Oncotarget

Meta-Analysis:

Genetic susceptibility to bone and soft tissue sarcomas: a field synopsis and meta-analysis

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Oncotarget. 2018; 9:18607-18626. https://doi.org/10.18632/oncotarget.24719

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Clara Benna _, Andrea Simioni, Sandro Pasquali, Davide De Boni, Senthilkumar Rajendran, Giovanna Spiro, Chiara Colombo, Calogero Virgone, Steven G. DuBois, Alessandro Gronchi, Carlo Riccardo Rossi and Simone Mocellin

Abstract

Clara Benna1,2, Andrea Simioni1, Sandro Pasquali1,4, Davide De Boni1, Senthilkumar Rajendran1, Giovanna Spiro1, Chiara Colombo4, Calogero Virgone5, Steven G. DuBois6, Alessandro Gronchi4, Carlo Riccardo Rossi1,3 and Simone Mocellin1,3

1Department of Surgery Oncology and Gastroenterology, University of Padova, Padova, Italy

2Clinica Chirurgica I, Azienda Ospedaliera Padova, Padova, Italy

3Surgical Oncology Unit, Istituto Oncologico Veneto (IOV-IRCCS), Padova, Italy

4Sarcoma Service, Department of Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy

5Pediatric Surgery, Department of Women's and Children's Health, University of Padua, Padua, Italy

6Department of Pediatric Hematology/Oncology, Dana-Farber/Boston Children's Cancer and Blood Disorders Center and Harvard Medical School, Boston, MA, USA

Correspondence to:

Clara Benna, email: clara.benna@unipd.it

Keywords: sarcoma; SNP; meta-analysis; polymorphisms; risk

Received: January 24, 2018     Accepted: March 07, 2018     Published: April 06, 2018

ABSTRACT

Background: The genetic architecture of bone and soft tissue sarcomas susceptibility is yet to be elucidated. We aimed to comprehensively collect and meta-analyze the current knowledge on genetic susceptibility in these rare tumors.

Methods: We conducted a systematic review and meta-analysis of the evidence on the association between DNA variation and risk of developing sarcomas through searching PubMed, The Cochrane Library, Scopus and Web of Science databases. To evaluate result credibility, summary evidence was graded according to the Venice criteria and false positive report probability (FPRP) was calculated to further validate result noteworthiness. Integrative analysis of genetic and eQTL (expression quantitative trait locus) data was coupled with network and pathway analysis to explore the hypothesis that specific cell functions are involved in sarcoma predisposition.

Results: We retrieved 90 eligible studies comprising 47,796 subjects (cases: 14,358, 30%) and investigating 1,126 polymorphisms involving 320 distinct genes. Meta-analysis identified 55 single nucleotide polymorphisms (SNPs) significantly associated with disease risk with a high (N=9), moderate (N=38) and low (N=8) level of evidence, findings being classified as noteworthy basically only when the level of evidence was high. The estimated joint population attributable risk for three independent SNPs (rs11599754 of ZNF365/EGR2, rs231775 of CTLA4, and rs454006 of PRKCG) was 37.2%. We also identified 53 SNPs significantly associated with sarcoma risk based on single studies.

Pathway analysis enabled us to propose that sarcoma predisposition might be linked especially to germline variation of genes whose products are involved in the function of the DNA repair machinery.

Conclusions: We built the first knowledgebase on the evidence linking DNA variation to sarcomas susceptibility, which can be used to generate mechanistic hypotheses and inform future studies in this field of oncology.


Genetic susceptibility to bone and soft tissue sarcomas: a field synopsis and meta-analysis | Benna | Oncotarget

INTRODUCTION

Sarcomas are a family of rare malignant tumors arising from bone and soft tissues with more than 50 different histologies accounting for about 1-2% of cancers in adults and 15-20% in children (worldwide incidence: approximately 200,000 cases per year). The pathogenesis of sarcomas is multifactorial including environmental (such as exposure to ionizing radiations or chemical carcinogens) and genetic components, although the disease rarity represents an objective hurdle to the research in this field of investigation. Significant advances have been made in the understanding of the acquired genetic events leading to sarcomagenesis. It has been recognized that three types of somatic DNA alterations, translocations, mutations, and copy number variations, play a key role in these tumors [1]. As a consequence, sarcomas are grouped into two categories: balanced translocation associated sarcomas (BATS) and complex genotype/karyotype sarcomas (CGKS), which are characterized by a stable genome and genomic instability, respectively [2]. A potential therapeutic implication of such genetic taxonomy classification is that some recurrent chromosomal translocations might be exploited for the development of drugs targeting the protein products of fusion oncogenes [1].

Conversely, knowledge on the role of germline DNA variations in sarcomagenesis is sparse and limited. Although a minority of sarcomas arise within well characterized heritable cancer predisposition syndromes (e.g., osteosarcoma and Bloom syndrome, desmoid tumors and familial adenomatous polyposis) [3], the vast majority of sarcomas occur sporadically and the role of the genetic background in their pathogenesis is to be uncovered. Recent advances in molecular high-throughput technology, which conduct of genome wide association studies (GWAS), is accelerating the pace of discovery of sarcoma predisposition loci.

Looking at the already existing international literature, some investigators have meta-analyzed the evidence regarding a handful of SNPs such as XRCC3 rs861539 [4], MDM2 rs2279744 [5, 6], and CTLA4 rs231775 [7]: however, to the best of our knowledge no comprehensive collection of the available data in this field of oncology has been published thus far.

With the present work we systematically reviewed and meta-analyzed the available evidence in this field in order to: 1) provide readers with the first knowledgebase dedicated to the relationship between germline DNA variation and sarcoma risk; 2) identify areas lacking of meaningful information thus helping to inform future studies; and 3) suggest a biological interpretation of current findings utilizing network and pathway analysis [8] after integrating multiple sources of biological data [9].

RESULTS

Characteristics of the eligible studies

We identified 90 eligible articles, comprising 47,796 subjects, 14,358 cases and 33,438 controls. The details of the literature search are summarized in Figure 1.

Flow diagram summarizing the search strategy and the study selection process.

Figure 1: Flow diagram summarizing the search strategy and the study selection process.

Based on the prevalent ancestry (ie. the race of at least 80% of the enrolled subjects) the majority of the studies were Asian (N=57 studies) the rest being Caucasian (N=25 studies), or mixed (N=8 studies). Based on study design, half of included studies were population based case-controls studies (N=40 studies), the remaining were hospital based (N=39 studies), with a few (N=11) being mixed or not specified. Two studies were GWAS [10, 11].

According to histology, the majority of the eligible studies investigated bone tumors (N=65) and the remaining investigated Ewing’s sarcoma (N=9), soft tissue sarcomas (N=6), chordoma (N=4), hemangiosarcoma (N=1), and mixed sarcomas (N=5). Thirteen studies investigated pediatric subjects or young adults. Although pediatric/young age ranged from 0 to 35 years old in eligible studies, most of the studies considered subjects < 20 years old.

We evaluated the included studies following the criteria of the Newcastle-Ottawa scale (NOS) scoring system. The mean score was 7.8. The main features of all the eligible studies and the NOS score are available on Table 1.

Table 1: Characteristics of the included studies and Newcastle-Ottawa quality assessment (NOS) evaluation

Included articles references

Subjects characteristics

NOS

First Author

Journal

Year

Cancer Type

Cases

Controls

Age

Ethnicity

Source of Controls

NOS 123

NOS [0–9]

Adiguzel M. [12]

Indian J Exp Biol

2016

Bone tumors

54

81

Adult

Caucasian

Population

413

8

Alhopuro P. [13]

J Med Genet

2005

Soft tissue sarcoma

68

185

Adult

Caucasian

Population

413

8

Almeida PSR. [14]

Genet Mol Res

2008

Soft tissue sarcoma

100

85

Adult

Mixed

not specified

213

6

Aoyama T. [15]

Cancer Letters

2002

Bone tumors

38

72

Adult

Asian

Population

313

7

Barnette P. [16]

Cancer Epidemiol Biomarkers Prev

2004

Mixed

42

326

Pediat/Young

Caucasian

Population

323

8

Biason P. [17]

Pharmacogenomics J

2012

Bone tumors

130

250

Adult

Caucasian

Hospital

323

8

Bilbao-Aldaiturriaga N. [18]

Pediatr Blood Cancer

2015

Bone tumors

99

387

Pediat/Young

Caucasian

Hospital

323

8

Chen Y. [19]

Tumor Biol

2016

Bone tumors

190

190

Adult

Asian

Hospital

323

8

Cong Y. [20]

Tumor Biol

2015

Bone tumors

203

406

Adult

Asian

Hospital

323

8

Cui Y. [21]

Biomarkers

2016

Bone tumors

251

251

Adult

Asian

Hospital

323

8

Cui Y. [22]

Tumor Biol

2016

Bone tumors

260

260

Adult

Asian

Hospital

323

8

Dong YZ. [23]

Genet Mol Res

2015

Bone tumors

185

201

Adult

Asian

Hospital

323

8

DuBois SG. [24]

Pediatr Blood Cancer

2011

Ewing's sarcoma

135

200

Pediat/Young

Caucasian

Hospital

213

6

Ergen A. [25]

Mol Biol Rep

2011

Bone tumors

50

50

Adult

Caucasian

not specified

313

7

Feng D. [26]

Genet Test Mol Biomarkers

2013

Ewing's sarcoma

308

362

Adult

Asian

Hospital

323

8

Gloudemans T. [27]

Cancer Res

1993

Soft tissue sarcoma

9

26

Adult

Caucasian

Population

303

6

Grochola LF. [28]

Clin Cancer Res

2009

Soft tissue sarcoma

130

497

Adult

Caucasian

Population

313

7

Grünewald TG. [29]

Nat Genet

2015

Ewing's sarcoma

343

251

Adult

Caucasian

Population

423

9

Guo J. [30]

Genet Mol Res

2015

Bone tumors

136

136

Adult

Asian

Hospital

313

7

He J. [31]

Endocr J

2013

Bone tumors

415

431

Adult

Asian

Hospital

323

8

He J. [32]

Endocrine

2014

Bone tumors

415

431

Adult

Asian

Hospital

323

8

He M. [33]

Tumor Biol

2014

Bone tumors

189

195

Adult

Asian

Hospital

323

8

He ML. [34]

Asian Pac J Cancer Prev

2013

Bone tumors

59

63

Adult

Asian

Hospital

313

7

He Y. [35]

Int Orthop

2014

Bone tumors

120

120

Adult

Asian

Hospital

323

8

Hu GL. [36]

Genet Mol Res

2015

Bone tumors

130

130

Adult

Asian

Hospital

323

8

Hu YS. [37]

BMC Cancer

2010

Bone tumors

168

168

Adult

Asian

Population

423

9

Hu YS. [38]

Med Oncol

2011

Bone tumors

168

168

Adult

Asian

Population

423

9

Hu Z. [39]

Genet Test Mol Biomarkers

2015

Bone tumors

368

370

Adult

Asian

not specified

213

6

Ito M. [40]

Clin Cancer Res

2010

Soft tissue sarcoma

155

37

Adult

Mixed

Hospital

203

5

Jiang C. [41]

Med Oncol

2014

Bone tumors

168

216

Adult

Asian

Hospital

323

8

Kelley MJ. [42]

Hum Genet

2014

Chordoma

103

160

Adult

Asian

Population

413

8

Koshkina NV. [43]

J Pediatr Hematol Oncol

2007

Bone tumors

123

510

Pediat/Young

Mixed

Population

413

8

Le Morvan V. [44]

Int J Cancer

2006

Mixed

93

53

Adult

Caucasian

Population

403

7

Li L. [45]

Genet Mol Res

2015

Bone tumors

52

100

Adult

Asian

Hospital

312

6

Liu Y. [46]

DNA Cell Biol

2011

Bone tumors

267

282

Adult

Asian

Population

313

7

Liu Y. [47]

PloSONE

2012

Bone tumors

326

433

Adult

Asian

Population

423

9

Lu H. [48]

Tumor Biol

2015

Bone tumors

388

388

Adult

Asian

Hospital

323

8

Lu XF. [49]

Asian Pac J Cancer Prev

2011

Bone tumors

110

226

Adult

Asian

Hospital

313

7

Lv H. [50]

Mol Med Rep

2014

Bone tumors

103

201

Adult

Asian

Hospital

213

6

Ma X. [51]

Genet Mol Res

2016

Bone tumors

141

282

Adult

Asian

Hospital

223

7

Martinelli M. [52]

Oncotarget

2016

Ewing's sarcoma

100

147

Pediat/Young

Caucasian

Population

423

9

Mei JW. [99]

Int J Clin Exp Pathol

2016

Bone tumors

97

120

Adult

Asian

Population

313

7

Miao C.[53]

Sci Rep

2015

Soft tissue sarcoma

138

131

Adult

Asian

Hospital

223

7

Mirabello L. [54]

Carcinogenesis

2010

Bone tumors

99

1430

Adult

Caucasian

mixed

323

8

Mirabello L. [55]

BMC Cancer

2011

Bone tumors

96

1426

Adult

Caucasian

mixed

323

8

Nakayama R. [56]

Cancer Sci

2008

Mixed

544

1378

Adult

Asian

mixed

323

8

Naumov VA. [57]

Bull Exp Biol Med

2012

Bone tumors

68

96

Adult

Caucasian

not specified

313

7

Oliveira ID. [58]

J Pediatr Hematol Oncol

2007

Bone tumors

80

160

Pediat/Young

Mixed

Hospital

323

8

Ozger H. [59]

Folia Biologica (Praha)

2008

Mixed

56

44

Adult

Caucasian

Population

403

7

Patino-Garcia A. [60]

J Med Genet

2000

Bone tumors

110

111

Pediat/Young

Caucasian

not specified

323

8

Pillay N. [61]

Nat Genet

2012

Chordoma

40

358

Adult

Caucasian

population

323

8

Postel-Vinay S. [10]

Nat Genet

2012

Ewing's sarcoma

401

4352

Adult

Caucasian

population

423

9

Qi Y. [62]

Tumor Biol

2016

Bone tumors

206

206

Adult

Asian

Hospital

323

8

Qu WR. [63]

Genetic Mol Res

2016

Bone tumors

153

252

Adult

Asian

Hospital

323

8

Ru JY. [64]

Int J Clin Exp Pathol

2015

Bone tumors

210

420

Adult

Asian

Hospital

323

8

Ruza E. [65]

J Pediatr Hematol Oncol

2003

Mixed

125

143

Pediat/Young

Caucasian

not specified

322

7

Saito T. [66]

Int J Cancer

2000

Hemangiosarcoma

22

84

Adult

Mixed

Population

213

6

Salinas-Souza C. [67]

Pharmacogenet Genomics

2010

Bone tumors

80

160

Pediat/Young

Mixed

Hospital

323

8

Savage SA. [68]

Cancer Epidemiol Biomarkers Prev

2007

Bone tumors

104

74

Pediat/Young

Caucasian

Hospital

213

6

Savage SA. [69]

Pediatr Blood Cancer

2007

Bone tumors

104

74

Pediat/Young

Caucasian

Hospital

213

6

Savage SA. [11]

Nat Genet

2013

Bone tumors

941

3291

Adult

Caucasian

Population

423

9

Shi ZW. [70]

Cancer Biomark

2016

Bone tumors

174

150

Adult

Asian

Hospital

313

7

Silva DS. [71]

Gene

2012

Ewing's sarcoma

24

200

Adult

Mixed

Population

323

8

Tang YJ. [72]

Medicine

2014

Bone tumors

160

250

Adult

Asian

Population

423

9

Thurow HS. [73]

Mol Biol Rep

2013

Ewing's sarcoma

24

91

Adult

Mixed

Population

323

8

Tian Q. [74]

Eur J Surg Oncol

2013

Bone tumors

133

133

Adult

Asian

Population

423

9

Tie Z. [75]

Int J Clin Exp Pathol

2014

Bone tumors

165

330

Adult

Asian

Population

423

9

Toffoli G. [76]

Clin Cancer Res

2009

Bone tumors

201

250

Adult

Caucasian

Population

423

9

Walsh KM. [77]

Carcinogenesis

2016

Bone tumors

660

6892

Pediat/Young

Caucasian

Population

423

9

Wang J. [78]

DNA Cell Biol

2012

Ewing's sarcoma

158

212

Adult

Asian

Population

323

8

Wang J. [79]

DNA Cell Biol

2013

Bone tumors

106

210

Adult

Asian

Population

323

8

Wang K. [80]

Biomed Rep

2014

Chordoma

65

65

Adult

Asian

Population

313

7

Wang K. [81]

Tumor Biol

2016

Bone tumors

126

168

Adult

Asian

Hospital

323

8

Wang W. [82]

DNA Cell Biol

2011

Bone tumors

205

216

Adult

Asian

Hospital

323

8

Wang W. [83]

Genet Test Mol Biomarkers

2011

Bone tumors

205

215

Adult

Asian

Hospital

323

8

Wang Z. [84]

Tumor Biol

2014

Bone tumors

330

342

Adult

Asian

Population

423

9

Wu Y. [85]

Tumor Biol

2015

Bone tumors

124

136

Adult

Asian

Hospital

323

8

Wu Z. [86]

Int J Mol Sci

2013

Chordoma

65

120

Adult

Asian

not specified

313

7

Xin DJ. [87]

Int J Clin Exp Pathol

2015

Bone tumors

90

100

Adult

Asian

Population

413

8

Xu H. [88]

Med Sci Monit

2016

Bone tumors

279

286

Pediat/Young

Asian

Hospital

323

8

Xu S. [89]

DNA Cell Biol

2014

Bone tumors

202

216

Adult

Asian

Population

423

9

Yang L. [90]

Int J Clin Exp Pathol

2015

Bone tumors

152

304

Adult

Asian

Population

423

9

Yang S. [91]

Genet Test Mol Biomarkers

2012

Ewing's sarcoma

223

302

Adult

Asian

Population

423

9

Yang W. [92]

Med Oncol

2014

Bone tumors

118

126

Adult

Asian

not specified

323

8

Zhang G. [93]

Genet Mol Res

2015

Bone tumors

180

360

Adult

Asian

Population

423

9

Zhang HF. [94]

Genet Mol Res

2015

Bone tumors

182

182

Adult

Asian

Population

423

9

Zhang N. [95]

Onco Targets Ther

2016

Bone tumors

276

286

Adult

Asian

Hospital

323

8

Zhang Y. [96]

Tumor Biol

2014

Bone tumors

610

610

Adult

Asian

Population

423

9

Zhao J. [97]

BioMed Res Int

2014

Bone tumors

247

428

Adult

Asian

Population

423

9

Zhi LQ. [98]

Tumor Biol

2014

Bone tumors

212

240

Adult

Asian

Hospital

323

8

NOS: Newcastle-Ottawa quality assessment scale evaluation (0-9). NOS1: selection of the study groups (0-4); NOS2: comparability of the groups (0-2); NOS3: ascertainment of the exposure or outcome (0-3).

Characteristics of the retrieved genetic variants

Overall, data on 1,126 polymorphisms involving 320 genes were retrieved. Variations were mainly SNPs, only six being insertion/deletions of more than one nucleotide. Based on the number of different genetic variations studied, the 11 most studied genes were the following: EGR2 (179 different SNPs), ADO (58 different SNPs), ZNF365 (40 different SNPs), TRAPPC9 (28 different SNPs), CASC8 (23 different SNPs), CD99 (20 different SNPs), EWSR1 (16 different SNPs) TP53, HSD17B2 (15 different SNPs each) and UGT1A8, LOC107984012 (12 different SNPs each).

Thirty-seven of these genetic variants were located no more than 2kb upstream the relevant gene, ten no more than 500bp downstream the relevant gene, 493 in introns, 100 in exons (non-UTRs), 19 in the 3’-UTR, seven in the 5’-UTR. Moreover, 413 SNPs were located in intergenic regions more than 2kb upstream or more than 500 bp downstream the relevant gene and 41 in non-coding transcripts. Among the exonic SNPs, 63 had a missense functional effect, while 37 were synonymous. Detailed information on all SNPs is reported in Supplementary Table 1.

Meta-analysis findings

At least two independent datasets were available for 51 genetic variations allowing us to perform 118 meta-analyses, 16 of them were histology-based meta-analysis on osteosarcoma and Ewing’s sarcoma. Moreover, 13 sensitivity analysis were performed considering the ethnicity of the different datasets. The results of data meta-analyses are comprehensively reported in Supplementary Table 2. Polymorphism “rs” identifier, nucleotide change and amino acid change are reported in Supplementary Table 3.

The eight most studied genetic variants were the following: TP53 rs1042522 (6 datasets), VEGF rs3025039 and GSTM1 deletion (5 datasets each), CTLA4 rs231775, CTLA4 rs5742909, MDM2 rs2279744, rs10434 VEGF and GSTT1 deletion (4 datasets each).

The number of subject (cases plus controls) enrolled in the 118 meta-analyses ranged from 144 to 5,347 (median: 1,195). Based on the number of subjects, the 10 most studied genetic variants, all with 5,347 subjects, were the following: EGR2 rs224292 and rs224278, ADO rs1848797 and rs1509966, MDM2 rs1690916, LOC107984012 rs9633562, rs944684 and rs6479860, ZNF365 rs11599754 and rs10761660.

Of the 118 meta-analyses and 13 sensitivity analysis (131 total analyses) performed, 55 resulted to be statistically significant (P-value <0.05). The level of summary evidence, among the significant associations identified by meta-analysis, was high, intermediate, and low in 9, 38, and 8 analyses respectively. The most frequent single cause of non-high-quality level of evidence was between-study heterogeneity followed by the small sample size. Considering all statistically significant meta-analyses FPRP was optimal (<0.2) at least at the 10E3 level for 10/55 analysis, 9 of them with high level of summary evidence.

The details of significant associations are reported in Table 2.

Table 2: Meta-analysis results: genetic variants significantly associated with sarcoma risk

SNP ID

Genes

Analysis

Model

Sarcoma type

data
sets

Meta-analysis Ethnicity

OR [95% CI]

I 2 %

P value

Cases

Controls

Ref/ Alt

Venice Criteria

FPRP (E-03)

Level of Evidence

rs11599754

ZNF365, ADO

primary

Per allele

Ewing's

2

Caucasian

1.48 [1.32, 1.66]

0

<0.00001

744

4603

T/C

AAA

Y

HIGH

rs1509966

ADO, EGR2

primary

Per allele

Ewing's

2

Caucasian

1.58 [1.42, 1.77]

0

<0.00001

744

4603

A/G

AAA

Y

HIGH

rs1848797

ADO, EGR2

primary

Per allele

Ewing's

2

Caucasian

1.57 [1.4, 1.77]

0

<0.00001

744

4603

G/A

AAA

Y

HIGH

rs224278

EGR2

primary

Per allele

Ewing's

2

Caucasian

1.73 [1.49, 2.02]

0

<0.00001

744

4603

T/C

AAA

Y

HIGH

rs9633562

EGR2, LOC107984012

primary

Per allele

Ewing's

2

Caucasian

1.46 [1.29, 1.65]

0

<0.00001

744

4603

A/C

AAA

Y

HIGH

rs10761660

ADO, EGR2

primary

Per allele

Ewing's

2

Caucasian

1.39 [1.21, 1.6]

0

<0.00001

744

4603

T/C

AAA

Y

HIGH

rs224292

ADO, EGR2

primary

Per allele

Ewing's

2

Caucasian

1.67 [1.42, 1.96]

0

<0.00001

744

4603

A/G

AAA

Y

HIGH

rs231775

CTLA4

primary

Per allele

Mixed

4

Asian

1.36 [1.2, 1.54]

0

<0.00001

1003

1162

G/A

AAA

Y

HIGH

rs454006

PRKCG

primary

Per allele

Osteo

2

Asian

1.35 [1.18, 1.54]

0

<0.0001

998

998

T/C

AAA

Y

HIGH

rs944684

LOC107984012

primary

Per allele

Ewing's

2

Caucasian

1.73 [1.4, 2.14]

49

<0.00001

744

4603

C/T

ABA

Y

INTERM

rs2305089

T

sensitivity

Per allele

Chordoma

2

Caucasian

3.91 [2.4, 6.38]

47

<0.00001

163

881

G/A

ABA

N

INTERM

rs1042522

TP53

primary

Dominant

Mixed

6

Mixed

0.67 [0.53, 0.84]

0

0.0007

788

950

G/C

AAA

N

INTERM

rs1042522

TP53

subgroup

Dominant

Osteo

3

Mixed

0.6 [0.43, 0.84]

15

0.002

509

737

G/C

AAA

N

INTERM

rs1129055

CD86

primary

Recessive

Mixed

2

Asian

0.6 [0.41, 0.88]

0

0.008

363

428

A/G

BAA

N

INTERM

rs11737764

NUDT6

primary

Dominant

Bone tumor

2

Caucasian

2.12 [1.34, 3.37]

0

0.001

164

1522

A/C

AAA

N

INTERM

rs1690916

MDM2

primary

Per allele

Ewing's

2

Caucasian

0.62 [0.46, 0.83]

0

0.001

164

1522

C/T

AAA

N

INTERM

rs17206779

ADAMTS6

primary

Per allele

Osteo

2

Mixed

0.79 [0.67, 0.93]

35

0.004

1109

3507

C/T

ABA

N

INTERM

rs17655

ERCC5

primary

Recessive

Mixed

2

Caucasian

2.04 [1.07, 3.9]

0

0.03

223

515

G/C

BAA

N

INTERM

rs1799793

ERCC2

primary

Per allele

Osteo

2

Mixed

0.75 [0.58, 0.97]

23

0.03

271

532

G/A

BAA

N

INTERM

rs1799793

ERCC2

primary

Dominant

Osteo

2

Mixed

0.63 [0.44, 0.89]

0

0.009

271

532

G/A

BAA

N

INTERM

rs1800896

IL10

primary

Per allele

Osteo

2

Mixed

1.33 [1.06,1.66]

0

0.01

340

420

A/G

BAA

N

INTERM

rs1906953

GRM4

sensitivity

Per allele

Osteo

2

Asian

0.68 [0.55, 0.84]

0

0.0004

294

384

G/A

BAA

N

INTERM

rs2279744

MDM2

primary

Per allele

Mixed

4

Mixed

1.36 [1.06, 1.76]

26

0.02

448

563

T/G

ABA

N

INTERM

rs2279744

MDM2

primary

Recessive

Mixed

4

Mixed

1.58 [1.03, 2.42]

20

0.04

448

563

T/G

AAA

N

INTERM

rs2279744

MDM2

primary

Dominant

Mixed

4

Mixed

1.55 [1.05, 2.29]

36

0.03

448

563

T/G

ABA

N

INTERM

rs231775

CTLA4

primary

Recessive

Mixed

4

Asian

2 [1.53, 2.62]

0

<0.00001

1003

1162

G/A

AAA

N

INTERM

rs231775

CTLA4

primary

Dominant

Mixed

4

Asian

1.35 [1.14, 1.61]

0

0.0007

1003

1162

G/A

AAA

N

INTERM

rs231775

CTLA4

subgroup

Per allele

Ewing's

2

Asian

1.36 [1.15, 1.61]

0

0.0003

531

664

G/A

AAA

N

INTERM

rs231775

CTLA4

subgroup

Recessive

Ewing's

2

Asian

2 [1.39, 2.89]

0

0.0002

531

664

G/A

AAA

N

INTERM

rs231775

CTLA4

subgroup

Dominant

Ewing's

2

Asian

1.36 [1.07, 1.72]

0

0.01

531

664

G/A

AAA

N

INTERM

rs231775

CTLA4

subgroup

Per allele

Osteo

2

Asian

1.36 [1.13, 1.64]

0

0.001

472

498

G/A

ABA

N

INTERM

rs231775

CTLA4

subgroup

Recessive

Osteo

2

Asian

2 [1.34, 2.98]

0

0.0007

472

498

G/A

ABA

N

INTERM

rs231775

CTLA4

subgroup

Dominant

Osteo

2

Asian

1.35 [1.04, 1.75]

0

0.02

472

498

G/A

ABA

N

INTERM

rs3025039

VEGFA

primary

Per allele

Osteo

5

Asian

1.28 [1.12, 1.47]

0

0.0004

987

1344

C/T

AAA

N

INTERM

rs3025039

VEGFA

primary

Recessive

Osteo

5

Asian

1.65 [1.19, 2.27]

6

0.002

987

1344

C/T

AAA

N

INTERM

rs3025039

VEGFA

primary

Dominant

Osteo

5

Asian

1.24 [1.04, 1.47]

0

0.02

987

1344

C/T

AAA

N

INTERM

rs454006

PRKCG

primary

Recessive

Osteo

2

Asian

1.99 [1.54, 2.58]

0

<0.0001

998

998

T/C

AAA

N

INTERM

rs6599400

FGFR3

primary

Per allele

Osteo

2

Caucasian

1.53 [1.19, 1.97]

0

0.001

164

1522

C/A

AAA

N

INTERM

rs699947

VEGFA

primary

Per allele

Osteo

2

Asian

1.46 [1.19, 1.79]

0

0.0003

347

512

C/A

BAA

N

INTERM

rs699947

VEGFA

primary

Recessive

Osteo

2

Asian

1.73 [1.17, 2.55]

0

0.006

347

512

C/A

BAA

N

INTERM

rs699947

VEGFA

primary

Dominant

Osteo

2

Asian

1.51 [1.14, 2]

0

0.004

347

512

C/A

BAA

N

INTERM

rs820196

RECQL5

primary

Recessive

Osteo

2

Asian

2.15 [1.41, 3.29]

0

0.0004

397

441

T/C

BAA

N

INTERM

rs820196

RECQL5

primary

Dominant

Osteo

2

Asian

1.49 [1.12, 1.98]

0

0.006

397

441

T/C

BAA

N

INTERM

rs861539

XRCC3, KLC1

primary

Per allele

Osteo

2

Asian

1.57 [1.25, 1.97]

0

0.0001

288

440

C/T

BAA

N

INTERM

rs861539

XRCC3, KLC1

primary

Recessive

Osteo

2

Asian

2.23 [1.4, 3.57]

0

0.0008

288

440

C/T

BAA

N

INTERM

rs861539

XRCC3, KLC1

primary

Dominant

Osteo

2

Asian

1.57 [1.16, 2.13]

0

0.003

288

440

C/T

BAA

N

INTERM

deletion

GSTT1

primary

Recessive

Mixed

4

Mixed

1.32 [1.01, 1.73]

4

0.04

355

938

non-null/ null

AAA

N

INTERM

rs1042522

TP53

primary

Per allele

Mixed

6

Mixed

0.6 [0.39, 0.93]

84

0.02

788

950

G/C

ACA

N

LOW

rs1042522

TP53

subgroup

Per allele

Osteo

3

Mixed

0.47 [0.23, 0.95]

93

0.04

509

737

G/C

ACA

N

LOW

rs1129055

CD86

primary

Per allele

Mixed

2

Asian

0.33 [0.11, 1.01]

93

0.05

363

428

A/G

BCA

N

LOW

rs2305089

T

primary

Per allele

Chordoma

3

Mixed

2.87 [1.35, 6.08]

86

0.006

228

1001

G/A

ACA

N

LOW

rs2305089

T

primary

Recessive

Chordoma

2

Mixed

4.16 [1.21, 14.25]

82

0.02

125

841

G/A

BCA

N

LOW

rs6479860

LOC107984012 NRBF2

primary

Per allele

Ewing's

2

Caucasian

1.79 [1.36, 2.34]

66

<0.0001

744

4603

C/T

ACA

N

LOW

rs7591996

GRM4

primary

Per allele

Osteo

2

Mixed

1.28 [1.02, 1.61]

53

0.03

1109

3507

A/C

ACA

N

LOW

deletion

GSTM1

sensitivity

Recessive

Bone tumor

3

Asian

1.69 [1.02, 2.81]

66

0.04

315

578

non-null/ null

BCA

N

LOW

OR [95%CI]: Summary Odds Ratio [95% Confidence Interval]; Ref: reference allele; Alt: alternative allele; Venice criteria: A (high), B (moderate), C (weak) credibility for three parameters (amount of evidence, heterogeneity and bias); FPRP: false positive report probability at a prior probability of 10E-3; Y: noteworthy association (FPRP cut-off value 0.2), N: non noteworthy association; Level of evidence: overall level of summary evidence according to the Venice criteria and FPRP.

In order to provide an estimate of the impact of germline variants on sarcoma risk, the PAR (population attributable risk) was calculated. As an example, we considered the following three independent SNPs with high quality evidence on their relationship with sarcoma risk: rs11599754 of ZNF365/EGR2 (chromosome 10, risk allele: C, risk allele frequency in European ancestry population: 0.39, meta-analysis OR: 1.48); rs231775 of CTLA4 (chromosome 2, risk allele: A, risk allele frequency in European ancestry population: 0.65, meta-analysis OR: 1.36); and rs454006 of PRKCG (chromosome 19, risk allele: C, risk allele frequency in European ancestry population: 0.25, meta-analysis OR: 1.35). The PAR resulted equal to 37.2%.

Associations based on single studies

Beside the variations resulted to be statistically significantly associated with sarcoma risk in this meta-analysis, we retrieved from the included articles 906 SNPs statistically significantly associated with sarcoma risk (P-value <0.05) based on single-study analysis. In Table 3 are reported 53 SNPs strongly associated with Ewing’s sarcoma or osteosarcoma risk (P-value <E-06), retrieved from the included studies.

Table 3: Statistically significant associations based on single studies (P-value threshold E-06)

Reference

Cancer type

Genes

SNP ID

Ref/Alt

Chr

OR [95%CI]

P-value

location

eQTL

eQTL P-value skeletal muscle

Postel-Vinay S. [10]

Ewing’s

C1orf127, TARDBP

rs9430161

T/G

1

2.20 [1.80, 2.70]

1.40E-20

intergene

Postel-Vinay S. [10]

Ewing’s

C1orf127

rs2003046

A/C

1

1.80 [1.50, 2.20]

1.30E-14

intron

Postel-Vinay S. [10]

Ewing’s

C1orf127

rs11576658

T/C

1

1.80 [1.40, 2.30]

9.40E-11

intron

Postel-Vinay S. [10]

Ewing’s

SRP14-AS1

rs4924410

C/A

15

1.50 [1.30, 1.70]

6.60E-09

intron

RP11-521C20.2

1.60E-07

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs10995305

G/A

10

1.59 [1.26, 2.00]

4.38E-07

intergene

ADO

1.40E-16

Zhao J. [97]

Osteo

ARHGAP35

rs1052667

C/T

19

2.25 [1.64, 3.09]

4.43E-07

utr 3 prime

ARHGAP35

Other tissue

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs224290

G/C

10

0.55 [0.43, 0.70]

7.80E-07

intergene

ADO

7.50E-14

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs224291

G/A

10

0.55 [0.43, 0.70]

7.80E-07

intergene

ADO

7.20E-14

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs224296

C/T

10

0.55 [0.43, 0.70]

7.80E-07

intergene

ADO

2.90E-14

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs224297

T/C

10

0.55 [0.43, 0.70]

7.80E-07

intergene

ADO

2.80E-14

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs224298

G/A

10

0.55 [0.43, 0.70]

7.80E-07

intergene

ADO

2.90E-14

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs224294

C/T

10

0.54 [0.43, 0.69]

1.01E-06

intergene

ADO

5.60E-14

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs224293

G/A

10

0.55 [0.44, 0.71]

1.02E-06

intergene

ADO

7.20E-14

Grünewald TG. [29]

Ewing’s

EGR2, ADO

rs1848796

C/T

10

1.80 [1.42, 2.29]

1.08E-06

intergene

ADO

2.90E-14

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs224282

A/G

10

0.55 [0.44, 0.71]

1.08E-06

intergene

ADO

7.20E-14

Savage SA. [11]

Osteo

ADAMTS17

rs2086452

T/C

15

1.35 [1.19, 1.52]

1.12E-06

intron

Grünewald TG. [29]

Ewing’s

EGR2

rs648746

G/T

10

0.56 [0.44, 0.71]

1.21E-06

upstream

ADO

5.10E-15

Grünewald TG. [29]

Ewing’s

EGR2

rs648748

G/A

10

0.56 [0.44, 0.71]

1.21E-06

upstream

ADO

5.10E-15

Grünewald TG. [29]

Ewing’s

EGR2

rs7076924

A/G

10

1.79 [1.41, 2.28]

1.21E-06

upstream

ADO

5.50E-15

Grünewald TG. [29]

Ewing’s

EGR2

rs224277

T/C

10

0.56 [0.44, 0.71]

1.40E-06

upstream

ADO

3.30E-15

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs224289

T/C

10

0.56 [0.44, 0.71]

1.42E-06

intergene

ADO

7.20E-14

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs7096645

G/T

10

1.78 [1.40, 2.27]

1.54E-06

intergene

ADO

8.60E-14

Grünewald TG. [29]

Ewing’s

LOC107984012, NRBF2

rs10740101

A/G

10

2.07 [1.55, 2.76]

2.29E-06

intergene

ADO

4.90E-10

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs7079482

C/T

10

2.06 [1.54, 2.76]

2.69E-06

intergene

ADO

1.70E-10

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs1115705

T/C

10

2.07 [1.55, 2.77]

2.73E-06

intergene

ADO

9.40E-11

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs983319

A/T

10

2.07 [1.55, 2.77]

2.99E-06

intergene

ADO

4.10E-10

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs1571918

A/G

10

2.05 [1.54, 2.74]

3.44E-06

intergene

ADO

2.80E-10

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs1888968

C/T

10

2.05 [1.54, 2.74]

3.44E-06

intergene

ADO

1.90E-10

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs1912369

G/A

10

2.05 [1.54, 2.74]

3.44E-06

intergene

ADO

3.50E-10

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs4147153

A/G

10

2.05 [1.54, 2.74]

3.44E-06

intergene

ADO

3.50E-10

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs4237316

C/T

10

2.05 [1.54, 2.74]

3.44E-06

intergene

ADO

1.90E-10

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs4746746

C/T

10

2.05 [1.54, 2.74]

3.44E-06

intergene

ADO

7.20E-10

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs6479854

C/T

10

2.05 [1.54, 2.74]

3.44E-06

intergene

ADO

1.50E-10

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs7100213

T/C

10

2.05 [1.54, 2.74]

3.44E-06

intergene

ADO

2.10E-10

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs4746745

T/C

10

2.03 [1.52, 2.72]

3.48E-06

intergene

ADO

5.90E-11

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs224301

G/A

10

0.60 [0.47, 0.76]

3.67E-06

intergene

ADO

1.20E-10

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs224302

G/A

10

0.60 [0.47, 0.76]

3.67E-06

intergene

ADO

3.70E-10

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs10822056

C/T

10

1.65 [1.31, 2.09]

3.70E-06

intergene

ADO

3.00E-13

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs224295

A/C

10

0.60 [0.48, 0.76]

4.80E-06

intergene

ADO

1.50E-10

Grünewald TG. [29]

Ewing’s

ADO, EGR2

rs224299

T/C

10

0.60 [0.48, 0.76]

4.80E-06

intergene

ADO

1.50E-10

Savage SA. [11]

Osteo

LOC105373401, LOC105373402

rs13403411

C/T

2

1.30 [1.16, 1.46]

5.20E-06

intergene

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs1509952

C/T

10

2.06 [1.54, 2.76]

5.28E-06

intergene

ADO

3.50E-10

Grünewald TG. [29]

Ewing’s

LOC107984012

rs10740095

T/C

10

2.03 [1.52, 2.72]

5.50E-06

intron

ADO

4.20E-11

Grünewald TG. [29]

Ewing’s

LOC107984012

rs925307

T/C

10

2.03 [1.52, 2.72]

5.50E-06

intron

ADO

6.00E-11

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs7073383

A/G

10

2.01 [1.50, 2.69]

5.98E-06

intergene

ADO

1.60E-10

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs10733780

G/T

10

2.01 [1.50, 2.69]

6.90E-06

intergene

ADO

2.90E-10

Grünewald TG. [29]

Ewing’s

LOC107984012

rs7071512

T/C

10

2.01 [1.50, 2.69]

6.90E-06

intron

ADO

4.20E-11

Savage SA. [11]

Osteo

FAM208B, GDI2

rs2797501

A/G

10

0.62 [0.51, 0.77]

7.88E-06

missense, downstream

Savage SA. [11]

Osteo

DLEU1, LOC107984568

rs573666

G/A

13

0.77 [0.68, 0.86]

8.59E-06

intergene

EBPL

Other tissue

Grünewald TG. [29]

Ewing’s

EGR2, LOC107984012

rs10740097

C/T

10

2.03 [1.51, 2.72]

9.03E-06

intergene

ADO

1.20E-10

Grünewald TG. [29]

Ewing’s

LOC107984012

rs6479848

T/C

10

2.01 [1.50, 2.69]

9.16E-06

intron

ADO

2.70E-11

Grünewald TG. [29]

Ewing’s

ZNF365, ADO, EGR2

rs224079

C/T

10

1.58 [1.25, 2.01]

9.24E-06

intergene

ADO

5.00E-22

Grünewald TG. [29]

Ewing’s

LOC107984012

rs965128

C/T

10

1.99 [1.49, 2.66]

9.48E-06

intron

ADO

3.10E-11

OR [95%CI]: Odds Ratio [95% Confidence Interval]; Ref: reference allele; Alt: alternative allele; eQTL: expression quantitative trait locus.

One dataset was available for each of those genetic variants. Although it was not possible to perform a meta-analysis, a strong association with sarcoma risk was found (P-values range from E-20 to E-06). Ewing’s sarcoma associations in European and US European-descendant population mainly involved the candidate risk loci at 1p36.22, 10q21 reported by Postel-Vinay et al [10] GWAS and in the following related study of Grünewald et al [29]. The 1p36.22 variants associated with Ewing’s sarcoma are located 25 kb proximal to the TARDBP gene. TARDBP (Tat activating regulatory DNA-binding protein, or TDP-43, transactive response DNA-binding protein) is a highly conserved DNA- and RNA-binding protein involved in RNA transcription and splicing. The 10q21 variants strongly associated with Ewing’s sarcoma are located in a block containing four genes: ADO (encoding cysteamine dioxygenase), ZNF365 (encoding zinc-finger protein 365), EGR2 (encoding early growth response protein 2) and LOC107984012 (unknown function).

A further association with osteosarcoma in Guangxi population was studied by Zhao et al [97] regarding the Rho GTPase-activating protein 35 (ARHGAP35), a Rho family GTPase-activating protein. Finally Savage et al [11] GWAS found associations with osteosarcoma and GMR4 (glutamate receptor metabotropic 4), which were part of our meta-analysis and ADAMTS protein family, as ADAM Metallopeptidase with Thrombospondin Type 1 Motif 17. Of note, most statistically significant associations based on single studies did not have a statistically significant eQTL effect.

Network and pathway analysis findings

Using the 36 genes whose SNPs were significantly associated with sarcoma risk (including data from both meta-analysis and single studies) and were also characterized by a significant eQTL effect, we found that the corresponding protein products interact with each other beyond chance (observed edges: 120; expected edges: 12; PPI enrichment P-value <10E-20), with an average node degree equal to 6.7 (see Figure 2). Such enrichment indicates that the input molecules - as a whole group - are at least partially biologically connected. This high connectivity prompted us to conduct pathway analysis, which showed that the identified network is significantly enriched in DNA repair proteins, as shown in Table 4.

Network analysis of proteins encoded by genes whose variants associated with sarcoma risk and characterized by an expression quantitative trait locus effect (eQTL).

Figure 2: Network analysis of proteins encoded by genes whose variants associated with sarcoma risk and characterized by an expression quantitative trait locus effect (eQTL). The figure illustrates the high degree of connectivity of these proteins, which result to be enriched in DNA repair pathway components.

Table 4: Pathway analysis main findings: gene set enrichment analysis based on 36 sarcoma risk genes. Enrichments with at least ten overlapping genes are shown

Pathway

Overlap

FDR

Genes

Database

Base excision repair (BER)

11/139

0.002374441

BLM;RAD50; PARP4; RECQL5; LIG1; MPG; PARP2; ERCC4; PNKP; FANCG; POLH

GO biol process

DNA 3' dephosphorylation involved in DNA repair

10/120

0.002376199

BLM; RAD50; PARP4; RECQL5; LIG1; PARP2; ERCC4; PNKP; FANCG; POLH

GO biol process

DNA dealkylation involved in DNA repair

12/128

0.000983329

BLM; RAD50; PARP4; RECQL5; LIG1; MPG; MGMT; PARP2; ERCC4; PNKP; FANCG; POLH

GO biol process

DNA ligation involved in DNA repair

11/132

0.002374441

BLM; RAD50; PARP4; RECQL5; LIG1; MGMT; PARP2; ERCC4; PNKP; FANCG; POLH

GO biol process

DNA repair

18/285

8.22494E-05

BLM; LIG1; CCNH; XRCC5; PARP2; MGMT; MPG; POLM; PNKP; FANCG; BRIP1; RAD50; NEIL2; ERCC4; ERCC2; ATM; ERCC5; POLH

Reactome

DNA synthesis involved in DNA repair

12/142

0.001514863

BLM; BRIP1; RAD50; PARP4; RECQL5; LIG1; PARP2; ERCC4; PNKP; ATM; FANCG; POLH

GO biol process

Double-strand break repair (DSBR)

12/164

0.002374441

BLM; BRIP1; RAD50; PARP4; RECQL5; LIG1; XRCC5; PARP2; ERCC4; PNKP; FANCG; POLH

GO biol process

Mismatch repair (MMR)

10/140

0.005835867

BLM; RAD50; PARP4; RECQL5; LIG1; PARP2; ERCC4; PNKP; FANCG; POLH

GO biol process

Mitochondrial DNA repair

10/123

0.002552586

BLM; RAD50; PARP4; RECQL5; LIG1; PARP2; ERCC4; PNKP; FANCG; POLH

GO biol process

Non homologous end joining (NHEJ)

10/120

0.002376199

BLM; RAD50; PARP4; RECQL5; LIG1; PARP2; ERCC4; PNKP; FANCG; POLH

GO biol process

Nucleotide excision repair (NER)

11/138

0.002374441

BLM; RAD50; PARP4; RECQL5; LIG1; PARP2; ERCC4; PNKP; ERCC2; FANCG; POLH

GO biol process

Nucleotide phosphorylation involved in DNA repair

10/120

0.002376199

BLM; RAD50; PARP4; RECQL5; LIG1; PARP2; ERCC4; PNKP; FANCG; POLH

GO biol process

Homologous recombination (HR)

10/132

0.00369711

BLM; RAD50; PARP4; RECQL5; LIG1; PARP2; ERCC4; PNKP; FANCG; POLH

GO biol process

Single strand break repair (SSBR)

11/124

0.001805921

BLM; RAD50; PARP4; RECQL5; LIG1; PARP2; ERCC4; PNKP; APTX; FANCG; POLH

GO biol process

UV-damage excision repair

11/158

0.003533915

BLM; RAD50; PARP4; RECQL5; LIG1; PARP2; ERCC4; PNKP; EIF2AK4; FANCG; POLH

GO biol process

XPC complex (NER)

15/160

8.19637E-06

WWOX; CCNH; XRCC5; MGMT; CD3EAP; FANCG; POC5; ERCC4; ERCC2; MDM2; OBFC1; ATM; ERCC5; POLH; UGT1A6

Jenesen compartments

FDR: false discovery rate.

In particular, many sarcoma risk genes appear to be involved in all main DNA repair pathways, including single strand break repair pathways (base excision repair [BER], nucleotide excision repair [NER], mismatch repair [MMR]) and double strand repair pathways (non homologous end joining [NHEJ], homologous recombination [HR]).

DISCUSSION

We described the findings of the first field synopsis and meta-analysis dedicated to the relationship between germline DNA variation and risk of developing bone and soft tissue sarcomas, which is based on genotyping data from 90 studies enrolling almost 48,000 people with a control-to-case ratio equal to 2. The resulting knowledgebase will be hosted by our cancer-dedicated website (at www.mmmp.org) [100] as a freely available online data repository that will be annually updated.

Overall, our findings support the hypothesis that genetic polymorphism does contribute to sarcoma susceptibility. This is exemplified by the population attributable risk (PAR=37.2%) calculated for three SNPs associated with the risk of sarcoma at a high level of evidence (rs11599754 of ZNF365/EGR2, rs231775 of CTLA4, and rs454006 of PRKCG), which indicates that more than one third of sarcoma cases would not occur in a hypothetical population where these three risk variants were absent. This remarkable influence of just three SNPs is linked not only to the high frequency of the risk alleles but also to the interesting fact that the risk, defined as odds ratio, associated with single variants ranged between 1.35 and 1.48, which are values higher than those usually observed for other malignancies such as breast [101], colorectal [102], and gastric carcinomas [103], which generally include odds ratios between 1.10 and 1.30. Considering that the mean risk among variants significantly associated with sarcoma predisposition was even higher (approximately 1.70, see Table 2), one might speculate that germline DNA variation is especially important in the determinism of the susceptibility to this family of tumors.

Overall, the quality of the available data, which was thoroughly assessed by means of both Venice criteria and false positive report probability (FPRP), was satisfactory considering that the statistically significant evidence on 47 of 55 variants for which a meta-analysis was feasible was classified as high to moderate level of quality with 10 SNPs considered adequate according to the FPRP (Table 2). A statistically significant association was also demonstrated for additional 906 SNPs, for which only a single data source was available, which pinpoints the urgent need for replication studies in order to validate or refute these findings.

Conventional meta-analysis of single variants led us to identify 55 SNPs significantly associated with sarcoma risk (Table 2), and additional 53 SNPs were reported in single studies (Table 3): these variants are linked to a variety of genes whose protein products are involved in several cell activities. Therefore, we tried to provide readers with a preliminary interpretation of these findings from the functional biology viewpoint. Using modern SNP-to-gene and gene-to-function approaches such as integrative analysis of genetic variation with expression quantitative trait locus (eQTL) data [9] and respectively pathway/network analysis [8], we hypothesize that germline variation of the DNA repair machinery might be of special relevance for the development of this type of cancer (Figure 2). This finding – which has been very recently confirmed in patients with Ewing’s sarcoma [104] - is in line with the complex gene and chromosome abnormalities that characterized some sarcoma histologies, as well as with the epidemiological observation that people accidentally [105] or therapeutically [106] exposed to ionizing radiations and thus prone to develop DNA damage are at higher risk of different types of sarcomas. In this regard, it is interesting to note that peripheral blood mononuclear cells of patients diagnosed with sarcomas show a higher sensitivity to mutagens in vitro as compared to controls [107], which supports the hypothesis that the genetic background can make the difference on an individual basis in terms of response to environmental carcinogens potentially involved in sarcomagenesis.

Finally, also somatic DNA alterations appear to confer a defective DNA repair capability to some sarcoma types such as Ewing’s sarcoma [108], and thus the combinatory study of germline and somatic DNA variations characterizing sarcomas might lead to better understand the cascade of molecular events underlying sarcomagenesis, as recently proposed for the EWSR1-FLI1 fusion gene and the SNPs near EGR2 in Ewing’s sarcoma patients [29].

Overall, these converging data suggest that more investigation aimed to fully elucidate whether the germline individual capacity of repairing genomic damage can actually affect the predisposition to a complex and heterogeneous trait such as sarcomas might be particularly fruitful.

In our work we also confirmed the association between sarcoma risk and variants of single genes, such as ZNF365, ADO, EGR2, CTLA4, TP53, CD86, NUDT6, MDM2, ERCC5 and ADAMTS6 just to mention the top ten by statistical significance. Many of these genes are not known to be involved in DNA repair and thus the relationship between these single gene findings and network/pathway analysis might appear of unclear interpretation and doubtful importance. However, we must remember that current evidence (and thus our analysis) is based on 88 candidate gene studies and only two GWAS: therefore, more extensive investigation is needed on the variation of pathways for which data on single genes are currently available. In this regard, our meta-analysis data can be utilized to inform future studies on candidate pathways whose genetic variation could affect sarcoma susceptibility.

This systematic review also underscores the main limitation of the evidence on the genetic susceptibility of sarcomas. In fact, most of current information is driven by data from studies investigating bone tumors (78 of 90, 86.6%). Studies focusing on soft tissue sarcomas are thus eagerly awaited, the formation of international consortia being advocated in order to overcome the hurdle of disease rarity. Hopefully, technological improvements in direct DNA sequencing such as next generation sequencing (NGS) methods will further accelerate the discovery pace in this field of investigation, as recently reported [104].

Nevertheless, we also recognize some limitations of this synopsis: data from different tumor types and population ethnicity were pooled together to find associations despite the diversity of sarcoma histologies, leading to high level of between-study heterogeneity. To overcome to this limitation we performed subgroup and sensitivity analysis whenever possible. Moreover, despite our efforts to avoid the issue of overlapping series, it is always possible that partial overlaps between multiple series published by the same research groups that cannot be detected by full text reading did remain included in pooled analyses: however, we believe that the influence of this potential residual overlapping on the overall results is reasonably low.

In conclusion, we hope that the creation of the first knowledgebase dedicated to the relationship between germline DNA variation and sarcoma risk can not only represent a valuable reference for investigators involved in sarcoma research but also inform future studies based on the gaps of the current literature.

MATERIALS AND METHODS

Search strategy, eligibility criteria, quality score assessment and data extraction

This study followed the principles proposed by the Human Genome Epidemiology Network (HuGeNet) for the systematic review of molecular association studies [109].

We considered eligible all the studies concerning the association between any genetic variant and the predisposition to sarcoma in humans, providing the raw data necessary to calculate risk of developing a sarcoma or the summary data. Exclusion criteria were: virus-induced sarcomas (HHV8 - Kaposi sarcoma); sarcomas secondary to radiation therapy; sarcomas secondary to burns/scars/surgery; associations between mitochondrial DNA variations and sarcomas; gastrointestinal stromal tumors (GIST).

Database search of original articles analyzing the association between any genetic variant and susceptibility to sarcoma was conducted independently by two investigators though the following database: MEDLINE (via the PubMed gateway); The Cochrane Library; Scopus; Web of Science. The search included the following three groups of keywords: 1) sarcoma, solitary fibrous tumor, chordoma, tenosynovitis, fibromatosis, desmoids, myofibroblastic, myopericytoma, myxoma, Ewing, desmoplastic, PEComa, haemangioendothelioma, lymphangioma, myoepithelioma; 2) risk, sarcomagenesis, tumorigenesis, predisposition, susceptibility; 3) polymorphism, SNP, variant, genome wide association study and its acronym GWAS. Searches were conducted using all combinations of at least one keyword from each group. References from eligible articles were also used to refine the literature search.

The quality of the studies was evaluated according to Newcastle-Ottawa quality assessment scale (NOS) [110]. In brief, the following three parameters were evaluated with a “star system”: the selection of the study groups (0 to 4 “stars”), the comparability of the groups (0 to 2 “stars”), and the ascertainment of either the exposure or outcome of interest for case-control or cohort studies respectively (0 to 3 “stars”). The maximum total score was 9 “stars” and represented the highest quality.

Data were extracted independently by two investigators using a template. Every disagreement was resolved by a third investigator in order to reach consensus. Authors were contacted whenever unreported data were potentially useful to enable the inclusion of the study into the systematic review. The data extracted from eligible studies were: authors, journal, year of publication, region or country where the study was conducted, hospital where the patients were diagnosed, number of patients with sarcoma enrolled and healthy control subjects, period of enrolment, prevalent ethnicity (>80%, categorized in Caucasian, Asian, African and mixed), subjects age, genetic polymorphisms and allelic frequency in both cases and controls (if no raw data were available, summary data were collected, i.e. odds ratios and confidence intervals), study design (population-based versus hospital-based), statistical methods used, and sarcoma histology.

We considered data published in different articles by the same Author/s with the same (or similar) number of subjects enrolled in the same period of time in the same hospital, to be derived by the same group of patients. In publications with either overlapping cases or controls, the most recent or largest population was chosen.

For analysis purposes, the search was closed in August 2017.

Statistical analysis

We calculated summary odds ratios (ORs) and their corresponding 95% confidence intervals (95%CI) starting from raw data to measure the strength of association between each polymorphism and sarcoma risk.

Whenever possible, we calculated the pooled ORs assuming 3 different genetic models: per-allele (additive), dominant and recessive. If the included studies reported exclusively per-allele ORs, as in GWAS, we calculated the pooled OR assuming the per-allele (additive) model.

Random effects meta-analysis based on the inverse variance method was used to calculate summary ORs; this model reduces to a fixed effect meta-analysis if between-study heterogeneity is absent. We chose this model for the large between-study heterogeneity usually expected in genetic association studies. A meta-analysis was performed only if at least two independent data sources were available. In case of GWAS, we considered as data source the joint analysis between the discovery and the validation phases. Subgroup analysis by histological subtype (Ewing’s sarcoma vs osteosarcoma) was planned if data permitted.

Regarding ethnicity, analyses were divided in 4 groups: African (if the datasets were all African population-based), Asian (if the datasets were all Asian population-based), Caucasian (if the datasets were all Caucasian population-based), and mixed (if the datasets were African, Asian and Caucasian or if the datasets were from mixed ethnicity). In order to test any dominant study driving effect, sensitivity analysis by ethnicity (Asian vs Caucasian/other) was performed in mixed meta-analyses, with more than two datasets, excluding either the Asian study or the Caucasian study from the meta-analysis.

Between-study heterogeneity was formally assessed by the Cochran Q-test and the I-squared statistic, the latter indicating the proportion of the variability in effect estimates linked to true between-study heterogeneity as opposed to within-study sampling error.

All statistical analyses were performed with RevMan 5 (Review Manager computer program, version 5.3; Copenhagen, The Nordic Cochrane Centre, The Cochrane Collaboration, 2014).

Assessment of cumulative evidence

With the aim to assess the credibility of statistically significant associations based on the results of data meta-analysis, we used the Venice criteria [111]. In brief, we defined credibility levels based on the strength (classified as A=strong, B=moderate or C=weak) of three following parameters: amount of the evidence, replication of the association and protection from bias. We graded the amount of evidence, which approximately depends on the study sample size, based on the sum of cases and controls. Grade A, B or C was assigned to meta-analyses with total sample size >1000, 100–1000 and <100, respectively. Also, the replication of the association was graded considering the amount of between-study heterogeneity. We assigned grade A, B or C to meta-analyses with I-squared <25%, 25–50% and >50%, respectively. We graded protection from bias as A if no bias was observed, B if bias was potentially present or C if bias was evident. While assessing protection from bias we also considered the magnitude of the association. We assigned a score of C to an association characterized by a summary OR<1.15 or a summary OR>0.87 if the effect of the polymorphism was protective.

In addition to the Venice criteria, we assessed the noteworthiness of significant findings by calculating the false positive report probability (FPRP) [112], which is defined as the probability of no true association between a genetic variant and disease (null hypothesis) given a statistically significant finding. FPRP is based not only on the observed P-value of the association test but also on the statistical power of the test and on the prior probability that the molecular association is real following a Bayesian approach. We calculated FPRP values for two levels of prior probabilities: at a low prior (10E-3) that would be similar to what is expected for a candidate variant, and at a very low prior (10E-6) that would be similar to what would be expected for a random variant. To classify a significant association as ‘noteworthy’, we used a FPRP cut-off value of 0.2.

Overall, we defined the credibility level of the cumulative evidence as high (Venice criteria A grades only coupled with “noteworthy” finding at FPRP analysis), low (one or more C grades combined with lack of noteworthiness), or intermediate (for all other combinations).

To estimate the impact of genetic variation on the risk of sarcomas, we calculated the so called population attributable risk (PAR) using the following formula:

Pr (RR − 1)/[1 + Pr (RR − 1)],

where Pr is the proportion of control subjects exposed to the allele of interest and the relative risk (RR) was estimated using the summary estimates (i.e. ORs) calculated by the meta-analysis. The joint PAR for combinations of polymorphisms was calculated as follows:

1 − (∏1→n[1 − PARi]),

where PARi corresponds to the individual PAR of the ith polymorphism and n is the number of polymorphisms considered [113].

Network and pathway analysis

In order to explore the mechanisms underlying the pathogenesis of sarcomas, we utilized network and pathway analysis to test the hypothesis that genes whose variations are associated with sarcoma risk interact with each other possibly within the frame of some specific molecular pathways [8].

To this aim, we first selected SNPs significantly associated with sarcoma risk. In case of SNPs located in intergenic regions we selected the first closest and the second closest genes, not necessarily upstream and downstream of the SNPs of interest.

Since most SNPs are intergenic or intronic and thus no obvious functional effect can be inferred, expression quantitative trait locus (eQTL) analysis was used to identify genes whose expression is affected by DNA variants [114]. The resulting gene list was the input for both network and pathway analysis.

For the former, the STRING web server was employed to study protein-protein interaction (PPI) across the selected genes [115], the confidence score being set >0.4. As a measure of across network connectivity STRING provides the average node degree, where degree is the conceptually simplest centrality measure as it measures the number of edges between protein connections attached to a protein; moreover, STRING computes the PPI enrichment P-value, which is significant when input proteins have more interactions among themselves than what would be expected for a random set of proteins of similar size, drawn from the genome.

As regards pathway analysis, the Enrichr web server was utilized to identify in our list over-representation of genes involved in specific pathways described in dedicated databases [116]. Hypergeometric distribution with Fisher’s exact test was used to calculate the statistical significance of gene overlapping, followed by correction for multiple hypotheses testing using the false discovery rate [FDR] method.

Declarations

Ethics approval and consent to participate: Not applicable

Consent for publication: Not applicable

Availability of data and material: All data generated or analysed during this study are included in this published article [and its supplementary information files].

Authors’ contributions

CB, AS, DDB, SR, GS: database search and data extraction; CC, CV: data revision, quality score assessment; CB, AS: statistical analysis, assessment of cumulative evidence and manuscript writing; SP, SM: network/pathway analysis, manuscript writing and revision; SGDB, AG, CRR: appraisal of manuscript.

CONFLICTS OF INTEREST

The authors declare that they have no conflicts of interests.

FUNDING

University of Padova, BIRD168075, “Germline polymorphisms of candidate genes as predictor of risk and prognosis in patients with cutaneous melanoma and soft tissue sarcoma.”

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