In the seminar, we will see how tumor-specific mutations (neo-antigens) can stimulate the immune recognition of cancer cells and be used as a therapeutic strategy. For such strategy to be successful, we need to be able to predict which endogenous peptide antigens will be presented on the cell surface by polymorphic HLA class I gene variants. We will present analyses of our single HLA peptide data which allowed us to develop improved rules for endogenous peptide presentation based on the physicochemical properties of binding peptides, patterns of peptide cleavage and abundance of cognate transcripts. Incorporating these findings into neural network models improved prediction of endogenous peptide binding as compared to current predictive algorithms. We will end by reviewing very encouraging results from a tumor vaccine trial in melanoma patients.