F-CAP: Functionalization of Variants in Clinically Actionable Pharmacogenes

F-CAP：临床上可行的药物基因变体的功能化

• 批准号: 9302807
• 负责人: Douglas M Fowler
• 金额: $ 73.7万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-15 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9302807
• 关键词: Address; Affect; Algorithms; Amino Acids; Applications Grants; Biochemical; Biological Assay; CYP2C19 gene; CYP2C9 gene; CYP2D6 gene; Catalogs; Cellular Assay; Classification; Clinical; Code; Communities; Custom; Data; Data Set; Databases; Dose; Elements; Funding; Gene Library; Generations; Genes; Genetic; Genetic Variation; Genome; Genotype; Goals; Guidelines; Health Personnel; High-Throughput DNA Sequencing; In Vitro; Individual; Large-Scale Sequencing; Libraries; Link; Machine Learning; Measurement; Measures; Medical; Medical Research; Methods; Monoclonal Antibody R24; Mutate; Mutation; Numerical value; Output; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Pharmacogenomics; Pharmacotherapy; Plant Roots; Positioning Attribute; Process; Resources; Series; Source; TPMT gene; Testing; Therapeutic Uses; Translations; Variant; clinical sequencing; clinically actionable; effective therapy; exome; genetic disorder diagnosis; genetic information; genetic variant; genotyped patients; human disease; improved; interest; mutation screening; new technology; novel; novel strategies; online resource; personalized medicine; programs; public health relevance; response; stability testing; tool; user-friendly; variant of unknown significance

 DESCRIPTION (provided by applicant): 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.

 描述（由申请人提供）：患者对药物反应的差异性对许多人类疾病的安全有效治疗构成了重大挑战。药物基因组学旨在通过将药物反应与重要基因座（称为药物基因组）的患者基因型联系起来来解决这一挑战，以便更好地定制患者治疗。药物基因组的遗传变异是广泛的。例如，在12种基因中，10%的人携带至少一种罕见的、潜在有害的变异。不幸的是，只有少数变异与药物反应的改变明确相关。显然，需要新的方法来注释巨大的未知意义的变异库的后果，包括那些已经通过现有的大规模测序程序确定的变异，以及那些随着临床测序成为常规而将被发现的变异。在这项提案中，我们试图通过利用测序和功能分析中的新技术，以前所未有的规模直接解决这个问题。我们的资源名为F-CAP（临床可操作药物基因中变体的功能化），将测试一些临床上最重要的药物基因中所有氨基酸残基的所有可能取代，并将这些数据传播给医学和研究界。为了实现这一目标，我们将使用深度突变扫描，这是我们开发的一种方法，可以对遗传变异库进行并行和定量测量。在目标1中，我们将创建这些文库，从5个最重要的CPIC水平A或B优先级基因（CYP 2C 9、CYP 2C 19、CYP 2D 6、TPMT和VKORC 1）开始，并使用合并选择策略检测每个变体基因的稳定性和酶活性。在目标2中，我们将整合这些数据以创建影响力评分。该影响评分为变体的功能效应提供了数值，该数值易于整合到药物基因组学社区正在开发的处方指南中。目标3将使用每种药效基因的治疗相关底物验证跨越影响评分谱的变体子集的该评分。最后，目标4描述了这一资源的一个关键组成部分：通过与CPIC和PharmGKB的合作，将我们的发现传播给整个药物基因组学社区。此外，我们将通过也将在Aim 4中开发的自定义网络资源提供我们的原始和处理后的数据。该资源将提供一系列完全注释的数据集，描述一系列关键药物基因中每个可能的单一突变的功能后果，从而大大推进个性化医疗领域。