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

项目总结 患结直肠癌(CRC)的终生风险为7%，疾病复发率令人震惊 32%的新诊断患者在治疗成功后。复发性疾病的患者有一种忧郁的 五年生存率14.3%。缺乏有效的生物标志物阻碍了对转移前疾病的早期检测， 影响结直肠癌患者的总体存活率。我们鉴定了一种有希望的播散性肿瘤细胞--一种 巨噬细胞(MФ)和癌细胞融合--这两种来源的细胞都具有基因和表型特征。 杂交细胞可以沿着转移级联被检测到，它可以启动肿瘤生长，响应MФ的迁移 受体-配体趋化作用和种子转移部位。在外周血液中，杂交种，称为循环杂交种 在结直肠癌患者中，细胞(CHC)的数量超过了传统定义的循环肿瘤细胞(CTCs)，克服了 CTC是一种主要的屏障，对用作疾病生物标志物的敏感性。CHC的表型多种多样 并反映原发肿瘤的蛋白表达。基于这些令人兴奋的发现，我们提出了杂交 细胞亚群包含不同的转移前细胞表型，可以用以下方法识别和定义 通过多路成像和与组学的多模式集成，基于单细胞图像的表型。至 为此，我们将分析来自早期和转移肿瘤的CHC，以进行基于图像的表型鉴定 单细胞基因表达。利用包括深度学习在内的定量和高级图像分析 对于基于图像的细胞轮廓，我们将在单个细胞中定义细胞间/亚细胞的空间特征来识别 新的亚群，并区分与转移特征相关的离散表型种群。在……里面 此外，成像和基因组技术都独立地应用于同一标本 测量高维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 生物标记物小组获得生物标记物的特异性和敏感性，在离散的结直肠癌患者队列中进行验证。我们的 总体目标是开发一种新的肿瘤生物标记物，基于CHC表型和组学分析，可以 用于提供新的量化见解并开发具有更高精度的机器驱动预测 确定结直肠癌患者的转移风险，以最终影响生存。

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.