Leveraging the molecular signatures of cancer for dynamic network

Drug resistance poses a significant challenge to the effectiveness of therapies, driven by accumulation of molecular alterations within dynamic cellular networks. In this thesis, we used a discrete dynamic model, Graph-based Cellular Automata (GCA), to reveal the network-based history of tumor progression and causal association between network modules and drug resistance by data integration. GCA can simulate dynamic systems using initial static information, set of states and simple transition rules. The reference graph is a tissue-specific interactome that consists of both protein-protein interactions and the regulatory network of the transcription factor to gene interactions composed of 8,228 nodes and 63,574 edges. By incorporating known biology and statistical rules of molecular alterations, including stimulations, repressions, and (non)-linear pairwise molecular correlations, GCA simulates molecular signalling and propagates mutation effects downstream of signalling pathways and complexes. Eventually, GCA gives a trajectory of subnetwork models for each context. In comparisons of simulations with and without mutations at the node level, we detected functional subnetworks within the dynamic network structure. We used publicly available omics data from a well-established cancer cell line repository to optimize the GCA model and construct dynamic networks for each cell-line-drug pair for interpreting drug resistance mechanisms at the pathway level. The accuracy of these drug representative networks were evaluated by cross-validation and on an independent test from Patient-Derived Xenografts (PDX). Notably, we found context-specific pathways (e.g. MAPK signalling) involving proteins from drug-resistant cell lines and PDX samples, thereby linking them to investigated drug resistance mechanisms. Overall, this approach, from molecular alterations to dynamic networks, transforms already available large datasets to gain new clinically relevant insights about drug resistance, offering potential implications for cancer therapy.