Evidence map›Paper›PMID 38948533›Full record

ReviewCancer innovation2024

A comprehensive review of 3D cancer models for drug screening and translational research.

Karthikey Sharma, Sreenath Dey, Rounik Karmakar, Aravind Kumar Rengan

Abstract readReview
In one paragraph

Review in Cancer innovation, 2024. 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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  9. Review
  10. Tools of the trade: leveraging 3DFrontiers in pharmacology · 2026
    Review
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  13. Review
  14. Article
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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

4 authors.

Karthikey SharmaDepartment of Biomedical Engineering Indian Institute of Technology (IIT) Hyderabad India.
Sreenath DeyDepartment of Biomedical Engineering Indian Institute of Technology (IIT) Hyderabad India.ORCID https://orcid.org/0000-0002-1328-0326
Rounik KarmakarDepartment of Biomedical Engineering Indian Institute of Technology (IIT) Hyderabad India.ORCID https://orcid.org/0009-0006-1233-6232
Aravind Kumar RenganDepartment of Biomedical Engineering Indian Institute of Technology (IIT) Hyderabad India.ORCID https://orcid.org/0000-0003-3994-6760

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The 3D cancer models fill the discovery gap of 2D cancer models and play an important role in cancer research. In addition to cancer cells, a range of other factors include the stroma, density and composition of extracellular matrix, cancer-associated immune cells (e.g., cancer-associated fibroblasts cancer cell-stroma interactions and subsequent interactions, and a number of other factors (e.g., tumor vasculature and tumor-like microenvironment in vivo) has been widely ignored in the 2D concept of culture. Despite this knowledge, the continued use of monolayer cell culture methods has led to the failure of a series of clinical trials. This review discusses the immense importance of tumor microenvironment (TME) recapitulation in cancer research, prioritizing the individual roles of TME elements in cancer histopathology. The TME provided by the 3D model fulfills the requirements of in vivo spatiotemporal arrangement, components, and is helpful in analyzing various different aspects of drug sensitivity in preclinical and clinical trials, some of which are discussed here. Furthermore, it discusses models for the co-assembly of different TME elements in vitro and focuses on their synergistic function and responsiveness as tumors. Furthermore, this review broadly describes of a handful of recently developed 3D models whose main focus is limited to drug development and their screening and/or the impact of this approach in preclinical and translational research.

Indexed as

3D modelscancerdrug developmenttranslational researchtumor microenvironment elements

Identifiers

PMID38948533
PMCPMC11212324

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.