Cancer is a heterogeneous disease characterized by unregulated cell growth and promoted by mutations in cancerdriver genes some of which encode suitable drug targets. Since the distinct set of cancer driver genes can varybetween and within cancer types, evidence-based selection of drugs is crucial for targeted therapy following theprecision medicine paradigm. However, many putative cancer driver genes cannot be targeted directly, suggestingan indirect approach that considers alternative functionally related targets in the gene interaction network . Oncepotential drug targets have been identified, it is essential to consider all available drugs. Since tools that offer supportfor systematic discovery of drug repurposing candidates in oncology are lacking, we developed CADDIE, a webapplication integrating six human gene-gene and four drug-gene interaction databases, information regardingcancer driver genes, cancer-type specific mutation frequencies, gene expression information, genetically relateddiseases, and anticancer drugs. CADDIE offers access to various network algorithms for identifying drug targetsand drug repurposing candidates. It guides users from the selection of seed genes to the identification of therapeutictargets or drug candidates, making network medicine algorithms accessible for clinical research. CADDIE isavailable at https://exbio.wzw.tum.de/caddie/ and programmatically via a python package athttps://pypi.org/project/caddiepy/.
Hartung Michael, Anastasi Elisa, Mamdouh Zeinab M, Nogales Cristian, Schmidt Harald H H W, Baumbach Jan, Zolotareva Olga, List Markus. Cancer driver drug interaction explorer. Nucleic Acids Research. Vol. 50(W1)2022. Oxford University Press (OUP). [Cross Ref]