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Record W4280508479 · doi:10.1002/bies.202200026

The integrated stress response in the induction of mutant <i>KRAS</i> lung carcinogenesis: Mechanistic insights and therapeutic implications

2022· article· en· W4280508479 on OpenAlexafffund
Antonis E. Koromilas

Bibliographic record

VenueBioEssays · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEndoplasmic Reticulum Stress and Disease
Canadian institutionsMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health Research
KeywordsIntegrated stress responseCarcinogenesisCancer researchBiologyKRASTranslation (biology)Tumor initiationCellular stress responsePsychological repressionLung cancerMAPK/ERK pathwayAdenocarcinomaMutationCancerCell biologyKinaseGeneticsGeneFight-or-flight responseMedicineGene expressionMessenger RNAOncology

Abstract

fetched live from OpenAlex

The integrated stress response (ISR) is a key determinant of tumorigenesis in response to oncogenic forms of stress like genotoxic, proteotoxic and metabolic stress. ISR relies on the phosphorylation of the translation initiation factor eIF2 to promote the translational and transcriptional reprogramming of gene expression in stressed cells. While ISR promotes tumor survival under stress, its hyperactivation above a level of tolerance can also cause tumor death. The tumorigenic function of ISR has been recently demonstrated for lung adenocarcinomas (LUAD) with KRAS mutations. ISR mediates the translational repression of the dual-specificity phosphatase DUSP6 to stimulate ERK activity and LUAD growth. The significance of this finding is highlighted by the strong anti-tumor responses of ISR inhibitors in pre-clinical LUAD models. Elucidation of the mechanisms of ISR action in LUAD progression via cell-autonomous and immune regulatory mechanisms will provide a better understanding of its tumorigenic role to fully exploit its therapeutic potential in the treatment of a deadly form of cancer.

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 score0.305
Threshold uncertainty score0.241

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 head0.011
GPT teacher head0.244
Teacher spread0.234 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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