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Record W2028895853 · doi:10.1016/j.ymthe.2005.07.533

The role of ICP0-Null HSV-1 and interferon signaling defects in the effective treatment of breast adenocarcinoma

2005· article· en· W2028895853 on OpenAlexaff
Jeff Hummel, Ekaterina Safroneeva, Karen Mossman

Bibliographic record

VenueMolecular Therapy · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsOncolytic virusHerpes simplex virusBiologyCytotoxic T cellCancer researchInterferonAdenocarcinomaImmune systemViral replicationVirologyCancerImmunologyVirusIn vitroGenetics

Abstract

fetched live from OpenAlex

Oncolytic viruses that selectively replicate in cancer cells have been described for over 50 years. Despite the observation by several groups that multimutated herpes simplex type 1 vectors are oncolytic in a variety of murine tumor models, the oncolytic potential of ICP0 null mutants has not been described. This study characterizes a novel second-generation oncolytic herpes simplex type 1 vector null for the ICP0 gene. We tested three mutant viruses and found that all were selectively cytotoxic in a variety of human and murine tumor cells in vitro. Furthermore, we provide evidence of a mechanistic link between ICP0's function in interferon signaling pathways and the observed oncolytic capacity of ICP0 mutants. Using an immunocompetent murine model of breast adenocarcinoma we demonstrate that the ICP0 mutant KM100 completely eradicates tumors in approximately 80% of treated animals and significantly increases survival. Our data suggest that active viral replication is necessary for effective tumor regression. In addition, we characterized the potential of KM100 as an anti-tumor vaccine since cured mice were found to elicit a robust anti-tumor immune response and were refractory to subsequent tumor growth upon rechallenge.

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 score0.110
Threshold uncertainty score0.400

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 head0.263
Teacher spread0.255 · 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

Citations59
Published2005
Admission routes1
Has abstractyes

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