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

描述（由申请人提供）：针对肿瘤相关抗原的抗体在患有多种癌症的人体内产生。虽然存在几种分析抗体的方法，但没有一种方法能够全面表征免疫反应中产生的抗体，然后合理地识别那些可能具有功能的抗体。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，利用DNA条形码对来自单个B细胞的成对重链（HC）和轻链（LC）抗体基因进行大规模测序。该技术能够在每个实验中从数百到数千个单个B细胞中对配对的HC+LC抗体基因进行测序，从而产生抗体序列数据集，从而能够生物信息学地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，表征其抗原靶点，并用于诊断或治疗。在癌症中，我们假设深入表征循环质母细胞产生的抗体库将揭示功能性抗肿瘤抗体反应。我们在转移性肺腺癌患者的血液中检测到高水平的浆母细胞（活化的B细胞），这些患者的癌症在治疗后几年内没有进展。我们将抗体库捕获（ARC）技术应用于从肺腺癌患者分离的质母细胞，并使用生物信息学生成抗体反应的系统发育树，并从大克隆家族中选择抗体进行重组表达。我们在免疫组织化学分析中发现了三种重组抗体，它们与80%的来自其他患者的肺腺癌结合。该应用包括四个目标：在目标1中，我们将(i)开发一种微流体前端，将ARC测序的吞吐量和深度提高一个数量级，以及（ii）从技术上验证ARC对抗癌抗体反应的测序。在Aim 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”的抗体反应。在Aim 3中，我们将从肺腺癌个体的质母细胞中克隆并表达合理选择的亲和成熟抗体，并鉴定其肿瘤抗原靶点。在Aim 4中，我们将评估选择抗肿瘤抗体的潜力

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.