Proteomic analysis of genetically stratified low-grade meningioma
Authors: Akther Yeasmin , Adams Claire , Sharma Vikram , Barros Claudia , Banton Matthew , Hanemann Oliver 
Published: October 2021

Aims

Meningioma is the most common primary intracranial tumour. Although ~80% are benign WHO grade I and show high rates of recurrence. Surgery is the main therapeutic approach, yet location can hamper complete resection and chemotherapies are ineffective. Moreover, accurate biomarkers for clinical management are lacking. Approximately 60% sporadic meningiomas harbour mutations in the NF2gene, while mutations in genes including TRAF7, KLF4, AKT1, SMO and PIK3CAhave been identified majority in the NF2-positive low grade-tumours. Moreover, mutations in TRAF7 mostly co-occur with a KLF4K409Q or with AKT1E17K mutation. The mutations and their molecular manifestations consequently affect the signalling pathways at the protein level. The molecular mechanisms behind meningioma tumourigenesis are still obscure and the identification of specific biomarker is necessary to enable their implementation in routine diagnostics and therapeutics. Therefore, we aim to identify novel biomarkers and therapeutic targets of genetically stratified low-grade meningioma by characterising the proteomic landscape.

Method

Frozen tumour samples have already been analysed for NF2-/- by next generation sequencing and genotyped for common mutational hotspots in non-NF2 meningioma such as TRAF7, KLF4 and AKT1 and grouped in to three different mutational groups: AKT1E17K/TRAF7, KLF4K409Q/TRAF7and NF2-/- and all these mutations will be compared to normal healthy meninges. For global proteomics, proteins were separated by SDS-PAGE followed by in-gel tryptic digestion and sample preparation for LC-MS/MS analysis. Raw mass spectrometry data files were processed by MaxQuant (1.6.2.10) and Perseus software (1.6.1.3). Quantitative phospho-proteomics was performed using TMT 10plex labelling approach followed by motif analysis using motif-X algorithm. GO enrichment analyses were performed using (DAVID) v6.8 against all human proteins. Potential candidates from expression data analysis will be validated via Western Blot and immunohistochemistry.

Results

We have quantified 4162 proteins across all mutational meningioma subgroups and normal meninges (n=31). Hierarchical clustering analysis showed distinct proteomic profiles of mutational subgroups revealing clusters of differentially expressed proteins. Comparative analysis showed 10 proteins were commonly significantly upregulated (log2 fold-change≥1; p&lt;0.05) among all mutational subtypes vs. normal meninges, indicating proteomic landscapes of mutational subtypes to be highly variable. In contrast, 257 proteins were commonly significantly downregulated (log2 fold-change≤-1; p&lt;0.05) and enriched with molecular functions including aldehyde dehydrogenase and oxido-reductase. Mutational subtype-specific analysis identified 162 proteins significantly upregulated in AKT1E17K/TRAF7 vs. remaining sample groups to be enriched in the oxidative phosphorylation pathway. However, only 14 and 7 proteins were commonly significantly upregulated in KLF4K409Q/TRAF7 and NF2-/- mutant meningioma subtypes respectively. Several of these up-regulated proteins including ANNEXIN-3, CRABP2, CLIC3 and Endoglin were already verified via WB. Lastly, analyses of 6600 phospho-sites (n=8) predicted regulatory kinases including CHEK1, CHEK2 and LCK.

Conclusion

Global proteomic and phos-phoproteomics analysis has led to the identification of proteins differentially expressed in mutant subtypes. Results of this study to date suggest that a proteomic approach is an effective tool to identify distinct patterns in genetically distinct meningioma subgroups. Further validation and functional verification (with inhibitory or knockdown approaches) of potential candidates will allow us to identify potential drug targets/biomarkers for meningiomas.