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Record W2169965735 · doi:10.1101/gad.1192704

Collaboration of Brca1 and Chk2 in tumorigenesis

2004· article· en· W2169965735 on OpenAlexaff
John Peter McPherson, Bénédicte Lemmers, Atsushi Hirao, Anne Hakem, Jacinth Abraham, Eva Migon, Elzbieta Matysiak‐Zablocki, Laura Tamblyn, Otto Sanchez-Sweatman, Rama Khokha, Jeremy A. Squire, M. Prakash Hande, Tak W. Mak, Razqallah Hakem

Bibliographic record

VenueGenes & Development · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversity of TorontoOntario Institute for Cancer Research
FundersNational Cancer InstituteNational University of SingaporeAmgen
KeywordsBiologyDNA damageCheckpoint Kinase 2CarcinogenesisContext (archaeology)Cell biologyPhosphorylationCancer researchSignal transductionSuppressorDNACancerGeneticsProtein-Serine-Threonine KinasesProtein kinase A

Abstract

fetched live from OpenAlex

Disruption of Brca1 results in cellular demise or tumorigenesis depending on cellular context. Inactivation of p53 contributes to Brca1-associated tumor susceptibility. However the activation of p53-dependent checkpoint/apoptotic signaling in the absence of Brca1 is poorly understood. Here, we show that Chk2 inactivation is partially equivalent to p53 inactivation, in that Chk2 deficiency facilitates the development, survival, and proliferation of Brca1-deficient T cells at the expense of genomic integrity. Brca1 deficiency was found to result in Chk2 phosphorylation and the Chk2-dependent accumulation and activation of p53. Furthermore, inactivation of Chk2 and Brca1 was cooperative in breast cancer. Our findings identify a critical role for Chk2 as a component of the DNA damage-signaling pathway activated in response to Brca1 deficiency.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.223
Teacher spread0.218 · 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

Citations67
Published2004
Admission routes1
Has abstractyes

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