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Record W4416812698 · doi:10.1016/j.addr.2025.115743

Implications of genetic and epigenetic aberrations to the tumorigenicity of human pluripotent stem cells

2025· article· en· W4416812698 on OpenAlexfundno aff
Gal Keshet, Ivana Barbaric, Nissim Benvenisty

Bibliographic record

VenueAdvanced Drug Delivery Reviews · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsnot available
FundersAzrieli FoundationIsrael Science FoundationUnited States-Israel Binational Science FoundationHORIZON EUROPE Framework ProgrammeRosetrees TrustEuropean Commission
KeywordsEpigeneticsInduced pluripotent stem cellEmbryonic stem cellHuman Induced Pluripotent Stem CellsEpigenesisGerm layerReprogramming

Abstract

fetched live from OpenAlex

Human pluripotent stem cells (hPSCs) hold immense promise for cell replacement therapies due to their capacity to give rise to derivatives of the three embryonic germ layers and their ability to divide indefinitely in culture. Since their first derivation less than 30 years ago, multiple hPSC-derived cell products are already in clinical trials for a range of pathologies. Nevertheless, hPSCs also possess an intrinsic tumorigenic potential and have been shown to acquire recurrent genetic and epigenetic aberrations strongly associated with cancer initiation and progression. These properties cast doubt on the safety of hPSCs and raise concerns regarding their use for transplantation. In this review, we summarize the different kinds of genetic and epigenetic abnormalities repeatedly observed in hPSCs, how they emerge, and their potential implications for the tumorigenicity of hPSC-based products. We also discuss shared and unique abnormalities found in hPSCs derived from different sources. Finally, we suggest possible methods for reducing the occurrence of these aberrations and managing their effects once they arise.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.299
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractno

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