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Bioinformatics Technology to Characterize Tumor Infiltrating Immune Repertoires

生物信息学技术表征肿瘤浸润免疫库

基本信息

批准号：
9888343
负责人：
Heng Li
金额：
$ 42.36万
依托单位：
DANA-FARBER CANCER INST
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-06 至 2022-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9888343
关键词：
Algorithms Antibodies Antigens B cell repertoire B-Lymphocytes Bioinformatics Biological Cancer Vaccines Cell Maturation Cell Separation Clinic Collaborations Collection Complementarity Determining Regions Computational algorithm Consumption Data Data Set 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 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 Tumor-infiltrating immune cells V(D)J Recombination algorithm development anticancer research bioinformatics infrastructure bioinformatics resource bioinformatics tool 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 transcriptome sequencing tumor tumor immunology tumor microenvironment user-friendly web interface

项目摘要

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.

项目摘要肿瘤浸润性T细胞和B细胞的库是关于癌症免疫应答的丰富信息来源。相互作用，并提供对癌症免疫治疗靶点的见解。已经努力描述B/T 使用细胞分选，随后进行靶向深度测序，在实体瘤中检测细胞库。但这些这些方法可能在组织解聚期间产生有偏差的估计，大样本队列。大规模并行mRNA测序（RNA-seq）技术已成为主流一种分析基因表达的方法和数千种实体瘤RNA-seq图谱可供公众使用域肿瘤RNA-seq数据集的丰富集合提供了研究肿瘤浸润的另一种方法，实体瘤中的B/T细胞库。我们的团队最近开发了一种用于反卷积的统计方法TIMER 肿瘤微环境中不同的免疫组分，以及用于推断高变蛋白的TRUST 在一个实施方案中，本发明提供了来自大部分肿瘤细胞的肿瘤浸润性T细胞受体（TCR）库的互补决定区（CDR）。肿瘤RNA-seq数据在公共领域。我们的初步分析表明，有大约十倍多的B细胞受体（BCR）读数，和TCR读段，这表明从大量肿瘤RNA-seq中提取BCR库可以揭示重要的对B细胞介导的肿瘤免疫的见解。这个建议的目的是：扩展我们的信任算法，从肿瘤RNA-seq数据中提取B细胞受体（BCR）库，并鉴定体细胞超突变，免疫球蛋白类别开关（目的1）;系统分析大规模肿瘤的TCR和BCR库 RNA-seq队列，并开发一个用户友好的网络界面，让癌症免疫学家或免疫肿瘤学家研究肿瘤免疫相关性（目标2）;通过以下方式促进我们的肿瘤免疫资源的利用：协作、云共享和外展（目标3）。我们将提供一个强大的生物信息学算法，系统地确定BCR / TCR库从大块肿瘤 RNA-seq数据和癌症免疫学家或免疫肿瘤学家探索肿瘤的用户友好资源- 来自公众中大型肿瘤分析队列的免疫相互作用以及它们未发表的数据。的该建议的成功实施具有告知癌症免疫疗法的临床实践的潜力，包括过继性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一个独特的除了生物信息学工具的阵列开发的信息国家癌症研究所的癌症研究技术。