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: 8664101
• 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/8664101
• 关键词: Address; Adenocarcinoma Cell; Affinity; Antibodies; Antibody Formation; Antibody Repertoire; 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; 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; public health relevance; 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）：针对肿瘤相关抗原的抗体是在各种癌症的人类中产生的。尽管存在几种用于分析抗体的方法，但没有一种方法能够全面地表征免疫反应中产生的抗体，然后合理地识别可能是功能性I.e。的抗体，那些是癌症遏制或发病机理的关键的抗体。为了应对这一挑战，我们正在开发使用DNA条形码的技术，用于对来自单个B细胞的配对重型（HC）和轻链（LC）抗体基因进行大规模测序。这项技术使配对的HC+LC抗体基因从数百到数千个单个B细胞中进行测序，从而产生抗体序列数据集，这些抗体序列数据集能够代表代表抗体库的生化生成的生物学生成，这些抗体依次是抗体库库，以及重新组合抗体的关键抗体选择，并诊断了抗体的合理选择。在癌症中，我们假设通过循环静脉曲张产生的抗体库的深入表征将发现功能性抗肿瘤抗体反应。我们检测到转移性肺腺癌个体的血液中高水平的浆膜（活化的B细胞），其癌症在治疗后几年没有进展。我们将抗体库捕获（ARC）技术应用于从肺腺癌患者中分离出来的浆膜，并使用生物信息学生成抗体反应的系统发育树，并从大型克隆家族中选择重组表达的大型抗体。我们确定了在免疫组织化学分析中结合的三种重组抗体，可衍生自其他患者的肺腺癌> 80％。该应用程序包含四个目标：在AIM 1中，我们将（i）开发一个微流体前端，该前端将ARC测序的吞吐量和深度提高到一个数量级，以及（ii）在技术上验证了对抗癌抗体反应测序的弧。在AIM 2中，我们将使用ARC来介绍和比较“非进步者”和“进步者”中的抗体反应与肺腺癌。在AIM 3中，我们将克隆并表达从患有肺腺癌个体的浆膜的合理选择，亲和力成熟的抗体，并鉴定其肿瘤抗原靶标。在AIM 4中，我们将评估精选抗肿瘤抗体的潜力和 肿瘤抗原可作为肺腺癌诊断/预后生物标志物或治疗剂。拟议的研究的成功将通过技术精炼和验证弧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剂的发展。