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.

免疫库序列，由个体数百万种独特的抗体和T细胞受体组成 (TCR)基因，编码一个动态的和高度个性化的记录个人的健康状况。我们长久以来- 我们的长期目标是开发必要的计算模型和工具来阅读这些记录，以便有一天能够 诊断各种感染，自身免疫性疾病，癌症和其他条件直接从剧目se- 顺序。关键问题是如何在库序列中发现特定疾病的模式， 是如此复杂。我们的假设是，自下而上（序列水平）和自上而下（系统- 水平）建模可以揭示这些模式，通过编码剧目作为简单但高度信息化的模型， 可用于构建高度敏感和特异的疾病分类器。在初步研究中，我们介绍了 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），并显示出他们能够阐明与疫苗接种状态相关的模式（97%的准确率）， 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 将根据自下而上的方法，统计生物物理学，开发模型和分类器;（2）我们将- velop自顶向下的方法，功能多样性，以改善这些分类器。为达致这些目标，我们会 使用我们广泛收集的公共免疫库数据集，从391抗体和TCR da开始， 我们之前描述过的tasets。我们的团队在开发方面拥有深厚的互补专业知识 计算工具，用于寻找免疫系统中的模式（Arnaout博士）和数学中的模式， 这些工具（Altschul博士），以及根据需要提供的关于机器学习的其他建议（Dr. AlQuraishi）。这一建议对于我们的两种新方法如何解决以前的问题具有高度创新性， 领域(i)统计生物物理学使用一种强大的机器学习方法，称为最大熵建模 （MaxEnt），通过定制MaxEnt来学习生物物理特性中编码的模式， (e.g.大小和电荷）;这些性质最终决定了抗体/TCR的氨基酸组成。 抗体/TCR可以结合什么样的靶点，因此在不同的疾病中存在哪些序列。（二） 功能多样性填补了迄今为止如何测量免疫多样性的一个关键空白， 不同的抗体/TCR是否可能结合相同的靶标。这一建议对（一） 开发一种高效、准确、可生成和可解释的机器学习方法， （ii）将一个强大的数学框架应用于免疫功能的测量， 逻辑多样性;（iii）影响临床诊断;以及（iv）为整合/大数据添加有价值的新工具 药这一提议的预期成果是一对综合的、稳健的、经过充分验证的新的 用于直接从库序列对特定疾病暴露进行分类的工具/模型。这项建议- 包括使这些工具广泛可用的计划，以最大限度地发挥其对医学的积极影响。