Genetic determinants of anti-tumor immunity
Genetic variation in immune genes creates inter-individual differences in immune traits. We are investigating the implications of these differences for cancer risk, progression and response to therapy. By jointly analyzing matched tumor and normal genomic data, we have found that individual immune genotypes can influence the evolution of tumor genomes, shape the tumor immune microenvironment and modify response to immunotherapy treatment. These studies will ultimately guide development of precision immunotherapies.
Predicting the effects of genetic variants on biological systems
Experimental assays to probe genetic variants at the single cell level provide new opportunities to model the effects of variants on biological processes and cellular activities. We are developing new experimental and computational approaches to investigate the context-specific consequences of genetic variation for biological systems to better understand their implications for human health.
Uncovering interactions between the inherited genome and tumor development
Cancer develops on an individual’s unique genetic background. While some inherited mutations are known to predispose individuals to developing tumors, little is known about the effects of such inherited genetic variation on the evolution of the tumor genome. We are working to uncover new regions of the genome that influence the probability that certain mutations will occur during tumor development. We previously described multiple loci that influence mutation rates of known cancer genes and the site at which a tumor will occur.
Discriminating cancer drivers from passengers
Each cancer genome is characterized by tens-to-thousands of DNA mutations, but only a minority of those mutations is expected to drive tumor development and progression. The vast majority of mutations are so called passenger mutations that are carried along in the genomes of proliferating tumor cells. Discriminating drivers from passengers remains an important challenge in cancer ‘omics research. We are developing new strategies to improve identification of drivers by studying mutations in the context of protein interaction networks.