ReviewNature reviews. Cancer2026
A guide to transcriptomic deconvolution in cancer.
Review in Nature reviews. Cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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
7 citing papers in PubMed.
- Patient-derived resources for decoding and targeting brain metastases ecosystems.EMBO molecular medicine · 2026Review
- Clinical overall survival prediction and disease characteristics of locally advanced non-small cell lung cancer: an integrated analysis based on the SEER and TCGA databases.Translational cancer research · 2026Article
- A transcriptional signature of resting mast cells is associated with improved disease outcome in HRGenes and immunity · 2026Article
- Transcriptomic subtypes in high-grade serous ovarian cancer are driven by tumor cellular composition.bioRxiv : the preprint server for biology · 2026Article
- Tumor microenvironment transcriptional activity enables robust stratification of chemotherapy response in triple-negative breast cancer.Cell reports. Medicine · 2026Article
- NLRP12 in cancer: a context-dependent regulator of tumor progression, immunity, and metabolism.Frontiers in oncology · 2026Review
- Editorial: Challenges and opportunities in tumor metabolomics.Frontiers in molecular biosciences · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Cancer tissues are heterogeneous mixtures of tumour, stromal and immune cells, where each component comprises multiple distinct cell types and/or states. Mapping this heterogeneity and understanding the unique contributions of each cell type to the tumour transcriptome is crucial for advancing cancer biology, yet high-throughput expression profiles from tumour tissues only represent combined signals from all cellular sources. Computational deconvolution of these mixed signals has emerged as a powerful approach to dissect both cellular composition and cell-type-specific expression patterns. Here, we provide a comprehensive guide to transcriptomic deconvolution, specifically tailored for cancer researchers, presenting a systematic framework for selecting and applying deconvolution methods, considering the unique complexities of tumour tissues, data availability and method assumptions. We detail 43 deconvolution methods and outline how different approaches serve distinctive applications in cancer research: from understanding tumour-immune surveillance to identifying cancer subtypes, discovering prognostic biomarkers and characterizing spatial tumour architecture. By examining the capabilities and limitations of these methods, we highlight emerging trends and future directions, particularly in addressing tumour cell plasticity and dynamic cell states.
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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.