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A systems pharmacology approach to model tyrosine kinase inhibitor‐induced cardiotoxicity gene interaction networks (844.17)
Author(s) -
Sarntivijai Sirarat,
Hur Junguk,
Ozgur Arzucan,
Burkhart Keith,
He Yongqun,
Omenn Gilbert,
Athey Brian,
Abernethy Darrell
Publication year - 2014
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.28.1_supplement.844.17
Subject(s) - adverse effect , cardiotoxicity , medicine , adverse event reporting system , kegg , dasatinib , gene interaction , computational biology , bioinformatics , gene , pharmacology , tyrosine kinase , computer science , gene ontology , biology , gene expression , receptor , genetics , chemotherapy
This study implements an integrative bioinformatics workflow to predict gene networks underlying TKI‐induced cardiac adverse events (AEs). The Ontology of Adverse Events (OAE) assisted Natural Language Processing (NLP) algorithm analyzes PubMed abstracts for gene interactions based on the gene centrality in a network.The algorithm utilizes support vector machine (SVM) learning to determine gene pairs of interaction. The gene interactions were mined in association with TKI‐induced cardiac AEs identified by disproportionality analysis of the FDA Adverse Event Reporting System data. These TKIs include dasatinib, imatinib, lapatinib, cetuximab, and trastuzumab. The result gene interaction networks were classified and interpreted for their relevance to the established knowledge confirmed by published experiments. Significant findings include mediator genes linking JUN and ABL1 to JAK‐STAT pathway, and inflammatory response mechanism.