Large-Scale Characterization of Anti-Cancer Antibody Responses in Lung Adenocarci

肺腺癌抗癌抗体反应的大规模表征

• 批准号: 8845528
• 负责人: William H Robinson
• 金额: $ 39.96万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8845528
• 关键词: Address; Adenocarcinoma Cell; Affinity; Antibodies; Antibody Repertoire; Antibody Response; Antigen Targeting; Antigens; B-Lymphocytes; Binding; Bioinformatics; Biological Markers; Blood; Cancer Model; Cell Line; Complementary DNA; Containment; DNA; Data Set; Deltastab; Development; Diagnostic; Disease; Disease Outcome; Emerging Technologies; Emulsions; Family; Generations; Genes; Health; Human; Immune response; Individual; Influenza vaccination; Large-Scale Sequencing; Light; Lung; Lung Adenocarcinoma; Lymphoid; Malignant Neoplasms; Memory B-Lymphocyte; Metastatic Melanoma; Methods; Microfluidics; Morbidity - disease rate; Organ; Pathogenesis; Patients; Phylogenetic Analysis; Plasmablast; Production; Prognostic Marker; Proteomics; Recombinant Antibody; Recombinants; Research; Resolution; Serum; Squamous Cell Lung Carcinoma; Staphylococcus aureus; Structure of parenchyma of lung; Surface Antigens; Technology; Therapeutic; Therapeutic antibodies; Toxin Conjugates; Trees; Tumor Antibodies; Tumor Antigens; United States; anticancer research; base; drug development; innovation; killings; mortality; new technology; novel; novel diagnostics; prognostic; research study; success; tool; tumor

DESCRIPTION (provided by applicant): Antibodies against tumor-related antigens are produced in humans with a variety of cancers. Although several methods exist for profiling antibodies, none are able to comprehensively characterize the antibodies produced in an immune response and to then rationally identify those likely to be functional-i.e., those that are key to the containment or pathogenesis of cancer. To address this challenge, we are developing technology that uses DNA barcoding for the large-scale sequencing of paired heavy- (HC) and light-chain (LC) antibody genes from individual B cells. This technology enables sequencing of the paired HC+LC antibody genes from hundreds to thousands of individual B cells in each experiment, thereby yielding antibody sequence datasets that enable bioinformatic generation of phylogenetic trees representing the antibody repertoire, as well as rational selection of key antibodies for recombinant expression, characterization of their antigen targets, and use as diagnostics or therapeutics. In cancer, we hypothesize that in-depth characterization of the antibody repertoire produced by circulating plasmablasts will uncover functional anti-tumor antibody responses. We detected high levels of plasmablasts (activated B cells) in the blood of individuals with metastatic lung adenocarcinoma whose cancer had not progressed several years after therapy. We applied our antibody repertoire capture (ARC) technology to plasmablasts isolated from a lung adenocarcinoma patient, and used bioinformatics to generate an phylogenetic tree of the antibody response and to select antibodies from large clonal families for recombinant expression. We identified three recombinant antibodies that bound in immunohistochemical analyses to >80% of lung adenocarcinomas derived from other patients. This application comprises four aims: In Aim 1, we will (i) develop a microfluidic front end that increases the throughput and depth of sequencing of ARC by an order of magnitude, and (ii) technically validate ARC for sequencing anti- cancer antibody responses. In Aim 2, we will use ARC to profile and compare the antibody responses in "non- progressors" and "progressors" with lung adenocarcinoma. In Aim 3, we will clone and express rationally selected, affinity-matured antibodies of plasmablasts from individuals with lung adenocarcinoma and identify their tumor antigen targets. In Aim 4, we will evaluate the potential of select anti-tumor antibodies and tumor antigens to serve as diagnostic/prognostic biomarkers or therapeutics for lung adenocarcinoma. Success of the proposed studies would transform cancer research by technically refining and validating ARC technology as a tool for the analysis of anti-cancer antibody responses-one that would advance our understanding of anti-cancer antibody responses and facilitate development of antibody-based diagnostics and therapeutics.

描述（由申请人提供）：在患有多种癌症的人中产生针对肿瘤相关抗原的抗体。尽管存在几种用于分析抗体的方法，但没有一种能够全面表征免疫应答中产生的抗体，然后合理地鉴定那些可能是功能性的-即，那些对癌症的控制或发病机制至关重要的基因。为了应对这一挑战，我们正在开发一种技术，该技术使用DNA条形码对来自单个B细胞的配对重链（HC）和轻链（LC）抗体基因进行大规模测序。该技术使得能够在每个实验中对来自数百至数千个个体B细胞的配对HC+LC抗体基因进行测序，从而产生抗体序列数据集，所述抗体序列数据集使得能够生物信息学生成代表抗体库的系统发育树，以及合理选择用于重组表达的关键抗体，表征其抗原靶标，并用作诊断或治疗。在癌症中，我们假设对循环浆母细胞产生的抗体库的深入表征将揭示功能性抗肿瘤抗体应答。我们检测到高水平的浆母细胞（活化的B细胞）在血液中的个人转移性肺腺癌的癌症治疗后几年没有进展。我们应用我们的抗体库捕获（ARC）技术从肺腺癌患者分离的浆母细胞，并使用生物信息学生成的抗体反应的系统发育树，并选择从大的克隆家族的抗体重组表达。我们鉴定了三种重组抗体，其在免疫组织化学分析中与来自其他患者的>80%的肺腺癌结合。该申请包括四个目标：在目标1中，我们将（i）开发微流体前端，其将ARC的测序的通量和深度增加一个数量级，以及（ii）在技术上验证ARC用于测序抗癌抗体应答。在目标2中，我们将使用ARC来分析和比较肺腺癌“非进展者”和“进展者”中的抗体应答。在目标3中，我们将从肺腺癌个体中克隆和表达合理选择的、亲和力成熟的浆母细胞抗体，并鉴定其肿瘤抗原靶标。在目标4中，我们将评估选择的抗肿瘤抗体的潜力， 肿瘤抗原用作肺腺癌的诊断/预后生物标志物或治疗剂。拟议研究的成功将通过技术上的改进和验证ARC技术作为分析抗癌抗体反应的工具来改变癌症研究，这将促进我们对抗癌抗体反应的理解，并促进基于抗体的诊断和治疗的发展。