The development of therapeutic vaccines in cancer has been boosted by the possibility to directly characterise the landscape of antigens on primary tumour tissue using liquid chromatography tandem mass spectrometry (LC-MS/MS) approaches1,2. Methods to directly identify MHC-bound peptides using LC-MS/MS have been reliant on the availability of accurate protein sequence databases for spectral interpretation. Therefore identification of tumour-specific neoantigens, that contain tumour specific mutations, is dependent on the availability of genomic sequence information and integration of the mutated residues into databases used for spectral interrogation.
New analysis tools, so called de novo search engines, have made it possible to interprete LC-MS/MS data independently of existing protein sequence databases. Therefore, these tools now allow us to screen MHC peptide ligand datasets for the occurrence of unforeseen peptide sequences, i.e. peptides that were generated by post-translational splicing. Such cis-spliced (within one molecule)3 or even trans-spliced4 (between two molecules) peptide sequences have been shown to be presented by MHC molecules and they therefore form a new family of neoantigens, that could be presented specifically in the context of cancer.
This PhD project aims to establish the relevant bioinformatics workflows to interrogate LC-MS/MS data outputs for the occurrence of spliced peptide ligands. The challenge of such workflows remains an unusually high false discovery rate (FDR) in de novo search engine data outputs, and therefore it is further important to establish methods to carefully control FDR and further minimize FDR by i.e. integration of binding prediction.
Finally, the focus of this project will be the interrogation of primary tumour tissue data, and the identification of tumour-specific neoantigen presentation relevant for the development of therapeutic vaccines in cancer.
1. Bassani-Sternberg, M. et al. Methods Mol Biol 1719, 209-221, doi:10.1007/978-1-4939-7537-2_14 (2018).
2. Ternette, N. et al. Proteomics 18, e1700465, doi:10.1002/pmic.201700465 (2018).
3. Mylonas, R. et al. Mol Cell Proteomics 17, 2347-2357, doi:10.1074/mcp.RA118.000877 (2018).
4. Faridi, P. et al. Sci Immunol 3, doi:10.1126/sciimmunol.aar3947 (2018).
The Ternette group specialized in the sequencing of MHC-associated peptidomes using liquid chromatography tandem mass spectrometry (LC-MS). Her group has now expanded their expertise to deep sequencing of immunopeptidomes in multiple pathogen infection models, analysis of the antigenic landscape of solid tumours and haematological cancers and characterisation of antigens involved in autoimmune diseases.
The project is suitable for a student with a bioinformatics background, and with enthusiasm for immunology and immunology related datasets. Informatics support will be available from a senior bioinformatician in the group, and several bioinformaticians working in the close network of collaborators.
In addition the student will have access to the wide range of training opportunities in the programme, and to seminars and courses offered at Oxford University.
Project reference number: 1030
|Dr Nicola Ternette||Jenner Institute||Oxford University, Old Road Campus Research Building||GBRemail@example.com|
|Professor Persephone Borrow||NDM Research Building||Oxford University, NDM Research Building||GBRfirstname.lastname@example.org|
There are no publications listed for this DPhil project.