Leveraging genetics and environment to predict presymptomatic multiple sclerosis

求职者：我是布里格姆妇女医院(BWH)的神经科医生，哈佛医学院(HMS)的讲师。我的研究兴趣在于将人类遗传学的发现转化为多发性硬化症(MS)和相关的神经免疫学和神经退行性疾病的临床应用。利用我在基础细胞和分子神经科学方面的博士论文工作，以及正在进行的统计遗传学和翻译基因组学博士后培训，我将通过指导项目和结构化的教育活动，精通下列新的研究领域：(1)领导研究人员团队，指导以患者为中心的研究；(2)设计和实施包含创新生物标记物和神经退行性疾病的神经成像结果的研究；(3)设计和实施遗传信息在临床上的相关应用，例如用于生物标志物选择、疾病预测和风险分层的遗传风险评分；(4)使用电子医疗记录(EMR)派生的数据和生物信息学工具进行翻译遗传学研究。这些新的翻译研究技能将使我能够实现向独立调查员过渡的职业目标。环境：我已经组建了一个由导师(将人类基因组技术应用于复杂神经疾病研究的专家Philip de Jager博士)和共同导师(Issac Kohane博士，生物信息学和预测建模专家；Daniel Reich博士，MS神经成像专家)、顾问和资深教职员工组成的多学科团队，他们拥有互补的专业知识，将指导我的研究并促进我的职业发展。这种多层次的导师结构嵌入在一个高度协作的环境中，该环境拥有无与伦比的智力水平，是哈佛附属机构的一部分：BWH神经病学系、BWH神经科学研究所、博德研究所和HMS。马丁·塞缪尔博士(BWH神经科主任)和David SilberSweig博士(BWH神经科学研究所主席)都支持我的职业规划。研究：多发性硬化症(MS)临床护理面临的一个重要挑战是缺乏强大的预测工具 指导多发性硬化症高危人群的个体化风险分层 这项研究旨在测试一种算法的有效性，该算法将现有的遗传和环境数据整合到对患多发性硬化症风险的单一、个人估计中。我们假设，这种算法可以为无症状的多发性硬化症风险分层，例如家庭成员或患有放射隔离综合征的患者(RIS：在脑磁共振成像(MRI)上偶然发现多发性硬化症样病变的无症状个人)。首先，我们将计算我们的多发性硬化症遗传和环境风险评分(GERSM)，包括500名多发性硬化症患者的无神经症状一级亲属，并评估与100名GERSM最低的受试者相比，GERSM最高的100名受试者是否表现出与多发性硬化症相关的外周血液生物标记物特征。其次，我们将评估GERSM在预测神经系统无症状一级亲属中是否存在MRI定义的MS病变和其他MS相关MRI结果方面的有效性，并评估与GERSM最低的50名受试者相比，GERSM最高的50名受试者是否有更多的MS样病变、更小的脑体积或更大的扩散张量成像(DTI)变化，这些变化表明白质束完整性的丧失。最后，我们将评估GERSM在预测现有EMR数据确定的RIS患者转化为临床MS方面的有效性。创新：这项建议是创新的，因为它应用了一种新的方法来解决一个未被研究的问题：我们如何识别患有多发性硬化症风险最高的无症状个体？我们将在两个独特的无症状受试者样本集合中测试该方法：(A)MS患者的无症状一级亲属，(B)来自最大EMR系统之一的RIS患者，具体地说，我们将评估该方法在选择尖端血液生物标记物和敏感神经成像指标方面的有效性。如果得到验证，我们的方法有可能通过识别从健康向多发性硬化症转变的风险最高的受试者子集来使无症状受试者的研究可行，从而打开多发性硬化症研究的一个全新领域，最终可能有助于设计预防多发性硬化症发病的策略。最后，利用EMR衍生的虚拟 翻译遗传学研究的队列为解决传统队列研究无法轻松解决的具有挑战性的翻译研究问题打开了丰富的资源：(1)神经疾病的不可预见的共病，(2)市场上药物，特别是新的免疫调节生物制剂的未知神经并发症。这些未来的研究并不局限于多发性硬化症领域，并创造了未来开展独立调查的领域。

DESCRIPTION (provided by applicant): Candidate: I am a board-certified neurologist at the Brigham and Women's Hospital (BWH) and instructor at the Harvard Medical School (HMS). My research interest lies in the translation of discoveries in human genetics to clinical application n multiple sclerosis (MS) and related neuroimmunological and neurodegerative disorders. Leveraging my PhD thesis work in basic cellular and molecular neuroscience and ongoing post- doctoral training in statistical genetics and translational genomics, I will gain proficiency in th following new research areas through mentored project and structured educational activities: (1) lead a team of researchers and direct patient-oriented research; (2) design and implement studies that incorporate innovative biomarkers and neuroimaging outcomes of neuroinflammation and neurodegeneration; (3) devise clinically relevant applications of genetic information such as a genetic risk score for biomarker selection, disease prediction, and risk stratification; (4) pursue translational genetics research using Electronic Medical Record (EMR)-derived data and bioinformatics tools. These new translational research skills will enable me to achieve the career goal of making the transition to an independent investigator. Environment: I have assembled a multidisciplinary team of mentor (Dr. Philip De Jager, an expert in incorporating human genomics techniques into the study of complex neurologic disorders) and co-mentors (Dr. Issac Kohane, an expert in bioinformatics and predictive modeling; Dr. Daniel Reich, an expert in MS neuroimaging), consultants and senior faculty members with complementary expertise who will guide my research and promote my career development. This multi-layered mentor structure is embedded in a highly collaborative environment of unparalleled intellectual caliber that is part of Harvard-affiliated institutions: BWH Department o Neurology, BWH Institute of Neurosciences, Broad Institute, and HMS. Dr. Martin Samuels (Chairman of BWH Department of Neurology) and Dr. David Silbersweig (Chairman of BWH Institute of Neurosciences) are both supportive of my career plan. Research: An important challenge facing clinical care in multiple sclerosis (MS) is the lack of robust predictive tools to guide individualized risk stratification for subjects at risk of developing MS. The overall goal of the study is to test the efficacy of an algorithm that integrates existing genetic and environmental data into a single, individual estimate of the risk of developing MS. We hypothesize that such an algorithm can provide MS risk stratification for asymptomatic subjects at risk of MS, such as family members or patients with Radiologically Isolated Syndrome (RIS: asymptomatic individuals with incidental findings of MS-like lesions on brain magnetic resonance imaging, MRI). First, we will calculate our MS genetic and environmental risk score (GERSMS) in a cohort of 500 neurologically asymptomatic first-degree relatives of MS patients and assess whether the 100 subjects with the highest GERSMS exhibit an MS-associated peripheral blood biomarker profile when compared to the 100 subjects with the lowest GERSMS. Second, we will assess the efficacy of GERSMS in predicting the presence of MRI-defined MS lesions and other MS-related MRI outcomes in neurologically asymptomatic first- degree relatives and assess whether the 50 subjects with the highest GERSMS have more MS-like lesions, smaller brain volume or greater diffusion tensor imaging (DTI) changes that suggest loss of white matter tract integrity when compared to the 50 subjects with the lowest GERSMS. Finally, we will assess the efficacy of GERSMS in predicting conversion to clinical MS in RIS patients identified from existing EMR-derived data. Innovation: This proposal is innovative since it applies a novel approach to address an understudied question: how do we identify asymptomatic individuals who are at the highest risk of developing multiple sclerosis? The proposed study leverages a recently published, robust analytic method that is further enhanced by the most up-to-date genetic information and validated epidemiological data to produce a novel single estimate of risk for MS. We will test the method in two unique sample collections of asymptomatic subjects: (a) asymptomatic first-degree relatives of MS patients, (b) RIS patients identified from one of the largest EMR systems Specifically, we will assess the efficacy of this method in the selection of cutting-edge blood biomarkers and sensitive neuroimaging measures. If validated, our approach has the potential to make the study of asymptomatic subjects feasible by identifying the subset of subjects at the highest risk of transitioning from health to MS, thus opening up a whole new area of investigation in MS that could ultimately shed light on the design of strategies to prevent the onset of MS. Finally, the utilization of an EMR-derived virtual cohort for translational genetics research opens a rich resource for tackling challenging translational research questions that cannot be easily addressed by traditional cohort studies: (1) unforeseen co-morbidities of neurological disorders, (2) unrecognized neurological complications of medications on the market, particularly new immune modulating biologic agents. These future studies are not restricted to the field of MS and create future areas to develop independent investigation.