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A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors
Published on: December 7, 2014
Kinase Inhibitor Cardiotoxicity Database (KICDB): a causality-oriented multi-omics database for kinase
Jiamin Wei1, Yin Liu2, Miaoqing Wu3
1Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Insights
Kinase inhibitors (KIs) used in cancer therapy can cause heart damage. We created a database linking KI targets to heart conditions, identifying shared toxicity mechanisms and potential protective factors.
Area of Science:
- Cardiovascular research
- Oncology
- Genomics
Background:
- Kinase inhibitors (KIs) are vital for targeted cancer therapy.
- KI-induced cardiotoxicity presents a significant clinical challenge.
- A systematic resource for understanding KI cardiotoxicity mechanisms is needed.
Purpose of the Study:
- To develop a comprehensive database for exploring kinase inhibitor cardiotoxicity.
- To identify molecular determinants and causal mechanisms of KI-induced cardiotoxicity.
Main Methods:
- Developed the Kinase Inhibitor Cardiotoxicity Database (KICDB).
- Integrated large-scale transcriptomic meta-analysis (5291 samples).
- Applied causal inference, including Mendelian randomization (MR).
Main Results:
- Identified convergent disruption of cellular mitosis as a shared KI toxicity mechanism.
- Found 26 robust causal associations between kinase targets and cardiovascular risks.
- Identified potential cardioprotective factors, including TYRO3 and JAK2.
Conclusions:
- KICDB links transcriptomic changes to genetically validated causal drivers of cardiotoxicity.
- The database facilitates biomarker discovery and mechanistic exploration.
- KICDB aids in designing cardioprotective strategies against kinase inhibitor-induced heart damage.
Background:
Kinase inhibitors (KIs) are essential in targeted cancer therapy but frequently cause cardiotoxicity, limiting their clinical utility. A systematic resource to explore the underlying causal mechanisms is urgently needed.
Methods:
We developed the Kinase Inhibitor Cardiotoxicity Database (KICDB ), an interactive web platform integrating large-scale transcriptomic meta-analysis with causal inference to identify molecular determinants of KI-induced cardiotoxicity.
Results:
Meta-analysis of 5291 samples revealed a convergent disruption of the cellular mitotic machinery, specifically chromosome segregation and nuclear division, as a shared mechanism of toxicity across multiple KI classes. Furthermore, Mendelian randomization (MR) analysis identified 26 robust causal associations, linking specific kinase targets (e.g. RING finger protein 13 [RNF13] and tyrosine kinase with immunoglobulin like and EGF like domains 1 [TIE1]) to increased risks of cardiomyopathy and myocardial infarction, while identifying TYRO3 protein tyrosine kinase [Tyro3] and Janus kinase 2 (JAK2) as potential cardioprotective factors.
Conclusions:
KICDB provides a mechanistic framework linking transcriptomic perturbations with genetically validated causal drivers. By linking transcriptomic perturbations with causal validation, it serves as a resource to advance biomarker discovery, mechanistic exploration and the design of cardioprotective strategies.
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