ArticleGenome medicine2019
Encircling the regions of the pharmacogenomic landscape that determine drug response.
Article in Genome medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
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- Mutation-informed gene pairs to predict melanoma metastasis.Cell communication and signaling : CCS · 2026Article
- Using Biological Networks to Guide Biomedical Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Integrative network analysis reveals novel moderators of Aβ-Tau interaction in Alzheimer's disease.Alzheimer's research & therapy · 2025Article
- Learning and actioning general principles of cancer cell drug sensitivity.Nature communications · 2025Article
- scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network.Cell reports. Medicine · 2024Article
- The Transcriptional Landscape of Immune-Response 3'-UTR Alternative Polyadenylation in Melanoma.International journal of molecular sciences · 2024Article
- Article
- DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19.Computers in biology and medicine · 2023Article
- Current trends and future prospects of drug repositioning in gastrointestinal oncology.Frontiers in pharmacology · 2023Review
- Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque.Nature communications · 2022Article
- Network-based machine learning approach to predict immunotherapy response in cancer patients.Nature communications · 2022Article
- Turning liabilities into opportunities: Off-target based drug repurposing in cancer.Seminars in cancer biology · 2021Review
- Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.Nature communications · 2020Article
- Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker.Nature biotechnology · 2020Article
- Overcoming cancer therapeutic bottleneck by drug repurposing.Signal transduction and targeted therapy · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundThe integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations, and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics.
methodsTo simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome.
resultsWe apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity, and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses.
conclusionsNetwork biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.
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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.