Vigneshwari Subramanian1,a, Bence Szalai1, Luis Tobalina1, Julio Saez-Rodriguez1,2,b Application of network diffusion approaches to drug screenings: A perspective on multilayered networks derived from drugs and cell lines References PrECISE is supported (in part) by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0324-2. The opinions expressed and arguments employed therein do not necessarily reﬂect the ofﬁcial views of the Swiss Government. The PrECISE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 668858. Funding www.saezlab.org @sysbiomed • RWR-MH can be used effectively to identify the most relevant genes associated with drug response • RWR-MH offers the possibility to predict patient-speciﬁc drug responses based on patient-cell line networks Conclusions / Perspectives PrECISE (Personalized Engine for Cancer Integrative Study and Evaluation) is a European H2020 project focused on prostate cancer, an international collaboration that aims to translate into clinical advances many of the technical and methodological developments achieved during the last years. • Identify the genes related to drug response by applying RWR-MH to a multilayered network that incorporates protein-protein interactions, drug-drug similarities, cell line-cell line similarities and co-expressed genes • Decipher targets for drugs whose mechanism of action is not well-elucidated • Guide drug sensitivity predictions based on the genomic status of a selected list of genes identiﬁed through RWR-MH Study Aims 1. RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, MTZ, Pauwelsstrasse 19, 52074-Aachen, Germany 2. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus CB10 1SD Hinxton, United Kingdom b) saezrodriguez@combine.rwth-aachen.de a) subramanian@combine.rwth-aachen.de • Multiplex heterogeneous networks (networks with multiple interconnected layers) could provide signiﬁcant information to understand the global topology, when compared to the networks that rely on single type of information1,2 • Random Walk with Restart on Multiplex Heterogeneous networks (RWR-MH) have been shown to effectively identify candidate genes of not well-studied diseases2 • Random walk with restart algorithm allows to explore all neighbours in close proximity to the seed nodes (diseases/ genes/ drugs) Background 1. Li, Y., Patra, J.C. (2010) Bioinformatics 26 (9), pp.1219-1224. 2. Valdeolivas, A., Ticket, L., Navarro, C. Et al. (2017) bioRxiv. DOI:10.1101/134734 3. Iorio, F., Knijnenburg, T.A., Vis, D.J. et al. (2016) Cell, 166(3), pp. 740-754. Drug-Drug similarity (766): Pearson’s correlation coefﬁcient based on normalised IC50s from GDSC (one drug against all cell lines) > 0.6 Cell line-Cell line similarity (1548): Pearson’s correlation coefﬁcient based on normalised IC50s from GDSC (one cell line against all drugs) > 0.6 Pathways / PPI (254766): Reactome, Biocarta, KEGG, InnateDB, Uniprot Coexpression (15319): Pearson’s correlation coefﬁcient based on Gene expression from GDSC (One gene across all cell lines) > 0.6 Construction of Drug-Gene-Cell Line Networks Seeds 265 drugs from GDSC3 Key Inferences Decipher targets of uncharacterised drugs based on Drug-Drug similarities Genes used Pearson’s correlation (Average of all drugs) All genes available in GDSC (17419) 0.42 Network genes identiﬁed by RWR-MH (500) 0.44 Performances of Random Forests drug response prediction models based on gene expression ANOVA: Prioritised genes from RWR-MH are among the most signiﬁcant ones that relate to drug response. Models based on network genes are as predictive as the models with all the available genes. ANOVA Correlation between Drug sensitivities and Mutation / CNV of network genes + MSI status +GDSC tissues IC50 ~ Mut / CNV / Gene Expression (gene) + MSI status + GDSC