bioRxivBioinformaticsPreprint
Communication breakdown and evolution of the cancer cell
Lorenzo Amir Nemati FardiD, Chakit AroraiD, Pasquale MiglionicoiD, Martina VariscoiD, Luisa BiscegliaiD, Ranka VukoticiD, Francesco RaimondiiD
Abstract
1We studied cell-cell interactions (CCIs) in large-scale transcriptomic datasets, which showed higher co-expression in cancer compared to healthy tissues. CCIs are more co-expressed than any other type of intracellular interaction and, likewise, they are the protein-protein interaction (PPI) class that is most co-evolved in sequenced genomes. Similar trends of stricter regulation and evolutionary pressure are observed when comparing extracellular versus intracellular interactions mediated by G protein Coupled Receptors (GPCRs), whose ligand interactions are also characterized by a higher mutational burden in later tumor stages when considering somatic mutations associated with tumor clonal evolution. CCIs undergo the most extensive rewiring of their tumor co-expression networks relative to healthy tissues, more so than any other PPI type, with a set of CCI hubs highly conserved across multiple tumor tissues, and a higher diversity on healthy ones. Cancer rewiring is also associated with the formation of recurrent circuits of co-expressed CCI pairs, represented by enriched network motifs such as triad or tetrad cliques. These act as integrative hotspots to facilitate the crosstalk of distinct processes and the interaction of the cancer cell with its tumor microenvironment (TME). Remarkably, many CCI circuits are significantly associated with patient survival and are predictive of patient response to immunotherapy. CCI circuits mapping to allograft rejection and inflammatory response inform immunotherapy response prediction, while those related to epithelial-mesenchymal transition are associated with poorer prognosis. Overall, we show that CCIs expression signatures could be effectively exploited to stratify patients and, at the same time, they highlight new combination therapeutic opportunities in personalized medicine settings.
- organism: human
- assay: bulk RNA-seq
- research focus: cell-cell communication
- research focus: differential expression
- approach: statistical model
- Citations
- 1
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