Evidence map›Paper›PMID 27259278›Full record

ArticleOncotarget2016

Extracellular vesicles secreted by highly metastatic clonal variants of osteosarcoma preferentially localize to the lungs and induce metastatic behaviour in poorly metastatic clones.

Rebecca Macklin, Haolu Wang, Dorothy Loo, Sally Martin, Andrew Cumming, Na Cai, Rebecca Lane, Natalia Saenz Ponce, Eleni Topkas, Kerry Inder and 2 more

Open access · diamondAbstract read
In one paragraph

Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 13% of its field
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

36 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.

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  18. Extracellular Vesicles in Osteosarcoma: Antagonists or Therapeutic Agents?International journal of molecular sciences · 2021
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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

12 authors at 2 institutions in 1 country.

Rebecca MacklinThe University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, Australia.
Haolu WangTherapeutics Research Centre, School of Medicine, University of Queensland, Brisbane, Australia.
Dorothy LooThe University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, Australia.
Sally MartinQueensland Brain Institute, The University of Queensland, Brisbane, Australia.
Andrew CummingThe University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, Australia.
Na CaiThe University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, Australia.
Rebecca LaneThe University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, Australia.
Natalia Saenz PonceThe University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, Australia.
Eleni TopkasThe University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, Australia.
Kerry InderThe University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, Australia.
Nicholas A SaundersThe University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, Australia.
Liliana Endo-MunozThe University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, Australia.
University of Queensland · AUTranslational Research Institute · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Osteosarcoma (OS) is the most common pediatric bone tumor and is associated with the emergence of pulmonary metastasis. Unfortunately, the mechanistic basis for metastasis remains unclear. Tumor-derived extracellular vesicles (EVs) have been shown to play critical roles in cell-to-cell communication and metastatic progression in other cancers, but their role in OS has not been explored. We show that EVs secreted by cells derived from a highly metastatic clonal variant of the KHOS cell line can be internalized by a poorly metastatic clonal variant of the same cell line and induce a migratory and invasive phenotype. This horizontal phenotypic transfer is unidirectional and provides evidence that metastatic potential may arise via interclonal co-operation. Proteomic analysis of the EVs secreted by highly metastatic OS clonal variants results in the identification of a number of proteins and G-protein coupled receptor signaling events as potential drivers of OS metastasis and novel therapeutic targets. Finally, multiphoton microscopy with fluorescence lifetime imaging in vivo, demonstrated a preferential seeding of lung tissue by EVs derived from highly metastatic OS clonal variants. Thus, we show that EVs derived from highly metastatic clonal variants of OS may drive metastatic behaviour via interclonal co-operation and preferential colonization of the lungs.

Indexed as

Cell CommunicationBone NeoplasmsCell Line, TumorCell MovementCell ProliferationClone CellsDisease ProgressionExtracellular VesiclesGene Expression Regulation, NeoplasticHumansLung NeoplasmsMicroscopy, ElectronMicroscopy, Fluorescence, MultiphotonNeoplasm InvasivenessOsteosarcomaProteomicsReceptors, G-Protein-Coupledextracellular vesiclesinterclonal communicationmetastasisosteosarcomapre-metastatic niche

Identifiers

PMID27259278
PMCPMC5190045
OpenAlexW2411792372

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.