An integrated transcriptomic and proteomic approach to antibody sequencing and repertoire characterization

用于抗体测序和库表征的集成转录组学和蛋白质组学方法

基本信息

批准号：
9254618
负责人：
Natalie Castellana
金额：
$ 38.84万
依托单位：
DIGITAL PROTEOMICS LLC
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-02-16 至 2019-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9254618
关键词：
Address Algorithms Animals Antibiotic Resistance Antibodies Antibody Diversity Antibody Repertoire Antibody Response Antigens Area Attention Autoimmune Diseases B-Lymphocytes Base Sequence Cells Clone Cells Data Databases Development Disease Drug Industry Drug resistance Genes High-Throughput Nucleotide Sequencing Human Hybridomas Immune response Immunization Immunize Infection Informatics Injectable Learning Legal patent Life Cycle Stages Longitudinal Studies Malignant Neoplasms Mass Spectrum Analysis Mature B-Lymphocyte Measurement Measures Memory B-Lymphocyte Methods Monoclonal Antibodies Mutation Nucleotides Organism Outcome Patients Peptide Sequence Determination Peptides Phage Display Pharmaceutical Preparations Phase II Clinical Trials Process Proteins Proteomics Proxy Reporting Research Resources Sampling Savings Somatic Mutation Specificity Survivors Technology Therapeutic Monoclonal Antibodies Therapeutic Polyclonal Antibodies Time TimeLine Work bacterial resistance base candidate identification candidate selection cost design digital drug candidate drug development drug discovery drug market falls improved in vivo infancy insight interest molecular sequence database next generation sequencing novel novel strategies novel therapeutics polyclonal antibody proteogenomics response screening therapeutic candidate therapeutic development tool transcriptomics vaccine trial

项目摘要

Antibodies are ideal drug candidates due to their high specificity for target molecules. Monoclonal antibodies represent one of the fastest growing segments of the drug market, however, recent attention has focused on polyclonal antibodies and monoclonal mixtures to reduce the opportunity for a disease to become drug resistant. Polyclonal antibodies sampled from disease survivors or immunized hosts offer a wealth of new drug candidates. Current pipelines for investigating the immune response rely on hybridoma technology, which is time-‐consuming and does not come close to mimicking the diversity of antibodies present in the organism. Next generation sequencing of B-‐cells can deeply interrogate the immune response, however, this technology falls short of providing insight into the best antibody drug candidates. We propose the development of Valens-‐Poly, which will integrate mass spectrometry-‐based proteomics data with next generation sequencing of B-‐cells, an emerging field called immunoproteogenomics. By interrogating the immune response at the protein-‐level, Valens-‐Poly will be able to rank antibody sequences based on their abundance, which is a proxy for specificity to the antigen of interest. It is impossible to sequence all memory B-‐cells in a host organism, therefore the antibody sequences reported would only represent a small fraction of the antibodies that could be present. Using our patented spectral network approach, pioneered in our monoclonal antibody sequencing tool, Valens, we will be able to recover complementarity-‐defining regions (CDRs) of antibodies even when the B-‐cell was not captured for next generation sequencing. Finally, we will characterize the broad spectrum of antibodies produced as part of the immune response, called the antibody repertoire. Identifying changes in germline gene usage and tracking clone abundance and lineage in response to immunization or across patients is an important component of characterizing diseases and guiding drug discovery.

抗体因其对靶点的高度特异性而成为理想的候选药物单克隆抗体是增长最快的部分之一。然而，药物市场最近的注意力集中在多克隆抗体上和单克隆混合物，以减少疾病成为药物的机会从疾病幸存者或免疫接种者身上采集的多克隆抗体。主办方提供了大量用于研究的新候选药物。免疫反应依赖于杂交瘤技术，该技术耗时且不可靠无法接近模仿生物体中存在的抗体的多样性。 B 细胞的下一代测序可以深入探究免疫反应，然而，这项技术不足以提供对最佳抗体药物的洞察。候选人。我们建议开发 Valens-Poly，它将整合质量基于光谱分析的蛋白质组学数据和 B 细胞的下一代测序称为免疫蛋白质组学的新兴领域。在蛋白质水平上的响应，Valens-Poly 将能够对抗体序列进行排序基于它们的丰度，这是感兴趣抗原特异性的代表。对宿主生物体中的所有记忆 B 细胞进行测序是不可能的，因此报告的抗体序列仅代表抗体的一小部分使用我们的专利光谱网络方法，该方法是在我们的单克隆抗体测序工具 Valens，我们将能够恢复即使 B 细胞是抗体的互补定义区 (CDR) 未捕获用于下一代测序。最后，我们将描述所产生的广谱抗体：免疫反应的一部分，称为抗体库的识别变化。种系基因的使用和跟踪克隆丰度和谱系以响应免疫或跨患者是表征的重要组成部分疾病并指导药物发现。