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

Abstract

Solid cancers are complex "ecosystems" comprised of diverse cell types and extracellular molecules, in which heterotypic interactions significantly influence disease etiology and therapeutic response. However, our current understanding of tumor microenvironments remains incomplete, hindering the development and implementation of novel tumor microenvironment-targeted drugs. To maximize cancer therapeutic development, we require a system-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 multiomic 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 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 transcriptomic studies that employed a marker-based approach for substate 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. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.