- Research article
- Open Access
- Open Peer Review
The genetic basis for inactivation of Wnt pathway in human osteosarcoma
© Du et al.; licensee BioMed Central Ltd. 2014
- Received: 27 December 2013
- Accepted: 5 June 2014
- Published: 18 June 2014
Osteosarcoma is a highly genetically unstable tumor with poor prognosis. We performed microarray-based comparative genomic hybridization (aCGH), transcriptome sequencing (RNA-seq), and pathway analysis to gain a systemic view of the pathway alterations of osteosarcoma.
aCGH experiments were carried out on 10 fresh osteosarcoma samples. The output data (Gene Expression Omnibus Series accession number GSE19180) were pooled with published aCGH raw data (GSE9654) to determine recurrent copy number changes. These were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to identify altered pathways in osteosarcoma. Transcriptome sequencing of six osteosarcomas was performed to detect the expression profile of Wnt signaling pathway genes. Protein expression of WNT1, β-catenin, c-myc, and cyclin D1 in the Wnt pathway was detected by immunohistochemistry (IHC) in an independent group of 46 osteosarcoma samples.
KEGG pathway analysis identified frequent deletions of Wnt and other Wnt signaling pathway genes. At the mRNA level, transcriptome sequencing found reduced levels of mRNA expression of Wnt signaling pathway transcripts. While WNT1 protein expression was detected by IHC in 69.6% (32/46) of the osteosarcomas, no β-catenin protein was detected in the nucleus. β-catenin protein expression was, however, detected in the membrane and cytoplasm of 69.6% (32/46) of the osteosarcomas. c-myc protein expression was detected in only 47.8% (22/46) and cyclin D1 protein expression in 52.2% (24/46) of osteosarcoma samples. Kaplan-Meier survival analysis showed that WNT1-negative patients had a trend towards longer disease free survival than WNT1-positive patients. Interestingly, in WNT1-negative patients, those who were also cyclin D1-negative had significantly longer disease free survival than cyclin D1-positive patients. However, there was no significant association between any of the investigated proteins and overall survival of human osteosarcoma patients.
Frequent deletions of Wnt and other Wnt signaling pathway genes suggest that the Wnt signaling pathway is genetically inactivated in human osteosarcoma.
- Wnt signal pathway
- Genetic aberration
- Microarray-based comparative genomic hybridization
Osteosarcoma is a malignant bone tumor, often associated with copy number alterations, that most commonly arises in the metaphyseal ends of long bones [1–3]. The survival of patients with osteosarcoma has not improved significantly in recent years and the prognosis of patients with metastatic osteosarcoma is especially poor . Identification of prognosis markers and key genetic and molecular events for osteosarcoma is critical for development of effective therapeutics [2, 3].
Fortunately, the discovery of signal transduction pathways and their importance in a variety of cancers has led to the development of many new targeted agents. With regard to osteosarcoma, preclinical investigations targeting the rapamycin (mTOR) pathway, as well as the VEGF pathway showed promising results [2, 4]. The Wnt pathway is clearly important in many forms of human cancer, particularly in epithelial cancer types where gain- or loss-of-function events appear to contribute to both inherited cancer risk and somatic carcinogenesis . In human osteosarcoma, most previous studies have suggested that active Wnt signaling contributes to osteosarcoma development, evidenced by cytoplasmic and/or membranous β-catenin staining or detection of Wnt pathway components [6, 7]. However, Cai and colleagues recently reported that the Wnt pathway is inactivated in osteosarcomas . These contradictory findings provoke debate and stimulate further research into the role of Wnt signaling in osteosarcoma .
In this study, we sought to gain a comprehensive understanding of the key driving pathways for osteosarcoma by performing pathway analysis of recurring gene copy number aberrations using array comparative genomic hybridization (aCGH) analysis. Transcriptome sequencing (RNA-seq) and immunohistochemistry (IHC) were used to explore the expression level of key signaling pathways affected by these genetic aberrations. The most surprising and intriguing finding was the deletion of Wnt pathway genes, suggesting the genetic inactivation of the Wnt signaling pathway, in human osteosarcoma.
Osteosarcoma tissues and clinical information
The clinical and pathological features in 46 human conventional osteosarcomas
Disease free survival
1.10E + 04
1.40E + 116
3.70E + 04
4.40E + 116
5.30E + 04
6.40E + 116
1.90E + 282
3.90E + 225
1.60E + 05
1.20E + 04
2.40E + 03
Array comparative genomic hybridization and pathway enrichment analysis
The aCGH data analysis was performed as previously described [2, 3]. aCGH data in this publication have been deposited in NCBI’s Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE19180. In addition, we obtained the raw aCGH data of another 10 osteosarcoma biopsy samples from the GEO database (GSE9654)  and pooled the two datasets for analysis. Briefly, the median-normalized log-2 ratio data were first subjected to a circular binary segmentation (CBS) algorithm to reduce the effect of noise . Then, the CGHcall algorithm was used to call segments of DNA sequences as amplified or deleted in each sample . A permutation analysis was further applied to call recurrent copy number aberration in osteosarcoma . As a result of this procedure, each target was given a label of “normal”, “deletion” or “amplification” as previously described [2, 3, 13]. A bacterial artificial chromosome (BAC) clone–based CGH array dataset from 36 cases of osteosarcoma was also analyzed to confirm the overall recurrent gene copy alteration patterns [2, 3, 14].
Pathway enrichment analysis was performed separately on the recurrent amplified and deleted gene lists from gene sets GSE19180 and GSE9654 as previously reported [2, 3]. Here, we used the gene annotations data in the Kyoto Encyclopedia of Genes and Genomes (KEGG). Pathways with more annotations in our gene list than expected by random (hypergeometric model, P < 0.05) were considered to be significantly enriched with amplified or deleted genes [2, 3].
Frozen tumors were crushed, then total RNA was isolated using TRIzol reagent (Invitrogen, Grand Island, NY). RNA was quantified by Qubit (Invitrogen, Grand Island, NY) and Nanodrop ND1000 (ThermoFisher Scientific, Waltham, MA) before quality assessment with the Agilent 2100 Bioanalyzer. Six of the 10 samples with high quality RNA were used for RNA library construction, followed by emulsion PCR and whole transcriptome 90 bp paired-end sequencing on Illumina HiSeq™ 2000 instruments at the Beijing Genomics Institute (BGI, Shenzhen, China). Each sequencing run produced approximately 50 million paired end reads.
Whole transcriptome sequencing reads were aligned against the GRCh37 human reference genome using Tophat version 2.0.4 . The number of overlapping reads was calculated for all exons and then for all genes annotated in Ensembl 67. Gene expression values were normalized across samples using median-of-ratios normalization . Briefly, an expression ratio between two samples for every gene (or exon) with > 500 reads is calculated, and the median of those ratios is determined. All gene expression values are then multiplied by the median-of-ratios.
Immunohistochemical analysis of WNT1, β-catenin, c-myc, and cyclin D1 expression
The 46 FFPE tissues were sectioned at 4 μm and mounted onto charged glass slides (ProbeOn Plus, Fisher Scientific, Pittsburgh, PA, USA) for immunohistochemical staining as described previously . Briefly, tissues were deparaffinized with xylene and ethanol, endogenous peroxidase activity was blocked with 0.3% H2O2 (Fisher Scientific, Fair Lawn, NJ, USA). Tissues were blocked for 30 min with normal serum (Vector Laboratories, Burlingame, CA, USA) and then incubated overnight at 4°C with appropriate antibodies. Antibodies for WNT1, β-catenin, c-myc, and cyclin D1 (Abcam, Cambridge, UK) were used at a dilution of 1:200, 1:100, 1:100, and 1:100, respectively. The same concentrations of non-immune rabbit serum were used as negative controls. Signal was detected using biotinylated anti-rabbit antibodies, followed by avidin, biotinylated enzyme and colorimetric detection using 3,3′-diaminobenzidine tetrahydrachloride (DAB) (DAKO Corporation, Carpinteria, CA, USA). Samples were then counterstained with Mayer’s hematoxylin (Polyscientific, Bay Shore, NY, USA).
Two pathologists, blinded to clinical information, evaluated and scored the immunohistochemical staining for WNT1, β-catenin, c-myc, and cyclin D1 in osteosarcoma tissues based on the overall intensity of membranous, cytoplasmic, and nuclear staining within the tumor cells and the percentage of cells stained [8, 17–20]. WNT1 and β-catenin staining were scored as described in [8, 17]. Specifically, intensity of staining was graded as follows: lost (score 0); weak (“+”, score 1); moderate (“++”, score 2); and strong (“+++”, score 3). Extent of staining was evaluated as the percentage of positive cells per 100 or more cells (at least 100 cells in 10 high-power fields) in each evaluated compartment and was graded into five classes as follows: < 5% (score 0); 6% to 25% (score 1); 26% to 50% (score 2); 51% to 75% (score 3); > 75% (score 4). A final staining score was calculated by adding intensity score and extent score for each compartment, and the expression was categorized into two classes based on the final score: positive (final score 2–7) and negative (final score 0–1) expression. For β-catenin staining evaluation, any intensity and extent of staining found in the nucleus was considered positive. The staining of c-myc and cyclin D1 was assessed on the basis of published data: negative (< 10% of the cells), and positive (> 10% of the cells) [18–20].
Student’s t-test, ANOVA, Chi-square, Fisher’s exact test, Kaplan-Meier, and Mantel-Cox survival analysis were performed using SPSS software 16.0 version when necessary. A P value of less than 0.05 was considered statistically significant in multivariate analysis.
Several signaling pathways are genetically altered in osteosarcoma
Genetic amplifications of key pathway genes in osteosarcoma
Gene name cohort
VEGF signaling pathway
MAPK14, AKT1, PLA2G2D, PLA2G2E, NFATC4, PIK3CD, PLA2G2A, PLA2G5, MAPK1, MAPK13, PTK2, RAC1, RAC3, PLA2G2F, VEGFA, CASP9, PIK3R3, SPHK1, SH2D2A, CDC42P2, CDC42
mTOR signaling pathway
AKT1, MTOR, RICTOR, PIK3CD, PRKAA1, PRKAA2, MAPK1, RPTOR, RPS6KA1, VEGFA, PIK3R3, ULK2
INADL, EXOC3, CSNK2B, CLDN19, AKT1, CLDN14, CLDN17, LLGL2, LLGL1, MYH6, MYH7, F11R, ASH1L, PRKCZ, CGN, JAM2, RAB3B, RAB13, ACTB, ACTG1, CLDN5, CLDN8, CRB3, TJAP1, CDC42P2, CDC42
Synthesis and degradation of ketone bodies
HMGCL, HMGCS1, HMGCS2, OXCT1
C21-Steroid hormone metabolism
CYP11B1, CYP11B2, HSD3B1, HSD3B2
PGLYRP2, PGLYRP3, PGLYRP4
Ether lipid metabolism
AGPAT1, PLA2G2D, PLA2G2E, PAFAH2, ENPP2, PLA2G2A, PLA2G5, AGPAT3, PLA2G2F, PPAP2B
Arachidonic acid metabolism
CYP2J2, CYP4A11, GPX6, PLA2G2D, GPX5, GPX7, PLA2G2E, CYP4F3, PLA2G2A, PLA2G5, PLA2G2F, CYP4F2, CBR3
Alpha-Linolenic acid metabolism
PLA2G2D, PLA2G2E, ACOX1, PLA2G2A, PLA2G5, PLA2G2F, FADS2
CTH, CA14, CA1, CA2, CA3, CA6, CA8, ASRGL1
Glycan structures-biosynthesis 2
PIGK, B3GALT5, ST6GALNAC3, PIGP, PIGV, ST3GAL1, ST3GAL3, ST6GALNAC5, B4GALT3, B4GALT2, B3GALT4, GPAA1, PIGM, PIGL
Biosynthesis of unsaturated fatty acids
ACOT7, FASN, ACOT11, FADS1, ACOX1, FADS2, TECR
Antigen processing and presentation
CTSS, HLA-DMA, HLA-DMB, HLA-DOA, HLA-DOB, HLA-DPA1, HLA-DQA2, HLA-DRB5, HLA-F, HSPA1B, HSPA1A, HSPA1L, HSP90AB1, NFYA, NFYC, RFX5, TAP1, TAP2, CALR
Fc epsilon RI signaling pathway
MAPK14, AKT1, FCER1A, FCER1G, PLA2G2D, GRB2, PLA2G2E, LYN, PIK3CD, PLA2G2A, PLA2G5, MAPK1, MAPK13, MAP2K3, MAP2K7, RAC1, RAC3, PLA2G2F, TNF, VAV1, PIK3R3
Insulin signaling pathway
FLOT1, CRKL, AKT1, FASN, EXOC7, MTOR, GRB2, INSR, PFKL, PIK3CD, PKLR, PRKAA1, PRKAA2, PRKAB2, PRKACA, PRKACB, PRKCZ, MAPK1, RPTOR, PTPRF, SHC1, SREBF1, PIK3R3, MKNK1, TRIP10
GnRH signaling pathway
ADCY8, MAPK14, ADCY4, PLA2G2D, GRB2, PLA2G2E, ITPR3, JUN, PLA2G2A, PLA2G5, PRKACA, PRKACB, MAPK1, MAPK7, MAPK13, MAP2K3, MAP2K7, PLA2G2F, CDC42P2, CDC42
NCSTN, BACE2, APP, APH1A, C1QA, TNF, C1QB, C1QC
FCER1A, FCER1G, HLA-DMA, HLA-DMB, HLA-DOA, HLA-DOB, HLA-DPA1, HLA-DQA2, HLA-DRB5, TNF
Systemic lupus erythematosus
FCGR1A, FCGR2A, FCGR2B, FCGR3A, HIST1H2AB, HIST1H2AE, HIST1H2BD, H3F3B, H3F3A, HLA-DMA, HLA-DMB, HLA-DOA, HLA-DOB, HLA-DPA1, HLA-DQA2, HLA-DRB5, HIST2H2AB, C1QA, TNF, C1QB, C1QC, C2, C3, C6, C7, C8A, C8B, C9, HIST1H2AG, SMCHD1, HIST1H2AI, HIST1H2AK, HIST1H2AL, HIST1H2AM, HIST1H2AJ, HIST1H2AC, HIST2H2AC, HIST1H2BC, HIST1H2BE, HIST1H2BF, HIST1H2BG, HIST1H2BI, HIST1H2BO, HIST2H2BE, HIST1H3A, HIST1H3B, HIST1H3C, HIST1H3D, HIST1H3E, HIST1H3F, HIST1H3G, HIST1H3H, HIST1H3I, HIST1H3J, HIST4H4, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E, HIST1H4F, HIST1H4H, HIST1H4I, HIST1H4J, HIST1H4K, HIST1H4L, HIST2H4A, HIST2H4B, HIST1H4G, HIST1H2AH, HIST1H2BK, HIST1H2BJ
HLA-DMA, HLA-DMB, HLA-DOA, HLA-DOB, HLA-DPA1, HLA-DQA2, HLA-DRB5, HLA-F, TNF
Genetic deletions of key pathway genes in osteosarcoma
Gene name cohort
Wnt signaling pathway
FRAT1, CSNK1A1L, CTBP2, PRICKLE2, FRAT2, GSK3B, PLCB2, SFRP5, MAP3K7, TCF7L2, WNT5A, WNT6, WNT8B, FZD5, WNT10A, FZD7, FZD8, BTRC
Hedgehog signaling pathway
CSNK1A1L, STK36, GSK3B, SUFU, WNT5A, WNT6, WNT8B, WNT10A, BTRC
SORBS1, WASF3, FER, FYN, PVRL3, MLLT4, PARD3, MAP3K7, TCF7L2, VCL, WASF1
Metabolism of xenobiotics by cytochrome P450
AKR1C4, GSTO2, CYP2C19, CYP2C8, CYP2C9, CYP2C18, AKR1C1, AKR1C2, UGT1A8, AKR1C3, GSTO1
PPAR signaling pathway
SORBS1, CYP27A1, FABP7, ACSL3, ACADL, ME1, ACSL5, SCD, ACOX2
DNTT, POLL, DCLRE1C, XRCC5, NHEJ1
Phosphatidylinositol signaling system
DGKH, INPP5D, PLCE1, CALML5, PIK3R1, GNG7, PIP4K2A, PLCB2, PTEN, PLCD4, DGKD
COL6A3, FNDC3A, SV2C, FN1, ITGA1, ITGB1, LAMA4, THBS1, THBS2, ITGA8, CD47
T cell receptor signaling pathway
RASGRP1, CHUK, MAP3K8, CTLA4, FYN, ICOS, NFKB2, PIK3R1, PRKCQ, PAK6, CBLB, CD28
ADCY5, CREB1, GSK3B, MITF, CALML5, PLCB2, TCF7L2, WNT5A, WNT6, WNT8B, FZD5, WNT10A, FZD7, FZD8
Basal cell carcinoma
STK36, GSK3B, SUFU, TCF7L2, WNT5A, WNT6, WNT8B, FZD5, WNT10A, FZD7, FZD8
Wnt pathway genes are deleted in human osteosarcomas
Reduced transcript and protein expression of Wnt signaling pathway components suggests the Wnt signaling pathway is inactivated in human osteosarcomas
WNT1 protein expression was detected in 69.6% (32/46) of the osteosarcomas (Figure 4A), however no β-catenin protein expression was observed in the nucleus (Figure 4B). β-catenin protein expression was detected in the membrane and cytoplasm of 69.6% (32/46) of the osteosarcomas (Figure 4B), negative c-myc protein expression was recorded for 52.2% (24/46) of osteosarcomas and negative cyclin D1 protein expression was recorded for 47.8% (22/46) of osteosarcomas (Figure 4C-D). Compared with previously published reports of c-myc and cyclin D1 expression frequencies in other tumors, such as endometrial carcinoma [22, 23], these detection frequencies (47.8% (22/46) c-myc-positive and 52.2% (24/46) cyclin D1-positive) are low. Combined with the low levels of mRNA expression for Wnt pathway genes, these data, especially the lack of β-catenin protein expression in the nucleus, suggest that the Wnt signaling pathway may be inactive.
Negative expression of WNT1 and cyclin D1 is associated with longer disease-free survival
While the expression of WNT1, β-catenin, c-myc, and cyclin D1 had no correlation with clinicopathological factors, WNT1 expression had significant positive correlation with β-catenin (χ2 = 15.97, P = 0.001, Pearson’s R = 0.59), c-myc (χ2 = 5.62, P = 0.018, Pearson’s R = 0.35), and cyclin D1 expression (χ2 = 11.58, P = 0.001, Pearson’s R = 0.50). β-catenin protein expression had significant positive correlation with c-myc (χ2 = 5.62, p = 0.018, Pearson’s R = 0.35) and cyclin D1 (χ2 = 16.36, p = 5.25E-5, Pearson’s R = 0.6). Because β-catenin is a key mediating factor and c-myc/cyclin D1 are key targets regulated by the Wnt signaling pathway, these consistent relationships suggest that the initial signal, mediating factor, and downstream events of the Wnt signaling pathway may all be inactivated in human osteosarcoma.To detect the effect of the Wnt signaling pathway on survival, the disease-free and total survival of patients were analyzed. Even though single protein expression of β-catenin, c-myc and cyclin D1 showed no significant effect on disease-free survival, K-M survival analysis showed that WNT1-negative patients had a trend towards longer disease-free survival than WNT1-positive patients, although this was not statistically significant (Log Rank = 2.452, P = 0.117) (Figure 4E).Patients negative for all of WNT1, β-catenin and cyclin D1/c-myc protein expression might be considered to have an inactivated Wnt signaling pathway. Therefore, the survival of such patients will reflect the effect of inactivation of the Wnt signaling pathway on survival. Indeed, our data showed that in the patients who were negative for WNT1 protein expression, those who were also negative for cyclin D1 expression had significantly longer disease-free survival than patients with positive cyclin D1 expression (Log Rank = 3.884, P = 0.049) (Figure 4F). However, expression of none of the proteins examined had significant correlation with the overall survival of human osteosarcoma patients.
In this study we describe, for the first time, significant deletion of genes involved in the Wnt signaling pathway, implying genetic inactivation of this important signaling pathway, in human osteosarcoma. Supporting this, transcriptome analysis determined that mRNA expression of genes in the Wnt signaling pathway was reduced. In addition, at the protein level, nuclear β-catenin expression was not observed and c-myc/cyclin D1 protein were detected at lower frequencies compared with the frequencies observed in other tumors. Our results are in agreement with published data from Cai and colleagues, who reported that the Wnt pathway is inactivated in bone cancers . Furthermore, similar results were reported by Matushansky and Gregory, indicating that inactivation of the Wnt pathway contributes to tumorigenesis in so-called malignant fibrous histiocytoma and melanoma [17, 21, 24]. In contrast to these results, most previous studies have suggested that active Wnt signaling contributes to osteosarcoma development [6, 7]. Furthermore, it is reported that the Wnt pathway is transcriptionally active in radiation-induced rat osteosarcomas , and that the Wnt/β-catenin pathway antagonists, curcumin and PKF118-310, demonstrate anti-tumor activity against human osteosarcoma cells . These seemingly conflicting results suggest that the complexity of this important signaling pathway is still poorly understood in human osteosarcoma .
An interesting but unanswered question raised in the present study is: at which time during osteosarcoma progression is the Wnt signaling pathway inactivated. As inactivation of the Wnt signaling pathway is associated with longer disease-free survival, it suggests that this inactivation may occur at an early time point in osteosarcoma progression. However, more investigations in vivo and in vitro are required before this question can be answered.
Based on evidence at the genomic, our data suggest that the Wnt signaling pathway may be genetically inactivated in osteosarcoma. These results remind us of the complexity of this important signaling pathway.
This work was partly supported by the National Nature Science Foundation of China (81372872 to JY and 81320108022 to KC), the funds from the University Cancer Foundation via the Sister Institution Network Fund (SINF) at the Tianjin Medical University Cancer Institute & Hospital (TMUCIH), Fudan University Shanghai Cancer Center (FUSCC), and University of Texas MD Anderson Cancer Center (UT MDACC), program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) in China (IRT1076), and National Key Scientific and Technological Project (2011ZX09307-001-04) (K. Chen).
The genomic studies were supported by Dr. Wei Zhang (email@example.com) and the Cancer Genomics Core Laboratory. We would like to thank Limei Hu and David Cogdell for performing the aCGH experiments.
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