Drug resistance and consequent tumor relapse are major clinical challenges in cancer treatment. While pre-existing subpopulations of genetically distinct drug-resistant cells have been shown to contribute to this phenomenon, recent studies reveal the role of drug-induced cell-state switching in aggravating disease outcomes by unlocking phenotypic plasticity and heterogeneity. To overcome this challenge, it is essential to understand the emergent dynamics of underlying regulatory networks enabling such switching. I will present several examples from our work integrating computational modeling of regulatory networks, single-cell high-throughput data and experimental validation to better understand what trajectories cells take to evade targeted therapy as well as immunotherapy in different cancers. We present an in silico platform to rationally identify combinatorial therapies to minimize cancer aggressiveness.