The role of nonsense mediated decay in antigenic HLA peptide generation

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Background: The nonsense mediated decay (NMD) pathway has recently been implicated asa novel target for cancer immunotherapy [1,2,3], however the fundamental biological linkbetween NMD and HLA presentation of mutated epitopes is unclear. Two competinghypotheses have been proposed, the first states that NMD must be bypassed, which henceleads to increased mutated protein abundance/HLA presentation via canonical mRNAtranslation. The second model proposes that the non-canonical "pioneer round oftranslation", which occurs directly after nuclear export in coordination with NMD, generatesthe major source of antigenic peptides [4,5]. Addressing this fundamental question is ofurgent biological/therapeutic relevance. In addition, the exon junction complex model of NMDefficiency provides a poor predictive classification of which variants will and won't be triggerdegradation, creating a challenge in interpreting the germline pathogenic and immunogenicpotential of individual mutations.Goals:1. To establish whether frameshift mutated proteins generate HLA presented neoepitopes viacanonical mRNA translation, or the "pioneer round of translation". Dr Litchfield's laboratorywill utilise genomics, transcriptomics and immunopeptidomics data to identify a set of knownframeshift mutated proteins which trigger validated HLA peptides. Dr Carlton's laboratory willuse live cellular imaging to track these mutated proteins in vivo, to establish the route oftranslation and HLA presentation, using an adapted version of the TRICK assay (PMID:25792328).2. To develop an improved predictive model to classify whether premature termination codonmutations will or won't trigger NMD, by using a saturation genome editing approach. Utilisingthe design generated from the rotation project below, premature termination codonmutations will be introduced at every amino acid position across the length of a selected setof reference genes using CRISPR-Cas9 editing. Cells will then be screened using afluorescence reporter to establish whether NMD was triggered at each mutated position. DrLitchfield's laboratory will then develop a novel machine learning classifier algorithm topredict which mutations do and don't trigger NMD.