Adapting technologies to predict immunogenic epitopes of cancer neo-antigens to predict immunogenic epitopes of new viral strains based on genomic similarity to other known viruses: the case of Sars-CoV-2.

The response to viruses needs to be quick and efficient to prevent the spread of the virus and protect risk groups such as elderly, infants and immunocompromised people. One of the major challenges here is to respond as quickly as possible and thus to have fast vaccine development. The current Covid-19 outbreak was the driver of this project as it undeniably points out the evident need for more efficient and faster methods. In addition, it is important to know that, while there is much evidence that various vaccine strategies against SARS-CoV-1 are safe and immunogenic, vaccinated animals still display significant disease upon challenge. Data suggest that new or combination strategies may be required for good protective efficacy against SARS-CoV-1 in humans. Unfortunately, as of today, almost twenty years in the run, the development of an efficient SARS-CoV-1 vaccine is still not completed, and research is still ongoing despite reduced funding in the past decade. This clearly indicates that there is a high need for efficient methods to select the most potent epitopes for vaccine development. The project has the aim to validate predicted immunogenic epitopes by the myNEO algorithm, combining in vitro validation datasets across viral strains within a family, through development of high-throughput tools. This will enable rapid screening of vaccination targets and this pre-screening will allow to limit the many screening assays that are now still required and are labor- and time intensive as well as require hazardous manipulations. Thus, the overall objective is to reduce the timeframe and cost that is associated with immunogenicity screening of a vast amount of possible target peptides for Covid-19 prophylactic vaccination.