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Record W4416140904 · doi:10.1093/neuonc/noaf201.0359

CSIG-25. EXPLORING THE ROLE OF THE CIC/YY1 COMPLEX IN MODULATING SENSITIVITY OF MEK INHIBITORS IN GLIOBLASTOMA

2025· article· en· W4416140904 on OpenAlexaff
Phooja Persaud, Severa Bunda, Gelareh Zadeh

Bibliographic record

VenueNeuro-Oncology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMAPK/ERK pathwayEffectorTranscription factorSuppressorKinaseTranscription (linguistics)Downregulation and upregulationCell growthSignal transduction

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Glioblastoma (GBM) is the most common and aggressive primary brain tumor, with a median survival of just 15 months following diagnosis. Standard therapies remain largely ineffective due to pronounced intra-tumoral heterogeneity and rapid development of resistance mechanisms, underscoring the need for novel therapeutic approaches. A hallmark of GBM is hyperactivation of the RTK/RAS/MEK/ERK signaling pathway, which drives tumor growth and progression. One critical downstream effector is Capicua (CIC), a tumor suppressor and HMG box transcription factor. Normally, CIC represses oncogenic transcription factors such as ETV1, ETV4, and ETV5. In GBM, sustained ERK activity leads to CIC degradation, lifting this repression and promoting oncogene expression. While this mechanism is well characterized, our data indicates that CIC degradation persists even when ERK is pharmacologically inhibited, suggesting alternative regulatory mechanisms. A phospho-kinase array identified p90 ribosomal S6 kinase (p90RSK), a downstream ERK effector, as significantly upregulated following ERK inhibition. Notably, p90RSK has been implicated in regulating CIC function and contributing to resistance in other malignancies. Additionally, CIC forms a co-repressor complex with Yin Yang 1 (YY1), a multifunctional transcription factor. The stability of this complex is essential for repressing oncogenic programs; however, hyperactive RTK signaling may destabilize it, potentially via p90RSK-mediated mechanisms. METHODS Biochemical, molecular, and in vivo assays, including Western blotting, qPCR, immunoprecipitation, ChIP-qPCR, cell viability assays, and xenograft models, were used to assess p90RSK’s role in regulating CIC/YY1 function and GBM growth. RESULTS MEK inhibition alone failed to restore CIC levels or suppress p90RSK activation. p90RSK interacted with and destabilized the CIC/YY1 complex, derepressing ETV1/4/5. Dual MEK and p90RSK inhibition restored CIC function, suppressed oncogenic transcription, reduced GBM stem cell viability, and inhibited tumor growth. CONCLUSION p90RSK decreases GBM sensitivity to MEK inhibitors by destabilizing the CIC/YY1 complex and sustaining oncogenic transcription. Dual inhibition restores CIC function, suppresses oncogenic programs, and enhances therapeutic efficacy in GBM.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.256
Teacher spread0.236 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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