Exploiting protein family and protein network data to identify novel drug targets for bladder cancer
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Tolulope Tosin Adeyelu1,2, Aurelio A. Moya-Garcia3,4 and Christine Orengo1
1 Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK
2 Louisiana State University, Department of Comparative Biomedical Science, Baton Rouge, LA 70803, USA
3 Laboratorio de Biología Molecular del Cáncer, Centro de Investigaciones Médico-Sanitarias (CIMES), Universidad de Málaga, Málaga 29071, Spain
4 Department of Molecular Biology and Biochemistry, Universidad de Málaga, Málaga 29071, Spain
|Aurelio A. Moya-Garcia,
Keywords: CATH-FunFams; bladder cancer; protein interaction network; drug targets; drug side effects
Received: June 01, 2021 Accepted: December 08, 2021 Published: January 12, 2022
Bladder cancer remains one of the most common forms of cancer and yet there are limited small molecule targeted therapies. Here, we present a computational platform to identify new potential targets for bladder cancer therapy. Our method initially exploited a set of known driver genes for bladder cancer combined with predicted bladder cancer genes from mutationally enriched protein domain families. We enriched this initial set of genes using protein network data to identify a comprehensive set of 323 putative bladder cancer targets. Pathway and cancer hallmarks analyses highlighted putative mechanisms in agreement with those previously reported for this cancer and revealed protein network modules highly enriched in potential drivers likely to be good targets for targeted therapies. 21 of our potential drug targets are targeted by FDA approved drugs for other diseases — some of them are known drivers or are already being targeted for bladder cancer (FGFR3, ERBB3, HDAC3, EGFR). A further 4 potential drug targets were identified by inheriting drug mappings across our in-house CATH domain functional families (FunFams). Our FunFam data also allowed us to identify drug targets in families that are less prone to side effects i.e., where structurally similar protein domain relatives are less dispersed across the human protein network. We provide information on our novel potential cancer driver genes, together with information on pathways, network modules and hallmarks associated with the predicted and known bladder cancer drivers and we highlight those drivers we predict to be likely drug targets.
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