LEAPS-MPS: Statistical Learning on Next Generation Sequencing of T/B Cell Receptor Repertoire Data

适应性免疫系统的两个关键组成部分是所谓的T细胞和B细胞，它们的功能是识别和应对“身体入侵者”，例如冠状病毒或癌细胞。在每个识别和反应过程之后，B细胞和T细胞在细胞表面留下持久的遗产，称为B/T细胞受体，或BCR/TCR，一旦再次检测到病原体，人体利用这种受体快速而强烈地做出反应。BCR/TCR谱系在一个人的一生中不断地形成，以应对疾病和感染，也可以作为一个人当前免疫概况的指纹。最近，新技术已经能够从单一的血液或组织样本中分析BCR/TCR谱系。然而，由于免疫谱系数据的复杂性，需要新的统计机器学习方法和计算工具来分析免疫谱系数据。这个项目将产生统计分析方法，这不仅将帮助我们了解免疫系统对疾病或感染的反应，还将帮助我们推进精准医学和免疫疗法，即为个人开发和定制治疗方法，以获得更好的疗效。这项研究旨在吸引数学和统计学的本科生和研究生，从而使他们接触到科学发现的兴奋，并为他们在学术界和工业界的高级学位课程和职业生涯中的成功做好准备。通过专注于招收和培训来自代表性不足群体的学生，PI将有助于科学工作队伍的多样化。T细胞和B细胞是适应性免疫系统的关键组成部分，已被证明介导抗体液免疫和对呼吸道冠状病毒的免疫反应。T和B细胞受体(TCR和BCR)的下一代测序可以作为分析TCR/BCR谱系的平台。由于曲谱数据的复杂特性(异质、高维、呈现三层信息：基因用途、丰度、克隆网络)，现有文献中的统计模型和推理工具非常有限。目前的分析工具缺乏识别与感兴趣的结果相关联的曲目签名或综合多层信息的能力。这个项目的主要目标是开发先进的统计方法和机器学习方法，以1)使用曲目的基因使用层确定与结果相关的基因和基因家族；2)使用曲目的网络层确定与结果相关的网络属性的优先顺序；3)整合多层曲目，以评估不同的曲目概况对结果的联合影响。特别是，将开发一个贝叶斯分层模型来区分基因用法，将开发一个置换辅助群套索来区分网络分析的局部和全局属性，并将使用各种核方法来模拟曲目特征和结果之间的复杂关系。我们将对一个公众可用的Covid数据库进行模拟研究和实际数据分析，以演示这些方法。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Two crucial components of the adaptive immune system are the so-called T cells and B cells, whose function is identifying and responding to “body invaders”, such as for example coronavirus or cancer cells. Following each of the identify-and-respond processes, B cells and T cells leave a lifetime lasting legacy on cell surfaces known as B/T cell receptors, or BCRs/TCRs, which the body uses to respond quickly and strongly once the pathogen is detected again. BCR/TCR repertoire, which are continually shaped throughout the lifetime of an individual in response to diseases and infections, can also serve as a fingerprint of one’s current immunological profile. Recently, new technologies have enabled the profiling of BCR/TCR repertoire from a single sample of blood or tissue. However, due to the complex nature of the repertoire data, there is a need for novel statistical machine learning approaches and computational tools for immune repertoire data analysis. This project will produce statistical analysis methods, which will not only help us understand how the immune system is responding to disease or infection, but also help us advance precision medicine and immunotherapy, where treatments are developed and tailored to an individual for greater efficacy. This research has been designed to engage undergraduate and graduate students of mathematics and statistics, thus exposing them to the excitement of scientific discovery and preparing them for success in advanced degree programs and careers in academia and industry. By focusing on recruiting and training students from underrepresented groups, the PI will contribute to the diversification of the scientific workforce.T cells and B cells represent a crucial component of the adaptive immune system and have been shown to mediate anti-humoral immunity and mediate immune response to respiratory coronavirus. Next generation sequencing of the T and B cell receptors (TCRs and BCRs) can be used as a platform to profile the TCR/BCR repertoire. Due to the complex characteristics of repertoire data (heterogeneous, high-dimensional, presents three layers of information: gene usage, abundance, clone network), there are very limited statistical models and inference tools existing in the literature. The current analyses tools lack the ability to identify the repertoire signatures that are associated with the outcome of interest or to integrate multiple layers of information. The main goal of this project is to develop advanced statistical methods and machine learning methods to 1) identify the gene and gene families associated with the outcome using the gene usage layer of repertoire; 2) prioritize the network properties associated with outcome using the network layer of repertoire; 3) integrate the multiple layers of repertoire to evaluate the joint effect of heterogenous repertoire profile on the outcome. Particularly, a Bayesian hierarchical model will be developed to differential gene usages, a permutation-assisted group lasso will be developed to prioritize both local and global properties for network analysis, and various kernel methods will be utilized to model the complex relationship between repertoire features and outcome. Simulation studies and real data analysis on a public-available covid database will be performed to demonstrate the methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.