Cancer Immunotherapies Ignited by a Thorough Machine Learning-Based Selection of Neoantigens

Abstract

Identification of neoantigens, derived from somatic DNA alterations, emerges as a promising strategy for cancer immunotherapies. However, not all somatic mutations result in immunogenicity, hence, efficient tools to predict the immunogenicity of neoepitopes are needed. A pipeline is presented that provides a comprehensive solution for the identification of neoepitopes based on genomic sequencing data. The pipeline consists of a data pre-processing step and three machine learning predictive steps. The pre-processing step analyzes genomic data for different types of alterations, produces a list of all possible antigens, and determines the human leukocyte antigen (HLA) type and T-cell receptor (TCR) repertoire. The first predictive step performs a classification into antigens and neoantigens, selecting neoantigens for further consideration. The next step predicts the strength of binding between neoantigens and available major histocompatibility complexes of class I (MHC-I). The third step is engaged to predict the likelihood of inducing an immune response. Neoepitopes satisfying all three predictive stages are assumed to be potent candidates to ensure immunogenicity. The predictive pipeline is used in two regimes: selecting neoantigens from patients' sequencing data and generating novel neoantigen candidates. Two different techniques — Monte Carlo and Reinforcement Learning – are implemented to facilitate the generative regime.