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. 2018 Jan 18;7:e30224. doi: 10.7554/eLife.30224

Loss of functional BAP1 augments sensitivity to TRAIL in cancer cells

Krishna Kalyan Kolluri 1,, Constantine Alifrangis 2,, Neelam Kumar 1,, Yuki Ishii 1,, Stacey Price 2, Magali Michaut 3, Steven Williams 2, Syd Barthorpe 2, Howard Lightfoot 2, Sara Busacca 4, Annabel Sharkey 4, Zhenqiang Yuan 1, Elizabeth K Sage 1, Sabarinath Vallath 1, John Le Quesne 4, David A Tice 5, Doraid Alrifai 1, Sylvia von Karstedt 6, Antonella Montinaro 6, Naomi Guppy 7, David A Waller 8, Apostolos Nakas 8, Robert Good 9, Alan Holmes 9, Henning Walczak 6, Dean A Fennell 4, Mathew Garnett 2, Francesco Iorio 10, Lodewyk Wessels 3, Ultan McDermott 2,, Samuel M Janes 1,
Editor: Joaquín M Espinosa11
PMCID: PMC5773178  PMID: 29345617

Abstract

Malignant mesothelioma (MM) is poorly responsive to systemic cytotoxic chemotherapy and invariably fatal. Here we describe a screen of 94 drugs in 15 exome-sequenced MM lines and the discovery of a subset defined by loss of function of the nuclear deubiquitinase BRCA associated protein-1 (BAP1) that demonstrate heightened sensitivity to TRAIL (tumour necrosis factor-related apoptosis-inducing ligand). This association is observed across human early passage MM cultures, mouse xenografts and human tumour explants. We demonstrate that BAP1 deubiquitinase activity and its association with ASXL1 to form the Polycomb repressive deubiquitinase complex (PR-DUB) impacts TRAIL sensitivity implicating transcriptional modulation as an underlying mechanism. Death receptor agonists are well-tolerated anti-cancer agents demonstrating limited therapeutic benefit in trials without a targeting biomarker. We identify BAP1 loss-of-function mutations, which are frequent in MM, as a potential genomic stratification tool for TRAIL sensitivity with immediate and actionable therapeutic implications.

Research organism: Human, Mouse

eLife digest

Two patients with the same disease who receive the same treatment may respond in different ways. This variation often arises from differences in each patient’s genetic code. Genes encode proteins, and proteins are the targets of most medical drugs and thus determine the patient’s response to treatment.

A major advance in the 21st century is that doctors recognise that patients can respond differently to the same treatment and now try to predict which patients will respond best to which drug – an approach known as personalised medicine. Cancer treatment has been at the forefront of personalised medicine because mutations in different genes underlie each different cancer. By analysing which mutations are present in a cancer, doctors can thus predict which drug (or combination of drugs) will be most effective. This approach has been used successfully in several cancers, including breast and lung cancer, leading to fewer patients being exposed to ineffective treatments and their associated side effects and costs.

Mesothelioma is a cancer of the lining of the lung that is associated with exposure to the mineral asbestos. Current treatment options for mesothelioma are unfortunately limited and not very effective. No personalised treatments are currently in use and new treatment approaches are desperately needed.

Kolluri, Alifrangis, Kumar, Ishii et al. set out to determine if any of the mutations commonly seen in mesothelioma affected how the cancer would respond to 94 anticancer drugs that are either in use or in development. In the laboratory, mesothelioma cells that have mutations in the gene that codes for a protein known as BRCA associated protein-1 (or BAP1 for short) were killed much more effectively by a drug known as TNF-related apoptosis-inducing ligand (TRAIL). The same link was seen in experiments with tumours of mesothelioma cells that had been transplanted into mice, and for fragments of mesothelioma tumours taken from patients. When Kolluri et al. studied why these tumours might be killed more effectively with TRAIL, they found that mutations in the gene for BAP1 result in a change in the levels of proteins that transmit the signal from the receptors targeted by the TRAIL drug.

These findings may one day result in a new approach to treating patients with mesothelioma. But first, the next step would be to conduct a clinical trial of TRAIL in patients with mesothelioma and assess if those with tumours that have mutations in the gene for BAP1 do indeed respond better. If this proves to be the case, this would result in a new personalised treatment option for patients that suffer from this disease.

Introduction

Amongst the most significant therapeutic breakthroughs in cancer has been the discovery of drug-sensitising genomic alterations. Drugs such as the tyrosine kinase inhibitors (TKIs) developed against the BCR-ABL fusion product in chronic myeloid leukaemia (CML) and the receptor products of HER2 mutations in breast cancer have transformed the prognosis of these cancers (Druker et al., 2006). Malignant mesothelioma (MM) currently has no biomarker-driven therapies in routine clinical use. The mainstay of medical therapy for all patients remains systemic cytotoxic chemotherapy that offers only limited survival benefit in unselected populations; as such the disease remains invariably fatal (Vogelzang et al., 2003). A plethora of genomic studies in MM has identified recurrent mutations in several genes considered to be tumour drivers. CDKN2A, NF2, BAP1 and TP53 are the most frequently mutated (Guo et al., 2015; Bueno et al., 2016) and there has been increased focus on these genes and their associated signaling pathways as potential therapeutic targets (LaFave et al., 2015).

We aimed to determine if the mutational status of these tumour driver genes could predict response to a range of existing anti-cancer compounds with a view to identifying genomic biomarkers for responsive subsets of MM. We have previously reported on the ability of such unbiased high-throughput chemical screens in cancer cell lines to identify drug-sensitising mutations in other cancer types (Garnett et al., 2012). To this end, we conducted a high-throughput chemical screen of molecularly characterised MM cell lines seeking associations between MM driver gene mutations and compound response. This strategy led to the discovery of a subset of MM cell lines defined by loss-of-function (LOF) mutations in BRCA associated protein-1 (BAP1) that demonstrated heightened sensitivity to the death receptor agonist recombinant tumour necrosis factor (TNF)-related apoptosis-inducing ligand (rTRAIL). We validated this finding using in vitro, in vivo and ex vivo models supporting the use of BAP1 as a genomic biomarker to identify TRAIL-sensitive MM tumours and a novel stratified approach to treat MM.

rTRAIL and other death receptor agonists selectively induce apoptosis in cancer cells and have long held promise as anti-cancer agents owing to their broad clinical utility and minimal off-target effects (Wiley et al., 1995; Pitti et al., 1996; Ashkenazi et al., 1999). Despite this, successful preclinical studies have not translated to clinical efficacy in trials of unselected populations (Herbst et al., 2010; Wainberg et al., 2013; Soria et al., 2010; Lemke et al., 2014a); there have been no trials to date in MM. However, within these trials some patients showed signs of therapeutic benefit and differential sensitivity within cell lines is well known. Retrospective biomarker identification has led to the stratified use of other anti-cancer therapies that initially failed in unselected trials such as activating EGFR mutations and EGFR TKIs (Lynch et al., 2004). We propose that BAP1 could potentially act as such a biomarker for the death receptor agonists. BAP1 is a nuclear deubiquitinase and forms multi-protein complexes that regulate the transcription of genes involved in key cellular functions including cell cycle regulation and DNA repair (Ismail et al., 2014; Machida et al., 2009). We investigated which BAP1 protein-binding partners, and thus which regulatory complexes, mediate TRAIL sensitivity identifying the BAP1-ASXL1 complex, the Polycomb repressive deubiquitinase (PR-DUB), as key. We further found that loss of BAP1 function modulates mRNA and protein expression of components of the extrinsic apoptotic pathway.

Results

A chemical screen uncovers genetic modifiers of drug response in mesothelioma

A 6 day viability screen using 94 drugs including small molecule inhibitors and cytotoxic chemotherapeutics (Supplementary file 1) was performed on 15 MM cell lines (Supplementary file 2) that had been characterised using whole-exome sequencing, copy number analysis and gene expression arrays. We generated 1425 single agent activity data profiles across the 15 cell lines (Figure 1A and Supplementary file 3). To detect novel markers of drug sensitivity, we sought statistical associations between drug response and the mutational status of the cell lines based on five genes identified as candidate drivers of tumourigenesis in MM (Guo et al., 2015) (Figure 1—figure supplement 1). There were 24 significant associations (false discovery rate (FDR) < 0.2) between single agent response and the presence of a genomic alteration. The most statistically significant sensitising association seen was between BAP1 LOF mutations (mt BAP1) and treatment with recombinant TRAIL (rTRAIL; FDR = 0.18, effect size −0.48) (Figure 1B,C and Supplementary file 4). No significant effect on cell viability was observed in BAP1 wild-type (wt BAP1) lines when treated with rTRAIL. We subsequently confirmed this association in a larger panel of MM cell lines (Figure 1D and Supplementary file 5). Strikingly, 6 of the 8 cell lines (75%) harbouring a BAP1 LOF mutation were sensitive or partially sensitive to a dose range of rTRAIL while 7 of the 9 cell lines (78%) harbouring wild-type BAP1 were resistant. BAP1 LOF mutations correlated with a loss of BAP1 protein expression in the majority of cell lines (Figure 1E). No sensitising association with BAP1 was observed for pemetrexed or cisplatin, which are current first line agents for the treatment of MM (Figure 1—figure supplement 2A and B). A marginal trend towards increased sensitivity in BAP1 mutant MM lines in response to treatment with the agonistic FAS receptor antibody CH11 and a TNF-α/IAP inhibitor combination was observed. However, this was not as pronounced as that observed with rTRAIL or the multivalent death receptor five superagonist MEDI3039 (Figure 1—figure supplement 2C,D and E). Thus, while the significant sensitising association observed in the screen appears most specific to death receptor agonists, the trend observed with other TNF superfamily agonists indicates the BAP1-rTRAIL association to be mediated by an underlying mechanism common to this family such as the cytoplasmic extrinsic apoptotic machinery.

Figure 1. A chemical screen in mesothelioma cell lines identifies a BAP1-mutant population sensitised to the death receptor ligand rTRAIL.

(A) Area under the curve (AUC) values for 15 malignant mesothelioma (MM) cells treated for 6 days with 94 compounds. Each dot indicates the AUC value for an individual cell line treated. AUC <0.7 is indicated by the red dotted line — only those compounds with ≥2 cell lines below this value were analysed for statistically significant associations with gene mutations. The AUC values for rTRAIL are indicated by the red asterisk. (B) A Welch t-test was used to test for significant pharmacogenomics interactions between the 94 single agents in the screen and the presence of driver mutations in any of 5 MM cancer genes. Each volcano plot circle corresponds to a significant gene–drug interaction whose position on the x-axis indicates the corresponding effect size. Both half-axes are positive; the right side (green circles) indicates the effect sizes of sensitivity associations, whereas the left side (red circles) corresponds with the effect sizes of resistance associations. The position on the y-axis indicates the statistical significance of the identified interaction. The size of a given circle is proportional to the number of samples in which the selected functional event involved in the corresponding interaction occurs. Specific examples of associations are indicated where the effect size is large (rTRAIL and BAP1 mutations) or highly significant (cisplatin and CDKN2A mutations). (C) Cell viability between wild-type BAP1 (wt BAP1) (n = 10) and mutant BAP1 (mt BAP1) (n = 5) MM lines following 6 days of treatment with rTRAIL (t-test; *p=0.015). (D) Cell viability data for 17 MM lines treated for 6 days with a concentration range of rTRAIL (0.4–50 ng/ml). MM lines are coloured according to their sensitivity pattern (green = sensitive (S); orange = partially sensitive (PS); red = resistant (R)). *Indicates cell lines harbouring BAP1 mutations. (E) Immunoblot of BAP1 protein expression in BAP1-mutant versus BAP1-wild-type MM cell lines. Sensitivity to rTRAIL treatment is indicated as font colour: green (S); orange (PS); red (R).

Figure 1.

Figure 1—figure supplement 1. Mutation status of 5 candidate tumour driver genes in the 15 MM lines used in the combinatorial chemical inhibitor screen.

Figure 1—figure supplement 1.

Figure 1—figure supplement 2. BAP1 and the response to alternative apoptotic stimuli in MM cells.

Figure 1—figure supplement 2.

72 hour cell viability results for 9 MM cell lines (4 BAP1-mutant - green and five wild-type - red) treated with (A) cisplatin (B) pemetrexed (C) FAS receptor agonistic antibody CH11, (D) TNF-α and 5 μM LCL161 or (E) DR5 agonist MEDI3039 assessed by MTT assay.

The association of loss of BAP1 function with TRAIL sensitivity extends to other tumour types

To determine if knockdown of BAP1 in wild-type MM cells led to TRAIL sensitivity, we silenced BAP1 expression in four wt BAP1 MM cell lines using a lentiviral shRNA construct. Knockdown of BAP1 resulted in increased cell death following rTRAIL treatment compared with empty vector (EV) control shRNA and the parental cell line in all four MM cell lines (Figure 2A and Figure 2—figure supplement 1B and C ). Loss of BAP1 expression has also been identified in several other tumour types including uveal melanoma (47%) (Harbour et al., 2010), clear cell renal carcinoma (CCRC) (14%) (Peña-Llopis et al., 2012) and cholangiocarcinoma (7%) (Fujimoto et al., 2015). Notably, knockdown of BAP1 in two CCRC lines resulted in increased sensitivity to rTRAIL in addition to the MDAMB-231 breast cancer line (Figure 2B and Figure 2—figure supplements 2 and 3). We also analysed a panel of 1001 cancer cell lines submitted for whole exome and copy number analysis as part of the COSMIC cell lines project (Forbes et al., 2015) and identified nine additional non-mesothelioma cell lines harbouring truncating mutations in BAP1 (http://cancer.sanger.ac.uk/cancergenome/projects/cell_lines/). These include CCRC, bladder and breast cancer lines. Treatment of cancer cell lines harbouring nonsense mutations in BAP1 with rTRAIL resulted in markedly reduced cell viability compared with cancer cell lines harbouring missense mutations (Figure 2—figure supplement 4).

Figure 2. BAP1-induced TRAIL resistance extends to other cancer subtypes and is dependent upon functional deubiquitinase and ASXL-binding sites.

(A) BAP1-wild-type H2818, MPP-89, H2373 and H2869 MM lines were transduced with BAP1 (shBAP1) or empty vector (EV) shRNA. Immunoblot confirmed BAP1 knockdown in the BAP1 shRNA-transduced cells. Parental and transduced cells were treated with rTRAIL (1000 ng/ml) and cell viability assessed after 72 hr by MTT assay (t-test; ****p<0.0001). (B) The BAP1-wild-type breast cancer line MDAMB-231 and the renal cell carcinoma (RCC) lines Caki-1 and BB65 were transduced with BAP1 (shBAP1) or empty vector (EV) shRNA. Immunoblot confirmed BAP1 knockdown in the BAP1 shRNA transduced cells. Parental and transduced cells were treated with rTRAIL (1000 ng/ml) and cell viability assessed after 72 hr by MTT assay (t-test; ****p<0.0001). (C) The rTRAIL-sensitive H226 MM line, which harbours a homozygous deletion of BAP1, was transduced with either a GFP control, wild-type BAP1 or a mutant BAP1 containing an inactive functional domain: C91A — inactivating mutation of deubiquitinase catalytic site; ΔHBM — deletion of HCF-1-binding motif; T493A — inactivating mutation of FOXK2-binding site; ΔASXL — deletion of ASXL1/2 protein-binding site; ΔCTD — deletion of C-terminal domain containing nuclear localisation signal. These transduced lines were treated with 50 ng/ml rTRAIL and cell death assessed with XTT assay (one-way ANOVA; **p<0.01). (D) The parental and transduced H226 MM lines were treated with a concentration range (1–100 pM) of the small molecule death receptor agonist MEDI3039 and cell viability assessed with XTT assay. (E) The BAP1-wild-type MPP-89 MM line was transduced with ASXL1 (shASXL1), ASXL2 (shASXL2) or empty vector (EV) shRNA. qPCR confirmed a decrease in ASXL1 and ASXL2 mRNA expression in the ASXL1 shRNA and ASXL2 shRNA-transduced cells, respectively (Figure 2—figure supplement 6). Parental and transduced cells were treated with a concentration range (1–100 pM) of MEDI3039 and cell viability assessed with XTT assay. (F) Differential gene expression of apoptosis regulator genes in the catalytically inactive BAP1-mutant (C91A) relative to the wild-type BAP1-transduced (wt BAP1) H226 cells. (G) Immunoblot of apoptosis regulator proteins in the catalytically inactive BAP1-mutant (C91A), inactive ASXL1/2-binding site BAP1-mutant (ΔASXL) or wild-type BAP1-transduced (wt BAP1) H226 cells. (H) Flow cytometry analysis of death receptor 4 (DR4) and 5 (DR5) cell surface expression in H226 cells transduced with the catalytically inactive BAP1-mutant (C91A) or wild-type BAP1 (wt BAP1) and of BAP1-wild-type H2818 MM cells transduced with BAP1 (KD) or empty vector (EV) shRNA. The values represent the median fluorescence intensity (MFI).

Figure 2.

Figure 2—figure supplement 1. shRNA knockdown of BAP1 increases sensitivity to rTRAIL in MM cells.

Figure 2—figure supplement 1.

Three BAP1-wild-type MM cell lines (A) MPP-89, (B) H2869 and (C) H2818 were transduced with empty vector (EV) or BAP1 shRNA (shBAP1). Immunoblot confirmed BAP1 knockdown. The parental, EV and shBAP1 cells were treated with rTRAIL for 24 hr and cell death measured by Annexin V/DAPI flow cytometry assay.
Figure 2—figure supplement 2. shRNA knockdown of BAP1 increases sensitivity to DR agonists in breast cancer cells.

Figure 2—figure supplement 2.

The MDAMB-231 breast cancer cell line was transduced with empty vector (EV) or BAP1 shRNA (shBAP1). Immunoblot confirmed BAP1 knockdown. Cells were treated with (A) rTRAIL and (B) MEDI3039 and cell viability measured with MTT assay at 72 hr. (C) Cells were treated with rTRAIL for 24 hr and cell death measured with Annexin V/DAPI flow cytometry assay.
Figure 2—figure supplement 3. shRNA knockdown of BAP1 increases sensitivity to DR agonists in clear cell renal carcinoma cells.

Figure 2—figure supplement 3.

Clear cell renal carcinoma cell lines, Caki-1 and BB65, were transduced either with either empty vector (EV) or BAP1 shRNA (shBAP1). Immunoblot confirmed BAP1 knockdown. Cells were treated with rTRAIL (A and C) or MEDI3039 (B and D) for 72 hr and cell viability measured by MTT assay.
Figure 2—figure supplement 4. Cell viability of non-mesothelioma BAP1-mutant cell lines following rTRAIL treatment.

Figure 2—figure supplement 4.

Bladder (RT4) and breast (HCC1187) cancer cell lines harbouring nonsense mutations in BAP1 show increased sensitivity to rTRAIL compared with renal cell carcinoma or bladder cancer cell lines harbouring missense (769P and RCC10RGB) or wild-type BAP1 (BB65RCC and SW1710). Cell viability was measured after 6 days of treatment with 100 ng/ml rTRAIL.
Figure 2—figure supplement 5. Overexpression of wild-type BAP1 induces resistance to rTRAIL in BAP1 mutant MM cells. .

Figure 2—figure supplement 5.

The rTRAIL-sensitive H2804(A) and H28(B) mesothelioma cell lines, which harbour mutations in BAP1, were transduced with wild-type BAP1 (wt BAP1) or BAP1 with an inactive deubiquitinase catalytic domain (C91A) and treated with a dose range of rTRAIL.
Cell death was assessed with Annexin V/DAPI apoptosis assay.
Figure 2—figure supplement 6. shRNA knockdown of ASXL1 increases sensitivity of MM cells to rTRAIL.

Figure 2—figure supplement 6.

(A) Cell viability of parental, empty vector, ASXL1 and ASXL2 shRNA-transduced MPP-89 cells treated with rTRAIL (0–1000 ng/ml) for 3 days measured with XTT assay. (B) Efficacy of ASXL1 and ASXL2 shRNA knockdown assessed by qPCR.
Figure 2—figure supplement 7. Ubiquitinated histone 2A at K119 (H2AK119Ub) expression and BAP1 function.

Figure 2—figure supplement 7.

(A) Immunoblot analysis of H2AK119Ub levels in the parental, GFP-, wild-type BAP1 (wt BAP1)-, deubiquitinase mutant BAP1 (C91A)- and ASXL-binding mutant BAP1 (ΔASXL)-transduced H226 MM cell lines. (B) Immunofluorescence images of H2AK119Ub staining in the parental, deubiquitinase mutant-transduced (C91A), ASXL-binding mutant-transduced (ΔASXL) and wild-type BAP1-transduced H226 cell lines. (C) Quantification of immunofluorescence staining in 2B (normalised to cell number; one-way ANOVA; ***p<0.001).
Figure 2—figure supplement 8. Differential gene expression data from H226 MM cells expressing C91A-mutant (mt BAP1) or wild-type BAP1 (wt BAP1).

Figure 2—figure supplement 8.

Only genes with logFC ≥2 and adj.p <0.05 are displayed.
Figure 2—figure supplement 9. Signalling pathway impact analysis of gene expression data from H226 MM cells expressing C91A-mutant (mt BAP1) or wild-type BAP1 (wt BAP1).

Figure 2—figure supplement 9.

The proteins in the pathway are highlighted in green if the expression in mt BAP1 is significantly less than wt BAP1 and red if the expression in mt BAP1 is significantly more than wt BAP1.

BAP1 modulates TRAIL sensitivity through PR-DUB activity

BAP1 is a nuclear deubiquitinase that forms multi-protein complexes with transcription factors to regulate gene transcription (Jensen et al., 1998; Ventii et al., 2008). To elucidate the mechanism by which BAP1 modulates sensitivity to TRAIL we generated expression vectors containing wild-type or mutant forms of BAP1, each with an inactive functional site or protein-binding domain. These included C91A (mutation in the deubiquitination catalytic site) (Jensen et al., 1998; Ventii et al., 2008), ΔHBM (deletion of the HCF-1-binding site) (Misaghi et al., 2009), T493A (mutation in the FOXK2-binding site) (Ji et al., 2014), ΔASXL (deletion of the ASXL1/2 protein-binding site) (Daou et al., 2015) and ΔCTD (deletion of the C-terminal domain containing the nuclear localisation signal) (Ventii et al., 2008). H226 MM cells, which harbour a homozygous deletion of BAP1 and demonstrate complete loss of BAP1 expression, were transduced with a GFP (vector control), a wild-type BAP1 expression vector or one of these five mutant BAP1 expression vectors. rTRAIL sensitivity of the parental BAP1-null H226 MM line was significantly diminished following expression of wild-type BAP1 and each of the mutant constructs except those with an inactive deubiquitinating or ASXL protein-binding site (Figure 2C), implicating the function of these sites in BAP1-induced TRAIL resistance. These effects were replicated using MEDI3039 (Figure 2D). Transduction of two further BAP1-mutant rTRAIL-sensitive cell lines, H28 and H2804, with wild-type BAP1 also induced resistance to rTRAIL while sensitivity was maintained with transduction of the deubiquitinase mutant (Figure 2—figure supplement 5).

The BAP1 deubiquitinase and ASXL-binding sites are key to the function of the PR-DUB, an epigenetic transcriptional regulatory complex composed of BAP1 and ASXL1. Deubiquitination of the main substrate of the PR-DUB, H2AK119Ub, alters chromatin architecture to modulate gene transcription (Scheuermann et al., 2010). This led us to hypothesise that PR-DUB, rather than exclusively BAP1, function might underlie rTRAIL sensitivity. Consistent with this shRNA silencing of ASXL1, but not ASXL2, induced sensitivity to MEDI3039 and rTRAIL in the BAP1/ASXL1/ASXL2-wild-type MM line MPP-89 (Figure 2E and Figure 2—figure supplement 6). Furthermore, H2AK119Ub expression was unaltered in the rTRAIL-sensitive H226 cells transduced with mutant constructs that disrupt PR-DUB activity, while the rTRAIL-resistant H226 cells transduced with a wild-type BAP1 construct exhibited lower H2AK119Ub levels (Figure 2—figure supplement 7). Thus, as the PR-DUB complex is implicated in transcriptional regulation, differential modulation of specific transcriptional programmes by BAP1 may determine rTRAIL sensitivity. We therefore compared differential gene expression data from BAP1-null H226 cells transduced with the C91A BAP1 mutant or with wild-type BAP1 and carried out a signalling pathway impact analysis (SPIA) ((Figure 2—figure supplements 8 and 9 [SPIA_H226 C91A mutant vs WT]) (http://www.genome.jp/dbget-bin/www_bget?path:map04210). Among those pathways significantly altered when comparing wild-type versus C91A BAP1 (FDR < 0.2) was that of apoptosis. In particular, there was altered mRNA expression of components of the extrinsic death pathway (Figure 2F and Supplementary file 6). This manifested as an imbalance in levels of pro- and anti-apoptotic mRNA expression with, for example, significantly decreased levels of the anti-apoptotic cIAP1/2 (p=2.32E-10) and increased levels of the pro-apoptotic death receptor 5 (p=7.79E-10) in the rTRAIL sensitive C91A BAP1-transduced cells relative to the rTRAIL resistant BAP1-wild-type transduced cells. Immunoblot analysis confirmed reduced protein expression of cIAP1/2 and c-FLIP in both C91A and ΔASXL BAP1-transduced cells relative to BAP1-wild-type transduced cells (Figure 2G). Flow cytometry analysis confirmed reduced DR4 and DR5 expression in C91A BAP1 transduced relative to BAP1-wild-type-transduced cells. Knockdown of BAP1 in the BAP1 wild-type H2818 line resulted in a significant increase in DR4 expression only (Figure 2H).

BAP1 loss-of-function sensitises human early passage mesothelial cell lines, human tumour explants and mouse mesothelioma xenograft models to rTRAIL

To support the clinical relevance of our finding we extended our assays to two further models derived from primary tumour tissue. 25 human early passage MM lines from the UK Mesobank (Rintoul et al., 2016) were assessed for BAP1 expression by immunohistochemistry, a technique known to correlate strongly with BAP1 LOF mutations in the absence of strong nuclear staining (Nasu et al., 2015). When treated with rTRAIL, those without strong nuclear staining were significantly more sensitive than those with strong nuclear staining (p=0.0067). Of the 12 lines that did not express nuclear BAP1 9 were sensitive, 2 partially sensitive and only one resistant to rTRAIL (Table 1, Figure 3A and Figure 3—figure supplement 1). Remarkably, rTRAIL treatment of tumour explants derived from three patients with MM also revealed increased levels of apoptosis (as measured by poly (ADP-ribose) polymerase (PARP) cleavage) in explants with low BAP1 expression compared with those with high BAP1 expression (Figure 3B and C, Figure 3—figure supplement 2).

Table 1. BAP1 immunoblot status, nuclear BAP1 staining and rTRAIL sensitivity (50 ng/ml) of the 25 human early passage MM cultures.

Sample name Western blot Nuclear BAP1-IHC Sensitivity
7T Sensitive
8T Sensitive
45 Sensitive
19 Sensitive
14T Sensitive
12 Sensitive
23T Sensitive
40 Sensitive
36 Low Expression Sensitive
26 + + Sensitive
12T + + Sensitive
3T + + Sensitive
52 Partially Sensitive
2 Partially Sensitive
30 Low Expression + Partially Sensitive
15 Low Expression + Partially Sensitive
35 + + Partially Sensitive
24 + + Partially Sensitive
43 Resistant
34 + + Resistant
50T + + Resistant
33T + + Resistant
18 + + Resistant
53T + + Resistant
38 + + Resistant

Figure 3. Loss of functional BAP1 leads to TRAIL sensitivity in early passage mesothelioma cell lines, human tumour explants and mouse xenograft models.

(A) Mean cell viability effect between human early passage MM cell lines (positive nuclear BAP1 staining; n = 13 and negative nuclear BAP1 staining; n = 12) as assessed by immunohistochemistry following 3 days of treatment with rTRAIL (50 ng/ml) (t-test, p=0.0067). (B) Immunohistochemical images of tumour explants derived from three MM patients treated with either vehicle or rTRAIL for 24 hr. Explants were stained with anti-BAP1 and anti-cleaved PARP (marker for apoptosis) antibodies. (C) The percentage of cleaved PARP-positive cells in tumour explants derived from three patients and treated with either vehicle or 0, 50, 100 and 200 ng/ml of rTRAIL for 24 hr was scored based on the percentage of cells with nuclear cleaved PARP-positive staining. (D) Weights of tumour xenografts derived from BAP1-wild-type (wt BAP1) versus catalytically inactive BAP1-mutant (C91A mt BAP1) transduced MM cells following treatment with either vehicle or TRAIL (600 μg per mouse) at the time of sacrifice (day 42) (t-test). (E) Serial bioluminescence imaging of BAP1-wild-type (wt BAP1) and catalytically inactive BAP1-mutant (C91A) MM xenografts in mice treated with either vehicle or TRAIL. Mice were imaged on day 0 (after tumour inoculation), day 13 (before TRAIL administration) and day 41 (time of sacrifice). The intensity of luminescence is denoted by colour: red - high luciferase signal (high tumour burden) and blue - low luciferase signal (low tumour burden). (F) A time-course of bioluminescence scores in BAP1-wild-type (wt BAP1) versus catalytically inactive BAP1-mutant (C91A) MM tumour xenografts. Bioluminescence was measured on days 0, 13, 19, 26 and 41, 15 min after injecting the mice with 0.2 ml luciferin intraperitoneally. The number of photons emitted per second indicates the tumour burden (two way ANOVA).

Figure 3.

Figure 3—figure supplement 1. BAP1 expression in early passage MM cultures.

Figure 3—figure supplement 1.

(A) Immunohistochemical analysis and (B) immunoblot. Cultures are grouped by sensitivity to rTRAIL.
Figure 3—figure supplement 2. Ex vivo experimental protocol.

Figure 3—figure supplement 2.

Tumour explants were obtained by cutting primary pleural tissue from patients with MM who underwent pleurectomy into fragments of approximately 2 mm3.
The explants were treated with vehicle or rTRAIL (50 ng/ml, 100 ng/ml or 200 ng/ml) for 24 hr, following which time explants were fixed and stained for cleaved-PARP (which is a marker of apoptosis).
Figure 3—figure supplement 3. In vivo experimental protocol.

Figure 3—figure supplement 3.

(A) Schematic of in vivo experimental protocol. Mice were injected with H226 cells transduced with wild-type BAP1 and luciferase or catalytically inactive BAP1-mutant (C91A) and luciferase on the right and left flanks, respectively. Mice were divided into two groups, each of which received 600 μg TRAIL or vehicle 6 days a week (day 14–40). Tumour size was assessed longitudinally with bioluminescence imaging on days 0, 13, 19, 26 and 41. (B) Size of tumours derived from BAP1-wild-type (wt) versus catalytically inactive (C91A) BAP1-mutant (mt) MM cells following treatment with either vehicle or TRAIL (600 μg per mouse) at time of sacrifice (day 42). A centimetre scale is included in the photograph for comparison.

To test the in vivo efficacy of TRAIL in inducing apoptosis in BAP1-mutant MM cells, we transduced the H226 BAP1-wild-type and the H226 C91A BAP1-mutant cell lines with luciferase and injected equal numbers of wild-type and mutant cells into the opposite flanks of mice (Figure 3—figure supplement 3A). On day 14 after injection the mice were divided into two groups and injected intraperitoneally with rTRAIL or vehicle for 6 days per week until day 40. At sacrifice rTRAIL-treated BAP1-mutant tumours weighed significantly less than rTRAIL-treated BAP1-wild-type tumours (p=0.020) and vehicle-treated BAP1-mutant tumours (p=0.019) (Figure 3D and Figure 3—figure supplement 3B). BAP1-wild-type tumours showed no response to rTRAIL compared with vehicle. The growth rate of rTRAIL-treated BAP1-mutant tumours was also significantly suppressed compared with rTRAIL-treated BAP1-wild-type and vehicle-treated tumours (p<0.05) as assessed by longitudinal bioluminescence intensity (Figure 3E and F).

Discussion

Malignant mesothelioma remains a devastating disease with limited systemic treatment options (Vogelzang et al., 2003). Biomarker-driven therapies have significantly improved the prognosis for subsets of patients within other cancer types however this strategy has yet to impact MM. Our data support the use of loss of function of BAP1 as a genomic stratification tool to identify rTRAIL-sensitive MM tumours, an approach that may extend to other cancer subtypes. We propose the underlying mechanism involves the transcriptional regulation of expression of components of the apoptotic pathway by the PR-DUB. Our finding has potentially significant and immediately actionable clinical implications for both MM treatment and for the death receptor agonist field.

BAP1 has emerged as a key driver of tumorigenesis in MM (Bueno et al., 2016). As such, there has been increased focus on this nuclear deubiquitinase and its associated pathways (LaFave et al., 2015). While next-generation sequencing reveals MM BAP1 mutation rates in the order of 20–30% (Guo et al., 2015; Bueno et al., 2016; Bott et al., 2011), immunohistochemical analysis has identified loss of BAP1 function in up to 67% of MM tumours (Nasu et al., 2015) opening our biomarker-driven approach to a significant proportion of MM patients. BAP1 immunohistochemistry accurately identifies loss of BAP1 function as a consequence of genetic and non-genetic mechanisms (Nasu et al., 2015) and is already in clinical use as a diagnostic tool; hence the clinical tools for our proposed approach are validated and ready. Our data indicate the BAP1-TRAIL association extends beyond MM to other tumours with loss of BAP1 function. Chromosomal deletions and somatic inactivating mutations have been identified at high frequency in uveal melanoma (Harbour et al., 2010), clear cell renal carcinoma (Peña-Llopis et al., 2012) and cholangiocarcinoma (Fujimoto et al., 2015), increasing the potential clinical impact of our discovery. Although loss of BAP1 function is seen at far lower rates in breast carcinoma (1%) (Stephens et al., 2012) and non-small cell lung carcinoma (1%) (Owen et al., 2017), the high incidence of these cancers translates to a large cohort of patients.

Focus on death receptor agonists as anti-cancer agents has generated two decades of preclinical studies and the development of numerous clinically tested compounds, all of which have demonstrated limited therapeutic efficacy at phase I/II trials (Herbst et al., 2010; von Pawel et al., 2014; Paz-Ares et al., 2013; Forero-Torres et al., 2013). Strategies to overcome this have included the development of increasingly potent death receptor agonists and combination therapies to address resistance factors within the apoptosis pathway (Holland, 2013; Lemke et al., 2014b). As differential sensitivity has been observed in trials, it has been accepted that identification of a biomarker predicting the therapeutic outcome is of paramount importance (Ashkenazi, 2015; von Karstedt et al., 2017). There have been previous attempts to identify predictive biomarkers largely focused on molecular expression panels (Passante et al., 2013). Ours is the first unbiased approach to address how the genetic make-up of tumours predicts response to rTRAIL treatment. The identification of BAP1 as a potential genomic biomarker has the potential to reignite the death receptor agonist field of research into which significant investment has already been made. The value of retrospective analysis of clinical trials based on the genomic landscape has clearly been demonstrated in the past (Lynch et al., 2004) and we wait with interest whether this will be performed on archived tumour tissue, in the context of BAP1 status, from previous trials. Notably there have been no trials of any death receptor agonists in MM or indeed any cancer with a high proportion of BAP1 mutations. We suspect a significantly higher proportion of responders would have been identified in such trials.

Our findings also have implications for death receptor agonists as a therapy for BAP1-wild-type tumours as delineation of the underlying mechanism would offer a novel avenue by which to sensitise these tumours. Our mechanistic data implicate transcriptional regulation by the PR-DUB as key to the capacity of BAP1 to modulate death receptor agonist sensitivity. BAP1 is a master genetic regulator and is known to influence the transcription of thousands of genes as supported by our and others’ gene expression data (Dey et al., 2012). While we highlight the extrinsic apoptotic pathway and proteins as being significantly altered by BAP1 status, identifying a single factor to explain BAP1-induced TRAIL resistance is extremely challenging. Of more direct clinical significance is our finding that loss of function of either component of the PR-DUB, BAP1 or ASXL1, results in an increase in death receptor agonist sensitivity. ASXL1 mutations have an important role in the pathogenesis of myeloid neoplasms primarily consisting of nonsense, missense and frameshift mutations resulting in a truncated ASXL1 protein that retains the BAP1-binding domain (Boultwood et al., 2010). It has yet to be clarified if this truncated protein possesses dominant-negative or gain-of-function properties in the context of PR-DUB activity (Balasubramani et al., 2015). In the case of the former, ASXL1 could potentially predict death receptor agonist sensitivity in myeloid neoplasms. Further research is needed in these malignancies to determine this.

Confirmation of the clinical value of BAP1 as a targeting biomarker for death receptor agonists in early phase clinical trials of mesothelioma is the first priority. The clinical tools for this approach are already validated and established facilitating the translation of our discovery into a desperately needed new therapy for this fatal thoracic cancer.

Materials and methods

Key resources table.

Reagent type (species) or resource Designation Source or reference Identifiers Additional information
gene
BRCA associated protein-1 (human) BAP1 Entrez Gene NCBI Gene ID: 8314
Additional sex combs like 1 (human) ASXL1 Entrez Gene NCBI Gene ID: 171023
strain, strain background
NOD.CB17-Prkdcscid/NcrCrl NOD SCID mice Charles River Laboratories, UK RRID:IMSR_CRL:394
cell line
Early passage mesotheliomacell cultures 7T, 8T, 45, 19, 14T,
23T, 40, 36, 26, 12T, 3T, 52, 2, 30, 15, 35, 24, 43, 34, 50T, 33T, 18, 53T, 38
MesobanK, Mesothelioma UK www.mesobank.com
Mesothelioma
Tissue Bank, Papworth Hospital NHS Trust, UK
NCI-H2373 H2373 Wellcome Trust Sanger Institute, UK RRID:CVCL_A533
NCI-H2803 H2803 Wellcome Trust Sanger Institute, UK RRID:CVCL_U997
NCI-H2452 H2452 Wellcome Trust Sanger Institute, UK RRID:CVCL_1553
NCI-H2722 H2722 Wellcome Trust Sanger Institute, UK RRID:CVCL_U994
NCI-H2369 H2369 Wellcome Trust Sanger Institute, UK RRID:CVCL_A532
NCI-H2795 H2795 Wellcome Trust Sanger Institute, UK RRID:CVCL_U996
NCI-H2869 H2869 Wellcome Trust Sanger Institute, UK RRID:CVCL_V001
NCI-H2591 H2591 Wellcome Trust Sanger Institute, UK RRID:CVCL_A543
MPP 89 MPP-89 Wellcome Trust Sanger Institute, UK RRID:CVCL_1427
NCI-H2810 H2810 Wellcome Trust Sanger Institute, UK RRID:CVCL_U999
NCI-H2818 H2818 Wellcome Trust Sanger Institute, UK RRID:CVCL_V000
NCI-H513 H513 Wellcome Trust Sanger Institute, UK RRID:CVCL_A570
NCI-H2595 H2595 Wellcome Trust Sanger Institute, UK RRID:CVCL_A545
NCI-H2461 H2461 Wellcome Trust Sanger Institute, UK RRID:CVCL_A536
NCI-H2731 H2731 Wellcome Trust Sanger Institute, UK RRID:CVCL_U995
NCI-H2804 H2804 Wellcome Trust Sanger Institute, UK RRID:CVCL_U998
NCI-H28 H28 Wellcome Trust Sanger Institute, UK RRID:CVCL_1555
NCI-H226 H226 Szlosarek lab, Barts Cancer Institute, UK RRID:CVCL_1544
MDA-MB-231 MDAMB-231 Wellcome Trust Sanger Institute, UK RRID:CVCL_0062
Caki-1 Caki-1 Wellcome Trust Sanger Institute, UK RRID:CVCL_0234
BB65 BB65 Wellcome Trust Sanger Institute, UK RRID:CVCL_1078
antibody
BAP1 (C-4) mouse mAb anti-BAP1 Santa Cruz Biotechnology, Santa Cruz, CA Cat# sc-28383 RRID:AB_626723 1:500 in milk; 1:50 for flow cytometry
Caspase-8 (1C12) mouse mAb anti-caspase 8 Cell Signaling Technology, Danvers, MA Cat# 9746 RRID:AB_2275120 1:1000 in BSA
FLIP (7F10) mouse mAb anti c-FLIP Enzo Life Sciences, Farmingdale, NY Cat# ALX-804-961-0100 RRID:AB_2713915 1:1000 in milk
c-IAP1 (D5G9) rabbit mAb anti-cIAP1 Cell Signaling Technology,Danvers, MA Cat# 7065S RRID:AB_10890862 1:1000 in BSA
c-IAP2 (58C7) rabbit mAb anti-cIAP2 Cell Signaling Technology,
Danvers, MA
Cat# 3130S RRID:AB_10693298 1:1000 in BSA
FADD rabbit pAb anti-FADD Cell Signaling Technology,
Danvers MA
Cat# 2782 RRID:AB_2100484 1:1000 in BSA
XIAP (3B6) rabbit mAb anti-XIAP Cell Signaling Technology,
Danvers, MA
Cat# 2045 RRID:AB_2214866 1:1000 in milk
survivin rabbit pAb anti-survivin Cell Signaling Technology,
Danvers, MA
Cat# 2803 RRID:AB_490807 1:1000 in BSA
α-Tubulin (11H10) Rabbit mAb anti-α-tubulin Cell Signaling Technology,
Danvers, MA
#2125 1:2000 in milk
Ubiquityl-Histone H2A (Lys119) (D27C4) XPRabbit mAb anti-H2AK119Ub Cell Signaling Technology,
Danvers, MA
Cat# 8240P RRID:AB_10891618 1:2000 in BSA
Histone H2A (D6O3A) Rabbit mAb anti-H2A Cell Signaling Technology,
Danvers, MA
Cat# 12349 RRID:AB_2687875 1:1000 in BSA
Anti-mouse IgG, HRP-linked antibody anti-mouse HRP Cell Signaling Technology,
Danvers, MA
Cat# 7076 RRID:AB_330924 1:2000 in milk
Anti-rabbit IgG, HRP-linked antibody anti-rabbit HRP Cell Signaling Technology,
Danvers, MA
Cat# 7074 RRID:AB_2099233 1:2000 in milk
Donkey anti-Mouse IgG (H + L) Highly Cross-Adsorbed Secondary Antibody, AlexaFluor 488 AlexaFluor 488-conjugated anti-mouse antibody Thermo Fisher Scientific, UK Cat# A-21202 RRID:AB_141607 1:200 for flow cytometry
Annexin V, AlexaFluor 647 conjugate Annexin V AlexaFluor 647-conjugated antibody Thermo Fisher Scientific, UK Cat# A23204 RRID:AB_2341149 1:100 for flow cytometry
PE anti-human CD261 (DR4, TRAIL-R1) antibody PE-conjugated antibody to DR4 Biolegend, UK Cat# 307205 RRID:AB_314669 1:100 for flow cytometry
PE anti-human CD262 (DR5, TRAIL-R2) antibody PE-conjugated antibody to DR5 Biolegend, UK Cat# 307405 RRID:AB_314677 1:100 for flow cytometry
PE Mouse IgG1, κ Isotype Ctrl Antibody PE isotype control antibody Biolegend, UK Cat# 400112 1:100 for flow cytometry
Goat anti-Rabbit IgG (H + L) Secondary Antibody, AlexaFluor 488 conjugate AlexaFluor 488-conjugated anti-rabbit secondary antibody Thermo Fisher Scientific, UK Cat# R37116 RRID:AB_2556544 1:200 for flow cytometry
Anti-Cleaved PARP1 (E51) mAb cleaved PARP primary antibody; anti-cleaved PARP Abcam, UK Cat# ab32064 RRID:AB_777102 (1:6000) for immunohistochemistry
recombinant DNA reagent
BAP1 (NM_004656) Human cDNA Clone pCMV6-AC BAP1 plasmid Origene, Rockville, MD Cat# SC117256
pHIV-Luc-ZsGreen ZS-green luciferase plasmid, pHIV-Luc-ZsGreen Bryan Welm Lab, University of Utah, Addgene, Logan, UT Cat# 39196
pCMVR8.74 pCMV-dR8.74 Thrasher lab, UCL, Addgene,
UK
Cat# 22036
pMD2.G pMD2.G Thrasher lab, UCL, Addgene,
UK
Cat# 12259
sequence based reagent
BAP1 GIPZ Lentiviral shRNA BAP1 shRNA UCL RNAi Library (Dharmacon, Lafayett, CO) V2LHS 41473
ASXL1 GIPZ Lentiviral shRNA ASXL1 shRNA UCL RNAi Library (Dharmacon, Lafayett, CO) V2LHS 78829
ASXL2 GIPZ Lentiviral shRNA ASXL2 shRNA UCL RNAi Library (Dharmacon, Lafayette, CO) V3LHS_313940
peptide, recombinant protein
Recombinant Human sTRAIL rTRAIL Peprotech, UK Cat# 310–04
commercial assay or kit
Cell Proliferation Kit XTT XTT reagent Applichem, UK A8088
Q5 Site-Directed Mutagenesis Kit Site directed mutagenesis New England Biolabs, Ipswich, MA Cat# E0554
Rabbit specific HRP/DAB (ABC) Detection IHC Kit rabbit-specific HRP/DAB (ABC) detection IHC kit Abcam, UK Cat# ab64261
chemical compound, drug
MEDI3039 MEDI3039 MedImmune, UK
software, algorithm
GraphPad Prism software Graphpad Prism GraphPad Software, CA, USA
CaVEMan algorithm CaVEMan https://github.com/cancerit/CaVEMan
Pindel algorithm Pindel https://github.com/genome/pindel
Predicting Integral Copy Numbers In Cancer algorithm PICNIC http://www.sanger.ac.uk/science/tools/picnic
FlowJo software Flowjo FlowJo LLC
Other
RIPA buffer RIPA Sigma-Aldrich, St. Louis, MO Cat# R0278
Syto™ 60 red fluorescent nucleic acid stain Syto 60 Thermo Fisher Scientific, UK Cat# S11342
Thiazolyl Blue Tetrazolium Bromide (MTT) MTT reagent Sigma-Aldrich, St. Louis, MO Cat# M2128
jetPEI DNA transfection reagent jetPEI Source Bioscience, UK Cat# 101–10
Polybrene Polybrene Sigma-Aldrich, St Louis, MO Cat# 107689 8 μg/ml
Hoechst 33342 Solution (20 mM) Hoechst 33342 Thermo Fisher Scientific, UK Cat# 62249
4’, 6-diamidino-2-phenylindole DAPI Sigma-Aldrich, St Louis, MO Cat# D9542 200 μg/ml

Drug screens

Drugs in the screen

Compounds were from academic collaborators or commercial vendors. Each compound, its therapeutically relevant target substrate and pathway and the minimum and maximum screening concentrations are listed in Supplementary file 1. Compounds were stored as 10 μM aliquots at −80°C and were subjected to a maximum of 5 freeze-thaw cycles. For the screen a fixed single 40 ng/ml concentration of rTRAIL was used, while each of the 94 agents was screened at a 5-point serial 4-fold dilution to give a 256-fold range from the lowest to highest concentration. The concentrations selected for each compound were based on in vitro data to cover the range of concentrations known to inhibit relevant kinase activity and cell viability.

Genomic/transcriptomic characterization of mesothelioma cell lines

The genomic data is available in the COSMIC database (Forbes et al., 2015) (http://cancer.sanger.ac.uk/cancergenome/projects/cell_lines/).

Substitution and insertion/deletion variant data

Exome sequencing was carried out using the Agilent SureSelectXT Human All Exon 50 Mb bait set giving an average 7 Gb of unique mapped reads per sample with an average of 85% of base pairs covered to >20 reads. Variants were identified by comparison to a reference single unmatched normal sample. Differences from the reference genome were identified using the CaVEMan and Pindel algorithms identifying substitution and small insertions/deletions respectively (https://github.com/cancerit/CaVEMan; https://github.com/genome/pindel) (Ye et al., 2009). The resulting variants were then screened against approximately 8000 normal samples to remove sequencing artefacts and germline variants (428 in-house normal exomes, 6500 normal exomes (NHLBI GO Exome Sequencing Project, June 20th 2012 release), 1000 genomes project (29th March 2012 release) and variants in the dbSNP database that had an associated minor allele frequency.

Copy number data

Genome-wide copy number data were obtained for the cell lines using the Affymetrix SNP6 microarray analysed using the ‘PICNIC’ algorithm, which segments the genome into integer value copy number segments (Greenman et al., 2010) (http://www.sanger.ac.uk/genetics/CGP/Software/PICNIC/). All genes were mapped onto this segmentation data to give a gene level copy number analysis. For genes to be classified as amplified the complete coding footprint of the gene had to map onto segment(s) present in eight or more copies. For genes to be classed as homozygously deleted a minimum of 1 bp of coding sequence had to be present within a segment of copy number ‘0’.

Cell viability assay in compound screen

Cells were seeded in either 96-well or 384-well microplates in RPMI-1640 or DMEM/F12. The optimal cell number for each cell line was determined to ensure that each was in growth phase at the end of the assay (~70% confluency). Adherent cell lines in the screens were plated 1 day prior to treatment with each compound using liquid handling robotics and assayed after 6 days of treatment with either the single agent or in combination with rTRAIL. Cells were fixed in 4% formaldehyde for 30 min and then stained with 1 μM of the fluorescent nucleic acid stain Syto 60 (Thermo Fisher Scientific, UK) for 1 hr. Quantitation of fluorescent signal intensity was performed using a fluorescent plate reader at excitation and emission wavelengths of 630/695 nm. The sensitivity of each cell line to various concentrations of compound was calculated as the fraction of viable cells relative to DMSO-treated cells following a 6 day exposure. All screening plates were subjected to stringent quality control measures and a Z-factor score comparing negative and positive control wells was calculated across all screening plates (median = 0.70, upper quartile = 0.86, lower quartile = 0.47, n = 4857 plates).

Calculation of AUC values from cell line viability data

We derived the area under the curve (AUC) parameter from the 6 day cell line viability data to identify cell lines that are sensitive to a specific compound, with decreasing AUC associated with increasing sensitivity. The AUCs were computed using a trapezoid integration below the five measured viability of the dose-response curve and scaled so that a constant viability of 1 gives AUC of 1.

Statistical analysis of the effect of genetic features on drug response

We used 15 mesothelioma cell lines with molecular and drug response data: H2369, H2373, H2461, H2591, H2722, H2731, H2803, H2804, H2810, H2818, H2869, H513, MPP-89, NCI-H2452 and NCI-H28. We selected five genes for inclusion in the analysis (BAP1, TAOK1, NF2, TP53 and CDKN2A). We defined groups of cell lines based on mutations and copy number alterations (homozygous deletions or amplifications) in these genes. This resulted in a set of input features of 4 genes altered in at least 2 of the cell lines (CUL1, RDX and PIK3C2B were not mutated and TAOK1 was only mutated in 1 cell line). For the association of gene mutations with sensitivity to each compound we restricted the set of drugs to test to those with ≥2 cell lines with AUC <0.7. This resulted in 45 drugs being suitable for analysis (of the overall 94 drugs).

Cell lines

All cell lines were sourced from the Wellcome Trust Sanger Institute except the H226 line that was a kind gift from Dr Peter Szlosarek, Barts Cancer Institute. All cell lines were authenticated by genotyping using Short Tandem Repeat (STR) and Sequenom profiling of a panel of 92 single nucleotide polymorphisms for each cell line to ensure non-synonymous cell lines were not used. As a cell line classified as mesothelioma, H513 (on the list of commonly misidentified cell lines) was included in the drug screen of 15 mesothelioma cell lines conducted. Use of this cell line however was not carried forward to further experiments in the paper. The 25 early passage MM cultures were purchased from MesobanK (Rintoul et al., 2016). All cell lines and cultures were tested for mycoplasma contamination and confirmed to be negative.

Cell culture

Cell lines were cultured in RPMI-1640 or Dulbecco's modified Eagle's medium and nutrient mix 12 medium (DMEM:F12) supplemented with 10% fetal bovine serum (FBS), penicillin/streptavidin and sodium pyruvate. Early passage human mesothelioma cultures were cultured in RPMI-1640 medium supplemented with 5% FBS, 25 mM HEPES, penicillin/streptavidin and sodium pyruvate. 293 T cells were cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 2 mM L-glutamine. All cells were maintained in a humidified environment at 37°C and 5% CO2.

Immunoblotting and antibodies

Cells were lysed in radioimmunoprecipitation assay (RIPA) buffer (Sigma-Aldrich, St. Louis, MO) with protease inhibitors (Complete-mini; Roche, Switzerland) on ice to extract protein. 20 μg of protein samples were separated by SDS–PAGE and transferred onto nitrocellulose membranes. Membranes were incubated with specific primary antibodies, washed, incubated with secondary antibodies and visualised using an ImageQuant LAS 4000 imaging system (GE Healthcare, Little Chalfont, NY). Antibodies used include BAP1 (Santa Cruz Biotechnology, Santa Cruz, CA) Cat# sc-28383, RRID:AB_626723), caspase 8 (Cell Signaling Technology, Danvers, MA) Cat# 9746, RRID:AB_2275120), c-FLIP (Enzo Life Sciences, Farmingdale, NY) Cat# ALX-804-961-0100 RRID:AB_2713915), cIAP1 (Cell Signaling Technology Cat# 7065S, RRID:AB_10890862), cIAP2 (Cell Signaling Technology Cat# 3130S, RRID:AB_10693298), FADD (Cell Signaling Technology Cat# 2782, RRID:AB_2100484), XIAP (Cell Signaling Technology Cat# 2045, RRID:AB_2214866), survivin (Cell Signaling Technology Cat# 2803, RRID:AB_490807), α-tubulin (Cell Signaling #2125), H2AK119Ub (Cell Signaling Technology Cat# 8240P, RRID:AB_10891618), H2A (Cell Signaling Technology Cat# 12349, RRID:AB_2687875), anti-mouse HRP (Cell Signaling Technology Cat# 7076, RRID:AB_330924) and anti-rabbit HRP (Cell Signaling Technology Cat# 7074, RRID:AB_2099233). To detect the ubiquitination status of the histones, the cells were lysed with TBS buffer containing 1% SDS, protease and phosphatase inhibitors. The cell extract was denatured by heating up at 95°C for 10 min and centrifuged at 13000 rpm for 10 min. The supernatant was collected and immunoblotted as described above.

XTT/MTT cell viability assay

Cells were seeded in 96-well plates in 100 μl media per well at a density of 40,000 cells/ml 1 day prior to treatment with soluble recombinant TRAIL (rTRAIL; Peprotech, UK) or MEDI3039 (Medimmune, UK). XTT (Applichem, UK; A8088) or MTT (M-2128, Sigma-Aldrich) reagent was added on day 3. The absorbance was measured with a spectrophotometer at a wavelength of 490 nm or 560 nm for XTT or MTT respectively. Relative cell viability was calculated as a fraction of viable cells relative to untreated cells.

Plasmids

Full-length BAP1 cDNA was amplified by PCR from pCMV6-AC BAP1 plasmid (Origene (Rockville, MD; SC117256) and cloned into the lentiviral plasmid pCCL-CMV-flT vector previously described (Yuan et al., 2015) in place of flT via BamHI and SalI sites, creating the BAP1 vector designated pCCL-CMV-BAP1. Vectors expressing mutant BAP1 constructs were generated by site-directed mutagenesis (New England Biolabs) of the pCCL-CMV-BAP1 vector. The primers used are listed below. All mutations were confirmed by sequencing.

BAP1-F CGTGGATCCGCCACCATGAATAAGGGCTGGCTGGA

BAP1-R GTCGGTCGACTCACTGGCGCTTGGCCTTGTA

C91A-F ATACCCAACTCTGCTGCAACTCATGCCTTGCTG

C91A-R CAGCTGGTGGGCAAAGAACATGTTATTCACAATATCATC

HBM-F CGCTGCTGCCAAGTCCCCCATGCAGGAGGA

HBM-R GCAGCGTCTAGAAAGGCCGGCAGCCGCT

CTD-F CGTGGATCCGCCACCATGAATAAGGGCTGGCTGGA

CTD-R GTCGTTCGAATCAGTCAGGCTTCCGCTGCTTGTGG

T493A-F GCAGACACGGCCTCTGAGATCGGCAGTGCT

T493A-R ACTCTCATTGCTGGGGGTGGGTGA

ASXL-F AACTACGATGAGTTCATCTGCACCT

ASXL-R CTGGTCATCAATCTTGAACTTCTTCCTC

The ZS-green luciferase plasmid, pHIV-Luc-ZsGreen (a gift from Bryan Welm, Addgene plasmid #39196) was used for generating ZS-Green luciferase-expressing lentivirus to transduce the H226 cells used in animal experiments.

RNA interference

Short hairpin RNAs (shRNAs) were expressed as part of a mir30-based GIPZ lentiviral vector (Dharmacon, Lafayette, CO). The clones used in this study include BAP1 (V2LHS_41473), ASXL1 (V2LHS_78829), ASXL2 (V3LHS_313940) and the empty GIPZ control vector.

Lentivirus production and cell transfection

Lentiviral vectors were produced by co-transfection of 293 T cells with construct plasmids together with the packaging plasmids pCMV-dR8.74 and pMD2.G (kind gifts from Dr Adrian Thrasher, UCL, Addgene plasmid #22036 and #12259) in the presence of a DNA transfection reagent jetPEI (Source Bioscience UK Ltd). Lentiviruses were concentrated by ultracentrifugation at 17,000 rpm (SW28 rotor, Optima LE80K Ultracentrifuge, Beckman Coulter, Brea, CA) for 2 hr at 4°C. To determine the titres of prepared lentiviruses 293 T cells were transduced with serial dilutions of viruses in the presence of 8 μg/ml Polybrene (Sigma-Aldrich) and BAP1 expression was assessed by flow cytometry. shRNA- and luciferase-expressing vectors were assessed by analysis of GFP expression. Cell lines were transduced in the presence of 8 μg/ml Polybrene at a range of MOIs and transduction efficacy was assessed by flow cytometry for BAP1 expression.

Gene expression analyses

We pre-processed and normalised raw CEL files from Affymetrix Human Genome U219 array plate hybridisations with the Multi-Array Average (RMA) method (Irizarry et al., 2003). We discarded transcripts with low sample variance and consolidated duplicated genes by averaging their expression values across duplicates. The resulting data were subsequently normalised (μ = 0, σ = 1) sample-wise and gene-median centred. Gene expression was averaged across three biological replicates of H226 transduced cells with either a C91A mutant or a wild-type BAP1 construct. SPIA pathway analysis as described in Tarca et al (Tarca et al., 2009) was performed on those genes with an adjusted p<0.05 and a fold change of >1.

Flow cytometry

All flow cytometry analysis was performed on a LSR Fortessa analyser (Becton Dickinson, Franklin Lakes, NJ). For analysis of BAP1 expression cells were stained with primary antibody to BAP1 (Santa Cruz Biotechnology Cat# sc-28383, RRID:AB_626723; 1:50) and then with an AlexaFluor 488-conjugated anti-mouse antibody (Thermo Fisher Scientific Cat# A-21202, RRID:AB_141607; 1:200). For analysis of apoptosis and cell death all floating and adherent cells were harvested and stained with an Annexin V AlexaFluor 647-conjugated antibody (Thermo Fisher Scientific Cat# A23204, RRID:AB_2341149) and 4’, 6-diamidino-2-phenylindole (DAPI; Sigma-Aldrich, 200 μg/ml). For analysis of DR4 and DR5 expression on cell surface cells were stained with PE-conjugated antibody (DR4 - BioLegend, San Diego, CA) Cat# 307205, RRID:AB_314669, DR5 - BioLegend Cat# 307405, RRID:AB_314677, Isotype control - Biolegend #400112; 1:100). FlowJo software was used to analyse all data.

Immunofluorescence

H226 cells were seeded at 2.5 × 103 cells per well into 96-well Greiner micro-clear imaging plates in DMEM 10% FBS. After 48 hr, cells were fixed in 4% PFA for 10 min at room temperature and permeabilised in 0.3% NP-40 in PBS for 10 min. Cells were blocked in 1% BSA in 0.1% PBS tween for 1 hr at room temperature. Ubiquityl-histone H2A (Lys119) primary antibody (Cell Signaling, #8240) was incubated overnight at 4°C, before incubating for 1 hr at room temperature with Alexafluor 488-conjugated anti-rabbit secondary antibody. Nuclei were stained with Hoechst 33342 (Thermo Fisher Scientific Cat# 62249). Images were acquired (n = 3) with a BioTek Cytation3 Multimode reader. Using a 10x objective 4 fields of view were acquired per well (n = 3) and the level of nuclear ubiquityl-histone H2A intensity was determined within the primary nuclear mask and normalised to total cell number.

Immunohistochemical analysis of early passage cultures

BAP1 immunohistochemistry of human early passage cell lines was conducted on sections of cell pellets mounted on slides. Automated staining on a Leica Bond III staining platform was used. Slides were incubated with BAP1 primary antibody (Santa Cruz Biotechnology Cat# sc-28383, RRID:AB_626723; 1:150) for 15 min at room temperature. Epitope retrieval was completed using HIER using Leica Bond ER2 (high pH) for 30 min and a Leica Bond Polymer Refine with DAB chromogen detections system used.

Mesothelioma patient explants

Appropriate ethical approval was obtained from the NHS Health Research Authority National Research Ethics Service to carry out this work (reference 14/LO/1527). Informed consent to conduct research on samples collected and to publish results was obtained from patients. The diagnosis of mesothelioma was confirmed histologically for all patients prior to consent and surgery. Patients underwent pleurectomy, following which primary pleural tissue was sectioned into fragments measuring approximately 2 mm3. These tissue explants were cultured in 50% neurobasal and 50% DMEM:F12, supplemented with B27 (2%), EGF (20 ng/ml) and FGF (10 ng/ml). After 24 hr the explants were treated with rTRAIL (vehicle, 50 ng/ml, 100 ng/ml or 200 ng/ml) for a further 24 hr, following which explants were either fixed for PARP immunohistochemistry. The explants were fixed in 10% neutral-buffered formalin (NBF) for 24 hr and then transferred into 70% ethanol followed by paraffin embedding. Subsequently, 5 μm sections were used for immunohistochemistry, as previously described (Busacca et al., 2016).

Immunohistochemistry of patient explants

Cleaved PARP primary antibody (Abcam Cat# ab32064, RRID:AB_777102) was used at a 1:6000 dilution and the rabbit-specific HRP/DAB (ABC) detection IHC kit (Abcam) was used for immunohistochemistry, according to the manufacturer’s instructions. Sections were counterstained with haematoxylin and mounted using Vectamount permanent mounting media (Vector Labs, Peterborough, United Kingdom). Images were taken at 40x magnification on a Hamamatsu Nanozoomer Digital slide scanner. Cleaved PARP-positive cells were scored as the percentage of cells with nuclear staining.

Animals

All animal studies were approved by the University College London Biological Services Ethical Review Committee and licensed under the UK Home Office regulations and the Evidence for the Operation of Animals (Scientific Procedures) Act 1986 (Home Office, London, UK). Mice were purchased from Charles River, kept in individually ventilated cages under specific pathogen-free conditions and had access to sterile irradiated food and autoclaved water ad libitum.

Xenograft mouse models

12 8 week old NOD.CB17-Prkdcscid/NcrCrl (NOD SCID) mice (Charles River, UK; RRID:IMSR_CRL:394) were injected with 1 × 106 H226 cells transduced with a plasmid containing wild-type BAP1 and luciferase on the right flank and with a plasmid containing a catalytically inactive BAP1-mutant (C91A) and luciferase on the left flank in a 1:1 mixture of Matrigel (Corning, Corning, NY) and medium. Tumour size was assessed by bioluminescence in vivo imaging system (IVIS, PerkinElmer, Waltham, MA) 15 min following intraperitoneal injection of 0.2 ml (2 mg) luciferin. Tumours were allowed to establish for 2 weeks prior to baseline assessment of size at day 13. Mice were then divided into two groups each of which received either 600 μg TRAIL or vehicle 6 days a week from day 14 until day 40. Bioluminescence was measured on days 0, 13, 19, 26 and 41. Mice were sacrificed on day 42 and tumours harvested for measurement. TRAIL used in the mouse experiment was made in Henning Walczak’s laboratory as per the established protocol (Ganten et al., 2006).

Statistical analysis

Statistical analysis was performed using GraphPad Prism (GraphPad Software, CA, USA). t-test was used to analyse differences between two groups whilst the analysis of variance (ANOVA) test with a Tukey post-hoc analysis was used to analyse differences between three groups. For multiple groups measured over multiple time points repeated measures ANOVA was used. All in vitro tests were performed in triplicate and all data are represented as mean values ± standard error of mean unless otherwise stated.

Acknowledgements

The authors would like to thank Dr Kate Gowers and Dr Rob Hynds (UCL Respiratory) for proof reading the manuscript.

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Ultan McDermott, Email: um1@sanger.ac.uk.

Samuel M Janes, Email: s.janes@ucl.ac.uk.

Joaquín M Espinosa, University of Colorado School of Medicine, United States.

Funding Information

This paper was supported by the following grants:

  • Wellcome WT097452MA to Constantine Alifrangis.

  • Wellcome Trust 106555/Z/14/Z to Neelam Kumar.

  • Cancer Research UK A17341 to Henning Walczak.

  • Cancer Research UK to Ultan McDermott.

  • Wellcome WT107963AIA to Samuel M Janes.

Additional information

Competing interests

No competing interests declared.

Employed in MedImmune, Inc.

Cofounder and shareholder of Apogenix AG.

Author contributions

Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing.

Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing.

Data curation, Investigation, Writing—original draft, Writing—review and editing.

Data curation, Formal analysis, Validation, Investigation, Methodology.

Data curation, Investigation.

Data curation, Formal analysis, Visualization.

Data curation, Methodology.

Data curation, Investigation.

Data curation, Formal analysis, Visualization.

Data curation, Methodology, Writing—review and editing.

Data curation.

Formal analysis, Investigation, Methodology.

Data curation, Investigation, Methodology.

Data curation, Investigation.

Formal analysis, Validation, Methodology.

Resources.

Data curation, Validation.

Data curation, Methodology, Writing—review and editing.

Data curation, Methodology.

Data curation, Formal analysis, Methodology.

Provided surgical samples.

Provided surgical samples.

Data curation.

Data curation, Formal analysis, Methodology.

Resources, Methodology.

Formal analysis, Visualization, Methodology.

Data curation, Formal analysis, Validation, Investigation, Methodology.

Data curation, Formal analysis, Visualization, Methodology.

Data curation, Formal analysis, Investigation, Visualization, Methodology.

Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing.

Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing.

Ethics

Human subjects: Appropriate ethical approval was obtained from the NHS Health Research Authority local National Research Ethics Committee Service to carry out this work (reference 14/LO/1527). Informed consent to conduct research on samples collected and to publish results was obtained from patients.

Animal experimentation: All animal studies were approved by the University College London Biological Services Ethical Review Committee and licensed under the UK Home Office regulations and the Evidence for the Operation of Animals (Scientific Procedures) Act 1986 (Home Office, London, UK).

Additional files

Supplementary file 1. List of 94 compounds used either as single agents or in combination with rTRAIL.

Listed are the unique ID number, the compound name and target, the cellular process targeted and the minimum and maximum concentration (micromolar) of the 5-point concentration range used for each compound.

elife-30224-supp1.xlsx (46.6KB, xlsx)
DOI: 10.7554/eLife.30224.021
Supplementary file 2. Name and histological subtype (where known) of the 15 mesothelioma cell lines.
elife-30224-supp2.xlsx (10.1KB, xlsx)
DOI: 10.7554/eLife.30224.022
Supplementary file 3. 1425 area under the curve (AUC) viability scores for 94 experimental agents tested against 15 mesothelioma cell lines after 6 days of treatment.
elife-30224-supp3.xlsx (55.6KB, xlsx)
DOI: 10.7554/eLife.30224.023
Supplementary file 4. Results of Welch’s two sample t-test from analysis of 45 single compounds that ≥2 cell lines demonstrated sensitivity to (AUC <0.7) and using the mutation status of eight genes implicated as drivers in mesothelioma in each cell line.

A 6 day viability assay was used to determine cell line sensitivity. False discovery associations < 0.2 are highlighted as red font. Whether a mutation is associated with resistance or sensitivity to that compound is indicated by red or green in the ‘effect’ column, respectively.

elife-30224-supp4.xlsx (38KB, xlsx)
DOI: 10.7554/eLife.30224.024
Supplementary file 5. Description of BAP1 mutations detected in 15 mesothelioma cell lines and the sensitivity of the cell lines to rTRAIL (as measured by a 6 day viability assay).

The sensitivity of each cell line is indicated in the last column as sensitive (green), partially sensitive (orange) or resistant (red).

elife-30224-supp5.xlsx (11.6KB, xlsx)
DOI: 10.7554/eLife.30224.025
Supplementary file 6. Differential gene expression values of apoptotic genes in H226 mesothelioma cells transduced with either the catalytically inactive C91A BAP1 mutant (C91A) or wild-type BAP1 (WT).
elife-30224-supp6.xlsx (11.4KB, xlsx)
DOI: 10.7554/eLife.30224.026
Transparent reporting form
DOI: 10.7554/eLife.30224.027

Major datasets

The following previously published dataset was used:

Garnett MJ, author; Edelman EJ, author; Heidorn SJ, author; Greenman CD, author; Dastur A, author; Lau KW, author. Data from the Cell Lines Project, V83. 2012 http://cancer.sanger.ac.uk/cell_lines/download Available at the Catalogue of Somatic Mutations in Cancer on registration and login. Downloads by academic and non-profit organisations are free but for-profit organisations are required to pay a license fee.

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Decision letter

Editor: Joaquín M Espinosa1

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Loss of functional BAP1 is a biomarker for TRAIL sensitivity in cancer" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Charles Sawyers as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Andrew Thorburn (Reviewer #1); Dan Longley (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

Kolluri et.al. present convincing data arising from a drug screen in a panel of mesothelioma cell lines that the nuclear deubiquitinase BAP1 regulates TRAIL sensitivity in mesothelioma cells (and some other tumor types too.) Loss of BAP1 expression, which occurs in some cancers, could potentially serve as a biomarker for sensitivity to agonists that target TRAIL receptors (TRAIL R). As noted by the authors TRAIL R-targeted therapeutics have been quite widely tested and usually shown to be safe, however the lack of an ability to identify patients who are likely to benefit from these treatments has stymied development of this class of drugs. Therefore, the area that the authors are addressing is potentially quite important. Reviewers found the data presented to be quite convincing and to support the authors' case that BAP1 can affect TRAIL sensitivity most likely through epigenetic regulation of gene expression by a mechanism involving the AXLS1 protein. However, a number of concerns were raised about the ultimate power of BAP1 status to serve as a biomarker in the clinic, as well as the minimal mechanistic insight provided in the report.

After thorough discussion, the reviewers agreed to invite a resubmission of a revised manuscript addressing the following major points:

1) A main concern with the paper in its current form is that it doesn't represent a major advance for the field, in the sense that there are many genes and processes that have been shown to confer TRAIL sensitivity and proposed as potential biomarkers, yet none of them so far have worked out to be useful. Reviewers agreed that more evidence is needed in this regard. First, it's not clear whether the effects are really specific to TRAIL. Many perturbations that make tumor cells more sensitive to apoptosis will appear to confer sensitivity on a canonical apoptosis inducer like TRAIL. Therefore, reviewers request a series of experiments addressing this issue, to define whether or not BAP1 mutation/loss is a general sensitizer to death ligands and/or general apoptotic stimuli. Reviewers suggest testing a different death ligand, such as a Fas agonist (e.g. CH-11 antibody) or, alternatively, a TNF/IAP antagonist combination. For the general apoptotic stimulus, the reviewers propose disease-relevant genotoxic agents such as pemetrexed or cisplatin.

2) Second, reviewers agreed that more work should be performed to establish the notion of BAP1 status as a biomarker for TRAIL efficacy. Although the pattern presented is very strong, many BAP1 positive cells are sensitive to TRAIL and vice versa, indicating that other factors in addition to BAP1 define sensitivity to TRAIL. The report will be significantly strengthened by performing cause-effect relationship experiments in a much larger number of cell lines. More specifically, reviewers would like to see the BAP1 depletion experiments done in a larger panel of cell lines. The report currently shows the effect of BAP1 knockdown in only two cell lines: H226 and MDA-MB-231. All other cause-effect experiments involve artifact-prone overexpression experiments of BAP1 (wt or various mutants). Given the ready access to cell lines and proven shRNAs for BAP1, the conclusions would be significantly strengthened by testing the effect of BAP1 depletion in two additional MM cell lines that are BAP1 positive and TRAIL-resistant. Additionally, BAP1 mutations are most frequent among kidney clear cell carcinomas (CRCC) estimated at 10% by tumorportal.org. The impact of the report would be significantly increased by measuring TRAIL sensitivity upon BAP1 depletion in two CCRC cell lines expressing wild type BAP1.

3) Third, the clinical relevance would be increased by testing a large number of patient samples in the ex vivo analyses in Figure 3B and C and/or to use a more clinically relevant TRAIL receptor agonist.

4) Another major issue was the minimal mechanistic insight as to how BAP1 status regulates sensitivity to TRAIL. What is the impact of BAP1 on known regulators and effectors of the TRAIL receptor pathway? Some data and discussion on the relationship between BAP1 mutation and gene expression of TRAIL-R1/2, FADD, FLIP, caspase-8, etc. is needed. What happens to these genes when WT versus mymutant BAP1 is overexpressed in BAP1 negative cell line?

5) Additionally, insight into how the ASXL1 connection translates into increased cell death should be provided, such as additional measures of key cell death signaling nodes as described in #4 above in experiments where ASXL1 activity/expression has been manipulated.

6) In the absence of true validation of BAP1 status as a biomarker in the clinical setting, the title was deemed overambitious, and reviewers request a more accurate title and some attenuation of some of the conclusions describing BAP1 as a biomarker.

eLife. 2018 Jan 18;7:e30224. doi: 10.7554/eLife.30224.032

Author response


[…] After thorough discussion, the reviewers agreed to invite a resubmission of a revised manuscript addressing the following major points:

1) A main concern with the paper in its current form is that it doesn't represent a major advance for the field, in the sense that there are many genes and processes that have been shown to confer TRAIL sensitivity and proposed as potential biomarkers, yet none of them so far have worked out to be useful. Reviewers agreed that more evidence is needed in this regard. First, it's not clear whether the effects are really specific to TRAIL. Many perturbations that make tumor cells more sensitive to apoptosis will appear to confer sensitivity on a canonical apoptosis inducer like TRAIL. Therefore, reviewers request a series of experiments addressing this issue, to define whether or not BAP1 mutation/loss is a general sensitizer to death ligands and/or general apoptotic stimuli. Reviewers suggest testing a different death ligand, such as a Fas agonist (e.g. CH-11 antibody) or, alternatively, a TNF/IAP antagonist combination. For the general apoptotic stimulus, the reviewers propose disease-relevant genotoxic agents such as pemetrexed or cisplatin.

We agree and highlight in our manuscript that to date a key limiting factor of TRAIL therapeutics has been the lack of a validated biomarker for sensitivity. However, no approach thus far has included the search for a sensitising mutation using an unbiased drug screen in sequenced cell lines. Importantly the retrospective identification of drug sensitising mutations has re-directed the use of other anticancer therapies with initially disappointing results in unselected populations.

To demonstrate the specificity of BAP1 as a sensitising mutation to TRAIL, and other death receptor agonists, we have treated a panel of BAP1 mutant and wild-type MM lines with additional apoptotic stimuli as suggested. No sensitising association with BAP1 was observed for pemetrexed or cisplatin, current first line agents for the treatment of MM (Figure 1—figure supplement 2A and B). A marginal trend towards increased sensitivity in BAP1 mutant MM lines in response to treatment with the agonistic FAS receptor antibody CH11 and a TNF-α/IAP inhibitor combination was observed. This was not however as pronounced as that observed with rTRAIL or the multivalent death receptor 5 superagonist MEDI3039 (Figure 1—figure supplement 2C, D and E). Thus, while the significant sensitising association observed in the screen appears most specific to death receptor agonists, the trend observed with other TNF superfamily agonists indicates the BAP1-rTRAIL association to be mediated by an underlying mechanism common to this family, such as the cytoplasmic extrinsic apoptotic machinery.

The following text was added to the Results section:

“No sensitising association with BAP1 was observed for pemetrexed or cisplatin, current first line agents for the treatment of MM (Figure 1—figure supplement 2A and B). […] Thus, while the significant sensitising association observed in the screen appears most specific to death receptor agonists, the trend observed with other TNF superfamily agonists indicates the BAP1-rTRAIL association to be mediated by an underlying mechanism common to this family, such as the cytoplasmic extrinsic apoptotic machinery.”

2) Second, reviewers agreed that more work should be performed to establish the notion of BAP1 status as a biomarker for TRAIL efficacy. Although the pattern presented is very strong, many BAP1 positive cells are sensitive to TRAIL and vice versa, indicating that other factors in addition to BAP1 define sensitivity to TRAIL. The report will be significantly strengthened by performing cause-effect relationship experiments in a much larger number of cell lines. More specifically, reviewers would like to see the BAP1 depletion experiments done in a larger panel of cell lines. The report currently shows the effect of BAP1 knockdown in only two cell lines: H226 and MDA-MB-231. All other cause-effect experiments involve artifact-prone overexpression experiments of BAP1 (wt or various mutants). Given the ready access to cell lines and proven shRNAs for BAP1, the conclusions would be significantly strengthened by testing the effect of BAP1 depletion in two additional MM cell lines that are BAP1 positive and TRAIL-resistant. Additionally, BAP1 mutations are most frequent among kidney clear cell carcinomas (CRCC) estimated at 10% by tumorportal.org. The impact of the report would be significantly increased by measuring TRAIL sensitivity upon BAP1 depletion in two CCRC cell lines expressing wild type BAP1.

We agree that factors other than BAP1 can affect TRAIL sensitivity as evidenced in our panel of MM lines (Figure 1D and E). This is not unusual, noting that TKI inhibition of EGFR activating mutant lung cancers show response rates of around 70%. We have strengthened our data by extending the knockdown of BAP1 wild-type cell lines to include four MM lines, two clear cell renal carcinoma lines and one breast cancer line.

We have amended the following text in the Results section:

“To determine if knockdown of BAP1 in wild-type MM cells led to TRAIL sensitivity, we silenced BAP1 expression in fourwt BAP1 MM cell lines using a lentiviral shRNA construct. […] Notably, knockdown of BAP1 in two CCRC lines resulted in increased sensitivity to rTRAIL in addition to the MDAMB-231 breast cancer line (Figure 2B and Figure 2—figure supplements 2 and 3).”

3) Third, the clinical relevance would be increased by testing a large number of patient samples in the ex vivo analyses in Figure 3B and C and/or to use a more clinically relevant TRAIL receptor agonist.

Since review we have obtained 7 video assisted thoracoscopic surgery (VATS) pleural biopsy samples from treatment naïve patients with a suspected diagnosis of MM. Four of these proved to comprise benign tissue only. We attempted to assess apoptosis in response to MEDI3039 treatment in explants from the remaining three. However, as these were VATS rather than pleurectomy biopsies, the tissue volume was extremely small precluding appropriate immunohistochemical analysis. Successful explant data is ultimately subject to the frequency of pleurectomy cases performed at our local surgical centre and within the suggested time period for resubmission there were none. We agree however that an expanded cohort would strengthen the clinical relevance and continue with our explant programme to this end.

4) Another major issue was the minimal mechanistic insight as to how BAP1 status regulates sensitivity to TRAIL. What is the impact of BAP1 on known regulators and effectors of the TRAIL receptor pathway? Some data and discussion on the relationship between BAP1 mutation and gene expression of TRAIL-R1/2, FADD, FLIP, caspase-8, etc. is needed. What happens to these genes when WT versus mymutant BAP1 is overexpressed in BAP1 negative cell line?

We have extended our mechanistic data as suggested. We have compared gene expression data from a BAP1 null MM line with overexpression of BAP1 wild-type or the catalytically inactive BAP1 C91A. Signaling pathway impact analysis highlighted apoptosis as being significantly altered by BAP1 function. Thus we proceeded as suggested to specifically assess the impact of BAP1 function on the death receptor pathway at a gene expression and protein level. This data supports a role for BAP1 in the modulation of transcription of death receptor pathway proteins.

We have added the following text to the Results section:

“We therefore compared differential gene expression data from BAP1-null H226 cells transduced with the C91A BAP1 mutant or with wild-type BAP1, and carried out a signalling pathway impact analysis SPIA ((Figure 2—figure supplement 7 and 8 [SPIA_H226 C91A mutant vs. WT]) (http://www.genome.jp/dbget-bin/www_bget?path:map04210). […] Flow cytometry analysis confirmed reduced DR4 and DR5 expression in C91A BAP1 transduced relative to wild-type BAP1 transduced cells. Knockdown of BAP1 in the BAP1 wild-type H2818 line resulted in a significant increase in DR4 expression only (Figure 2H).”

5) Additionally, insight into how the ASXL1 connection translates into increased cell death should be provided, such as additional measures of key cell death signaling nodes as described in #4 above in experiments where ASXL1 activity/expression has been manipulated.

We have conducted immunoblot analysis of the death receptor pathway on BAP1 null MM cells transduced with the ASXL1/2 binding mutant ΔASXL BAP1. The changes observed in this analysis are consistent with that of those cells transduced with the catalytically inactive C91A BAP1 mutant. This supports the role of both functions, deubiquitinase activity and ASXL binding, in the regulation of expression of components of the death receptor pathway.

These changes to the manuscript are detailed above in point 4.

6) In the absence of true validation of BAP1 status as a biomarker in the clinical setting, the title was deemed overambitious, and reviewers request a more accurate title and some attenuation of some of the conclusions describing BAP1 as a biomarker.

We have amended the title to:

“Loss of functional BAP1 augments TRAIL sensitivity in cancer cells.”

We have minimised the reference of BAP1 as a biomarker replacing such instances with reference to its use as a ‘genomic stratification tool’ or ‘potential biomarker’.

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Supplementary file 1. List of 94 compounds used either as single agents or in combination with rTRAIL.

    Listed are the unique ID number, the compound name and target, the cellular process targeted and the minimum and maximum concentration (micromolar) of the 5-point concentration range used for each compound.

    elife-30224-supp1.xlsx (46.6KB, xlsx)
    DOI: 10.7554/eLife.30224.021
    Supplementary file 2. Name and histological subtype (where known) of the 15 mesothelioma cell lines.
    elife-30224-supp2.xlsx (10.1KB, xlsx)
    DOI: 10.7554/eLife.30224.022
    Supplementary file 3. 1425 area under the curve (AUC) viability scores for 94 experimental agents tested against 15 mesothelioma cell lines after 6 days of treatment.
    elife-30224-supp3.xlsx (55.6KB, xlsx)
    DOI: 10.7554/eLife.30224.023
    Supplementary file 4. Results of Welch’s two sample t-test from analysis of 45 single compounds that ≥2 cell lines demonstrated sensitivity to (AUC <0.7) and using the mutation status of eight genes implicated as drivers in mesothelioma in each cell line.

    A 6 day viability assay was used to determine cell line sensitivity. False discovery associations < 0.2 are highlighted as red font. Whether a mutation is associated with resistance or sensitivity to that compound is indicated by red or green in the ‘effect’ column, respectively.

    elife-30224-supp4.xlsx (38KB, xlsx)
    DOI: 10.7554/eLife.30224.024
    Supplementary file 5. Description of BAP1 mutations detected in 15 mesothelioma cell lines and the sensitivity of the cell lines to rTRAIL (as measured by a 6 day viability assay).

    The sensitivity of each cell line is indicated in the last column as sensitive (green), partially sensitive (orange) or resistant (red).

    elife-30224-supp5.xlsx (11.6KB, xlsx)
    DOI: 10.7554/eLife.30224.025
    Supplementary file 6. Differential gene expression values of apoptotic genes in H226 mesothelioma cells transduced with either the catalytically inactive C91A BAP1 mutant (C91A) or wild-type BAP1 (WT).
    elife-30224-supp6.xlsx (11.4KB, xlsx)
    DOI: 10.7554/eLife.30224.026
    Transparent reporting form
    DOI: 10.7554/eLife.30224.027

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