ArticleBMC genomics2015
An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 20 citations in OpenAlex.
- Advances in the management of pediatric low-grade glioma.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026Review
- Host kinase regulation of Plasmodium vivax dormant and replicating liver stages.PLoS neglected tropical diseases · 2026Article
- Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis.BMC genomics · 2023Article
- Gene expression profile analysis to discover molecular signatures for early diagnosis and therapies of triple-negative breast cancer.Frontiers in molecular biosciences · 2022Article
- Exploiting vulnerabilities in cancer signalling networks to combat targeted therapy resistance.Essays in biochemistry · 2018Review
- Differential prioritization of therapies to subtypes of triple negative breast cancer using a systems medicine method.Oncotarget · 2017Article
- Identifying host regulators and inhibitors of liver stage malaria infection using kinase activity profiles.Nature communications · 2017Article
- GIW and InCoB, two premier bioinformatics conferences in Asia with a combined 40 years of history.BMC genomics · 2015Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundTriple-Negative Breast Cancer (TNBC) is an aggressive disease with a poor prognosis. Clinically, TNBC patients have limited treatment options besides chemotherapy. The goal of this study was to determine the kinase dependency in TNBC cell lines and to predict compounds that could inhibit these kinases using integrative bioinformatics analysis.
resultsWe integrated publicly available gene expression data, high-throughput pharmacological profiling data, and quantitative in vitro kinase binding data to determine the kinase dependency in 12 TNBC cell lines. We employed Kinase Addiction Ranker (KAR), a novel bioinformatics approach, which integrated these data sources to dissect kinase dependency in TNBC cell lines. We then used the kinase dependency predicted by KAR for each TNBC cell line to query K-Map for compounds targeting these kinases. We validated our predictions using published and new experimental data.
conclusionsIn summary, we implemented an integrative bioinformatics analysis that determines kinase dependency in TNBC. Our analysis revealed candidate kinases as potential targets in TNBC for further pharmacological and biological studies.
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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.