Defining Immunity to Placental Malaria using a Multi-assay Predictive Model

描述(申请人提供)：由恶性疟原虫引起的疟疾在孕妇中尤其严重，因为受感染的红细胞(IE)表达VAR2CSA，这是一种与滋养层细胞上的软骨素硫酸盐A(CSA)结合的配体，导致IE在胎盘积聚。因此，会发生炎症和病理，增加自然流产、早产和低出生体重婴儿的风险。幸运的是，针对VAR2CSA的抗体显著改善了妊娠结局。基于VAR2CSA的疫苗正在设计中，目标是诱导高水平的保护性抗体，一种候选疫苗的安全性测试将于今年开始在人类身上进行。然而，目前还没有确定女性是否对胎盘性疟疾(PM)免疫的方法。提供一种成本效益高的诊断方法来确定妇女的免疫水平，将使1)医生能够提供更好的产前护理，2)疫苗开发人员评估妇女接种疫苗前后的免疫水平，3)政府官员为孕妇制定明智的卫生政策，特别是在实施干预战略导致疟疾形势不断变化的情况下。因此，我们的目标是使用血清学和功能分析相结合的方法来表征介导IE从胎盘清除的抗体，然后使用这些数据来建立统计模型，预测女性是否有足够的免疫力来1)预防胎盘病理和2)预防PM对对PM有不同免疫力的喀麦隆怀孕妇女的档案样本将进行筛查，以测量VAR2CSA抗体的不同特征，包括特异性、亲和力和功能。这部分研究是直截了当的，因为我们的实验室已经对分析进行了优化。此外，我们试图开发两种新的功能分析方法来衡量抗体的能力：(A)阻断VAR2CSA与CsA的相互作用；(B)防止暴露于IE的胎盘滋养层细胞的激活和失调，这是导致胎盘病理的一个机制。血清学和功能检测的结果将有助于鉴定保护性抗体的特性。然后，将该数据用于构建多分析统计预测模型。将使用两种统计方法。首先，将基于抗体特征和其他与免疫相关的关键变量(如年龄、妊娠、红细胞压积、孕期)的组合建立多变量Logistic模型。这种方法将产生两个“用户友好的”简单风险指数(公式)，基于最少的分析和变量，为预防病理和感染提供最佳预测。其次，将采用递归划分的方法，利用这些数据建立分类和回归树(CART)和随机森林(RF)。由此产生的二进制分类树将很容易解释，并允许纳入更复杂的免疫途径。一旦模型被开发出来，从怀孕期间每月收集的档案样本中获得的数据将被用来确定在怀孕期间模型在疟疾传播高和低的情况下具有最佳预测价值的时间。

DESCRIPTION (provided by applicant): Malaria, caused by Plasmodium falciparum, is especially severe in pregnant women because infected erythrocytes (IE) express VAR2CSA, a ligand that binds to chondroitin sulfate A (CSA) on trophoblasts, causing IE to accumulate in the placenta. As a result, inflammation and pathology occur, increasing the risk of spontaneous abortions, premature deliveries, and low birth weight babies. Fortunately, antibodies (Ab) against VAR2CSA significantly improve pregnancy outcomes. VAR2CSA-based vaccines are being designed with the goal of inducing high levels of protective Ab, and safety testing of one candidate vaccine will begin in humans this year. However, there is no method for determining if a woman is immune to placental malaria (PM). The availability of a cost-effective diagnostic approach that identifies a woman's level of immunity will allow 1) doctors to provide better prenatal care, 2) vaccine developers to assess the level of immunity women have before and after vaccination, and 3) government officials to make intelligent health policies for pregnant women, especially with the changing malaria landscape due to implementation of intervention strategies. Therefore, our goal is to use a combination of serological and functional assays to characterize Ab that mediate clearance of IE from the placenta and then use the data to develop statistical models that predict whether a woman has sufficient immunity to 1) prevent placental pathology and 2) prevent PM. Archival samples from pregnant Cameroonian women with different levels of immunity to PM will be screened in assays that measure different characteristics of Ab to VAR2CSA, including specificity, avidity, and function. This part of the study is straight-forward as the assays are already optimized in our laboratory. In addition, we seek to develop two new functional assays that measure the ability of Ab to (a) block the interaction of VAR2CSA with CSA and (b) prevent activation and dysregulation of placental trophoblasts exposed to IE, a mechanism that contributes to placental pathology. Results from the serological and functional assays will help identify characteristics of protective Ab. Then, th data will be used to build multi-assay statistical prediction models. Two statistical approaches will be used. First, multivariable logistic models will be built based on a combination of Ab characteristics and other key variables related to immunity (e.g., age, gravidity, hematocrit, length of gestation). This approach will result in two "user friendly" simple risk indexes (formulas) based on the least number of assays and variables that provide the optimal prediction for prevention of pathology and infection. Second, a recursive partitioning approach will be taken to develop classification and regression trees (CART) and random forests (RF) using the data. The resulting binary classification trees will be easy to interpret and allow more complex immunological pathways to be incorporated. Once the models are developed, data from archival samples collected monthly throughout pregnancy will be used to determine when during pregnancy the models have the best predictive value in high and low malaria transmission settings.