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Record W4388588920 · doi:10.1093/neuonc/noad179.1198

MODL-47. A NEW MOUSE MODEL OF IDH MUTATED GLIOMAS IDENTIFIES TUMOR CELLS OF ORIGIN AND DETERMINANTS OF SENSITIVITY TO IDH INHIBITORS

2023· article· en· W4388588920 on OpenAlexaff
Eric Laugesen, Ruxiao Tian, Annette Wu, Samah El Ghamrasni, Adrian Levine, Zoya Aamir, Liana Nobre, Kevin Petrecca, Samuel K. McBrayer, Mario L. Suvà, Cynthia Hawkins, Uri Tabori, Daniel Schramek, Tak W. Mak, Jérôme Fortin

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMcGill UniversityHospital for Sick ChildrenPrincess Margaret Cancer Centre
Fundersnot available
KeywordsBiologyIDH1MutantCancer researchIDH2GliomaTumor progressionGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Driver mutations in IDH1 and IDH2 characterize a substantial proportion of diffuse gliomas. These tumors are typically of lower grade at diagnosis, but many eventually transform to more aggressive disease. Furthermore, there is a lack of effective curative treatments. Mutated IDH molecules acquire neomorphic enzymatic activity, favoring the synthesis of D-2-hydroxyglutarate (D-2-HG). Accumulation of this metabolite perturbs many cellular functions and drives gliomagenesis through incompletely understood mechanisms. IDH mutations are early events in gliomagenesis, but the identity of the initiating cell type has been debated. Here, using genetically engineered mice, we found that combining Idh1R132H and Trp53 loss in oligodendrocyte progenitors leads to fully penetrant development of diffuse gliomas. The tumors recapitulate the cardinal features of the corresponding human disease. While heterogeneous, the mouse Idh1R132H and Idh1WT tumors show distinct patterns of transcriptional alterations and oncogenic copy number variants. Mutant IDH itself is an attractive drug target, as it is clonally expressed and usually retained during tumor evolution, even though its relevance for disease progression has been questioned. Nevertheless, recent clinical trials have demonstrated clear, but variable, anti-tumor effects of mutant IDH inhibitors in patients. To better understand the mechanistic basis for heterogeneous responses to mutant IDH inhibition, we performed CRISPR/Cas9 functional genomic screens in cell lines derived from the mouse Idh1R132H;Trp53MUT tumors. These revealed interactions between the mutant IDH inhibitor vorasidenib and molecules related to astrocytic differentiation, Notch signaling, and cell growth and metabolism. The translational relevance of these findings was supported by molecular data from human tumors, patients treated with IDH inhibitors, and human-derived cell models. Overall, these studies nominate oligodendrocyte progenitors as candidate cells of origin for IDH mutated gliomas, and point to strategies that may enhance the efficacy of IDH inhibitors.

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)0.000
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.035
GPT teacher head0.316
Teacher spread0.281 · 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 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

Citations3
Published2023
Admission routes1
Has abstractyes

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