2020 AACR: Precision neoantigen discovery using a pan-predictive machine learning model integrated into ImmunoID NeXT Platform®

Technologies for neoantigen discovery are critical for developing personalized cancer vaccines and neoantigen-based biomarkers. Precision neoantigen discovery entails comprehensive detection of tumor-specific genomic variants and accurate prediction of MHC presentation of epitopes originating from such variants. Our ImmunoID NeXT Platform enables a comprehensive survey of putative neoantigens by combining highly sensitive and exome scale DNA and RNA sequencing with advanced analytics. Here, we present Systematic HLA Epitope Ranking Pan Algorithm (SHERPA), our pan-predictive machine learning model for predicting MHC class I presentation and identifying potentially immunogenic patient-specific neoantigens.

Our immuno-oncology platform (ImmunoID NeXT Platform) enables researchers to analyze both a tumor and its microenvironment from a single tumor sample. In-depth interrogation of tumor and normal samples and identification of tumor-specific genomic events allows us to comprehensively profile the landscape of potential neoantigens, a critical aspect of precision neoantigen discovery.