Evidence map›Paper›PMID 35272694›Full record

ReviewBiomarker research2022

Clinical translation of patient-derived tumour organoids- bottlenecks and strategies.

Malia Alexandra Foo, Mingliang You, Shing Leng Chan, Gautam Sethi, Glenn K Bonney, Wei-Peng Yong, Edward Kai-Hua Chow, Eliza Li Shan Fong, Lingzhi Wang, Boon-Cher Goh

Abstract readReview
In one paragraph

Review in Biomarker research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
60citing 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

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

  1. Pooled it
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  4. Organoid technology in cancer research.Molecular biomedicine · 2026
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  6. Tumor organoids modeling reveals timed responses and interplay of radiotherapy and chemotherapy in pancreatic cancer.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026
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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

10 authors.

Malia Alexandra Foo *Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
Mingliang You *Hangzhou Cancer Institute, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou, 31002, China.
Shing Leng ChanCancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
Gautam SethiNUS Centre for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Glenn K BonneyDepartment of Surgery, National University Hospital, Singapore, Singapore.
Wei-Peng YongCancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
Edward Kai-Hua ChowCancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
Eliza Li Shan FongCancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
Lingzhi WangCancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore. csiwl@nus.edu.sg.ORCID http://orcid.org/0000-0001-5585-5426
Boon-Cher GohCancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore. phcgbc@nus.edu.sg.

Funding

Joint NCIS and N2CR Seed Funding R713-009-272-733National Medical Research Council, Singapore NMRC/CSASI/ 0006/2016National Medical Research Council, Singapore NMRC/ STaR/ MOH-000709
6 · The paper itself

Abstract

Multiple three-dimensional (3D) tumour organoid models assisted by multi-omics and Artificial Intelligence (AI) have contributed greatly to preclinical drug development and precision medicine. The intrinsic ability to maintain genetic and phenotypic heterogeneity of tumours allows for the reconciliation of shortcomings in traditional cancer models. While their utility in preclinical studies have been well established, little progress has been made in translational research and clinical trials. In this review, we identify the major bottlenecks preventing patient-derived tumour organoids (PDTOs) from being used in clinical setting. Unsuitable methods of tissue acquisition, disparities in establishment rates and a lengthy timeline are the limiting factors for use of PDTOs in clinical application. Potential strategies to overcome this include liquid biopsies via circulating tumour cells (CTCs), an automated organoid platform and optical metabolic imaging (OMI). These proposed solutions accelerate and optimize the workflow of a clinical organoid drug screening. As such, PDTOs have the potential for potential applications in clinical oncology to improve patient outcomes. If remarkable progress is made, cancer patients can finally benefit from this revolutionary technology.

Indexed as

MedicineOrganoidPrecisionThree-Dimensional (3D)Tumour

Identifiers

PMID35272694
PMCPMC8908618

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.