Evidence map›Paper›PMID 26942108›Full record

ArticleCurrent opinion in chemical engineering2016

The Predictive Link between Matrix and Metastasis.

L E Barney, L E Jansen, S R Polio, S Galarza, M E Lynch, S R Peyton

Abstract read
In one paragraph

Article in Current opinion in chemical engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
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  16. Article
  17. Vascularized Biomaterials to Study Cancer Metastasis.Advanced healthcare materials · 2020
    Review
  18. Article
  19. Article
  20. Review
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.

L E BarneyDepartment of Chemical Engineering, University of Massachusetts, Amherst Amherst, MA 01003.
L E JansenDepartment of Chemical Engineering, University of Massachusetts, Amherst Amherst, MA 01003.
S R PolioDepartment of Chemical Engineering, University of Massachusetts, Amherst Amherst, MA 01003.
S GalarzaDepartment of Chemical Engineering, University of Massachusetts, Amherst Amherst, MA 01003.
M E LynchDepartment of Chemical Engineering, University of Massachusetts, Amherst Amherst, MA 01003; Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst Amherst, MA 01003.
S R PeytonDepartment of Chemical Engineering, University of Massachusetts, Amherst Amherst, MA 01003.

Funding

CHEMISTRY-BIOLOGY INTERFACE PREDOCTORAL TRAINING GRANTST32GM008515 · NIGMS · UNIVERSITY OF MASSACHUSETTS AMHERST · PI THOMPSON, LYNMARIE K. · 1995 to 2020
$4.3M
Tissue-specific stem cells and breast cancer tissue tropism in bioengineered microenvironmentsDP2CA186573 · NCI · UNIVERSITY OF MASSACHUSETTS AMHERST · PI PEYTON, SHELLY R. · 2013 to 2015
$2.6M
NCI NIH HHS DP2 CA186573NIGMS NIH HHS T32 GM008515
6 · The paper itself

Abstract

Cancer spread (metastasis) is responsible for 90% of cancer-related fatalities. Informing patient treatment to prevent metastasis, or kill all cancer cells in a patient's body before it becomes metastatic is extremely powerful. However, aggressive treatment for all non-metastatic patients is detrimental, both for quality of life concerns, and the risk of kidney or liver-related toxicity. Knowing when and where a patient has metastatic risk could revolutionize patient treatment and care. In this review, we attempt to summarize the key work of engineers and quantitative biologists in developing strategies and model systems to predict metastasis, with a particular focus on cell interactions with the extracellular matrix (ECM), as a tool to predict metastatic risk and tropism.

Identifiers

PMID26942108
PMCPMC4770819

What OpenQuestion holds

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Registered trials

None linked

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.