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Record W4411253612 · doi:10.1016/j.xcrm.2025.102183

BRAF/MEK inhibition induces cell state transitions boosting immune checkpoint sensitivity in BRAFV600E-mutant glioma

2025· article· en· W4411253612 on OpenAlexaff
Yao Lulu Xing, Dena Panovska, Jong‐Whi Park, Stefan Grossauer, Katharina Koeck, Brandon Bui, Emon Nasajpour, Jeffrey Nirschl, Zhi-Ping Feng, Pierre Cheung, Pardes Habib, Ruolun Wei, Jie Wang, Wes Thomason, Michelle Monje, Joanne Xiu, Alexander Beck, Katharina J. Weber, Patrick N. Harter, Michael Lim, Kelly B. Mahaney, Laura M. Prolo, Gerald A. Grant, Xuhuai Ji, Kyle M. Walsh, Jean M. Mulcahy Levy, Dolores Hambardzumyan, Claudia Petritsch

Bibliographic record

VenueCell Reports Medicine · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsInstitute of Infection and Immunity
FundersStanford Cancer InstituteBaton Rouge Area FoundationNational Institutes of HealthNational Cancer InstituteNational Institute of Neurological Disorders and StrokeUniversity of California, San Francisco
KeywordsBoosting (machine learning)MutantGliomaCancer researchImmune checkpointSensitivity (control systems)Immune systemMelanomaChemistryBiologyImmunotherapyImmunologyComputer scienceGeneticsGeneArtificial intelligence

Abstract

fetched live from OpenAlex

Xing et al. report that combined BRAF and MEK inhibition (BRAFi+MEKi) in BRAFV600E-mutant high-grade glioma shifts tumor cell states and upregulates PD-L1, via Galectin-3 secretion, contributing to T cell suppression. Concurrent but not sequential immune checkpoint inhibition in mice overcomes these tumor-intrinsic adaptations, highlighting its translational promise for glioma therapy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.018
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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 teacher head, 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

Citations5
Published2025
Admission routes1
Has abstractyes

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