Abstract Background Meningioma is the most frequent primary intracranial tumour. Surgical resection remains the main therapeutic option as pharmacological intervention is hampered by poor knowledge of their proteomic signature. There is an urgent need to identify new therapeutic targets and biomarkers of meningioma. Methods We performed proteomic profiling of grade I, II and III frozen meningioma specimens and three normal healthy human meninges using LC-MS/MS to analyse global proteins, enriched phosphoproteins and phosphopeptides. Differential expression and functional annotation of proteins was completed using Perseus, IPA® and DAVID. We validated differential expression of proteins and phosphoproteins by Western blot on a meningioma validation set and by immunohistochemistry. Findings We quantified 3888 proteins and 3074 phosphoproteins across all meningioma grades and normal meninges. Bioinformatics analysis revealed commonly upregulated proteins and phosphoproteins to be enriched in Gene Ontology terms associated with RNA metabolism. Validation studies confirmed significant overexpression of proteins such as EGFR and CKAP4 across all grades, as well as the aberrant activation of the downstream PI3K/AKT pathway, which seems differential between grades. Further, we validated upregulation of the total and activated phosphorylated form of the NIMA-related kinase, NEK9, involved in mitotic progression. Novel proteins identified and validated in meningioma included the nuclear proto-oncogene SET, the splicing factor SF2/ASF and the higher-grade specific protein, HK2, involved in cellular metabolism. Interpretation Overall, we generated a proteomic thesaurus of meningiomas for the identification of potential biomarkers and therapeutic targets. Fund This study was supported by Brain Tumour Research. Keywords: Meningioma, Proteomics, Phosphoproteins, Differential expression, Grade-specific, RNA metabolism __________________________________________________________________ Research in context. Evidence before this study There is currently no effective pharmacological intervention for meningioma, the most frequent primary intracranial tumour. There is now a wealth of genomic studies of meningioma that have been pivotal in elucidating the mutational profile of these tumours. However, the mechanistic involvement of these mutations in meningioma development or their potential as therapeutic targets has not yet been established. In the molecular landscape of meningioma, proteomics attempts to bridge the gap between the increasing expanse of genomic, epigenomic and transcriptomic knowledge of these tumours and the execution of these instructions at the proteome level. Previous proteomic studies have been limited by relatively small cohorts encompassing all WHO grades, have lacked an appropriate control or did not include tumour microenvironment. Added value of this study In this study, we performed extensive proteomic analyses of frozen tumour tissue covering all WHO grades of meningioma and healthy human meningeal tissue as a control, including to our knowledge, the largest sample size analysed to date of the rarest and most therapeutically challenging, grade III meningiomas. We did additional mutation screening of all samples and used a validation cohort. We describe the enrichment of numerous novel pathways including RNA processing and proteins in meningiomas, and oncoproteins commonly upregulated among all grades. Further, we also show upregulation of proteins including NEK9 and its activated phosphorylated form, previously undescribed in these tumours. We also demonstrate grade specific protein upregulation and activation of proteins. Implications of all the available evidence This study demonstrates that proteomic and phosphoproteomic analyses are instrumental in identifying new candidates for targeted therapies or biomarkers of this most common brain tumour. Indeed, our results confirm the significant overexpression of several new proteins and phosphoproteins in meningioma, including grade-specific candidates that with further investigation may have clinical importance when determining an effective treatment strategy. Alt-text: Unlabelled Box 1. Introduction Meningiomas are the most common primary intracranial tumour accounting for up to 36% of all primary central nervous system (CNS) tumours [[37]1]. They are classified by the World Health Organization (WHO) as slow growing benign WHO grade I (~80%), atypical WHO grade II (18%) or malignant WHO grade III (1–3%) [[38]1]. Many meningiomas can be treated effectively by surgical resection [[39]2]. Excision is occasionally associated with morbidity and can be difficult to perform depending on tumour location, often preventing complete removal [[40]2]. Radiotherapy is used as an adjunct, whilst current chemotherapies remain ineffective [[41]2]. Prediction of tumour recurrence and prognosis is based primarily on histological grade and extent of surgical resection: 5 year recurrence rates following gross total resection, for grade I, II and III meningiomas are approximately 3%, 38% and 78%, respectively [[42]2]. However, a recently described DNA methylation-based meningioma classification may improve future predictions of tumour recurrence and prognosis [[43]3]. The genomic landscape of meningiomas is well characterised. Approximately 60% sporadic meningiomas harbour mutations in the Neurofibromatosis 2 (NF2, merlin) gene, while mutations in genes including TRAF7, KLF4, AKT1, SMO, PIK3CA, POLR2A, PRKAR1A, AKT3 and SUFU have been identified in the remaining 40% non-NF2 mutant meningiomas [[44][4], [45][5], [46][6]]. More recently, studies have defined distinct genetic profiles underlying primary and recurrent atypical meningioma as well as the presence of BAP1 mutations in a subset of WHO grade III rhabdoid meningiomas [[47]7,[48]8]. Potential molecular targets of meningiomas have previously been identified including the growth factor receptors EGFR, PDGFR and VEGFR, along with their signalling pathway activation, yet their pharmaceutical targeting has yielded mixed results in clinical trials [[49][9], [50][10], [51][11]]. Treatment of recurrent meningioma with the EGFR inhibitors erlotinib and gefitinib produced no significant response; whilst the VEGFR and PDGFR inhibitor sunitinib (Sutent®), demonstrated some efficacy in recurrent/progressive high-grade meningiomas but was associated with considerable toxicity [[52]10,[53]11]. Therefore, the identification of aberrantly activated pathways and the associated molecular targets/biomarkers remains crucial for developing novel therapeutic strategies for meningioma. Proteomic approaches provide a powerful tool to detect differentially or uniquely expressed proteins and reveal alterations in signalling pathways. Previous studies have described differential proteomic profiles between meningioma grades. Okamoto et al. analysed pure populations of tumour cells, however not considering a control; whilst, Sharma et al. analysed tumour specimens but using glial in place of meningeal tissue as healthy control [[54]12,[55]13]. Most recently, Parada et al. investigated the phosphoproteome across WHO grades of meningiomas and identified reduced expression of AKAP12 as a potential prognostic marker of high-grade meningioma [[56]14]. In this study, we present the comparative proteomic analyses of different WHO grades of meningiomas vs. healthy human meninges aiming to characterise the pathogenic signature of these tumours. We analysed the global proteome as well as enriched phosphoproteins and phosphopeptides of 22 meningiomas and three normal meninges and identified the differential expression of novel proteins like the oncoproteins SET and SF2/ASF among all meningioma grades. In addition, we revealed among others, the activation of phosphoproteins like phospho-AKT and phospho-NEK9. Grade-specific analysis allowed for the identification of upregulated proteins and phosphoproteins and their associated enriched biological processes in high-grade aggressive meningiomas. We validated differential expression using additional techniques and a validation cohort. Our results describe to our knowledge, the largest proteomic study of meningioma tissue and suggest several candidates with promise as therapeutic targets or biomarkers in meningioma. 2. Materials and methods 2.1. Clinical material Anonymised meningioma samples under the ‘J’ series were provided by the BRain Archive and Information Network (BRAIN UK) under ethical approval by the South West research ethics committee (REC No: 14/SC/0098; IRAS project ID: 143874, BRAIN UK Ref: 15/011). Anonymised ‘MN’ meningioma samples were collected under ethical approval by the South West research ethics committee (REC No: 14/SW/0119; IRAS project ID: 153351) and local research and development approval (Plymouth Hospitals NHS Trust: R&D No: 14/P/056 and North Bristol NHS Trust: R&D No: 3458). Clinical and histological details about the specimens are presented in Supplementary Tables S1 and S2. Tumours were separated into a ‘discovery set’ for MS analysis consisting of 22 meningiomas (WHO grade I: n = 8, WHO grade II: n = 8, WHO grade III: n = 6) and a ‘validation set’ composed of 15 meningioma samples (WHO grade I: n = 5, WHO grade II: n = 5, WHO grade III: n = 5). Two frozen normal meninges were obtained from Analytical Biological Services Inc. and one human brain cerebral meninges was purchased from Novus Biologicals® (NB820-59183; lot B105014). 2.2. Phosphoprotein and phosphopeptide enrichment For both phosphoprotein and phosphopeptide enrichment 2·5 mg of protein lysate was used as starting material. Phosphoproteins were enriched from frozen tissue using the commercially available Qiagen® PhosphoProtein Purification Kit (Qiagen) according to the manufacturer's instructions. Previous studies using this kit have reported an 88% elution recovery of phosphoproteins [[57]15]. Protein concentration was determined as before [[58]16]. For phosphopeptide enrichment, samples were homogenised from frozen in lysis buffer (8 M urea, 100 mM Tris-HCl, pH 8·0). Samples were frozen at −80 °C for 24 h, thawed on ice and centrifuged at 16,000 ×g for 15 min at 4 °C. Supernatant was collected in Eppendorf® Protein LoBind microcentrifuge tubes and protein concentration determined [[59]16]. Prior to enrichment using titanium dioxide (TiO[2]) beads, 2·5 mg of protein lysate was subjected to in-solution digestion. Proteins were reduced and alkylated by incubation with 0·1 M dithiotheitol (DTT) for 30 min at room temperature (RT), followed by further incubation with 50 mM 2-iodoacetamide in the dark for 15 min at RT. Lys-C protease (Lysyl Endopeptidase®, Mass Spectrometry Grade, Wako) was then added at a protease: protein ratio of 1: 100 (w/w) in 50 mM ammonium bicarbonate (ABC) and incubated overnight (O/N) at 37 °C. Samples were diluted in 50 mM ABC to a final concentration of 2 M urea and incubated O/N at 37 °C with trypsin (Promega, Wisconsin, US), added at a protease: protein ratio of 1: 50 (w/w). Digested samples were acidified to a final concentration of 0·1% trifluoroacetic acid (TFA) and peptides desalted using HyperSep™ C18 Cartridges (Thermo Fisher Scientific, Massachusetts, US). Columns were washed with buffer A (1% TFA, 0·5% acetic acid), phosphopeptides eluted in buffer B (80% acetonitrile (ACN, LC-MS grade), 0·5% acetic acid, 1% TFA) and dried in a vacuum concentrator. Phosphopeptides were enriched by batch-wise incubation with Titansphere 10 μm TiO[2] beads (GL Sciences) as described by Lasonder et al. [[60]17] with the following modifications. Initially, TiO[2] beads (1 mg beads per incubation) were incubated in wash buffer A (80% ACN, 5% TFA) followed by incubation in buffer B (60% ACN, 5% TFA, 5% glycerol) for 5 min at 1000 rpm, RT. TiO[2] beads were sedimented by centrifugation at 2000 ×g for 1 min, buffer removed, sample peptide digests added in 1·5 ml Eppendorf® vials and incubated with the beads under continuous shaking for 1 h. Beads were washed three times with 100 μl buffer B, followed by three washes with 100 μl buffer A. Phosphopeptides were eluted following a two-step elution protocol by Fukuda et al. [[61]18], acidified with TFA, purified by stop and go extraction (STAGE) tips [[62]19] and stored at −20 °C prior to mass spectrometry (MS) analysis. 2.3. Protein fractionation and in-gel digestion Proteins and phosphoproteins were separated using SDS-PAGE on 4–15% Mini-PROTEAN® TGX™ Precast Gels (Bio-Rad). For global proteome analysis 50 μg of protein lysate was separated. Gels were stained with Coomassie Blue R-350 (GE Healthcare Life Sciences) until lanes were visible. Destaining was performed using a destaining solution (50% LC/MS grade water, 40% MeOH and 10% acetic acid) O/N at RT. Sample lanes were excised from gels, sliced into 6 fractions that were cut into 1 × 1 mm pieces before in-gel digestion. In-gel digestion was performed following Shevchenko et al. [[63]20]. Briefly, gel pieces were equilibrated with alternate incubation of 100% acetonitrile and 50 mM ABC. Proteins were reduced by incubation with 10 mM DTT in 50 mM ABC for 20 min at 56 °C in shaking at 700 rpm and then alkylated by incubation with 50 mM 2-iodoacetamide in 50 mM ABC for 20 min at RT in the dark. Proteins were digested in 12·5 ng/μl trypsin (Promega, Wisconsin, US) in 50 mM ABC O/N at 37 °C. Digested peptides were acidified with a final concentration of 2% TFA for 20 min at RT 1400 rpm. Peptides were then extracted by two 5 min incubations of gel pieces with buffer B (80% ACN, 0.5% acetic acid, 1% TFA) at 1400 rpm. Supernatant was pooled together for each sample and ACN was evaporated in a vacuum concentrator. Digested peptides and phosphopeptides were purified by STAGE tips [[64]19]. Peptides were loaded onto C18-StageTips (Empore™ SPE Disks C18, 3 M) conditioned by 50 μl of methanol and equilibrated using 50 μl buffer B (80% ACN, 0·5% acetic acid, 1% TFA) followed by 50 μl buffer A (1% TFA, 0·5% acetic acid). Tips were washed with 50 μl buffer A and peptides eluted in 40 μl buffer B into LoBind microcentrifuge tubes. Eluted peptides were dried down completely in a vacuum concentrator and peptides resuspended in buffer A. 2.4. Liquid chromatography tandem mass spectrometry and protein identification MS and protein identification was carried out as previously described [[65]16], with the following modifications. MS data was analysed to identify proteins with the Andromeda peptide database search engine integrated into the computational proteomics platform MaxQuant version (1.5.0.30) [[66][21], [67][22], [68][23]]. Andromeda search parameters for protein identification specified a first search mass tolerance of 20 ppm and a main search tolerance of 4·5 ppm for the parental peptide. Minimal peptide length was set at six amino acids. Proteins were quantified with label free quantification (LFQ) values representing normalised summed peptide intensities correlating with protein abundances, where the ‘match between run’ option was permitted between runs with a 0·7 min elution time interval. Venn diagrams depicting the distribution of identified proteins and phosphoproteins were created with Venny 2.1 ([69]http://bioinfogp.cnb.csic.es/tools/venny/index.html). 2.5. Protein quantification analysis and functional annotation analysis LFQ values were log[2] transformed using the Perseus software suite 1.5.0.31 [[70]22] to achieve normal data distribution, which was verified by visual inspection of histogram distribution plots of log[2] transformed data generated in Perseus for each sample. Proteins identified in at least three runs were considered for LFQ and entries with an LFQ equal to zero were kept. Statistical significance of changes in abundance between sample groups was calculated by a two-tailed t-test, with p-values adjusted for multiple testing by a permutation-based FDR at 5%. Microsoft Excel was used to calculate ratios and fold changes (FC) followed by log[2] transformation. A log[2]FC ≥ 1·5 and log[2] FC ≤ −1·5 with adjusted p-value < 0·05 were considered. Results are visualised by Volcano plots. Differentially expressed proteins (DEPs) for hierarchical clustering were obtained by submitting relative expression profiles identified in at least three runs to Perseus and performing a four-group one-way ANOVA (p-value < 0·05) on imputation supplemented data. Venn diagrams depicting upregulated proteins and phosphoproteins among all meningioma grades were created using euler APE ([71]http://www.eulerdiagrams.org/eulerAPE/). Pathway enrichment analysis was generated through the use of IPA® (QIAGEN Inc., [72]https://www.qiagenbioinformatics.com/products/ingenuity-pathway-ana lysis). Statistical significance of enriched pathways was assessed by right-tailed Fisher's exact test and considered significant for p (FET) < 0.05. GO enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8 ([73]https://david.ncifcrf.gov/) against a background of the H. sapiens proteome [[74]24]. Enrichment of GO FAT terms was considered statistically significant when corrected for multiple testing by the Benjamini-Hochberg method with adjusted p-values < 0·05. Cytoscape plugin Enrichment Map ([75]http://www.cytoscape.org/) was used to visualise enriched GO terms [[76]25]. Molecular signatures for higher grade meningiomas were generated with the GeneSign module in BubbleGUM software ([77]https://omictools.com/bubblegum-tool). Grade II and III samples were defined as subsets of interest (test classes), with NMT and grade I samples as references. The Mean (test)/ Mean method was applied for