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

描述(由申请人提供)：抗肿瘤相关抗原的抗体在患有各种癌症的人类中产生。虽然存在几种分析抗体的方法，但没有一种方法能够全面描述免疫反应中产生的抗体的特征，然后合理地识别那些可能起作用的抗体--即那些对遏制癌症或癌症发病至关重要的抗体。为了应对这一挑战，我们正在开发使用DNA条形码对来自单个B细胞的成对的重链(HC)和轻链(LC)抗体基因进行大规模测序的技术。这项技术能够在每个实验中对成对的HC+LC抗体基因进行测序，从而产生抗体序列数据集，从而能够以生物信息学的方式生成代表抗体库的系统发育树，以及合理选择关键抗体进行重组表达，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的抗肿瘤抗体反应。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(激活的B细胞)，这些转移性肺腺癌患者的癌症在治疗几年后没有进展。我们将我们的抗体库捕获(ARC)技术应用于从一个肺腺癌患者分离的浆母细胞，并使用生物信息学来生成抗体反应的系统发育树，并从大的克隆家族中挑选抗体进行重组表达。在免疫组织化学分析中，我们确定了三种重组抗体，它们结合了80%来自其他患者的肺腺癌。这项应用包括四个目标：在目标1中，我们将(I)开发一种微流控前端，将ARC的吞吐量和测序深度增加一个数量级，以及(Ii)从技术上验证ARC用于对抗癌抗体反应进行测序。在目标2中，我们将使用ARC来描述和比较“非进展者”和“进展者”与肺腺癌的抗体反应。在目标3中，我们将克隆和表达合理选择的、亲和力成熟的肺腺癌患者血浆母细胞抗体，并鉴定其肿瘤抗原靶点。在目标4中，我们将评估精选的抗肿瘤抗体和 肿瘤抗原作为肺腺癌的诊断/预后生物标志物或治疗药物。拟议研究的成功将通过从技术上改进和验证ARC技术作为分析抗癌抗体反应的工具来改变癌症研究-这将增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。

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.