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Abstract
Malignant peripheral nerve sheath tumours (MPNSTs) are high grade soft-tissue sarcomas with an unmet need for novel therapies. Tumour antigen-based approaches, including neoantigen and tumour-associated antigens (TAA) directed therapies, offer potential opportunities for immunotherapy. Here, we integrated public domain tumour DNA and RNA sequencing data with in-silico predictions in order to characterise the potential (neo)antigenic landscape of MPNST. We stratified the computational predictions across the two known sub-groups of MPNST, those associated without and with Polycomb Repressor Complex 2 (PRC2) loss of function variants (PRC2-Loss). Using pVACtools, computationally identified high-confidence neoantigens based on predicted pMHC affinity were derived from somatic mutations and gene fusions, as well as recurrently overexpressed cell-surface TAAs. All predicted neoantigens were private to individual MPNST cases and across both tumour subtypes. Using ImSig and CIBERSORTx, PRC2-Loss tumours displayed reduced immune infiltration with downregulation of antigen processing and presentation pathways compared to PRC2-WT, confirming known intrinsic constraints to effective neoantigen-directed immune priming. Moreover, PRC2-Loss MPNSTs demonstrated recurrent copy number driven overexpression of cell surface TAAs derived from chromosome 8 amplification, providing potential immunotherapeutic targets that are pMHC independent. Overall, these predictive findings confirm a PRC2-independent private immuno-antigenic peptide repertoire, with an immune resistant MPNST microenvironment in PRC-Loss. These data provide further impetus for personalised functional validation and immune based treatment strategies, including personalised neoantigen vaccines and cell surface protein TAA-directed therapies dependent on PRC2 status.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
1. Refined manuscript title. 2. Minor edits to abstract and manuscript text. 3. Correction of author middle initial
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