls (n = 5) (E-GEOD-16122) (c). Box and whiskers plots show the median and interquartile ranges for each cohort. *p < 0.05; **** p < 0.0001; Kruskal-Wallis test with Dunn’s multiple comparison tests. d Representative image of a BM trephine section from an MM patient stained with anti-CD138 (red) and anti-PTTG1 (green), showing plasma cell-specific protein expression of PTTG1. A negative (no primary antibody) control is shown. e Kaplan-Meier plot of PTTG1 high patients (quartile 4; n = 71) vs PTTG1 low patients (quartiles 1–3; n = 214) (TT2 patients from GSE4581). f MM patients from GSE4581 (n = 414) were stratified into subgroups based on the UAMS criteria; namely, patients characterised by increased proliferation-related genes (PR), chromosomal translocations involving cyclin D1 and cyclin D3 (CD1 and CD2), MAF (MF) or MMSET (MS), as well as patients exhibiting hyperdiploidy (HY) and decreased prevalence of lytic bone disease (LB) [4]. The expression of PTTG1 was analysed in each subset. Box and whiskers plots show the median and interquartile ranges for each cohort; ^p < 0.0001 relative to CD1, CD2, MF, MS, HY and LB; #p < 0.01 relative to CD2, MF, MS, LB; Kruskal-Wallis test with Dunn’s multiple comparison tests

Full size image

Collectively, these data suggest that over-expression of PTTG1 is a feature of MM disease in approximately 36–70 % of MM patients. In order to determine whether the increased expression of PTTG1 in MM patients was related to patient survival, newly diagnosed MM patients enrolled in the total therapy 2 (TT2) trial, from publically available microarray dataset GSE4581, were stratified based on PTTG1 expression levels. The quartile with the highest PTTG1 expression was classified as PTTG1 high (n = 71 patients), while the remaining patients (n = 214) were classified as PTTG1 low. Subsequent analysis of overall survival identified a significantly poorer survival in the PTTG1 high group (hazard ratio 2.49; 95 % CI 1.28 to 4.86; p = 0.0075; log-rank [Mantel-Cox] test; Fig. 2e).

When MM patients in dataset GSE4581 were partitioned into gene expression profiling-defined subgroups (UAMS classifications) [4], elevated PTTG1 was found to be associated with specific disease subtypes (Fig. 2f). Specifically, PTTG1 expression was elevated in the PR subgroup, characterised by expression of proliferation-related genes, when compared with the subgroups characterised by chromosomal translocations involving cyclin D1 and D3 (CD1 and CD2), MAF (MF) or MMSET (MS), by hyperdiploidy (HY) or by a decreased prevalence of lytic bone disease (LB) (Fig. 2f). Additionally, significantly elevated PTTG1 was observed in the HY group, when compared with the CD2, LB, MF and MS subgroups.

PTTG1 expression in MM patients is associated with an increase in expression of cell cycle-associated genes

As elevated PTTG1 expression was shown to be associated with poor outcomes in MM patients, we next compared MM plasma cell gene expression between PTTG1 high and PTTG1 low MM patients in four independent microarray datasets. Twenty-nine genes were found to be significantly down-regulated, and 1459 genes significantly up-regulated (excluding PTTG1) in at least one dataset (Fisher’s p value <0.05). Of these, 155 genes were significantly up-regulated, and no genes were down-regulated, in all four datasets. Of the 119 of these genes that were classified in DAVID, there was an enrichment for cell cycle-related genes, with 82/119 (68.9 %) being genes associated with mitosis. Genes up-regulated by more than twofold, all with a strong (p < 0.0001) positive correlation (Spearman) with PTTG1 expression, included key cell cycle regulators (eg. CCNB1, CCNB2, CDK1, CKS2), genes associated with DNA replication (eg. MCM2, GINS1, RRM2), response to DNA damage (eg. CHEK1, PBK), mitotic spindle and microtubule organisation (eg. AURKA, NEK1, PRC1), chromosome segregation during mitosis (eg. CENPA, CENPH, PENPK, BIRC5) and ubiquitin ligase activity and protein catabolism (eg. UBE2C, CDC20, MAD2L1, DTL) (Table 1; Fig. 3). Other non-cell cycle genes associated with high PTTG1 include DEPDC1 and the histone demethylase EZH2.

Table 1 Gene significantly upregulated more than twofold in PTTG1 high MM patients
Full size table
Fig. 3
figure 3

PTTG1 expression strongly correlates with expression of cell proliferation-related genes in MM patients. PTTG1 expression in 328 newly diagnosed MM patients (E-GEOD-19784) plotted against expression of cell cycle-related genes CDK1 (a), CCNB1 (b), CCNB2 (c), BIRC5 (d), RRM2 (e) and the non-cell cycle gene DEPCD1 (f). r and p values are shown for Pearson correlation analyses

Full size image

Pttg1 knockdown in 5TGM1 cells inhibits cell proliferation in vitro

Consistent with the elevated expression of Pttg1 in KaLwRij-derived plasma cells and the KaLwRij mouse tumour origin of the 5TGM1 cell line [27, 28], 5TGM1 cells express high levels of Pttg1 (data not shown). Using lentiviral transduction, we stably introduced a short hairpin ribonucleic acid (shRNA) within an mCherry-tagged vector to specifically knockdown Pttg1 in luciferase-expressing 5TGM1 cells. Pttg1 expression levels, as assessed by qRT-PCR (Fig. 4a) and Western blot (Fig. 4b), were reduced by 70 % in the knockdown cell line (denoted 5TGM1-PTTG-kd) when compared with a scrambled shRNA control line (5TGM1-SCRAM). These cell lines were used for subsequent in vivo and in vitro experiments.

Fig. 4
figure 4

Pttg1 knockdown reduces 5TGM1 proliferation in vitro. a A 70 % knockdown of Pttg1 mRNA was confirmed in 5TGM1-PTTG-kd cells compared with 5TGM1-SCRAM controls by qRT-PCR. b Reduced protein expression of Pttg1 was confirmed by Western blot. c Cells were seeded at 1 × 105 cells/mL in a 96-well plate, and cell numbers over 3 days were quantitated by WST-1 assay, read at 450 nm. d Cells were seeded at 4 × 105 cells/mL in a 96-well plate, BrdU substrate was added and BrdU incorporation was quantitated after 2 h by ELISA, measured by absorbance at 370 nm. *p < 0.05, t test. e Cells were seeded at 4 × 105 cells/mL in a six-well plate and cultured for 24 h, and cell cycle distribution was assessed following PI staining. Representative FACS plots (PI histograms) are shown. Graphs depict mean + SEM of three independent experiments

Full size image

As high PTTG1 expression in MM patients correlates with increased expression of cell cycle- and proliferation-associated genes, we next examined whether Pttg1 may play a role in modulating cell cycle progression and proliferation in murine myeloma plasma cells. After 3 days of culture, cell number, as determined by water-soluble tetrazolium salt (WST-1) assay, was significantly decreased by 75 % in the 5TGM1-PTTG-kd cells when compared with the 5TGM1-SCRAM controls (Fig. 4c, p < 0.01, two-way ANOVA with Sidak’s multiple comparison tests). Furthermore, the proliferative capacity of the 5TGM1-PTTG-kd line in vitro was decreased by 83 % compared with the 5TGM1-SCRAM control, as determined by bromodeoxyuridine (BrdU) incorporation over 2 h (Fig. 4d, p = 0.018, t test). However, there was no effect of Pttg1 knockdown on cell cycle distribution, as determined by propidium iodide (PI) staining (Fig. 4e, p = 0.998, two-way ANOVA). Furthermore, cell viability, as assessed by trypan blue exclusion, was not affected by Pttg1 knockdown (data not shown). Taken together, these results suggest that elevated expression of Pttg1 in the malignant 5TGM1 cells may be associated with increased cell proliferation.

Pttg1 knockdown reduces the expression of proliferation-related genes in the 5TGM1 mouse myeloma cell line

In order to elucidate a potential mechanism through which loss of Pttg1 inhibits proliferation, we examined the expression of a selection of proliferation-related genes identified in the human patient datasets (see Table 1) in the 5TGM1-PTTG1-kd cell line compared with the 5TGM1-SCRAM control. Our findings show that expression of genes encoding the cell cycle regulator Ccnb1 (p = 0.0039, t test) and the kinetochore-associated protein Birc5 (p = 0.0008, t test) were decreased by 52 and 48 %, respectively, in the PTTG1-kd cells, compared with the scramble control cells (Fig. 5). In contrast, expression of genes encoding the cell cycle regulator Cdk1 and the deoxyribonucleotide synthesis enzyme Rrm2 were not affected by Pttg1 knockdown (p = 0.2391, t test). Additionally, expression of Depdc1, which has previously been implicated in MM [29], was decreased by 38 % (p = 0.0055, t test) in the PTTG1-kd cells.

Fig. 5
figure 5

Pttg1 knockdown results in deregulation of proliferation-related genes in 5TGM1 cells. Expression of Ccnb1, Birc5, Cdk1, Rrm2 and Depcd1 was quantitated in 5TGM1-PTTG-kd cells compared with 5TGM1-SCRAM controls by qRT-PCR. Graphs depict mean + SD of triplicates from a single experiment. *p < 0.05, t test

Full size image

Pttg1 knockdown in 5TGM1 cells reduces tumour burden in vivo

To ascertain whether the observed decrease in proliferation in the 5TGM1-PTTG-kd cells corresponded with reduced tumour growth in vivo, the 5TGM1-PTTG-kd and 5TGM1-SCRAM cell lines were injected i.v. into C57BL/KaLwRij mice and tumour burden was monitored at weekly intervals by bioluminescent imaging. As seen in Fig. 6, tumour burden was decreased by 83 % in the Pttg1 knockdown group compared with the control group at 4 weeks (p < 0.0001, two-way ANOVA with Sidak’s post-test), suggesting that high basal expression of Pttg1 in 5TGM1 cells is important for in vivo tumour growth.

Fig 6
figure 6

Pttg1 knockdown reduces tumour growth in vivo. a Total tumour burden was measured at 2-, 3- and 4-weeks post-tumour cell inoculation using bioluminescence imaging techniques. A significant reduction in total tumour burden was observed in the Pttg kd group (n = 15) compared with SCRAM controls (n = 10); ****p < 0.001, two-way ANOVA with Sidak’s post-test. b Representative bioluminescent images of mice injected with 5TGM1-SCRAM control (left) and 5TGM1-PTTG-kd (right) cells at 4-weeks post-tumour cell inoculation are shown

Full size image

Discussion

Over-expression of PTTG1 has previously been linked to neoplastic transformation in a wide range of cell types [23, 30–32]. Increased expression of PTTG1 has been extensively studied in pituitary adenomas, as well as a range of other endocrine cancers (reviewed by [33]). By comparison, little is known about the role PTTG1 may play in haematological malignancies. Early studies demonstrated that PTTG1 was highly expressed in approximately 70 % of patients with leukaemia, lymphoma or other myelodysplastic diseases but not in healthy donors [26, 34]. More recently, high PTTG1 protein expression has been observed in 63 % of MM patients [24, 25]; however, the biological and prognostic significance of PTTG1 over-expression was not investigated in these studies. In the current study, we show a significant increase in PTTG1 expression in human MM patients, with approximately 36–70 % of patients showing increased expression of PTTG1 above that seen in healthy controls, findings which were subsequently confirmed by analysis of our own MM patient specimens. Importantly, increased PTTG1 expression was associated with poor overall survival, suggesting that high PTTG1 expression may be implicated in MM disease severity and poor patient outcome. Additionally, our analyses show that PTTG1 is upregulated in MM patients, but not in the asymptomatic precursor MGUS, suggesting an association with disease progression.

Chromosomal rearrangements and duplications are early events in the development of myeloma with the presence of genomic instability being associated with poor prognosis [35]. In this study, elevated PTTG1 was specifically observed in a subgroup of MM patients displaying a hyperdiploid phenotype (HY group). This is consistent with the role of PTTG1 in regulating sister chromatid separation during mitosis [36, 37] as dysregulation of this key function commonly leads to genomic instability [38–42]. Over-expression of PTTG1 promotes dysregulated chromosome segregation resulting in aneuploidy in human cancer cell lines [40, 41, 43]. The chromosomal instability induced by PTTG1 over-expression has been proposed as a mechanism whereby PTTG1 drives malignant transformation [44]. Interestingly, gene expression profiling studies have shown that the presence of chromosomal instability in newly diagnosed myeloma patients is associated with a gene expression profile that includes upregulation of PTTG1 [35]. These data suggest that expression of PTTG1 in MM patients may contribute to the development of chromosome duplications characteristic of the HY group.

In addition to its association with hyperdiploidy, we found a significant increase in PTTG1 expression in subset of patients with a proliferation-related gene expression profile (PR group), which is associated with high-risk disease and poor prognosis [4]. Elevated PTTG1 has previously been identified as part of a gene signature that is associated with increased proliferative index and is an independent predictor of poor prognosis in newly diagnosed MM patients [45]. The increase in PTTG1 in MM patients with highly proliferative disease is consistent with data from other systems which show that increased expression of PTTG1 correlates with high levels of cellular proliferation [46–48]. While, in the short term, over-expression of PTTG1 prevents exit from mitosis, leading to cell cycle arrest and increased cell death [49, 50], sustained; stable over-expression of PTTG1 generally leads to enhanced cellular proliferation [32, 51, 52]. Notably, our Pttg1 knockdown studies in the KaLwRij-derived 5TGM1 myeloma plasma cell line resulted in a reduction in cellular proliferation in vitro, as well as decreased tumour development in vivo. This is consistent with a number of animal knockout and knockdown studies in a range of different cell types, which show that reduction of PTTG1 expression inhibits cell proliferation [52–59]. Taken together, these data are consistent with increased expression of Pttg1 being a core requirement for the growth of malignant plasma cells in some patients.

In support of the pro-proliferative role of PTTG1 in myeloma, analysis of four independent MM patient gene expression datasets revealed that the majority (68.9 %) of genes significantly up-regulated in PTTG1 high patients had proliferation-related functions, specifically cell cycle regulation, DNA replication, mitotic spindle formation, chromosome segregation and DNA damage pathways. These included 20 of the top 50 genes originally identified as being upregulated in the PR subgroup [4]. Notable exclusions include the cancer/testis antigens MAGEA1, MAGEA3, MAGEA6, GAGE1, GAGE2, GAGE4 and GAGE5, which were originally identified as being strongly upregulated in the PR subgroup [4] but are not upregulated in our PTTG1 high patients. This, combined with the strength of the association between PTTG1 expression and the expression of cell cycle-associated genes, suggests that the upregulation of proliferation-related genes with high PTTG1 expression is not simply due to an increased representation of PR patients in this group.

Indeed, we found that knockdown of Pttg1 was associated with downregulation of cell cycle regulatory genes in the 5TGM1 cell line, suggesting a potential mechanism for the decreased proliferation observed in these cells. Cell cycle genes CcnB1 (cyclin B1) and Birc5 (survivin), which were among the most highly up-regulated genes identified in our patient analysis, were down-regulated by approximately 50 % by Pttg1 knockdown in the 5TGM1 cells. Both CCNB1 and BIRC5 have been identified as part of gene expression signatures predictive of high-risk disease and poor prognosis in MM patients in several studies [4, 5, 45, 60–63]. PTTG1, CCNB1 and BIRC5 expression are under tight transcriptional control during cell cycle progression, being switched on during G2/M phase [46, 49, 64, 65]. While this suggests that downregulation of Ccnb1 and Birc5 expression following Pttg1 could be a consequence of a decrease in G2/M phase cells, we saw no change in cell cycle distribution in the PTTG1-kd cells to support this. Cyclin B1, an essential regulator of cell cycle transition during mitosis, has previously been identified as a PTTG1-regulated gene whose up-regulation is associated with enhanced proliferation in keratinocytes [52]. BIRC5 is an inhibitor of apoptosis proteins (IAP) family member which regulates microtubule dynamics and chromosomal segregation during mitosis. Over-expression and knockdown studies in human myeloma cell lines have implicated BIRC5 in proliferation and protection of MM cells from apoptosis [62, 64, 66]. Taken together, these data suggest a potential role for cyclin B1 and BIRC5 in PTTG1-mediated tumour growth.

In addition to its role as a securin during chromatid segregation, PTTG1 can directly regulate gene expression. PTTG1 has been shown to physically interact with p53, repressing its transcriptional activity [36]. Additionally, p53 expression can be transcriptionally and translationally repressed by transient over-expression of PTTG1 [67]. Notably, some of the genes found to strongly correlate with PTTG1 expression in this study, including CDK1, CCNB1, CCNB2 and BIRC5 are known to be inhibited downstream of p53 [68–70]; p53-mediated regulation of gene expression down stream of PTTG1 could be a focus of future studies. However, we found no correlation between PTTG1 and p53 expression in MM patients in any of the datasets examined (data not shown), suggesting that PTTG1 is unlikely to transcriptionally downregulate p53 expression in plasma cells from MM patients. Additionally, our data suggests that PTTG1 upregulation in MM patients is not a consequence of deletion of the p53 locus 17p13, or associated TP53 mutations [71], which is observed in approximately 10 % of patients and is itself associated with poor prognosis [72–74].

In addition to cell cycle genes, we found that expression of DEPDC1 (Dishevelled, EGL-10, Pleckstrin domain containing 1) was significantly up-regulated in PTTG1 high patients. Importantly, Pttg1 knockdown in myeloma cells leads to a 40 % reduction in DEPDC1 expression. While the function of DEPDC1 is unknown, its expression has been associated with poor prognosis in lung cancer [75] and advanced disease in breast cancer [76]. Knockdown of DEPDC1 or inhibition using a specific peptide results in decreased cell proliferation and apoptosis in bladder cancer cell lines [77, 78]. In myeloma, DEPDC1 expression has been associated with poor prognosis [29]. In addition, shRNA-mediated DEPCD1 knockdown in human myeloma cell lines was shown to significantly inhibit cell proliferation and induce accumulation in G2/M in TP53 wild-type cells and marked apoptosis in TP53 mutant cells [29]. These data suggest that regulation of DEPDC1 expression may be a mechanism whereby PTTG1 regulates cell proliferation in myeloma.

Although studies have identified a role for PTTG1 in regulating epithelial-mesenchymal transition (EMT) [79–81] and recent studies by Azab and colleagues [82] have identified a role for the EMT processes in the dissemination and homing of MM plasma cells to the BM, we showed no association between the expression of EMT-related genes and high PTTG1 expression in MM patients. In addition, PTTG1 has previously been shown to directly mediate pro-angiogenic pathways through regulation of secreted factors VEGF and FGF-2 [19, 83, 84], which in turn are commonly expressed in MM and associated with increased disease severity [85–87]. However, our analyses did not identify an increase in these, or other, secreted pro-angiogenic factors in the presence of increased PTTG1 expression in MM patients. These data suggest that PTTG1 is unlikely to function through EMT or angiogenic pathways in modulating MM disease.

Conclusions

In summary, we have identified PTTG1 as a gene which is over-expressed in the MM-susceptible KalwRij mouse and in MM patients with hyperdiploidy or with hyperproliferative disease, suggesting a role in MM disease development. Knockdown of Pttg1 significantly inhibited the proliferation of myeloma cells in vitro, with an associated decrease in the expression of mitosis-related genes, and slowed tumour development in vivo. While expression of PTTG1 has previously been noted in gene expression signatures defining myeloma patients with highly proliferative disease [4] and chromosomal instability [35] and, by association, poor outcomes, this is the first study to show that PTTG1 expression alone is sufficient to identify a subset of patients with poor overall survival. Collectively, our data suggest that the poor prognosis associated with PTTG1 expression is due to a hyperproliferative state in these patients, which may result from the PTTG1-mediated upregulation of key drivers of cell cycle progression.

Materials and methods

Mouse tissue and plasma cell isolation

C57BL/6 mice were obtained from the Animal Resources Centre (Perth, Australia). C57BL/KaLwRij mice, originally kindly provided by Prof. Andrew Spencer (Monash University, Melbourne, Australia), were bred and housed at the SA Pathology Animal Care Facility (Adelaide, Australia). Tissues from C57BL/6 and C57BL/KaLwRij mice were snap frozen in liquid nitrogen and homogenised in TRIzol (Life Technologies, Carlsbad, CA). Blood was obtained from mice by cardiac puncture, collected in microfuge tubes containing 50 μL 0.5 M EDTA and centrifuged for 10 min at 500g and the cell pellet resuspended in TRIzol by vigorous vortexing. Femora and tibiae from age- and sex-matched C57BL/6 and C57BL/KaLwRij mice were extracted and cleaned thoroughly. A 21-gauge needle was inserted into the BM cavity, and the BM was flushed with ice-cold PFE (PBS/2 %FCS/2 mM EDTA). The resulting cell suspension was subjected to Ficoll density gradient separation and collected in 10 mL PFE, followed by centrifugation at 300g for 5 min at 4 °C. BM cells were immediately lysed in TRIzol, or CD138+ plasma cells were isolated by FACS. Briefly, cells were resuspended at 1 × 107 cells/mL in PFE and blocked with 110 μg/mL murine gamma globulin (Jackson Laboratories, Bar Harbor, ME) for 30 min at 4 °C. Cells were stained for 30 min at 4 °C protected from light with rat anti-mouse CD138 (R & D Systems, Minneapolis, MN) or an isotype control, washed twice with PFE and stained with secondary goat anti-rat IgG PE (Southern Biotech, Birmingham, AL) for 30 min at 4 °C protected from light. Cells were washed three times with PFE, followed by sorting for CD138+ cells on a FACSAria II (BD Biosciences, San Jose, CA). Total RNA was isolated from sorted cells using an RNAqueous Micro kit (Life Technologies).

Microarray analysis

For comparison of PTTG1 expression in CD138+ BM plasma cells, isolated by CD138-MACS, from newly diagnosed MM or MGUS patients or normal controls, three independent datasets were used: E-GEOD-6477 (normal, n = 5; MGUS, n = 11; MM, n = 133 [61]), E-GEOD-16122 (normal, n = 15; MGUS, n = 22; MM, n = 73 [88]) and E-MTAB-363 (normal, n = 5; MGUS, n = 5; MM, n = 156 [89]). Analysis of PTTG1 expression in different gene expression-defined (UAMS) patient subsets was conducted in GSE4581 (n = 414) [4]. Analysis of patient survival in PTTG1 high and PTTG1 low newly diagnosed MM patients was carried out using GSE4581 analysing patients included in the total therapy 2 (TT2) trial (n = 256). Four independent datasets were used for analysis of gene expression in PTTG1 high (quartile 4) and PTTG1 low (quartiles 1–3) patient subsets in CD138-selected BM plasma cells from newly diagnosed MM patients: E-GEOD-19784 (n = 328) [90], E-GEOD-26863 (n = 304) [91], E-MTAB-317 (n = 226) [45] and E-MTAB-363 (n = 156). E-MTAB-363, E-GEOD-26863, E-MTAB-317 and GSE4581 were conducted on Affymetrix GeneChip Human Genome U133 plus 2.0 arrays; E-GEOD-6477 and E-GEOD-16122 were conducted on Affymetrix GeneChip Human Genome U133A arrays. For all datasets except GSE4581, raw microarray data (CEL files) were downloaded from ArrayExpress (EMBL-EBI) and were normalised by RMA using the bioconductor package (affy) [92] and R (version 3.03) and log2 transformed. One patient in E-MTAB-363 (V0681) failed quality control (normalised unscaled standard error [NUSE] >1.05) and was excluded, and the remaining 165 files were re-normalised. For GSE4581, MAS5-normalised data were downloaded from the Gene Expression Omnibus (GEO) and were log2 normalised prior to analysis. GO annotation (http://www.geneontology.org) and DAVID [93, 94] were used to classify genes by related function.

MM patient samples

BM trephines and aspirates were collected, with informed consent, from patients with MM at the time of diagnosis and prior to initiation of therapy. This study was approved by the Royal Adelaide Hospital Human Research Ethics Committee (application # RAH 030206 and 131133).

CD138+ plasma cell isolation from MM patients

CD138+ plasma cells were isolated from diagnostic MM patient BM samples using CD138 microbeads (Miltenyi Biotech, Auburn, CA) as previously described [15]. Briefly, cryopreserved human BM samples were thawed into 10 mL DMEM (high glucose) with 15 % FCS and DNase I (80 U/mL). Samples were centrifuged at 300g for 10 min and supernatant removed. The cell pellet was resuspended in MACS buffer (2 mM EDTA, 0.5 % deionised BSA in PBS) and CD138 microbeads added, followed by incubation on ice for 15 min. Cell-bead conjugates were washed in 1 mL MACS buffer and centrifuged at 300g for 10 min. Cells were resuspended in MACS buffer, applied to a pre-rinsed MS column and washed three times with MACS buffer and eluted in 1 mL. Subsequent FACS analysis confirmed >85 % CD138+ following MACS. Total RNA was isolated using an All Prep DNA/RNA Micro Kit (Qiagen, Valencia, CA).

Dual colour staining BM trephines

Paraffin-embedded BM trephine sections were mounted on silicane-coated slides and dried. Endogenous peroxidase was blocked with 0.5 % H2O2 in methanol at room temperature for 30 min, followed by blocking with 3 % normal horse serum (NHS) for 30 min. Slides were incubated with anti-PTTG1 antibody (diluted 1:50; DCS-280; Abcam) at room temperature overnight. Slides were washed twice in PBS and incubated with biotinylated anti-mouse IgG (Vector Laboratories, Burlingame, CA) diluted 1:250 for 30 min at room temperature, washed in PBS and incubated with streptavidin Alexa Fluor 488 (diluted 1:500; Life Technologies) diluted 1:500 for 1 h at room temperature. Slides were re-blocked with 3 % NHS for 30 min and incubated with mouse anti-human CD138 (diluted 1:40; MI15, Dako, Denmark) overnight. Slides were washed twice with PBS followed by incubation for 1 h at room temperature with anti-mouse Alexa Fluor 594 (diluted 1:500, Life Technologies) and mounted in aqueous mounting solution. Images were taken on a Zeiss LSM 700 confocal system (Zeiss, Oberkocken, Germany) at ×40 magnification.

Cell lines

The murine myeloma plasma cell line 5TGM1 was kindly provided by Ass. Prof. Claire Edwards (University of Oxford, Oxford, UK), and was maintained in Iscove’s modified Dulbecco’s medium (Sigma) with 20 % FCS. Unless otherwise specified, all culture medium was supplemented with 2 mM l-glutamine, 100 U/mL penicillin, 100 μg/mL streptomycin, 10 mM HEPES buffer (Life Technologies) and 1 mM sodium pyruvate.

Generation of stable PTTG1 knockdown lines

To generate stable knockdown cell lines, an RNA duplex targeting murine Pttg1 (GGGAAATTGCAGGTTTCAACG) was cloned into the pFIV-H1-mCherry vector. A scrambled sequence was used as a control. pFIV-H1-mCherry was created by excising the GFP cassette from pFIV-H1-GFP (System Biosciences, Mountain View, CA) using XbaI and SalI and replacing it with the mCherry cassette from pMSCV-mCherry. Following lentiviral infection of 5TGM1-luc cells (expressing a dual GFP and luciferase reporter construct [16, 96, 97]), single-cell clones were generated from the top 10 % GFP- and mCherry-expressing cells using preparative cell sorting and the automatic cell deposition unit on a Beckman Coulter Epics Altra HyperSort, using Expo MultiComp Software version 1.2B (Beckman Coulter, Miami, FL). Clonal 5TGM1-PTTG-kd and 5TGM1-SCRAM lines were used for subsequent in vitro and in vivo assays.

Real-time PCR

Total RNA was isolated using TRIzol (Life Technologies) as per the standard protocol (unless otherwise specified). For mouse and human CD138+ plasma cells, RNA was reverse transcribed using Sensiscript (Qiagen). For all other tissues and cell lines, RNA (1 μg) was reverse transcribed with Superscript III (Life Technologies) as per the manufacturer’s protocol. Real-time PCR was conducted on the Corbett Rotorgene using the following primers: human β-actin (F: 5'-TTGCTGACAGGATGCAGAAG-3' and R: 5'-AAGGGTGTAAAACGCAGCTC-3'), human PTTG1 (F: 5'-CGGCCTCAGATGAATGCGGCT-3' and R: 5'-TTGATTGAAGGTCCAGACCCCAGC-3'), mouse Gapdh (F: 5'- ACCCAGAAGACTGTGGATGG-3' and R: 5'-CAGTGAGCTTCCCGTTCAG-3'), mouse β-actin (F: 5'-TTG CTGACAGGATGCAGAAG-3' and R: 5'-CAGTGAGCTTCCCGTTCA-3'), mouse Pttg1 (F: 5'-GCTCCTGATGATGCCTACCC-3' and R: 5'-CGCCATTCAAGGGGAGAAGT-3'), mouse Ccnb1 (F: 5'-GATGATGGGGCTGACCCAAA-3' and R: 5'-ACATGGTCTCCTGAAGCAGC-3'), mouse Cdk1 (F: 5'-GTCCGTCGTAACCTGTTGAG-3' and R: 5'-TGACTATATTTGGATGTCGAAG-3') [98], mouse Rrm2 (F: 5'-GATTTAGCCAAGAAGTTCAAGTTACAG-3' and R: 5'-TCACACAAGGCATAGTTTCAATAGC-3') [99], mouse Birc5 (F: 5'-GAACCCGATGACAACCCGAT–3' and R: 5'-TGGTCTCCTTTGCAATTTTGTTCT-3') and mouse Depcd1 (F: 5'-AGCTGCAGTGGAGAAACATCT-3' and R: 5'-TGGTCTCCTTTGCAATTTTGTTCT-3'). Gene expression was represented relative to β-actin or Gapdh expression, calculated using the 2-ΔCT method.

Western blot analysis

Cells were lysed in lysis buffer (1 % NP-40, 20 mM HEPES, 150 mM NaCl, 10 % glycerol, 2 mM Na3VO4, 10 mM Na4P2O7, 2 mM NaF and Complete EDTA-free Protease Inhibitor Cocktail (Roche, Mannheim, Germany)). Total lysate (10 μg) was loaded on an 11 % acrylamide gel and subjected to SDS-PAGE. Proteins were transferred to PVDF membrane overnight. Membrane was incubated for 1 h in blocking buffer (Tris-buffered saline containing 0.1 % Tween20 and 2.5 % ECL Blocking Agent (GE Healthcare, Little Chalfont, UK)) and for 2 h at RT with mouse monoclonal anti-PTTG1 antibody (DCS-280; Abcam; Cambridge, MA) diluted 1:1000 in blocking buffer, followed by alkaline phosphate-conjugated anti-mouse IgG (Millipore, Billerica, MA) diluted 1:4000 in blocking buffer for 1 h at RT. Proteins were visualised using ECL detection reagent (GE Healthcare) on a Typhoon FLA 7000 IP2 (GE Healthcare).

Proliferation assays

For WST-1 assays, 5TGM1 cells (PTTG-kd or SCRAM controls) were seeded at 1 × 105 cells/well in triplicate in 96-well plates and were incubated at 37 °C with 5 % CO2. At 24-h intervals, WST-1 reagent (Roche) was added to the cells and incubated for 2 h prior to reading absorbance at 450 nm.

For BrdU assays, 5TGM1 cells were seeded at 4 × 105 cells/well in triplicate in a 96-well plate. BrdU (Roche) was immediately added to the cells and incubated for 2 h at 37 °C with 5 % CO2. BrdU incorporation was measured using a BrdU Cell Proliferation ELISA kit (Roche) as per manufacturer’s protocol and absorbance measured at 370 nm.

For cell cycle analysis, cells were seeded at 4 × 105 cells/well in a six-well plate and incubated for 24 h at 37 °C with 5 % CO2. Cells were fixed in ice-cold 70 % (v/v) ethanol, washed twice in PBS and stained with propidium iodide (PI; 40 μg/mL; Sigma) containing 20 μg/mL RNase A (Qiagen) for 30 min prior to analysis on a Gallios flow cytometer (Beckman Coulter). Cell cycle distribution was analysed using FCS Express version 4.

Animals

Ethical approval for this study was obtained from the SA Pathology/Adelaide Health Service Animal Ethics Committee (application # 136/10). C57BL/KaLwRij mice at 6–8 weeks of age were injected with 5 × 105 luciferase-expressing 5TGM1-luc cells (5TGM1-SCRAM or 5TGM1-PTTG-kd) in 100-μl sterile PBS via the tail vein. At weekly intervals, mice were administered luciferin (150 mg/kg) i.p. and imaged using the Xenogen IVIS 100 bioluminescence imaging system (Caliper Life Sciences, Hopkinton, MA) until termination of the experiment at day 28, prior to the development of lethal disease (hind limb paralysis) [16, 96]. Total tumour burden was measured as total flux (photons/second) for each animal using Living Image software (PerkinElmer, Waltham, MA), as described previously [15, 16, 97, 100–103].

Statistical analyses

Statistical analysis was performed using GraphPad Prism version 6.03 for Windows (GraphPad Software, San Diego, CA). Variance between patient groups was assessed using Kruskal-Wallis tests with Dunn’s multiple comparison tests. In each of the E-GEOD-19784, E-GEOD-26863, E-MTAB-317 and E-MTAB-363 datasets, gene expression was compared in PTTG1 high and PTTG1 low patients, using t tests with Bonferroni’s correction for multiple testing and p values from the four datasets were combined using Fisher’s method. Survival curves were compared using the log-rank (Mantel-Cox) test with hazard ratios calculated using the Mantel-Haenszel calculation. In vivo data, WST-1 assays and cell cycle distribution were analysed by two-way ANOVA with Sidak’s multiple comparison tests. qRT-PCR data and BrdU incorporation were compared between groups using unpaired two-tailed t tests. A p value of 0.05 was considered statistically significant. Unless otherwise described, all plots depict mean + SEM of three independent experiments.

References

  1. Al-Farsi K. Multiple myeloma: an update. Oman Med J. 2013;28(1):3–11.

    Article  PubMed Central  PubMed  Google Scholar 

  2. Landgren O, Morgan GJ. Biologic frontiers in multiple myeloma: from biomarker identification to clinical practice. Clin Cancer Res. 2014;20(4):804–13.

    Article  CAS  PubMed  Google Scholar 

  3. Dzikiewicz-Krawczyk A. MicroRNA-binding site polymorphisms in hematological malignancies. J Hematol Oncol. 2014;7(1):83.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  4. Zhan F, Huang Y, Colla S, Stewart JP, Hanamura I, Gupta S, et al. The molecular classification of multiple myeloma. Blood. 2006;108(6):2020–8.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  5. Shaughnessy Jr JD, Zhan F, Burington BE, Huang Y, Colla S, Hanamura I, et al. A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. Blood. 2007;109(6):2276–84.

    Article  CAS  PubMed  Google Scholar 

  6. Decaux O, Lode L, Magrangeas F, Charbonnel C, Gouraud W, Jezequel P, et al. Prediction of survival in multiple myeloma based on gene expression profiles reveals cell cycle and chromosomal instability signatures in high-risk patients and hyperdiploid signatures in low-risk patients: a study of the Intergroupe Francophone du Myelome. J Clin Oncol. 2008;26(29):4798–805.

    Article  CAS  PubMed  Google Scholar 

  7. Kumar SK, Dispenzieri A, Lacy MQ, Gertz MA, Buadi FK, Pandey S, et al. Continued improvement in survival in multiple myeloma: changes in early mortality and outcomes in older patients. Leukemia. 2014;28(5):1122–8.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Radl J, Hollander CF, van den Berg P, de Glopper E. Idiopathic paraproteinaemia. I. Studies in an animal model—the ageing C57BL/KaLwRij mouse. Clin Exp Immunol. 1978;33(3):395–402.

    PubMed Central  CAS  PubMed  Google Scholar 

  9. Radl J, Croese JW, Zurcher C, Van den Enden-Vieveen MH, de Leeuw AM. Animal model of human disease. Multiple myeloma. Am J Pathol. 1988;132(3):593–7.

    PubMed Central  CAS  PubMed  Google Scholar 

  10. Fowler JA, Mundy GR, Lwin ST, Lynch CC, Edwards CM. A murine model of myeloma that allows genetic manipulation of the host microenvironment. Dis Model Mech. 2009;2(11-12):604–11.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Garrett IR, Dallas S, Radl J, Mundy GR. A murine model of human myeloma bone disease. Bone. 1997;20(6):515–20.

    Article  CAS  PubMed  Google Scholar 

  12. Oyajobi BO, Munoz S, Kakonen R, Williams PJ, Gupta A, Wideman CL, et al. Detection of myeloma in skeleton of mice by whole-body optical fluorescence imaging. Mol Cancer Ther. 2007;6(6):1701–8.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Radl J, De Glopper ED, Schuit HR, Zurcher C. Idiopathic paraproteinemia. II. Transplantation of the paraprotein-producing clone from old to young C57BL/KaLwRij mice. J Immunol. 1979;122(2):609–13.

    CAS  PubMed  Google Scholar 

  14. Vanderkerken K, De Raeve H, Goes E, Van Meirvenne S, Radl J, Van Riet I, et al. Organ involvement and phenotypic adhesion profile of 5T2 and 5T33 myeloma cells in the C57BL/KaLwRij mouse. Br J Cancer. 1997;76(4):451–60.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Noll JE, Hewett DR, Williams SA, Vandyke K, Kok C, To LB, et al. SAMSN1 is a tumor suppressor gene in multiple myeloma. Neoplasia. 2014;16(7):572–85.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  16. Noll JE, Williams SA, Tong CM, Wang H, Quach JM, Purton LE, et al. Myeloma plasma cells alter the bone marrow microenvironment by stimulating the proliferation of mesenchymal stromal cells. Haematologica. 2014;99(1):163–71.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  17. Fowler JA, Lwin ST, Drake MT, Edwards JR, Kyle RA, Mundy GR, et al. Host-derived adiponectin is tumor-suppressive and a novel therapeutic target for multiple myeloma and the associated bone disease. Blood. 2011;118(22):5872–82.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Saez C, Japon MA, Ramos-Morales F, Romero F, Segura DI, Tortolero M, et al. hpttg is over-expressed in pituitary adenomas and other primary epithelial neoplasias. Oncogene. 1999;18(39):5473–6.

    Article  CAS  PubMed  Google Scholar 

  19. Zhang X, Horwitz GA, Heaney AP, Nakashima M, Prezant TR, Bronstein MD, et al. Pituitary tumor transforming gene (PTTG) expression in pituitary adenomas. J Clin Endocrinol Metab. 1999;84(2):761–7.

    Article  CAS  PubMed  Google Scholar 

  20. Heaney AP, Singson R, McCabe CJ, Nelson V, Nakashima M, Melmed S. Expression of pituitary-tumour transforming gene in colorectal tumours. Lancet. 2000;355(9205):716–9.

    Article  CAS  PubMed  Google Scholar 

  21. Boelaert K, McCabe CJ, Tannahill LA, Gittoes NJ, Holder RL, Watkinson JC, et al. Pituitary tumor transforming gene and fibroblast growth factor-2 expression: potential prognostic indicators in differentiated thyroid cancer. J Clin Endocrinol Metab. 2003;88(5):2341–7.

    Article  CAS  PubMed  Google Scholar 

  22. Rehfeld N, Geddert H, Atamna A, Rohrbeck A, Garcia G, Kliszewski S, et al. The influence of the pituitary tumor transforming gene-1 (PTTG-1) on survival of patients with small cell lung cancer and non-small cell lung cancer. J Carcinog. 2006;5:4.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  23. Tfelt-Hansen J, Kanuparthi D, Chattopadhyay N. The emerging role of pituitary tumor transforming gene in tumorigenesis. Clin Med Res. 2006;4(2):130–7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  24. Chiriva-Internati M, Ferrari R, Prabhakar M, Yu Y, Baggoni L, Moreno J, et al. The pituitary tumor transforming gene 1 (PTTG-1): an immunological target for multiple myeloma. J Transl Med. 2008;6:15.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  25. Wang Z, Lu QY, Chen P, Zhang P, Cong YQ. Expression of pituitary tumor-transforming gene in patients with multiple myeloma. Zhongguo Shi Yan Xue Ye Xue Za Zhi. 2006;14(6):1143–5.

    CAS  PubMed  Google Scholar 

  26. Dominguez A, Ramos-Morales F, Romero F, Rios RM, Dreyfus F, Tortolero M, et al. hpttg, a human homologue of rat pttg, is overexpressed in hematopoietic neoplasms. Evidence for a transcriptional activation function of hPTTG. Oncogene. 1998;17(17):2187–93.

    Article  CAS  PubMed  Google Scholar 

  27. Asosingh K, Radl J, Van Riet I, Van Camp B, Vanderkerken K. The 5TMM series: a useful in vivo mouse model of human multiple myeloma. Hematol J. 2000;1(5):351–6.

    Article  CAS  PubMed  Google Scholar 

  28. Dallas SL, Garrett IR, Oyajobi BO, Dallas MR, Boyce BF, Bauss F, et al. Ibandronate reduces osteolytic lesions but not tumor burden in a murine model of myeloma bone disease. Blood. 1999;93(5):1697–706.

    CAS  PubMed  Google Scholar 

  29. Kassambara A, Schoenhals M, Moreaux J, Veyrune JL, Reme T, Goldschmidt H, et al. Inhibition of DEPDC1A, a bad prognostic marker in multiple myeloma, delays growth and induces mature plasma cell markers in malignant plasma cells. PLoS One. 2013;8(4):e62752.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  30. Kakar SS. Molecular cloning, genomic organization, and identification of the promoter for the human pituitary tumor transforming gene (PTTG). Gene. 1999;240(2):317–24.

    Article  CAS  PubMed  Google Scholar 

  31. Zhang X, Horwitz GA, Prezant TR, Valentini A, Nakashima M, Bronstein MD, et al. Structure, expression, and function of human pituitary tumor-transforming gene (PTTG). Mol Endocrinol. 1999;13(1):156–66.

    Article  CAS  PubMed  Google Scholar 

  32. Hamid T, Malik MT, Kakar SS. Ectopic expression of PTTG1/securin promotes tumorigenesis in human embryonic kidney cells. Mol Cancer. 2005;4(1):3.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  33. Salehi F, Kovacs K, Scheithauer BW, Lloyd RV, Cusimano M. Pituitary tumor-transforming gene in endocrine and other neoplasms: a review and update. Endocr Relat Cancer. 2008;15(3):721–43.

    Article  CAS  PubMed  Google Scholar 

  34. Saez C, Pereda T, Borrero JJ, Espina A, Romero F, Tortolero M, et al. Expression of hpttg proto-oncogene in lymphoid neoplasias. Oncogene. 2002;21(53):8173–7.

    Article  CAS  PubMed  Google Scholar 

  35. Chung TH, Mulligan G, Fonseca R, Chng WJ. A novel measure of chromosome instability can account for prognostic difference in multiple myeloma. PLoS One. 2013;8(6):e66361.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  36. Bernal JA, Luna R, Espina A, Lazaro I, Ramos-Morales F, Romero F, et al. Human securin interacts with p53 and modulates p53-mediated transcriptional activity and apoptosis. Nat Genet. 2002;32(2):306–11.

    Article  CAS  PubMed  Google Scholar 

  37. Zou H, McGarry TJ, Bernal T, Kirschner MW. Identification of a vertebrate sister-chromatid separation inhibitor involved in transformation and tumorigenesis. Science. 1999;285(5426):418–22.

    Article  CAS  PubMed  Google Scholar 

  38. Kim D, Pemberton H, Stratford AL, Buelaert K, Watkinson JC, Lopes V, et al. Pituitary tumour transforming gene (PTTG) induces genetic instability in thyroid cells. Oncogene. 2005;24(30):4861–6.

    Article  CAS  PubMed  Google Scholar 

  39. Winnepenninckx V, Debiec-Rychter M, Belien JA, Fiten P, Michiels S, Lazar V, et al. Expression and possible role of hPTTG1/securin in cutaneous malignant melanoma. Mod Pathol. 2006;19(9):1170–80.

    Article  CAS  PubMed  Google Scholar 

  40. Yu R, Heaney AP, Lu W, Chen J, Melmed S. Pituitary tumor transforming gene causes aneuploidy and p53-dependent and p53-independent apoptosis. J Biol Chem. 2000;275(47):36502–5.

    Article  CAS  PubMed  Google Scholar 

  41. Yu R, Lu W, Chen J, McCabe CJ, Melmed S. Overexpressed pituitary tumor-transforming gene causes aneuploidy in live human cells. Endocrinology. 2003;144(11):4991–8.

    Article  CAS  PubMed  Google Scholar 

  42. Jallepalli PV, Waizenegger IC, Bunz F, Langer S, Speicher MR, Peters JM, et al. Securin is required for chromosomal stability in human cells. Cell. 2001;105(4):445–57.

    Article  CAS  PubMed  Google Scholar 

  43. Mu YM, Oba K, Yanase T, Ito T, Ashida K, Goto K, et al. Human pituitary tumor transforming gene (hPTTG) inhibits human lung cancer A549 cell growth through activation of p21(WAF1/CIP1). Endocr J. 2003;50(6):771–81.

    Article  CAS  PubMed  Google Scholar 

  44. Heaney AP, Nelson V, Fernando M, Horwitz G. Transforming events in thyroid tumorigenesis and their association with follicular lesions. J Clin Endocrinol Metab. 2001;86(10):5025–32.

    Article  CAS  PubMed  Google Scholar 

  45. Hose D, Reme T, Hielscher T, Moreaux J, Messner T, Seckinger A, et al. Proliferation is a central independent prognostic factor and target for personalized and risk-adapted treatment in multiple myeloma. Haematologica. 2011;96(1):87–95.

    Article  PubMed Central  PubMed  Google Scholar 

  46. Ramos-Morales F, Dominguez A, Romero F, Luna R, Multon MC, Pintor-Toro JA, et al. Cell cycle regulated expression and phosphorylation of hpttg proto-oncogene product. Oncogene. 2000;19(3):403–9.

    Article  CAS  PubMed  Google Scholar 

  47. Filippella M, Galland F, Kujas M, Young J, Faggiano A, Lombardi G, et al. Pituitary tumour transforming gene (PTTG) expression correlates with the proliferative activity and recurrence status of pituitary adenomas: a clinical and immunohistochemical study. Clin Endocrinol (Oxf). 2006;65(4):536–43.

    Article  Google Scholar 

  48. Menicanin D, Bartold PM, Zannettino AC, Gronthos S. Identification of a common gene expression signature associated with immature clonal mesenchymal cell populations derived from bone marrow and dental tissues. Stem Cells Dev. 2010;19(10):1501–10.

    Article  CAS  PubMed  Google Scholar 

  49. Yu R, Ren SG, Horwitz GA, Wang Z, Melmed S. Pituitary tumor transforming gene (PTTG) regulates placental JEG-3 cell division and survival: evidence from live cell imaging. Mol Endocrinol. 2000;14(8):1137–46.

    Article  CAS  PubMed  Google Scholar 

  50. Yu R, Cruz-Soto M, Li Calzi S, Hui H, Melmed S. Murine pituitary tumor-transforming gene functions as a securin protein in insulin-secreting cells. J Endocrinol. 2006;191(1):45–53.

    Article  CAS  PubMed  Google Scholar 

  51. Pei L, Melmed S. Isolation and characterization of a pituitary tumor-transforming gene (PTTG). Mol Endocrinol. 1997;11(4):433–41.

    Article  CAS  PubMed  Google Scholar 

  52. Ishitsuka Y, Kawachi Y, Taguchi S, Maruyama H, Fujisawa Y, Furuta J, et al. Pituitary tumor-transforming gene 1 enhances proliferation and suppresses early differentiation of keratinocytes. J Invest Dermatol. 2012;132(7):1775–84.

    Article  CAS  PubMed  Google Scholar 

  53. Chesnokova V, Kovacs K, Castro AV, Zonis S, Melmed S. Pituitary hypoplasia in Pttg-/- mice is protective for Rb+/- pituitary tumorigenesis. Mol Endocrinol. 2005;19(9):2371–9.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  54. Cho-Rok J, Yoo J, Jang YJ, Kim S, Chu IS, Yeom YI, et al. Adenovirus-mediated transfer of siRNA against PTTG1 inhibits liver cancer cell growth in vitro and in vivo. Hepatology. 2006;43(5):1042–52.

    Article  PubMed  CAS  Google Scholar 

  55. Genkai N, Homma J, Sano M, Tanaka R, Yamanaka R. Increased expression of pituitary tumor-transforming gene (PTTG)-1 is correlated with poor prognosis in glioma patients. Oncol Rep. 2006;15(6):1569–74.

    CAS  PubMed  Google Scholar 

  56. Tfelt-Hansen J, Yano S, Bandyopadhyay S, Carroll R, Brown EM, Chattopadhyay N. Expression of pituitary tumor transforming gene (PTTG) and its binding protein in human astrocytes and astrocytoma cells: function and regulation of PTTG in U87 astrocytoma cells. Endocrinology. 2004;145(9):4222–31.

    Article  CAS  PubMed  Google Scholar 

  57. Rubinek T, Chesnokova V, Wolf I, Wawrowsky K, Vlotides G, Melmed S. Discordant proliferation and differentiation in pituitary tumor-transforming gene-null bone marrow stem cells. Am J Physiol Cell Physiol. 2007;293(3):C1082–92.

    Article  CAS  PubMed  Google Scholar 

  58. Tong Y, Tan Y, Zhou C, Melmed S. Pituitary tumor transforming gene interacts with Sp1 to modulate G1/S cell phase transition. Oncogene. 2007;26(38):5596–605.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  59. Zhou C, Tong Y, Wawrowsky K, Melmed S. PTTG acts as a STAT3 target gene for colorectal cancer cell growth and motility. Oncogene. 2014;33(7):851–61.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  60. Kuiper R, Broyl A, de Knegt Y, van Vliet M, van Beers E, van der Holt B, et al. A gene expression signature for high-risk multiple myeloma. Leukemia. 2012;26(11):2406-2413.

  61. Chng WJ, Kumar S, Vanwier S, Ahmann G, Price-Troska T, Henderson K, et al. Molecular dissection of hyperdiploid multiple myeloma by gene expression profiling. Cancer Res. 2007;67(7):2982–9.

    Article  CAS  PubMed  Google Scholar 

  62. Romagnoli M, Seveno C, Wuilleme-Toumi S, Amiot M, Bataille R, Minvielle S, et al. The imbalance between Survivin and Bim mediates tumour growth and correlates with poor survival in patients with multiple myeloma. Br J Haematol. 2009;145(2):180–9.

    Article  CAS  PubMed  Google Scholar 

  63. Bergsagel PL, Kuehl WM, Zhan F, Sawyer J, Barlogie B, Shaughnessy Jr J. Cyclin D dysregulation: an early and unifying pathogenic event in multiple myeloma. Blood. 2005;106(1):296–303.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  64. Li F, Ambrosini G, Chu EY, Plescia J, Tognin S, Marchisio PC, et al. Control of apoptosis and mitotic spindle checkpoint by survivin. Nature. 1998;396(6711):580–4.

    Article  CAS  PubMed  Google Scholar 

  65. Pines J, Hunter T. Isolation of a human cyclin cDNA: evidence for cyclin mRNA and protein regulation in the cell cycle and for interaction with p34cdc2. Cell. 1989;58(5):833–46.

    Article  CAS  PubMed  Google Scholar 

  66. Romagnoli M, Trichet V, David C, Clement M, Moreau P, Bataille R, et al. Significant impact of survivin on myeloma cell growth. Leukemia. 2007;21(5):1070–8.

    CAS  PubMed  Google Scholar 

  67. Hamid T, Kakar SS. PTTG/securin activates expression of p53 and modulates its function. Mol Cancer. 2004;3:18.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  68. Badie C, Bourhis J, Sobczak-Thepot J, Haddada H, Chiron M, Janicot M, et al. p53-dependent G2 arrest associated with a decrease in cyclins A2 and B1 levels in a human carcinoma cell line. Br J Cancer. 2000;82(3):642–50.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  69. Taylor WR, Schonthal AH, Galante J, Stark GR. p130/E2F4 binds to and represses the cdc2 promoter in response to p53. J Biol Chem. 2001;276(3):1998–2006.

    Article  CAS  PubMed  Google Scholar 

  70. Hoffman WH, Biade S, Zilfou JT, Chen J, Murphy M. Transcriptional repression of the anti-apoptotic survivin gene by wild type p53. J Biol Chem. 2002;277(5):3247–57.

    Article  CAS  PubMed  Google Scholar 

  71. Lode L, Eveillard M, Trichet V, Soussi T, Wuilleme S, Richebourg S, et al. Mutations in TP53 are exclusively associated with del(17p) in multiple myeloma. Haematologica. 2010;95(11):1973–6.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  72. Neri A, Baldini L, Trecca D, Cro L, Polli E, Maiolo AT. p53 gene mutations in multiple myeloma are associated with advanced forms of malignancy. Blood. 1993;81(1):128–35.

    CAS  PubMed  Google Scholar 

  73. Corradini P, Inghirami G, Astolfi M, Ladetto M, Voena C, Ballerini P, et al. Inactivation of tumor suppressor genes, p53 and Rb1, in plasma cell dyscrasias. Leukemia. 1994;8(5):758–67.

    CAS  PubMed  Google Scholar 

  74. Chang H, Qi C, Yi QL, Reece D, Stewart AK. p53 gene deletion detected by fluorescence in situ hybridization is an adverse prognostic factor for patients with multiple myeloma following autologous stem cell transplantation. Blood. 2005;105(1):358–60.

    Article  CAS  PubMed  Google Scholar 

  75. Okayama H, Kohno T, Ishii Y, Shimada Y, Shiraishi K, Iwakawa R, et al. Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res. 2012;72(1):100–11.

    Article  CAS  PubMed  Google Scholar 

  76. Kretschmer C, Sterner-Kock A, Siedentopf F, Schoenegg W, Schlag PM, Kemmner W. Identification of early molecular markers for breast cancer. Mol Cancer. 2011;10(1):15.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  77. Kanehira M, Harada Y, Takata R, Shuin T, Miki T, Fujioka T, et al. Involvement of upregulation of DEPDC1 (DEP domain containing 1) in bladder carcinogenesis. Oncogene. 2007;26(44):6448–55.

    Article  CAS  PubMed  Google Scholar 

  78. Harada Y, Kanehira M, Fujisawa Y, Takata R, Shuin T, Miki T, et al. Cell-permeable peptide DEPDC1-ZNF224 interferes with transcriptional repression and oncogenicity in bladder cancer cells. Cancer Res. 2010;70(14):5829–39.

    Article  CAS  PubMed  Google Scholar 

  79. Shah PP, Fong MY, Kakar SS. PTTG induces EMT through integrin alphaVbeta3-focal adhesion kinase signaling in lung cancer cells. Oncogene. 2012;31(26):3124–35.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  80. Shah PP, Kakar SS. Pituitary tumor transforming gene induces epithelial to mesenchymal transition by regulation of Twist, Snail, Slug, and E-cadherin. Cancer Lett. 2011;311(1):66–76.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  81. Yoon CH, Kim MJ, Lee H, Kim RK, Lim EJ, Yoo KC, et al. PTTG1 oncogene promotes tumor malignancy via epithelial to mesenchymal transition and expansion of cancer stem cell population. J Biol Chem. 2012;287(23):19516–27.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  82. Azab AK, Hu J, Quang P, Azab F, Pitsillides C, Awwad R, et al. Hypoxia promotes dissemination of multiple myeloma through acquisition of epithelial to mesenchymal transition-like features. Blood. 2012;119(24):5782–94.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  83. Ishikawa H, Heaney AP, Yu R, Horwitz GA, Melmed S. Human pituitary tumor-transforming gene induces angiogenesis. J Clin Endocrinol Metab. 2001;86(2):867–74.

    CAS  PubMed  Google Scholar 

  84. McCabe CJ, Boelaert K, Tannahill LA, Heaney AP, Stratford AL, Khaira JS, et al. Vascular endothelial growth factor, its receptor KDR/Flk-1, and pituitary tumor transforming gene in pituitary tumors. J Clin Endocrinol Metab. 2002;87(9):4238–44.

    Article  CAS  PubMed  Google Scholar 

  85. Giatromanolaki A, Bai M, Margaritis D, Bourantas KL, Koukourakis MI, Sivridis E, et al. Hypoxia and activated VEGF/receptor pathway in multiple myeloma. Anticancer Res. 2010;30(7):2831–6.

    CAS  PubMed  Google Scholar 

  86. Gupta D, Treon SP, Shima Y, Hideshima T, Podar K, Tai YT, et al. Adherence of multiple myeloma cells to bone marrow stromal cells upregulates vascular endothelial growth factor secretion: therapeutic applications. Leukemia. 2001;15(12):1950–61.

    Article  CAS  PubMed  Google Scholar 

  87. Sato N, Hattori Y, Wenlin D, Yamada T, Kamata T, Kakimoto T, et al. Elevated level of plasma basic fibroblast growth factor in multiple myeloma correlates with increased disease activity. Jpn J Cancer Res. 2002;93(4):459–66.

    Article  CAS  PubMed  Google Scholar 

  88. Agnelli L, Mosca L, Fabris S, Lionetti M, Andronache A, Kwee I, et al. A SNP microarray and FISH-based procedure to detect allelic imbalances in multiple myeloma: an integrated genomics approach reveals a wide gene dosage effect. Genes Chromosomes Cancer. 2009;48(7):603–14.

    Article  CAS  PubMed  Google Scholar 

  89. Reme T, Hose D, Theillet C, Klein B. Modeling risk stratification in human cancer. Bioinformatics. 2013;29(9):1149–57.

    Article  CAS  PubMed  Google Scholar 

  90. Broyl A, Hose D, Lokhorst H, de Knegt Y, Peeters J, Jauch A, et al. Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients. Blood. 2010;116(14):2543–53.

    Article  CAS  PubMed  Google Scholar 

  91. Chapman MA, Lawrence MS, Keats JJ, Cibulskis K, Sougnez C, Schinzel AC, et al. Initial genome sequencing and analysis of multiple myeloma. Nature. 2011;471(7339):467–72.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  92. Gautier L, Cope L, Bolstad BM, Irizarry RA. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004;20(3):307–15.

    Article  CAS  PubMed  Google Scholar 

  93. Dennis Jr G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003;4(5):3.

    Article  Google Scholar 

  94. da Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57.

    Article  CAS  Google Scholar 

  95. Schweitzer KM, Vicart P, Delouis C, Paulin D, Drager AM, Langenhuijsen MM, et al. Characterization of a newly established human bone marrow endothelial cell line: distinct adhesive properties for hematopoietic progenitors compared with human umbilical vein endothelial cells. Lab Invest. 1997;76(1):25–36.

    CAS  PubMed  Google Scholar 

  96. Diamond P, Labrinidis A, Martin SK, Farrugia AN, Gronthos S, To LB, et al. Targeted disruption of the CXCL12/CXCR4 axis inhibits osteolysis in a murine model of myeloma-associated bone loss. J Bone Miner Res. 2009;24(7):1150–61.

    Article  CAS  PubMed  Google Scholar 

  97. Cheong CM, Chow AW, Fitter S, Hewett DR, Martin SK, Williams SA, et al. Tetraspanin 7 (TSPAN7) expression is upregulated in multiple myeloma patients and inhibits myeloma tumour development in vivo. Exp Cell Res. 2015;332(1):24–38.

    Article  CAS  PubMed  Google Scholar 

  98. Diril MK, Ratnacaram CK, Padmakumar VC, Du T, Wasser M, Coppola V, et al. Cyclin-dependent kinase 1 (Cdk1) is essential for cell division and suppression of DNA re-replication but not for liver regeneration. Proc Natl Acad Sci U S A. 2012;109(10):3826–31.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  99. Lei W, Feng XH, Deng WB, Ni H, Zhang ZR, Jia B, et al. Progesterone and DNA damage encourage uterine cell proliferation and decidualization through up-regulating ribonucleotide reductase 2 expression during early pregnancy in mice. J Biol Chem. 2012;287(19):15174–92.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  100. Mrozik KM, Cheong CM, Hewett D, Chow AW, Blaschuk OW, Zannettino AC et al. Therapeutic targeting of N-cadherin is an effective treatment for multiple myeloma. Br J Haematol. 2015

  101. Zinonos I, Luo KW, Labrinidis A, Liapis V, Hay S, Panagopoulos V, et al. Pharmacologic inhibition of bone resorption prevents cancer-induced osteolysis but enhances soft tissue metastasis in a mouse model of osteolytic breast cancer. Int J Oncol. 2014;45(2):532–40.

    PubMed Central  CAS  PubMed  Google Scholar 

  102. Martin SK, Gan ZY, Fitter S, To LB, Zannettino AC. The effect of the PI3K inhibitor BKM120 on tumour growth and osteolytic bone disease in multiple myeloma. Leuk Res. 2015;39(3):380–7.

    Article  CAS  PubMed  Google Scholar 

  103. Gan ZY, Fitter S, Vandyke K, To LB, Zannettino AC, Martin SK. The effect of the dual PI3K and mTOR inhibitor BEZ235 on tumour growth and osteolytic bone disease in multiple myeloma. Eur J Haematol. 2015;94(4):343–54.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This research was supported by the Beat Cancer Project of the Cancer Council of South Australia (AZ). JN was supported by a Veronika Sacco Clinical Cancer Research Fellowship (Florey Medical Research Foundation, University of Adelaide); KV was supported by a Research Fellowship from the Multiple Myeloma Research Foundation; KV and CK were supported by Mary Overton Early Career Research Fellowships (Royal Adelaide Hospital).

Author information

Authors and Affiliations

  1. Myeloma Research Laboratory, Department of Physiology, School of Medicine, Faculty of Health Sciences, University of Adelaide and Cancer Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, Australia

    Jacqueline E. Noll, Kate Vandyke, Duncan R. Hewett, Krzysztof M. Mrozik, Rachel J. Bala, Sharon A. Williams & Andrew CW Zannettino

  2. SA Pathology, Adelaide, Australia

    Kate Vandyke

  3. Leukaemia Research Group, Cancer Theme, SAHMRI, Adelaide, Australia

    Chung H. Kok

  4. Discipline of Physiology, School of Medicine, Faculty of Health Sciences, University of Adelaide, Cancer Theme, Level 5 South, SAHMRI, PO Box 11060, Adelaide, SA, 5001, Australia

    Andrew CW Zannettino

Authors
  1. Jacqueline E. Noll
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kate Vandyke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Duncan R. Hewett
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Krzysztof M. Mrozik
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rachel J. Bala
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sharon A. Williams
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chung H. Kok
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Andrew CW Zannettino
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrew CW Zannettino.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

JN performed and designed experimental work and wrote the paper; KV performed in silico analyses and experimental work and wrote the paper; DH performed experimental work and wrote the paper; RB, SW and KM performed experimental work; CK performed in silico analyses; AZ facilitated research and wrote the paper. All authors read and approved the final manuscript.

Jacqueline E. Noll and Kate Vandyke equal first author.

Additional file

Additional file 1: Figure S1.

PTTG1 expression is upregulated in MM PC. PTTG1 expression was quantitated in CD138-selected BM PC from newly diagnosed MM patients (n = 11) using qRT-PCR. Graph shows mean + SD of triplicates from a single experiment.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Noll, J.E., Vandyke, K., Hewett, D.R. et al. PTTG1 expression is associated with hyperproliferative disease and poor prognosis in multiple myeloma. J Hematol Oncol 8, 106 (2015). https://doi.org/10.1186/s13045-015-0209-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13045-015-0209-2

Keywords

Journal of Hematology & Oncology

ISSN: 1756-8722

Contact us