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Record W2894744183 · doi:10.1038/s41419-018-1069-9

Dianhydrogalactitol induces replication-dependent DNA damage in tumor cells preferentially resolved by homologous recombination

2018· article· en· W2894744183 on OpenAlexafffund
Beibei Zhai, Anne Steinø, Jeffrey Bacha, Dennis Brown, Mads Daugaard

Bibliographic record

VenueCell Death and Disease · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsPrevention of Organ FailureDelmar (Canada)University of British Columbia
FundersMitacs
KeywordsHomologous recombinationDNA damageDNA repairGuanineDNADNA replicationBiologyCancer researchCytotoxicityCell cycleChemistryCell biologyMolecular biologyCellIn vitroBiochemistryGene

Abstract

fetched live from OpenAlex

Abstract 1,2:5,6-Dianhydrogalactitol (DAG) is a bifunctional DNA-targeting agent causing N 7 -guanine alkylation and inter-strand DNA crosslinks currently in clinical trial for treatment of glioblastoma. While preclinical studies and clinical trials have demonstrated antitumor activity of DAG in a variety of malignancies, understanding the molecular mechanisms underlying DAG-induced cytotoxicity is essential for proper clinical qualification. Using non-small cell lung cancer (NSCLC) as a model system, we show that DAG-induced cytotoxicity materializes when cells enter S phase with unrepaired N 7 -guanine DNA crosslinks. In S phase, DAG-mediated DNA crosslink lesions translated into replication-dependent DNA double-strand breaks (DSBs) that subsequently triggered irreversible cell cycle arrest and loss of viability. DAG-treated NSCLC cells attempt to repair the DSBs by homologous recombination (HR) and inhibition of the HR repair pathway sensitized NSCLC cells to DAG-induced DNA damage. Accordingly, our work describes a molecular mechanism behind N 7 -guanine crosslink-induced cytotoxicity in cancer cells and provides a rationale for using DAG analogs to treat HR-deficient tumors.

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 score0.004
Threshold uncertainty score0.570

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 head0.008
GPT teacher head0.230
Teacher spread0.221 · 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

Citations20
Published2018
Admission routes2
Has abstractyes

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