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: 10704738
• 负责人: Michel Nederlof
• 金额: $ 81.82万
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10704738
• 关键词: Address; Antibodies; Architecture; Biological; Biological Assay; Biological Markers; Biology; Blood; Blood Tests; Blood specimen; Cancer Detection; Cancer Patient; Cataloging; 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; Health; Hybrid Cells; Image; Image Analysis; Immunofluorescence Immunologic; Indirect Immunofluorescence; Individual; 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; Phosphoproteins; Population; Prediction of Response to Therapy; Primary Neoplasm; Process; Proteins; Rectal Cancer; Research; Risk; Sampling; Science; 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; 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; prognostication; prototype; spatial relationship; success; symposium; targeted treatment; technology platform; tool; translational impact; translational potential; 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