Ading color to cancer, adenovirus and flow cytometry to identify and capture CTCs

“Ad'ing color to cancer：adenovirus & flow cytometry to identify & capture CTC“ 纳米排序 研究及相关其他项目信息 7.项目摘要 我们提出了一种新的技术来识别和捕获循环肿瘤细胞（CTC），使用工程化的 腺病毒和复杂的流式细胞术。目前用于检测CTC的技术包括反向检测。 转录酶-聚合酶链反应（RT-PCR）、流式细胞术、荧光原位杂交，以及， 最近，微流体。不幸的是，RT-PCR不能区分存活的转移性CTC 与源自原发性肿瘤的核酸或细胞片段相比。!基于抗体的技术 不能用于检测所有的癌症，而只能用于检测那些表达最常见和最好的 特征标记。因此，迫切需要开发新的诊断试剂和工具， 不仅检测和捕获CTC，而且还量化其恶性潜力，并确定“前期”治疗 最有效地切除病人的肿瘤尽管复杂多变， 在基因组规模的癌症，一个统一的主题是他们的生长失调表型，所谓的标志 癌症，这是由相对较少的关键途径的突变所赋予的。而不是专注于 检测个体遗传病变，这些病变在肿瘤之间数量众多且高度可变，我们建议 创建诊断病毒，在其基因组中整合多个转录和分子模块， 感染并检测患者肿瘤，报告其分子“特征”及其对不同疗法的反应， 前面。使用这些药物，任何给定肿瘤的分子病变和恶性特征将被 通过标准化自动化平台快速识别（24小时内）并评分。而且这些 试剂也可以用作报告者，以快速和直接地确定患者的肿瘤是否可能响应 一个特定的治疗。我们的目标是开发一个标准化的自动化平台， 在分子复杂性和预后能力水平上为临床决策提供信息的诊断， 可以使用任何其它检测系统、生物标记或相关基因表达标记。实现 为此，我们将联合收割机结合索尔克、加州大学圣地亚哥分校和纳米排序所开发的变革性新技术平台 根据肿瘤细胞获得的分子损伤， 进展和对治疗的反应，使用集成的方法促进其检测、量化和分离 “芯片实验室”流式细胞仪。!

"Ad'ing color to cancer: adenovirus & flow cytometry to identify & capture CTCs " NanoSort RESEARCH & RELATED Other Project Information 7. PROJECT SUMMARY We propose a novel technique to identify and capture circulating tumor cels (CTCs) using enginered adenoviruses and sophisticated flow cytometry. Current techniques for detection of CTCs include reverse transcriptase-polymerase chain reaction (RT-PCR), flow cytometry, fluorescence in situ hybridization, and, more recently, microfluidics. Unfortunately, RT-PCR does not distinguish between viable metastatic CTC versus nucleic acids or celular fragments originating from the primary tumor. ! Antibody-based techniques cannot be used for detection of all cancers, but only those cancers that express the most common and well- characterized markers. As such, there is a desperate need to develop new diagnostic agents and tools that not only detect and capture CTCs but also quantify their malignant potential and identify 'up-front' the therapies that are most effective in ablating an individual patient's tumor. Despite the complexity and variability of cancers at a genome scale, a unifying theme is their growth deregulation phenotypes, the so-called hallmarks of cancer, which are conferred by mutations in a relatively small number of key pathways. Rather than focus on detecting individual genetic lesions that are numerous and highly variable between tumors, we propose to create diagnostic viruses that incorporate multiple transcriptional and molecular modules in their genomes to infect and detect a patient's tumor, report its molecular 'hallmarks' and its response to different therapies 'up- front'. Using these agents, the molecular lesions and malignant characteristics of any given tumor wil be rapidly discerned (within 24 hours) and scored via a standardized automated platform. Furthermore, these agents could also be used as reporters to determine rapidly and directly if a patient's tumor is likely to respond to a particular therapy. Our goal is to develop a standardized automated platform that provides point-of-care diagnostics to inform clinical decisions at a level of molecular sophistication and prognostic power that is not possible with any other detection system, biomarkers or correlative gene expression signatures. To achieve this, we will combine transformative new technological platforms developed at the Salk, UCSD, and NanoSort that label tumor cells in different colors based on their acquisition of molecular lesions that dictate malignant progression and response to therapy, facilitating their detection, quantification and isolation using an integrated 'lab-on a chip' flow cytometer. !