ArticleCPT: pharmacometrics & systems pharmacology2013
Metasignatures identify two major subtypes of breast cancer.
Article in CPT: pharmacometrics & systems pharmacology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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.
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Who cites it
12 citing papers in PubMed.
- Precision omics data integration and analysis with interoperable ontologies and their application for COVID-19 research.Briefings in functional genomics · 2021Article
- Article
- Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine.Frontiers in pharmacology · 2020Review
- Integrative clustering of multi-level 'omic data based on non-negative matrix factorization algorithm.PloS one · 2017Article
- Drug-induced adverse events prediction with the LINCS L1000 data.Bioinformatics (Oxford, England) · 2016Article
- Oncogenic epigenetic control.Aging · 2016Article
- Histone H3 lysine 4 acetylation and methylation dynamics define breast cancer subtypes.Oncotarget · 2016Article
- Publisher’s Note:Abstraction for data integration:Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction.Computational biology and chemistry · 2015Article
- Drug/Cell-line Browser: interactive canvas visualization of cancer drug/cell-line viability assay datasets.Bioinformatics (Oxford, England) · 2014Article
- Lean Big Data integration in systems biology and systems pharmacology.Trends in pharmacological sciences · 2014Article
- Bayesian consensus clustering.Bioinformatics (Oxford, England) · 2013Article
- Network2Canvas: network visualization on a canvas with enrichment analysis.Bioinformatics (Oxford, England) · 2013Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Genome-wide expression data from tumors and cell lines in breast cancer, together with drug response of cell lines, open prospects for integrative analyses that can lead to better personalized therapy. Drug responses and expression data collected from cell lines and tumors were used to generate tripartite networks connecting clusters of patients to cell lines and cell lines to drugs, to connect drugs to patients. Various approaches were applied to connect cell lines to tumor clusters: a standard method that uses a biomarker gene set, and new methods that compute metasignatures for transcription factors and histone modifications given upregulated genes in cell lines or tumors. The results from the metasignature analysis identify two major clusters of patients: one enriched for active histone marks and one for repressive marks. The tumors enriched for activation marks are correlated with poor prognosis. Overall, the analyses suggest new patient clustering, discover dysregulated pathways, and recommend individualized use of drugs to treat subsets of patients.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e35; doi:10.1038/psp.2013.11; advance online publication 27 March 2013.
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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.