Evidence map›Paper›PMID 33493799›Full record

ReviewTranslational oncology2021

3D Bioprinted cancer models: Revolutionizing personalized cancer therapy.

Robin Augustine, Sumama Nuthana Kalva, Rashid Ahmad, Alap Ali Zahid, Shajia Hasan, Ajisha Nayeem, Lana McClements, Anwarul Hasan

Abstract readReview
In one paragraph

Review in Translational oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
89citing 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

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

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29 more citing papers are in PubMed but not listed here.

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.

Robin AugustineDepartment of Mechanical and Industrial Engineering, College of Engineering, Qatar University, 2713 Doha, Qatar; Biomedical Research Center (BRC), Qatar University, PO Box 2713 Doha, Qatar. Electronic address: robin.augustine@qu.edu.qa.
Sumama Nuthana KalvaDepartment of Mechanical and Industrial Engineering, College of Engineering, Qatar University, 2713 Doha, Qatar; Biomedical Research Center (BRC), Qatar University, PO Box 2713 Doha, Qatar.
Rashid AhmadDepartment of Mechanical and Industrial Engineering, College of Engineering, Qatar University, 2713 Doha, Qatar; Biomedical Research Center (BRC), Qatar University, PO Box 2713 Doha, Qatar.
Alap Ali ZahidDepartment of Mechanical and Industrial Engineering, College of Engineering, Qatar University, 2713 Doha, Qatar; Biomedical Research Center (BRC), Qatar University, PO Box 2713 Doha, Qatar.
Shajia HasanDepartment of Mechanical and Industrial Engineering, College of Engineering, Qatar University, 2713 Doha, Qatar; Biomedical Research Center (BRC), Qatar University, PO Box 2713 Doha, Qatar.
Ajisha NayeemDepartment of Biotechnology, St. Mary's College, Thrissur, 680020, Kerala, India.
Lana McClementsSchool of Life Sciences, Faculty of Science, University of Technology Sydney, 2007, NSW, Australia.
Anwarul HasanDepartment of Mechanical and Industrial Engineering, College of Engineering, Qatar University, 2713 Doha, Qatar; Biomedical Research Center (BRC), Qatar University, PO Box 2713 Doha, Qatar. Electronic address: ahasan@qu.edu.qa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

After cardiovascular disease, cancer is the leading cause of death worldwide with devastating health and economic consequences, particularly in developing countries. Inter-patient variations in anti-cancer drug responses further limit the success of therapeutic interventions. Therefore, personalized medicines approach is key for this patient group involving molecular and genetic screening and appropriate stratification of patients to treatment regimen that they will respond to. However, the knowledge related to adequate risk stratification methods identifying patients who will respond to specific anti-cancer agents is still lacking in many cancer types. Recent advancements in three-dimensional (3D) bioprinting technology, have been extensively used to generate representative bioengineered tumor in vitro models, which recapitulate the human tumor tissues and microenvironment for high-throughput drug screening. Bioprinting process involves the precise deposition of multiple layers of different cell types in combination with biomaterials capable of generating 3D bioengineered tissues based on a computer-aided design. Bioprinted cancer models containing patient-derived cancer and stromal cells together with genetic material, extracellular matrix proteins and growth factors, represent a promising approach for personalized cancer therapy screening. Both natural and synthetic biopolymers have been utilized to support the proliferation of cells and biological material within the personalized tumor models/implants. These models can provide a physiologically pertinent cell-cell and cell-matrix interactions by mimicking the 3D heterogeneity of real tumors. Here, we reviewed the potential applications of 3D bioprinted tumor constructs as personalized in vitro models in anticancer drug screening and in the establishment of precision treatment regimens.

Indexed as

3D cancer modelsBioprintingCancerCancer modelsPersonalized medicine

Identifiers

PMID33493799
PMCPMC7823217

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.