How to Design a Cancer Vaccine: Neoantigens and MHC Science
UNC's PIRL lab founders discuss neoantigen detection, MHC prediction, and the engineering challenges of personalized cancer immunotherapy in a three-hour deep dive.
Alex Rubinsteyn and Benjamin Vincent, co-founders of UNC-Chapel Hill's Personalized Immunotherapy Research Lab (PIRL), discuss the design and computational foundations of cancer vaccines in an extended conversation. The talk covers technical ground including the distinction between tumor-associated antigens (TAAs) and neoantigens, MHC/HLA presentation mechanisms, the limits of computational antigen prediction versus immunopeptidomic verification, and the information-theoretic challenge posed by tens of thousands of MHC molecules per cell. They also examine whether TCR-T and CAR-T cell engineering could complement or replace neoantigen vaccine approaches.
The discussion extends well beyond pure science into practical territory: how FDA approval works for hyperpersonalized drugs, how much of the computational pipeline needs to stay proprietary, and the field's lack of rigorous benchmarking. They scrutinize whether Moderna and BioNTech's reported successes hold up, along with challenges posed by tumor mutational burden in low-mutation cancers like glioblastoma.
For engineers, the real value lies in seeing where machine learning, bioinformatics, and clinical deployment collide with hard real-world constraints — the gap between predicting an antigen and actually knowing it, how immunodominance can make even a well-designed vaccine fail, and the need for a scalable, automated 'tumor in, RNA therapeutic out' pipeline for personalized medicine at scale.