Open Access

Whole exome sequencing of a single osteosarcoma case—integrative analysis with whole transcriptome RNA-seq data

Human Genomics20148:20

https://doi.org/10.1186/s40246-014-0020-0

Received: 30 July 2014

Accepted: 10 November 2014

Published: 11 December 2014

Abstract

Background

Osteosarcoma (OS) is a prevalent primary malignant bone tumour with unknown etiology. These highly metastasizing tumours are among the most frequent causes of cancer-related deaths. Thus, there is an urgent need for different markers, and with our study, we were aiming towards finding novel biomarkers for OS.

Methods

For that, we analysed the whole exome of the tumorous and non-tumour bone tissue from the same patient with OS applying next-generation sequencing. For data analysis, we used several softwares and combined the exome data with RNA-seq data from our previous study.

Results

In the tumour exome, we found wide genomic rearrangements, which should qualify as chromotripsis—we detected almost 3,000 somatic single nucleotide variants (SNVs) and small indels and more than 2,000 copy number variants (CNVs) in different chromosomes. Furthermore, the somatic changes seem to be associated to bone tumours, whereas germline mutations to cancer in general. We confirmed the previous findings that the most significant pathway involved in OS pathogenesis is probably the WNT/β-catenin signalling pathway. Also, the IGF1/IGF2 and IGF1R homodimer signalling and TP53 (including downstream tumour suppressor gene EI24) pathways may have a role. Additionally, the mucin family genes, especially MUC4 and cell cycle controlling gene CDC27 may be considered as potential biomarkers for OS.

Conclusions

The genes, in which the mutations were detected, may be considered as targets for finding biomarkers for OS. As the study is based on a single case and only DNA and RNA analysis, further confirmative studies are required.

Keywords

Osteosarcoma Whole exome sequencing Integrative analysis

Introduction

Osteosarcoma (OS) is a most prevalent primary malignant bone tumour and mostly occurs in children and adolescents—75% of patients with OS are 15 to 25 years old. The etiology is unknown; however, a genetic predisposition has been suggested [1],[2]. Reviewed in [3], these tumours have high potential to metastasize and are one of the most frequent causes of cancer-related deaths. The survival rate increased up to 70% after chemotherapy became available [4]. However, no further improvements have been made in the last decades in terms of survival. Thus, the survival plateau forces scientists to look for new biomarkers (diagnostic, disease monitoring, response, resistance markers, drug targets), which could lead to, i.e. applying new therapeutic agents. While OS is rare and very heterogeneous (inter-patient, inter-tumour and intra-tumour heterogeneity), the clinical study progress is slow; thus, the preclinical studies are vital. Furthermore, finding the biomarkers and detecting the potential targets for new drugs are essential to improve the present situation.

There are several next-generation sequencing (NGS) and genome-wide association studies (GWAS) about OS, which associate different genes and pathways with pathogenesis of OS [5]-[7]. With whole exome sequencing (WES) and whole genome sequencing (WGS) studies, TP53, PTEN and PRB2 are found to be mutated in significant frequency [5]. High mutation rate in TP53 has also demonstrated in OS cell lines. Additionally, deletion of CDKN2A/B locus and amplification of MDM2 were detected [8]. With GWAS studies, a single nucleotide variant (SNV) in GRM4 was detected as potential biomarker for OS [7]. Gene expression studies reveal that, i.e. WNT inhibitory factor (WIF1) has a loss of expression in OS cell lines [9]; however, we found in our previous work that the expression has increased significantly [10]. Thus, as demonstrated, the expression pattern of WNT pathway genes in different OS cases may not be similar. When correlating the expression patterns of miRNA/mRNA pairs, miRNAs regulating TGFBR2, IRS1, PTEN and PI3K have been detected [11]. In addition, several serine/threonine kinases (mechanistic target of rapamycin (mTOR)) or tyrosine kinases (SRC, IGF1R, PDGFR, KIT) are considered as targets in OS treatment [3],[12],[13].

When observing the related pathways, the WNT/β-catenin pathway is one of the most thoroughly studied among bone malignancies. For example, the tumour growth is regulated through this pathway and the overexpression of BMP9 suppresses its activity [14],[15]. Furthermore, PI3K/AKT/mTOR signalling pathway was brought forward as a potential target for therapy, and also, pathways associated to TP53 may be altered [5]. Hypoxia-HIF-1α-CXCR4 pathway plays a crucial role during the migration of human osteosarcoma cells [16]. These are just a few examples—the network of associated genes and pathways is complex.

OS has a very unstable genome—it may contain aberrant number of chromosomes, and in most cases, these chromosomes display major structural abnormalities including amplification, deletions and translocations. For example, several studies have demonstrated the gain of chromosomal arms 6p, 8q and 17p in the case of OS [17],[18]. To be more precise, i.e. VEGFA amplification and LSAMP deletion have been detected in OS [19],[20]. Thus, it is suggested that genomic instability is linked to the development of this tumour [18],[21]-[23]. Furthermore, the genomic aberrations are more frequent in metastases than in primary tumours [24]. The genes responsible for cell cycle regulation are suggested to be associated to DNA breakage and genomic instability, i.e. CDC5L overexpression and mutations in TP53 gene are correlated to the high genomic instability in OS [23],[25].Moreover, the chromothripsis event is characteristic to OS—it generates new fusion products. This may explain the sudden onset of OS and the complexity and heterogeneity of OS genome [26]. All these changes make it difficult to find biomarkers suitable for targeting OS, as there are so many different subtypes.

In the present work, we analysed the whole exome of the tumorous and non-tumour bone tissue from the same patient with osteosarcoma. We used next-generation sequencing to study how the coding region of the tumour genome has altered. Additionally, we analysed together the WES genotyping and RNA expression data (from our previous RNA-seq analysis).

Materials and methods

Subject

The protocols and informed consent form used in this study were approved by the Ethical Review Committee on Human Research of the University of Tartu. The patient signed a written informed consent, which also includes the acceptance of the report to be published. A 16-year-old Caucasian male patient with an OS diagnosis was studied. In more detail, the patient became ill with complaints of pain in the left knee area. History of trauma was missing, and GP administered painkillers and vitamins. After 6 months, the patient returned to GP with complaints of pain, swelling and dysfunction in the left distal femur and knee area. The swelling line was observed in the left femoral distal region, and the area was thicker and painful to touch. No changes in skin colour were detected. The X-ray investigation showed additional shading and structural change in the distal part of the left femur. For detailed investigation, the MRI was performed and as a result, malignant process was suspected. Patient was hospitalized, and bone biopsy was taken for histological investigation. The diagnosis of osteosarcoma was confirmed. Chemotherapy for osteosarcoma started by Scandinavian Sarcoma Group (SSG) XIV treatment protocol. The patient responded well to the therapy—the histological analysis confirmed the necrotic tissue in tumour. After 3 month of chemotherapy, surgical removal of tumour (distal part of femoral bone with knee joint) and replacement of the knee and the lower part of the femur with megaprosthesis was performed. Pathologist confirmed that resection line was without tumour cells and OS was referred as NAS (Not Further Specified). After the patient had recovered from surgery, the SSG XIV chemotherapy treatment protocol was followed. Materials for this study were collected from the surgically removed tissue.

Exome sequencing

The genomic DNA (gDNA) was extracted from two bone samples from different locations—one sample from tumour area and another sample from the uninvolved normal bone tissue as a control. For gDNA extraction, the tissue was homogenized applying liquid nitrogen and a mortar, and after that, the PureLink Genomic DNA kit (Life Technologies Corp., Carlsbad, CA, USA) was used according to manufacturer’s protocol. The Target Seq Exome Enrichment System and SOLiD 5500 barcoded adaptors (Life Technologies Corp., Carlsbad, CA, USA) were used to prepare the libraries. The SOLiD 5500xl platform and paired-end DNA sequencing chemistry (75 bp forward and 35 bp reverse direction) were applied to sequence the samples.

The data analysis

Offline cluster was used for data processing and analysis. For bioinformatic analysis, LifeScope version 2.5 was applied. LifeScope performed colour space mapping and pairing. Tertiary analysis consisted of SNV discovery (diBayes algorithm) and detection of small indels. Hg19 (GRCh37.p13) was used as a reference, and before mapping, the multifasta file was verified in order to increase the mapping quality.

The SNVs and small indel .gff3 files were used as input in ANNOVAR software (AS; www.openbioinformatics.org/annovar/) [27] and Ingenuity Variant Analysis (IVA; http://www.ingenuity.com) QIAGEN, Redwood City, MD, USA) software. Applying refGene hg19, dbSNP135 and dbCOSMIC67 databases, AS annotated and predicted the effects of SNVs and small indels we detected in our study samples. AS also provides other prediction tools in order to get prediction scores (PolyPhen-2, SIFT, ljb2 etc.) [28]-[30]. Comparative distribution of SNVs and small indels between different samples was performed with Galaxy software bundle [31],[32]. IVA provided tools to annotate SNVs and small indels, which may be associated to cancer. The tumour and control samples were compared, and the lists for diseases, processes and pathways related to cancer were received as output.

The .bam and .bai files were used as input in CEQer software (CS) (www.ngsbicocca.org/html/ceqer.html), which is a tool for analysing copy number variants (CNVs) and loss of heterozygosity (LOH).

About the RNA-seq data analysis, please see our previous article, where we used the bone samples from the same patient [10].

Results

For comparing the tumour tissue and non-tumour tissue (control tissue) from the same individual, different approaches were applied. After mapping the data to a reference genome, we used several tools to perform the tertiary analysis.

Sequencing statistics from LifeScope software

In the case of the tumour tissue, over 130 million (58%) mappable reads were in target and the enrichment fold was 48%. Eighty-five percent of the detected targets were covered over 20 times, and the average coverage was 185.5. In the case of the control tissue, over 154 million (61%) mappable reads were in target and the enrichment fold was 51%. Eighty-three percent of the detected targets were covered over 20 times, and the average coverage was 157.

SNVs, small indels and CNVs

1)Results from ANNOVAR software

Using refGene hg19 database, AS was able to annotate 37,990 SNVs and 1,484 small indels. In the case of SNVs, we considered the data reliable, if the coverage was over 20; thus, 25,914 SNVs remained. In the case of SNVs, there were 23,767 germline mutations (9,067 in homozygous form and 14,700 in heterozygous form) and 2,147 somatic mutations (in the tumour tissue—116 in homozygous form and 2,031 in heterozygous form) (Table 1, Additional file 1). Furthermore, there were 896 germline small indels (278 in homozygous form and 618 in heterozygous form) and 588 somatic indels (in the tumour tissue—177 in homozygous form and 411 in heterozygous form).
Table 1

The numbers of SNV and small indel findings received from data analysis with ANNOVAR software

 

Germline mutations

Somatic mutations

 

Homozygous: non-reference

Heterozygous

Homozygous in tumour

Heterozygous in tumour

Homozygous in tumour

Heterozygous in tumour

   

Heterozygous in control

Homozygous in control

Homozygous (reference) in control

Homozygous (reference) in control

SNVs

Altogether

9,067

14,700

48

237

68

1,794

Exonic (includes ncRNA)

5,244

8,702

21

103

29

967

Nonsynounymous

2,435

4,035

15

52

18

500

Stopgain

6

50

0

2

0

7

Stoploss

2

5

0

0

0

1

Splicing (includes exonic)

11

20

0

0

0

2

Intronic (includes ncRNA)

3,091

4,846

22

111

35

681

5′ UTR and 3′ UTR

515

797

3

19

1

91

Downstream and upstream

51

76

1

2

1

13

Intergenic

155

259

1

2

2

40

Small indels

Altogether

278

618

89

75

88

336

Exonic (includes ncRNA)

33

99

14

11

2

29

Frameshift

9

30

4

4

1

12

Stopgain

0

1

0

0

0

0

Splicing (includes exonic)

4

16

0

4

1

3

Intronic (includes ncRNA)

212

419

64

51

75

270

5′ UTR and 3′ UTR

25

73

10

7

8

24

Downstream and upstream

1

2

0

1

1

6

Intergenic

3

9

1

1

1

4

Applying dbSNP135, we were able to annotate 5,281 SNVs and 239 small indels. With dbCOSMIC67, we annotated 2,569 SNVs and 59 small indels—none of these were noted to be associated to bone cancer. Applying ljb2 database, we found 469 SNVs to potentially cause a disease (average ljb2 score over 0.918), including 31 germline mutations and 4 somatic mutations (ESX1: c.A578G/p.K193R; CDC27: c.A17G/p.E6G; TMEM120B: c.G274A/p.D92N; TMEM131: c.C3947T/p.P1316L) in homozygous form in the tumour tissue.

2) Results from Ingenuity Variant Analysis software

Altogether, 207 cancer driver variants (CD-SNVs) were found in 123 genes according to IVA (Additional file 2). Fourteen CD-SNVs potentially gain and 186 lose the gene function. Only seven SNVs may have no drastic effect on gene function in the tumour tissue. Furthermore, according to IVA, none of these 207 SNVs affect the gene functionality in the control tissue. Thirteen of the CD-SNVs were homozygous in the tumour tissue (Table 2). There were no cancer-associated homozygous mutations present in the control tissue; thus, the homozygous CD-SNVs in the tumour tissue are all somatic.
Table 2

The somatic cancer driver SNVs and small indels found in data analysis with Ingenuity Variant Analysis software

Gene symbol

Chr number

Position

REF/ALT

Tumour zygosity

Effect on function

Control zygosity

Effect on function

dbSNP

SIFT function

Polyphen function

Transcript ID

Nucleotide change

Amino acid change

Gene region

Translation impact

SNVs

RGPD3 (includes others)

2

110585652

A/G

1/1

Loss

0/0

Normal

 

Damaging

Benign

NM_001037866.1,

c.2393A > G

p.E798G

Exonic

Missense

NM_001123363.3,

 

NM_005054.2,

 

NM_032260.2

 

PRDM9

5

23527251

C/T

1/1

Loss

0/0

Normal

 

Tolerated

Probably damaging

NM_020227.2

c.2054C > T

p.T685I

Exonic

Missense

FOXK1

7

4722436

A/G

1/1

Loss

0/0

Normal

 

Damaging

Benign

NM_001037165.1

c.497A > G

p.N166S

Exonic

Missense

CCZ1/CCZ1B

7

6841033

T/A

1/1

Loss

0/1

Normal

 

Tolerated

 

NM_198097.3

c.1228A > T

p.M410L

Exonic

Missense

PLATa

8

42044965

G/A

1/1

Normal

0/0

Normal

2020921

Tolerated

Benign

NM_033011.2/

c.352C > T/

p.R118W/

Exonic

Missense

       

NM_000930.3

c.490C > T

p.R164W

 

AGTPBP1a

9

88292495

C/T

1/1

Loss

0/1

Normal

 

Tolerated

Benign

NM_015239.2

c.292G > A

p.G98R

Exonic

Missense

SARDH

9

136597592

T/C

1/1

Loss

0/0

Normal

149002589

Tolerated

Benign

NM_001134707.1,

c.463A > G

p.I155V

Exonic

Missense

       

NM_007101.3

   

FAH

15

80472526

C/T

1/1

Normal

0/1

Normal

11555096

Damaging

Probably damaging

NM_000137.2

c.1021C > T

p.R341W

Exonic

Missense

CDC27

17

45266522

T/C

1/1

Loss

0/0

Normal

62077279

Damaging

Probably damaging

NM_001114091.1,

c.17A > G

p.E6G

Exonic

Missense

       

NM_001256.3

   

SBF1a

22

50893287

T/C

1/1

Loss

0/1

Normal

200488568

Tolerated

Benign

NM_002972.2

c.4768A > G

p.T1590A

Exonic

Missense

LRRC37A3a (includes others)

17

44632540

T/C

1/1

Gain

0/0

Normal

144051917

Activating

Benign

NM_001006607.2

c.4882 T > C

p.W1628R

Exonic

Missense

ARL17A

17

44632540

T/C

1/1

Gain

0/0

Normal

144051917

Activating

Benign

NM_001113738.1/

c.*2182A > G/

-/

3'UTR/

 
       

NM_016632.2

c.259 + 15585A > G

-

Intronic

LILRB3

19

54725835

G/C

1/1

Gain

0/0

Normal

201948566

Activating

Benign

NM_001081450.1,

c.523C > G

p.R175G

Exonic

Missense

        

NM_006864.2

   

Small indels

CTCFL

20

56073500

(N)103/T

1/1

Loss

0/0

Normal

   

NM_001269041.1/

c.*4_*105del(N)103/

 

3′ UTR/

 
     

NM_001269043.1/

c.1988 + 8_1988 + 109del(N)103/

Intronic/

     

NM_001269040.1/

c.*4_*105del(N)103/

3′ UTR/

     

NM_001269042.1/

c.*4_*105del(N)103/

3′ UTR/

     

NM_080618.3/

c.*4_*105del(N)103/

3′ UTR/

     

NM_001269046.1

c.*4_*105del(N)103

3′ UTR

PRR23C

3

138763627

GTGC/G

1/1

Loss

0/1

Normal

63140560

  

NM_001134657.1

c.-168_-166delGCA

 

5′ UTR

 

CDCA7L

7

21941867

CTTAG/C

1/1

Loss

0/0

Normal

   

NM_001127371.2/

c.*69_*72delCTAA/

 

3′ UTR/

 
      

NM_001127370.2/

c.*69_*72delCTAA/

 

3′ UTR/

       

3′ UTR

      

NM_018719.4

c.*69_*72delCTAA

 

ALK

2

29416029

G/GATTG

1/1

Loss

0/0

Normal

   

NM_004304.4

c.*60_*61insCAAT

 

3′ UTR

 

DSPP

4

88537081

CAGCAGCAAT/C

0/1

Loss

0/0

Normal

   

NM_014208.3

c.3268_3276delAGCAGCAAT

p.S1090_N1092del

Exonic

In-frame

RELA

11

65422086

CTC/CTGTAGT

0/1

Loss

0/0

Normal

   

NM_001145138.1/

c.1408delGinsACTAC/

p.E470fs*19

Exonic/

Frameshift/

     

NM_021975.3/

c.1417delGinsACTAC/

 

Exonic/

Frameshift/

     

NM_001243984.1/

c.1210delGinsACTAC/

 

Exonic/

Frameshift/

     

NM_001243985.1

c.1216-108delGinsACTAC

 

Intronic

-

aThe expression pattern of these genes has changed in the tumour tissue compared to that in the control tissue.

According to IVA, six cancer-associated small indels were found (Table 2). Four of them are homozygous and two are heterozygous in the tumour tissue—the effect is most probably the loss of gene function. These indels are predicted to have no effect in the control tissue.

In most of the genes brought front by IVA, one CD-SNV was found in coding region in heterozygous form. However, some of the genes have more CD-SNVs in coding regions: MUC4 had even 22, ZNF717 had 8, CTBP2 had 7 and OR4C3 had 5 CD-SNVs, whereas these were not present in the control tissue (data not shown). When observing from a slightly different angle—the gene complexes, we can see that the mucin complex has the highest significance—three genes and 27 CD-SNVs are considered (Table 3). There are also other gene complexes, which are potentially associated to cancer processes, and in different complexes, the CD-SNVs are either somatic or germline (Table 3).
Table 3

The gene complexes which are potentially associated to cancer processes

Complex name

p value

Number of genes associated

Number of variances found

Tumour tissue

Control tissue

Mucin

9.54E-05

3: MUC2, MUC4, MUC6

27

1

0

Bcl9-Cbp/p300-Ctnnb1-Lef/Tcf

2.46E-03

2: CREBBP, TCF3

2

1

0

Sox

4.55E-03

2: SOX7, SOX10

2

1

0

Cholesterol monooxygenase (side-chain-cleaving)

1.06E-02

1

1

1

0

CYP11A

1.06E-02

1

1

1

0

Sarcosine dehydrogenase

1.06E-02

1

1

1

0

Ctbp

1.59E-02

1

7

1

0

Cbp/p300

1.59E-02

1

1

1

0

Dimethylglycine dehydrogenase

1.59E-02

1

1

1

0

DRD1/5

1.59E-02

1

1

1

0

MAGI

2.64E-02

1

2

1

1

Magi-Pten

3.68E-02

1

2

1

1

Fumarylacetoacetase

1.06E-02

1

1

1

1

There are both somatic and germline cancer driver SNVs found in the tumour and control tissues.

In the case of cancer-associated small indels, the statistically most significant results were with complexes related to RELA gene—NFKB1-RELA and RELA-REL complexes both had p value 7.56E-4.

IVA provided the first 100 cancer-associated processes and diseases related to CD-SNVs and small indels. Seventy-three genes and 135 CD-SNVs were found associated to process named as “disorder of genitourinary system” (Table 4). These findings were present in both the tumour and control tissues. There were also two processes associated to bone “myelopoiesis of bone marrow” (associated genes NPM1, RARA) and “quantity of trabecular bone” (associated genes CREBBP, SMO)—these findings were present only in the tumour tissue. In the case of small indels, all the findings were somatic and ALK and RELA genes were associated to “outgrowth of bone marrow cells” and “inflammatory response of bone marrow-derived macrophages”, respectively.
Table 4

The cancer-associated processes detected by IVA

Process name

p value

Number of genes associated

Number of variances found

Tumour tissue

Control tissue

CD-SNVs

Disorder of genitourinary system

9.05E-14

73

135

1

1

Cell biology

4.08E-04

69

132

1

1

Cell signalling

3.83E-03

25

31

1

1

Morphology of body region

2.55E-03

23

24

1

1

Abnormal morphology of cells

1.73E-03

18

19

1

1

Abnormal morphology of body cavity

6.17E-04

17

18

1

1

Morphology of body cavity

1.36E-03

17

18

1

1

Morphology of cardiovascular system

5.80E-04

13

14

1

1

Abnormal morphology of cardiovascular system

7.22E-04

12

13

1

1

Abnormal morphology of thoracic cavity

1.22E-03

11

12

1

1

Myelopoiesis of bone marrow

3.64E-03

2: NPM1, RARA

2

1

0

Quantity of trabecular bone

4.08E-03

2: CREBBP, SMO

2

1

0

Small indels

Tissue development

1.19E-03

5

5

1

0

Developmental process of tissue

1.35E-03

5

5

1

0

Development of organ

5.05E-03

4

4

1

0

Organogenesis

5.32E-03

4

4

1

0

Colony formation of tumour cell lines

6.25E-05

3

3

1

0

Colony formation of cells

4.44E-04

3

3

1

0

Colony formation

5.46E-04

3

3

1

0

Developmental process of tumour cells

3.81E-03

3

3

1

0

Colony formation of carcinoma cell lines

5.94E-05

2

2

1

0

Apoptosis of nervous tissue cell lines

2.49E-04

2

2

1

0

Outgrowth of bone marrow cells

7.56E-04

1: ALK

1

1

0

Inflammatory response of bone marrow-derived macrophages

1.26E-03

1: RELA

1

1

0

The sorting is performed by number of genes.

The bold data reflects the processes directly associated to bone.

IVA found 111 genes with 202 germline CD-SNVs associated to cancer (Table 5). Fifteen genes, which had 43 somatic CD-SNVs were associated to “bone marrow cancer and tumours”. In the case of small indel, all six genes, with a finding, are associated to cancer and the found small indels are all somatic. The disease named as “tumourigenesis of bone tumour” was associated to small indel in ALK gene and was present only in the tumour tissue.
Table 5

The diseases associated to CD-SNVs and small indels

Disease name

p value

Number of genes associated

Number of variances found

Tumour tissue

Control tissue

CD-SNVs

Cancer

7.04E-23

111

202

1

1

Tumourigenesis

8.21E-16

111

202

1

1

Cancers and tumours

3.37E-15

111

202

1

1

Organismal injury and abnormalities

9.45E-17

105

194

1

1

Carcinoma

3.46E-25

99

186

1

1

Solid tumour

2.64E-24

99

186

1

1

Epithelial neoplasia

3.34E-23

99

186

1

1

Epithelioma

3.34E-23

99

186

1

1

Breast or colorectal cancer

5.45E-23

83

164

1

1

Malignant neoplasm of abdomen

6.93E-20

83

169

1

1

Bone marrow cancer

1.69E-03

15: CREBBP, EPHA2, FGFR2, KCNJ12, KMT2C, LILRB3, MUC17, MUC4, MYBPC3, NPM1, RARA, SMO, TCF3, TTN, TUBG1

43

1

0

Bone marrow cancer and tumours

1.69E-03

43

1

0

Small indels

Cancer

9.07E-03

6

6

1

1a

Hematologic cancer

2.36E-04

4

4

1

1a

Hematologic cancer and tumours

2.36E-04

4

4

1

1a

Hematological neoplasia

8.01E-04

4

4

1

1a

Lymphohematopoietic cancer

9.12E-04

4

4

1

1a

Disease of colon

7.88E-03

4

4

1

0

Hematological disease

8.15E-03

4

4

1

1a

Immunological disease

1.28E-02

4

4

1

1a

Gastrointestinal tract cancer

2.00E-02

4

4

1

0

Gastrointestinal tract cancer and tumours

2.02E-02

4

4

1

0

Tumourigenesis of bone tumour

7.04E-03

1: ALK

1

1

0

aHere, only one gene PRR23C has a small indel in heterozygous form, which most likely does not affect the gene function. See Table 2.

The bold data reflects the diseases directly associated to bone.

With the osteosarcoma patient’s tumour and control tissue, WES data IVA found six pathways associated to CD-SNVs and six to cancer driver small indels (Table 6). All the mutations considered here were somatic. In the case of CD-SNVs, the statistically most significant association was between tumour and WNT/β-catenin signalling pathway. In the case of small indels, associations with different cytokine pathways were found. Also, a pathway directly linked to the bone tissue—“RANK signalling in osteoclasts” was brought front.

3) Results from CEQer software
Table 6

The pathways associated to cancer

Pathway name

p value

Number of genes

Genes

Number of variants

Tumour tissue

Control tissue

CD-SNVs

Wnt/β-catenin signalling

7.07E-04

6

CREBBP, RARA, SMO, SOX10, SOX7, TCF3

6

1

0

Epithelial adherens junction Ssignalling

1.26E-02

4

IQGAP1, KEAP1, TCF3, TUBG1

4

1

0

Germ cell-sertoli cell junction signalling

2.10E-02

4

GSN, IQGAP1, KEAP1, TUBG1

5

1

0

Mouse embryonic stem cell pluripotency

2.59E-02

3

CREBBP, SMO, TCF3

3

1

0

Regulation of the epithelial-mesenchymal transition pathway

3.40E-02

4

FGFR2, SMO, TCF3, ZEB2

4

1

0

Hereditary breast cancer signalling

4.95E-02

3

CREBBP, NPM1, TUBG1

3

1

0

Small indels

IL-17A signalling in gastric cells

8.79E-03

1

RELA

1

1

0

Role of JAK1, JAK2 and TYK2 in interferon signalling

9.54E-03

1

RELA

1

1

0

Interferon signalling

9.79E-03

1

RELA

1

1

0

IL-15 production

1.00E-02

1

RELA

1

1

0

TNFR2 signalling

1.05E-02

1

RELA

1

1

0

RANK signalling in osteoclasts

2.86E-02

1

RELA

1

1

0

We applied CS to analyse CNVs in tumour and non-tumour tissue exomes. Compared to the control tissue, in the tumour tissue, the loss of coding sequences was found in 6 chromosomes and 183 genes and gain of coding sequences in 4 chromosomes and 65 genes (Figure 1). The loss or gain of coding sequences was altogether in 8 chromosomes, and the most altered were chromosomes 2 and 19 (193,701 bp and 115,358 bp, respectively; Figure 2). The loss of heterozygosity was detected altogether in 68 regions in 37 genes, located in 15 different chromosomes (Additional file 3).
Figure 1

Circos plot illustrating the CNVs and LOHs in the OS tissue compared to that in the control tissue. CNVs are marked as lines in the centre: red—gain and green—loss. LOHs are marked as dots in the centre: black—copy neutral, green—copy gain and red—copy loss.

Figure 2

The CNVs in chromosomes 2 and 19 in the osteosarcoma tissue compared to that in the control tissue. Data analysis performed with CEQer software.

Integrative analysis

The integrative analysis narrows down the large list of findings from NGS data. When combining the results from WES data (AS, IVA, CS) and RNA-seq data [10], we found some interesting and rather logical associations, which we would like to emphasize.

SNVs, small indels and RNA expression

To reduce down the complexity of data we received from AS, we decided to perform as follows. In the case of SNV data, we observed both somatic and germline SNVs, which are homozygous in the tumour tissue and should have an effect on translation (nonsynonymous, stopgain, stoploss findings). Thus, we got 527 homozygous germline SNVs (in 392 genes) and 8 homozygous somatic SNVs (in 7 genes), which are located in genes with altered expression in the tumour tissue compared to that in the control tissue. If also considering the ljb2 database scores, seven homozygous SNVs with high disease-causing probability remained (Table 7).
Table 7

The integrative analysis—genes with altered expression pattern [10] and SNVs annotated with ANNOVAR software

Gene name

Transcript name—exon number: nucleotide change/amino acid change

ljb2 score/indel

Chr number

Start

End

REF/ALT

logFC

FDR

Germline mutations homozygous in tumour tissue

STEAP4

NM_024636exon2: c.G364A/p.A122T

0.647

Chr7

87913221

87913221

C/T

3.015

1.44E-19

NM_001205316exon2: c.G364A/p.A122T

NM_001205315exon3: c.G364A/p.A122T

DDX60L

NM_001012967exon18: c.T2491C/p.C831R

0.711

Chr4

169341435

169341435

A/G

2.349

2.67E-14

MT1A

NM_005946exon3: c.A152G/p.K51R

0.785

Chr16

56673828

56673828

A/G

−3.094

0.00795

ACOX1

NM_004035exon7: c.C936G/p.I312M

0.872

Chr17

73949540

73949540

G/C

−0.809

0.01538

NM_007292exon7: c.C936G/p.I312M

NM_001185039exon7: c.C822G/p.I274M

TMC7

NM_001160364exon6: c.G431A/p.G144E

0.695

Chr16

19041595

19041595

G/A

1.266

0.01726

NM_024847exon6: c.G761A/p.G254E

MYO7A

NM_001127179exon27: c.3514_3535del/p.1172_1179del

Frameshift deletion

Chr11

76895771

76895792

GGAGGCGGGGACACCAGGGCCT/-

1.541

0.03810

ATRNL1

NM_001276282exon8: c.1399_1400insTT/p.L467fs

Frameshift insertion

Chr10

116931101

116931101

-/TT

2.321

0.04535

Somatic mutations homozygous in the tumour tissue

TMEM120B

NM_001080825exon3: c.G274A/p.D92N → X → COSM1599921

0.981

Chr12

122186317

122186317

G/A

−1.548

0.00064

TMEM131

NM_015348exon31: c.C3947T/p.P1316L

0.945

Chr2

98409046

98409046

G/A

−0.799

0.01371

EI24

NM_001007277exon9: c.733dupC/p.R244fs

Frameshift insertion

Chr11

125452300

125452300

-/C

−0.815

0.01569

These germline or somatic SNVs are all nonsynonymous and homozygous in the tumour tissue and according to ljb2 database have a disease-causing effect.

In the case of small indels detected with AS, we observed the somatic and germline indels, which were homozygous in the tumour tissue. There was 52 germline and 26 somatic indels in introns of the genes, which expression pattern has also changed (data not shown). Furthermore, there was five germline and three somatic indels in exons of the genes with altered expression. Thus, we found altogether three frameshift small indels, which possibly have an effect on translation (frameshift insertions and deletion in exons) (Table 7).

In the case of homozygous cancer driver SNVs and small indels found with IVA (Table 2), only four genes have altered expression pattern in the tumour tissue compared to that in the control tissue. The mRNA expression was increased in the case of PLAT (log fold change (logFC) = 3.65, false discovery rate (FDR; corrected statistical significance) = 8.27E-27), AGTPBP1 (logFC = 0.91, FDR = 0.039) and LRRC37A3 (logFC = 1.14, FDR = 0.0072) and decreased in the case of SFB1 (logFC = −1.33, FDR = 0.0037).

CNVs, LOHs and RNA expression

When analysing the CNV results together with RNA expression results, we found that with gained copy numbers, there were altogether 22 genes, with altered expression profile—20 genes with increased and 2 genes with decreased mRNA expression. In the case of loss copy of number, 74 genes’ expression profile had changed—11 genes with increased and 63 genes with decreased mRNA expression. In Table 8, the genes with the lowest FDR values for gene expression results are presented. Here, we would emphasize that the INSR, which has copy number loss in area covering 174,552 bp has also a remarkable decrease in mRNA expression (3.36 times; FDS = 9.67E-31). However, there are also several genes with CNVs, which could be associated to cancer.
Table 8

The integrative analysis—CNVs and RNA expression data [10] is observed together

 

CNVs

RNA expression

Gene name

Chr number

Start

End

Area length

CNV p value

Copy number fold change

logFC

FDR

Loss

INSR

Chr19

7119459

7294011

174,552

3.18E-11

−6.64

−3.36

9.67E-31

NFIX

Chr19

13106583

13201204

94,621

0

−10.82

−2.45

1.63E-17

FARSA

Chr19

13034964

13044558

9,594

0

−10.82

−2.62

1.96E-16

RAD23A

Chr19

13056627

13063667

7,040

0

−10.82

−2.40

4.46E-16

GINS4

Chr8

41386724

41399418

12,694

8.28E-05

−3.94

−2.79

1.31E-15

GADD45GIP1

Chr19

13064971

13068050

3,079

0

−10.82

−2.82

3.67E-15

IFIH1

Chr2

163123588

163175218

51,630

0

−10.11

2.20

3.69E-14

RPL31

Chr2

101618690

101622885

4,195

0

−9.39

−2.05

5.08E-13

PLEKHG4B

Chr5

156185

181790

25,605

1.08E-05

−4.40

−2.17

1.58E-12

ZNF358

Chr19

7581003

7581135

132

3.18E-11

−6.64

−2.59

2.56E-11

ARHGEF18

Chr19

7459998

7532004

72,006

3.18E-11

−6.64

−1.96

1.44E-10

STX10

Chr19

13255223

13260987

57,64

0

−10.82

−2.55

1.96E-10

COL5A3

Chr19

10102679

10121147

18,468

4.14E-04

−3.53

−1.92

5.45E-10

MGAT4A

Chr2

99242185

99347589

105,404

0

−10.22

1.87

9.95E-10

Gain

SLC40A1

Chr2

190428309

190428951

642

1.83E-05

4.28

2.22

1.05E-14

KIT

Chr4

55524094

55603446

79,352

1.69E-06

4.79

2.54

1.17E-13

PTPLAD2

Chr9

21008019

21031635

23,616

7.73E-14

7.48

3.02

4.49E-13

ATP8A1

Chr4

42571177

42629126

57,949

1.83E-07

5.22

2.65

4.54E-10

FOCAD

Chr9

20658308

20993327

335,019

7.73E-14

7.48

1.94

9.12E-08

FAM200B

Chr4

15683351

15692070

8,719

3.54E-05

4.14

1.83

8.59E-07

SLIT2

Chr4

20255234

20512189

256,955

4.16E-05

4.10

1.35

1.73E-05

MLLT3

Chr9

20353522

20622514

268,992

7.73E-14

7.48

1.94

2.19E-05

LCORL

Chr4

17887690

18023483

135,793

4.16E-05

4.10

1.40

5.04E-05

Only the genes with lowest FDR value are presented.

Combining the LOH and mRNA expression data, we found that in the tumour tissue, the expression of four genes with LOH has increased significantly and expression of five genes with LOH has decreased significantly (Table 9). The rest of the genes with LOHs had no significant changes in mRNA expression level, and two genes were not detected with RNA-seq (FLJ20518, MANSC4) [10].
Table 9

The integrative analysis - loss of heterozygosity and RNA expression data observed together

Gene name

Chr number

LOHs

RNA expression

LOH position

Alleles

LOH

LOH p value

logFC

FDR

MS4A14

Chr11

60165358–60165379

G/C

CopyNeutralLOH

0.025

2.46

3.20E-08

DSC2

Chr18

28666554–28666556

A/C

CopyNeutralLOH

0.025

1.87

3.82E-07

RPS4X

ChrX

71495409–71495414

G/C

CopyNeutralLOH

0.01

−1.44

7.25E-07

RPS23

Chr5

81571874

A/C

CopyNeutralLOH

0.005

−1.43

1.04E06

IL7R

Chr5

35874575

C/T

1AlleleGain

0.025

1.59

6.69E-06

PCNXL2

Chr1

233398713

C/T

CopyNeutralLOH

0.01

1.20

0.00027

HILPDA (C7orf68)

Chr7

128098270

T/G

CopyNeutralLOH

0.0001

−1.12

0.00094

HRNR

Chr1

152188041

C/T

Allele(s)Loss

0.025

−3.09

0.00796

MUC4

Chr3

195515594, 195516630

C/G

CopyNeutralLOH

0.025

−2.22

0.01230

Only the genes with significant mRNA expression changes in the tumour tissue compared to that in the control tissue are presented.

For additional information, please see the supplementary material as separate files for AS, IVA and CS combined with RNA-seq data.

Discussion

In this study, the exome profiles of the osteosarcoma patient’s tumour and normal bone tissue were compared. Additionally, the RNA-seq data from our previous work was used [10]. For WES data analysis, several softwares were applied and possibly some of them are better in detecting some mutations and not so effective in detecting others. Still, we think it is more beneficial to use different approaches and we believe it is easier to follow, if we discuss separately the results gained from each software.

The ANNOVAR software annotated a large amount of genes with SNVs and small indels, applying refGene hg19 database. Over 2,700 somatic SNVs and small indels were detected specifically in the tumour tissue, from which almost 300 are homozygous. These findings are located all over the exome. This demonstrates that the changes in OS genome are not concentrated into a single or few areas but are rather distributed.

When using ljb2 database, AS detected four homozygous somatic mutations in the tumour tissue, which could potentially cause a disease. These nonsynonymous mutations were located in ESX1, CDC27, TMEM120B and TMEM131. Additionally, in the case of TMEM120B and TMEM131, the mRNA expression has decreased substantially in the tumour tissue compared to that in the control tissue [10]; however, further studies are needed to confirm the possible associations between found mutations and gene expression level. Available data about the possible associations between OS and these genes is very limited. In TMEM120B, a gene with an unclear function, the mutation COSM1599921 has been previously detected in glioma [33]. The CDC27 is a gene possibly controlling the timing of mitosis and may have an important role in tumour cell division [34]. In addition to the somatic mutation, the CDC27 had 33 heterozygous germline disease-causing mutations (nonsynonymous) (data not shown). In the case of breast cancer, the CDC27 has been demonstrated to be a promising biomarker in predicting the disease progression and prognostication [35]. Thus, these somatic mutations may have some effect on OS pathogenesis. Especially the abundant changes in CDC27 may be important in terms of regulating OS tumour cell division.

In the tumour tissue, we detected homozygous somatic small indels causing the frameshift in five genes—EI24, ALG1L2, TIGD6, GPATCH4 and SSPO. None of these genes have previously been associated to OS, and according to our RNA-seq data, only EI24 of these five genes has altered mRNA expression—it has decreased in the tumour tissue [10], which could be due to the insertion in exon 9. The EI24 encodes a tumour suppressor and is an immediate-early induction target of TP53-mediated apoptosis—it binds to antiapoptotic BLC2. Furthermore, the EI24 has found to be highly mutated in the case of aggressive breast cancer and is rather associated to tumour invasiveness than development of the primary tumour [36]-[38]. In the present case, we found no mutations in TP53 nor was the expression altered [10]; thus, according to this data, we may suggest that the TP53 is functional in the tumour tissue. However, the TP53 pathway may still be suppressed due to mutated and downregulated EI24. Moreover, the aggressive nature of OS is correlated to this finding.

Appling Ingenuity Variant Analysis software, we found over 200 cancer driver variants and 93% of these possibly cause the loss of gene function. Thirteen homozygous somatic CD-SNVs were detected in different genes—RGPD3, PRDM9, FOXK1, CCZ1, PLAT, AGTPBP1, SARDH, FAH, CDC27, SBF1, LRRC37A3, ARL17A and LILRB3. The mRNA expression of PLAT, AGTPBP1 and LRRC37A3 has increased and of SFB1 has decreased significantly [10]. We found no previous data about the associations between OS and these genes, except SBF1. With previous OS studies, another missense mutation (p.E1539K) has detected in SBF1 [39]. SBF1 is a SET (a nuclear oncogene) binding factor 1 and may inhibit the cell division [40]. The decreased expression in the tumour tissue may be responsible for the increased cell proliferation. Some other associations, which might be interesting—PLAT gene is important for cell migration and tissue remodelling and the overexpression might cause hyperfibrinolysis [41], which has not previously described in the case of OS. Two mutations in ARL17A have detected in chondrosarcoma cells [42]. In the case of CDC27, the same mutation (p.E6G) was also brought front by AS as potentially disease causing, which is discussed above. Thus, it is highly likely that at least some of these genes participate in some level of OS pathogenesis.

Additionally, with IVA four homozygous somatic small indels were detected in the tumour tissue. These were in noncoding regions of genes CTCFL, PRR23C, CDCA7L and ALK; thus, the effect might be post-transcriptional. CTCFL is a genetic paralog of CTCF; latter is an important methylation pattern regulator. In the case of CTCF, it has previously demonstrated that in the OS tissue, the changes in its methylation pattern may also cause loss of imprinting of IGF2 and H19 genes, which further alters their expression pattern [43]. In our OS patient’s tumour tissue, the mRNA expression of both IGF2 and H19 has increased significantly (FDR = 3.46E-15 and FDR = 0.0015, respectively) [10]. Thus, the association may be valid here also. In PRR23C, one missense mutation (p.R190W) has detected previously in the OS tissue [42]. ALK encodes a receptor tyrosine kinase and is rearranged, mutated or amplified in several tumours. However, in the case of OS, there are only few reports about ALK [44],[45]. In addition, two heterozygous somatic small indels were detected in DSPP and RELA exons; however, we found no previous data about these findings and associations to OS. The small indels might have an effect on the expression of these genes both pre- and post-transcriptional level; however, these suggestions need to be further studied.

According to IVA, there were several genes with more than one mutation—in MUC4, there were even 22 somatic mutations in exons and 44 in introns, although they all were heterozygous. Thus, we found MUC4 locus to be the most altered in the tumour tissue compared to that in the control tissue. This might explain why its mRNA expression in the tumour tissue has decreased (FDR = 0.012) [10]. Mucin 4 is among major constituents of mucus, and it has demonstrated that primary bone tumours rarely express MUC4 protein [46], which correlates to our finding. Furthermore, with IVA, we found mucin complex (MUC2, MUC4, MUC6) to have a highest significance in OS among others. However, there are also other mucin genes (MUC16, MUC17, MUC20) with somatic heterozygous CD-SNVs. The expression pattern of all other detected mucins has not changed significantly. Thus, mucins may have a role in OS pathogenesis, but we dear not to make any further conclusions.

With IVA, there was four bone-related processes brought front only in the case of the tumour tissue—“myelopoiesis of bone marrow” (NPM1, RARA), “quantity of trabecular bone” (CREBBP, SMO), “outgrowth of bone marrow cells” (ALK) and “inflammatory response of bone marrow-derived macrophages” (RELA). Furthermore, in disease list, 16 genes with over 40 somatic variations were associated to “bone marrow cancer” and “bone tumour”; however, there were also over 200 germline CD-SNVs associated to cancer. Thus, here, we would like to emphasize that in the case of both cancer-associated processes and diseases, the ones associated with bone are somatic mutations; however, the findings possibly promoting cancer are germline mutations. This is one of the phenomena, which we would like to observe in our future studies.

The most significant pathway found with IVA was “WNT/β-catenin signalling pathway” (altered genes: CREBBP, RARA, SMO, SOX10, SOX7, TCF3). Reviewed in [15], the pathway is required for bone development and has demonstrated to be altered in pathogenesis of OS—overexpression of numerous WNT pathway components including WNT ligands, FZDs and LRP receptors and epigenetic silencing of the pathway inhibiting genes, i.e. WIF1. However, in our previous study, we found WNT7B and WNT11 to be downregulated and WNT2B and WNT5B upregulated; FZD4 and FZD8 upregulated and LRP8 and LRP12 downregulated and DVL3 downregulated and WIF1 and SOST upregulated. Additionally, genes with CD-SNVs—RARA, SMO and SOX7 were upregulated [10]. Thus, our results are rather controversial to several previous studies demonstrating the WNT/β-catenin pathway to be upregulated [47]-[49]. However, there are also studies correlating to our findings [50],[51]. As our study is based on a single case, we dear not to conclude, why the WNT/β-catenin pathway is rather downregulated here, but we suggest the controversial results may occur due to major heterogeneity of OS. Nevertheless, the present study demonstrates that in addition to altered expression patter, the genes involved in WNT/β-catenin signalling pathway carry the CD-SNVs.

In the case of small indels, the IVA brought front the pathways associated to RELA and these are mostly cytokine signalling pathways (Table 6). Previously, it has demonstrated that interaction of IL17A and IL17AR promotes metastasis in OS cells. Furthermore, IL17 stimulates osteoclast resorption [52]. In our previous study, we found IL17AR to be significantly upregulated [10]. Osteoclasts are important in pathogenesis of OS—the more active they are, the more aggressive the tumour is [53]. RELA is demonstrated to enhance the osteoclast differentiation [54]. As IVA predicts the loss of RELA functionality (at least partially, as the small indel is heterozygous), in the present case, the OS might not have been as aggressive as it usually would.

Previously, it has demonstrated that chromothripsis event is common to early stage of OS—hundreds of genomic rearrangements will appear in a single instability event [26]. In the present case, the CEQer software detected nearly 2,400 gain and loss events in 8 chromosomes involved, which should qualify as the chromothripsis. However, the initiating cause of this massive rearrangement is unknown, as there were no traumas or other environmental causes we are aware of.

In general, the gain of copy number should increase the mRNA expression and loss of copy number should decrease the expression [6]. In present work, this pattern was valid in the case of 86.5% of the genes with CNVs and altered expression. One of the strongest findings here was the amount of CNVs in INSR, which expression has decreased remarkably (Table 8). The main physiological role of the insulin receptor appears to be metabolic regulation [55]. However, together with IGF1R it forms a hybrid receptor for IGF1, latter together with IGF2 is thought to have a key role in driving the proliferation and survival of sarcoma cells [56]. Furthermore, the growth hormone and IGF1 axis controls the growth and bone modelling/remodelling [57]. Additionally, the IRS1, which is phosphorylated by the INSR, is important for both metabolic and mitogenic pathways [58]. In the present case, the mRNA expression of both IGF1 and IGF2 has increased (FDR = 4.65E-35 and FDR = 3.46E-15, respectively); however, the expression of IGF1R remained the same in the tumour tissue compared to that in the control tissue [10]. Furthermore, in IGF1R we found a heterozygous germline nonsynonymous mutation (p.G1117R) with AS, which according to ljb2 database is a disease causing (data not shown). Similarly to INSR, the mRNA expression of IRS1 is decreased in the tumour tissue compared to that in the control tissue (FDR = 2.62E-10) [10]. Thus, in the present case it seems, the proliferation of tumour cells might be rather supported by increased effect of IGF1, IGF2 and IGF1R homodimer associations, than IGF1, IGF2 and INSR-IGF1R heterodimer associations or INSR effects on IRS1.

The loss of heterozygosity has been reported to be extensive in OS exomes [39]. In the present case, we did not detect whole chromosome or gene region loss; however, we did detect the loss of heterozygosity in smaller regions. The genes with LOH findings and increased mRNA expression—MS4A14, DSC2, IL7R and PCNXL2 have not associated to OS previously. However, in the case of DSC2, the overexpression has demonstrated to be inversely correlated to bone metastasis-free survival [59]. The mutations in IL7R exon 6 have been demonstrated to be present in leukaemia patients’ bone marrow samples but not associated to other solid tumours [60]. The five genes with LOHs and decreased mRNA expression—RPS4X, RPS23, HILPDA (C7orf68), HRNR and MUC4 also do not have previous information associated to OS. Nonetheless, also the LOH analysis brought forward different genes in mucin family. In addition to MUC4, there were also other genes with LOHs but with insignificant mRNA expression changes in the tumour tissues—MUC2, MUC6 and MUC17. Thus, these results also support the idea that mucins might have a role in pathogenesis of osteosarcoma.

In summary, the present case has several characteristics previously demonstrated in OS. The wide genomic arrangements have appeared—SNVs and small indels all over the genome and CNVs in some chromosomes; and in several cases, these rearrangements may have an effect on gene expression. Furthermore, the germline mutations seem to be associated to cancer in general and somatic mutations to bone tumours. The most significant pathway was the one probably most thoroughly studied in the case of OS—the WNT/β-catenin signalling pathway. We found several genes in this pathway carrying the cancer driver variances. Additionally, the IGF1/IGF2 and IGF1R homodimer signalling might have an essential effect on OS pathogenesis. Which also needs to be emphasized is that according to our data (based on DNA and RNA studies), there is no evidence of a nonfunctional TP53; however, the TP53 pathway might be suppressed in further levels—the downregulation of EI24. In addition, with this study, we found associations between different genes and OS pathogenesis, which have not demonstrated before in earlier studies. We found the MUC4 locus to be the most altered in the tumour tissue compared to that in the control tissue; furthermore, several other mucin genes are also possibly associated to OS. The somatic mutation in CDC27 was brought front by two different data analysis softwares and might have a role in OS pathogenesis.

Conclusions

All genes, in which the mutations were detected, may be considered as potential targets for additional studies (i.e. functional, histopathological, clinical studies) for finding OS biomarkers. The present study brought front the WNT pathway genes, IGF1/IGF2 and IGF1R homodimer signalling pathway genes, TP53 together with EI24, MUC4 together with other mucin genes and CDC27 as potential biomarkers for OS. Finally, as this study is based on a single case and only DNA and RNA analysis, these data may not be taken as conclusive evidence and further studies are needed to confirm the present findings.

Additional files

Declarations

Acknowledgements

This work was supported by the institutional research funding IUT (IUT20-46) of the Estonian Ministry of Education and Research, by the Centre of Translational Genomics of University of Tartu (SP1GVARENG) and by the European Regional Development Fund (Centre of Translational Medicine, University of Tartu).

Authors’ Affiliations

(1)
Department of Pathophysiology, University of Tartu
(2)
Department of Reproductive Biology, Estonian University of Life Sciences
(3)
Department of Traumatology and Orthopaedics, University of Tartu
(4)
Department of Oncology, Hue University of Medicine and Pharmacy
(5)
Traumatology and Orthopaedics Clinic, Tartu University Hospital

References

  1. Picci P: Osteosarcoma (osteogenic sarcoma). Orphanet J Rare Dis. 2007, 2: 6-10.1186/1750-1172-2-6.PubMed CentralView ArticlePubMedGoogle Scholar
  2. Ottaviani G, Jaffe N: The etiology of osteosarcoma. Cancer Treat Res. 2009, 152: 15-32. 10.1007/978-1-4419-0284-9_2.View ArticlePubMedGoogle Scholar
  3. Botter SM, Neri D, Fuchs B: Recent advances in osteosarcoma. Curr Opin Pharmacol. 2014, 16C: 15-23. 10.1016/j.coph.2014.02.002.View ArticleGoogle Scholar
  4. Allison DC, Carney SC, Ahlmann ER, Hendifar A, Chawla S, Fedenko A, Angeles C, Menendez LR: A meta-analysis of osteosarcoma outcomes in the modern medical era. Sarcoma. 2012, 2012: 704872-10.1155/2012/704872. http://www.ashg.org/2013meeting/programguide/files/assets/basic-html/page258.htmlPubMed CentralView ArticlePubMedGoogle Scholar
  5. Kiezun A, Janeway K, Tonzi P, Mora J, Aguiar S, Mercado G, Melendez J, Garraway L, Rodriguez-Galindo C, Orkin S, Golub T, Getz G, Yunes JA: Next generation sequencing of osteosarcoma identifies the PI3K/mTOR pathway as a unifying vulnerability to be exploited for targeted therapy. In.: ASHG meeting 2013; 2013.Google Scholar
  6. Kuijjer ML, Hogendoorn PC, Cleton-Jansen AM: Genome-wide analyses on high-grade osteosarcoma: making sense of a genomically most unstable tumor. Int J Cancer. 2013, 133 (11): 2512-2521.PubMedGoogle Scholar
  7. Savage SA, Mirabello L, Wang Z, Gastier-Foster JM, Gorlick R, Khanna C, Flanagan AM, Tirabosco R, Andrulis IL, Wunder JS, Gokgoz N, Patiño-Garcia A, Sierrasesúmaga L, Lecanda F, Kurucu N, Ilhan IE, Sari N, Serra M, Hattinger C, Picci P, Spector LG, Barkauskas DA, Marina N, de Toledo SR, Petrilli AS, Amary MF, Halai D, Thomas DM, Douglass C, Meltzer PS, et al: Genome-wide association study identifies two susceptibility loci for osteosarcoma. Nat Genet. 2013, 45 (7): 799-803. 10.1038/ng.2645.PubMed CentralView ArticlePubMedGoogle Scholar
  8. Ottaviano L, Schaefer KL, Gajewski M, Huckenbeck W, Baldus S, Rogel U, Mackintosh C, de Alava E, Myklebost O, Kresse SH, Meza-Zepeda LA, Serra M, Cleton-Jansen AM, Hogendoorn PC, Buerger H, Aigner T, Gabbert HE, Poremba C: Molecular characterization of commonly used cell lines for bone tumor research: a trans-European EuroBoNet effort. Genes Chromosomes Cancer. 2010, 49 (1): 40-51. 10.1002/gcc.20717.View ArticlePubMedGoogle Scholar
  9. Kansara M, Tsang M, Kodjabachian L, Sims NA, Trivett MK, Ehrich M, Dobrovic A, Slavin J, Choong PF, Simmons PJ, Dawid IB, Thomas DM: Wnt inhibitory factor 1 is epigenetically silenced in human osteosarcoma, and targeted disruption accelerates osteosarcomagenesis in mice. J Clin Invest. 2009, 119 (4): 837-851. 10.1172/JCI37175.PubMed CentralView ArticlePubMedGoogle Scholar
  10. Märtson A, Kõks S, Reimann E, Prans E, Erm T, Maasalu K: Transcriptome analysis of osteosarcoma identifies suppression of wnt pathway and up-regulation of adiponectin as potential biomarker. Genomic Discovery. 2013, 1: 1-9. 10.7243/2052-7993-1-1.View ArticleGoogle Scholar
  11. Namlos HM, Meza-Zepeda LA, Baroy T, Ostensen IH, Kresse SH, Kuijjer ML, Serra M, Burger H, Cleton-Jansen AM, Myklebost O: Modulation of the osteosarcoma expression phenotype by microRNAs. PLoS One. 2012, 7 (10): e48086-10.1371/journal.pone.0048086.PubMed CentralView ArticlePubMedGoogle Scholar
  12. Hingorani P, Zhang W, Gorlick R, Kolb EA: Inhibition of Src phosphorylation alters metastatic potential of osteosarcoma in vitro but not in vivo. Clin Cancer Res. 2009, 15 (10): 3416-3422. 10.1158/1078-0432.CCR-08-1657.View ArticlePubMedGoogle Scholar
  13. Yap TA, Arkenau HT, Camidge DR, George S, Serkova NJ, Gwyther SJ, Spratlin JL, Lal R, Spicer J, Desouza NM, Leach MO, Chick J, Poondru S, Boinpally R, Gedrich R, Brock K, Stephens A, Eckhardt SG, Kaye SB, Demetri G, Scurr M: First-in-human phase I trial of two schedules of OSI-930, a novel multikinase inhibitor, incorporating translational proof-of-mechanism studies. Clin Cancer Res. 2013, 19 (4): 909-919. 10.1158/1078-0432.CCR-12-2258.View ArticlePubMedGoogle Scholar
  14. Lv Z, Wang C, Yuan T, Liu Y, Song T, Liu Y, Chen C, Yang M, Tang Z, Shi Q, Weng Y: Bone morphogenetic protein 9 regulates tumor growth of osteosarcoma cells through the Wnt/beta-catenin pathway. Oncol Rep. 2014, 31 (2): 989-994.PubMedGoogle Scholar
  15. Cai Y, Cai T, Chen Y: Wnt pathway in osteosarcoma, from oncogenic to therapeutic. J Cell Biochem. 2014, 115 (4): 625-631. 10.1002/jcb.24708.View ArticlePubMedGoogle Scholar
  16. Guo M, Cai C, Zhao G, Qiu X, Zhao H, Ma Q, Tian L, Li X, Hu Y, Liao B, Ma B, Fan Q: Hypoxia promotes migration and induces CXCR4 expression via HIF-1alpha activation in human osteosarcoma. PLoS One. 2014, 9 (3): e90518-10.1371/journal.pone.0090518.PubMed CentralView ArticlePubMedGoogle Scholar
  17. Raymond A, Ayala A, Knuutila S: Conventional osteosarcoma, genetics. 2002, IARC Press, LyonGoogle Scholar
  18. Lau CC, Harris CP, Lu XY, Perlaky L, Gogineni S, Chintagumpala M, Hicks J, Johnson ME, Davino NA, Huvos AG, Meyers PA, Healy JH, Gorlick R, Rao PH: Frequent amplification and rearrangement of chromosomal bands 6p12-p21 and 17p11.2 in osteosarcoma. Genes Chromosomes Cancer. 2004, 39 (1): 11-21. 10.1002/gcc.10291.View ArticlePubMedGoogle Scholar
  19. Kresse SH, Ohnstad HO, Paulsen EB, Bjerkehagen B, Szuhai K, Serra M, Schaefer KL, Myklebost O, Meza-Zepeda LA: LSAMP, a novel candidate tumor suppressor gene in human osteosarcomas, identified by array comparative genomic hybridization. Genes Chromosomes Cancer. 2009, 48 (8): 679-693. 10.1002/gcc.20675.View ArticlePubMedGoogle Scholar
  20. Pasic I, Shlien A, Durbin AD, Stavropoulos DJ, Baskin B, Ray PN, Novokmet A, Malkin D: Recurrent focal copy-number changes and loss of heterozygosity implicate two noncoding RNAs and one tumor suppressor gene at chromosome 3q13.31 in osteosarcoma. Cancer Res. 2010, 70 (1): 160-171. 10.1158/0008-5472.CAN-09-1902.View ArticlePubMedGoogle Scholar
  21. Tarkkanen M, Karhu R, Kallioniemi A, Elomaa I, Kivioja AH, Nevalainen J, Bohling T, Karaharju E, Hyytinen E, Knuutila S: Gains and losses of DNA sequences in osteosarcomas by comparative genomic hybridization. Cancer Res. 1995, 55 (6): 1334-1338.PubMedGoogle Scholar
  22. Tarkkanen M, Elomaa I, Blomqvist C, Kivioja AH, Kellokumpu-Lehtinen P, Bohling T, Valle J, Knuutila S: DNA sequence copy number increase at 8q: a potential new prognostic marker in high-grade osteosarcoma. Int J Cancer. 1999, 84 (2): 114-121. 10.1002/(SICI)1097-0215(19990420)84:2<114::AID-IJC4>3.0.CO;2-Q.View ArticlePubMedGoogle Scholar
  23. Overholtzer M, Rao PH, Favis R, Lu XY, Elowitz MB, Barany F, Ladanyi M, Gorlick R, Levine AJ: The presence of p53 mutations in human osteosarcomas correlates with high levels of genomic instability. Proc Natl Acad Sci U S A. 2003, 100 (20): 11547-11552. 10.1073/pnas.1934852100.PubMed CentralView ArticlePubMedGoogle Scholar
  24. Yen CC, Chen WM, Chen TH, Chen WY, Chen PC, Chiou HJ, Hung GY, Wu HT, Wei CJ, Shiau CY, Wu YC, Chao TC, Tzeng CH, Chen PM, Lin CH, Chen YJ, Fletcher JA: Identification of chromosomal aberrations associated with disease progression and a novel 3q13.31 deletion involving LSAMP gene in osteosarcoma. Int J Oncol. 2009, 35 (4): 775-788.PubMedGoogle Scholar
  25. Lu XY, Lu Y, Zhao YJ, Jaeweon K, Kang J, Xiao-Nan L, Ge G, Meyer R, Perlaky L, Hicks J, Chintagumpala M, Cai WW, Ladanyi M, Gorlick R, Lau CC, Pati D, Sheldon M, Rao PH: Cell cycle regulator gene CDC5L, a potential target for 6p12-p21 amplicon in osteosarcoma. MCR. 2008, 6 (6): 937-946. 10.1158/1541-7786.MCR-07-2115.PubMed CentralView ArticlePubMedGoogle Scholar
  26. Stephens PJ, Greenman CD, Fu B, Yang F, Bignell GR, Mudie LJ, Pleasance ED, Lau KW, Beare D, Stebbings LA, McLaren S, Lin ML, McBride DJ, Varela I, Nik-Zainal S, Leroy C, Jia M, Menzies A, Butler AP, Teague JW, Quail MA, Burton J, Swerdlow H, Carter NP, Morsberger LA, Iacobuzio-Donahue C, Follows GA, Green AR, Flanagan AM, Stratton MR, et al: Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell. 2011, 144 (1): 27-40. 10.1016/j.cell.2010.11.055.PubMed CentralView ArticlePubMedGoogle Scholar
  27. Wang K, Li M, Hakonarson H: ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010, 38 (16): e164-10.1093/nar/gkq603.PubMed CentralView ArticlePubMedGoogle Scholar
  28. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR: A method and server for predicting damaging missense mutations. Nat Methods. 2010, 7 (4): 248-249. 10.1038/nmeth0410-248.PubMed CentralView ArticlePubMedGoogle Scholar
  29. Gonzalez-Perez A, Lopez-Bigas N: Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score. Condel Am J Human Genetics. 2011, 88 (4): 440-449. 10.1016/j.ajhg.2011.03.004.View ArticleGoogle Scholar
  30. Kumar P, Henikoff S, Ng PC: Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009, 4 (7): 1073-1081. 10.1038/nprot.2009.86.View ArticlePubMedGoogle Scholar
  31. Blankenberg D, Von Kuster G, Coraor N, Ananda G, Lazarus R, Mangan M, Nekrutenko A, Taylor J: Galaxy: a web-based genome analysis tool for experimentalists. Current protocols in molecular biology/edited by Frederick M Ausubel [et al.] 2010, Chapter 19:Unit 19.10.1:1–21.,Google Scholar
  32. Giardine B, Riemer C, Hardison RC, Burhans R, Elnitski L, Shah P, Zhang Y, Blankenberg D, Albert I, Taylor J, Miller W, Kent WJ, Nekrutenko A: Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 2005, 15 (10): 1451-1455. 10.1101/gr.4086505.PubMed CentralView ArticlePubMedGoogle Scholar
  33. Yost SE, Pastorino S, Rozenzhak S, Smith EN, Chao YS, Jiang P, Kesari S, Frazer KA, Harismendy O: High-resolution mutational profiling suggests the genetic validity of glioblastoma patient-derived pre-clinical models. PLoS One. 2013, 8 (2): e56185-10.1371/journal.pone.0056185.PubMed CentralView ArticlePubMedGoogle Scholar
  34. Topper LM, Campbell MS, Tugendreich S, Daum JR, Burke DJ, Hieter P, Gorbsky GJ: The dephosphorylated form of the anaphase-promoting complex protein Cdc27/Apc3 concentrates on kinetochores and chromosome arms in mitosis. Cell Cycle. 2002, 1 (4): 282-292. 10.4161/cc.1.4.139.View ArticlePubMedGoogle Scholar
  35. Talvinen K, Karra H, Pitkanen R, Ahonen I, Nykanen M, Lintunen M, Soderstrom M, Kuopio T, Kronqvist P: Low cdc27 and high securin expression predict short survival for breast cancer patients. APMIS: acta pathologica, microbiologica, et immunologica Scandinavica. 2013, 121 (10): 945-953. 10.1111/apm.12110.View ArticlePubMedGoogle Scholar
  36. Zhao YG, Zhao H, Miao L, Wang L, Sun F, Zhang H: The p53-induced gene Ei24 is an essential component of the basal autophagy pathway. J Biol Chem. 2012, 287 (50): 42053-42063. 10.1074/jbc.M112.415968.PubMed CentralView ArticlePubMedGoogle Scholar
  37. Gu Z, Flemington C, Chittenden T, Zambetti GP: ei24, a p53 response gene involved in growth suppression and apoptosis. Mol Cell Biol. 2000, 20 (1): 233-241. 10.1128/MCB.20.1.233-241.2000.PubMed CentralView ArticlePubMedGoogle Scholar
  38. Zhao X, Ayer RE, Davis SL, Ames SJ, Florence B, Torchinsky C, Liou JS, Shen L, Spanjaard RA: Apoptosis factor EI24/PIG8 is a novel endoplasmic reticulum-localized Bcl-2-binding protein which is associated with suppression of breast cancer invasiveness. Cancer Res. 2005, 65 (6): 2125-2129. 10.1158/0008-5472.CAN-04-3377.View ArticlePubMedGoogle Scholar
  39. Joseph CG, Hwang H, Jiao Y, Wood LD, Kinde I, Wu J, Mandahl N, Luo J, Hruban RH, Diaz LA, He TC, Vogelstein B, Kinzler KW, Mertens F, Papadopoulos N: Exomic analysis of myxoid liposarcomas, synovial sarcomas, and osteosarcomas. Genes, chromosomes & cancer. 2014, 53 (1): 15-24. 10.1002/gcc.22114.View ArticleGoogle Scholar
  40. Firestein R, Cleary ML: Pseudo-phosphatase Sbf1 contains an N-terminal GEF homology domain that modulates its growth regulatory properties. J Cell Sci. 2001, 114 (Pt 16): 2921-2927.PubMedGoogle Scholar
  41. Booth NA, Bennett B, Wijngaards G, Grieve JH: A new life-long hemorrhagic disorder due to excess plasminogen activator. Blood. 1983, 61 (2): 267-275.PubMedGoogle Scholar
  42. Tarpey PS, Behjati S, Cooke SL, Van Loo P, Wedge DC, Pillay N, Marshall J, O’Meara S, Davies H, Nik-Zainal S, Beare D, Butler A, Gamble J, Hardy C, Hinton J, Jia MM, Jayakumar A, Jones D, Latimer C, Maddison M, Martin S, McLaren S, Menzies A, Mudie L, Raine K, Teague JW, Tubio JM, Halai D, Tirabosco R, Amary F, et al: Frequent mutation of the major cartilage collagen gene COL2A1 in chondrosarcoma. Nat Genet. 2013, 45 (8): 923-926. 10.1038/ng.2668.PubMed CentralView ArticlePubMedGoogle Scholar
  43. Ulaner GA, Vu TH, Li T, Hu JF, Yao XM, Yang Y, Gorlick R, Meyers P, Healey J, Ladanyi M, Hoffman AR: Loss of imprinting of IGF2 and H19 in osteosarcoma is accompanied by reciprocal methylation changes of a CTCF-binding site. Hum Mol Genet. 2003, 12 (5): 535-549. 10.1093/hmg/ddg034.View ArticlePubMedGoogle Scholar
  44. Pant V, Jambhekar NA, Madur B, Shet TM, Agarwal M, Puri A, Gujral S, Banavali M, Arora B: Anaplastic large cell lymphoma (ALCL) presenting as primary bone and soft tissue sarcoma—a study of 12 cases. Ind J Pathology & Microbiology. 2007, 50 (2): 303-307.Google Scholar
  45. Choy E, Hornicek F, MacConaill L, Harmon D, Tariq Z, Garraway L, Duan Z: High-throughput genotyping in osteosarcoma identifies multiple mutations in phosphoinositide-3-kinase and other oncogenes. Cancer. 2012, 118 (11): 2905-2914. 10.1002/cncr.26617.PubMed CentralView ArticlePubMedGoogle Scholar
  46. Tirabosco R, Berisha F, Ye H, Halai D, Amary MF, Flanagan AM: Assessment of MUC4 expression in primary bone tumours. Histopathology. 2013, 63 (1): 142-143. 10.1111/his.12134.View ArticlePubMedGoogle Scholar
  47. Flores RJ, Li Y, Yu A, Shen J, Rao PH, Lau SS, Vannucci M, Lau CC, Man TK: A systems biology approach reveals common metastatic pathways in osteosarcoma. BMC Syst Biol. 2012, 6: 50-10.1186/1752-0509-6-50.PubMed CentralView ArticlePubMedGoogle Scholar
  48. Ma Y, Ren Y, Han EQ, Li H, Chen D, Jacobs JJ, Gitelis S, O’Keefe RJ, Konttinen YT, Yin G, Li TF: Inhibition of the Wnt-beta-catenin and Notch signalling pathways sensitizes osteosarcoma cells to chemotherapy. Biochem Biophys Res Commun. 2013, 431 (2): 274-279. 10.1016/j.bbrc.2012.12.118.PubMed CentralView ArticlePubMedGoogle Scholar
  49. Leow PC, Tian Q, Ong ZY, Yang Z, Ee PL: Antitumor activity of natural compounds, curcumin and p KF118–310, as Wnt/beta-catenin antagonists against human osteosarcoma cells. Investig New Drugs. 2010, 28 (6): 766-782. 10.1007/s10637-009-9311-z.View ArticleGoogle Scholar
  50. Cleton-Jansen AM, Anninga JK, Briaire-de Bruijn IH, Romeo S, Oosting J, Egeler RM, Gelderblom H, Taminiau AH, Hogendoorn PC: Profiling of high-grade central osteosarcoma and its putative progenitor cells identifies tumourigenic pathways. Br J Cancer. 2009, 101 (12): 2064-10.1038/sj.bjc.6605482.PubMed CentralView ArticlePubMedGoogle Scholar
  51. Cai Y, Mohseny AB, Karperien M, Hogendoorn PC, Zhou G, Cleton-Jansen AM: Inactive Wnt/beta-catenin pathway in conventional high-grade osteosarcoma. J Pathol. 2010, 220 (1): 24-33. 10.1002/path.2628.View ArticlePubMedGoogle Scholar
  52. Van Bezooijen RL, Papapoulos SE, Lowik CW: Effect of interleukin-17 on nitric oxide production and osteoclastic bone resorption: is there dependency on nuclear factor-kappaB and receptor activator of nuclear factor kappaB (RANK)/RANK ligand signalling?. Bone. 2001, 28 (4): 378-386. 10.1016/S8756-3282(00)00457-9.View ArticlePubMedGoogle Scholar
  53. Avnet S, Longhi A, Salerno M, Halleen JM, Perut F, Granchi D, Ferrari S, Bertoni F, Giunti A, Baldini N: Increased osteoclast activity is associated with aggressiveness of osteosarcoma. Int J Oncol. 2008, 33 (6): 1231-1238.PubMedGoogle Scholar
  54. Vaira S, Alhawagri M, Anwisye I, Kitaura H, Faccio R, Novack DV: RelA/p65 promotes osteoclast differentiation by blocking a RANKL-induced apoptotic JNK pathway in mice. J Clin Invest. 2008, 118 (6): 2088-2097.PubMed CentralPubMedGoogle Scholar
  55. Lee J, Pilch PF: The insulin receptor: structure, function, and signalling. Am J Physiol. 1994, 266 (2 Pt 1): C319-C334.PubMedGoogle Scholar
  56. Kim SY, Toretsky JA, Scher D, Helman LJ: The role of IGF-1R in pediatric malignancies. Oncologist. 2009, 14 (1): 83-91. 10.1634/theoncologist.2008-0189.PubMed CentralView ArticlePubMedGoogle Scholar
  57. Canalis E, McCarthy T, Centrella M: Growth factors and the regulation of bone remodeling. J Clin Invest. 1988, 81 (2): 277-281. 10.1172/JCI113318.PubMed CentralView ArticlePubMedGoogle Scholar
  58. Dearth RK, Cui X, Kim HJ, Kuiatse I, Lawrence NA, Zhang X, Divisova J, Britton OL, Mohsin S, Allred DC, Hadsell DL, Lee AV: Mammary tumorigenesis and metastasis caused by overexpression of insulin receptor substrate 1 (IRS-1) or IRS-2. Mol Cell Biol. 2006, 26 (24): 9302-9314. 10.1128/MCB.00260-06.PubMed CentralView ArticlePubMedGoogle Scholar
  59. Sanz-Pamplona R, Garcia-Garcia J, Franco S, Messeguer X, Driouch K, Oliva B, Sierra A: A taxonomy of organ-specific breast cancer metastases based on a protein-protein interaction network. Mol BioSyst. 2012, 8 (8): 2085-2096. 10.1039/c2mb25104c.View ArticlePubMedGoogle Scholar
  60. Kim MS, Chung NG, Kim MS, Yoo NJ, Lee SH: Somatic mutation of IL7R exon 6 in acute leukemias and solid cancers. Hum Pathol. 2013, 44 (4): 551-555. 10.1016/j.humpath.2012.06.017.View ArticlePubMedGoogle Scholar

Copyright

© Reimann et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.

Advertisement