Project 1: DNA Methylation-Based Blood Biomarkers for Prognosis, Molecular Stratification and Treatment Response in Glioma Patients

项目总结/摘要 神经胶质瘤是一组异质性肿瘤，具有不同的临床结局。而异柠檬酸脱氢酶 IDH-MT突变和其他遗传特征改变了胶质瘤的诊断和预后 对于低级别胶质瘤，这些相同的标志物不能解释治疗反应和生存的异质性， 胶质母细胞瘤（GBM）。个体免疫因素可能在胶质瘤预后中发挥作用。为了解决这个问题，我们必须 开创性的免疫甲基组学，一种定义和定量免疫细胞扩展库的方法， 群体（例如，幼稚和记忆性CD 4、CD 8 T细胞和B细胞、NK细胞、单核细胞、嗜中性粒细胞）和 来自新鲜或冷冻外周全血的异常骨髓源性抑制细胞（MDSC）。 免疫甲基化组学是一种基于免疫细胞中DNA甲基化模式的强大方法学 基因组在这次更新中，我们将使用免疫甲基组学来解决高优先级和尚未解决的临床问题。 使用三个目标解决GBM患者管理中的问题。在目标1中，我们提出了一种算法， 根据预期的存活率对GBM患者进行分层。从历史上看，使用的是个别措施，即，年龄， IDH-MT（<10%的GBM）和DNA甲基转移酶（MGMT）甲基化。这一单变量的重要差距 方法包括缺乏对皮质类固醇激素免疫抑制和MDSC影响的评估。 我们将通过建立整合的IDH-WT GBM生存模型来解决这些差距， 免疫概况数据。在目标2中，我们将根据IDH状态建立基于血液的胶质瘤亚组分层， 年级目前缺乏在手术前鉴定肿瘤IDH状态的方法，限制了新辅助治疗和 在临床试验设计中越来越重要的术中治疗策略。目标2解决了这个问题 未满足的需求在目标3中，我们将创建对免疫治疗和放射治疗反应的预测性血液生物标志物。 迫切需要非侵入性预测因子来帮助区分早期真正进展的放射学证据 （约30%的GBM患者）假性进展（PsP;约20-30%）。与PsP相比，真实进展的不确定性 仅基于磁共振成像（MRI）导致患者遭受风险和费用 再次手术进行进一步治疗。我们和其他人最近的研究表明，PsP和GBM 存活率受患者免疫因素的影响，特别是MDSC的浓度， 在外周血中积累。目标3A通过创建基于血液的生物标志物来解决这一未满足的需求， 区分PsP与化疗/放疗后GBM患者的真实进展。没有标准化的 评估全身免疫系统对免疫疗法应答的影响的综合方法。 在Aim 3B中，我们在代表两种不同免疫治疗方式的四项临床试验中测试了我们的方法 （抗PD 1/PD-L1和CART过继细胞疗法）。总之，该项目将继续确定新的 神经胶质瘤的预后和预测因素，通过使用最新的基因组 技术和创新的生物信息学技术与注释良好的患者临床数据相结合。

Project Summary/Abstract Gliomas are a heterogeneous group of tumors with diverse clinical outcomes. While isocitrate dehydrogenase mutation (IDH-MT) and other genetic features of glioma have changed the landscape of diagnosis and prognosis for lower-grade glioma, these same markers do not explain heterogeneity in treatment response and survival for glioblastoma (GBM). Individual immune factors may play a role in glioma outcomes. To address this, we have pioneered immunomethylomics, an approach that defines and quantitates an extended library of immune cell populations (e.g., naïve and memory CD4, CD8 T-cells, and B cells, NK cells, monocytes, neutrophils) and aberrant myeloid-derived suppressor cells (MDSCs) from fresh or frozen peripheral whole blood. Immunomethylomics is a powerful methodology based on DNA methylation patterns in the immune cell genomes. In this renewal, we will use immunomethylomics to address high-priority and yet unresolved clinical problems in GBM patient management using three aims. In Aim 1, we propose to develop an algorithm for stratifying GBM patients according to expected survival. Historically, individual measures were used, i.e., age, IDH-MT (<10% of GBMs), and DNA methyltransferase (MGMT) methylation. Important gaps in this univariate approach include the lack of assessment of corticoid steroid immunosuppression and the influence of MDSCs. We will address these gaps by creating integrated IDH-Wildtype (IDH-WT) GBM survival models with longitudinal immune profile data. In Aim 2, we will create a blood-based stratification of glioma subgroups by IDH status and grade. The current lack of methods to identify tumor IDH status before surgery limits neoadjuvant and intraoperative therapeutic strategies that are increasingly important in clinical trial design. Aim 2 addresses this unmet need. In Aim 3, we will create predictive blood biomarkers for response to immunotherapy and radiation. Non-invasive predictors are urgently needed to help distinguish radiologic evidence of early true progression (~30% of GBM patients) from pseudoprogression (PsP; ~20-30%). Uncertainty about true progression vs. PsP based on magnetic resonance imaging (MRI) alone results in patients being subjected to the risk and expense of re-operation for further management. Our and others’ recent studies demonstrate that both PsP and GBM survival are influenced by patient immunologic factors, specifically, the concentrations of MDSCs that accumulate in peripheral blood. Aim 3A addresses this unmet need by creating a blood-based biomarker to distinguish PsP from true progression in GBM patients after chemo/radiation. There are no standardized comprehensive methods to assess the effect of the systemic immune system on response to immunotherapies. In Aim 3B we test our methodology in four clinical trials representing two different immunotherapy modalities (anti-PD1/PD-L1 and CART adoptive cell therapy). In summary, this project will continue to identify novel prognostic and predictive factors for glioma through carefully conducted studies using the latest genomic technologies and innovative bioinformatics techniques in combination with well-annotated patient clinical data.