期刊
CANCER DISCOVERY
卷 12, 期 8, 页码 1847-1859出版社
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/2159-8290.CD-21-0282
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资金
- Eunice Kennedy Shriver National Institute of Child Health and Human Development of the NIH [F31HD097958]
- National Health and Medical Research Council of Australia [GNT1088122, GNT1181230, GNT1184009, GNT2012848]
- Australian Research Council [DP220104036]
- National Breast Cancer Foundation [IIRS-19-092]
- Cancer Institute New South Wales Fellowship [CDF181243]
- Chan-Zuckerberg Initiative [182702, CZF2019-002440]
- National Science Foundation [2047856]
- Sloan Fellowship [FG-2021-15883]
- NIH [R01GM135929, R01GM130847]
- Ramaciotti Foundation Biomedical Research Award
- Nelune Foundation Rebecca Wilson Fellowship
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [2047856] Funding Source: National Science Foundation
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.
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.
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