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Record W2970333537 · doi:10.1016/j.omto.2019.08.003

Combination Therapy with Reovirus and ATM Inhibitor Enhances Cell Death and Virus Replication in Canine Melanoma

2019· article· en· W2970333537 on OpenAlexaff
Masaya Igase, Shusaku Shibutani, Yosuke Kurogouchi, N Fujiki, Chung Chew Hwang, Matt Coffey, Shunsuke Noguchi, Yuki Nemoto, Takuya Mizuno

Bibliographic record

VenueMolecular Therapy — Oncolytics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsOncolytics Biotech (Canada)
FundersMinistry of Education, Culture, Sports, Science and TechnologyJapan Society for the Promotion of ScienceYamaguchi UniversityUniversity of Michigan
KeywordsOncolytic virusCancer researchCombination therapyMelanomaVirotherapyCell cultureCancerVirologyBiologyMedicineVirusPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Oncolytic virotherapy using reovirus is a promising new anti-cancer treatment with potential for use in humans and dogs. Because reovirus monotherapy shows limited efficacy in human and canine cancer patients, the clinical development of a combination therapy is necessary. To identify candidate components of such a combination, we screened a 285-compound drug library for those that enhanced reovirus cytotoxicity in a canine melanoma cell line. Here, we show that exposure to an inhibitor of the ataxia telangiectasia mutated protein (ATM) enhances the oncolytic potential of reovirus in five of six tested canine melanoma cell lines. Specifically, the ATM inhibitor potentiated reovirus replication in cancer cells along with promoting the lysosomal activity, resulting in an increased proportion of caspase-dependent apoptosis and cell cycle arrest at G2/M compared to those observed with reovirus alone. Overall, our study suggests that the combination of reovirus and the ATM inhibitor may be an attractive option in cancer therapy.

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 score0.012
Threshold uncertainty score0.929

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 head0.012
GPT teacher head0.277
Teacher spread0.265 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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