Core 2: Immune Bioinformatics and Computational Biology Core

项目总结（免疫生物信息学和预防生物学核心） 本P01提案的总体目标是采用系统的方法来理解 免疫抗性，可以指导合理设计新的组合免疫疗法， 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， 表观基因组学测定和外显子组测序，以及临床试验数据的生物统计学分析。我们的团队 具有互补的专业知识，包括HNSCC遗传学和生物学，先天性和适应性免疫 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 和全基因组监管数据，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 为P01研究者提供研究设计、分析、整合和 广泛的基于组学的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，我们建议 研究设计的策略，并提供特定的免疫生物信息学分析。这包括但不 仅限于突变负荷分析和新抗原预测、HLA I类等位基因分析和免疫原性分析。 细胞型反褶积第二个目的是为研究设计和进行各种分析提供专业知识 和单细胞生物信息学方法的发展。我们将利用几个最近开发的scRNA-seq QC方法、批次校正和标准化、插补、创新的可视化、自动细胞类型 鉴定和差异表达测试。第三个目标是提供实验咨询 设计并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， 在不同免疫检查点阻断（ICB）治疗方案之前和之后的转录组学数据。目的 四是为临床试验分析，功效分析， 以及上文未提及的其他生物信息学支持，包括数据管理、透明度和数据 共享

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.