KICDB: A Causality-Oriented Multi-Omics Database for Kinase Inhibitor-Induced Cardiotoxicity

Background

Kinase inhibitors (KIs) are mainstays of targeted cancer therapy, but their clinical utility is frequently limited by cardiotoxicity. A systematic resource to explore the underlying causal mechanisms is urgently needed.

Results

This database centralizes the findings from a comprehensive meta-analysis of 26 kinase inhibitors (KIs) across 7 studies (n=5291) identified 8,907 significant gene expression changes in human cardiomyocytes. To establish causality, we performed a two-pronged Mendelian randomization (MR) analysis testing hundreds of downstream genes and a panel of 43 key kinase proteins against 46 cardiovascular outcomes. This large-scale analysis revealed 26 significant causal associations, implicating novel molecular mediators in KI-induced cardiotoxicity.

Conclusions

KICDB serves as a valuable and accessible platform for the cardio-oncology community. By integrating transcriptomic signatures with causal inference data, the database empowers researchers to formulate mechanistic hypotheses, accelerate biomarker validation, and guide the design of future cardioprotective strategies.

URL: https://zhang-lab-database.shinyapps.io/KICDB/

Key Points

• We developed KICDB, a comprehensive and publicly accessible web server, to systematically investigate the causal mechanisms of KI-induced cardiotoxicity.

• KICDB integrates a large-scale meta-analysis of transcriptomic data from 26 KIs with a robust Mendelian randomization (MR) framework to move beyond correlation and infer causality.

• The analysis identified 8,907 significant gene expression changes and 26 significant causal associations between KI-associated genes and 46 cardiovascular outcomes.