ArticleBioinformatics (Oxford, England)2015
Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- miRNAs in the Box: Potential Diagnostic Role for Extracellular Vesicle-Packaged miRNA-27a and miRNA-128 in Breast Cancer.International journal of molecular sciences · 2023Pooled it
- Host kinase regulation of Plasmodium vivax dormant and replicating liver stages.PLoS neglected tropical diseases · 2026Article
- Pharmacological approaches to understanding protein kinase signaling networks.Frontiers in pharmacology · 2023Review
- Application of High Throughput Technologies in the Development of Acute Myeloid Leukemia Therapy: Challenges and Progress.International journal of molecular sciences · 2022Review
- Kinomic profile in patient-derived glioma cells during hypoxia reveals c-MET-PI3K dependency for adaptation.Theranostics · 2021Article
- Exploiting vulnerabilities in cancer signalling networks to combat targeted therapy resistance.Essays in biochemistry · 2018Review
- Identifying host regulators and inhibitors of liver stage malaria infection using kinase activity profiles.Nature communications · 2017Article
- Kinase activity ranking using phosphoproteomics data (KARP) quantifies the contribution of protein kinases to the regulation of cell viability.Molecular & cellular proteomics : MCP · 2017Article
- Progress towards a public chemogenomic set for protein kinases and a call for contributions.PloS one · 2017Article
- Exploiting receptor tyrosine kinase co-activation for cancer therapy.Drug discovery today · 2017Review
- An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer.BMC genomics · 2015Article
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Authors and funding
8 authors.
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Abstract
motivationTargeted kinase inhibitors have dramatically improved cancer treatment, but kinase dependency for an individual patient or cancer cell can be challenging to predict. Kinase dependency does not always correspond with gene expression and mutation status. High-throughput drug screens are powerful tools for determining kinase dependency, but drug polypharmacology can make results difficult to interpret.
resultsWe developed Kinase Addiction Ranker (KAR), an algorithm that integrates high-throughput drug screening data, comprehensive kinase inhibition data and gene expression profiles to identify kinase dependency in cancer cells. We applied KAR to predict kinase dependency of 21 lung cancer cell lines and 151 leukemia patient samples using published datasets. We experimentally validated KAR predictions of FGFR and MTOR dependence in lung cancer cell line H1581, showing synergistic reduction in proliferation after combining ponatinib and AZD8055. AVAILABILITY AND IMPLEMENTATION: KAR can be downloaded as a Python function or a MATLAB script along with example inputs and outputs at: http://tanlab.ucdenver.edu/KAR/. CONTACT: aikchoon.tan@ucdenver.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.