Evidence map›Paper›PMID 42895119›Full record

ReviewCell biochemistry and function2026

Patient-Derived Organoids in Functional Precision Oncology: Tumor Modeling, Drug-Response Prediction, and Clinical Translation.

Abdullah Jabri, Mohamed Alsharif, Abdulaziz Mhannayeh, Bader Taftafa, Dania Sibai, Abdulrahman Elsalti, Raghad Alsharif, Tanveer Ahmad Mir, Mohammed Imran Khan, Jahan Salma and 1 more

Abstract readReview
In one paragraph

Review in Cell biochemistry and function, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Abdullah JabriCollege of Medicine, Alfaisal University, Riyadh, Saudi Arabia.ORCID https://orcid.org/0009-0000-8048-9528
Mohamed AlsharifCollege of Medicine, Alfaisal University, Riyadh, Saudi Arabia.ORCID https://orcid.org/0000-0003-0546-4210
Abdulaziz MhannayehCollege of Medicine, Alfaisal University, Riyadh, Saudi Arabia.ORCID https://orcid.org/0009-0009-4809-7554
Bader TaftafaCollege of Medicine, Alfaisal University, Riyadh, Saudi Arabia.ORCID https://orcid.org/0009-0002-3146-9218
Dania SibaiCollege of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Abdulrahman ElsaltiDepartment of Internal Medicine, Acıbadem MAA University, Istanbul, Turkey.
Raghad AlsharifCollege of Medicine, AlMaarefa University, Riyadh, Saudi Arabia.
Tanveer Ahmad MirKing Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.ORCID https://orcid.org/0000-0002-0093-0924
Mohammed Imran KhanKing Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia.ORCID https://orcid.org/0000-0001-5832-5017
Jahan SalmaKing Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.ORCID https://orcid.org/0009-0000-2989-8895
Ahmed YaqinuddinCollege of Medicine, Alfaisal University, Riyadh, Saudi Arabia.ORCID https://orcid.org/0000-0001-7536-910X

Funding

Alfaisal University, Office of Research
6 · The paper itself

Abstract

Patient-derived organoids (PDOs) have emerged as an effective experimental platform for bridging the long-standing gap between traditional in vitro cancer models and the biological complexity of actual tumors. These three-dimensional cultures (3D), derived directly from patient tissues, preserve essential architectural, genomic, and phenotypic characteristics of their tumors of origin, enabling more accurate modeling of tumor heterogeneity and disease evolution. Over the last decade, rapid methodological advances in organoid culture systems, extracellular matrices (ECMs), and growth factor optimization have facilitated the establishment of organoid models for a wide range of malignancies and the development of large living biobanks representing diverse patient populations. These models are increasingly being utilized to study cancer biology, elucidate mechanisms of treatment resistance, and evaluate patient-specific therapeutic responses, highlighting their potential as tools for precision oncology. However, important challenges remain, including incomplete representation of the tumor microenvironment (TME), variability in culture conditions, and limited scalability, and the need for greater standardization and validation. Consequently, although PDOs show considerable promise for translational research and personalized medicine, their routine clinical implementation remains largely investigational, and their clinical utility has not yet been fully established. This review examines the technological foundations of PDO generation, assesses their role as experimental models of tumor biology, and discusses their expanding applications in translational cancer research and precision oncology while highlighting key challenges that must be addressed before their widespread clinical adoption.

Indexed as

Antineoplastic AgentsModels, BiologicalNeoplasmsOrganoidsPrecision MedicineTranslational Research, BiomedicalHumansTumor MicroenvironmentAntineoplastic Agentscancer organoidsclinical translationCRISPR functional genomicsdrug response predictionfunctional precision oncologyorgan‐on‐chippatient‐derived organoidsthree‐dimensional cell culturetumor heterogeneitytumor microenvironment

Identifiers

PMID42895119
PMCPMC13678655

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.