Ultra-high content analyses of circulating and solid tumor cells: A diagnostic reference system for disease burden

循环肿瘤细胞和实体肿瘤细胞的超高内涵分析：疾病负担的诊断参考系统

• 批准号: 10259230
• 负责人: Michel Nederlof
• 金额: $ 22.37万
• 依托单位: QUANTITATIVE IMAGING SYSTEMS, LLC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-16 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10259230
• 关键词: Address; Age; Antibodies; Architecture; Biological; Biological Markers; Biology; Blood; Blood Tests; Blood specimen; Cancer Patient; Cataloging; Cations; Cell model; Cells; Clinical; Collaborations; Color; Colorectal Cancer; Complex; Computer software; Data Analyses; Derivation procedure; Detection; Development; Diagnostic; Disease; Disease Progression; Early Diagnosis; Ensure; Evaluation; Excision; Foundations; Goals; Gold; Health Sciences; Hybrid Cells; Image; Image Analysis; Immunofluorescence Immunologic; Indirect Immunofluorescence; Individual; Institutes; Label; Link; Malignant Neoplasms; Malignant neoplasm of pancreas; Maps; Methodology; Monitor; Neoplasm Metastasis; Oligonucleotides; Operative Surgical Procedures; Oregon; Pathologic; Patient Care; Patients; Pattern; Periodicity; Phase; Phenotype; Population; Prediction of Response to Therapy; Primary Neoplasm; Process; Proteins; Rectal Cancer; Research; Risk; Sampling; Signal Pathway; Signal Transduction; Small Business Innovation Research Grant; Solid Neoplasm; Specificity; Stains; System; Technology; Testing; Time; Tissue Model; Tissues; Translations; Tumor Tissue; Universities; Validation; Visual; Visualization; Work; antibody conjugate; base; biomarker identification; biomarker panel; burden of illness; cancer cell; cancer type; cell type; cellular imaging; clinical assay development; cohort; colon cancer patients; data visualization; high risk; imaging platform; imaging software; multiplexed imaging; neoplastic cell; novel; peripheral blood; personalized cancer therapy; prognostic assays; protein expression; prototype; spatial relationship; success; symposium; targeted treatment; tool; translational impact; treatment response; tumor; tumor progression; validation studies

ABSTRACT We propose to create a modular suite of products to facilitate highly multiplexed imaging and quantita- tive analyses of individual cells in intact tissue, and cells circulating in blood to be used to establish disease status. Our imaging software analyses platform supporting interactive data visualization will connect protein-based cellular features between tissue resident cells and disseminated cells to lever- age mechanisms of cancer cell dissemination to detect disease earlier, to identify risk for metastases, and to track response to treatment. Our goal is to develop an imaging software analyses platform, biomarker panels for highly multiplexed imaging using cyclic immunostaining and a newly discovered tumor cell population for translation to clinical assay development. Our suite of products will fill an unmet clinical need as no visual platforms exist that can link analyses of individual highly metastatic cells poised to escape the primary tumor with their disseminated counterparts in blood to inform disease status. Our technology has translational impact in developing a non-invasive biomarker for prognosti- cation and for monitoring treatment response in cancer patients. To ensure successful development of a visualization platform with translational potential, we will extend our close partnership with Oregon Health and Science University (OHSU) and strong collaborations at the Knight Cancer Institute for biologic analyses with translation to patient care. We will leverage a novel disseminated tumor popula- tion (i.e., circulating hybrid cells [CHCs]), discovered at OHSU, and a novel cyclic immunofluorescence (cyCIF) technology based on oligonucleotide conjugated antibodies. Currently, little functionality exists to optimally extract the wealth of phenotypic information from highly multiplexed cyCIF images of cells produced on ours or similar staining platforms. Herein, we introduce a novel tool to manage, process, and dynamically visualize such images, with superior single cell analytics even in complex tissue. The proposed labeling platform is the foundation for a new field of advanced multi-parametric analytics that can correlate architectural and functional aspects of intact tissue then apply these finding to corre- sponding cells from blood; a critical aspect of lethal tumor progression. In Phase I, a prototype imaging platform will be built and tested with an integral set of biomarkers for identification of CHC in blood and their corresponding hybrid cell in the primary tumor. Upon confirming feasibility, Phase II will focus on expansion of this technology to address three critical clinical questions: (1) Can cancer be reliably de- tected with a blood test? (2) Can occult metastatic disease be detected in early stage cancers? (3) Can a blood test aid in treatment monitoring to personalize cancer therapy? Successful completion of this work will result in biomarker panels and a software solution to answer these questions as demonstrated in pancreatic and colorectal cancers herein, that can be readily expanded to other cancer types.

摘要 我们建议创建一个模块化的产品套件，以促进高度多路复用的成像和定量， 完整组织中的单个细胞和血液中循环的细胞的活性分析，用于建立 疾病状态。我们的成像软件分析平台支持交互式数据可视化， 将组织驻留细胞和播散细胞之间基于蛋白质的细胞特征连接起来， 癌细胞扩散的年龄机制，以更早地检测疾病，识别转移的风险， 并追踪治疗的反应。我们的目标是开发一个成像软件分析平台， 使用循环免疫染色进行高度多重成像的生物标志物面板和新发现的 肿瘤细胞群转化为临床试验开发。我们的产品将填补一个未满足的 临床需要，因为不存在可以将单个高转移性细胞的分析联系起来的可视平台 准备逃离原发性肿瘤与他们的传播对应物在血液中通知疾病 status.我们的技术在开发非侵入性生物标志物方面具有转化影响， 阳离子和用于监测癌症患者的治疗反应。为确保成功开发 一个具有转化潜力的可视化平台，我们将扩大与俄勒冈州的密切合作关系 健康与科学大学（OHSU）和骑士癌症研究所的强大合作， 生物学分析并转化为患者护理。我们将利用一种新的扩散性肿瘤群体- 作用（即，循环杂交细胞[CHCs]），发现在OHSU，和一种新的循环免疫荧光 基于寡核苷酸缀合的抗体的免疫荧光（cyCIF）技术。目前，几乎没有功能 为了从细胞的高度复用的cyCIF图像中最佳地提取丰富的表型信息， 在我们的或类似的染色平台上产生。在这里，我们介绍了一种新的工具来管理，处理， 并动态可视化这些图像，即使在复杂组织中也具有上级单细胞分析。的 建议的标记平台是先进的多参数分析的新领域的基础， 可以将完整组织的结构和功能方面联系起来，然后将这些发现应用于校正， 从血液中产生细胞;致命肿瘤进展的一个关键方面。在第一阶段， 将建立一个平台，并使用一套完整的生物标志物进行测试，以识别血液中的CHC， 它们在原发性肿瘤中的相应杂交细胞。在确认可行性后，第二阶段将重点关注 这项技术的扩展，以解决三个关键的临床问题：（1）癌症是否可以可靠地去， 做了血液检测吗(2)在早期癌症中可以检测到隐匿性转移性疾病吗？(3)可以 血液检测有助于治疗监测，以个性化癌症治疗？成功完成本 工作将导致生物标志物面板和软件解决方案，以回答这些问题，如所示 在本文的胰腺癌和结肠直肠癌中，可以容易地扩展到其它癌症类型。