Allam Mayar, Cai Shuangyi, Coskun Ahmet F
1Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA USA.
2Interdisciplinary Program in Bioengineering, Georgia Institute of Technology, Atlanta, GA USA.
NPJ Precis Oncol. 2020 May 1;4:11. doi: 10.1038/s41698-020-0114-1. eCollection 2020.
Cancers exhibit functional and structural diversity in distinct patients. In this mass, normal and malignant cells create tumor microenvironment that is heterogeneous among patients. A residue from primary tumors leaks into the bloodstream as cell clusters and single cells, providing clues about disease progression and therapeutic response. The complexity of these hierarchical microenvironments needs to be elucidated. Although tumors comprise ample cell types, the standard clinical technique is still the histology that is limited to a single marker. Multiplexed imaging technologies open new directions in pathology. Spatially resolved proteomic, genomic, and metabolic profiles of human cancers are now possible at the single-cell level. This perspective discusses spatial bioimaging methods to decipher the cascade of microenvironments in solid and liquid biopsies. A unique synthesis of top-down and bottom-up analysis methods is presented. Spatial multi-omics profiles can be tailored to precision oncology through artificial intelligence. Data-driven patient profiling enables personalized medicine and beyond.
癌症在不同患者中表现出功能和结构的多样性。在这种肿块中,正常细胞和恶性细胞共同创造了肿瘤微环境,而这种微环境在不同患者之间是异质性的。原发性肿瘤的残余物以细胞簇和单细胞的形式渗入血液,为疾病进展和治疗反应提供线索。这些分层微环境的复杂性需要阐明。尽管肿瘤包含多种细胞类型,但标准的临床技术仍然是仅限于单一标志物的组织学检查。多重成像技术为病理学开辟了新方向。现在,在单细胞水平上获取人类癌症的空间分辨蛋白质组学、基因组学和代谢图谱已成为可能。本文观点讨论了用于解析实体活检和液体活检中微环境级联反应的空间生物成像方法。文中提出了一种自上而下和自下而上分析方法的独特综合。空间多组学图谱可以通过人工智能定制以用于精准肿瘤学。数据驱动的患者剖析能够实现个性化医疗及更多目标。