Network integration of multi-tumour omics data suggests novel targeting strategies. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Valle, Ítalo; Menichetti, Giulia; Simonetti, Giorgia; Bruno, Samantha; Zironi, Isabella; Durso, Danielle; Mombach, José; Martinelli, Giovanni; Castellani, Gastone; Remondini, Daniel
• Is Part Of: Nature communications. Volume 9:Issue 1(2018)
• Page Start: 1
• Page End: 10
• Publication Date: 2018-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-018-06992-7
• Languages: English
• ISSNs: 2041-1723

Abstract

Abstract We characterize different tumour types in search for multi-tumour drug targets, in particular aiming for drug repurposing and novel drug combinations. Starting from 11 tumour types from The Cancer Genome Atlas, we obtain three clusters based on transcriptomic correlation profiles. A network-based analysis, integrating gene expression profiles and protein interactions of cancer-related genes, allows us to define three cluster-specific signatures, with genes belonging to NF-κB signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis biological processes. These signatures have been characterized by different approaches based on mutational, pharmacological and clinical evidences, demonstrating the validity of our selection. Moreover, we define new pharmacological strategies validated by in vitro experiments that show inhibition of cell growth in two tumour cell lines, with significant synergistic effect. Our study thus provides a list of genes and pathways that could possibly be used, singularly or in combination, for the design of novel treatment strategies. Tumours of different tissues can show similarities in genomic alterations. Here, the authors combine tumour transcriptome and protein interaction data in a network-based analysis of 11 tumours types, and identify clusters of tumours with specific signatures for multi-tumour drug targeting and survival prognosis.