Filling the knowledge gaps in the prediction of bona fide immunogenic neoantigens
OncoImmunity offers software solutions powered by a machine-learning engine that predicts from next generation sequencing data, true neoantigens for personalized cancer immunotherapy and cancer immunotherapy biomarkers.
Recent advances in the field of immuno-oncology have established that mutated antigens (neoantigens), are viable targets of the immune system, potentially specific to each individual cancer patient.
This plus many other breakthroughs in immuno-oncology and the rapid advances in next generation sequencing (NGS), opens up exciting new opportunities to harness neoantigens for their therapeutic and biomarker potental in cancer immunotherapy.
The rapid identification of neoantigens in a clinically actionable timeframe is very difficult, laborious and expensive. The in silico prediction of neoantigens from next generation sequence data offers a powerful alternative. However, serious computational challenges need to be overcome in order to predict true neoantigens from NGS data, above and beyond current predictions of binding to the HLA type of a cancer patient.
AI has made inroads toward the ultimate goal of predicting antigen presentation and subsequent immunogenicity. The proprietary developments by the OncoImmunity team fills the existing machine-learning gaps and predicts antigen presentation to the cell surface to facilitate the automated screening of epitopes for their immunogenic and clinical potential.
Clinical applications of this improved neoantigen prediction enables the availability of true personalized cancer immunotherapy. The integrated solutions at OncoImmunity to predict neoantigen effectively guides the selection of targets for personalized cancer vaccines, targeted cell therapies, and patient stratification.