SynthesisBMC cancer2026
Spatial and temporal intratumoral heterogeneity in breast cancer: a systematic and conceptual review of single-cell and spatial omics studies.
Synthesis in BMC cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Rapid relapse in triple-negative breast cancer: clinical patterns, platinum resistance, and implications for clonal evolution.Frontiers in pharmacology · 2026Article
- The heterogeneous and dynamic immune-tumor interface in breast cancer.Frontiers in oncology · 2026Review
- Integrating single-cell and spatial multi-omics for precision oncology: from tumor ecosystems to clinical decision-making.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSpatial and temporal intratumoral heterogeneity (ITH) remains a major challenge in the diagnosis, prognosis, and treatment of breast cancer. Recent advances in single-cell and spatial omics technologies have enabled unprecedented resolution of subclonal architectures, evolutionary trajectories, and microenvironmental interactions. This systematic and conceptual review aimed to synthesize and integrate current evidence on spatiotemporal ITH in human breast cancer, bridging empirical data with mechanistic interpretation through high-resolution profiling platforms.
methodsWe conducted a systematic review following PRISMA 2020 guidelines, searching three databases (PubMed, Scopus, and Web of Science) and screening 1037 records published between January 2018 and May 2025. 19 original studies were included based on predefined eligibility criteria targeting single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, or multi-omics approaches applied to human breast tumor samples. Data extraction focused on study design, technologies used, subclonal dynamics, spatial/temporal resolution, tumor–immune interactions, and risk of bias.
resultsThe included studies analyzed over 400,000 single cells from diverse breast cancer subtypes, with a predominance of triple-negative breast cancer. Subclonal plasticity was a recurrent feature, often characterized by EMT (epithelial-to-mesenchymal transition) signatures, cell-cycle heterogeneity, and immune evasion. Spatial analyses revealed discrete ecological niches shaped by immune exclusion and stromal patterning, while temporal assessments uncovered therapy-driven clonal selection, metabolic reprogramming, and enhancer remodeling. Interclonal and tumor–immune communication were consistently associated with poor prognosis or therapeutic resistance. Most studies were judged to have low or moderate risk of bias, with transparent reporting and accessible data pipelines.
conclusionsSingle-cell and spatial omics studies provide critical insights into the evolutionary ecology of breast cancer. By conceptually integrating spatial, temporal, and microenvironmental dimensions, this review highlights convergent evolutionary programs underlying tumor aggressiveness and resistance. Spatiotemporal ITH is a key driver of disease progression, and its systematic characterization could inform biomarker development, personalized therapies, and future multi-modal diagnostics. Continued integration of spatial, temporal, and functional data is essential to move from descriptive maps to clinically actionable frameworks.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.