ReviewBiology2026
Spatial Transcriptomics in Breast Cancer: Advances and Applications.
Review in Biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
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Authors and funding
6 authors.
Funding
Abstract
BACKGROUND/
objectivesWhile traditional transcriptomics and single-cell RNA sequencing can reveal differences in cell type and gene expression, they cannot provide spatial information within tissues. Spatial transcriptomics (ST), as an emerging technology in recent years, has achieved significant progress in resolving gene expression along the spatial dimension. This technology quantifies gene expression at defined spatial coordinates and describes the spatial distribution of transcripts and the co-localization patterns between cells within intact tissue, allowing for an integrated analysis of molecular and spatial information. This review aims to systematically trace the development of ST and highlight its application value in breast cancer research.
methodsWe systematically reviewed the recent literature on ST platforms, on combined analyses of single-cell RNA sequencing (scRNA-seq) and ST, and on integrated spatial multi-omics in breast cancer. Key topics include tumor microenvironment organization, intra-tumor heterogeneity, the spatial distribution of immune cells, cancer-associated fibroblast function, treatment-response prediction, and personalized-treatment strategy development.
resultsST can characterize the spatial organization of interactions between breast cancer cells and the tumor microenvironment, describe the spatial dimensions of tumor heterogeneity, and provide multi-dimensional information that may support refined subtype classification and prognostic assessment. Existing studies indicate that ST shows significant potential to inform personalized treatment strategies, but the technology also faces bottlenecks in data integration, spatial resolution, standardization, and the need for functional validation.
conclusionsST provides an important tool for an in-depth description of the complex spatial organization within breast cancer tumors. When integrated with functional perturbation, longitudinal cohorts, and orthogonal omics, it has the potential to ultimately improve clinical outcomes for breast cancer patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.