Ryan Hartmaier, Foundation Medicine
Intersection of genomics and cancer immunotherapy: neooantigens are critical – identified through NGS and prediction algorithms. Can be used for immune checkpoint inhibitors or cancer vaccines.
Extensive genetic diversity within a a given tumour. (mutanome)
Difficult to manufacture and scale, thus expensive therapeutics. However, TCGA datasets (and others) reinforce that individualized therapies make sense. No comprehensive analysis data set on this approach has yet been done.
NGS-based genomic profiling for solid tumours. FoundationCore holds data.
At time of analysis, 63,220 tumours available. Genetic diversity was very high.
Mutanomes are unique and rarely share more than 1-2 driver mutations. Thus, define smaller set of alterations that are found across many tumours. Can be done at genes, type, variant or coding short variants. Led to about 25% of tumours having at least one overlap with 10 shortlist genes.
Instead of trying to do single immunogen therapy for each person, look for those that could be used commonly across many people. Use MHC-I binding prediction to identify specific neoantigens. 1-2% will have at least one of these variants.
Multi-epitope, non-individualized vaccines could be used, but, only apply to 1-2%.
Evidence of immunoediting in driver alterations. Unfortunately, driver mutations produce fewer neoantigens.
Discussions of limits of method, but much room for improvement and expansion of experiment
conclusion: Tumour mutanomes are highly unique. 25% of tumours have at least one coding mutation, potential to build vaccines is limited to 1-2% of the population. Drivers tend not to produce neoantigens.