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Record W4409393897 · doi:10.1101/2025.04.07.647626

Identification of Injury-Response State Determinants in Glioblastoma Stem Cells

2025· preprint· en· W4409393897 on OpenAlexaff
Fatemeh Molaei, Graham MacLeod, Shahan Haider, Aleczandria S. Tiffany, Frank M Oteng, Jacob M. Berman, Arianna Skirzynska, Molly S. Shoichet, Daniel Schramek, Peter B. Dirks, Stéphane Angers

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsGlioblastomaIdentification (biology)Stem cellState (computer science)MedicineCancer researchBiologyComputer scienceCell biologyEcology

Abstract

fetched live from OpenAlex

Abstract Glioblastoma (GBM) is a common and highly lethal type of primary brain tumor in adults. Therapeutic failure is partly attributed to a fraction of Glioblastoma Stem Cells (GSCs) that show high levels of heterogeneity and plasticity. GSCs exist in a transcriptional gradient between two states: Developmental (Dev) and Injury Response (IR) in which IR-GSCs exhibit more invasive behaviors. While previous studies have identified fitness genes in GSCs, the genes required to establish and maintain the Dev and IR states remain poorly defined. To identify the regulators of the IR GSC state, we performed a phenotypic genome-wide CRISPR-Cas9 knockout (KO) screen in patient-derived GSCs based on cell surface expression of the IR marker CD44. Notably, we found that perturbations of the histone acetyltransferase EP300 in IR GSCs led to decreased CD44 cell surface expression, significant downregulation of gene expression signatures associated with the IR state, and to decreased self-renewal and invasion. Furthermore, genetic targeting of Ep300 in a mouse GBM model delayed tumor initiation and/or progression. Collectively, our results establish EP300 as a regulator of the IR state in GSCs and provide a mechanistic basis for its therapeutic targeting in GBM. Significance A genome-wide phenotypic CRISPR-Cas9 screen in a patient-derived Glioblastoma Stem Cell line identified the genes required to maintain the Injury-Response cellular state, with a focus on the histone acetyl transferase gene EP300 . This study suggests how therapeutic targeting of cellular state could reduce the aggressiveness of GBM tumors.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.302
Teacher spread0.277 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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 abstractyes

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