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Cook AL, Sur S, Dobbyn L, Watson E, Cohen JD, Ptak B, Lee BS, Paul S, Hsiue E, Popoli M, Vogelstein B, Papadopoulos N, Bettegowda C, Gabrielson K, Zhou S, Kinzler KW, Wyhs N. Identification of nonsense-mediated decay inhibitors that alter the tumor immune landscape. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.28.573594. [PMID: 38234817 PMCID: PMC10793421 DOI: 10.1101/2023.12.28.573594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Despite exciting developments in cancer immunotherapy, its broad application is limited by the paucity of targetable antigens on the tumor cell surface. As an intrinsic cellular pathway, nonsense-mediated decay (NMD) conceals neoantigens through the destruction of the RNA products from genes harboring truncating mutations. We developed and conducted a high throughput screen, based on the ratiometric analysis of transcripts, to identify critical mediators of NMD. This screen implicated disruption of kinase SMG1's phosphorylation of UPF1 as a potential disruptor of NMD. This led us to design a novel SMG1 inhibitor, KVS0001, that elevates the expression of transcripts and proteins resulting from truncating mutations in vivo and in vitro . Most importantly, KVS0001 concomitantly increased the presentation of immune-targetable HLA class I-associated peptides from NMD-downregulated proteins on the surface of cancer cells. KVS0001 provides new opportunities for studying NMD and the diseases in which NMD plays a role, including cancer and inherited diseases. One Sentence Summary Disruption of the nonsense-mediated decay pathway with a newly developed SMG1 inhibitor with in-vivo activity increases the expression of T-cell targetable cancer neoantigens resulting from truncating mutations.
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Dickson KA, Field N, Blackman T, Ma Y, Xie T, Kurangil E, Idrees S, Rathnayake SNH, Mahbub RM, Faiz A, Marsh DJ. CRISPR single base-editing: in silico predictions to variant clonal cell lines. Hum Mol Genet 2023; 32:2704-2716. [PMID: 37369005 DOI: 10.1093/hmg/ddad105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
Engineering single base edits using CRISPR technology including specific deaminases and single-guide RNA (sgRNA) is a rapidly evolving field. Different types of base edits can be constructed, with cytidine base editors (CBEs) facilitating transition of C-to-T variants, adenine base editors (ABEs) enabling transition of A-to-G variants, C-to-G transversion base editors (CGBEs) and recently adenine transversion editors (AYBE) that create A-to-C and A-to-T variants. The base-editing machine learning algorithm BE-Hive predicts which sgRNA and base editor combinations have the strongest likelihood of achieving desired base edits. We have used BE-Hive and TP53 mutation data from The Cancer Genome Atlas (TCGA) ovarian cancer cohort to predict which mutations can be engineered, or reverted to wild-type (WT) sequence, using CBEs, ABEs or CGBEs. We have developed and automated a ranking system to assist in selecting optimally designed sgRNA that considers the presence of a suitable protospacer adjacent motif (PAM), the frequency of predicted bystander edits, editing efficiency and target base change. We have generated single constructs containing ABE or CBE editing machinery, an sgRNA cloning backbone and an enhanced green fluorescent protein tag (EGFP), removing the need for co-transfection of multiple plasmids. We have tested our ranking system and new plasmid constructs to engineer the p53 mutants Y220C, R282W and R248Q into WT p53 cells and shown that these mutants cannot activate four p53 target genes, mimicking the behaviour of endogenous p53 mutations. This field will continue to rapidly progress, requiring new strategies such as we propose to ensure desired base-editing outcomes.
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Affiliation(s)
- Kristie-Ann Dickson
- Translational Oncology Group, Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Natisha Field
- Translational Oncology Group, Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Tiane Blackman
- Translational Oncology Group, Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Yue Ma
- Translational Oncology Group, Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Tao Xie
- Translational Oncology Group, Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Ecem Kurangil
- Translational Oncology Group, Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Sobia Idrees
- Faculty of Science, School of Life Sciences, Centre for Inflammation, Centenary Institute and the University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Senani N H Rathnayake
- Respiratory Bioinformatics and Molecular Biology (RBMB), Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Rashad M Mahbub
- Respiratory Bioinformatics and Molecular Biology (RBMB), Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Alen Faiz
- Respiratory Bioinformatics and Molecular Biology (RBMB), Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Deborah J Marsh
- Translational Oncology Group, Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
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