Core B: Bioinformatics/Biostatistics Core

项目总结/摘要 这个P01项目的目标是确定生物标志物，使我们能够预测可能的滞后时间 在停止抗逆转录病毒治疗（ART）后， 艾滋病毒感染者。在我们的研究中，大量的病毒学和免疫学参数将被 在约125名接受分析治疗的特征明确的HIV感染者队列中进行测量 中断（ATI），以确定这些测量是否允许我们可靠地预测病毒的动力学 停止抗逆转录病毒疗法后反弹。包括下一代测序在内的大量高维数据 数据（转录组和microRNA谱）和CyTOF数据将在我们提出的实验中生成。 生物信息学和生物统计学核心将在编译，策划，分析和 传播与此P01应用程序相关的所有三个项目中生成的数据。最大化我们 识别有意义的签名预测时间，直到病毒反弹的机会，我们将实施几个 统计方法和集成学习方法（例如，梯度推进，随机森林）来开发 理论，并且我们将依赖于已建立的分类器性能评估程序（例如，交叉验证， 递归特征消除和特征重要性度量）来严格地确定预测潜力 生物标志物的数量。 在我们的生物信息学和生物统计学核心项目的目标1中，我们将评估单个假定的 在项目2中研究了血细胞相关生物标志物，以预测ATI后病毒反弹的时间。 测量包括CD 4 + T细胞中有复制能力的前病毒基因组的频率和全球的 宿主细胞转录组的表征。在目标2中，我们将评估单个假定细胞的能力- 在项目3中研究了游离血浆和CSF衍生的生物标志物，以预测ATI后病毒反弹的时间。 测量包括循环microRNA谱、细胞外囊泡表型和多重细胞因子 和抗体表征。最后，在目标3中，我们将对所有3种生物标志物进行综合分析 项目（包括项目1中生成的CyTOF免疫表型数据），以评估其相对性能 并确定预测因素之间的潜在协同作用。封闭式学习方法是发现 预测特征的复杂组合。它们还提供了一个评估预测性 候选生物标志物单独或与其他生物标志物组合的重要性。 生物信息学和生物统计学核心将在实现我们的P01目标和 推进艾滋病毒治疗议程，将丰富多样的高维数据转化为强大的 抗逆转录病毒治疗中断后艾滋病毒反弹的预测因素。

PROJECT SUMMARY/ABSTRACT The goal of this P01 project is to identify biomarkers that will enable us to predict the likely duration of the lag phase or “remission” period prior to HIV rebound following discontinuation of antiretroviral therapy (ART) in HIV-infected individuals. In our study, a large number of virologic and immunologic parameters will be measured in a cohort of ~125 well-characterized HIV-infected individuals undergoing analytical treatment interruption (ATI), to determine if any of these measurements allow us to reliably predict the kinetics of viral rebound post-ART cessation. A large amount of high-dimensional data including next-generation sequencing data (transcriptomes and microRNA profiles) and CyTOF data will be generated in our proposed experiments. The Bioinformatics and Biostatistics Core will play a leading role in compiling, curating, analyzing and disseminating data generated in all three projects associated with this P01 application. To maximize our chances of identifying meaningful signatures predicting time until viral rebound, we will implement several statistical approaches and ensemble learning methods (e.g., gradient boosting, random forests) to develop theories, and we will rely on established classifier performance evaluation procedures (e.g. cross validation, recursive feature elimination, and feature importance measures) to rigorously determine the predictive potential of biomarkers under consideration. In Aim 1 of our Bioinformatics and Biostatistics Core project, we will evaluate the capacity of individual putative blood cell-associated biomarkers studied in Project 2 to predict time until viral rebound following ATI. Measurements include the frequency of replication-competent proviral genomes in CD4+ T cells and global characterization of the host cell transcriptome. In Aim 2, we will evaluate the capacity of individual putative cell- free plasma- and CSF-derived biomarkers studied in Project 3 to predict time until viral rebound following ATI. Measurements include circulating microRNA profile, extracellular vesicle phenotype, and multiplex cytokine and antibody characterization. Lastly, in Aim 3, we will perform a combined analysis of biomarkers across all 3 projects (including CyTOF immunophenotypic data generated in Project 1) to assess their relative performance and to identify potential synergies between predictors. Ensemble learning methods are ideal for discovering complex combinations of predictive features. They also provide a framework for evaluating the predictive importance of candidate biomarkers both individually and in combination with other biomarkers. The Bioinformatics and Biostatistics Core will play a central role in achieving our P01 objectives and in advancing the HIV cure agenda, transforming copious and diverse, high-dimensional data into robust predictors of HIV rebound following ART interruption.