Bioinformatics Technology to Characterize Tumor Infiltrating Immune Repertoires

生物信息学技术表征肿瘤浸润免疫库

• 批准号: 9507415
• 负责人: Xiaole Shirley Liu
• 金额: $ 44.3万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-06 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9507415
• 关键词: Algorithms; Antibodies; Antigens; B cell repertoire; B-Lymphocytes; Bioinformatics; Biological; Cancer Immunology Science; Cancer Vaccines; Cell Maturation; Cell Separation; Clinic; Collaborations; Collection; Complementarity Determining Regions; Computational algorithm; Data; Data Set; Development; Education and Outreach; Gene Expression; Genomic Data Commons; Immune; Immune system; Immunity; Immunoglobulin Class Switching; Immunoglobulin Somatic Hypermutation; Immunoglobulin Variable Region; Immunologist; Immunotherapy; Infiltration; Informatics; Information Technology; Mainstreaming; Malignant Neoplasms; Mediating; Mediation; Methods; National Cancer Institute; Oncologist; Patients; Property; Public Domains; RNA analysis; Receptor Cell; Receptors, Antigen, B-Cell; Research Infrastructure; Resources; Sampling; Solid Neoplasm; Source; Statistical Methods; T cell therapy; T-Cell Receptor; T-Lymphocyte; T-cell receptor repertoire; Technology; The Cancer Genome Atlas; Therapeutic; Time; Tissues; Tumor Immunity; Tumor Tissue; Tumor stage; Tumor-Infiltrating Lymphocytes; V(D)J Recombination; anticancer research; bioinformatics resource; cancer cell; cancer immunotherapy; cancer therapy; cancer type; clinical practice; cohort; computing resources; data mining; deep sequencing; genomic data; heuristics; immunoglobulin receptor; immunological diversity; improved; improved functioning; insight; mRNA sequencing; neoplasm resource; novel; online resource; outreach; simulation; sound; tool; transcriptome sequencing; tumor; tumor microenvironment; user-friendly; web interface

PROJECT SUMMARY The repertoires of tumor-infiltrating T cells and B cells are rich sources of information about cancer-immune interactions and provide insights on cancer immunotherapy targets. Efforts have been made to characterize B/T cell repertoires in solid tumors using cell sorting followed by targeted deep sequencing. However, these approaches may produce biased estimates during tissue disaggregation and can be expensive when applied to large sample cohorts. Massively parallel mRNA sequencing (RNA-seq) technology has become the mainstream method to profile gene expression and thousands of solid tumor RNA-seq profiles are available in the public domain. The rich collection of tumor RNA-seq datasets provides an alternative approach to study tumor-infiltrating B/T cell repertoires in solid tumors. Our team has recently developed a statistical method TIMER for deconvolving different immune components in the tumor microenvironment, and TRUST for inferring the hypervariable complementarity determining regions (CDRs) of the tumor infiltrating T cell receptor (TCR) repertoire from bulk tumor RNA-seq data in the public domain. Our preliminary analysis indicated that there are approximately ten times as many B cell receptor (BCR) reads and TCR reads, suggesting that extracting the BCR repertoires from bulk tumor RNA-seq could reveal important insights on B cell mediated tumor immunity. The aims of this proposal are: to extend our TRUST algorithm to extract B cell receptor (BCR) repertoires from tumor RNA-seq data, and identify somatic hypermutations and immunoglobin class switches (Aim 1); to systematically analyze TCR and BCR repertoires from large scale tumor RNA-seq cohorts, and develop a user friendly web interface to allow cancer immunologists or immuno-oncologists to investigate tumor-immune associations (Aim 2); to promote the utility of our tumor immune resource through collaborations, cloud sharing, and outreach (Aim 3). We will deliver a robust bioinformatics algorithm to systematically identify BCR / TCR repertoires from bulk tumor RNA-seq data and a user-friendly resource for cancer immunologists or immuno-oncologists to explore tumor- immune interactions from large tumor profiling cohorts in the public as well as their unpublished data. The successful execution of this proposal has the potential to inform clinical practice of cancer immunotherapies, including adoptive T cell transfer, therapeutic cancer vaccines or antibodies. Our proposed cancer immunology algorithm and resource will be a unique addition to the array of bioinformatics tools developed by the Information Technology for Cancer Research at the National Cancer Institute.

项目摘要 肿瘤浸润的T细胞和B细胞的曲目是有关癌症免疫信息的丰富来源 相互作用并提供有关癌症免疫疗法靶标的见解。已经努力表征b/t 使用细胞分选，然后进行靶向深测序，在实体瘤中的细胞谱。但是，这些 方法可能会在组织分解过程中产生偏见的估计，并且应用于 大型样品队列。大量平行的mRNA测序（RNA-SEQ）技术已成为主流 公众提供了谱图基因表达和成千上万的实体瘤RNA-seq轮廓的方法 领域。丰富的肿瘤RNA-seq数据集的收集提供了一种研究肿瘤浸润的替代方法 实体瘤中的B/T细胞库。我们的团队最近开发了一个统计方法计时器，以进行反应 肿瘤微环境中不同的免疫成分，并信任推断高变量 从肿瘤浸润T细胞受体（TCR）库的互补性确定区域（CDR） 公共领域中的肿瘤RNA-seq数据。 我们的初步分析表明，大约有许多B细胞受体（BCR）读取的十倍 TCR读取，这表明从散装肿瘤RNA-Seq中提取BCR曲目可能会揭示重要的 对B细胞介导的肿瘤免疫的见解。该提案的目的是：将我们的信任算法扩展到 从肿瘤RNA-seq数据中提取B细胞受体（BCR）曲目，并鉴定体内过度过度和 免疫球蛋白类开关（AIM 1）;系统地分析大规模肿瘤的TCR和BCR曲目 RNA-seq队列，并开发一个用户友好的网络界面，以允许癌症免疫学家或免疫肿瘤学家 研究肿瘤免疫相关（AIM 2）；通过 合作，云共享和外展（AIM 3）。 我们将提供强大的生物信息学算法，以系统地识别大量肿瘤的BCR / TCR库 RNA-seq数据和癌症免疫学家或免疫肿瘤学家的用户友好资源探索肿瘤 - 大型肿瘤分析队列中的免疫相互作用及其未发表的数据。这 该提案的成功执行有可能为癌症免疫疗法的临床实践提供信息， 包括收养T细胞转移，治疗性癌症疫苗或抗体。我们提出的癌症免疫学 算法和资源将是通过信息开发的一系列生物信息学工具的独特补充 国家癌症研究所的癌症研究技术。