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Record W3044717029 · doi:10.1038/s41419-020-02780-8

Dianhydrogalactitol synergizes with topoisomerase poisons to overcome DNA repair activity in tumor cells

2020· article· en· W3044717029 on OpenAlexafffund
Beibei Zhai, Yue Li, Sudha Sravanti Kotapalli, Jeffrey Bacha, Dennis Brown, Anne Steinø, Mads Daugaard

Bibliographic record

VenueCell Death and Disease · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer therapeutics and mechanisms
Canadian institutionsPrevention of Organ FailureRead Jones Christoffersen (Canada)Delmar (Canada)University of British Columbia
FundersMitacs
KeywordsTopoisomeraseDNA repairDNA damageDNACell biologyBiologyComputational biologyCancer researchChemistryGenetics

Abstract

fetched live from OpenAlex

1,2:5,6-Dianhydrogalactitol (DAG) is a bi-functional DNA-targeting agent currently in phase II clinical trial for treatment of temozolomide-resistant glioblastoma (GBM). In the present study, we investigated the cytotoxic activity of DAG alone or in combination with common chemotherapy agents in GBM and prostate cancer (PCa) cells, and determined the impact of DNA repair pathways on DAG-induced cytotoxicity. We found that DAG produced replication-dependent DNA lesions decorated with RPA32, RAD51, and γH2AX foci. DAG-induced cytotoxicity was unaffected by MLH1, MSH2, and DNA-PK expression, but was enhanced by knockdown of BRCA1. Acting in S phase, DAG displayed selective synergy with topoisomerase I (camptothecin and irinotecan) and topoisomerase II (etoposide) poisons in GBM, PCa, and lung cancer cells with no synergy observed for docetaxel. Importantly, DAG combined with irinotecan treatment enhanced tumor responses and prolonged survival of tumor-bearing mice. This work provides mechanistic insight into DAG cytotoxicity in GBM and PCa cells and offers a rational for exploring combination regimens with topoisomerase I/II poisons in future clinical trials.

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 score0.047
Threshold uncertainty score0.575

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 head0.217
Teacher spread0.209 · 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

Citations8
Published2020
Admission routes2
Has abstractyes

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