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Related Concept Videos

Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Cancer Stem Cells and Tumor Maintenance02:40

Cancer Stem Cells and Tumor Maintenance

Early diagnosis and treatment can often cure cancer. However, even with treatment, residual cells called cancer stem cells (CSC) might remain, often causing tumor recurrence. These cancer stem cells possess the potential for self-renewal and multi-lineage differentiation and are often responsible for the therapeutic resistance displayed in most cancers.
Cancer stem cells are thought to originate from tissue-specific normal stem cells or progenitor cells. The normal stem cells usually reside in...
Cancer Stem Cells and Tumor Maintenance02:40

Cancer Stem Cells and Tumor Maintenance

Early diagnosis and treatment can often cure cancer. However, even with treatment, residual cells called cancer stem cells (CSC) might remain, often causing tumor recurrence. These cancer stem cells possess the potential for self-renewal and multi-lineage differentiation and are often responsible for the therapeutic resistance displayed in most cancers.
Cancer stem cells are thought to originate from tissue-specific normal stem cells or progenitor cells. The normal stem cells usually reside in...
Treatment Resistant Cancers02:56

Treatment Resistant Cancers

Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...

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Updated: May 28, 2026

An In Vitro System to Study Tumor Dormancy and the Switch to Metastatic Growth
09:14

An In Vitro System to Study Tumor Dormancy and the Switch to Metastatic Growth

Published on: August 11, 2011

Computational Approaches to Cancer Cell Dormancy: From Detection to Dynamic Modelling.

Lucas G N Spink1, Shi Pan1, Minyoung Kim1

  • 1UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, UK.

Biomolecules
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Cancer cell dormancy, a state of reversible growth arrest, poses challenges in understanding recurrence. Computational methods are crucial for defining dormant cell identity and dynamics, moving beyond static classifications.

Keywords:
cancer cell dormancycell state transitionscomputational modellingdisseminated tumour cellsdrug-tolerant persister cellstumour reactivation

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A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer Cells
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Last Updated: May 28, 2026

An In Vitro System to Study Tumor Dormancy and the Switch to Metastatic Growth
09:14

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Published on: August 11, 2011

An In Vitro Dormancy Model of Estrogen-sensitive Breast Cancer in the Bone Marrow: A Tool for Molecular Mechanism Studies and Hypothesis Generation
08:48

An In Vitro Dormancy Model of Estrogen-sensitive Breast Cancer in the Bone Marrow: A Tool for Molecular Mechanism Studies and Hypothesis Generation

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A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer Cells
12:48

A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer Cells

Published on: February 16, 2018

Area of Science:

  • Oncology
  • Computational Biology
  • Systems Biology

Background:

  • Cancer cell dormancy is a critical yet poorly understood state characterized by reversible growth arrest and delayed recurrence.
  • Advances in single-cell and multi-omic technologies aid in detecting dormant and persister cell populations, but their molecular underpinnings remain elusive.

Purpose of the Study:

  • To review the application of computational methods in elucidating cancer cell dormancy.
  • To explore how these methods infer dormant cell identity, heterogeneity, microenvironmental influences, state transitions, and reactivation dynamics.

Main Methods:

  • Review of computational approaches applied to single-cell transcriptomics, lineage tracing, spatial profiling, and integrative multi-omic analyses.
  • Examination of mathematical and statistical frameworks for modeling dormancy and reactivation.

Main Results:

  • Computational analyses reveal significant context-dependent variability in dormant cell states, challenging the concept of a universal dormancy signature.
  • Existing methods struggle with fragmented definitions, rare-state detection, and analyzing temporal processes from static data.

Conclusions:

  • Progress in understanding cancer dormancy requires computational frameworks that treat it as a dynamic, multi-scale systems problem, not a static classification.
  • Integrating diverse computational approaches is essential for resolving the complexities of dormancy and its clinical implications.