Mapping Phenotypic Plasticity upon the Cancer Cell State Landscape Using Manifold Learning

Phenotypic plasticity describes the ability of cancer cells to undergo dynamic, non -genetic cell state changes that amplify cancer heterogeneity to promote metastasis and therapy evasion. Thus, cancer cells occupy a continuous spectrum of phenotypic states connected by trajectories defining dynamic transitions upon a cancer cell state landscape. With technologies prolif-erating to systematically record molecular mechanisms at single-cell resolution, we illuminate manifold learning techniques as emerging computational tools to effectively model cell state dynamics in a way that mimics our understanding of the cell state landscape. We anticipate that state-gating therapies targeting phenotypic plasticity will limit cancer heterogeneity, metastasis, and therapy resistance.Significance: Nongenetic mechanisms underlying phenotypic plasticity have emerged as significant drivers of tumor heterogeneity, metastasis, and therapy resistance. Herein, we discuss new experimen-tal and computational techniques to defi ne phenotypic plasticity as a scaffold to guide accelerated progress in uncovering new vulnerabilities for therapeutic exploitation.

Automated summary

Phenotypic plasticity refers to the ability of cancer cells to undergo dynamic non-genetic changes in cell state, which amplifies cancer heterogeneity and promotes metastasis and therapy evasion. With the advancement of technologies to record molecular mechanisms at single-cell resolution, manifold learning techniques can effectively model cell state dynamics, resembling our understanding of the cell state landscape. State-gating therapies targeting phenotypic plasticity are anticipated to limit cancer heterogeneity, metastasis, and therapy resistance.