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Record W2784678115 · doi:10.1126/scitranslmed.aao1613

Dimethyl fumarate potentiates oncolytic virotherapy through NF-κB inhibition

2018· article· en· W2784678115 on OpenAlexafffund
Mohammed Selman, Paula Ou, Christopher Rousso, Anabel Bergeron, Ramya Krishnan, Larissa A. Pikor, Andrew Chen, Brian A. Keller, Carolina S. Ilkow, John C. Bell, Jean‐Simon Diallo

Bibliographic record

VenueScience Translational Medicine · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCanadian Institutes of Health ResearchTerry Fox Research InstituteCanadian Cancer Society
KeywordsOncolytic virusVirotherapyCancer researchVirologyMedicineChemistryTumor cells

Abstract

fetched live from OpenAlex

Resistance to oncolytic virotherapy is frequently associated with failure of tumor cells to get infected by the virus. Dimethyl fumarate (DMF), a common treatment for psoriasis and multiple sclerosis, also has anticancer properties. We show that DMF and various fumaric and maleic acid esters (FMAEs) enhance viral infection of cancer cell lines as well as human tumor biopsies with several oncolytic viruses (OVs), improving therapeutic outcomes in resistant syngeneic and xenograft tumor models. This results in durable responses, even in models otherwise refractory to OV and drug monotherapies. The ability of DMF to enhance viral spread results from its ability to inhibit type I interferon (IFN) production and response, which is associated with its blockade of nuclear translocation of the transcription factor nuclear factor κB (NF-κB). This study demonstrates that unconventional application of U.S. Food and Drug Administration-approved drugs and biological agents can result in improved anticancer therapeutic outcomes.

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 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 score0.016
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.360
Teacher spread0.329 · 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

Citations66
Published2018
Admission routes2
Has abstractyes

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