Advances in sequencing technology are fueling the rapid discovery of genetic variants in cancer, but the ability to interpret whether or not these variants actually affect the function of the encoded protein is often challenging. Instead, “look-up” tables are needed to provide physicians information about the functional impact of any possible genetic variant. We have pioneered several high-throughput functional methods and propose to refine and implement these tools to generate these tables for two important pediatric cancer genes, TP53 and SMAD4.
Proposed specific aims
· Leverage advances in DNA mutagenesis and DNA sequencing to perform Deep Mutational Scans to study the effects of all possible gene variants affecting the protein sequences of TP53 and SMAD4.
· Perform additional detailed assays on all possible protein sequence mutants in TP53, and use machine learning algorithms to develop classifiers to more accurately predict variant pathogenicity.
Potential impact on child health
The results of these high-throughput functional studies will be immediately useful for patient care, and could also identify patient-specific novel therapeutics.