背景:确定与强大的HIV-1疫苗诱导的免疫反应相关的基线宿主决定因子可能有助于HIV-1疫苗的开发。我们旨在评估在9个不同的1-2期HIV-1疫苗临床试验(26个疫苗方案,在非洲和美洲进行)作为高HIV-1疫苗应答者。
方法:这是对个体参与者数据的荟萃分析,根据参与者水平选择的研究(与研究水平总结)HIV-1疫苗试验网络内的数据可用性。我们评估了25个基线特征的表现(人口统计,安全血液学测量,生命体征,分析背景测量),并估计了每个特征在将831名参与者分类为高(定义为在阳性应答者中的前25个百分位数内或高于分析定量上限)与非高应答者中的相对重要性。免疫应答结果包括最后一次给药后两周评估的HIV-1特异性血清IgG结合抗体和Env特异性CD4+T细胞应答。全部在中央HVTN实验室测量。考虑了三种基于SuperLearner集成机器学习的可变重要性方法。
结果:总体而言,30.1%,50.5%,36.2%,13.9%的参与者被归类为gp120IgG的高反应者,gp140IgG,gp41IgG,和Env特异性CD4+T细胞疫苗诱导的反应,分别。当包括所有基线特征时,对于结合抗体应答的高应答者状态的分类,实现了中等性能,gp120IgG的ROC曲线下交叉验证面积(CV-AUC)为0.72(95%CI:0.68,0.76),0.73(0.69,0.76)gp140IgG,gp41IgG为0.67(95%CI:0.63,0.72)。相比之下,收集所有基线特征后,预测高Env特异性CD4+T细胞应答的几率改善不大[CV-AUC:0.53(0.48,0.58)].虽然估计的变量重要性模式在三种方法中有所不同,出生时分配的女性性别,较低的高度,使用方法1,在多个免疫应答结果中,较高的白细胞总数是高应答者状态的重要预测因子。在这三个基准变量中,在预测疫苗诱导的gp41和gp140高应答者状态的所有三种方法中,白细胞总数排名很高。
结论:应进一步研究确定的特征,以寻求改善疫苗反应的干预策略,并可能在分析免疫反应数据时进行调整,以提高统计能力。
背景:国家过敏和传染病研究所(UM1AI068635至YH,UM1AI068614至GDT,UM1AI068618至MJM,和UM1AI069511到MCK),杜克CFARP30AI064518到GDT,和国家牙科和颅面研究所(R01DE027245至JJK)。这项工作也得到了比尔和梅琳达·盖茨基金会的支持。内容完全由作者负责,不一定代表任何资金来源的官方观点。
BACKGROUND: The identification of baseline host determinants that associate with robust HIV-1 vaccine-induced immune responses could aid HIV-1 vaccine development. We aimed to assess both the collective and relative performance of baseline characteristics in classifying individual participants in nine different Phase 1-2 HIV-1 vaccine clinical trials (26 vaccine regimens, conducted in Africa and in the Americas) as High HIV-1 vaccine responders.
METHODS: This was a meta-analysis of individual participant data, with studies chosen based on participant-level (vs. study-level summary) data availability within the HIV-1 Vaccine Trials Network. We assessed the performance of 25 baseline characteristics (demographics, safety haematological measurements, vital signs, assay background measurements) and estimated the relative importance of each characteristic in classifying 831 participants as High (defined as within the top 25th percentile among positive responders or above the assay upper limit of quantification) versus Non-High responders. Immune response outcomes included HIV-1-specific serum IgG binding antibodies and Env-specific CD4+ T-cell responses assessed two weeks post-last dose, all measured at central HVTN laboratories. Three variable importance approaches based on SuperLearner ensemble machine learning were considered.
RESULTS: Overall, 30.1%, 50.5%, 36.2%, and 13.9% of participants were categorized as High responders for gp120 IgG, gp140 IgG, gp41 IgG, and Env-specific CD4+ T-cell vaccine-induced responses, respectively. When including all baseline characteristics, moderate performance was achieved for the classification of High responder status for the binding antibody responses, with cross-validated areas under the ROC curve (CV-AUC) of 0.72 (95% CI: 0.68, 0.76) for gp120 IgG, 0.73 (0.69, 0.76) for gp140 IgG, and 0.67 (95% CI: 0.63, 0.72) for gp41 IgG. In contrast, the collection of all baseline characteristics yielded little improvement over chance for predicting High Env-specific CD4+ T-cell responses [CV-AUC: 0.53 (0.48, 0.58)]. While estimated variable importance patterns differed across the three approaches, female sex assigned at birth, lower height, and higher total white blood cell count emerged as significant predictors of High responder status across multiple immune response outcomes using Approach 1. Of these three baseline variables, total white blood cell count ranked highly across all three approaches for predicting vaccine-induced gp41 and gp140 High responder status.
CONCLUSIONS: The identified features should be studied further in pursuit of intervention strategies to improve vaccine responses and may be adjusted for in analyses of immune response data to enhance statistical power.
BACKGROUND: National Institute of Allergy and Infectious Diseases (UM1AI068635 to YH, UM1AI068614 to GDT, UM1AI068618 to MJM, and UM1 AI069511 to MCK), the Duke CFAR P30 AI064518 to GDT, and National Institute of Dental and Craniofacial Research (R01DE027245 to JJK). This work was also supported by the Bill and Melinda Gates Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding sources.