Genetic Variants Associated with Increased Risk of Malignant Pleural Mesothelioma: A Genome-Wide Association Study
Asbestos exposure is the main risk factor for malignant pleural mesothelioma (MPM), a rare aggressive tumor. Nevertheless, only 5–17% of those exposed to asbestos develop MPM, suggesting the involvement of other environmental and genetic risk factors. To identify the genetic risk factors that may contribute to the development of MPM, we conducted a genome-wide association study (GWAS; 370,000 genotyped SNPs, 5 million imputed SNPs) in Italy, among 407 MPM cases and 389 controls with a complete history of asbestos exposure. A replication study was also undertaken and included 428 MPM cases and 1269 controls from Australia. Although no single marker reached the genome-wide significance threshold, several associations were supported by haplotype-, chromosomal region-, gene- and gene-ontology process-based analyses. Most of these SNPs were located in regions reported to harbor aberrant alterations in mesothelioma (SLC7A14, THRB, CEBP350, ADAMTS2, ETV1, PVT1 and MMP14 genes), causing at most a 2–3-fold increase in MPM risk. The Australian replication study showed significant associations in five of these chromosomal regions (3q26.2, 4q32.1, 7p22.2, 14q11.2, 15q14). Multivariate analysis suggested an independent contribution of 10 genetic variants, with an Area Under the ROC Curve (AUC) of 0.76 when only exposure and covariates were included in the model, and of 0.86 when the genetic component was also included, with a substantial increase of asbestos exposure risk estimation (odds ratio, OR: 45.28, 95% confidence interval, CI: 21.52–95.28). These results showed that genetic risk factors may play an additional role in the development of MPM, and that these should be taken into account to better estimate individual MPM risk in individuals who have been exposed to asbestos.
Genetic Variants Associated with Increased Risk of
Malignant Pleural Mesothelioma: A Genome-Wide
*, Simonetta Guarrera
, Marta Betti
, Giovanni Fiorito
, Daniela Ferrante
, Gemma Cadby
, Cornelia Di Gaetano
, Alessia Russo
, Ari Hirvonen
, Sara Tunesi
, Marina Padoan
, Mara Giordano
, Caterina Casadio
, Enrico Ruffini
, Pier Giacomo Betta
, Roberto Guaschino
, Monica Neri
, Arthur W. B. Musk
, Nicholas H. de Klerk
, Jennie Hui
, John Beilby
Alan L. James
, Jenette Creaney
, Bruce W. Robinson
, Sutapa Mukherjee
, Donatella Ugolini
, Stefano Bonassi
, Corrado Magnani
1 Human Genetics F oundation, HuGeF, Turin, Italy, 2 De par tmen t of Medical Scienc es, University of Turin, Turin, Italy, 3 Laboratory of Genetic Pathology, Department Health Sciences,
University of Piemonte Orientale, Novara, Italy, 4 CPO-Piemonte and Unit of Medical Statistics and Epidemiology, Department Translational Medicine, University of Piemonte Orientale,
Novara, Italy, 5 Genetic Epidemiology and Biostatistics Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada, 6 Prosserman Centre for Health Research, Samuel
Lunenfeld Research Institute, Toronto, Ontario, Canada, 7 Centre for Genetic Epidemiology and Biostatistics, University of Western Australia, Nedlands, Western Australia, Australia,
8 Centreof Expertise for Health andWork Ability,Finnish Institute ofOccupational Health,Helsinki, Finland, 9 Laboratoryof Genetics, DepartmentHealthSciences, University of Piemonte
Orientale, Novara, Italy, 10 Thoracic Surgery Unit, University of Piemonte Orientale, Novara, Italy, 11 Chest Surgery, Department of Clinical and Biological Sciences, University of Turin,
Orbassano, Italy, 12 Thoracic Surgery Unit, University of Turin, Turin, Italy, 13 Pathology Unit, Azienda Ospedaliera Nazionale SS, Antonio e Biagio e Cesare Arrigo, Alessandria, Italy,
14 Transfusion Centre, Azienda Ospedaliera Nazionale SS, Antonio e Biagio e Cesare Arrigo, Alessandria, Italy, 15 Pneumology Unit, Santo Spirito Hospital, Casale Monferrato, Italy,
16 Unit of Clinical and Molecular Epidemiology IRCCSSan Raffaele Pisana, Rome, Italy,17 Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Nedlands, WesternAustralia,
Australia, 18 National Centre for Asbestos Related Disease, School of Medicine and Pharmacology, University of Western Australia, Nedlands, Western Australia, Australia, 19 Centre for
Child Health Research, The University of Western Australia, Nedlands, Western Australia, Australia, 20 PathWest Laboratory Medicine WA, Nedlands, Western Australia, Australia,
21 Department of Medicine, University of Toronto, Toronto, Ontario, Canada, 22 Women’s College Research Institute and Women’s College Hospital, Toronto, Ontario, Canada, 23 Unit
of Cancer Epidemiology, CPO-Piemonte and University of Turin, Turin, Italy, 24 Interdepartmental Center for Studies on Asbestos and other Toxic Particulates ‘‘G. Scansetti’’, University of
Turin, Turin, Italy, 25 Department of Internal Medicine, University of Genoa and IRCSS AOU San Martino-IST-Istituto Nazionale per la Ricerca sul Cancro, Genoa, Italy
Asbestos exposure is the main risk factor for malignant pleural mesothelioma (MPM), a rare aggressive tumor. Nevertheless,
only 5–17% of those exposed to asbestos develop MPM, suggesting the involvement of other environmental and genetic risk
factors. To identify the genetic risk factors that may contribute to the development of MPM, we conducted a genome-wide
association study (GWAS; 370,000 genotyped SNPs, 5 million imputed SNPs) in Italy, among 407 MPM cases and 389 controls
with a complete history of asbestos exposure. A replication study was also undertaken and included 428 MPM cases and 1269
controls from Australia. Although no single marker reached the genome-wide significance threshold, several associations
were supported by haplotype-, chromosomal region-, gene- and gene-ontology process-based analyses. Most of these SNPs
were located in regions reported to harbor aberrant alterations in mesothelioma (SLC7A14, THRB, CEBP350, ADAMTS2, ETV1,
PVT1 and MMP14 genes), causing at most a 2–3-fold increase in MPM risk. The Australian replication study showed significant
associations in five of these chromosomal regions (3q26.2, 4q32.1, 7p22.2, 14q11.2, 15q14). Multivariate analysis suggested
an independent contribution of 10 genetic variants, with an Area Under the ROC Curve (AUC) of 0.76 when only exposure and
covariates were included in the model, and of 0.86 when the genetic component was also included, with a substantial increase
of asbestos exposure risk estimation (odds ratio, OR: 45.28, 95% confidence interval, CI: 21.52–95.28). These results showed
that genetic risk factors may play an additional role in the development of MPM, and that these should be taken into account
to better estimate individual MPM risk in individuals who have been exposed to asbestos.
Citation: Matullo G, Guarrera S, Betti M, Fiorito G, Ferrante D, et al. (2013) Genetic Variants Associated with Increased Risk of Malignant Pleural Mesothelioma: A
Genome-Wide Association Study. PLoS ONE 8(4): e61253. doi:10.1371/journal.pone.0061253
Editor: Xiao-Ping Miao, MOE Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazho ng University of Science and
Received January 7, 2013; Accepted March 6, 2013; Published April 23, 2013
Copyright: ß 2013 Matullo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was partially supported by the Regione Piemonte Ricerca Sanitaria Finalizzata 2007, 2008, 2009 (to I.D.), Fondazione Buzzi Unicem Onlus 2007 (to
I.D., S.B), CIPE (to I.D.), AIRC (to I.D., D.U., S.B) and Human Genetics Foundation - HuGeF (to G.M.). The Turin case-control study was supported by a grant from Regione
Piemonte, Ricerca Scientifica Applicata 2003 (to D.M.). The Casale case-control study was supported by a grant from Regione Piemonte, Ricerca Sanitaria Finalizzata
2004 (to C.M.). The Australian studies have been supported by the Australian National Health and Medical Research Council, the Sir Charles Gairdner Hospital and
PathWest laboratory Medicine of WA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors declare no competing financial interest. In fact, the ‘‘PathWest Laboratory Medicine WA’’ is not a commercial funder of this
research. The authors Jennie Hui and John Beilby are employed by PathWest and do not have any additional consultancy, patents, products in developmentor
marketed products with competing interests relating to this research. Thus, PathWest affiliation does not alter the authors’ adherence to all the PLOS ONE policies on
sharing data and materials.
* E-mail: email@example.com (GM); firstname.lastname@example.org (ID)
PLOS ONE | www.plosone.org 1 April 2013 | Volume 8 | Issue 4 | e61253
Malignant pleural mesothelioma (MPM) is a rare, aggressive
tumor that generally causes death within 2 years. The only clearly
established risk factors for MPM are asbestos exposure, and
exposure to erionite, other mineral fibers and x-ray for medical
purposes . Asbestos fibers retained in the lung and pleura may
be carcinogenic, either through direct mechanical or biochemical
effects, or through the activation of inflammatory cells. Persistent
inflammation can induce chronic oxidative stress, genotoxic
lesions, chromosomal aberrations and epigenetic alterations
[2,3]. Asbestos fibers may also interfere with chromosome
segregation and mitosis .
Although asbestos has been banned in many Western countries,
it is still used in several parts of the world, and some developing
countries are actually increasing the industrial use of asbestos, as
well as its production and importation [5,6,7]. In Western Europe,
over 5,000 people with MPM die each year [8,9,10,11].
Considering the long median latency period between initial
asbestos exposure and MPM diagnosis [12,13], MPM incidence
is expected to peak around 2020 in Western countries [9,14,15].
Only 5%–17% of individuals heavily exposed to asbestos
develop MPM , suggesting a genetic component in the etiology
of the disease, which is also supported by reports of familial
clustering [8,16,17,18] and candidate-gene association studies
[8,11]. Dominant mutations in the BAP1 (BRCA1-associated
protein 1) gene were recently reported to cause a new, rare cancer-
prone syndrome that renders the individual susceptible to
mesothelioma and melanoma, among others .
The aim of this study was to identify genetic risk factors that
might contribute to the development of MPM. To this end, we
performed a GWAS in an Italian study sample of 407 MPM cases
and 389 healthy controls, and a replication study in an Australian
study sample of 428 MPM cases and 1269 controls.
The general characteristics of the Italian study sample, after
quality controls (QC), are reported in Table 1 (392 MPM cases
and 367 controls; 540 males, 219 females). A total of 330,879
SNPs were included in the analyses. The principal component
analysis (PCA) (Figure S1) showed population stratification with
two distinct clusters, which was further confirmed by K-mean
analysis (data not shown). After correction of the regression
analyses by PCA-cluster, the l inflation factor was ,1.03 for both
the overall and the exposed-only samples (Quantile- Quantile, QQ
plots, Figure S2). Manhattan plots of the two-sided logistic
regression analyses (per allele additive model) are also reported
The genotyped SNPs with the highest significance levels are
listed in Table 2. The imputed SNPs with the highest significance
levels are listed in Table S1. Nine intragenic SNPs (7 genotyped
and 2 imputed) were located in genes. When analyzing these nine
genes in a Gene Set Enrichment Analysis (GSEA, File S1),
significant enrichment involving MMP14 and ADAMTS2 was
shown for gene-ontology (GO, File S1) biological processes
including lung development (P = 0.0087), respiratory tube devel-
opment (P = 0.0087), respiratory system development (P = 0.0087),
metalloendopeptidase activity (P = 0.0140), and metallopeptidase
activity (P = 0.0210) (Table S2).
When the GSEA (File S1) was extended to SNPs with a
significance level of P#10
in the regression analysis (additive
model, 201 genes), another metallopeptidase, namely MMP8, was
included in the gene list, further reinforcing the putative role of the
metalloendopeptidase pathway in MPM.
Haplotype association was investigated in the Italian study
sample for the 20 genes/chromosomal regions with the highest
significance levels. The most significant haplotype associations
were found in the chromosomal region 3p24.2, where the THRB
Table 1. Summary statistics of all the subjects included in the Italian GWAS.
CASALE M. TURIN GENOA ALL SAMPLE
Cases Controls Cases Controls Cases Controls Cases Controls
N (%) N (%) N (%) N (%) N (%) N (%) N (%) N (%)
Eligible 241 (48.88) 252 (51.12) 91 (61.9) 56 (38.1) 75 (48.08) 81 (51.92) 407 (51.13) 389 (48.87)
After QC filtering 230 (49.25) 237 (50.75) 89 (61.81) 55 (38.19) 73 (49.32) 75 (50.68) 392 (51.65) 367 (48.35)
Males 155 (67.39) 162 (68.35) 62 (69.66) 38 (69.09) 67 (91.78) 56 (74.67) 284 (72.45) 256 (69.75)
Females 75 (32.61) 75 (31.65) 27 (30.34) 17 (30.91) 6 (8.22) 19 (25.33) 108 (27.55) 111 (30.25)
North Italy 204 (90.27) 186 (78.81) 62 (69.66) 35 (63.64) 54 (78.26) 53 (76.81) 320 (83,33) 274 (76,11)
Center Italy 6 (2.65) 12 (5.08) 5 (5.62) 1 (1.82) 8 (11.59) 5 (7.25) 19 (4,95) 18 (5)
South Italy 14 (6.19) 33 (13.98) 16 (17.98) 17 (30.91) 4 (5.8) 4 (5.8) 34 (8.85) 54 (15)
Sardinia 0 (0) 2 (0.85) 3 (3.37) 2 (3.64) 1 (1.45) 3 (4.35) 4 (1.04) 7 (1.94)
Other Caucasians 2 (0.88) 3 (1.27) 3 (3.37) 0 (0) 2 (2.9) 4 (5.8) 7 (1.82) 7 (1.94)
Non exposed 4 (2.06) 54 (22.78) 3 (3.37) 18 (32.73) 10 (13.7) 41 (54.67) 17 (4.87) 113 (30.79)
Medium exposed 106 (54.64) 103 (43.46) 33 (37.08) 25 (45.45) 7 (9.59) 22 (29.33) 146 (41.01) 150 (40.87)
High exposed 84 (43.3) 80 (33.76) 53 (59.55) 12 (21.82) 56 (76.71) 12 (16) 193 (54.21) 104 (28.34)
Age (mean±s.e.) 66.46610.81 66.42612.26 68.5369.28 68.7067.69 64.16613.70 63.44614.47 66.5611.01 66.12612.06
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gene is located (P = 2.04610
), and in 19q13.42 (P = 7.02610
(Table S3), strengthening the importance of these chromosomal
Seven chromosomal regions were significantly associated with
MPM in the region-based analysis (P,0.0025, Table 3, Figure 2,
Figure S3) . The gene-based analysis confirmed the signifi-
cance of the THRB gene (P = 2.29610
) and showed a borderline
significance for the PVT1 gene (P = 0.02) (Table 3). Finally, the
regional GO (File S1) process-based analysis supported the
involvement of the metalloendopeptidase and metallopeptidase
GO (File S1) processes (Table 3, P = 0.0005 and 0.0039,
We detected a substantial improvement in accuracy comparing
the first multivariate model, which used asbestos exposure as a
predictor and adjusted for demographic covariates, with the
second one, which also included 10 selected SNPs with indepen-
dent effects (Table 4). The average Akaike Information Criterion
(AIC) and area under ROC curve (AUC) across 10,000 random
splits of the entire Italian study sample were 871.34 and 0.76 for
the first model, and 730.27 and 0.86 for the second model,
respectively (Figure 3, Table 4). The analysis stratified by center
(Casale Monferrato versus Turin-Genoa) confirmed the stability of
the risk estimates and 95% CIs (data not shown).
The first multivariate model confirmed asbestos exposure as the
main risk factor for MPM (high exposure: OR 17.33, 95% CI
; low exposure: OR 8.01, 95% CI 4.41–
14.54, P = 8.52610
) (Table 4). The second model, which
included the genetic component, showed that the 10 selected SNPs
had an independent contribution to MPM risk (Table 4), and also
increased the estimate for the effect of asbestos exposure (high
Figure 1. Manhattan plot of genotyped SNPs from logistic additive model. A) all samples, B) exposed samples.
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Table 2. Italian top 12 genotyped SNP list (2-tailed logistic regression, n = 759 overall, n = 593 exposed only).
CHR Location SNP Ref. Allele OR (95% CI) P Typed Gene Name Left Gene Right Gene Group
6q21 rs742109 A 0.55(0.43–0.71) 2.70610
Genotyped PRDM1 ATG5 OVERALL
3q26.2 rs7632718 A 1.83(1.42–2.37) 3.71610
CLDN11 RPL22L1 EXPOSED
3p24.2 rs9833191 C 0.54(0.41–0.71) 7.67610
Genotyped THRB NR1D2 MIR4792 EXPOSED
5q23.1 rs1508805 A 1.85(1.41–2.44) 1.04610
Genotyped PRR16 FTMT EXPOSED
1q25.2 rs2501618 A 2.18(1.53–3.10) 1.49610
Genotyped CEP350 TOR1AIP1 QSOX1 EXPOSED
5q35.3 rs4701085 G 1.84(1.39–2.44) 1.93610
Genotyped ADAMTS2 ZNF354C AX747985 EXPOSED
4q22.1 rs4290865 A 1.98(1.44–2.71) 2.16610
Genotyped FAM190A GRID2 EXPOSED
13q14.3 rs9536579 A 0.54(0.40–0.72) 2.33610
Genotyped OLFM5 MIR1297 OVERALL
7p21.2 rs3801094 A 1.75(1.35–2.27) 2.52610
Genotyped ETV1 ARL4A DGKB OVERALL
8q24.21 rs7841347 A 0.60(0.47–0.76) 2.60610
Genotyped PVT1 MYC TMEM75 OVERALL
15q21.1 rs10519201 A 2.36(1.57–3.56) 3.82610
Genotyped SHC4 EID1 SECISBP2L EXPOSED
22q12.3 rs5756444 G 0.60(0.47–0.76) 3.95610
Genotyped CSF2RB2 C22orf33/TEX33 EXPOSED
Table 3. Region-, Gene- and GO process-based analysis on top SNPs (1-tailed binomial test, n = 759, alpha 0.0025, alpha = 0.01,
alpha = 0.025, respectively).
Region/Gene/GO processes based Cytogenetic Band Position (from - to) Number of SNPs Significant SNPs P
- 1q25.2 (178192161–178267165) 5 4 8.31610
- 3p24.2 (24311166–24397755) 17 7 3.86610
- 3q26.2 (171668688–171738200) 12 6 9.47610
- 4q22.1 (92842088–92925574) 11 3 0.05
- 4q32.1 (160680345–160763147) 11 3 0.04
- 5q23.1 (120950796–121034917) 11 3 0.08
- 5q35.2 (173515657–173599925) 16 4 7.23610
- 5q35.3 (178559043–178654962) 19 5 0.01
- 6q21 (106656091–106738553) 18 5 8.00610
- 7p21.2 (13877273–13974190) 20 6 4.36610
- 7p22.2 (4339181–4436371) 17 9 5.96610
- 8q24.21 (128837336–128935399) 7 6 1.04610
- 9p24.1 (5363441–5453988) 12 5 0.02
- 12q23.3 (107375486–107461372) 13 7 5.78610
- 13q14.3 (53429288–53513774) 12 4 0.02
- 14q11.2 (22334110–22425388) 13 2 0.14
- 15q14 (34381353–34470568) 13 5 2.04610
- 15q21.1 (46959609–47047893) 18 2 0.23
- 19q13.42 (59189856–59266559) 9 1 0.47
- 22q12.3 (35660028–35754794) 19 5 0.03
CEP350 1q25.2 (179933906–180093734) 17 2 0.31
THRB 3p24.2 (24162088–24541232) 54 15 2.29610
SLC7A14 3q26.2 (170167538–171715102) 13 2 0.16
SDK1 7p22.2 (3341374–4303003) 90 5 0.61
PVT1 8q24.21 (128808953–129119976) 34 7 0.02
METALLOENDOPEPTIDASE - - 197 19 4.65610
METALLOPEPTIDASE - - 470 32 0.04
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exposure: OR 45.28, 95% CI 21.52–95.28, P,2610
exposure: OR 15.31, 95% CI 7.78–30.14, P =2610
SNP validation and repli cation
The Italian and Australian study samples showed a marked
degree of heterogeneity (I
statistics, range 0.62–0.97)  (Table
S5). None of the 12 genotyped SNPs with the highest significance
levels in the Italian study were found in the Australian replication
study (Table S4), and nor of these were confirmed by the meta-
analysis (Table S5). Nevertheless, when a regional analysis was
performed in the Australian study sample, we found significant
associations in five chromosomal regions (3q26.2, 4q32.1, 7p22.2,
14q11.2, 15q14) that have reported to be altered in mesothelioma
(Table 5) .
Gene expression analysis in blood and in normal pleural
Gene expression analysis on lymphocytes from Italian healthy
subjects (Text S1) showed a possible expression Quantitative Trait
Locus (eQTL) for the PVT1 (rs7841347) gene (non-parametric
Kruskal-Wallis test P,0.001) (Figure 4). However, expression
analysis from Italian healthy subjects pleural tissue stratified by
PVT1 rs7841347 genotypes did not show any gradient, although a
statistically significant difference (P = 0.01) was found (Figure S4).
Published expression data  (Text S1) confirmed the dysreg-
ulation of MMP14, THRB and MYC genes in MPM, supporting
SNP predictive functional analysis
Using the GenomePipe tool, none of the SNPs with the highest
significance levels included in the present analysis might predict
damage, nor were they located in a regulatory or splicing site.
Figure 2. Regional association plots for 4 of the most consistent chromosome regions. a. 3p24.2, b. 8q24.21, c. 14q11.2, d. 7p22.2.
Consistency was based on haplotype, gene-, region- and pathway analysis. Each SNP is plotted with respect to its chromosomal location (x axis) and
transformed P value (y axis on the left) for associations with MPM. The tall blue spikes indicate the recombination rate (y axis on the right) at
that region of the chromosome. The red-outlined diamond indicate the index SNP and other diamond indicate the genotyped SNPs, the squares
indicate imputed SNPs using as reference 1000 Genomes Pilot 1 CEU population. LD values were calculated only on our control population.
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Even when SNPs in Linkage Disequilibrium (LD) with our top
SNPs (LD$0.8 as measured by pairwise r
) were included in the
analysis no evidence of functional properties of the proxy SNPs
was found. LD refers to two different populations, i.e. HapMap
TSI from Tuscany (Italy) and CEU (HapMap3, File S1), for a total
of 33 and 72 SNPs respectively.
In order to identify genetic risk factors that might contribute to
the development of MPM, we performed a GWAS on 407 Italian
MPM cases and 389 controls.
We performed an independent replication study in an
Australian sample, which included 428 MPM cases (Genetic
Understanding of Asbestos-Related Disease, GUARD, study) and
1,269 controls (Busselton Health Study, BHS).
Among the top SNPs identified in our Italian study sample,
there were several genes previously reported to be involved in
MPM or other cancer types, as well as chromosomal regions
reported to be altered in MPM .
Although no single SNP replicated in the Australian sample,
probably due to the high genetic heterogeneity between the two
studies, regional analyses showed significant signals in 5 of the
chromosomal regions where the Italian top SNPs are located. The
chromosomal region 7p22.2 found in the replication study
includes the SDK1  and FOXK1  genes. Interestingly,
FOXK1 has been reported to interact with BAP1 , which was
recently found to be mutated in mesothelioma . Chromosomal
region 7p22 is located in a fragile sequence (FRA7B) containing
two miRNA genes (mir589 and mir339) and three large genes
(SDK1, THSD7A, MAD1L1), and is highly prone to gaps and
breaks in several cancers .
Another Italian genotyped top-signal (rs7632718) is located in
the SLC7A14 (solute carrier family 7 member 14) gene, which lies
on 3q26.2, which was one of the replicating regions in the
Australian study. Although no link with MPM has been previously
reported for SLC7A14, a chromosomal gain has been described in
this region , suggesting a possible involvement of other genes
The PVT1 (Pvt1 oncogene (non-protein coding)) gene is
involved in several types of cancer [26,27,28,29,30]. It is located
in a large (.300 kb) locus downstream of MYC (53 Kb apart) on
chromosomal region 8q24. The PVT1 locus produces a wide
variety of spliced non-coding RNAs as well as a cluster of six
annotated miRNAs: miR-1204, miR-1205, miR-1206, miR-1207-
5p, miR-1207-3p, and miR-1208 [31,32]. PVT1 was proposed to
regulate c-Myc gene transcription over a long distance . A
functional variant (rs378854) in chromosomal region 8q24 that
modulates PVT1 expression has been associated with prostate
Table 4. Nested multivariate logistic regression models: 1) model 1, without genetic component; 2) model 2, with genetic
MODEL 1 MODEL 2
OR OR_95L OR_95U P OR OR_95L OR_95U P
LOW vs NO EXPOSURE 8.01 4.41 14.54 8.52610
15.31 7.78 30.14 2.86610
HIGH vs NO EXPOSURE 17.33 9.28 32.37 ,2610
45.28 21.52 95.28 ,2610
CLUSTER 2 vs 1 1.76 1.1 2.79 1.74610
2.21 1.29 3.79 4.09610
rs2501618 - - - - 2.23 1.47 3.37 1.52610
rs9833191 - - - - 0.55 0.41 0.73 4.39610
rs7632718 - - - - 1.85 1.41 2.42 9.07610
rs4701085 - - - - 2.05 1.41 2.97 1.75610
rs73034881 - - - - 0.44 0.29 0.67 1.12610
rs3801094 - - - - 1.86 1.39 2.48 2.78610
rs7841347 - - - - 0.51 0.39 0.67 1.56610
rs10815216 - - - - 0.41 0.27 0.60 8.53610
rs2236304 - - - - 1.72 1.19 2.51 4.39610
rs7178364 - - - - 0.45 0.28 0.71 5.66610
*adjusted for age, gender and center of recruitment.
MODEL 1: AIC = 871.3, AUC = 0.76.
MODEL 2: AIC = 730.27, AUC = 0.86.
Figure 3. Receiver Opera ting Cur ves (ROC) for the t wo
multivariate models including asbestos exposure 1) without
and 2) with the 10 most robust and significant genetic variants.
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cancer . In vitro, the rs378854-G allele has been associated with
reduced binding of the transcription factor YY1, a putative tumor
suppressor, and with repressed global transcription in prostate
cancer . The regulation of this chromosomal region is very
complex, as is suggested by the association of several SNPs with
different cancer types , and involves miRNA, lincRNA and
other epigenetic regulations .
The gene-expression analysis on lymphocytes from Italian
healthy subjects showed a possible eQTL for PVT1. Functional
studies are needed to clarify the link between PVT1-associated
SNPs, gene expression regulation and cancer risk taking into
account that in our study PVT1 seems to act only at an early stage
of carcinogenesis as its deregulation has not been observed at later
stages in tumor tissue .
Two other genes that have been reported to be dysregulated in
MPM, are THRB and MMP14 [22,37]. THRB encodes for thyroid
hormone receptor beta (TRb), which could function as a tumor
suppressor. Cell-based studies and xenograft models have dem-
onstrated that TRb is a suppressor of ras-mediated cell prolifer-
ation, transformation, and tumorigenesis . Moreover, TRb
disrupts mitogenic growth factors by suppressing the activation of
extracellular signal-regulated kinases and phosphatidylinositol 3-
kinase signaling pathways to suppress tumor cell invasiveness and
metastasis [39,40]. THRB is located about 28 Mb telomeric to the
BAP1 gene, which is mutated in MPM . A down-regulation of
Table 5. Regional replication of Italian top signals in the Australian study for 5 out of the 20 regions.
Cytogenetic Band BP_start
p Binomial test
p Binomial test
3q26.2 171668688 171738200 9.47338E-05 0.01643691 1.61610
4q32.1 160680345 160763147 0.042137914 0.000649 3.15610
7p22.2 4339181 4436371 5.95584E-05 0.01403811 1.26610
14q11.2 22334110 22425388 0.139471486 0.00100497 1.38610
15q14 34381353 34470568 0.002040183 0.01305659 3.07610
(1-tailed binomial test and meta-analysis).
Figure 4. eQTL:
gene-expression levels in blood cells across rs78941347 genotypes.
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THRB has been documented in MPM versus parietal pleura 
and it is frequently methylated/deleted in non-squamous-cell lung
MMP14 (matrix metallopeptidase 14) has been reported to
influence overall survival in MPM cases , and was significantly
highlighted in our enrichment analysis, together with ADAMTS2,
because of their metalloendopeptidase and metallopeptidase
activities. The matrix metalloproteinases are a family of zinc-
containing enzymes with proteolytic activity against a wide range
of extracellular proteins. Extracellular matrix proteases are
involved in several steps of cancer development and progression,
including angiogenesis and metastasis.
Some of the SNPs with highest significance levels were located
in the genes: CEP350, ETV1 and SHC4. Although they have not
been directly associated with MPM, their involvement in several
cancer types has been described [43,44,45], suggesting the
necessity to further investigate their possible role in MPM
pathogenesis. Considering the closest flanking genes of intergenic
SNPs, the following are noteworthy and could contribute to the
carcinogenic process, as has been reported for other cancer types:
PRDM1 , ATG5 , MYC , EID , RLN1 , CD274
Although our sample size is clearly a limitation for a GWAS, the
Italian and the Australian study samples are, to the best of our
knowledge, the largest MPM series with available DNA, as
mesothelioma is a very rare cancer. A further limitation of GWAS
is that they do not take into account rare variants. The availability
of methods for complete genome sequencing (and the decrease of
the sequencing costs) will allow to circumvent the problem linked
to the identification of rare variants, whose involvement should be
better investigated in future studies.
The negative replication of the Italian top SNPs in the
Australian study should be revised on the basis of the following
considerations: i) the two studies had a marked degree of
heterogeneity as shown by the I
statistics; ii) no exposure
assessment was available for the Australian control group.
Notwithstanding these discrepancies, we observed an intriguing
significant regional replication in the Australian study for 5 out of
20 Italian top signals.
Most of the top-signals we identified were located in chromo-
somal regions reported to harbor aberrant alterations in meso-
thelioma, and cause an at most 2–3 fold increase in MPM risk.
Moreover, asbestos exposure in our study was associated with a
remarkable increase in MPM risk, which became even more
evident when the contribution of genetic factors was taken into
account, with a significant improvement of asbestos exposure risk
In conclusion, our results support the complementary role of
genetic background in asbestos-related carcinogenesis of the
pleura, indicating that genetic risk factors should be taken into
account to understand MPM physiopathology, and to better
define the MPM risk profile of people with a high exposure to
All MPM cases reported on in the present report gave written
informed consent. This study was performed according to the
principles of the Declaration of Helsinki and in agreement with
ethical requirements. Approval was obtained from the Istituto
Nazionale per la Ricerca sul Cancro Ethics Committee for the
studies in Genoa and La Spezia, and from the Human Genetics
Foundation (HuGeF) Ethics Committee for the studies in Casale
Monferrato and Turin. The Australian replication study was
specifically approved by the Human Research Ethics Committee
of the University of Western Australia.
Italian study sample
The Italian study sample is composed of MPM cases and
controls from cities located in Northern Italy: Casale Monferrato
and Turin in the Piedmont Region, and Genoa and La Spezia in
the Liguria Region (Table 1; details in Text S1). The study in
Casale Monferrato was a population-based MPM case-control
study , and included 241 MPM patients and 252 population
controls of Italian nationality and Caucasian ethnicity. The study
in Turin was a hospital-based MPM case-control study , and
consisted of 91 MPM patients and 56 controls of Italian nationality
and Caucasian ethnicity. The hospital-based study in Genoa and
La Spezia included 75 incident MPM cases . Controls are 81
healthy subjects or patients hospitalized for non-neoplastic/non-
All the three of the above-mentioned Italian studies were
registry-based and therefore no selection criteria were applied to
MPM cases; they needed only to be residing in the study area at
the time of diagnosis. Only cases with a pathological diagnosis
(based on histology or cytology with confirmatory immunohisto-
chemical staining) were eligible for inclusion in the present
analysis. Study periods in the Italian studies were different (Casale
Monferrato: January 2001 to December 2006; Turin: January
2004 to October 2008; Genoa and La Spezia: April 1996 to
February 2006 for cases and February 1997 and November 2006
for controls). For practical reasons, the study in Turin was limited
to cases admitted to the main metropolitan hospitals.
Asbestos exposure was carefully assessed in all the Italian cases
and controls. After reviewing individual occupational histories,
asbestos exposure was reclassified for the overall sample by the
same expert (D.M.) as ‘‘no/unlikely’’ (no acknowledged occupa-
tional or environmental exposure), ‘‘low’’ (low exposure probabil-
ity, or definite low exposure), and ‘‘high’’ (definite and high
exposure; asbestos-cement and asbestos-textile workers, insulators,
shipyard workers and dockers).
Australian replication study
Australian MPM cases were part of the GUARD study, which
consisted of individuals who had been exposed to asbestos and
diagnosed with MPM (n = 428) and who attended a hospital clinic
in Perth, Western Australia between 1988 and 2010 . DNA
samples and clinical data from these individuals were obtained and
MPM diagnosis was confirmed after pathological, radiological and
clinical review with confirmation from respective cancer registries
in Western Australia (Western Australia Mesothelioma Registry)
The GUARD study subjects are primarily male (88.8%) with an
average age of 67610.3 years. Most BHS study subjects are female
(57.4%) and the average age is 54617.2 years. Control samples
(n = 1,269), with no information on asbestos exposure, were
obtained from the population-based BHS . MPM cases were
excluded after genotyping if they were: related to another
individual, had a low call GWAS rate (,97%), were not
Caucasian/European based on principal component analysis,
had ambiguous sex, or had low heterozygosity compared to the
rest of the sample.
Whole-genome genotyping was done on a HumanCNV370-
Quad BeadChip (Illumina Inc., San Diego, CA, USA) for 716
samples. The remaining 80 samples were tested on a Human610-
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PLOS ONE | www.plosone.org 8 April 2013 | Volume 8 | Issue 4 | e61253
Quad (which includes 100% of the HumanCNV370 BeadChip
SNPs) as the HumanCNV370-Quad had been discontinued.
Genotypes were assessed by GenomeStudio V2011.1(Illumina
Inc., San Diego, CA). The 12 most significant SNPs from the
Italian studyS were individually genotyped in the Australian
replication study with a 59-nuclease assay (AppliedBiosystems, CA,
Genotyping quality controls. A cut-off a genotyping call
rate of 0.98 was set, leading to the exclusion of 18 study subjects.
SIdentity By Descent (IBD) estimation using the Identity By State
(IBS) distance was used to check genotypic identity or relatedness
among subjects (PLINK software , File S1). Subjects with
IBD$0.05 (n = 16) were considered consanguineous and excluded
from further analyses. We additionally excluded three samples
with an X chromosome inbreeding homozygosity estimate of
about 0.5. Thirty-seven subjects (4.64%) were removed from the
analysis, leaving 759 subjects (392 cases and 367 controls).
SNPs with minor allele frequency ,1% (n = 15,252), those
having .0.05 missing genotypes (n = 11,535) and those deviating
from Hardy-Weinberg equilibrium (HWE) in the control popula-
tion (P,0.001, n = 1,157) were excluded from the analysis, for a
final study data-set of 330,879 SNPs, which were analyzed for
their potential association with mesothelioma.
Population structure and association analysis. The
population structure was investigated by PCA (PLINK Software,
File S1, Covariance Method ). A new discrete covariate was
defined by the two principal components (Figure S1), and was
included in the following logistic regression analysis. PCA results
were further confirmed by the K-means clustering analysis 
(data not shown). The effective removal of any population
structure bias was checked by the l-inflation factor parameter
 (Figure S2).
We tested for 330,879 SNPs for their association with
mesothelioma by 2-sided logistic regression analysis on a per-
allele additive model after adjusting for age, gender, PCA cluster,
center of recruitment and exposure level, both in the overall
Italian sample (n = 759) and among exposed-only Italian subjects
(n = 593) (high and low exposure). After Bonferroni correction, we
considered alpha = 1.51610
(0.05/330879) as a threshold of
significance. The analyses were performed with PLINKv1.07 (File
S1)  and Rv2.10.1  software. The software Impute.v2
[61,62] was used to impute 5,333,982 SNPs, using the 1000
genomes (http://www.1000genomes.org/) and HapMap3 (File
S1) genotype panels as reference datasets.
Haplotypes (Table S3) within the chromosomal regions where
the most significant SNPs were located (considering sliding
windows from 2 to 10 SNPs; PLINK Software, File S1) were
also tested for any association with MPM in the overall Italian
Meta-analysis and replication. A meta-analysis of the
Italian-study top 12 genotyped SNPs was done on data from the
whole genome genotyping (Human610-Quad BeadChip, Illumina)
of 428 cases and 1269 Australian controls of European descent
(GWAMA software, File S1 ). A random-effects model was
used due to the presence of genetic heterogeneity (I
.50%; Table S5).
Multivariate analysis. The cumulative effect of the SNPs
with highest significance levels was investigated by two-sided
multivariate logistic regression analysis, comparing the prediction
accuracy of two models: the first considering asbestos exposure as
a predictor and adjusting for demographic covariates (recruitment
center, gender, age, geographical cluster), and the second identical
to the first, but also including the genetic component (genotypes).
SNPs included in the second multivariate model were selected
among the top 20 markers (12 genotyped and 8 imputed),
excluding 4 SNPs (rs4290865, rs1354252, rs1072577, rs10519201)
because of negative internal replication between Casale Mon-
ferrato and pooled Turin-Genoa studies, and 6 SNPs (rs742109,
rs1508805, rs9536579, rs5756444, rs6897549, rs71365421) be-
cause they did not replicate in the Australian study on the regional
analysis and were not intragenic.
An internal validation of the two models was done by randomly
splitting the overall Italian sample in two groups 10,000 times,
each time performing a two-sided logistic regression in the first
group and verifying the accuracy of estimation in the second
group. The average AIC under 10,000 permutations and AUC
were used as measures of the fit and the prediction power of the
Gene-region enrichment and SNP functional prediction
A GSEA (File S1)  was performed on the genes in
which the top SNPs are located (9 genes out of 20 signals): PVT1
(gene ID 5820), CEP350 (ID 9857), THRB (ID 7068), ETV1 (ID
2115), C9orf46 (also known as PLGRKT; ID 55848), MMP14 (ID
4323), ADAMTS2 (ID 9509), SLC7A14 (ID 57709), SHC4 (ID
399694). The list was tested for over-representation using the
curated Molecular Signatures Database (MSigDB) 7, specifically i)
KEGG 8 (File S1), REACTOME and BioCarta pathway
databases, ii) the GO (File S1) gene set 9. Gene set enrichment
significance was tested by a hyper-geometric test that evaluates the
distribution of overlapping genes over all genes in the gene set
Region-, gene- and GO (File S1) process-based analyses were
also performed . We investigated the occurrence of multiple
signals in those genes and chromosomal regions, where the
significant SNPs from the single SNP analysis are located, as well
as those from genes belonging to the pathways identified by the
GO (File S1) process-based analysis (Table 3).
We tested 20 candidate chromosomal regions, and five genes
(CEP350, THRB, SLC7A14, SDK1 and PVT1) for which there were
enough representative SNPs genotyped, and two GSEA significant
GO processes (File S1) (metalloendopeptidase activity and
metallopeptidase activity). After Bonferroni correction, we adopted
the following significance thresholds: alpha = 0.0025, alpha = 0.01,
alpha = 0.025, for region-based, gene-based and GO (File S1)
process-based analysis respectively.
Prediction of functional SNPs has been carried out with several
softwares, including GenomePipe software, which is freely
available at website of the National Institute of Environmental
Health Sciences (http://snpinfo.niehs.nih.gov/seleGWAs.htm)
and the Pupasuite3.1 software (http://pupasuite.bioinfo.cipf.es/).
Gene-expression analysis. The expression levels of the nine
genes corresponding to the most common intragenic SNPs
(Table 2) and of MYC, which is neighbor to PVT1, were examined
using data from the HapMap (File S1) CEU gene-expression
database, and the GenoPheno database , an internal database
which includes genotypic, phenotypic, and gene-expression data
from the peripheral blood of 120 healthy Italian volunteers (Text
S1). We considered the average expression levels of probes and,
when feasible, tested for differential expression among the three
genotypes (Kruskal-Wallis test).
In addition, the mRNA levels of the PVT1, MYC and THRB
genes were measured by quantitative real-time PCR in 79 normal
pleural tissues from donors that underwent thoracoscopy for
conditions other than MPM, who signed an informed consent
form (Text S1).
A GWAS on Malignant Pleural Mesothelioma
PLOS ONE | www.plosone.org 9 April 2013 | Volume 8 | Issue 4 | e61253
Figure S1 Principal Component Analysis (PCA) plots:
first vs second PC. A) Cases and controls are plotted for the
overall study and for each of the three study samples (Turin,
Casale Monferrato and Genoa); B) birth places (Northern,
Central, Southern Italy, Sardinians and Other Caucasians) are
plotted for the overall study and for each of the three study
Figure S2 Supplementary figure 1:Q-Q plots for GWAS
of mesothelioma in the Italian population. This Q-Q plots
are based on logistic regression allelic P after standard quality
control. The estimated l inflation factor was ,1.03. Plot A shows
the Q-Q plot for the overall Italian population, whereas Plot B
refers to the exposed-only population.
Figure S3 Regional association plots for additional 4 regions (a.
3q26.2, b. 4q32.1, c. 7p21.2, d. 15q14) replicating in the
Australian study. Each SNP is plotted with respect to its
chromosomal location (x axis) and its log
transformed P value
(y axis on the left) for associations with MPM. The tall blue spikes
indicate the recombination rate (y axis on the right) at that region
of the chromosome. The red-outlined diamond indicate the index
SNP and other diamond indicate the genotyped SNPs, the squares
indicate imputed SNPs using as reference 1000 Genomes Pilot 1
CEU population. LD values were calculated only on our control
Figure S4 RT-PCR of PVT1 and MYC genes-expression
levels in 79 normal pleural tissues expression levels
across rs78941347 genotypes.
Table S1 Italian top 8 imputed SNP list.
Table S2 Gene Set Enrichment Analysis.
Table S3 Significant Haplotype Results for 3p24 and
Table S4 Replication of the 12 genotyped Italian top
SNPs on GUARD-BHS Study.
Table S5 Meta-analysis of Italian and Australian stud-
ies for the top 12 genotyped Italian SNPs.
Text S1 Supplementary Materials.
File S1 URLs.
We wish to thank all the patients and healthy controls that generously
participated in the study, the attending MDs that supported it, Dr Silvia
Polidoro for her contribution to our the first technical issues with the
Illumina platform, and Progetto RoPHS, Regione Piemonte - Bando
Scienze umane e sociali (I.D.).
Contributed to writing the manuscript: SG CDG AR GF FR. Evaluated
asbestos exposure: DM. Obtained and/or supervised clinical information:
RG EP AH FA ER CC SM. Obtained funding for sample collection and
genotyping: ID GM CM LJP ALJ SB DU DM PGB. Participated in critical
review of the manuscript for intellectual content: GM SG MB GF DF FV
GC CDG FR AR AH EC ST MP MG AA CC FA ER PGB RL RG EP
MN AWBM NHdK JH JB ALJ JC BWR SM LJP DM DU SB CM ID.
Conceived and designed the experiments: ID GM SB CM. Performed the
experiments: SG AR MB AA EC FR ID GM JH. Analyzed the data: GF
CM DF ST FV GC CDG FR MP. Contributed reagents/materials/
analysis tools: MB MN DU RL MG PGB SB AH JC BWR AWBM LJP
ALJ NHdK JB CC FA. Wrote the paper: GM ID CM.
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A GWAS on Malignant Pleural Mesothelioma
PLOS ONE | www.plosone.org 11 April 2013 | Volume 8 | Issue 4 | e61253