Personalis has launched its Systemic HLA Epitope Ranking Pan Algorithm (SHERPA) machine learning-based tool for identifying and characterizing cancer neoantigens, as well as its Neoantigen Presentation Score (NEOPS) for predicting cancer immunotherapy response.
SHERPA assesses the potential MHC-binding affinity and stability of identified peptides, and incorporates features linked to antigen processing machinery and RNA abundance to produce a presentation rank for each detected peptide. The rank determines the relative likelihood of a given neoantigen being presented and undergoing immunosurveillance. Integrated into the firm's NeXT platform, the tool allows for the development of new neoantigen-based diagnostic biomarkers, such as its NEOPS biomarker, and neoantigen-targeting personalized cancer therapies.
Personalis' NEOPS combines the tumor genomic and immune-related analytics of the firm's NeXT platform to create a composite biomarker, that the firm believes can be more effective in predicting immunotherapy responses than other biomarkers.
NEOPS and SHERPA are the latest updates to the comprehensive suite of advanced analytical engines of the Personalis NeXT Platform for biopharmaceutical customers, the company said.