Stochastic State Transitions Give Rise to Phenotypic Equilibrium in Populations of Cancer Cells
Open accessAbstract
Cancer cells within individual tumors often exist in distinct phenotypic states that differ in functional attributes. While cancer cell populations typically display distinctive equilibria in the proportion of cells in various states, the mechanisms by which this occurs are poorly understood. Here, we study the dynamics of phenotypic proportions in human breast cancer cell lines. We show that subpopulations of cells purified for a given phenotypic state return towards equilibrium proportions over time. These observations can be explained by a Markov model in which cells transition stochastically between states. A prediction of this model is that, given certain conditions, any subpopulation of cells will return to equilibrium phenotypic proportions over time. A second prediction is that breast cancer stem-like cells arise de novo from non-stem-like cells. These findings contribute to our understanding of cancer heterogeneity and reveal how stochasticity in single-cell behaviors promotes phenotypic equilibrium in populations of cancer cells.
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Ergodic rate analysis accurately inferred cell cycle trajectories from single-cell measurements of fixed steady-state populations.
“In pioneering work based on a few markers, ergodic rate analysis (ERA) was used to accurately infer trajectories along the cell cycle from single-cell measurements of fixed steady-state populations136.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [136]
Because cells are unsynchronized, single cells captured at any time point stochastically include cells at different instantaneous points along a temporal trajectory.
“Since cells are unsynchronized, the set of single cells captured at any time point will stochastically contain cells positioned in different instantaneous time points along the temporal trajectory59,135,136 (Fig. 1b).”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [136]
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- Authors
- Gupta, P. B., Fillmore, C. M., Jiang, G., Shapira, S. D., Tao, K., Kuperwasser, C., & Lander, E. S.
- Affiliations
- Broad Institute (US)
- Tufts Medical Center (US)
- Tufts University (US)
- Corresponding author
- Gupta, Piyush B.
- Lander, Eric S.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2011-08
- Volume
- 146
- Issue
- 4
- Pages
- 633-644
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W2129375080
- Cited by
- 1,475
- References
- 68
- Citations by year
- 2026: 45 · 2025: 68 · 2024: 56 · 2023: 84 · 2022: 63 · 2021: 98 · 2020: 112 · 2019: 116 · 2018: 112 · 2017: 93 · 2016: 128 · 2015: 141 · 2014: 139 · 2013: 116 · 2012: 92
- FWCI
- 55.67
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- Breast Cancer Research Foundation
- Raymond and Beverly Sackler Foundation
- Topics
- Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- tumor heterogeneity, stochastic state transitions, Markov model, breast cancer cell lines, breast cancer stem cells, single-cell behavior, tumor cell populations
- MeSH terms
- Animals, Breast Neoplasms, Female, Flow Cytometry, Humans, Markov Chains, Neoplasm Transplantation, Stochastic Processes, Transplantation, Heterologous, Neoplastic Stem Cells, Mice, SCID, Mice, Inbred NOD, Gene Expression Profiling, Mice
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