Our primary laboratory's active line of research is developing -omics data-based artificial intelligence algorithms to design, enhance and personalize cancer immunotherapy based on tumor peptides or CAR-T cells.
Immune checkpoint inhibitors (CPIs) are a type of cancer immunotherapy that plays a vital role in the treatment of multiple malignancies, including melanoma. However, nearly 50% of patients do not respond to CPIs.
One can combine CPIs with tumor-associated antigen (TAAs)-targeted cancer therapy to circumvent this. TAAs and their associated peptides can originate from somatic mutations or overexpressed proteins in the tumor, and activate the body's immune response against cancer.
In our laboratory, we investigate if one can combine patient gene expression profiling data with artificial intelligence and computational modelling to select effective TAAs, enhancing the anticancer immune response with minimal risk of potential side effects.