Non-small cell lung cancer (NSCLC) has poor survival even for those receiving modern checkpoint inhibitor therapies. Personalised vaccines based on short peptide neoantigens containing tumour mutations, presented to cytotoxic T-cells by human leukocyte antigen (HLA) molecules, are an attractive strategy. However, identifying therapeutically relevant neoantigens is challenging, with existing methods yielding positive responses in only 6% of candidates tested, and neoantigen-based vaccines in melanoma, glioblastoma and pancreatic cancer producing an immune responses in about 50% of patients.Here we report a proteogenomics approach to identify neoantigens in tumours from a cohort of 24 NSCLC patients: 15 adenocarcinoma, 9 squamous cell carcinoma. We characterised the mutational and HLA immunopeptide landscapes of NSCLC using whole exome sequencing, transcriptomics and mass spectrometry immunopeptidomics. We directly identified one neoantigen, and additional predicted neoantigens were generated using an existing in silico neoantigen prediction workflow. Using the immunopeptidomes to filter for candidate predicted neoantigens we identified positive functional assay responses for 5 out of the 6 patients we tested, with an overall success rate of 13%, inclusive of the directly observed neoantigen. Finally, for one patient using scRNAseq we identified a CD8+ effector T-cell clonotype expanded only in response to the putative class I HLA neoantigen.These results represent an improvement in both the quantity of neoantigens identified and the specificity of immune responses to neoantigens, utilising knowledge of the HLA peptides presented on a tumour. Thus immunopeptidomics has the potential to improve the efficacy of neoantigen based personalised cancer vaccine workflows.Competing Interest StatementThe authors have declared no competing interest.