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Pan-Cancer Spatial Profiling Reveals Conserved Subtypes and Niches of Cancer-Associated Fibroblasts

计算生物学 间质细胞 生物 肿瘤微环境 转录组 癌症 鉴定(生物学) 背景(考古学) 生物信息学 遗传学 癌症研究 基因 生态学 基因表达 古生物学
作者
Hani Jieun Kim,Travis Ruan,Alexander Swarbrick
出处
期刊:Cancer Research [American Association for Cancer Research]
标识
DOI:10.1158/0008-5472.can-25-2181
摘要

Abstract Solid cancers are complex ‘ecosystems’ comprised of diverse cell types and extracellular molecules, where heterotypic interactions significantly influence disease etiology and therapeutic response. However, our current understanding of tumor microenvironments (TMEs) remains incomplete, hindering the development and implementation of novel TME-targeted drugs. To maximize cancer therapeutic development we require a systems-level understanding of the malignant, stromal, and immune states that define the tumor and determine treatment response. In their recent study, Liu and colleagues took a new approach to resolving the complexity of stromal heterogeneity. They leveraged extensive single-cell spatial multi-omics datasets across various cancer types and platforms to identify four conserved spatial Cancer-Associated Fibroblast (CAF) subtypes, classified by their spatial organization and cellular neighborhoods. Their work expands upon prior efforts to develop a CAF taxonomy, which primarily relied on single-cell RNA-Sequencing (scRNA-Seq) and yielded a multitude of classification systems. This study advances our understanding of CAF biology by establishing a link between spatial context and CAF identity across diverse tumor types. Departing from recent single-cell transcriptomics studies that employed a marker-based approach for sub-state identification, Liu and colleagues conducted de novo discovery of CAF subtypes using spatial neighborhood information alone. By positioning spatial organization as the defining axis of CAF heterogeneity, this research redefines CAF classification and provides a new framework for exploring the rules governing tumor ecosystems and developing novel ecosystem-based therapeutic strategies.
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