Core 2: Immune Bioinformatics and Computational Biology Core

项目总结(免疫生物信息学和计算生物学核心) 这项P01提案的总体目标是应用系统的方法来理解 免疫抵抗力可以指导合理设计有效的新型组合免疫疗法 治疗头颈部鳞状细胞癌(HNSCC)患者。为了实现这一目标，我们将使用几个 基于批量和单细胞RNA测序(scRNA-seq)数据的尖端生物信息学策略， 表观基因组分析，外显子组测序，以及临床试验数据的生物统计分析。我们的团队 拥有互补的专业知识，包括HNSCC遗传学和生物学、先天免疫和适应性免疫 反应，免疫生物信息学，单细胞动力学和scRNA-seq分析，癌症表观基因组学 和全基因组调控数据，以及HNSCC临床试验数据分析。免疫的目的 生物信息学和计算生物学(IBCB)的核心是提供针对特定疾病的全面 并为P01研究人员提供创新支持，以进行研究设计、分析、整合和 对广泛的基于组学和临床试验研究的解释。 IBCB核心将通过四个具体目标支持P01的三个项目。首先，我们将推荐 针对免疫生物信息学的研究设计和分析策略。这包括但不包括 仅限于突变负荷分析和新抗原预测、人类白细胞抗原I类等位基因分析和免疫 胞型去卷积。第二个目标是为研究设计和执行各种分析提供专业知识 以及单细胞生物信息学的方法发展。我们将利用最近开发的几种scRNA-seq QC方法、批量校正和归一化方法、归责方法、创新可视化方法、自动细胞类型方法 鉴定和差异表达测试。第三个目标是提供关于实验的咨询 设计和提供表观基因组学数据分析。我们将分析全基因组DNA甲基化和 不同免疫检查点阻断(ICB)治疗方案前后的转录组学数据。目标 四是为临床试验分析、动力分析、 以及上面没有提到的其他生物信息学支持，包括数据管理、透明度和数据 分享。

PROJECT SUMMARY (IMMUNE BIOINFORMATICS AND COMPUTATIONAL BIOLOGY CORE) The overall goal of this P01 proposal is to apply a systematic approach to understanding mechanisms of immune resistance that can guide the rational design of novel combinatorial immunotherapies to effectively treat head and neck squamous cell carcinoma (HNSCC) patients. To accomplish this goal, we will use several cutting-edge bioinformatics strategies on data from bulk and single cell RNA sequencing (scRNA-seq), epigenomics assays, and exome sequencing, as well as biostatistics analysis of clinical trial data. Our team has complementary expertise encompassing HNSCC genetics and biology, innate and adaptive immune response, immune bioinformatics, single cell dynamics and the analysis of scRNA-seq, cancer epigenomics and genome-wide regulatory data, and HNSCC clinical trial data analyses. The objective of the Immune BioInformatics and Computational Biology (IBCB) Core is to provide, disease-specific comprehensive and innovative support to the P01 investigators for the study design, analysis, integration and interpretation of a broad range of omics-based and clinical trial studies. The IBCB Core will support the three projects of the P01 through four specific aims. First, we will recommend strategies for study design and provide analyses specific to immune bioinformatics. This includes, but is not limited to, mutational burden analysis and neoantigen prediction, analysis of HLA class I alleles, and immune cell type deconvolution. The second aim is to provide expertise for study design and perform various analyses and methods development for single cell bioinformatics. We will utilize several recently developed scRNA-seq methods for QC, batch correction and normalization, imputation, innovative visualizations, automatic cell type identification, and differential expression testing. The third aim is to provide consultation on experimental design and provide analyses for epigenomics data. We will analyze genome-wide DNA methylation and transcriptomics data before and after different immune checkpoint blockade (ICB) therapeutic regimens. Aim four is to provide advanced biostatistics and bioinformatics support for clinical trial analyses, power analyses, and other bioinformatics support not mentioned above, including data management, transparency, and data sharing.