ReviewMolecular oncology2025
Decrypting cancer's spatial code: from single cells to tissue niches.
Review in Molecular oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Atlas-Guided Nanocarrier Strategies Targeting Spatial NTRK2/MAPK Signaling in EGFR-TKI-Resistant Niches of Esophageal Squamous Cell Carcinoma.Pharmaceutics · 2026Review
- Deciphering immunosuppressive niches by spatial single-cell proteomics: spatial interaction networks and translational opportunities.Frontiers in immunology · 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
5 authors.
Funding
Abstract
Spatial transcriptomics (ST) has emerged as a powerful tool to map gene expression patterns to the local tissue structure in cancer, enabling unprecedented insights into cellular heterogeneity and tumour microenvironments. As the technology matures, developing new, spatially informed analytical frameworks will be essential to fully leverage its potential to elucidate the complex organisation and emerging properties of cancer tissues. Here, we highlight key challenges in cancer spatial transcriptomics, focusing on three emerging topics: (a) defining cell states, (b) delineating cellular niches and (c) integrating spatial data with other modalities that can pave the way towards clinical translation. We discuss multiple analytical approaches that are currently implemented or could be adapted in the future in order to tackle these challenges, including classical biostatistics methods as well as methods inherited from geospatial analytics or artificial intelligence. In the rapidly expanding landscape of ST, such methodologies lay the foundation for biological discoveries that conceptualise cancer as an evolving system of interconnected niches.
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.