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

项目摘要/摘要 胶质瘤是一组具有不同临床结果的异质性肿瘤。而异柠檬酸脱氢酶 胶质瘤的突变(IDH-MT)和其他遗传特征改变了诊断和预后的格局 对于较低级别的胶质瘤，这些相同的标记物不能解释治疗反应和存活率的异质性。 胶质母细胞瘤(GBM)。个体免疫因素可能在胶质瘤的预后中发挥作用。为了解决这个问题，我们有 开创性的免疫甲基组学，一种定义和量化扩展免疫细胞库的方法 人群(例如，幼稚和记忆的CD4、CD8 T细胞和B细胞、NK细胞、单核细胞、中性粒细胞)和 来自新鲜或冷冻的外周全血的异常髓系来源的抑制细胞(MDSCs)。 免疫甲基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但尚未解决的临床问题 三个目标在GBM患者管理中存在的问题在目标1中，我们建议开发一种算法，用于 根据预期生存期对GBM患者进行分层。在历史上，使用的是单独的衡量标准，即年龄， IDH-MT(占GBM的10%)和DNA甲基转移酶(MGMT)甲基化。这一单变量中的重要差距 方法包括缺乏对皮质类固醇免疫抑制的评估和MDSCs的影响。 我们将通过创建集成的IDH-Wildtype(IDH-WT)GBM生存模型来解决这些差距 免疫档案数据。在目标2中，我们将根据IDH状态和IDH状态创建基于血液的胶质瘤亚型分层 年级。目前缺乏术前确定肿瘤IDH状态的方法，限制了新辅助治疗和 在临床试验设计中日益重要的术中治疗策略。AIM 2解决了这一问题 未得到满足的需求。在目标3中，我们将创建用于免疫治疗和辐射反应的预测性血液生物标记物。 迫切需要非侵入性预测指标来帮助区分早期真实进展的放射学证据。 (约30%的GBM患者)来自假性进展(PSP；~20-30%)。关于真实进展与PSP的不确定性 仅根据磁共振成像(MRI)的结果，患者将受到风险和费用的影响 重新运营，以便进一步管理。我们和其他人最近的研究表明，PSP和GBM 患者的存活率受到患者免疫因素的影响，特别是 在外周血液中积聚。Aim 3A通过创建一种基于血液的生物标记物来满足这一未得到满足的需求 鉴别GBM患者化疗/放疗后的PSP和真实进展。没有标准化的 评估系统免疫系统对免疫疗法反应的综合方法。 在Aim 3B中，我们在代表两种不同免疫疗法的四个临床试验中测试了我们的方法 (抗PD1/PD-L1和CART过继细胞治疗)。综上所述，该项目将继续确定小说 通过使用最新基因组进行仔细研究来预测胶质瘤的预后和预测因素 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.