F-CAP: Functionalization of Variants in Clinically Actionable Pharmacogenes

 描述(由申请人提供)：患者对药物的反应不同，对许多人类疾病的安全和有效治疗造成了巨大的挑战。药物基因组学试图通过将药物反应与患者在重要基因座上的基因类型联系起来来应对这一挑战，以便更好地定制患者的治疗方法。药源中的遗传变异是广泛的。例如，在12个CYP基因中，10%的人携带至少一个罕见的、潜在有害的变异。不幸的是，只有少数变异被明确地与药物反应的改变联系在一起。显然，需要新的方法来诠释大量未知意义的变异库的后果，包括那些已经被现有的大规模测序程序识别的变异体，以及那些将随着临床测序成为常规而被发现的变异体。在这项提案中，我们试图通过利用测序和功能分析方面的新技术，以前所未有的规模直接解决这一问题。我们的资源名为F-CAP(临床可操作的药物起源中变异的功能化)，将测试一些临床上最重要的药物起源中所有氨基酸残基的所有可能的替代，并将这些数据传播给医学和研究社区。为了实现这一点，我们将使用深度突变扫描，这是我们开发的一种方法，允许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，从五个最重要的CPIC Level A或B优先基因(CYP2C9、CYP2C19、CYP2D6、TPMT和VKORC1)开始，并使用混合选择策略测试每个变体的稳定性和酶活性。在目标2中，我们将整合这些数据以创建影响评分。这一影响分数为变体的功能效应提供了一个数值，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。AIM 3将使用每个药物基因的治疗相关底物，为跨越影响评分谱的变体子集验证这一分数。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的伙伴关系，向整个药物基因组学社区传播我们的研究结果。此外，我们将通过一个也将在AIM 4中开发的定制网络资源来提供我们的原始和处理数据。该资源将提供一系列完全注释的数据集，描述一系列关键药源中每一个可能的单一突变的功能后果，从而极大地推动个性化药物领域的发展。

 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.