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.

项目概要肿瘤浸润 T 细胞和 B 细胞的库是有关癌症免疫的丰富信息来源相互作用并提供有关癌症免疫治疗靶点的见解。努力刻画B/T特征使用细胞分选和靶向深度测序来分析实体瘤中的细胞库。然而，这些方法可能会在组织分解过程中产生有偏差的估计，并且当应用于大样本队列。大规模并行mRNA测序（RNA-seq）技术已成为主流基因表达谱分析方法和数以千计的实体瘤 RNA-seq 谱已向公众开放领域。丰富的肿瘤 RNA-seq 数据集为研究肿瘤浸润提供了另一种方法实体瘤中的 B/T 细胞库。我们团队最近开发了一种用于反卷积的统计方法TIMER 肿瘤微环境中的不同免疫成分，以及推断高变量的信任肿瘤浸润 T 细胞受体 (TCR) 库的互补决定区 (CDR) 公共领域的肿瘤 RNA-seq 数据。我们的初步分析表明，B 细胞受体 (BCR) 读取数量大约是其十倍和 TCR 读数，表明从大块肿瘤 RNA-seq 中提取 BCR 库可以揭示重要的信息对 B 细胞介导的肿瘤免疫的见解。该提案的目的是：将我们的 TRUST 算法扩展到从肿瘤 RNA-seq 数据中提取 B 细胞受体 (BCR) 库，并识别体细胞超突变和免疫球蛋白类别转换（目标 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，以允许癌症免疫学家或免疫肿瘤学家研究肿瘤-免疫关联（目标 2）；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）。我们将提供强大的生物信息学算法，系统地从大块肿瘤中识别 BCR / TCR 库 RNA-seq 数据和用户友好的资源，供癌症免疫学家或免疫肿瘤学家探索肿瘤来自公众大型肿瘤分析群体的免疫相互作用以及他们未发表的数据。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，包括过继性 T 细胞移植、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成为信息中心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。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Landscape of B cell immunity and related immune evasion in human cancers.

人类癌症中 B 细胞免疫和相关免疫逃避的概况。

DOI：
发表时间：
2019
期刊：
影响因子：
30.8
作者：
Hu, Xihao;Zhang, Jian;Wang, Jin;Fu, Jingxin;Li, Taiwen;Zheng, Xiaoqi;Wang, Binbin;Gu, Shengqing;Jiang, Peng;Fan, Jingyu;Ying, Xiaomin;Zhang, Jing;Carroll, Michael C;Wucherpfennig, Kai W;Hacohen, Nir;Zhang, Fan;Zhang, Peng;Liu, Jun S;Li
通讯作者：
Li

Immune receptor repertoires in pediatric and adult acute myeloid leukemia.

儿童和成人急性髓系白血病的免疫受体库。

DOI：
发表时间：
2019
期刊：
影响因子：
12.3
作者：
Zhang, Jian;Hu, Xihao;Wang, Jin;Sahu, Avinash Das;Cohen, David;Song, Li;Ouyang, Zhangyi;Fan, Jingyu;Wang, Binbin;Fu, Jingxin;Gu, Shengqing;Sade;Hacohen, Nir;Li, Wuju;Ying, Xiaomin;Li, Bo;Liu, X Shirley
通讯作者：
Liu, X Shirley

Evaluation of immune repertoire inference methods from RNA-seq data.

根据 RNA-seq 数据评估免疫组库推断方法。