Patient-to-patient variability in response to drugs creates a significant challenge for the safe and effective treatment of many human diseases. Pharmacogenomics seeks to address this challenge by linking drug response to patient genotypes at important loci, termed pharmacogenes, in order to better customize patient treatments. Genetic variation in pharmacogenes is extensive. For example, amongst 12 CYP genes, 10% of people carry at least one rare, potentially deleterious variant. Unfortunately, only a small number of variants have been unambiguously linked to alterations in drug response. Clearly, new approaches are needed to annotate the consequences of the huge pool of variants of unknown significance, including those already identified by existing large-scale sequencing programs, and those that will be discovered as clinical sequencing becomes routine. In this proposal, we seek to address this problem directly and at a scale never before possible by taking advantage of new technologies in sequencing and functional analysis. Our resource, termed F-CAP (Functionalization of Variants in Clinically Actionable Pharmacogenes) will test all possible substitutions at all amino acid residues in some of the most clinically important pharmacogenes and disseminate these data to the medical and research communities. In order to accomplish this, we will use deep mutational scanning, a method we have developed that allows parallelized, and quantitative measurements to be performed on libraries of genetic variants.
In Aim 1 we will create these libraries, starting with five of the most important CPIC level A or B priority genes (CYP2C9, CYP2C19, CYP2D6, TPMT and VKORC1), and test the stability and enzymatic activity of each variant en masse using a pooled selection strategy.
In Aim 2, we will integrate these data to create an impact score. This impact score provides a numerical value for a variant's functional effects that is amenable to easy integration into prescribing guidelines being developed by the pharmacogenomics community.
Aim 3 will validate this score for a subset of variants that span the impact score spectrum using therapeutically relevant substrates for each pharmacogene. Finally, Aim 4 describes a key component of this resource: the dissemination of our findings to the entire pharmacogenomics community through partnership with CPIC and PharmGKB. In addition, we will make available our raw and processed data via a custom web resource that will also be developed in Aim 4. This resource will provide a series of fully annotated datasets describing the functional consequences of every possible single mutation in a series of key pharmacogenes, thereby greatly advancing the field of personalized medicine.
Pharmacogenomics seeks to identify genetic sources of inter-individual variability in drug response, with the goal of personalizing drug selection and dose to improve patient outcomes. A key barrier to using pharmacogenomic information is lack of clarity about the functional impact of variants, which hampers the provision of clear, unambiguous guidance to health care providers. We propose to connect pharmacogenomic variant discovery with novel high-throughput experimental approaches to deliver a resource to guide the use of individual pharmacogenomic information in personalizing drug treatment.
