Integrative single cell analyses of inflammation-derived circulating hybrid cells to identify aggressive disease

对炎症衍生的循环杂交细胞进行综合单细胞分析以识别侵袭性疾病

• 批准号: 10608043
• 负责人: Young Hwan Chang
• 金额: $ 60.67万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10608043
• 关键词: Advanced Development; Age; American Cancer Society; Biological; Biological Markers; Biological Process; Cancer Etiology; Cell fusion; Cell physiology; Cells; Cessation of life; Characteristics; Chemotaxis; Colorectal Cancer; Computational Biology; Data; Detection; Development; Diagnosis; Disease; Early Diagnosis; Foundations; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Genes; Genomics; Genotype; Goals; Heterogeneity; Hybrid Cells; Hybrids; Image; Image Enhancement; Individual; Inflammation; Ligands; Macrophage; Measures; Modality; Molecular; Names; Neoplasm Circulating Cells; Neoplasm Metastasis; Newly Diagnosed; Operative Surgical Procedures; Pathway interactions; Patient risk; Patients; Phenotype; Population; Primary Neoplasm; Prognosis; Proliferating; Property; Recurrence; Recurrent disease; Risk; Sample Size; Sampling; Sensitivity and Specificity; Side; Site; Specificity; Specimen; Survival Rate; System; Technology; Testing; Time; Tumor Markers; Validation; biomarker development; biomarker panel; biomarker validation; cancer cell; cancer imaging; cellular imaging; cohort; colon cancer patients; deep learning; detection assay; early detection biomarkers; experience; genomic signature; high dimensionality; imaging approach; imaging biomarker; imaging modality; improved; insight; lifetime risk; liquid biopsy; macrophage product; metastatic colorectal; migration; multidisciplinary; multimodality; multiplexed imaging; neoplastic cell; novel; peripheral blood; predictive modeling; prognostic; protein expression; receptor; response; screening; screening guidelines; sensor; single cell analysis; single-cell RNA sequencing; treatment response; tumor; tumor growth; tumor initiation

PROJECT SUMMARY The lifetime risk for acquiring colorectal cancer (CRC) is 7%, with an astounding rate of disease recurrence in 32% of newly diagnosed patients after their “successful” treatment. Patient with recurrent disease have a dismal 14.3% five-year survival. Lack of effective biomarkers hampers early detection of pre-metastatic disease, impacting overall survival from CRC. We identified a promising disseminated tumor cell—a product of macrophage (MФ) and cancer cell fusion—that harbors genotypic and phenotypic features of both cells of origin. Detectable along the metastatic cascade, hybrid cells can initiate tumor growth, migrate in response to MФ receptor-ligand chemotaxis, and seed metastatic sites. In peripheral blood, hybrids, named circulating hybrid cells (CHCs) outnumber conventionally defined circulating tumor cells (CTCs) in CRC patients, overcoming the sensitivity of CTC—a primary barrier—to usage as a biomarker for disease. CHCs are phenotypically diverse and reflect protein expression of the primary tumor. Based on these exciting findings, we propose that hybrid cells subpopulations harbor discrete phenotypes of pre-metastatic cells that can be identified and defined using single cell image-based phenotyping through multiplexed imaging and multimodal integration with –omics. To this end, we will analyze CHCs derived from early stage and metastatic tumors for image-based phenotyping with single cell gene expression. Utilizing quantitative and advanced image analytics including deep learning approach for image-based cell profiling, we will define inter/subcellular spatial features in single cells to identify new subpopulations and differentiate discrete phenotypic populations associated with metastatic signatures. In addition, the application of both imaging and genomic technologies to the same specimen independently measures highly dimensional, yet non-orthogonal, sets of cellular features. Multimodal integration of imaging and single cell data will quantify systems-level biological functions of cellular subpopulation and enhance imaging biomarker panel to gain biomarker specificity and sensitivity for validation in a discrete CRC patient cohort. Our overall goal is to develop a novel tumor biomarker, based upon CHC phenotyping and –omics analyses that can be used to provide new quantitative insights and develop machine-driven prediction with superior accuracy for identifying risk of metastases in CRC patients to ultimately impact survival.

项目摘要 终身获取结直肠癌（CRC）的风险为7％，疾病复发的速度令人惊讶 32％的新诊断患者在“成功”治疗后。复发性疾病的患者有惨淡的 14.3％的五年生存。缺乏有效的生物标志物会阻碍早期检测中转移性疾病， 影响CRC的总体生存。我们确定了一个承诺的散布肿瘤细胞 - 巨噬细胞（MO）和癌细胞融合 - 具有两个原始细胞的基因型和表型特征。 沿着转移性级联反应可检测的杂化细胞可以启动肿瘤生长，响应MOS迁移 受体配体趋化性和种子转移性位点。在外周血中，杂种，命名为循环混合动力 CRC患者的细胞（CHC）外部数量常规定义的循环肿瘤细胞（CTC），克服了 CTC的敏感性（主要障碍）用作疾病的生物标志物。 CHC在表型上是潜水员 并反映原发性肿瘤的蛋白质表达。基于这些令人兴奋的发现，我们提出了混合 细胞亚群具有可以使用和定义的，可以使用和定义的分散细胞的离散表型 基于单细胞图像的表型通过多重成像和与–omics的多模式整合。到 这一目的，我们将分析从早期和转移性肿瘤衍生的CHC进行基于图像的表型 单细胞基因表达。利用定量和高级图像分析，包括深度学习 基于图像的细胞分析的方法，我们将在单个单元中定义/亚细胞间空间特征以识别 新的亚群和与转移性特征相关的离散表型种群。在 此外，独立地将成像和基因组技术应用于同一标本 测量高度尺寸但非正交的细胞特征集。成像的多模式集成 单细胞数据将量化细胞亚群的系统级生物学功能并增强成像 生物标志物面板以获得离散CRC患者队列中验证的生物标志物特异性和灵敏度。我们的 总体目标是基于CHC表型和 - 可以分析的新型肿瘤生物标志物 可用于提供新的定量见解，并以优越的准确性来开发机器驱动的预测 确定CRC患者转移的风险最终影响生存。