DEMYSTIFYING ALTERNATIVE IMMUNOTHERAPY MYSTERY TARGETS BY COMBINING ARTIFICIAL INTELLIGENCE- BASED TARGET DISCOVERY AND LARGER-SCALE TARGET VALIDATION

While society invests big budgets in drug development, many results still end up in the Valley of Death. This could be explained by insufficient target discovery/validation and suboptimal target selection. However, even though the human genome has been mapped in 2001, a third of its coding genome remains mysterious (i.e. without annotation). Thus, this is a goldmine for target discovery, knowledge gain and drug development. This doctoral dissertation aims on the one hand at using this source of knowledge to predict the immunosuppressive (ImmuSup) function of mystery genes in endothelial cells, which will be useful for alternative immunotherapy (AlterIT), and on the other hand aims at discovering new immunosuppressive mystery genes in immune cells. Indeed, ECs restrict activation and access of immune cells in tumors, thus impairing efficacy and promoting resistance to traditional immunotherapy. As a result, we hypothesize that by inhibiting immunosuppressive mystery genes in ECs we will improve the efficiency and overcome the resistance to immunotherapy. To do so, we will develop a Brain-for-BioTech (BfBio) AI-tool to predict the immunosuppressive function of mystery genes with greater confidence and accuracy, but also to gain objective filters to guide the suitable selection of immunosuppressive mystery genes for alternative immunotherapy development. This will be done under the supervision of AI expert Yvan Sayes (Ghent VIB campus). We aspire to validate 100 new targets and patent 10 new targets for immunosuppressive genes, and this BfBio tool can be made generic to discover any type of function for mystery genes in any cell type, which makes this project a game-changer of how society can adapt its mindset to combine exciting research with fruitful clinical translation.