ArticleCell reports2016
Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.
Article in Cell reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03941496 (Phase 1b/2a Safety and Immunogenicity of the DNMT Inhibitor Azacitidine During Anti-Tuberculosis Therapy), which is not on this map. Cited by 60 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.
Phase 1b/2a Safety and Immunogenicity of the DNMT Inhibitor Azacitidine During Anti-Tuberculosis Therapy
Who cites it
60 citing papers in PubMed, 123 citations in OpenAlex.
- Evaluating and Optimizing Mass Spectrometry Proteomics Data to Deconvolve Cell-Type-Specific Protein Expression in Tumors.Journal of proteome research · 2026Article
- A robust workflow to benchmark deconvolution of multi-omic data.Genome biology · 2025Article
- The multifaceted roles of non-coding RNAs in cell-cell communication in cardiovascular health and disease.Cell & bioscience · 2025Review
- Examining cellular heterogeneity in human DNA methylation studies: Overview and recommendations.STAR protocols · 2025Review
- STsisal: a reference-free deconvolution pipeline for spatial transcriptomics data.Frontiers in genetics · 2025Article
- Inferring tumor purity using multi-omics data based on a uniform machine learning framework MoTP.Briefings in bioinformatics · 2024Article
- TCA metabolism regulates DNA hypermethylation in LPS andProceedings of the National Academy of Sciences of the United States of America · 2024Article
- Identifying cell lines across pan-cancer to be used in preclinical research as a proxy for patient tumor samples.Communications biology · 2024Article
- Computational deconvolution of DNA methylation data from mixed DNA samples.Briefings in bioinformatics · 2024Review
- Frozen tissue coring and layered histological analysis improves cell type-specific proteogenomic characterization of pancreatic adenocarcinoma.Clinical proteomics · 2024Article
- Mapping three-dimensional intratumor proteomic heterogeneity in uterine serous carcinoma by multiregion microsampling.Clinical proteomics · 2024Article
- Novel Pan-ERR Agonists Ameliorate Heart Failure Through Enhancing Cardiac Fatty Acid Metabolism and Mitochondrial Function.Circulation · 2024Article
- Stromal cell-expressed malignant gene patterns contribute to the progression of squamous cell carcinomas across different sites.Frontiers in genetics · 2024Article
- DECOMICS, a shiny application for unsupervised cell type deconvolution and biological interpretation of bulk omic data.Bioinformatics advances · 2024Article
- A novel STAT3/ NFκB p50 axis regulates stromal-KDM2A to promote M2 macrophage-mediated chemoresistance in breast cancer.Cancer cell international · 2023Article
- Breast cancers as ecosystems: a metabolic perspective.Cellular and molecular life sciences : CMLS · 2023Review
- Methylome-wide association study of anxiety disorders.Molecular psychiatry · 2023Article
- Deconvolution of cancer cell states by the XDec-SM method.PLoS computational biology · 2023Article
- Article
- Stroma-derived miR-214 coordinates tumor dissemination.Journal of experimental & clinical cancer research : CR · 2023Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 1 country.
Funding
Abstract
Cancer progression depends on both cell-intrinsic processes and interactions between different cell types. However, large-scale assessment of cell type composition and molecular profiles of individual cell types within tumors remains challenging. To address this, we developed epigenomic deconvolution (EDec), an in silico method that infers cell type composition of complex tissues as well as DNA methylation and gene transcription profiles of constituent cell types. By applying EDec to The Cancer Genome Atlas (TCGA) breast tumors, we detect changes in immune cell infiltration related to patient prognosis, and a striking change in stromal fibroblast-to-adipocyte ratio across breast cancer subtypes. Furthermore, we show that a less adipose stroma tends to display lower levels of mitochondrial activity and to be associated with cancerous cells with higher levels of oxidative metabolism. These findings highlight the role of stromal composition in the metabolic coupling between distinct cell types within tumors.
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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.