Evidence map›Paper›PMID 26085954›Full record

ArticleQuantitative biology (Beijing, China)2013

Population dynamics of cancer cells with cell state conversions.

Da Zhou, Dingming Wu, Zhe Li, Minping Qian, Michael Q Zhang

Abstract read
In one paragraph

Article in Quantitative biology (Beijing, China), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Da ZhouMOE Key Laboratory of Bioinformatics; Bioinformatics Division/Center for Synthetic & Systems Biology, TNLIST; Department of Automation, Tsinghua University, Beijing 100084, China.
Dingming WuMOE Key Laboratory of Bioinformatics; Bioinformatics Division/Center for Synthetic & Systems Biology, TNLIST; Department of Automation, Tsinghua University, Beijing 100084, China.
Zhe LiComputational Neuroscience Lab, School of Medicine, Tsinghua University, Beijing 100084, China.
Minping QianSchool of Mathematical Sciences, Peking University, Beijing 100871, China.
Michael Q ZhangDepartment of Molecular and Cell Biology, Center for Systems Biology, The University of Texas at Dallas, Richardson, TX 75080, USA ; MOE Key Laboratory of Bioinformatics; Bioinformatics Division/Center for Synthetic & Systems Biology, TNLIST; Department of Automation, Tsinghua University, Beijing 100084, China.

Funding

The San Diego Epigenome CenterU01ES017166 · NIEHS · LUDWIG INSTITUTE FOR CANCER RES LTD · PI REN, BING · 2008 to 2013
$16.3M
NIEHS NIH HHS U01 ES017166
6 · The paper itself

Abstract

Cancer stem cell (CSC) theory suggests a cell-lineage structure in tumor cells in which CSCs are capable of giving rise to the other non-stem cancer cells (NSCCs) but not vice versa. However, an alternative scenario of bidirectional interconversions between CSCs and NSCCs was proposed very recently. Here we present a general population model of cancer cells by integrating conventional cell divisions with direct conversions between different cell states, namely, not only can CSCs differentiate into NSCCs by asymmetric cell division, NSCCs can also dedifferentiate into CSCs by cell state conversion. Our theoretical model is validated when applying the model to recent experimental data. It is also found that the transient increase in CSCs proportion initiated from the purified NSCCs subpopulation cannot be well predicted by the conventional CSC model where the conversion from NSCCs to CSCs is forbidden, implying that the cell state conversion is required especially for the transient dynamics. The theoretical analysis also gives the condition such that our general model can be equivalently reduced into a simple Markov chain with only cell state transitions keeping the same cell proportion dynamics.

Identifiers

PMID26085954
PMCPMC4467561

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.