Cellular adaptation to cancer therapy along a resistance continuum [CRISPR-Screen: Kuramochi]

Advancements in precision oncology over the past decades have led to new therapeutic interventions, but the efficacy of such treatments is generally limited by an adaptive process that fosters drug resistance. In addition to genetic mutations, recent research has identified a role for non-genetic plasticity in transient drug tolerance and the acquisition of stable resistance. However, the dynamics of cell-state transitions that occur in the adaptation to cancer therapies remain unknown and require a systems-level longitudinal framework. Here we demonstrate that resistance develops through trajectories of cell-state transitions accompanied by a progressive increase in cell fitness, which we denote as the 'resistance continuum'. This cellular adaptation involves a stepwise assembly of gene expression programmes and epigenetically reinforced cell states underpinned by phenotypic plasticity, adaptation to stress and metabolic reprogramming. Our results support the notion that epithelial-to-mesenchymal transition or stemness programmes-often considered a proxy for phenotypic plasticity-enable adaptation, rather than a full resistance mechanism. Through systematic genetic perturbations, we identify the acquisition of metabolic dependencies, exposing vulnerabilities that can potentially be exploited therapeutically. The concept of the resistance continuum highlights the dynamic nature of cellular adaptation and calls for complementary therapies directed at the mechanisms underlying adaptive cell-state transitions. Kuramochi cells from C (untreated) and adapted (olaparib resistant) T10 and T320 populations were sorted based on their CD24 and CD44 profiles and expanded before being transfected with CRISPR/Cas9 knockout metabolism library. After puromycin selection, cells were collected (initial time point) and expanded by 10 population doublings without drug treatment (C, T10 and T320) and on drug treatment (CD on 1 μM and T320D on 40 μM of olaparib), thus constituting the end time point of the screen. Guide RNAs were sequenced and depletion scores were determined for each sample based on the abundance of gRNAs at the end time point relative to the initial time point.