ArticlePLoS computational biology2009
Transcriptional profiling of the dose response: a more powerful approach for characterizing drug activities.
Article in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Review
- Modeling omics dose-response at the pathway level with DoseRider.Computational and structural biotechnology journal · 2025Article
- Transcriptional characterization of iPSC-derived microglia as a model for therapeutic development in neurodegeneration.Scientific reports · 2024Article
- DOSE-L1000: unveiling the intricate landscape of compound-induced transcriptional changes.Bioinformatics (Oxford, England) · 2023Article
- Chemical genetics of regeneration: Contrasting temporal effects of CoClDevelopmental dynamics : an official publication of the American Association of Anatomists · 2021Article
- A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response.PLoS computational biology · 2021Article
- Gene-signature-derived ICScientific reports · 2020Article
- Dose-Response Metabolomics To Understand Biochemical Mechanisms and Off-Target Drug Effects with the TOXcms Software.Analytical chemistry · 2020Article
- Therapy-induced stress response is associated with downregulation of pre-mRNA splicing in cancer cells.Genome medicine · 2018Article
- Intestinal toxicity of the type B trichothecene mycotoxin fusarenon-X: whole transcriptome profiling reveals new signaling pathways.Scientific reports · 2017Article
- Identifying ultrasensitive HGF dose-response functions in a 3D mammalian system for synthetic morphogenesis.Scientific reports · 2016Article
- A negative-feedback loop between the detoxification/antioxidant response factor SKN-1 and its repressor WDR-23 matches organism needs with environmental conditions.Molecular and cellular biology · 2013Article
- Array-based pharmacogenomics of molecular-targeted therapies in oncology.The pharmacogenomics journal · 2012Review
- Characterization of BMS-911543, a functionally selective small-molecule inhibitor of JAK2.Leukemia · 2012Article
- SDRS--an algorithm for analyzing large-scale dose-response data.Bioinformatics (Oxford, England) · 2011Article
- Gene Expression Profiling via Multigene Concatemers.PloS one · 2011Article
- FDR-FET: an optimizing gene set enrichment analysis method.Advances and applications in bioinformatics and chemistry : AABC · 2011Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data.
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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.