Next Article in Journal
Citrus aurantium L. and Its Flavonoids Regulate TNBS-Induced Inflammatory Bowel Disease through Anti-Inflammation and Suppressing Isolated Jejunum Contraction
Next Article in Special Issue
Special Issue on Mechanisms of Mesothelioma Heterogeneity: Highlights and Open Questions
Previous Article in Journal
Cost-Effective Biochar Produced from Agricultural Residues and Its Application for Preparation of High Performance Form-Stable Phase Change Material via Simple Method
Previous Article in Special Issue
Mesotheliomas in Genetically Engineered Mice Unravel Mechanism of Mesothelial Carcinogenesis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 19
Issue 10
10.3390/ijms19103056
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Communication

Genomic Deletion of BAP1 and CDKN2A Are Useful Markers for Quality Control of Malignant Pleural Mesothelioma (MPM) Primary Cultures

by
Kadir Harun Sarun
1,
Kenneth Lee
1,2,3,
Marissa Williams
1,3,
Casey Maree Wright
1,
Candice Julie Clarke
2,
Ngan Ching Cheng
4,
Ken Takahashi
1 and
Yuen Yee Cheng
1,3,*
1
Asbestos Diseases Research Institute, University of Sydney, Sydney, NSW 2139, Australia
2
Anatomical Pathology Department, Concord Repatriation General Hospital, Sydney, NSW 2139, Australia
3
School of Medicine, University of Sydney, Sydney, NSW 2006, Australia
4
Liver Injury and Cancer Program, Centenary Institute, Sydney, NSW 2050, Australia
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2018, 19(10), 3056; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms19103056
Submission received: 7 September 2018 / Revised: 25 September 2018 / Accepted: 30 September 2018 / Published: 7 October 2018
(This article belongs to the Special Issue Mesothelioma Heterogeneity: Potential Mechanisms)
Browse Figures
Versions Notes

Abstract

:
Malignant pleural mesothelioma (MPM) is a deadly cancer that is caused by asbestos exposure and that has limited treatment options. The current standard of MPM diagnosis requires the testing of multiple immunohistochemical (IHC) markers on formalin-fixed paraffin-embedded tissue to differentiate MPM from other lung malignancies. To date, no single biomarker exists for definitive diagnosis of MPM due to the lack of specificity and sensitivity; therefore, there is ongoing research and development in order to identify alternative biomarkers for this purpose. In this study, we utilized primary MPM cell lines and tested the expression of clinically used biomarker panels, including CK8/18, Calretinin, CK 5/6, CD141, HBME-1, WT-1, D2-40, EMA, CEA, TAG72, BG8, CD15, TTF-1, BAP1, and Ber-Ep4. The genomic alteration of CDNK2A and BAP1 is common in MPM and has potential diagnostic value. Changes in CDKN2A and BAP1 genomic expression were confirmed in MPM samples in the current study using Fluorescence In situ Hybridization (FISH) analysis or copy number variation (CNV) analysis with digital droplet PCR (ddPCR). To determine whether MPM tissue and cell lines were comparable in terms of molecular alterations, IHC marker expression was analyzed in both sample types. The percentage of MPM biomarker levels showed variation between original tissue and matched cells established in culture. Genomic deletions of BAP1 and CDKN2A, however, showed consistent levels between the two. The data from this study suggest that genomic deletion analysis may provide more accurate biomarker options for MPM diagnosis.

Graphical Abstract

1. Introduction

Malignant pleural mesothelioma (MPM) is a tumor originating from the mesothelium, the membrane lining the thoracic and peritoneal cavities [1]. MPM is strongly linked to previous asbestos exposure [2] and asbestiform minerals such as erionite and fluoroedenite [3]. Australia has one of the world’s highest incidences of MPM due to the heavy industrial utilization of asbestos in the past [4]. MPM is a deadly cancer with poor prognosis [1,5,6], and treatment options are mainly palliative [7]. Most MPM patients are diagnosed at a late stage of the disease where limited treatment options are available; this is due to a lack of symptoms at early stages and the long latency period between asbestos exposure and the development of MPM. Mesothelioma is especially difficult to diagnose, as symptoms closely resemble those of lung cancer. Delays or errors in diagnosis hinder treatment intervention that can subsequently adversely affect the patients’ survival and quality-of-life (QoL); therefore, accurate diagnosis is essential for prognostic and therapeutic purposes [8].
Immunohistochemistry (IHC) is the standard method for biomarker detection of MPM, and multiple mesothelial markers have been identified to enable the distinction between epithelioid MPM and adenocarcinomas in routine practice. The three predominant subtypes differentiated by their MPM histomorphology are epithelioid, biphasic, and sarcomatoid. The proteins assessed using IHC vary in different laboratories, but the use of antibodies for the identification of calretinin and CEA is prominent [8]. To date, it is generally accepted that no single biomarker is absolutely sensitive or specific for MPM, and multipanel immunohistochemical tests are essential for diagnosis [9,10]. Therefore, further molecular characterization of the tumor is required to potentially identify more specific markers to aid in the diagnosis of MPM.
As well as intertumor heterogeneity, MPM tumors also exhibit intratumor heterogeneity. This tumour complexity limits the ability to delegate suitable treatment options due to the existence of several tumor clones and subclones within a single patient [11]. Intertumor heterogeneity is inclusive of the variable molecular phenotype of MPM. While genomic loss and gain are evident in MPM tumors, they exhibit low levels of drivers and recurrent mutations in comparison to other cancers [12]. The most commonly reported mutations are identified in genes such as NF2, BAP1, TP53, NRAS, and EGFR [13,14]. Asbestos fibers have been demonstrated to induce chromosome instability resulting in dysfunctional DNA damage response [15]. The most frequently reported chromosomal losses are those affecting chromosomal arms 3p, 9p, and 22q. Genes located in these regions include BAP1, CDKN2A, and NF2, respectively [16,17]. Among the three, CDKN2A represents the highest number of homozygous deletions in MPM-patient tumors [18]. To date, the mechanisms underlying the poor response of MPM patients to a wide range of therapeutic interventions are largely undetermined. Molecular intertumor heterogeneity, including a diversity of mutation, epigenetic, expression, and microscopic (phenotypic) changes may cause inefficacy of the treatment regimens. In contrast to nonsmall cell lung cancer, many mutations, such as in EGFR or TP53, are uncommon in the majority of MPM cases [9,19].
Due to the lack of single, accurate biomarkers for MPM, recent studies have focused on the analysis of biomarker combinations or panels, as well as the development of new diagnostic methods separate from IHC. For example, the determination of p16 (CDKN2A) homozygous deletion using Fluorescence In Situ Hybridization (FISH) and identification of BRCA1-associated protein 1 (BAP1) loss by IHC are particularly useful to differentiate mesothelial hyperplasia (MH) from MPM. Currently, these two markers are not widely used in the clinic, potentially due to the low sensitivity of the existing detection method [20,21,22,23]. Considering this, new markers with increased sensitivity and specificity are thus required. Cell-line models derived from MPM tumors are useful for the discovery of biomarkers and testing their efficacy. Cell culture is limitlessly renewable and can be manipulated to study cell function and gene signatures. Cultured cells derived from tumors have been shown to maintain many of the hallmarks of cancer apart from tumor-specific angiogenesis [24,25]. The development of a primary MPM cell culture provides an inexpensive and more homogeneous MPM cell population for genomic marker identification. Primary MPM cell lines have provided a medium to better understand the genomic alterations that exist in mesothelioma [26]. Further, in tumor samples, the inevitable infiltration of stromal and inflammatory cell populations can influence the molecular phenotype. The implementation of cell-culture models allows the exclusion of such populations, and allows exclusive testing of the tumor cells and accurate estimation of gene copy number.
This study aimed to utilize the MPM cell lines that were established between 2013–2017 [27] to study different types of biomarkers, including protein markers using IHC and genomic markers using qualitative FISH analysis coupled with absolute quantification analysis with droplet digital PCR (ddPCR).

2. Results

2.1. Immunohistochemistry Analysis Demonstrates Variable Marker Expression between MPM Tissue and Derivative Cell-Line Samples

A total of 15 biomarkers used in clinical practices for differential diagnosis of MPM were assessed in all samples, including MPM tumor samples together with derivative primary cell-line samples from the corresponding parent tumor tissue and/or plural effusion. Short Tandem Repeat (STR) profiling was employed to provide genomic signatures for confirmation of cell-line identity (Supplementary Table S1). Figure 1 shows the IHC staining of a MPM tumor and the expression of 15 protein markers used in the clinic. MPM primary cells were extracted from tumor tissue and/or pleural effusion samples and grown in cell culture until they reached passage 15 to eliminate normal cell contamination, after which they were cultured into 3D. Two-dimensional and three-dimensional MPM cell blocks were used for protein marker analysis, and our results indicated that the 3D model more closely represents the tumor architecture (Supplementary Figure S1). Protein levels were compared between MPM tumor samples and subsequent derivative cell lines from the same tissue. The detailed percentage scoring of protein marker expression in MPM tumor samples and cell lines is listed in Table 1 (each MM ID represents samples from one patient). It was found that the majority of IHC protein markers are not correlative between tumor tissue and their derived cells (Table 1). Due to the observed variability of protein markers between tissue and cell lines, other molecular biomarker strategies were considered.

2.2. Genomic Deletion of CDKN2A Was Identified in MPM Samples Using FISH

In this study, we have established FISH analysis to identify the genomic deletion of CDKN2A using specific probes in MPM tissue samples. FISH staining identified heterozygous or homozygous loss of the CDNK2A region in MPM tumor samples (Figure 2); alternatively, normal cells retained expression of both alleles. Figure 2 demonstrates the homozygous loss in the majority of tumor cells (Figure 2A), whereas a smaller portion of samples displayed heterozygous loss (Figure 2B) or no loss (Figure 2C). Of the 12 MPM tumor samples analyzed, 66% (8/12, cut-off 15%) showed homozygous and 16% (2/12, cut-off 40%) showed heterozygous loss of CDKN2A. These data indicate that FISH provides a qualitative presentation of genomic deletion in MPM. Although FISH is a well-established technique for the identification of genomic deletion, it is not largely accessible in every laboratory and difficult to provide quantitative assessment; therefore, a more accessible approach to identify genomic changes would be beneficial. To be able to quantitatively analyze genomic deletion, we have assessed the absolute quantification of genomic expression using ddPCR.

2.3. Copy Number Variation Contributes to Loss of BAP1 and CDKN2A Expression in MPM

Our initial attempt to study the DNA content of MPM cells was carried out using metaphase spread and flow-cytometry analysis of DNA content (Supplementary Figure S2). Results indicated cell lines 1180, 1843, 2164, 1157, and 1518 showed tetraploidy. However, these data did not provide conclusive information in regard to specific genes containing copy number variation (CNV). Prior to this study, we reviewed that about 50% MPM cases tested show loss of BAP1 protein expression. Many studies have reported that loss of BAP1 protein expression is due to genetic mutation or DNA methylation in the genomic region. To better understand the mechanism causing BAP1 loss in MPM, we performed genomic Sanger sequencing of the genetic regions spanning exon 6 and 7, where the majority of mutations reside, as reported in the literature [28,29]. In addition, DNA methylation status was determined by methylation-specific PCR (MSP) analysis to study the involvement of DNA methylation in BAP1 loss. Results from DNA sequencing and MSP studies (Supplementary Figure S3) indicated no evidence of genomic mutation near exon 6 and 7, and there was no promoter hypermethylation in the nine samples tested. We therefore performed CNV to assess loss of heterozygosity (LOH) of BAP1 using ddPCR. Results obtained from ddPCR analysis confirm BAP1 deletion (Figure 3) in MPM samples that correlate with the loss of BAP1 protein expression observed using IHC analysis (Figure 1). Loss of CDKN2A is a common event in mesothelioma [30]. We tested its genomic alteration using FISH and, similar to previous studies, we showed that either heterozygous or homozygous loss of CDKN2A is prevalent in our MPM cohort. To assess the potential of detecting copy number loss of CDKN2A in MPM samples using ddPCR, we performed CNV ddPCR analysis in MPM tissues and their matched primary cell lines. The percentage of CDKN2A loss assessed using ddPCR correlated to results observed using FISH ananlysis (Figure 2). Our results also showed the stability and consistency of CNV detection across formalin-fixed paraffin-embedded (FFPE) samples and established cell lines. Using a normal mesothelial cell line and healthy individual buffy coat (BCN7) samples as normal controls for the presence of both CDKN2A alleles and MPM cell lines (H2052 and H28) as controls for gene deletion. Seven percent (one out of 14) of samples showed the retention of both alleles, and the majority of cases (93%: 13 out of 14) showed deletion of the CDKN2A genomic region.

2.4. Concordance of BAP1 Protein Expression and Genomic Deletion

Table 2 demonstrates BAP1 protein expression and the corresponding ddPCR CNV analysis in matched cell line and tissue samples. In our sample cohort, 54% of MPM FFPE samples showed high levels of BAP1 protein expression, and 46% showed low or no expression of the BAP1 protein. When examining cell lines established from tissue collection (matched samples), 64% showed a high level of expression, while 36% showed low to no expression of BAP1. Strong correlation (92%, 24 out of 26) was demonstrated between the BAP1 protein and genomic expression in FFPE and cell lines samples, as measured by IHC and ddPCR, respectively. Our results indicated low correlation between BAP1 protein and genomic expression, as determined by IHC and CNV. respectively (Supplementary Figure S4). Both FFPE and matched cell-line samples exhibited 50% of BAP1 genomic retention (13 out of 26) and 70% BAP1 protein expression (19 out of 27), whereas 42% (11 out of 26) of samples that displayed low BAP1 protein expression also showed genomic deletion in the BAP1 region. SPSS software was utilized for the measurement of sensitivity and specificity of biomarkers for detection of MPM. CDKN2A genomic loss had 96.4% of sensitivity and 100% specificity for MPM detection. BAP1 protein expression (using IHC) and genomic deletion (using ddPCR) results were analyzed for their concordance by measuring their combined sensitivity and specificity. Results from SPSS indicated there was 93.3% sensitivity and 63.6% specificity when combining the protein expression and genomic deletion of BAP1 as a marker for MPM. When analyzing genomic deletion (ddPCR results) alone, BAP1 genomic loss had a sensitivity of 42.9% and specificity of 100% in identifying MPM compared to normal mesothelial cells and healthy donor buffy-coat controls. In comparison, BAP1 protein expression identifies MPM with lower sensitivity and specificity levels of 67.9% and 28.6%, respectively. These results indicate that detection of BAP1 and CDKN2A by genomic analysis (CNV using ddPCR) is a more distinctive method to identify MPM.

3. Discussion

MPM tumors are histologically heterogeneous and have distinct morphological subtypes that range from epithelioid, sarcomatoid, and biphasic. The biphasic histological subtype consists of epithelioid and sarcomatoid components, with each contributing to at least 10% of the tumor. Further, histomorphological features such as mitotic numbers and nuclear atypia have been included to conclude a total score [31]. This heterogeneity within morphological subgroups further adds to the complexity of the definitive identification of MPM [32]. It is therefore important to establish cultured cells from mesothelioma biopsies and pleural effusions to be utilized for studying the cellular, molecular, and genetic levels of this tumor. Over a period of four years (2013–2017), we established fifteen cell lines (defined as successful subculture) from a primary culture [27] from fourteen human tumor and/or plural effusion samples. In the current study, we utilized these established mesothelioma cell lines to determine biomarker expression in a system that closely parallels the tumor.
Currently, no single immunohistochemical marker offers high specificity and sensitivity or definitive negative predictive value for the diagnosis of mesothelioma. The most useful mesothelial and epithelial markers proposed for the diagnosis of mesothelioma are a combination of markers, often including calretinin (a vitamin D-dependent calcium-binding protein involved in calcium signalling) [33], HBME-1, thrombomodulin, WT-1, mesothelin, and podoplanin as mesothelial markers, and pCEA, Ber-EP4, TTF-1, and TAG72 as epithelial markers [34]. The cytokeratin-19/CEA ratio is a useful marker for mesothelioma diagnosis due to the high level of cytokeratin (18 and 19) expression in mesothelial cells. Additionally, cytokeratin-19 was previously found in two hereditary cases of mesothelioma [35]. The IHC panel included a total of 15 markers, and these were tested in the matched tumor and primary cell-line sample set. Unexpectedly, we did not observe a complimentary pattern in expression between tumor samples and derivative cell lines; often, the cell-line marker expression scores deviated from what was observed in the tumor samples. This finding suggests that in terms of MPM identification, IHC marker subtyping is not an ideal method for use in cell lines. Cell-to-cell communication in tumors creates distinct protein expression phenotypes that differ from those in cell culture [36] and this could explain the observed difference in protein marker expression. Analysis of the genomic phenotype could, instead, provide an alternative method of tumor identification.
Homozygous deletion (HD) of CDKN2A is one of the most common gene alterations associated with MPM [30]. Detection of CDKN2A HD using FISH can be used to differentiate between MPM and RMH (43% to 93% sensitivity; 100% specificity) [37]. However, the CDKN2A FISH assay only provides qualitative measurement of genomic expression and is inaccessible to many laboratories due to high costs and a highly specialised workflow. Furthermore, the complex structural chromosomal instability in mesothelioma cell lines can lead to the complication of karyotype differentiation and problems in chromosomal abnormality detection that may not be revealed by routine G-banding or FISH techniques [38]. ddPCR offers an alternative method for genomic analysis that provides absolute quantification, thereby enabling CNV analysis. Additionally, it is relatively cost-effective for routine use [39], thus enabling high-throughput application in the clinic. Hida et al. previously carried out BAP1 IHC analysis and CDKN2A-specific FISH in 40 MPM and 20 reactive mesothelial hyperplasia (RMH) samples [40]. Results indicated that BAP1 expression loss and CDKN2A homozygous deletion were present in 27 (67.5%) and 17 (42.5%) MPM cases, respectively. Three MPM cases (7.5%) and all 20 RMH cases had neither BAP1 loss nor CDKN2A homozygous deletion. The combination of two markers produced higher sensitivity (92.5%, 37/40) and estimated probability than BAP1 IHC and CDKN2A FISH used alone. In our study, the combination of the two markers produced 96.4% of sensitivity and 100% specificity. Our results indicate that CNV analysis of tumor and matched cell-line samples were concurrent and both indicated CDKN2A deletion. BAP1 loss in MPM is attributed to multiple mechanisms including mutation, DNA methylation, or copy number loss [41]. These data show that BAP1 protein loss was due to genomic deletion and BAP1 CNV at the tumor level was also found to be reiterated in the matched MPM cell lines.
The discovery of alternative molecular markers for MPM is required to facilitate effective diagnosis to improve the dire prognosis of the disease. This study suggests that the CNV of CDKN2A is identifiable in MPM tumor samples and derivative cell lines alike using ddPCR. Additionally, BAP1 CNV was demonstrated using ddPCR and was correlated between tumor and cell-line samples. This highlights the stability of CDKN2A and BAP1 genomic deletion in MPM tumors and suggests identification of CNV could offer a potential alternative in MPM diagnostic testing.

4. Materials and Methods

4.1. Patient Tissue-Sample Collection and MPM Cell-Line Establishment

MPM Cell-Line Establishment

Formalin-fixed paraffin-embedded (FFPE) tumor tissues and fresh tumor samples were collected from MPM patients. All patients gave informed written consent and the project was approved by the Human Research Ethics Committees at Concord Repatriation General Hospital (HREC/11/CRGH/75 approved since 2011). Patient demographics are listed in Table 3.

4.2. Immunohistochemical Analysis of MPM Tissue Sections and Established Cell-Line Blocks

MPM cell lines were cultured into a 3D model and embedded into cell blocks that were further processed into paraffin blocks. MPM tissue blocks and cell blocks were sectioned at 0.4 μm thickness, deparaffinised, and rehydrated in graded concentrations of xylene and ethanol. Antigen retrieval and immunohistochemical staining were performed on an automated Leica Bond III (Leica Microsystems, Melbourne, Australia) using a Bond Polymer Refine Detection Kit (Leica Biosystems, Milton Keynes, UK). Either enzyme 1 (Leica Biosystems, UK) or Heat-Induced Epitope Retrieval (HIER) was performed on all slides in either Bond Epitope Retrieval Solution (Leica Biosystems, UK) 1 (pH6) or 2 (pH9) for 20 min. Primary antibody was applied and incubated for 20 min at room temperature (Table 4). Slides were then immersed in H2O2 for 5 min to quench endogenous peroxidases. Slides were processed for postprimary detection for 15 min, followed by a polymer for 15 min. 3,3′-diaminobenzidine (DAB with enhancer) chromogenic detection and haematoxylin counterstaining were used. Diagnostic clinical procedures related to diagnosis of the cases were performed in a NATA-approved laboratory using external quality-assurance program (QAP)-validated tests. The method of scoring for each antibody in each case was as per usual clinical diagnostic practice. A negative staining pattern was defined as no staining. Positive staining cells were defined as 1+ (weak), 2+ (moderate), or 3+ (strong) staining intensity in the cells, and the number of cells showing the relevant positive intensity were scored as a percentage over the total number of cells present.

4.3. FISH Analysis of CDKN2A Genomic Analysis

FISH dual-color analysis was performed with a CEP9 Spectrum Green-labelled probe and a Spectrum Orange-labelled, locus-specific CDKN2A (p16) probe (Cat. 05J51-001, Abbott Molecular, Sydney, Australia). Briefly, paraffin sections were deparaffinised, dehydrated in ethanol, and washed 3 times with H2O. Sections were digested with protease K (0.5 mg/mL) at 37 °C for 20 min. The slides were washed in SSC twice, dehydrated with ethanol, and air-dried. The probes were denatured for 5 min at 95 °C before hybridization. Slides were hybridized overnight at 37 °C and washed in 0.2 × SSC/NP40 71 °C for 2 min. Nuclei were counterstained with DAPI/antifade (Vysis, Abbott Molecular, Sydney, Australia). Each FISH assay included normal lung-tissue sections as a negative control, and sections of mesothelioma previously identified as carrying p16 deletion as a positive control. Analyses were performed with a fluorescence microscope (Axio M2, ZEISS, Oberkochen, Germany) equipped with filter sets with single- and dual-band exciters for Spectrum Green, Spectrum Orange, and DAPI (UV 360 nm). The histologic areas previously selected on the hematoxylin-eosin-stained sections were identified on the FISH-treated slides. Overlapping cells were excluded and individual, and well-defined cells were analyzed and scored. At least 100 cells were scored for each mesothelioma case. Homozygous deletion was defined as the absence of both red CDKN2A signals (9p21) in the presence of at least one green chromosome 9 signal (CEP9). A heterozygous deletion was defined as the presence of one 9p21 signal, and two CEP9 signals. The cut-off values were established by methods previously described [42]. The cut-off value was the mean percentage plus three SD using normal mesothelial cell nuclei. We established a cut-off value of 15% for homozygous deletion and 40% for heterozygous deletion.

4.4. CDKN2A and BAP1 Genomic Loss Were Suggested by CNV Using ddPCR

Primers for the amplification of the genomic region of BAP1, CDKN2A, and RPP30 were optimized using ddPCR EvaGreen (Bio-Rad, California, CA, USA) according to the manufacturer’s recommendations. Eighty nanograms of total genomic DNA was isolated from MPM tissues and MPM cell lines for use as a template for ddPCR. ddPCR reaction mixtures were assembled using 2× EvaGreen ddPCR Supermix (Bio-Rad) and primers at a final concentration of 0.2 μM in a total reaction volume of 20 μL. Reactions were dispensed into each well of droplet generator DG8 cartridge (Bio-Rad). Seventy microliters of Evagreen specific droplet generation oil (Bio-Rad) was used to generate approximately 15,000 to 20,000 droplets using the droplet generator (Bio-Rad). A 40 µL droplet emulsion was then loaded onto a 96-well PCR plate (Bio-Rad). The plate was then heat-sealed with a pierceable foil in the PX1 PCR Plate Sealer (Bio-Rad), and placed in the thermocycler (Bio-Rad T1000). Optimal thermal-cycling conditions were used: 95 °C for 5 min; 35 cycles of 95 °C for 30 s, 60 °C for 30 s, 72 °C for 1 min; and a final step at 72 °C for 1 min. The reaction mixtures were then held at 4 °C until needed. The cycled droplets were read individually with the QX200 droplet reader (Bio-Rad), and analyzed with QuantaSoft droplet-reader software, version 1.7 (Bio-Rad). The error reported for a single well was the Poisson 95% confidence interval. No template controls (NTC) were used to monitor contaminations and primer–dimer formation and determination of the cut-off threshold (Supplementary Figure S5), copy number for each genomic region was calculated by normalization to the included reference gene RPP30 (retains two copies per cell). Homozygous deletion was considered in cases where no detection of the target genomic region was determined, but a distinctive RPP30 population was apparent. MPM tissue samples and matched cell-line positive populations were used to calculate the positive expression values, and results were plotted as copy number detected per sample.

4.5. Statistics

All statistical analyses were carried out using IBM SPSS software version 25. The sensitivity and specificity of BAP1 and CDKN2A deletion were performed using the crosstabs function of in the descriptive statistics of SPSS.

5. Conclusions

Loss of BAP1 and CDKN2A are important diagnostic biomarkers in MPM. This study demonstrated the feasibility of genomic deletion as an appropriate biomarker for MPM detection that is consistent in both MPM tumor tissue and matched MPM cell lines.

Supplementary Materials

Supplementary materials can be found at https://0-www-mdpi-com.brum.beds.ac.uk/1422-0067/19/10/3056/s1.

Author Contributions

K.H.S. carried out the majority of the experiments in this study, including establishment and maintenance of the cell lines, preparation of cell blocks, sectioning, DNA isolation, and drafting the manuscript. K.L. is an experienced pathologist that provided professional scoring of MPM biomarkers using IHC staining and CDKN2A FISH analysis. M.W. provided conceptual input and edited the manuscript, C.J.C. carried out IHC and CDKN2A staining, C.M.W. and N.C.C. established the MPM cell lines, K.T. was involved in establishing the concept of the study and editing the manuscript. Y.Y.C. conceived the project, established the MPM cell lines, performed sectioning, prepared cell blocks, performed ddPCR analysis of copy number variation, and drafted the manuscript. All authors approved the submitted version.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Robinson, B.W.; Lake, R.A. Advances in malignant mesothelioma. N. Engl. J. Med. 2005, 353, 1591–1603. [Google Scholar] [CrossRef] [PubMed]
  2. LaDou, J.; Castleman, B.; Frank, A.; Gochfeld, M.; Greenberg, M.; Huff, J.; Joshi, T.K.; Landrigan, P.J.; Lemen, R.; Myers, J.; et al. The case for a global ban on asbestos. Environ. Health Perspect. 2010, 118, 897–901. [Google Scholar] [CrossRef] [PubMed]
  3. Carbone, M.; Kanodia, S.; Chao, A.; Miller, A.; Wali, A.; Weissman, D.; Adjei, A.; Baumann, F.; Boffetta, P.; Buck, B.; et al. Consensus Report of the 2015 Weinman International Conference on Mesothelioma. J. Thorac. Oncol. 2016, 11, 1246–1262. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Kao, S.C.; Reid, G.; Lee, K.; Vardy, J.; Clarke, S.; van Zandwijk, N. Malignant mesothelioma. Intern. Med. J. 2010, 40, 742–750. [Google Scholar] [CrossRef] [PubMed]
  5. Tsao, A.S.; Wistuba, I.; Roth, J.A.; Kindler, H.L. Malignant pleural mesothelioma. J. Clin. Oncol. 2009, 27, 2081–2090. [Google Scholar] [CrossRef] [PubMed]
  6. Johnson, D.H.; Schiller, J.H.; Bunn, P.A., Jr. Recent clinical advances in lung cancer management. J. Clin. Oncol. 2014, 32, 973–982. [Google Scholar] [CrossRef] [PubMed]
  7. Scherpereel, A.; Astoul, P.; Baas, P.; Berghmans, T.; Clayson, H.; de Vuyst, P.; Dienemann, H.; Galateau-Salle, F.; Hennequin, C.; Hillerdal, G.; et al. Guidelines of the European Respiratory Society and the European Society of Thoracic Surgeons for the management of malignant pleural mesothelioma. Eur. Respir. J. 2010, 35, 479–495. [Google Scholar] [CrossRef] [PubMed]
  8. Linton, A.; Kao, S.; Vardy, J.; Clarke, S.; van Zandwijk, N.; Klebe, S. Immunohistochemistry in the diagnosis of malignant pleural mesothelioma: Trends in Australia and a literature review. Asia Pac. J. Clin. Oncol. 2013, 9, 273–279. [Google Scholar] [CrossRef] [PubMed]
  9. Husain, A.N.; Colby, T.; Ordonez, N.; Krausz, T.; Attanoos, R.; Beasley, M.B.; Borczuk, A.C.; Butnor, K.; Cagle, P.T.; Chirieac, L.R.; et al. Guidelines for pathologic diagnosis of malignant mesothelioma: 2012 Update of the consensus statement from the International Mesothelioma Interest Group. Arch. Pathol. Lab. Med. 2013, 137, 647–667. [Google Scholar] [CrossRef] [PubMed]
  10. Tischoff, I.; Neid, M.; Neumann, V.; Tannapfel, A. Pathohistological diagnosis and differential diagnosis. Recent Results Cancer Res. 2011, 189, 57–78. [Google Scholar] [PubMed]
  11. Oehl, K.; Vrugt, B.; Opitz, I.; Meerang, M. Heterogeneity in Malignant Pleural Mesothelioma. Int. J. Mol. Sci. 2018, 19, 1603. [Google Scholar] [CrossRef] [PubMed]
  12. Guo, G.; Chmielecki, J.; Goparaju, C.; Heguy, A.; Dolgalev, I.; Carbone, M.; Seepo, S.; Meyerson, M.; Pass, H.I. Whole-exome sequencing reveals frequent genetic alterations in BAP1, NF2, CDKN2A, and CUL1 in malignant pleural mesothelioma. Cancer Res. 2015, 75, 264–269. [Google Scholar] [CrossRef] [PubMed]
  13. Hylebos, M.; Van Camp, G.; van Meerbeeck, J.P.; Op de Beeck, K. The Genetic Landscape of Malignant Pleural Mesothelioma: Results from Massively Parallel Sequencing. J. Thorac. Oncol. 2016, 11, 1615–1626. [Google Scholar] [CrossRef] [PubMed]
  14. Kim, J.E.; Kim, D.; Hong, Y.S.; Kim, K.P.; Yoon, Y.K.; Lee, D.H.; Kim, S.W.; Chun, S.M.; Jang, S.J.; Kim, T.W. Mutational Profiling of Malignant Mesothelioma Revealed Potential Therapeutic Targets in EGFR and NRAS. Transl. Oncol. 2018, 11, 268–274. [Google Scholar] [CrossRef] [PubMed]
  15. Jaurand, M.C.; Fleury-Feith, J. Pathogenesis of malignant pleural mesothelioma. Respirology 2005, 10, 2–8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Borczuk, A.C.; Pei, J.; Taub, R.N.; Levy, B.; Nahum, O.; Chen, J.; Chen, K.; Testa, J.R. Genome-wide analysis of abdominal and pleural malignant mesothelioma with DNA arrays reveals both common and distinct regions of copy number alteration. Cancer Biol. Ther. 2016, 17, 328–335. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Ivanova, A.V.; Goparaju, C.M.; Ivanov, S.V.; Nonaka, D.; Cruz, C.; Beck, A.; Lonardo, F.; Wali, A.; Pass, H.I. Protumorigenic role of HAPLN1 and its IgV domain in malignant pleural mesothelioma. Clin. Cancer Res. 2009, 15, 2602–2611. [Google Scholar] [CrossRef] [PubMed]
  18. Illei, P.B.; Rusch, V.W.; Zakowski, M.F.; Ladanyi, M. Homozygous deletion of CDKN2A and codeletion of the methylthioadenosine phosphorylase gene in the majority of pleural mesotheliomas. Clin. Cancer Res. 2003, 9, 2108–2113. [Google Scholar] [PubMed]
  19. Dahabreh, I.J.; Linardou, H.; Siannis, F.; Kosmidis, P.; Bafaloukos, D.; Murray, S. Somatic EGFR mutation and gene copy gain as predictive biomarkers for response to tyrosine kinase inhibitors in non-small cell lung cancer. Clin Cancer Res. 2010, 16, 291–303. [Google Scholar] [CrossRef] [PubMed]
  20. Nasu, M.; Emi, M.; Pastorino, S.; Tanji, M.; Powers, A.; Luk, H.; Baumann, F.; Zhang, Y.A.; Gazdar, A.; Kanodia, S.; et al. High Incidence of Somatic BAP1 alterations in sporadic malignant mesothelioma. J. Thorac. Oncol. 2015, 10, 565–576. [Google Scholar] [CrossRef] [PubMed]
  21. Cigognetti, M.; Lonardi, S.; Fisogni, S.; Balzarini, P.; Pellegrini, V.; Tironi, A.; Bercich, L.; Bugatti, M.; Rossi, G.; Murer, B.; et al. BAP1 (BRCA1-associated protein 1) is a highly specific marker for differentiating mesothelioma from reactive mesothelial proliferations. Mod. Pathol. 2015, 28, 1043–1057. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Bott, M.; Brevet, M.; Taylor, B.S.; Shimizu, S.; Ito, T.; Wang, L.; Creaney, J.; Lake, R.A.; Zakowski, M.F.; Reva, B.; et al. The nuclear deubiquitinase BAP1 is commonly inactivated by somatic mutations and 3p21.1 losses in malignant pleural mesothelioma. Nat. Genet. 2011, 43, 668–672. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Sheffield, B.S.; Hwang, H.C.; Lee, A.F.; Thompson, K.; Rodriguez, S.; Tse, C.H.; Gown, A.M.; Churg, A. BAP1 immunohistochemistry and p16 FISH to separate benign from malignant mesothelial proliferations. Am. J. Surg. Pathol. 2015, 39, 977–982. [Google Scholar] [CrossRef] [PubMed]
  24. Sato, G. Tissue culture: The unrealized potential. Cytotechnology 2008, 57, 111–114. [Google Scholar] [CrossRef] [PubMed]
  25. Sato, G.H.; Sato, J.D.; Okamoto, T.; McKeehan, W.L.; Barnes, D.W. Tissue culture: The unlimited potential. In Vitro Cell. Dev. Biol. Anim. 2010, 46, 590–594. [Google Scholar] [CrossRef] [PubMed]
  26. Ivanov, S.V.; Ivanova, A.V.; Goparaju, C.M.; Chen, Y.; Beck, A.; Pass, H.I. Tumorigenic properties of alternative osteopontin isoforms in mesothelioma. Biochem. Biophys. Res. Commun. 2009, 382, 514–518. [Google Scholar] [CrossRef] [PubMed]
  27. Cheng, Y.Y.S.K.; Lee, K.; Clarke, C.J.; Cheng, N.C.; van Zandwijk, N.; Klebe, S.; Reid, G. Establishing malignant pleural mesothelioma cell lines using the spheroid method produces a model with better 3D architecture. J. Thorac. Oncol. 2017, 12, S2265–S2266. [Google Scholar] [CrossRef]
  28. Yoshikawa, Y.; Sato, A.; Tsujimura, T.; Emi, M.; Morinaga, T.; Fukuoka, K.; Yamada, S.; Murakami, A.; Kondo, N.; Matsumoto, S.; et al. Frequent inactivation of the BAP1 gene in epithelioid-type malignant mesothelioma. Cancer Sci. 2012, 103, 868–874. [Google Scholar] [CrossRef] [PubMed]
  29. Cheung, M.; Testa, J.R. BAP1, a tumor suppressor gene driving malignant mesothelioma. Transl. Lung Cancer Res. 2017, 6, 270–278. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Port, J.; Murphy, D.J. Mesothelioma: Identical Routes to Malignancy from Asbestos and Carbon Nanotubes. Curr. Biol. 2017, 27, R1173–R1176. [Google Scholar] [CrossRef] [PubMed]
  31. Rosen, L.E.; Karrison, T.; Ananthanarayanan, V.; Gallan, A.J.; Adusumilli, P.S.; Alchami, F.S.; Attanoos, R.; Brcic, L.; Butnor, K.J.; Galateau-Sallé, F.; et al. Nuclear grade and necrosis predict prognosis in malignant epithelioid pleural mesothelioma: A multi-institutional study. Mod. Pathol. 2018, 31, 598–606. [Google Scholar] [CrossRef] [PubMed]
  32. Marusyk, A.; Almendro, V.; Polyak, K. Intra-tumour heterogeneity: A looking glass for cancer? Nat. Rev. Cancer 2012, 12, 323–334. [Google Scholar] [CrossRef] [PubMed]
  33. Kao, S.C.; Klebe, S.; Henderson, D.W.; Reid, G.; Chatfield, M.; Armstrong, N.J.; Yan, T.D.; Vardy, J.; Clarke, S.; van Zandwijk, N.; et al. Low calretinin expression and high neutrophil-to-lymphocyte ratio are poor prognostic factors in patients with malignant mesothelioma undergoing extrapleural pneumonectomy. J. Thorac. Oncol. 2011, 6, 1923–1929. [Google Scholar] [CrossRef] [PubMed]
  34. Marchevsky, A.M. Application of immunohistochemistry to the diagnosis of malignant mesothelioma. Arch. Pathol. Lab. Med. 2008, 132, 397–401. [Google Scholar] [PubMed]
  35. Relan, V.; Morrison, L.; Parsonson, K.; Clarke, B.E.; Duhig, E.E.; Windsor, M.N.; Matar, K.S.; Naidoo, R.; Passmore, L.; McCaul, E.; et al. Phenotypes and karyotypes of human malignant mesothelioma cell lines. PLoS ONE 2013, 8, e58132. [Google Scholar] [CrossRef] [PubMed]
  36. Ravi, M.; Mohan, D.K.; Sahu, B. Protein Expression Differences of 2-Dimensional and Progressive 3-Dimensional Cell Cultures of Non-Small-Cell-Lung-Cancer Cell Line H460. J. Cell. Biochem. 2017, 118, 1648–1652. [Google Scholar] [CrossRef] [PubMed]
  37. Nabeshima, K.; Matsumoto, S.; Hamasaki, M.; Hida, T.; Kamei, T.; Hiroshima, K.; Tsujimura, T.; Kawahara, K. Use of p16 FISH for differential diagnosis of mesothelioma in smear preparations. Diagn. Cytopathol. 2016, 44, 774–780. [Google Scholar] [CrossRef] [PubMed]
  38. Riegel, M. Human molecular cytogenetics: From cells to nucleotides. Genet. Mol. Biol. 2014, 37, 194–209. [Google Scholar] [CrossRef] [PubMed]
  39. Cheng, Y.Y.; Mok, E.; Tan, S.; Leygo, C.; McLaughlin, C.; George, A.M.; Reid, G. SFRP Tumour Suppressor Genes Are Potential Plasma-Based Epigenetic Biomarkers for Malignant Pleural Mesothelioma. Dis. Mark. 2017. [Google Scholar] [CrossRef] [PubMed]
  40. Hida, T.; Hamasaki, M.; Matsumoto, S.; Sato, A.; Tsujimura, T.; Kawahara, K.; Iwasaki, A.; Okamoto, T.; Oda, Y.; Honda, H.; et al. BAP1 immunohistochemistry and p16 FISH results in combination provide higher confidence in malignant pleural mesothelioma diagnosis: ROC analysis of the two tests. Pathol. Int. 2016, 66, 563–570. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Bueno, R.; Stawiski, E.W.; Goldstein, L.D.; Durinck, S.; De Rienzo, A.; Modrusan, Z.; Gnad, F.; Nguyen, T.T.; Jaiswal, B.S.; Chirieac, L.R.; et al. Comprehensive genomic analysis of malignant pleural mesothelioma identifies recurrent mutations, gene fusions and splicing alterations. Nat. Genet. 2016, 48, 407–416. [Google Scholar] [CrossRef] [PubMed]
  42. Chung, C.T.; Santos Gda, C.; Hwang, D.M.; Ludkovski, O.; Pintilie, M.; Squire, J.A.; Tsao, M.-S. FISH assay development for the detection of p16/CDKN2A deletion in malignant pleural mesothelioma. J. Clin. Pathol. 2010, 63, 630–634. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Ijms 19 03056 g001a 550Ijms 19 03056 g001b 550
Figure 1. Representative immunohistochemical (IHC) staining of a malignant pleural mesothelioma (MPM) (sample ID 1157) patient tumor sample with the 15 biomarkers currently used for clinical diagnosis. BAP1 is not expressed in sample MM ID 1157, therefore BAP1 staining of sample MM ID 1518 is included as an example of positive BAP1 expression in this figure. All pictures are taken at same magnification with scale bar indicated left bottom corner of TFF-1 staining.
Figure 1. Representative immunohistochemical (IHC) staining of a malignant pleural mesothelioma (MPM) (sample ID 1157) patient tumor sample with the 15 biomarkers currently used for clinical diagnosis. BAP1 is not expressed in sample MM ID 1157, therefore BAP1 staining of sample MM ID 1518 is included as an example of positive BAP1 expression in this figure. All pictures are taken at same magnification with scale bar indicated left bottom corner of TFF-1 staining.
Ijms 19 03056 g001aIjms 19 03056 g001b
Ijms 19 03056 g002 550
Figure 2. Representative example of Fluorescence In situ Hybridization (FISH) to visualise method of CDKN2A deletion in MPM samples. Images depict (A) homozygous, (B) heterozygous and (C) no loss of CDKN2A in MPM. The bottom-right corner of each image shows CDNK2A (red) as well as control CEP9 (green) signals. Images were taken using ZEISS Axio Imager M2. All pictures taken at same magnification with scale bar indicated at panel C.
Figure 2. Representative example of Fluorescence In situ Hybridization (FISH) to visualise method of CDKN2A deletion in MPM samples. Images depict (A) homozygous, (B) heterozygous and (C) no loss of CDKN2A in MPM. The bottom-right corner of each image shows CDNK2A (red) as well as control CEP9 (green) signals. Images were taken using ZEISS Axio Imager M2. All pictures taken at same magnification with scale bar indicated at panel C.
Ijms 19 03056 g002
Ijms 19 03056 g003 550
Figure 3. CDKN2A and BAP1 protein loss is due to copy number. Copy number (Blue = results from cell line, grey = results from tumor tissue, red line = two copies, red dotter line = one copy) of (A) CDKN2A and (B) BAP1 in MPM tumor and its derivative cells normalized to the ribonuclease P protein subunit p30 (RPP30) assessed by droplet digital PCR (ddPCR).
Figure 3. CDKN2A and BAP1 protein loss is due to copy number. Copy number (Blue = results from cell line, grey = results from tumor tissue, red line = two copies, red dotter line = one copy) of (A) CDKN2A and (B) BAP1 in MPM tumor and its derivative cells normalized to the ribonuclease P protein subunit p30 (RPP30) assessed by droplet digital PCR (ddPCR).
Ijms 19 03056 g003
Table
Table 1. Mesothelioma biomarker scoring.
Table 1. Mesothelioma biomarker scoring.
MM IDSample TypeCK 8/18CalretininCK 5/6CD141HBME-1WT-1D2-40EMACEATAG72BG8CD15TTF-1BAP1Ber-EP4
1137Tissue(+++) 90%-(+++) <5%(++) 10%-(++) 10%(+) <5%---(++) 70%--(+++) 90–100%-
1137 T3D cells(+++) 100%---------(++/+++) 90–100%--(+++) 30%-
2D cells(+++) 90%(++) 5%(+) 10%(+) 20%(+++) 10%(+) 10%-(+) <5%(++) 10%-(++) 60%-(+++) <5%-(++/+++) 60%
1157tissue(+++) 90–100%(+++) 80%(+++) 90–100%(+) 50%(+++) 10%(++) 90%(++) 30%(+++) 40%--(+++) 90–100%----
1157 T3D cells(+++) 100%(+) 40%(+++) 70%--(+++) 90%(+) <10%(+++) 40%--(+) 40%----
2D cells(+++) 90–100%-(++/+++) 90%(+) <5%---(+) <5%------(+) 50%
1180Tissue(++++) 90–100%(+++) 30%(+++) <5%(+++) 30%(+++) <5%-(+++) 10%---(++) 80%--(+++) 80%-
1180 T3D cells(+++) 100%----(++) 30%-(++) 40%--(+) 30%-(+) 10%(+++) 95%-
2D cells(+++) 90–100%----(+++) <10%------(++) <10%(+++) 95%-
1187tissue(+++) 90–100%(+++) 90–100%(+++) 90–100%(+++) 90–100%(+++) 90–100%(+++) 90–100%(+++) 40%--(+++) 90–100%--(++) 90–100%-(++) 90–100%
1187 T3D cells(++) 10%----(+++) 90%(++) 40%------(+++) 80%-
2D cells(+++) 100%--(++) 40%-(++) 80%(+) 10%---(+) 70%--(+++) 95%(+) 80%
1505Tissue(+++) 90–100%(+++) 40%(+++) 10%(++) 30%-(++) <5%(+++) <5%---(+++) 90–100%-(+++) 40%(+++) 90–100%-
1505 T3D cells(+++) 100%(+) 20%(+) <5%(+) <5%(+) <5%(+) <5%-(++) 60%(++) 60%(+) 20%(+++) 100%-(+++) 80%(+++) 100%(+++) 50%
2D cells(+++) 100%(++) 80%(+) 80%(+) 70%(+) 20%-(+) 30%-(+) <5%(++) 60%(+++) 100%(+) 80%(+++) 80%(+++) 100%(+++) 100%
1506Tissue(+++) 90%(+++) 80%-(++) 70%(+++) 100%(++) 60%(+) 10%---(++) 40%--(+++) 90%(+) 10%
1506 T3D cells--(+) <5%(+++) 40%---(++) 80%--(+++) 90%--(+++) 100%(+++) 80%
2D cells(+) 10%-(+) 40%(+) 10%(+) <5%--(++) 40%(+) 20%(++) 70%(+++) 100%(+) 90%(+) <5%(+++) 80%(+++) 100%
1518Tissue(+++) 40%-(+++) 30%(+) 20%-(++) 10%(++) 30%(++) 10%--(+) 40%--(+++) 20%-
1518 P3D cells(+++) 100%(+) 10%(+) <5%-(+) 5%(+++) 40%(+) <5%(+) <5%--(+++) 100%--(+++) 90%(+++) 90%
2D cells(+++) 100%(+) 5%-(+) 10%-(+) 10%(+) <5%(+) <5%(+) 10%-(+++) 100%(+) 80% (++) 80%(+++) 100%
1518 T3D cells(+++) 70%(++) 10%-(+) <5%----------(+) 10%
2D cells-(++) 4%-(+) <5%-(+++) 40%(++) 30%-(++/+++) 10%(+) 10%--(+++) 20%-(+++) 40%
1170 T3D cells(++/+++) 70%--(+) 40%(++) <5%(+++) 20%-(+++) 30%--(+) 10%(+) <5%(+++) 90–100%(+++) 95%(++/+++) 40%
2D cells(++) 70%---(++) 70%(++/+++) 80%------(+++) 80%(+++) 95%(++) 20%
1843Tissue(++) 40%(+) <10%(+++) 40%(+++) 30%(+++) 30%(+) 5%-(+++) 20%--(+) 20%-(+) <5%--
1843 T3D cells(+++) 50%----(+++) 100%(+) 20%(++/+++) 40%----(+++) 90%--
2D cells(++) 80%----(++) 70%----(+) 50%-(++) 80%-(+) 50%
2164Tissue(+++) 40%(++) 40%-(++) 40%-(+) <5%(+++) 70%(+++) 60%--(+) 10%----
2164 P3D cells-----(++) 90%---------
2D cells-----(++) 90–100%(++) 30%(+) 10%--(+) 40%----
2170Tissue(+++) 90%(+++) 60%(+) 5%(++) 40%(+++) 90%(+++) 40%(+++) 80%(+++) 80%--(++) 10%---(+++) 5%
2170 T3D cells-(+) <5%------(+) 15%-(+++) 100%(++) 5%-(+++) 100%-
2D cells---(++) 10%(+) 5%-(+) <5%(+) 10%--(+++) 100%(+) 10%-(+++) 100%(+) 80%
2174 P3D(+++) 100%(++) <5%-(+++) 50%(+++) 100%(+++) 80%-(++) 40%--(+++) 100%--(+++) 100%-
2D(+++) 80%(++) 80%-(+++) 90%(+) 20%-(+++) 90%(+++) 80%(+) 10%-(+++) 100%(+) 90%-(+++) 70%(+) 90%
2175Tissue(+++) 100%(+++) 60%(+++80%)-(+) 10%(+) 20%(+) 50%(+++) 70%-----(+++) 80%-
2175 P3D cells---(+++) 60%---(++/+++) 80%-----(+++) 95%-
2D cells---(++) <5%---(+) <5%-----(+++) 95%-
2280Tissue(+++) 100%(+) <5%-(+++) 90%---(+) <5%-----(+) 100%-
2280 T3D cells---(+++) 60%---(+++) 80%--(+++) 80%--(+++) 100%-
2D cells(+++) <5%--(+) 5%--(+) <5%(+) 10%--(+++) 100%(+) 20%-(+++) 100%(+) 90%
T = cell lines established from MPM tumor tissue; P = cell lines established from MPM pleural effusion. +, ++, +++ = 1 positive, 2, positive, 3 positive of IHC intensity.
Table
Table 2. Concordance of BAP1 IHC and ddPCR analysis.
Table 2. Concordance of BAP1 IHC and ddPCR analysis.
MM IDSample TypeBAP1 IHCBAP1 ddPCR (Reference to RPP30)
1137Tissue(+++) 90–100%1.94
cells(+++) 30%2
1157TissueND0.028
cellsND0.07
1180Tissue(+++) 80%2.91
cells(+++) 95%1.69
1187TissueND3.28
cells(+++) 80%2
1505Tissue(+++) 90–100%1.87
cells(+++) 100%2.01
1506Tissue(+++) 90%1.96
cells(+++) 100%1.87
1518Tissue(+++) 20%0.82
cells(+++) 90%0.86
1170Tissue(+++) 95%No tissue availible
Cells(+++) 95%1.98
1843TissueND1
CellsND0.94
2164TissueND0.24
CellsND0.39
2170TissueND1.15
Cells(+++) 100%1
2174TissueNo tissue availibleNo tissue availible
Cells(+++) 70%1.327814
2175Tissue(+++) 80%1.91
Cells(+++) 95%1.92
2280Tissue(+) 100%1.88
Cells(+++) 100%1.85
ND = not detected. +, ++, +++ = 1 positive, 2, positive, 3 positive of IHC intensity.
Table
Table 3. Patient demographics.
Table 3. Patient demographics.
MM IDAga at DiagnoseGenderHistological SubtypeSurgery ProcedureSurvival (months)Asbestos Exposure
113783MaleDesmoplasticBiopsy, Decortication, Pleurodesis0.7Yes
115751MaleEpitheliodBiopsy, Decortication, Pleurodesis20.6Yes
117074MaleBiphasicBiopsy, Decortication, Pleurodesis0.8Yes
118072MaleBiphasicBiopsy, Decortication, Pleurodesis7.4Yes
118764MaleBiphasicExtrapleural pneumonectomy33.5Yes
150557MaleEpitheliodBiopsy, Surgical exploration7.2Yes
150672MaleEpitheliodBiopsy, Pleurodesis24.0Yes
151880MaleBiphasicBiopsy, Decortication, Pleurodesis22.7Yes
184360MaleBiphasicDecortication, Pleurodesis15.6Yes
216473MaleEpitheliodBiopsy, Decortication, Pleurodesis*Yes
217075MaleEpitheliodBiopsy, Decortication, Pleurodesis*ND
217478MaleEpitheliodBiopsy, Decortication, Pleurodesis3.0Yes
217566MaleEpitheliodBiopsy, Decortication, Pleurodesis12.5Yes
228069MaleEpitheliodExtrapleural pneumonectomy*Yes
* = still alive; ND = no data provided.
Table
Table 4. Antibodies used in this study.
Table 4. Antibodies used in this study.
AntibodyCloneManufacturerProduct CodeSpeciesDilution
CK8/18EP17/30DakoM3652Rabbit1:100
CalretininPolyclonalBiocareCP092CRabbit1:100
CK5/6D5 & 16B4Cell Marque358M-16Mouse1:150
CD14115CBNovocastraNCL-CD141Mouse1:50
HBME1HBME-1DakoM3505Mouse1:50
WT1WT49NovocastraNCL-L-WT1-562Mouse1:50
D2-40D2-40BiocareCM266CMouse1:100
EMAGP1.4NovocastraNCL-L-EMAMouse1:350
CEA11-7DakoM7072Mouse1:200
TAG72B72-3Cell Marque337M-84Mouse1:2000
BG8F3CovanceSIG-3317-1000Mouse1:100
CD15Carb-3DakoM3631Mouse1:100
TTF-1SPT24NovocastraNCL-L-TTF-1Mouse1:100
BAP1C-4Santa CruzSC-28383Mouse1:200
HEABer-EP4DakoM0804Mouse1:100

Share and Cite

MDPI and ACS Style

Sarun, K.H.; Lee, K.; Williams, M.; Wright, C.M.; Clarke, C.J.; Cheng, N.C.; Takahashi, K.; Cheng, Y.Y. Genomic Deletion of BAP1 and CDKN2A Are Useful Markers for Quality Control of Malignant Pleural Mesothelioma (MPM) Primary Cultures. Int. J. Mol. Sci. 2018, 19, 3056. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms19103056

AMA Style

Sarun KH, Lee K, Williams M, Wright CM, Clarke CJ, Cheng NC, Takahashi K, Cheng YY. Genomic Deletion of BAP1 and CDKN2A Are Useful Markers for Quality Control of Malignant Pleural Mesothelioma (MPM) Primary Cultures. International Journal of Molecular Sciences. 2018; 19(10):3056. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms19103056

Chicago/Turabian Style

Sarun, Kadir Harun, Kenneth Lee, Marissa Williams, Casey Maree Wright, Candice Julie Clarke, Ngan Ching Cheng, Ken Takahashi, and Yuen Yee Cheng. 2018. "Genomic Deletion of BAP1 and CDKN2A Are Useful Markers for Quality Control of Malignant Pleural Mesothelioma (MPM) Primary Cultures" International Journal of Molecular Sciences 19, no. 10: 3056. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms19103056

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop