Identification of public antigens for off-the-shelf cancer immunotherapy

Cancer immunotherapy has revolutionised the treatment of several malignancies, yet many current strategies rely on highly personalised approaches that target patient-specific neoantigens. While effective, such personalised therapies are costly, time-consuming, and difficult to scale. An alternative strategy is to identify shared (“public”) tumour antigens that are recurrently presented across multiple patients and cancer types, enabling the development of off-the-shelf immunotherapies. This project aims to identify and characterise such public antigens using immunopeptidomics and multi-omics analysis. By analysing peptides naturally presented by HLA molecules on tumour cells, we seek to discover tumour-associated antigens that are recurrent across patients and therefore represent promising targets for broadly applicable T cell-based therapies. The student will work with large-scale datasets generated from mass spectrometry-based immunopeptidomics, transcriptomics, and proteomics to identify candidate antigens shared across tumour samples. Computational analysis will be used to prioritise peptides based on recurrence, HLA presentation, tumour specificity, and immunogenic potential. The findings may contribute to the development of next-generation off-the-shelf cancer immunotherapies targeting shared tumour antigens.