ArticleNature communications2019
Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Drug repositioning for immunotherapy in breast cancer using single-cell analysis.NPJ systems biology and applications · 2024Pooled it
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Multiomics integration of serum proteome and autoantibody profiles reveals diagnostic and prognostic biomarkers in glioma.Journal of neuro-oncology · 2026Article
- Article
- Integrative multi-omic analysis reveals a PAX8-driven gene network linking tumor stemness to therapy response in ovarian cancer.NAR genomics and bioinformatics · 2025Article
- A distinct mechanism of epigenetic reprogramming silences PAX2 and initiates endometrial carcinogenesis.The Journal of clinical investigation · 2025Article
- Single-cell analysis of chromatin and expression reveals age- and sex-associated alterations in the human heart.Communications biology · 2024Article
- O-GlcNAcylation of MITF regulates its activity and CDK4/6 inhibitor resistance in breast cancer.Nature communications · 2024Article
- Article
- SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data.Nature biotechnology · 2023Article
- Comprehensive single cell aging atlas of mammary tissues reveals shared epigenomic and transcriptomic signatures of aging and cancer.bioRxiv : the preprint server for biology · 2023Article
- Article
- Dynamic regulatory module networks for inference of cell type-specific transcriptional networks.Genome research · 2022Article
- Chromatin interaction-aware gene regulatory modeling with graph attention networks.Genome research · 2022Article
- CharPlant: A De Novo Open Chromatin Region Prediction Tool for Plant Genomes.Genomics, proteomics & bioinformatics · 2021Article
- SPaRTAN, a computational framework for linking cell-surface receptors to transcriptional regulators.Nucleic acids research · 2021Article
- Expanding the phenotype of E318K (c.952G > A) MITF germline mutation carriers: case series and review of the literature.Hereditary cancer in clinical practice · 2021Article
- Fra-1 regulates its target genes via binding to remote enhancers without exerting major control on chromatin architecture in triple negative breast cancers.Nucleic acids research · 2021Article
- Single-cell RNA-seq dissects the intratumoral heterogeneity of triple-negative breast cancer based on gene regulatory networks.Molecular therapy. Nucleic acids · 2021Article
- Mechanical properties of nucleoprotein complexes determined by nanoindentation spectroscopy.Nucleus (Austin, Tex.) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
Chromatin accessibility data can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators. We develop a computational strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to combine chromatin accessibility data with large tumor expression data and model the effect of enhancers on transcriptional programs in multiple cancers. We generate a new ATAC-seq data profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and apply PSIONIC to 723 patient and 96 cell line RNA-seq profiles from ovarian, uterine, and basal breast cancers. Our computational framework enables us to share information across tumors to learn patient-specific TF activities, revealing regulatory differences between and within tumor types. PSIONIC-predicted activity for MTF1 in cell line models correlates with sensitivity to MTF1 inhibition, showing the potential of our approach for personalized therapy. Many identified TFs are significantly associated with survival outcome. To validate PSIONIC-derived prognostic TFs, we perform immunohistochemical analyses in 31 uterine serous tumors for ETV6 and 45 basal breast tumors for MITF and confirm that the corresponding protein expression patterns are also significantly associated with prognosis.
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.