Context-specific signaling pathway construction in cancer through network motif search

Abnormal alterations in intracellular signaling networks are common features in many cancer types. Yet, accurately representing signaling networks by identifying context-specific interactions within complex interactomes remains a significant challenge. In this thesis, we focused on searching significant network motifs - repeating patterns in complex networks - to reconstruct context-specific networks and identify clinically important target proteins and pathways. In our novel method, we employed a motif analysis by screening a series of three-node kinase-mediated subnetworks including feed-forward and feed-back loops in a reference directed interactome and tested the significance of their presence compared to random interactomes. As a result, we found five prominent motifs: positive cascade, positive feedback loops, and coherent type-1, coherent type-2, and incoherent type-1 feed-forward loops (FFLs). Using this method, we generated tumor-specific networks for 69 ovarian cancer patients by combining their phosphoproteomic profiles in CPTAC with three-node motifs. Merging significant motifs having at least two differentially expressed phosphoproteins in the corresponding tumor followed by filtering out non-specific edges gives the final patient-specific network. On one side patient-specific networks contains intermediate nodes; on the other side, they represent causal relations between altered proteins and eventually pathways. All pair comparison of similarities between patient-specific networks resulted in 30% node and 5% network motif overlap on average. We utilized an unsupervised method to align patient groups with ovarian cancer cell lines and performed clustering based on mRNA expression profiles of four drug targets showing significant effects on patient survival. Consequently, we identified enriched targets and pathways within the clusters, and linked them to potential drugs, providing personalized therapeutic options for each patient cluster. This approach facilitated the assessment of kinase inhibitors' efficacy in a clinically relevant context and yielded valuable insights for developing personalized treatment strategies using personalized network motif profiles.