2019 SITC: Comprehensive and accurate prediction of presented neoantigens using ImmunoID NeXT and advanced machine learning algorithms

Comprehensive detection of potential neoantigens and accurate prediction of their MHC presentation are critical prerequisites for selecting neoepitopes that can be used for creating personalized cancer vaccines. However, prediction models developed using in-vitro MHC-peptide purification followed by mass spectrometry (IP-MS) have enabled direct detection of MHC-bound peptides and can therefore be used for modelling native MHC-peptide presentation. Furthermore, genetically engineered cell lines that express a single HLA allele enable unambiguous HLA peptide assignment. Here, we present an overview of our MHC presentation prediction framework based on a large collection of such monoallelic cell lines and discuss its utility in conjunction with ImmunoID NeXT, our commercially available exome scale DNA and RNA sequencing and analytics platform specifically designed to enable the development of immunotherapies.

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