Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools.

Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies. © 2023. Springer Nature America, Inc.

Citation

Maalavika Pillai, Emilia Hojel, Mohit Kumar Jolly, Yogesh Goyal. Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools. Nature computational science. 2023 Apr;3(4):301-313

Mesh Tags

PMID: 38177938

search → result