Therapeutic Antibody Discovery from Pancreatic Cancer B Cell Repertoires

描述(申请人提供)：该SBIR第一阶段项目的具体目标是建立一种从罕见的原代B细胞中发现胰腺癌候选单抗(MAb)的技术。胰腺癌已被证明对针对肿瘤学“常见嫌疑人”的药物具有弹性，包括与血管生成、增殖和转移相关的蛋白质(Mackenzie&McCollum，2009)。单抗是肿瘤学的首选治疗剂(Gura，2002；Elder，2011)。传统上，mAb发现程序识别前导目标抗原，并使用噬菌体展示或小鼠杂交瘤筛选抗体库的亲和力。作为另一种选择，一些学术和工业团体一直在使用人类B细胞杂交瘤(Lang等人，1990；Yu等人，2008)来挖掘候选治疗性抗体的天然谱系。B细胞杂交瘤在技术上具有挑战性且效率低下，但天然抗体具有几个优点，包括低免疫原性、体内亲和力成熟以及在哺乳动物细胞中良好的表达能力(Hoet等人，2005年；Beerli&Rader，2010年)。下一代测序(NGS)深度谱系测序方法可能有助于克服B细胞谱系挖掘的技术问题。经典的NGS方法用于分析克隆B或浆细胞产生的重链或轻链免疫球蛋白(Ig)。然而，传统的NGS方法错过了配对Ig的单细胞背景，需要猜测Ig单体和亲和筛选(Reddy等人，2010年)。GigaGen使用高科技基因组学首次从数百万单个人类B细胞中产生具有天然免疫球蛋白配对的DNA文库。我们通过以下创新解决了现有方法的许多缺点：(I)深液滴数字单细胞基因组学；(Ii)体内亲和力成熟的检测；(Iii)组织、时间点和患者之间的比较；以及(Iv)与蛋白质展示的集成。在第一阶段，我们将调整我们的技术，专门用于胰腺癌单抗的发现。首先，为了捕获罕见的抗体，我们将把我们的微流控设备的细胞吞吐量增加至少十倍。其次，我们将开发胰腺肿瘤中肿瘤浸润性B细胞(TIL-B)的解聚方案。我们将通过执行以下任务来实现这一特定目标：(I)验证一种新的微流控芯片几何形状，使细胞吞吐量提高10倍；(Ii)胰腺肿瘤TIL-B解聚和分选的测试方法；以及(Iii)对胰腺癌患者外周血和TIL-B进行测序，以发现正在亲和成熟的谱系。如果我们实现以下指标，我们将成功：(I)产生直径60-200μm的液滴，液滴合并5%，多细胞包封率3%，细胞吞吐量至少400赫兹；(Ii)在新旧芯片上生成的文库之间重链和轻链Ig的错误连接不应有显著差异(两比例z检验；α=0.05；Power=0.8)；(Iii)从6个胰腺TIL-B文库中恢复至少100,000个独特的B细胞克隆；以及(Iv)发现和GT；10个亲和成熟的Ig序列，并在同一患者的TIL-B和外周血之间共享，或经历趋同进化。第一阶段将生成一份具有强大治疗潜力的候选抗体的精选名单。在第二阶段，我们将使我们的候选单抗通过标准的临床前开发(赵等人，2009年)。我们预计这一开发阶段将耗资100万至200万美元，持续18至24个月。到第二阶段结束时，我们希望有足够的数据用于与生物制药合作伙伴或通过筹集风险资本进行新型先导单抗的临床开发。

DESCRIPTION (provided by applicant): The Specific Aim of this SBIR Phase I project is to build a technology for discovery of monoclonal antibody (mAb) drug candidates for pancreatic cancer from rare primary B cells. Pancreatic cancer has proven resilient to drugs that target oncology's "usual suspects", including proteins related to angiogenesis, proliferation, and metastasis (Mackenzie & McCollum, 2009). mAbs are therapeutic agents of choice for oncology (Gura, 2002; Elder, 2011). Traditionally, mAb discovery programs identify a lead target antigen and screen antibody libraries for affinity using either phage display or mouse hybridomas. As an alternative, some academic and industry groups have been using human B cell hybridomas (Lang et al., 1990; Yu et al., 2008) to mine natural repertoires for candidate therapeutic antibodies. B cell hybridomas are technically challenging and inefficient, but natural antibodies have several advantages, including low immunogenicity, in vivo affinity maturation, and excellent expressability in mammalian cells (Hoet et al., 2005; Beerli & Rader, 2010). Next-generation sequencing (NGS) deep repertoire sequencing methods may help overcome the technical problems with B cell repertoire mining. Canonical NGS methods are used to deep sequence immunoglobulin (Ig) heavy or light monomers produced by clonal B or plasma cells. However, conventional NGS methods miss the single-cell context of paired Ig, requiring guesswork to pair Ig monomers and affinity screen (Reddy et al., 2010). GigaGen uses high-tech genomics to generate, for the first time, DNA libraries with native Ig pairing from millions o single human B cells. We address many of the shortcomings with existing methodologies through the following innovations: (i) deep droplet digital single cell genomics; (ii) detection of affinity maturation in vivo; (iii) comparisons among tissues, time points, and patients; and (iv) integration with protein display. In Phase I, we will adapt our technology specifically for pancreatic cancer mAb discovery. First, in order to capture rare antibodies, we will increase the cell throughput of our microfluidic device at least tenfold. Second, we will develop protocols for disaggregation of tumor infiltrating B cells (TIL-Bs) from pancreatic tumors. We will accomplish the Specific Aim by performing the following tasks: (i) Validate a novel microfluidic chip geometry for >10x higher cell throughput; (ii) Test methods for disaggregation and sorting of TIL-Bs from pancreatic tumors; and (iii) Sequence pancreatic cancer patient peripheral blood and TIL-Bs to discover lineages undergoing affinity maturation. We will be successful if we achieve the following metrics: (i) Generate droplets at 60-200μm in diameter, <5% droplet merging, multiple-cell encapsulation rates of <3%, and cell throughput of at least 400Hz across at least 20 experiments; (ii) Mis-linking of heavy and light chain Ig should not differ significanty between libraries generated on the old and new chips (two-proportion z-test; α=0.05; power=0.8); (iii) Recover at least 100,000 unique B cell clones from each of 6 pancreatic TIL-B libraries; and (iv) Discover >10 Ig sequences undergoing affinity maturation and shared between TIL-Bs and peripheral blood from the same patient, or undergoing convergent evolution. Phase I will generate a curated list of candidate antibodies with strong therapeutic potential. In Phase II, we will bring our candidate mAbs through standard preclinical development (Zhao et al., 2009). We expect this stage of development to cost $1-2m and last 18-24 months. By the end of Phase II, we hope to have sufficient data for clinical development of a novel lead mAb, either with a biopharma partner or by raising venture capital.