This project aimed to develop a computational approach to identify potential therapeutic opportunities by examining protein coding changes across a large number of tumours sequenced within the Michael Smith Genome Sciences Centre, as well as data generated by other groups and consortia.
The primary goal was to optimize targets for cancer antibody targeting (immunotherapy) by validating differentially expressed genes or splicing variants, and to generate antibodies for antibody-drug conjugates (ADC) development on lead selection.
A pan-cancer analysis was conducted in 53 cancer types and identified 892 putative cancer-associated differentially expressed genes with 399 predicted to localize to the cell surface. A complex and refined criterion (Analytic Hierarchy Process) was created to identify cancer targets for ADC development. Four cancer targets were validated; two of them went through antibody discovery campaigns and one of them went through preliminary assessment of ADCs.