Evidence map›Paper›PMID 42528148›Full record

ReviewCancer medicine2026

Patient-Derived Organoid-Based CRISPR Screens in Cancer Research: Applications, Advances, and Challenges.

Julianne du Plessis, Aadilah Omar

Abstract readReview
In one paragraph

Review in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Julianne du PlessisDepartment of Internal Medicine, University of the Witwatersrand, Johannesburg, South Africa.
Aadilah OmarDepartment of Internal Medicine, University of the Witwatersrand, Johannesburg, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patient-derived organoids (PDOs) have emerged as physiologically relevant cancer models that preserve key genetic, histological, and functional features of the tumors from which they are derived. In parallel, CRISPR-based perturbation technologies have transformed functional genomics by enabling scalable interrogation of gene function. Their integration provides a powerful framework for identifying cancer dependencies, modeling oncogenic evolution, and investigating mechanisms of drug response and resistance in patient-relevant settings. This review examines how CRISPR knockout, CRISPR interference/activation, and precision editing approaches have been applied in PDO systems to uncover context-specific vulnerabilities, reconstruct mutational trajectories, and study tumor heterogeneity. We further compare pooled and arrayed screening formats and discuss what is uniquely enabled by performing CRISPR screens in organoids rather than conventional 2D models. Particular emphasis is placed on the technical and analytical constraints of organoid-based screening, including variable editing efficiency, clonal bottlenecks, biological heterogeneity, and limited scalability. We argue that the major value of organoid-based CRISPR screening lies in its ability to identify functionally actionable cancer vulnerabilities in a patient-contextualized model, while also introducing methodological challenges that must be addressed for robust clinical translation.

Indexed as

Clustered Regularly Interspaced Short Palindromic RepeatsCRISPR-Cas SystemsNeoplasmsOrganoidsAnimalsGene EditingGenomicsHumansPrecision Medicinecancer functional genomicsCRISPR screeningdrug resistancepatient‐derived organoidsprecision oncologysynthetic lethality

Identifiers

PMID42528148
PMCPMC13420333

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.