Communication breakdown and evolution of the cancer cell
Nemati Fard, L. A.; Arora, C.; Miglionico, P.; Varisco, M.; Bisceglia, L.; Vukotic, R.; Raimondi, F.
Show 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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Identification of Relevant Genetic Alterations in Cancer using Topological Data Analysis 96%
- Broad misappropriation of developmental splicing profile by cancer in multiple organs 96%
- GZMKhigh CD8+ T effector memory cells are associated with CD15high neutrophil abundance in early-stage colorectal tumors and predict poor clinical outcome. 95%
Similar papers in this journal
- KDML: a machine-learning framework for inference of multi-scale gene functions from genetic perturbation screens 96%
- Explainable Machine Learning Identifies Dosage Compensation Factors in Aneuploid Human Cancer Cells 95%
- Pan-Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival 95%
Similar papers in this journal
- DUX4 is a common driver of immune evasion and immunotherapy failure in metastatic cancers 96%
- MGPfactXMBD: A Model-Based Factorization Method for scRNA Data Unveils Bifurcating Transcriptional Modules Underlying Cell Fate Determination 96%
- Gene interaction perturbation network deciphers a high-resolution taxonomy in colorectal cancer 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.