Evidence map›Paper›PMID 22566967›Full record

ArticleFrontiers in immunology2012

Selection of Mesenchymal-Like Metastatic Cells in Primary Tumors - An in silico Investigation.

Vipin Narang, Shek Yoon Wong, Shiang Rong Leong, Bindu Harish, Jean-Pierre Abastado, Alexandre Gouaillard

Abstract read
In one paragraph

Article in Frontiers in immunology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Vipin NarangAgency for Science, Technology and Research, Singapore Immunology Network Singapore.
Shek Yoon Wong
Shiang Rong Leong
Bindu Harish
Jean-Pierre Abastado
Alexandre Gouaillard

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to metastasize, cancer cells must undergo phenotypic transition from an anchorage-dependent form to a motile form via a process referred to as epithelial to mesenchymal transition. It is currently unclear whether metastatic cells emerge late during tumor progression by successive accumulation of mutations, or whether they derive from distinct cell populations already present during the early stages of tumorigenesis. Similarly, the selective pressures that drive metastasis are poorly understood. Selection of cancer cells with increased proliferative capacity and enhanced survival characteristics may explain how some transformations promote a metastatic phenotype. However, it is difficult to explain how cancer cells that disseminate can emerge due to such selective pressure, since these cells usually remain dormant for prolonged periods of time. In the current study, we have used in silico modeling and simulation to investigate the hypothesis that mesenchymal-like cancer cells evolve during the early stages of primary tumor development, and that these cells exhibit survival and proliferative advantages within the tumor microenvironment. In an agent-based tumor microenvironment model, cancer cell agents with distinct sets of attributes governing nutrient consumption, proliferation, apoptosis, random motility, and cell adhesion were allowed to compete for space and nutrients. These simulation data indicated that mesenchymal-like cancer cells displaying high motility and low adhesion proliferate more rapidly and display a survival advantage over epithelial-like cancer cells. Furthermore, the presence of mesenchymal-like cells within the primary tumor influences the macroscopic properties, emergent morphology, and growth rate of tumors.

Indexed as

agent-based modeling and simulationcomplex systemsepithelial to mesenchymal transitiontumor microenvironment

Identifiers

PMID22566967
PMCPMC3342023

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Registered trials

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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.