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Record W1966826063 · doi:10.1016/j.celrep.2012.02.010

Targeting Synthetic Lethal Interactions between Myc and the eIF4F Complex Impedes Tumorigenesis

2012· article· en· W1966826063 on OpenAlexafffund
Chen‐Ju Lin, Zeina Nasr, Prem K. Premsrirut, John A. Porco, Yoshitaka Hippo, Scott W. Lowe, Jerry Pelletier

Bibliographic record

VenueCell Reports · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsMcGill University
FundersNational Institute of General Medical SciencesNational Cancer InstituteFaculty of Medicine, McGill UniversityHoward Hughes Medical Institute
KeywordsCarcinogenesisPI3K/AKT/mTOR pathwayBiologyCancer researchRNA interferenceCell biologySignal transductionCancerGeneticsRNAGene

Abstract

fetched live from OpenAlex

The energetically demanding process of translation is linked to multiple signaling events through mTOR-mediated regulation of eukaryotic initiation factor (eIF)4F complex assembly. Disrupting mTOR constraints on eIF4F activity can be oncogenic and alter chemotherapy response, making eIF4F an attractive antineoplastic target. Here, we combine a newly developed inducible RNAi platform and pharmacological targeting of eIF4F activity to define a critical role for endogenous eIF4F in Myc-dependent tumor initiation. We find elevated Myc levels are associated with deregulated eIF4F activity in the prelymphomatous stage of the Eμ-Myc lymphoma model. Inhibition of eIF4F is synthetic lethal with elevated Myc in premalignant pre-B/B cells resulting in reduced numbers of cycling pre-B/B cells and delayed tumor onset. At the organismal level, eIF4F suppression affected a subset of normal regenerating cells, but this was well tolerated and rapidly and completely reversible. Therefore, eIF4F is a key Myc client that represents a tumor-specific vulnerability.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.297

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 science0.0000.000
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 head0.016
GPT teacher head0.256
Teacher spread0.240 · 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

Citations88
Published2012
Admission routes2
Has abstractyes

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