Prediction of neoepitopes with bioinformatic techniques for immunotherapy against cancer

CARRI, IBEL; PODAZA, ENRIQUE; KOILE, DANIEL; YANKILEVICH, PATRICIO; BARRIO, MARÍA MARCELA; NIELSEN, MORTEN

Mar del Plata

Congreso; IX Congreso Argentino de Bioinformática y Biología Computacional; 2018

Asociación Argentina de Bioinformática y Biología Computacional (A2B2C)

BACKGROUNDImmunotherapy is a type of cancer treatment that boosts the body´s natural defenses, aiding the removal of carcinogenic, metastatic cells. Under these conditions, neoepitopes from somatic mutations are potential targets of T cell immune responses, and therefore are the focus of diverse experimental and in-silico studies.In the last years, advances in high-throughput sequencing techniques have made possible to identify mutations related to potential neoepitope candidates in human tumors. However, a relatively big part of these candidates are not immunogenic, and can therefore be classified as false positives. In this context, an improvement in neoepitope prediction methods may lead to better and faster cancer treatments.RESULTSIn this project, we define a novel approach to rationally obtain variants and accurately select only potential neoepitopes from whole exome sequencing. From a collection of mutations our pipeline facilitates the extraction of neoepitope candidates, based on predicted MHC binding affinity, stability of peptide-MHC complex, similarity between mutant and wild type peptides, and the presence of mutated transcripts in RNAseq data from tumor samples.We applied such pipeline to whole exome sequencing data of melanoma cells from a patient who received the cellular CSF-470 vaccine in a Phase II clinical study and obtained in vitro validated neoepitopes. CONCLUSIONSIn this work, we successfully introduce a pipeline for neoepitope search and selection out of an assortment of mutated genes.