Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Blood. 2013 Jan 31;121(5):801-11. doi: 10.1182/blood-2012-06-436295. Epub 2012 Dec 11.
The development of immunomonitoring models to determine HIV-1 vaccine efficacy is a major challenge. Studies suggest that HIV-1–specific CD8 T cells play a critical role in subjects achieving spontaneous viral control (HIV-1 controllers) and that they will be important in immune interventions. However, no single CD8 T-cell function is uniquely associated with controller status and the heterogeneity of responses targeting different epitopes further complicates the discovery of determinants of protective immunity. In the present study, we describe immunomonitoring models integrating multiple functions of epitope-specific CD8 T cells that distinguish controllers from subjects with treated or untreated progressive infection. Models integrating higher numbers of variables and trained with the least absolute shrinkage and selection operator (LASSO) variant of logistic regression and 10-fold cross-validation produce “diagnostic tests” that display an excellent capacity to delineate subject categories. The test accuracy reaches 75% area under the receiving operating characteristic curve in cohorts matched for prevalence of protective alleles. Linear mixed-effects model analyses show that the proliferative capacity, cytokine production, and kinetics of cytokine secretion are associated with HIV-1 control. Although proliferative capacity is the strongest single discriminant, integrated modeling of different dimensions of data leverages individual associations. This strategy may have important applications in predictive model development and immune monitoring of HIV-1 vaccine trials.
Immune monitoring models integrating multiple functions of HIV-1-specific CD8 T cells distinguish controllers from subjects with progressive HIV-1 infection. This strategy may have important applications in predictive model development and immune monitoring of HIV-1 vaccine trials.
开发用于确定 HIV-1 疫苗疗效的免疫监测模型是一个主要挑战。研究表明,HIV-1 特异性 CD8 T 细胞在实现自发性病毒控制的受试者(HIV-1 控制器)中发挥关键作用,并且它们在免疫干预中很重要。然而,没有单一的 CD8 T 细胞功能与控制器状态具有独特的相关性,针对不同表位的反应的异质性进一步使保护性免疫决定因素的发现复杂化。在本研究中,我们描述了整合针对不同表位的 CD8 T 细胞的多种功能的免疫监测模型,这些模型可将控制器与经治疗或未经治疗的进展性感染的受试者区分开。整合更多变量的模型,并使用逻辑回归的最小绝对收缩和选择算子(LASSO)变体以及 10 倍交叉验证进行训练,可产生“诊断测试”,其在与保护性等位基因流行率匹配的队列中具有出色的区分受试者类别的能力。测试准确性在匹配保护性等位基因流行率的队列中达到了 75%的接收者操作特征曲线下面积。线性混合效应模型分析表明,增殖能力,细胞因子产生和细胞因子分泌的动力学与 HIV-1 控制有关。尽管增殖能力是最强的单一判别因素,但不同数据维度的综合建模利用了个体关联。该策略可能在 HIV-1 疫苗试验的预测模型开发和免疫监测中具有重要应用。
整合 HIV-1 特异性 CD8 T 细胞的多种功能的免疫监测模型可将控制器与进展性 HIV-1 感染的受试者区分开。该策略可能在 HIV-1 疫苗试验的预测模型开发和免疫监测中具有重要应用。