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Record W2734667085 · doi:10.18632/oncotarget.19199

Checkpoint kinase 1 inhibition sensitises transformed cells to dihydroorotate dehydrogenase inhibition

2017· article· en· W2734667085 on OpenAlexfundno aff
Stéphanie Arnould, Geneviève Rodier, Gisèle Matar, Charles Vincent, Nelly Pirot, Yoann Delorme, Charlène Berthet, Yoan Buscail, Jean Noel, Simon Lachambre, Marta Jarlier, Florence Bernex, Hélène Delpech, Pierre‐Olivier Vidalain, Yves L. Janin, Charles Theillet, Claude Sardet

Bibliographic record

VenueOncotarget · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsnot available
FundersUniversité de MontpellierInstitute of Cancer ResearchInstitut National Du CancerLigue Contre le CancerDirection Générale de l’offre de SoinsInstitut National de la Santé et de la Recherche Médicale
KeywordsHumanitiesPhilosophyMolecular biologyBiology

Abstract

fetched live from OpenAlex

// Stéphanie Arnould 1, 2, 3, 4 , Geneviève Rodier 1, 2, 3, 4 , Gisèle Matar 1, 2, 3, 4 , Charles Vincent 1, 2, 3, 4 , Nelly Pirot 1, 2, 3, 4, 5 , Yoann Delorme 1, 2, 3, 4 , Charlène Berthet 1, 2, 3, 4, 5 , Yoan Buscail 1, 2, 3, 4, 5 , Jean Yohan Noël 1, 2, 3, 4, 5 , Simon Lachambre 6 , Marta Jarlier 1, 2, 3, 4 , Florence Bernex 1, 2, 3, 4, 5 , Hélène Delpech 1, 2, 3, 4 , Pierre Olivier Vidalain 7 , Yves L. Janin 8 , Charles Theillet 1, 2, 3, 4 and Claude Sardet 1, 2, 3, 4 1 Institut de Recherche en Cancérologie de Montpellier, Montpellier, France 2 INSERM U1194, Montpellier, France 3 Université de Montpellier, Montpellier, France 4 Institut Régional du Cancer de Montpellier, Montpellier, France 5 Réseau d'Histologie Expérimentale de Montpellier, BioCampus, UMS3426 CNRS-US009 INSERM-UM, Montpellier, France 6 Montpellier RIO Imaging, BioCampus, UMS3426 CNRS-US009 INSERM-UM, Montpellier, France 7 Laboratoire de Chimie et Biochimie Pharmacologiques et Toxicologiques, Equipe Chimie and Biologie, Modélisation et Immunologie pour la Thérapie, CNRS UMR 8601 CNRS-Université Paris Descartes, Paris, France 8 Institut Pasteur, Unité de Chimie et Biocatalyse, CNRS UMR3523, Paris, France Correspondence to: Claude Sardet, email: claude.sardet@inserm.fr Stéphanie Arnould, email: stephanie.arnould@inserm.fr Keywords: DHODH, Chk1, cytotoxicity, triple negative breast cancer, DNA damage Received: December 20, 2016      Accepted: June 17, 2017      Published: July 12, 2017 ABSTRACT Reduction in nucleotide pools through the inhibition of mitochondrial enzyme dihydroorotate dehydrogenase (DHODH) has been demonstrated to effectively reduce cancer cell proliferation and tumour growth. The current study sought to investigate whether this antiproliferative effect could be enhanced by combining Chk1 kinase inhibition. The pharmacological activity of DHODH inhibitor teriflunomide was more selective towards transformed mouse embryonic fibroblasts than their primary or immortalised counterparts, and this effect was amplified when cells were subsequently exposed to PF477736 Chk1 inhibitor. Flow cytometry analyses revealed substantial accumulations of cells in S and G2/M phases, followed by increased cytotoxicity which was characterised by caspase 3-dependent induction of cell death. Associating PF477736 with teriflunomide also significantly sensitised SUM159 and HCC1937 human triple negative breast cancer cell lines to dihydroorotate dehydrogenase inhibition. The main characteristic of this effect was the sustained accumulation of teriflunomide-induced DNA damage as cells displayed increased phospho serine 139 H2AX (γH2AX) levels and concentration-dependent phosphorylation of Chk1 on serine 345 upon exposure to the combination as compared with either inhibitor alone. Importantly a similar significant increase in cell death was observed upon dual siRNA mediated depletion of Chk1 and DHODH in both murine and human cancer cell models. Altogether these results suggest that combining DHODH and Chk1 inhibitions may be a strategy worth considering as a potential alternative to conventional chemotherapies.

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 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 score0.015
Threshold uncertainty score1.000

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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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