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Record W2913127881 · doi:10.1038/s41467-018-08087-9

CIC protein instability contributes to tumorigenesis in glioblastoma

2019· article· en· W2913127881 on OpenAlexafffund
Severa Bunda, Pardeep Heir, Julie Metcalf, Annie Si Cong Li, Sameer Agnihotri, Stefan Pusch, Yasin Mamatjan, Mira Li, Kelly Burrell, Sheila Mansouri, Olivia Singh, Mark R. Wilson, Amir Alamsahebpour, Romina Nejad, Bethany Choi, David Kim, Andreas von Deimling, Gelareh Zadeh, Kenneth Aldape

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsUniversity Health NetworkToronto Western HospitalPrincess Margaret Cancer Centre
FundersPrincess Margaret Cancer Foundation
KeywordsMAPK/ERK pathwayCarcinogenesisUbiquitin ligaseCancer researchGene knockdownReceptor tyrosine kinasePhosphorylationRepressorKinaseSignal transductionMdm2BiologyProteasomeUbiquitinChemistryCell biologyGeneTranscription factorGenetics

Abstract

fetched live from OpenAlex

Capicua (CIC) is a transcriptional repressor that counteracts activation of genes downstream of receptor tyrosine kinase (RTK)/Ras/ERK signaling. It is well-established that tumorigenesis, especially in glioblastoma (GBM), is attributed to hyperactive RTK/Ras/ERK signaling. While CIC is mutated in other tumors, here we show that CIC has a tumor suppressive function in GBM through an alternative mechanism. We find that CIC protein levels are negligible in GBM due to continuous proteasome-mediated degradation, which is mediated by the E3 ligase PJA1 and show that this occurs through binding of CIC to its DNA target and phosphorylation on residue S173. PJA1 knockdown increased CIC stability and extended survival using in-vivo models of GBM. Deletion of the ERK binding site resulted in stabilization of CIC and increased therapeutic efficacy of ERK inhibition in GBM models. Our results provide a rationale to target CIC degradation in Ras/ERK-driven tumors, including GBM, to increase efficacy of ERK 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 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

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 science0.0010.000
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 head0.006
GPT teacher head0.262
Teacher spread0.256 · 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

Citations90
Published2019
Admission routes2
Has abstractyes

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