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Record W2580129211 · doi:10.1158/1535-7163.mct-16-0504

Ras–MEK Signaling Mediates a Critical Chk1-Dependent DNA Damage Response in Cancer Cells

2017· article· en· W2580129211 on OpenAlexaff
Ho-June Lee, Yi Cao, Victoria C. Pham, Elizabeth Blackwood, Catherine Wilson, Marie Evangelista, Christiaan Klijn, David Stokoe, Jeff Settleman

Bibliographic record

VenueMolecular Cancer Therapeutics · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsDiscovery Centre
Fundersnot available
KeywordsDNA damageCHEK1Cancer researchBiologyCancerDNA repairCancer cellG2-M DNA damage checkpointKinaseSignal transductionCell biologyDNACell cycleCell cycle checkpointGenetics

Abstract

fetched live from OpenAlex

Abstract Cancer cell line profiling to identify previously unrecognized kinase dependencies revealed a novel nonmutational dependency on the DNA damage response checkpoint kinase Chk1. Although Chk1 is a promising therapeutic target in p53-deficient cancers, we found that Ras–MEK signaling engages Chk1 in a subset of osteosarcoma, ovarian, and breast cancer cells to enable their survival upon DNA damage, irrespective of p53 mutation status. Mechanistically, Ras–MEK signaling drives Chk1 expression and promotes cancer cell growth that produces genotoxic stress that requires Chk1 to mediate a response to the consequent DNA damage. Reciprocally, Chk1 engages a negative feedback loop to prevent hyperactivation of Ras–MEK signaling, thereby limiting DNA damage. Furthermore, exogenous DNA damage promotes Chk1 dependency, and pharmacologic Chk1 inhibition combined with genotoxic chemotherapy potentiates a DNA damage response and tumor cell killing. These findings reveal a mechanism-based diagnostic strategy to identify cancer patients that may benefit from Chk1-targeted therapy. Mol Cancer Ther; 16(4); 694–704. ©2017 AACR.

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 categoriesMeta-epidemiology (narrow)
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 score0.006
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.026
GPT teacher head0.326
Teacher spread0.300 · 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.

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

Citations25
Published2017
Admission routes1
Has abstractyes

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