Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics - Groves, Sarah M.; Quaranta, Vito (Frontiers in Network Physiology, 2023)
Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment. In both scenarios, quantifying plasticity is essential for identifying high-plasticity states or elucidating transition paths between states. Currently, methods to quantify plasticity tend to focus on (1) quantification of quasipotential based on the underlying gene regulatory network dynamics of the system; or (2) inference of cell potency based on trajectory inference or lineage tracing in single-cell dynamics. Here, we explore both of these approaches and associated computational tools. We then discuss implications of each approach to plasticity metrics, and relevance to cancer treatment strategies.