Toward improved understanding of sex differences in drug response: developing gene and pathway-based informatics methods to examine sex-differential genetic effects

研究综述 女性发生不良事件的风险是女性的1.5倍以上，包括严重的不良事件，如 药物性肝损伤。1997至2000年间，每十种药物中就有八种退出市场 对妇女提出了更大的健康风险。虽然风险增加的部分原因是女性服药过量，但 这些差异中的很大一部分也与生理性别差异有关；然而， 在这些差异的背后，是人们对此认识不足。此外，不研究女性的一个经常被引用的理由是 月经周期引起的性别间的变异性。在整个药物开发流程中， 性很少被考虑，标签经常被排除在分析之外，即使是在计算层面上也是如此。遗传 以全基因组关联研究(GWAS)和基因表达水平的形式提供的数据提供了独特的 分析性行为影响的机会；它们允许对生物功能的洞察和对 在这种情况下，没有标记的数据是可能的，因为性很容易被归因于。此外，基于网络的 对这些数据的分析具有通过依赖先验信息来提高信噪比的好处 关于基因-基因相互作用和途径，也有助于对结果的生物学解释。为了提高 了解性别差异的影响，我建议利用遗传数据来实现以下具体 目的：1)使用基因表达数据来研究性别差异在 由于月经周期激素变化而导致的器官水平和性别差异，2)基于网络的发展 性别差异效应的检测方法，以及3)性别变异之间和性别内变异之间的联系 对药物反应的反应。我的工作将提高对性和药物反应之间相互作用的理解，以及 深入了解这些交互作用背后的机制。随着成功和进一步的评估，这 分析将通过考虑与性别相关的变异性来改进药物开发过程，减少 不良事件。 我的长期职业目标是成为一名开发信息学方法的独立学术研究员 研究生物学、疾病和药物反应中的性别变异性。在我的团契期间 通过培训，我将通过深化我的研究技能，建立合作关系，发表论文， 参加研讨会和会议，参加额外的相关课程，以及教学和辅导 学生们。我为实现这些目标做好了准备；我的训练将与拉斯博士一起进行 Altman在指导学生方面有着极其成功的记录，他在斯坦福大学学习， 拥有令人难以置信的教育资源和协作的尖端研究环境。

RESEARCH SUMMARY Women are at more than 1.5-fold higher risk for adverse events, including serious adverse events such as drug induced liver injury. Between 1997 and 2000, eight out of the ten drugs withdrawn from the market proposed greater health risks to women. While some of this increased risk is due to women being overdosed, a large portion of these differences are also related to biological sex differences; however, the mechanisms behind these differences are poorly understand. In addition, an often-cited reason for not studying women is the presence of within-sex variability due to the menstrual cycle. Throughout the drug development pipeline, sex is rarely considered, and labels are routinely left out of analysis, even at the computational level. Genetic data, in the forms of Genome-Wide Association Studies (GWAS) and gene expression levels, provide unique opportunities for analyzing the effects of sex; they allow for insights into biological function and examination of unlabeled data is possible in this case because sex can be easily imputed. Additionally, network-based analysis of these data has the benefit of increasing the signal-to-noise ratio by relying on prior information about gene-gene interactions and pathways, and also aids in the biological interpretation of results. To improve understanding of sex-differential effects, I propose to leverage genetic data to accomplish following specific aims: 1) use gene expression data to investigate the molecular effects of between-sex differences at the organ-level and within-sex differences due to menstrual cycle hormone variability, 2) develop network-based methods for detecting sex-differential effects in GWAS, and 3) link identified between- and within-sex variability to drug response. My work will improve understanding of interactions between sex and drug response, and provide insight into the mechanisms behind these interactions. With success and further evaluation, this analysis will improve the drug development process by taking sex-related variability into account, decreasing adverse events. My long-term career goal is to become an independent academic researcher developing informatics methods to study between and within sex variability in biology, disease, and drug response. During my fellowship training, I will work toward this goal by deepening my research skills, building collaborations, publishing papers, attending seminars and conferences, taking additional relevant coursework, and teaching and mentoring students. I am exceptionally well-poised to achieve these goals; my training will take place with Dr. Russ Altman, who has an extremely successful track record of mentoring students, and at Stanford University, which has incredible educational resources and a collaborative cutting-edge research environment.