An Integrated Multilevel Modeling Framework for Repertoire-Based Diagnostics

用于基于指令的诊断的集成多级建模框架

• 批准号: 10165490
• 负责人: Ramy Arnaout
• 金额: $ 52.89万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10165490
• 关键词: Address; Affect; Aging; Amino Acid Motifs; Amino Acids; Antibodies; Autoimmune Diseases; Autoimmunity; B-Lymphocytes; Base Sequence; Big Data; Binding; Biophysics; Characteristics; Charge; Classification; Clinical; Code; Collection; Complex; Computer Models; Data Set; Dependence; Diagnosis; Diagnostic; Diagnostic tests; Disease; Ensure; Entropy; Fostering; Gene Frequency; Genes; Goals; Health; Human; Immune; Immunology; Individual; Infection; Influenza vaccination; Intuition; Learning; Letters; Machine Learning; Malignant Neoplasms; Mathematics; Measurement; Measures; Medicine; Methods; Mission; Modeling; Outcome; Pattern; Performance; Persons; Physics; Play; Population Heterogeneity; Privatization; Property; Public Health; Reading; Reporting; Research; Role; Sample Size; Sampling; Sampling Errors; Signs and Symptoms; Speed; Statistical Study; System; T-Cell Receptor; T-Cell Receptor Genes; T-Lymphocyte; Testing; United States National Institutes of Health; Vaccination; Virus Diseases; Work; base; biophysical properties; clinical diagnostics; computerized tools; diagnostic accuracy; human disease; immunological diversity; improved; information model; innovation; machine learning method; multidisciplinary; multilevel analysis; novel; novel strategies; tool

Immune-repertoire sequence, which consists of an individual's millions of unique antibody and T-cell receptor (TCR) genes, encodes a dynamic and highly personalized record of an individual's state of health. Our long- term goal is to develop the computational models and tools necessary to read this record, to one day be able diagnose diverse infections, autoimmune diseases, cancers, and other conditions directly from repertoire se- quence. The key problem is how to find patterns of specific diseases in repertoire sequence, when repertoires are so complex. Our hypothesis is that a combination of bottom-up (sequence-level) and top-down (systems- level) modeling can reveal these patterns, by encoding repertoires as simple but highly informative models that can be used to build highly sensitive and specific disease classifiers. In preliminary studies, we introduced two new modeling approaches for this purpose: (i) statistical biophysics (bottom-up) and (ii) functional diversity (top-down), and showed their ability to elucidate patterns related to vaccination status (97% accuracy), viral infection, and aging. Building on these studies, we will test our hypothesis through two specific aims: (1) We will develop models and classifiers based on the bottom-up approach, statistical biophysics; and (2) we will de- velop the top-down approach, functional diversity, to improve these classifiers. To achieve these aims, we will use our extensive collection of public immune-repertoire datasets, beginning with 391 antibody and TCR da- tasets we have characterized previously. Our team has deep and complementary expertise in developing computational tools for finding patterns in immune repertoires (Dr. Arnaout) and in the mathematics that under- lie these tools (Dr. Altschul), with additional advice available as needed regarding machine learning (Dr. AlQuraishi). This proposal is highly innovative for how our two new approaches address previous issues in the field. (i) Statistical biophysics uses a powerful machine-learning method called maximum-entropy modeling (MaxEnt), improving on past work by tailoring MaxEnt to learn patterns encoded in the biophysical properties (e.g. size and charge) of the amino acids that make up antibodies/TCRs; these properties ultimately determine what targets antibodies/TCRs can bind, and therefore which sequences are present in different diseases. (ii) Functional diversity fills a key gap in how immunological diversity has been measured thus far, by factoring in whether different antibodies/TCRs are likely to bind the same target. This proposal is highly significant for (i) developing an efficient, accurate, generative, and interpretable machine-learning method for finding diagnostic patterns in repertoire sequence; (ii) applying a robust mathematical framework to the measurement of immuno- logical diversity; (iii) impacting clinical diagnostics; and (iv) adding a valuable new tool for integrative/big-data medicine. The expected outcome of this proposal is an integrated pair of robust and well validated new tools/models for classifying specific disease exposures directly from repertoire sequence. This proposal in- cludes plans to make these tools widely available, to maximize their positive impact across medicine.

免疫曲目序列，由个人数百万种独特的抗体和T细胞受体组成 (TCR)基因，编码一个人的健康状态的动态和高度个性化的记录。我们的长- 学期目标是开发阅读这一记录所需的计算模型和工具，以便有一天能够 直接诊断各种感染、自身免疫性疾病、癌症和其他疾病- 奎斯。关键问题是如何在剧目序列中找到特定疾病的模式 都是如此复杂。我们的假设是，自下而上(序列级别)和自上而下(系统- 级别)建模可以揭示这些模式，方法是将曲目编码为简单但信息丰富的模型， 可用于构建高度敏感和特定的疾病分类器。在初步研究中，我们引入了 为此采取了两种新的建模方法：(一)统计生物物理学(自下而上)和(二)功能多样性 (自上而下)，并显示了他们阐明与疫苗接种状态相关的模式的能力(97%的准确率)，病毒 感染和衰老。在这些研究的基础上，我们将通过两个具体目标来检验我们的假设：(1)我们 将基于自下而上的方法(统计生物物理学)开发模型和分类器；以及(2)我们将去 采用自上而下的方法，即功能多样性，来改进这些分类器。为了实现这些目标，我们将 使用我们广泛收集的公共免疫谱系数据集，从391抗体和TCR da开始- 我们之前描述过的流苏。我们的团队在开发方面拥有深厚和互补的专业知识 在免疫系统和数学中寻找模式的计算工具(阿纳奥特博士) 利用这些工具(Altschul博士)，并根据需要提供关于机器学习的其他建议(Dr.Altschul) AlQuraishi)。这项建议在我们的两个新方法如何解决 菲尔德。(I)统计生物物理学使用一种名为最大熵建模的强大机器学习方法 (MaxEnt)，通过定制MaxEnt来学习生物物理属性中编码的模式，从而改进了过去的工作 (例如，大小和电荷)组成抗体/TCR的氨基酸；这些特性最终决定 什么靶标抗体/TCR可以结合，因此在不同的疾病中存在哪些序列。(Ii) 功能多样性填补了迄今为止在衡量免疫多样性方面的一个关键空白，因为它考虑了 不同的抗体/TCR是否可能结合相同的靶点。这项建议对(I)具有重要意义 开发一种高效、准确、生成性和可解释的机器学习方法来查找诊断 曲目序列中的模式；(Ii)将稳健的数学框架应用于免疫- 逻辑多样性；(Iii)影响临床诊断；以及(Iv)为综合/大数据增加一个有价值的新工具 医药。这项提议的预期结果是一对强大和经过良好验证的综合新的 用于直接根据曲目序列对特定疾病暴露进行分类的工具/模型。这项建议载于- 包括让这些工具广泛使用的计划，以最大限度地发挥它们在医学领域的积极影响。