Evidence map›Paper›PMID 25500073›Full record

ReviewTranslational oncology2014

Personalized in vitro cancer modeling - fantasy or reality?

Richard Bartlett, William Everett, Santi Lim, Natasha G, Marilena Loizidou, Gavin Jell, Aaron Tan, Alexander M Seifalian

Abstract readReview
In one paragraph

Review in Translational oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Relevance of 3d culture systems to study osteosarcoma environment.Journal of experimental & clinical cancer research : CR · 2018
    Review
  12. Article
  13. Characterization andFrontiers in bioengineering and biotechnology · 2018
    Article
  14. Review
  15. Review
  16. Article
  17. 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

8 authors.

Richard BartlettCentre for Nanotechnology & Regenerative Medicine, Research Department of Nanotechnology, UCL Division of Surgery & Interventional Science, University College London (UCL), London, UK; UCL Medical School, University College London (UCL), London, UK.
William EverettCentre for Nanotechnology & Regenerative Medicine, Research Department of Nanotechnology, UCL Division of Surgery & Interventional Science, University College London (UCL), London, UK; UCL Medical School, University College London (UCL), London, UK.
Santi LimCentre for Nanotechnology & Regenerative Medicine, Research Department of Nanotechnology, UCL Division of Surgery & Interventional Science, University College London (UCL), London, UK; UCL Medical School, University College London (UCL), London, UK.
Natasha GCentre for Nanotechnology & Regenerative Medicine, Research Department of Nanotechnology, UCL Division of Surgery & Interventional Science, University College London (UCL), London, UK; UCL Medical School, University College London (UCL), London, UK.
Marilena LoizidouCentre for Nanotechnology & Regenerative Medicine, Research Department of Nanotechnology, UCL Division of Surgery & Interventional Science, University College London (UCL), London, UK.
Gavin JellCentre for Nanotechnology & Regenerative Medicine, Research Department of Nanotechnology, UCL Division of Surgery & Interventional Science, University College London (UCL), London, UK.
Aaron TanCentre for Nanotechnology & Regenerative Medicine, Research Department of Nanotechnology, UCL Division of Surgery & Interventional Science, University College London (UCL), London, UK; UCL Medical School, University College London (UCL), London, UK; Biomaterials & Advanced Drug Delivery Laboratory (BioADD), Stanford School of Medicine, Stanford University, Stanford, CA, USA.
Alexander M SeifalianCentre for Nanotechnology & Regenerative Medicine, Research Department of Nanotechnology, UCL Division of Surgery & Interventional Science, University College London (UCL), London, UK; Royal Free London NHS Foundation Trust Hospital, London, UK. Electronic address: a.seifalian@ucl.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With greater technological advancements and understanding of pathophysiology, "personalized medicine" has become a more realistic goal. In the field of cancer, personalized medicine is the ultimate objective, as each cancer is unique and each tumor is heterogeneous. For many decades, researchers have relied upon studying the histopathology of tumors in the hope that it would provide clues to understanding the pathophysiology of cancer. Current preclinical research relies heavily upon two-dimensional culture models. However, these models have had limited success in recreating the complex interactions between cancer cells and the stroma environment in vivo. Thus, there is increasing impetus to shift to three-dimensional models, which more accurately reflect this phenomenon. With a more accurate in vitro tumor model, drug sensitivity can be tested to determine the best treatment option based on the tumor characteristics. Many methods have been developed to create tumor models or "tumoroids," each with its advantages and limitations. One significant problem faced is the replication of angiogenesis that is characteristic of tumors in vivo. Nonetheless, if three-dimensional models could be standardized and implemented as a preclinical research tool for therapeutic testing, we would be taking a step towards making personalized cancer medicine a reality.

Identifiers

PMID25500073
PMCPMC4311045

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.