Open access peer-reviewed chapter

Genetic Alterations of Malignant Pleural Mesothelima

Written By

Benjamin Wadowski, David T. Severson, Raphael Bueno and Assunta De Rienzo

Submitted: 14 January 2020 Reviewed: 26 August 2020 Published: 11 November 2020

DOI: 10.5772/intechopen.93756

From the Edited Volume


Edited by Sonia Maciá

Chapter metrics overview

475 Chapter Downloads

View Full Metrics


Malignant pleural mesothelioma (MPM) is a highly aggressive tumor that arises from the mesothelial cells lining the pleural cavity. Asbestos is considered the major factor in the pathogenesis of this malignancy, with more than 80% of patients with a history of asbestos exposure. MPM is characterized by a long latency period, typically 20–40 years from the time of asbestos exposure to diagnosis, suggesting that multiple somatic genetic alterations are required for the tumorigenic conversion of a mesothelial cell. In the last few years, advancements in next-generation sequencing and “–omics” technologies have revolutionized the field of genomics and medical diagnosis. The focus of this chapter is to summarize recent studies which explore the molecular mechanisms underlying this disease and identify potential therapeutic targets in MPM.


  • pleural mesothelioma
  • next-generation sequencing
  • transcriptome
  • exome sequencing
  • tumor suppressor gene

1. Introduction

Malignant pleural mesothelioma (MPM) is a lethal cancer of the mesothelial cells lining the pleural cavity and, less frequently, the pericardium, peritoneum, and tunica vaginalis [1]. Many years after the peak of asbestos use in United States, 3200 cases of MPM continue to be diagnosed annually, indicating that the U.S. population remains at risk of exposure to asbestos and development of mesothelioma [2]. There are two major histological variants: epithelioid, which accounts for about 60% of cases and has the more favorable prognosis, and sarcomatoid, whose incidence is 10%. The remaining cases demonstrate histologic characteristics of both types and are classified as biphasic [3]. The prognosis for patients with MPM is poor, with a median survival of 5–15 months [3]. However, some patients with early MPM who undergo multimodality therapy including surgical resection and chemotherapy demonstrate longer-term survival of up to 25% at 5 years [4].

Many studies have shown a causal relationship between exposure to asbestos and mesothelioma (reviewed by Bianche et al. [5]). Although it has been suggested that brief asbestos exposure is sufficient to induce disease, MPM is the consequence of prolonged exposure in most cases. However, only a small percentage of individuals exposed to asbestos develop MPM, suggesting that genetic predisposition may modulate the effect of exposure to asbestos. In addition, 20% of MPM cases with unknown asbestos exposure have been related to other risk factors such as radiation therapy and thorotrast [6].

Studies conducted on large numbers of patients indicate that the time between asbestos exposure and diagnosis of MPM is generally more than 20 years. The molecular mechanisms for the transformation of mesothelial cells are unknown; it has been suggested that asbestos induces multiple chromosomal aberrations, particularly deletions, facilitating oncogenesis [7].

Investigations prior to the advent of next-generation sequencing (NGS) revealed the complexity of the genetic alterations observed in MPM tumors by using karyotypic and comparative genomic hybridization (CGH) analyses [8, 9]. Chromosomal losses were found to be more frequent than gains and particular chromosomal regions (1p22, 3p21, 4q, 6q, 9p21, 13q13–14, 15q11–15, and 22q12) were deleted at higher frequency in MPM tissues and cell lines [10, 11, 12]. Two tumor suppressor genes (TSGs) were identified by positional cloning approaches: CDKN2A at 9p21 and NF2 at 22q12. In the last few years, the genetic landscape of MPM has been characterized using high-throughput technologies [13, 14, 15]. The focus of this chapter is to summarize the major genetic changes occurring in MPM as identified by high-throughput sequencing and to describe the novel insights obtained through transcriptomic studies.


2. Exome sequencing studies

NGS technologies have allowed the sequencing of DNA and RNA at unprecedented speed, uncovering potential driver genes and creating novel biological applications [16]. In the last decade, NGS has been used to detect driver genetic mutations in cancer and provide new insights into tumorigenesis.

Shotgun pyrosequencing was used to characterize RNA expression levels and mutations of four patients in the first effort to investigate MPM by NGS. Several different mutations were found in the four transcriptomes. In addition, RNA editing gene deletions and gene silencing were identified [17].

In 2010, the first whole genome sequence of one MPM tumor and matching normal tissue was conducted using a combination of sequencing-by-synthesis and pyrosequencing methodologies [18]. This study showed that aneuploidy and chromosomal rearrangements were more numerous than point mutations in this tumor. One large deletion in the dipeptidyl peptidase like 10 (DPP10) gene, altering the expression of the corresponding transcript, was further investigated in 53 additional MPM tumors. Patients expressing DPP10 had statistically longer survival compared to patients lacking DPP10 expression [18].

In 2016, Bueno et al. conducted an extensive analysis of the mutational landscape of MPM. Ninety-nine MPM tumors were examined by whole exome sequencing, whereas additional 103 samples were characterized by targeted exome sequencing [13]. BAP1, NF2, TP53, SETD2, DDX3X, ULK2, RYR2, CFAP45, SETDB1 and DDX51 were found to be significantly mutated (q-score ≥ 0.8), and recurrent mutations were found in SF3B1 (2%) and TRAF7 (2%).

In 2018, The Cancer Genome Atlas (TCGA) program performed a comprehensive molecular profiling of 74 primary MPM samples including exome sequencing, copy-number arrays, mRNA sequencing, noncoding RNA profiling, DNA methylation, and reverse-phase protein arrays [15]. The significantly mutated genes in this study were BAP1, NF2, TP53, LATS2, and SETD2. Furthermore, this study identified a new near-haploid molecular MPM subtype.

The TCGA study performed a comparison of the significantly mutated genes between the Bueno and TCGA cohorts [15]. This analysis identified five genes that were frequently mutated in both studies: BRCA1-associated protein-1 (BAP1), neurofibromin 2 (NF2), tumor protein P53 (TP53), SET domain containing 2, histone lysine methyltransferase (SETD2), and SET domain bifurcated histone lysine methyltransferase 1 (SETDB1). The large tumor suppressor kinase 2 (LATS2) gene was found frequently altered in the TCGA cohort alone, whereas four additional genes, DEAD-box helicase 3 X-linked (DDX3X), Unc-51-like autophagy-activating kinase 2 (ULK2), ryanodine receptor 2 (RYR2), and DEAD-box helicase 51 (DDX51) were identified as commonly mutated in the series from Bueno et al. (Table 1).

Gene symbol Gene ID Chromosomal location Number of mutations in Bueno’s cohort Number of mutations in Hmeljak’s cohort Total
BAP1 ENSG00000163930 3p21.1 55 17 72
NF2 ENSG00000186575 22q12.2 39 19 58
TP53 ENSG00000141510 17p31.1 17 10 27
SETD2 ENSG00000181555 3p21.31 18 8 26
SETDB1 ENSG00000143379 1q21 7 3 10
LATS2 ENSG00000150457 13q12.11 2 9 11
DDX3X ENSG00000215301 Xp11.4 8 0 8
RYR2 ENSG00000198626 1q43 4 1 5
ULK2 ENSG00000083290 17p11.2 4 0 4
DDX51 ENSG00000185163 12q24.33 3 0 3
Total 157 67 224

Table 1.

Number of mutations in each gene in the two studies.

2.1 BAP1

BAP1 is located on the short (p) arm of chromosome 3, at position 21.1., a region frequently deleted in MPM [9]. This gene encodes for a deubiquitinase involved in cell cycle regulation, modulation of gene transcription, cellular differentiation, and DNA repair [19]. BAP1 is one of the most commonly mutated genes in MPM [1315, 20, 21]. Germline BAP1 mutations have been linked to the development of BAP1 tumor predisposition syndrome, which includes uveal and cutaneous melanoma, atypical Spitz tumors, renal cell carcinoma, and MPM. In all these malignancies but MPM, BAP1 mutations are associated with poor prognosis [22, 23]. In contrast, some studies have shown that patients with MPM carrying BAP1 mutations have longer overall survival compared to patients with wild-type BAP1 [24, 25]. In one study, BAP1 immunohistochemistry (IHC) was performed using tissue microarray including 229 MPM tumors. The results showed that loss of BAP1 nuclear staining was associated with longer median survival of 16.11 months (95% CI: 12.16–20.06) versus 6.34 months for patients with nuclear BAP1 staining (95% CI: 5.34–7.34) (P < 0.01) [24]. Baumann et al. compared the survival in 23 patients with MPM carrying germline mutations in BAP1 with a control group of MPM patients from the Surveillance, Epidemiology, and End Results (SEER) database and found a 7-fold increase in long-term survival in patients with BAP1 mutation [25].

Given its prevalence in MPM, loss of nuclear BAP1 expression by IHC is commonly used as a diagnostic marker in MPM [26, 27].

Recently, BAP1 status has been associated with drug response [28, 29]. In vitro studies showed MPM cell lines carrying BAP1 mutations were significantly less sensitive to gemcitabine compared to wild-type cells. Silencing of BAP1 in MPM wild-type cells significantly increased resistance to gemcitabine, suggesting a role of BAP1 in drug response [28]. Kumar et al. performed a retrospective study analyzing presence or absence of nuclear BAP1 by IHC in MPM tumors from 60 patients in the MS01 trial (NCT00075699) [29]. Nuclear BAP1 expression was associated with a small but statistically nonsignificant decrease in survival in patients treated with vinorelbine.

2.2 NF2

NF2 is located on the long (q) arm of chromosome 22 at position 12.2. Loss of chromosome 22 is a common alteration in MPM [9]. This gene codes for a protein known as merlin (moesinezrin-radixin-like protein) or schwannomin, which regulates key signaling pathways involved in cell growth, adhesion, and microtubule stabilization [30]. Germline mutation or chromosomal deletion of NF2 causes the neurofibromatosis type 2 syndrome, which is associated with tumors of the cranial and peripheral nerves as well as meningioma and ependymoma [31]. Germline mutations in NF2 have also been linked to MPM; however, patients with both neurofibromatosis type 2 syndrome and MPM are extremely rare [32]. Recent studies have shown that NF2 mutations occur in 14–19% of MPM [13, 14, 15, 20]. In addition, karyotype and/or FISH analyses demonstrated that 56% MPMs have shown loss of chromosome 22q. Deletions of 22q are more frequently associated with epithelioid than non-epithelioid MPM (p = 0.037) [20].

In 2009, a study suggested that NF2 may be inactivated by upstream regulators in MPM tumors where no NF2 aberration can be detected [33]. In an investigation of 204 MPM patients, low cytoplasmic merlin expression was found to predict shorter recurrence interval and shorter overall survival [34]. Lopez-Lago et al. investigated the association between loss of merlin and mTORC1 activation in MPM cell lines and found that merlin-negative or merlin-depleted cell lines were more sensitive to the growth-inhibitory effect of rapamycin [35]. In 2014, low merlin expression was found to be associated to increased sensitivity of MPM cell lines to a FAK inhibitor, VS-471 [36]. However, in clinical trials, the FAK inhibitor defactinib did not improve progression free or overall survival in patients with MPM after first-line chemotherapy [37].

2.3 TP53

Located at 17p31.1, TP53 codes for tumor protein p53 (p53), which is a sequence-specific DNA binding protein that regulates transcription and has a tumor suppressor function controlling cell apoptosis in presence of DNA damage [38]. Named “the guardian of the genome,” p53 is involved in many cellular processes such as checkpoint control, cellular senescence, and BCL-2 mediated apoptosis [39]. TP53 is, overall, the most frequently altered gene in human cancer [40]. The frequency of TP53 mutations in MPM across different studies is variable, but overall it is much lower than in other solid tumors [13, 14, 15, 20]. TP53 was significantly more frequently mutated in women (10/40; 25%) compared to men (17/169, 10%) (Fisher’s exact P = 0.044) when all samples included in two large MPM studies [13, 15] were analyzed. In addition, Bueno et al. reported that MPM patients with mutations in TP53 had shorter overall survival than those with wild-type TP53 (p = 0.0167) [13].

2.4 SETD2

SETD2 maps to 3p21.31. It encodes a histone methyltransferase specific for lysine-36 of histone H3 which regulates transcription through epigenetic mechanisms [41]. Inactivating SETD2 mutations have been identified in multiple cancers [42]. In particular, targeted sequencing revealed SETD2 bi-allelic inactivation in clear cell renal cell carcinoma tumors suggesting for the first time that SETD2 may contribute to tumor formation [43]. In MPM, single nucleotide mutations in SETD2 as well as 3p losses are frequently observed [13, 15, 44]. In the last few years, SETD2 alterations have been linked to mechanisms of resistance to DNA-damaging chemotherapy in several cancers [45, 46].

2.5 SETDB1

SETDB1 is positioned at 1q21, another region frequently deleted in MPM [9], and codes for histone-lysine N-methyltransferase SETDB1 which trimethylates Lys-9 of histone H3 [47]. As an epigenetic modulator, SETDB1 has a critical role in several biological processes such as embryonic development, adipocyte differentiation, and inflammation, as well as providing regulation of several signaling pathways including the P13K-AKT axis, p53, the STAT1-CCND1/CDK6 axis, and gene promoter methylation [48].

Targeted deep sequencing has revealed somatic SETDB1 mutations in 10% (7/69) patients with MPM [49]. No significant correlation between mutation in SETDB1 and survival was found (p = 0.351). Mutations in SETDB1 were also identified in 3% (7/202) of MPMs in a different cohort [13]. Hmeljak et al. found that SETDB1 mutations were present together with TP53 and extensive loss of heterozygosity in 3% of MPM. This rare genomic subtype was associated with female sex and younger age at diagnosis [15].

2.6 LATS2

LATS2, located on 13q12.11, encodes for a serine/threonine kinase which is involved in a broad array of programs such as cell cycle regulation, cell motility, and differentiation [50]. Loss of LATS2, either through copy number alteration or mutation, has been identified in several different cancer types [51], as well as in MPM [1552]. In a cohort of 266 MPM samples, mutations in LATS2 were observed in 5% of the samples, with lower frequency in epithelioid compared to non-epithelioid samples. In addition, LATS2 mutations were more frequent in patients without asbestos exposure (7%) than those exposed (2%) [53]. Another study identified a new molecular subgroup of MPM characterized by a co-occurring mutation in LATS2 and NF2. MPM patients in this subgroup had poor prognosis compared to the cohort at large [54].

Several investigations have linked LATS2 to the transcription regulator YAP involved in the Hippo pathways. Mizuno et al. found that inactivation of LATS2 leads to YAP overexpression, which, when knocked down, inhibits cell motility and invasion in vitro [55]. Another study demonstrated that LATS2 is a key binding partner of AJUBA, which suppresses YAP activity in mesothelioma [56].

2.7 DDX3X

DDX3X resides on Xp11.4 and encodes an ATP-dependent RNA helicase with RNA-independent ATPase activity stimulated by either DNA or RNA [57]. DDX3X has both cytoplasmic and nuclear functions including translation, regulation of transcription, pre-mRNA splicing, and mRNA export [58]. Its functions are complex and varied: DDX3X has been recognized as both an oncogene and a tumor suppressor, sometimes within the context of a single type of cancer [59]. An analysis of the COSMIC database found that 12% of genetic abnormalities in DDX3 are typical for tumor suppressors, while 81% are more typical for gain of function [59].

2.8 RYR2

RYR2 is located at 1q43. It encodes a member of the ryanodine receptor family of calcium channels, highly expressed in cardiac muscle but also found in smooth muscle and the nervous system [60]. The release of calcium from the sarcoplasmic reticulum into the cytoplasm via RyR2 triggers contraction in myocytes, whereas in the brain, it aids in functions related to learning and memory [60]. Although mutations in RYR2 have been reported in other cancers [61], RYR2 mutations in MPM have been identified only in one study [13].

2.9 ULK2

ULK2 maps on 17p11.2. It codes for an Atg1 homolog and serine/threonine kinase which normally localizes to the membrane of autophagosomes and plays a key role in autophagy, particularly in the setting of nutrient deprivation or mTOR inhibition [62]. ULK2 has been linked to the development of astrocytoma [63], and colorectal cancer [64]. Rare ULK2 mutations have been identified in MPM [13]. In spheroid models of MPM, autophagy was successfully inhibited by the ULK1/2 inhibitor MRT 68921 [65].

2.10 DDX51

DDX51 resides on 12q24.33. It is a ribosome synthesis factor required for the formation of the 3′ end of 28S rRNA [66]. Abnormal function of DDX51 has been linked to NSCLC, leukemia, and breast cancer [67, 68, 69]. Few DDX51 mutations have been found in MPM [13].


3. Transcriptome sequencing studies

Since gene expression is linked to tumor behavior, bulk expression profiling of tumors has revolutionized our understanding of cancer by giving insight into the expression levels of thousands of genes measured at once. In addition, the allocation of cancer specimens into molecular clusters having similar biological and clinical characteristics has improved the understanding of the molecular biology of tumors and identified both actionable targets for therapies as well as biomarkers for prediction of response [70].

In 2005, Gordon et al. profiled 40 MPM tumors using microarray technologies [71]. Four normal pleura specimens and four normal lung tissues were included in the analysis as controls because MPM arises from mesothelial cells of the pleura and often involves the lung parenchyma [71]. Unsupervised cluster analysis revealed four distinct subclasses with two, named C1 and C2, consisting only of MPM samples. These two clusters had epithelial (88%) and mixed (78%) subtypes, respectively, showing a partial correlation with tumor histology. Differential gene expression analysis demonstrated genes related to cytoskeletal/support, such as keratins, cadherins, and other proteoglycans, were over-expressed in cluster C1, whereas genes associated with extracellular matrix and structural proteins such as collagen, actin, biglycan, and fibronectin were highly expressed in subclass C2 [71].

In 2014, a study from de Reynies et al. generated a transcriptomic classification of MPM using 38 primary cultures [72]. Consensus clustering of the expression profiles identified two groups of MPM, C1 and C2, which are partially related to histology. Epithelioid MPM were found in both clusters, whereas sarcomatoid tumors clustered only in C2. In addition, tumor samples in C1 tended to have more frequent mutations in BAP1 (P = 0.09) and deletions of the chromosomal region 3p21 (P < 0.01), where BAP1 is located. Furthermore, 40 genes that discriminated the two groups were used to validate the molecular classification in 108 MPM tumors. Survival analyses showed that patients in C2 had shorter survival compared to the survival of patients in cluster C1 (P = 0.02). This difference persisted when only epithelioid samples were included (P < 0.01) [72]. Pathway analyses revealed that the most deregulated pathways were those related to the epithelial-to-mesenchymal transition (EMT) process [72].

In 2016, a seminal publication on genomics in MPM described unsupervised consensus clustering of RNA sequencing data from 211 MPM tumors. This analysis classified the samples into four distinct molecular clusters: epithelioid, biphasic-epithelioid (biphasic-E), biphasic-sarcomatoid (biphasic-S), and sarcomatoid [13]. The clusters were loosely associated with the spectrum from epithelioid to sarcomatoid histology. Epithelioid and biphasic samples were distributed in all four subgroups, whereas sarcomatoid tumors were only in one cluster. Biphasic samples clustered according to the proportion of epithelioid and sarcomatoid cells contained in the specimen; biphasic tumors with the highest portion of sarcomatoid cells grouped with the sarcomatoid samples. Notably, patients in the epithelioid cluster had longer overall survival compared to the survival of patients in the other three groups. Differential expression analysis of the sarcomatoid and epithelioid clusters revealed that genes related to the EMT process were differently expressed between the two groups, and that ratio of two genes CLDN15 and VIM (C/V score) significantly differentiated the four clusters [13].

A different approach to classify MPM tumors was used by Hmeljak et al. [15]. To determine whether a multi-platform molecular profiling may offer additional power to identify subsets of MPM, two clustering algorithms, iCluster [73] and PARADIGM [74] were used to integrate somatic copy-number alteration, gene expression, and epigenetic data from 74 MPM samples. Both algorithms grouped the samples into four distinct clusters with high concordance between the two methods in the assignment of the sample into the groups. Survival analyses showed significant differences in survival across the four groups. In addition, the four clusters were significantly associated with histology: cluster 1 contained many epithelioid samples, whereas cluster 4 was enriched for sarcomatoid tumors as found in previous studies [13, 71, 72]. This study, using a small number of samples, mostly epithelial, confirmed that genes related to the EMT process were differentially expressed between the two most extreme clusters [15].

In 2019, unsupervised clustering of microarray profiles assigned 63 primary MPMs into four groups (C1A, C1B, C2A, and C2B) [75]. Then, a meta-analysis of mesothelioma expression profiles was conducted to compare these clusters with the groups from previous classifications [13, 15, 71, 72, 75, 76]. This analysis identified two highly correlated MPM clusters present in all expression profiles, which corresponded to the extreme epithelioid and the sarcomatoid phenotypes. The remaining groups did not associate closely suggesting that they may represent different points of a continuum or “histo-molecular gradient” of epithelioid and sarcomatoid components. A deconvolution approach was used to identify novel insights into the intra-tumor heterogeneity of MPM by dissecting whole tissue RNA-sequencing signatures into biologically relevant components. This analysis produced two molecular signatures of 150 genes, E-score and S-score, which were related to histology and recapitulated the molecular classification. These signatures reflected the proportion of epithelioid-like and sarcomatoid-like components within each MPM tumor. In addition, the proportions of these cellular components were significantly associated with prognosis [75].

Regardless of the metric used, the whole transcriptome studies indicate that MPM is characterized by a molecular gradient associated with the EMT process. Most recently, the relationship between the C/V score [18] and other published metrics [75, 77] associated with the EMT process has been investigated [78] demonstrating a significant correlation of the C/V score with other molecular signatures. These results indicate that the ratio of just two genes can be sufficient to determine the “EMT-component” in each MPM [78].


4. Clinical significance

While further work is needed before these data can be applied directly to patient care, an understanding of the molecular heterogeneity of MPM and the mutations that contribute to different subtypes can have a meaningful impact on the direction of clinical research in this field. In 2014, in vitro and tumor xenograft experiments suggested that low Merlin (NF2 protein) expression may predict increased sensitivity of MPM cells to a FAK inhibitor, VS-4718 [36]. Subsequently, the use of defactinib, a FAK inhibitor, was investigated in the neoadjuvant setting for surgically resectable disease (a “window of opportunity” study). The treatment was well tolerated and resulted in successful inhibition of FAK, as well as inhibition of multiple cancer stem cell markers such as CD133 and SOX2 (Bueno et al., 2018 personal communication, International Mesothelioma Interest Group (IMIG) Conference, 2016 Birmingham UK). The use of defactinib as maintenance therapy following first-line chemotherapy in advanced MPM was also assessed in the COMMAND trial, a phase II randomized placebo-controlled study. Three hundred forty-four patients were stratified by merlin expression and randomized; however, there was no significant improvement in progression-free survival (4.1 [95% CI: 2.9–5.6] versus 4 [95% CI: 2.9–4.2] months) or overall survival (12.7 [95% CI: 9.1–21] versus 13.6 [95% CI: 9.6 to 21.2] months) of patients treated with defactinib compared to placebo [37].

Knowledge of key mutations in MPM has guided investigations into other forms of targeted therapy, although many are still at the preclinical stage. For example, LaFave and colleagues found evidence that loss of Bap1 expression increases Ezh2 expression in xenograft and Bap1 knock-out mice and enhances sensitivity to EZH2 inhibition in vitro. Szlosarek and colleagues studied arginine deprivation in 68 patients with advanced ASS1-deficient malignant pleural mesothelioma (defined by >50% low expressor cells on immunohistochemical analysis) [79]. Treatment with the deprivation agent ADI-PEG20 improved progression-free survival (3.2 vs. 2 months, p = 0.03) with no significant difference in life expectancy or adverse events.

Beyond identifying therapeutic targets, multi-omic data have enhanced the understanding of tumor biology, providing novel ways to stratify patients, determining prognosis and predicting sensitivity to existing treatments (reviewed in [80]).

We have developed a gene expression ratio-based method to translate expression profiling data into clinical tests based on the expression levels of a small number of genes [81]. This method uses standard supervised methods for microarray analysis to compare gene expression in two types of tissues differing by a single clinical parameter such as histology or outcome. Genes with the most significant difference in expression are selected and used in combination to calculate ratios of gene expression able to predict the clinical parameter associated with a random patient sample.

Using this method, a 6-gene 3-ratio test has been developed to distinguish MPM from adenocarcinoma using resection specimens and fine needle biopsies [81, 82]. A similar approach was used to generate a 4-gene 3-ratio prognostic test to identify patients likely to benefit from tumor resection in the preoperative setting [83, 84], as well as a 4-gene 3-ratio signature to distinguish the epithelioid from the sarcomatoid MPM subtype [85].

Despite rapidly decreasing sequencing costs [86], there remain several barriers to introducing the use of NGS technology in clinical practice, especially in MPM. In many solid tumors, the development of targeted sequencing panels has led to targeted therapies and prediction of survival of cancer patients. MPM is rare, making large-scale validation studies difficult to perform, and heterogeneous, characterized by mutations highly variable among tumors. In addition, loss of TSGs is a common feature of MPM making potential treatments associated with these genes difficult to be applied to real life treatment. Clinical trials focused on specific mutated genes [2937] have been infrequent and the results never translated to practice. Transcriptome analyses have classified MPM patients into several groups stratifying patients into categories of risk; however, a substantial margin of error in these predictions persists because the sensitivity and specificity of these tests are difficult to define [87]. Precision medicine based on cancer genomics is still far from being applied in clinical practice in MPM. Nevertheless, we are confident in the value of NGS for personalized medicine and believe additional efforts are needed for the implementation of NGS in identifying patients who might benefit from targeted treatments.


5. Conclusions

NGS has revolutionized the study of human genetics by transforming our ability to analyze the causes of disease, develop new diagnostics, and identify potential therapeutic targets. NGS studies have led to the discovery of several commonly mutated genes in MPM [13, 15]. Although analyses of transcriptome data have contributed to the understanding of the molecular biology of MPM subtypes, these studies were based on bulk profiling where tumors were profiled as a single entity averaging the gene expression of all the cells in the specimen and ignoring the intra-tumor heterogeneity that regulates many critical aspects of tumor biology [88]. The importance of intra-tumor heterogeneity in MPM is becoming evident. Future single-cell RNA sequencing work will be able to elucidate molecular roles of immune infiltrates and stroma in MPM as well as to clarify whether the molecular mechanisms associated with the genetic heterogeneity are due to subclonal mutations, epigenetic programs, or other environmental factors such as cell-cell interaction or nutrient availability.



This work was supported by grants to RB from the National Cancer Institute (NCI 2 R01 CA120528-11A1) and the International Mesothelioma Program at Brigham and Women’s Hospital. The study sponsors played no role in the study design, collection, analysis, interpretation of data, writing of the report, or decision to submit the chapter for publication.


Conflict of interest

The authors disclose no potential conflicts of interest. Dr. Bueno reports grants from Medgenome, grants from Roche, grants from Verastem, grants from Merck, grants from Gristone, grants from Epizyme, grants from Siemens, grants from NCI, grants from DoD, and grants from NIH. In addition, Dr. Bueno has a patent 7,622,260 licensed to BWH, a patent 8,450,057 licensed to BWH, a patent 8,551,700 licensed to BWH, and a patent 9,446,050 licensed to BWH, and Patents/Equity in Navigation Sciences.


  1. 1. Robinson BW, Lake RA. Advances in malignant mesothelioma. The New England Journal of Medicine. 2005;353(15):1591-1603
  2. 2. Henley SJ, Larson TC, Wu M, et al. Mesothelioma incidence in 50 states and the District of Columbia, United States, 2003-2008. International Journal of Occupational and Environmental Health. 2013;19(1):1-10
  3. 3. Raja S, Murthy SC, Mason DP. Malignant pleural mesothelioma. Current Oncology Reports. 2011;13(4):259-264
  4. 4. Sugarbaker DJ, Wolf AS, Chirieac LR, et al. Clinical and pathological features of three-year survivors of malignant pleural mesothelioma following extrapleural pneumonectomy. European Journal of Cardio-Thoracic Surgery. 2011;40(2):298-303
  5. 5. Bianchi C, Bianchi T. Malignant mesothelioma: Global incidence and relationship with asbestos. Industrial Health. 2007;45(3):379-387
  6. 6. Prazakova S, Thomas PS, Sandrini A, Yates DH. Asbestos and the lung in the 21st century: An update. The Clinical Respiratory Journal. 2014;8(1):1-10
  7. 7. Mutsaers SE. The mesothelial cell. The International Journal of Biochemistry & Cell Biology. 2004;36(1):9-16
  8. 8. Carbone M, Kratzke RA, Testa JR. The pathogenesis of mesothelioma. Seminars in Oncology. 2002;29(1):2-17
  9. 9. Lee WC, Testa JR. Somatic genetic alterations in human malignant mesothelioma (review). International Journal of Oncology. 1999;14(1):181-188
  10. 10. Balsara BR, Bell DW, Sonoda G, et al. Comparative genomic hybridization and loss of heterozygosity analyses identify a common region of deletion at 15q11.1-15 in human malignant mesothelioma. Cancer Research. 1999;59(2):450-454
  11. 11. De Rienzo A, Balsara BR, Apostolou S, Jhanwar SC, Testa JR. Loss of heterozygosity analysis defines a 3-cM region of 15q commonly deleted in human malignant mesothelioma. Oncogene. 2001;20(43):6245-6249
  12. 12. De Rienzo A, Jhanwar SC, Testa JR. Loss of heterozygosity analysis of 13q and 14q in human malignant mesothelioma. Genes, Chromosomes & Cancer. 2000;28(3):337-341
  13. 13. Bueno R, Stawiski EW, Goldstein LD, et al. Comprehensive genomic analysis of malignant pleural mesothelioma identifies recurrent mutations, gene fusions and splicing alterations. Nature Genetics. 2016;48(4):407-416
  14. 14. Guo G, Chmielecki J, Goparaju C, et al. Whole-exome sequencing reveals frequent genetic alterations in BAP1, NF2, CDKN2A, and CUL1 in malignant pleural mesothelioma. Cancer Research. 2015;75(2):264-269
  15. 15. Hmeljak J, Sanchez-Vega F, Hoadley KA, et al. Integrative molecular characterization of malignant pleural mesothelioma. Cancer Discovery. 2018;8(12):1548-1565
  16. 16. Zhang J, Chiodini R, Badr A, Zhang G. The impact of next-generation sequencing on genomics. Journal of Genetics and Genomics. 2011;38(3):95-109
  17. 17. Sugarbaker DJ, Richards WG, Gordon GJ, et al. Transcriptome sequencing of malignant pleural mesothelioma tumors. Proceedings of the National Academy of Sciences of the United States of America. 2008;105(9):3521-3526
  18. 18. Bueno R, De Rienzo A, Dong L, et al. Second generation sequencing of the mesothelioma tumor genome. PLoS One. 2010;5(5):e10612
  19. 19. Carbone M, Yang H, Pass HI, Krausz T, Testa JR, Gaudino G. BAP1 and cancer. Nature Reviews. Cancer. 2013;13(3):153-159
  20. 20. De Rienzo A, Archer MA, Yeap BY, et al. Gender-specific molecular and clinical features underlie malignant pleural mesothelioma. Cancer Research. 2016;76(2):319-328
  21. 21. Nasu M, Emi M, Pastorino S, et al. High incidence of somatic BAP1 alterations in sporadic malignant mesothelioma. Journal of Thoracic Oncology. 2015;10(4):565-576
  22. 22. Rai K, Pilarski R, Cebulla CM, Abdel-Rahman MH. Comprehensive review of BAP1 tumor predisposition syndrome with report of two new cases. Clinical Genetics. 2016;89(3):285-294
  23. 23. Wang A, Papneja A, Hyrcza M, Al-Habeeb A, Ghazarian D. Gene of the month: BAP1. Journal of Clinical Pathology. 2016;69(9):750-753
  24. 24. Farzin M, Toon CW, Clarkson A, et al. Loss of expression of BAP1 predicts longer survival in mesothelioma. Pathology. 2015;47(4):302-307
  25. 25. Baumann F, Flores E, Napolitano A, et al. Mesothelioma patients with germline BAP1 mutations have 7-fold improved long-term survival. Carcinogenesis. 2015;36(1):76-81
  26. 26. Carbone M, Adusumilli PS, Alexander HR Jr, et al. Mesothelioma: Scientific clues for prevention, diagnosis, and therapy. CA: A Cancer Journal for Clinicians. 2019;69(5):402-429
  27. 27. Pillappa R, Maleszewski JJ, Sukov WR, et al. Loss of BAP1 expression in atypical mesothelial proliferations helps to predict malignant mesothelioma. The American Journal of Surgical Pathology. 2018;42(2):256-263
  28. 28. Guazzelli A, Meysami P, Bakker E, et al. BAP1 status determines the sensitivity of malignant mesothelioma cells to gemcitabine treatment. International Journal of Molecular Sciences. 2019;20(2):429
  29. 29. Kumar N, Alrifai D, Kolluri KK, et al. Retrospective response analysis of BAP1 expression to predict the clinical activity of systemic cytotoxic chemotherapy in mesothelioma. Lung Cancer. 2019;127:164-166
  30. 30. Smole Z, Thoma CR, Applegate KT, et al. Tumor suppressor NF2/merlin is a microtubule stabilizer. Cancer Research. 2014;74(1):353-362
  31. 31. Petrilli AM, Fernandez-Valle C. Role of merlin/NF2 inactivation in tumor biology. Oncogene. 2016;35(5):537-548
  32. 32. Baser ME, De Rienzo A, Altomare D, et al. Neurofibromatosis 2 and malignant mesothelioma. Neurology. 2002;59(2):290-291
  33. 33. Thurneysen C, Opitz I, Kurtz S, Weder W, Stahel RA, Felley-Bosco E. Functional inactivation of NF2/merlin in human mesothelioma. Lung Cancer. 2009;64(2):140-147
  34. 34. Meerang M, Berard K, Friess M, et al. Low merlin expression and high Survivin labeling index are indicators for poor prognosis in patients with malignant pleural mesothelioma. Molecular Oncology. 2016;10(8):1255-1265
  35. 35. Lopez-Lago MA, Okada T, Murillo MM, Socci N, Giancotti FG. Loss of the tumor suppressor gene NF2, encoding merlin, constitutively activates integrin-dependent mTORC1 signaling. Molecular and Cellular Biology. 2009;29(15):4235-4249
  36. 36. Shapiro IM, Kolev VN, Vidal CM, et al. Merlin deficiency predicts FAK inhibitor sensitivity: A synthetic lethal relationship. Science Translational Medicine. 2014;6(237):237ra268
  37. 37. Fennell DA, Baas P, Taylor P, et al. Maintenance defactinib versus placebo after first-line chemotherapy in patients with merlin-stratified pleural mesothelioma: COMMAND-A double-blind, randomized, phase II study. Journal of Clinical Oncology. 2019;37(10):790-798
  38. 38. Laptenko O, Prives C. Transcriptional regulation by p53: One protein, many possibilities. Cell Death and Differentiation. 2006;13(6):951-961
  39. 39. Anbarasan T, Bourdon JC. The emerging landscape of p53 isoforms in physiology, cancer and degenerative diseases. International Journal of Molecular Sciences. 2019;20(24):6257
  40. 40. Kandoth C, McLellan MD, Vandin F, et al. Mutational landscape and significance across 12 major cancer types. Nature. 2013;502(7471):333-339
  41. 41. Yuan W, Xie J, Long C, et al. Heterogeneous nuclear ribonucleoprotein L is a subunit of human KMT3a/Set2 complex required for H3 Lys-36 trimethylation activity in vivo. The Journal of Biological Chemistry. 2009;284(23):15701-15707
  42. 42. Li J, Duns G, Westers H, Sijmons R, van den Berg A, Kok K. SETD2: An epigenetic modifier with tumor suppressor functionality. Oncotarget. 2016;7(31):50719-50734
  43. 43. Duns G, Hofstra RM, Sietzema JG, et al. Targeted exome sequencing in clear cell renal cell carcinoma tumors suggests aberrant chromatin regulation as a crucial step in ccRCC development. Human Mutation. 2012;33(7):1059-1062
  44. 44. Hylebos M, Van Camp G, Vandeweyer G, et al. Large-scale copy number analysis reveals variations in genes not previously associated with malignant pleural mesothelioma. Oncotarget. 2017;8(69):113673-113686
  45. 45. Mar BG, Chu SH, Kahn JD, et al. SETD2 alterations impair DNA damage recognition and lead to resistance to chemotherapy in leukemia. Blood. 2017;130(24):2631-2641
  46. 46. Sheng Y, Ji Z, Zhao H, et al. Downregulation of the histone methyltransferase SETD2 promotes imatinib resistance in chronic myeloid leukaemia cells. Cell Proliferation. 2019;52(4):e12611
  47. 47. Ishimoto K, Kawamata N, Uchihara Y, et al. Ubiquitination of lysine 867 of the human SETDB1 protein upregulates its histone H3 lysine 9 (H3K9) methyltransferase activity. PLoS One. 2016;11(10):e0165766
  48. 48. Karanth AV, Maniswami RR, Prashanth S, et al. Emerging role of SETDB1 as a therapeutic target. Expert Opinion on Therapeutic Targets. 2017;21(3):319-331
  49. 49. Kang HC, Kim HK, Lee S, et al. Whole exome and targeted deep sequencing identify genome-wide allelic loss and frequent SETDB1 mutations in malignant pleural mesotheliomas. Oncotarget. 2016;7(7):8321-8331
  50. 50. Furth N, Aylon Y. The LATS1 and LATS2 tumor suppressors: Beyond the hippo pathway. Cell Death and Differentiation. 2017;24(9):1488-1501
  51. 51. Visser S, Yang X. LATS tumor suppressor: A new governor of cellular homeostasis. Cell Cycle. 2010;9(19):3892-3903
  52. 52. Murakami H, Mizuno T, Taniguchi T, et al. LATS2 is a tumor suppressor gene of malignant mesothelioma. Cancer Research. 2011;71(3):873-883
  53. 53. Quetel L, Meiller C, Assie JB, et al. Genetic alterations of malignant pleural mesothelioma: Association with tumor heterogeneity and overall survival. Molecular Oncology. 2020;14(6):1207-1223
  54. 54. Tranchant R, Quetel L, Tallet A, et al. Co-occurring mutations of tumor suppressor genes, LATS2 and NF2, in malignant pleural mesothelioma. Clinical Cancer Research. 2017;23(12):3191-3202
  55. 55. Mizuno T, Murakami H, Fujii M, et al. YAP induces malignant mesothelioma cell proliferation by upregulating transcription of cell cycle-promoting genes. Oncogene. 2012;31(49):5117-5122
  56. 56. Tanaka I, Osada H, Fujii M, et al. LIM-domain protein AJUBA suppresses malignant mesothelioma cell proliferation via hippo signaling cascade. Oncogene. 2015;34(1):73-83
  57. 57. Franca R, Belfiore A, Spadari S, Maga G. Human DEAD-box ATPase DDX3 shows a relaxed nucleoside substrate specificity. Proteins. 2007;67(4):1128-1137
  58. 58. Sharma D, Jankowsky E. The Ded1/DDX3 subfamily of DEAD-box RNA helicases. Critical Reviews in Biochemistry and Molecular Biology. 2014;49(4):343-360
  59. 59. Bol GM, Xie M, Raman V. DDX3, a potential target for cancer treatment. Molecular Cancer. 2015;14:188
  60. 60. Amador FJ, Stathopulos PB, Enomoto M, Ikura M. Ryanodine receptor calcium release channels: Lessons from structure-function studies. The FEBS Journal. 2013;280(21):5456-5470
  61. 61. Schmitt K, Molfenter B, Laureano NK, et al. Somatic mutations and promotor methylation of the ryanodine receptor 2 is a common event in the pathogenesis of head and neck cancer. International Journal of Cancer. 2019;145(12):3299-3310
  62. 62. Lee EJ, Tournier C. The requirement of uncoordinated 51-like kinase 1 (ULK1) and ULK2 in the regulation of autophagy. Autophagy. 2011;7(7):689-695
  63. 63. Shukla S, Patric IR, Patil V, et al. Methylation silencing of ULK2, an autophagy gene, is essential for astrocyte transformation and tumor growth. The Journal of Biological Chemistry. 2014;289(32):22306-22318
  64. 64. Choi EJ, Lee JH, Kim MS, Song SY, Yoo NJ, Lee SH. Intratumoral heterogeneity of somatic mutations for NRIP1, DOK1, ULK1, ULK2, DLGAP3, PARD3 and PRKCI in colon cancers. Pathology Oncology Research. 2018;24(4):827-832
  65. 65. Follo C, Cheng Y, Richards WG, Bueno R, Broaddus VC. Inhibition of autophagy initiation potentiates chemosensitivity in mesothelioma. Molecular Carcinogenesis. 2018;57(3):319-332
  66. 66. Srivastava L, Lapik YR, Wang M, Pestov DG. Mammalian DEAD box protein Ddx51 acts in 3′ end maturation of 28S rRNA by promoting the release of U8 snoRNA. Molecular and Cellular Biology. 2010;30(12):2947-2956
  67. 67. Sun W, Cang S, Lv X, et al. DDX51 gene promotes proliferation by activating Wnt/beta-catenin signaling in breast cancer. International Journal of Clinical and Experimental Pathology. 2017;10(11):10892-10900
  68. 68. Taylor KH, Pena-Hernandez KE, Davis JW, et al. Large-scale CpG methylation analysis identifies novel candidate genes and reveals methylation hotspots in acute lymphoblastic leukemia. Cancer Research. 2007;67(6):2617-2625
  69. 69. Wang X, Liu H, Zhao C, Li W, Xu H, Chen Y. The DEAD-box RNA helicase 51 controls non-small cell lung cancer proliferation by regulating cell cycle progression via multiple pathways. Scientific Reports. 2016;6:26108
  70. 70. Zhao L, Lee VHF, Ng MK, Yan H, Bijlsma MF. Molecular subtyping of cancer: Current status and moving toward clinical applications. Briefings in Bioinformatics. 2019;20(2):572-584
  71. 71. Gordon GJ, Rockwell GN, Jensen RV, et al. Identification of novel candidate oncogenes and tumor suppressors in malignant pleural mesothelioma using large-scale transcriptional profiling. The American Journal of Pathology. 2005;166(6):1827-1840
  72. 72. de Reynies A, Jaurand MC, Renier A, et al. Molecular classification of malignant pleural mesothelioma: Identification of a poor prognosis subgroup linked to the epithelial-to-mesenchymal transition. Clinical Cancer Research. 2014;20(5):1323-1334
  73. 73. Shen R, Olshen AB, Ladanyi M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics. 2009;25(22):2906-2912
  74. 74. Vaske CJ, Benz SC, Sanborn JZ, et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics. 2010;26(12):i237-i245
  75. 75. Blum Y, Meiller C, Quetel L, et al. Dissecting heterogeneity in malignant pleural mesothelioma through histo-molecular gradients for clinical applications. Nature Communications. 2019;10(1):1333
  76. 76. Lopez-Rios F, Chuai S, Flores R, et al. Global gene expression profiling of pleural mesotheliomas: Overexpression of aurora kinases and P16/CDKN2A deletion as prognostic factors and critical evaluation of microarray-based prognostic prediction. Cancer Research. 2006;66(6):2970-2979
  77. 77. Creighton CJ, Gibbons DL, Kurie JM. The role of epithelial-mesenchymal transition programming in invasion and metastasis: A clinical perspective. Cancer Management and Research. 2013;5:187-195
  78. 78. Severson DT, De Rienzo A, Bueno R. Mesothelioma in the age of “Omics”: Before and after the cancer genome atlas. The Journal of Thoracic and Cardiovascular Surgery. 2020;S0022-5223, 20:30998
  79. 79. Szlosarek PW, Steele JP, Nolan L, et al. Arginine deprivation with Pegylated arginine deiminase in patients with argininosuccinate synthetase 1-deficient malignant pleural mesothelioma: A randomized clinical trial. JAMA Oncology. 2017;3(1):58-66
  80. 80. Kuperstein I, Grieco L, Cohen DP, Thieffry D, Zinovyev A, Barillot E. The shortest path is not the one you know: Application of biological network resources in precision oncology research. Mutagenesis. 2015;30(2):191-204
  81. 81. Gordon GJ, Jensen RV, Hsiao LL, et al. Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Research. 2002;62(17):4963-4967
  82. 82. De Rienzo A, Dong L, Yeap BY, et al. Fine-needle aspiration biopsies for gene expression ratio-based diagnostic and prognostic tests in malignant pleural mesothelioma. Clinical Cancer Research. 2011;17(2):310-316
  83. 83. Gordon GJ, Dong L, Yeap BY, et al. Four-gene expression ratio test for survival in patients undergoing surgery for mesothelioma. Journal of the National Cancer Institute. 2009;101(9):678-686
  84. 84. Gordon GJ, Jensen RV, Hsiao LL, et al. Using gene expression ratios to predict outcome among patients with mesothelioma. Journal of the National Cancer Institute. 2003;95(8):598-605
  85. 85. De Rienzo A, Richards WG, Yeap BY, et al. Sequential binary gene ratio tests define a novel molecular diagnostic strategy for malignant pleural mesothelioma. Clinical Cancer Research. 2013;19(9):2493-2502
  86. 86. Payne K, Gavan SP, Wright SJ, Thompson AJ. Cost-effectiveness analyses of genetic and genomic diagnostic tests. Nature Reviews. Genetics. 2018;19(4):235-246
  87. 87. Pass HI. Commentary: Tasting individual ingredients of meso soup: Can ’omics bring out the flavor? The Journal of Thoracic and Cardiovascular Surgery. 2020;160(4):1084-1085
  88. 88. Suva ML, Tirosh I. Single-cell RNA sequencing in cancer: Lessons learned and emerging challenges. Molecular Cell. 2019;75(1):7-12

Written By

Benjamin Wadowski, David T. Severson, Raphael Bueno and Assunta De Rienzo

Submitted: 14 January 2020 Reviewed: 26 August 2020 Published: 11 November 2020