Nickerson
Co-Investigator
Rettie
Co-Principal Investigator
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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. |
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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.
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. |
F-CAP intends to catalog the functional impact of every genetic variant at all amino acid positions in a set of prioritized pharmacogenes. CPIC have identified 84 gene-drug pairs that are assigned a level A or level B priority, which reflects the group’s view that genetic information either should or could be used to change prescribing of the affected drug (https://www.pharmgkb.org/cpic/pairs). Collectively, nine of these genes (CYP2C9, CYP2C19, TPMT, VKORC1, CYP2D6, G6PD, IFNL3, SLCO1B1 and DPYD) encompass 86% of the highest priority CPIC genepairs. In the 3 year period of funding available through this R24 grant application, we will concentrate on five of these genes; CYP2C9, CYP2C19, CYP2D6, TPMT and VKORC1, that interact with widely used anti-cancer, antidepressant, anti-coagulant/platelet, anti-epileptic, and non-steroidal anti-inflammatory drugs, and account for over half of the highest priority CPIC genes, in order to establish the high throughput methodologies and validation processes that underpin development of our cataloging resource.
Nickerson
Co-Investigator
Rettie
Co-Principal Investigator
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This part of the project capitalizes on the facile expression of large libraries of protein variants in yeast where each cell harbors one, and only one, protein variant. We are adapting this technology for use in mammalian cells. All variants of interest are bar-coded for ease of identification by high-throughput sequencing. Functional capabilities of each variant will be assessed with fluorescent assays for catalytic activity and protein stability.
Dunham
Co-Investigator
Fowler
Co-Principal Investigator
Rettie
Co-Principal Investigator
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A major goal of our resource is to develop an integrated, numerical descriptor of variant impact. The motivation for producing such an impact score is to enable clinicians and other users of the resource to easily gauge the consequences of a particular variant. To calculate an impact score, we will interrogate models that integrate the activity and stability scores. A very simple product model is I = A * S, where I is the impact score, A is the wild type normalized activity score and S is the wild type-normalized stability score. In this model, an impact score of one means that a variant is identical to wild type. Lessened stability or activity result in a lower impact score, and decreases in both activity and stability can exert combined effects.
Fowler
Co-Principal Investigator
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Key to the validation of the impact scores derived from the high throughput screen involving fluorescent probes as reporters is the detailed evaluation of pharmacogene catalytic activity in a subset of variants using therapeutically relevant drug substrates, as identified by CPIC level A/B gene-pairs. In a similarly low throughput, but detailed fashion, we will determine the catalytic activity of VKOR variants towards that enzyme’s physiologically relevant epoxide substrate. Additionally, protein stability will be assessed here as the steady-state accumulation of immunodetectable protein in microsomes or cytosol. These particular measures of enzyme activity and stability, while distinct from those used for the high throughput screens, are key parameters for validation because they are the most direct in vitro indicators of drug metabolizing capabilities of the enzyme variants. As in Aim 2, impact scores (I) will be assigned (initially) as the product of enzyme activity (A) and stability (S). Analyses of these data and comparison with the high throughput data will enable us to estimate the accuracy of the impact scores of all variants assigned in Aim 2. Additionally, we will derive empirical false discovery rates for classifications of variant effect also determined in Aim 2. Beyond the clear clinical significance of the data obtained, these efforts are important because they will enable new biochemical insights into pharmacogene function and structure.
Rettie
Co-Principal Investigator
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We will disseminate our highly annotated impact score and functional classifications to all appropriate databases, including PharmGKB. Furthermore, through our interactions with CPIC we will explore the clinical utility of providing our predictions more widely to the ClinGen Resource, which includes the NCBI ClinVar database. We will disseminate our raw sequencing data through the short read sequence archive. We will also develop a web resource to make all data easily accessible to the research community beyond its availability in public databases.
Nickerson
Co-Investigator
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