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

项目摘要 患结直肠癌（CRC）的终生风险为7%， 32%的新诊断患者在“成功”治疗后。复发性疾病的病人有一个令人沮丧的 5年生存率14.3%。缺乏有效的生物标志物阻碍了转移前疾病的早期检测， 影响CRC的总生存率。我们发现了一种有希望的播散性肿瘤细胞， 巨噬细胞（M β）和癌细胞融合-其具有两种细胞起源的基因型和表型特征。 可检测到的沿着转移级联，杂交细胞可以启动肿瘤生长，迁移，以响应M 受体-配体趋化性和种子转移位点。在外周血中，杂种，称为循环杂种 在CRC患者中，CHC细胞（CHC）的数量超过了常规定义的循环肿瘤细胞（CTC），克服了 CTC作为疾病生物标志物的敏感性--主要障碍。CHC具有表型多样性 并反映原发肿瘤的蛋白表达。基于这些令人兴奋的发现，我们提出， 细胞亚群具有转移前细胞的离散表型， 通过多重成像和与组学的多模式整合进行基于单细胞图像的表型分析。到 为此，我们将分析来自早期和转移性肿瘤的CHC，以进行基于图像的表型分析 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方法，我们将定义单个细胞中的细胞间/亚细胞空间特征，以识别 新的亚群和分化与转移标志相关的离散表型群体。在 此外，将成像和基因组技术独立地应用于同一标本 测量高度维度但非正交的细胞特征集。成像的多模态集成 单细胞数据将量化细胞亚群的系统水平生物功能， 生物标志物面板，以获得生物标志物的特异性和灵敏度，用于在离散的CRC患者队列中进行验证。我们 总体目标是开发一种新的肿瘤生物标志物，基于CHC表型和组学分析， 用于提供新的定量见解，并开发具有上级准确性的机器驱动预测， 确定CRC患者的转移风险，最终影响生存率。

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.