Striking progress in the field of Next Generation Sequencing (NGS) along with the development of various NGS analysis tools has made it possible to identify somatic mutations in a cancer patient by comparing the genetic sequences of normal and tumor samples. Peptides generated from these identified tumor neoantigens are potential candidates for peptide vaccines. The goal of the current project was to build a pipeline that identifies tumor neoantigens created by a somatic mutation in various transcripts that are affected by it, and generate peptides overlapping this mutation. The generated peptides, both wild type and mutated, were synthesized and tested against the patient's Tumor infiltrating lymphocytes (TIL)/ Peripheral Blood Mononuclear cells (PBMC) by performing Interferon gamma Enzyme-Linked ImmunoSpot (IFN-γ ELISPOT). Information that could impact the likelihood of a peptide to be immunogenic were recorded, namely the mapping quality associated with the identification of the somatic mutation, RNA expression level of the mutated neoantigen, MHC class I and II binding predictions of the peptide, immunogenicity scores for MHC class I of the peptide, amino acid difference associated with the mutation and similarity of the peptide to commensal microbiota. These scores were tested for their capacity to predict if a given neoantigen derived peptide will elicit an immune response. This project thus provides the foundation for the longer-term goal of establishing a predictive model that prioritizes neoantigen-derived peptides that are more likely to be immunogenic.