Searching neoepitopes with bioinformatic techniques for immunotherapy against cancer

CARRI, IBEL; BARRIO, MARÍA MARCELA; NIELSEN, MORTEN

Posadas

Congreso; 8th Argentinian Congress of Bioinformatics and Computational Biology; 2017

Asociación Argentina de Bioinformática y Biología Computacional (A2B2C)

BACKGROUNDImmunotherapy exploits the body´s natural defenses against cancer cells. Neoepitopes from somatic mutations are potential targets of T cell immune responses.Advances in high-throughput sequencing techniques have made it possible to identify mutations which are potential neoepitope candidates in a patient’s tumor. However, most of these candidates are not immunogenic and false positives. When the mutant candidate is very similar to the wild type peptide, self-tolerance is involved in this lack of immune response.RESULTSWe define a pipeline to rationally obtain variants and select only potential neoepitopes, based on predicted MHC binding, stability and self similarity. Adding a score of self similarity allow us to select neoepitope candidates from mutations, even when the wild type has strong binding affinity.We apply this pipeline to whole exome sequence data from the cellular CSF-470 vaccine and from a melanoma patient who was treated successfully with this vaccine. We choose different values of self-similarity to set a threshold of tolerance to mutant peptides.CONCLUSIONSThis pipeline introduces a new feature that help us to narrow down the neoepitope candidate list.