Neighbours of cancer-related proteins have key influence on pathogenesis and could increase the drug target space for anticancer therapies

Abstract

Even targeted chemotherapies against solid cancers show a moderate success increasing the need to novel targeting strategies. To address this problem, we designed a systems-level approach investigating the neighbourhood of mutated or differentially expressed cancer-related proteins in four major solid cancers (colon, breast, liver and lung). Using signalling and protein–protein interaction network resources integrated with mutational and expression datasets, we analysed the properties of the direct and indirect interactors (first and second neighbours) of cancer-related proteins, not found previously related to the given cancer type. We found that first neighbours have at least as high degree, betweenness centrality and clustering coefficient as cancer-related proteins themselves, indicating a previously unknown central network position. We identified a complementary strategy for mutated and differentially expressed proteins, where the affect of differentially expressed proteins having smaller network centrality is compensated with high centrality first neighbours. These first neighbours can be considered as key, so far hidden, components in cancer rewiring, with similar importance as mutated proteins. These observations strikingly suggest targeting first neighbours as a novel strategy for disrupting cancer-specific networks. Remarkably, our survey revealed 223 marketed drugs already targeting first neighbour proteins but applied mostly outside oncology, providing a potential list for drug repurposing against solid cancers. For the very central first neighbours, whose direct targeting would cause several side effects, we suggest a cancer-mimicking strategy by targeting their interactors (second neighbours of cancer-related proteins, having a central protein affecting position, similarly to the cancer-related proteins). Hence, we propose to include first neighbours to network medicine based approaches for (but not limited to) anticancer therapies.

A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in a complex disease

“…Beside using interaction networks to identify disease-related modules and key proteins, it has also been used for finding novel pathogenetic players among the interactor partners of already known, key pathogenic genes by "guilt by association". We previously used this approach to identify potential drug repurposing targets in cancer 28 . In the current study we integrated all these three network reconstruction and analysis methodologies to understand the pathogenesis of complex diseases, such as UC better.…”

“…The network footprint of each patient contained the proteins encoded by the SNP-affected genes and the interactors of these proteins, i.e. their first neighbour proteins 28 . Unsupervised hierarchical clustering using different linkage algorithms and multidimensional scaling of the network footprints of 377 patients stratified the patients into the same four distinct clusters (Figures 3a and 3b).…”

“…Using network biology approaches, that we have previously exploited to uncover novel important proteins in cancer biology, 28 we aimed to further understand the pathogenic pathways of UC and to identify novel disease associated hidden proteins. These proteins are often hidden if one looks only conventional mutation and expression screens as they mostly act as direct interactors (first neighbours) of the proteins affected by the mutation.…”

PROTEINATOR: Web-UI exploring repurposing hypotheses of PROTEIN InhibiTORs based on protein interactions

“…(1) Disrupting a PPI by directly binding to the interfacial surface (2) Disrupting a PPI by allosteric interference (3) Allosterically increasing the binding affinity to the protein partner (4) Stabalize PPI by direct binding to the interacting surface Recent studies ((Modos et al, 2017)) show that neighbors of proteins implicated in cancer have a high degree of centrality in biological networks. They have clearly shown that first neighbor of cancer-related proteins have high local and global centrality in the network.…”

Regulatory network analysis of Paneth cell and goblet cell enriched gut organoids using transcriptomics approaches

“…In the presented analysis, the nodes of the interaction networks represent genes/molecules of interest from the transcriptomics data and the edges represent regulatory connections (molecular interactions) between the nodes inferred from databases. Studying regulatory interactions using interaction networks has been proved useful to uncover how cells respond to changing environments at a transcriptional level, to prioritise drug targets and to investigate the downstream effects of gene mutations and knockouts (32)(33)(34).…”