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Record W2032795930 · doi:10.2147/vaat.s12980

Oncolytic viruses: a step into cancer immunotherapy

2011· article· en· W2032795930 on OpenAlexaff
Brian D. Lichty, Jonathan Pol, Resseguier

Bibliographic record

VenueVirus Adaptation and Treatment · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsMcMaster University Medical CentreMcMaster University
Fundersnot available
KeywordsOncolytic virusVirotherapyImmunotherapyImmune systemCancerImmunologyVirusMedicineCancer immunotherapyCancer researchVirologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract: Oncolytic virotherapy is currently under investigation in phase I–III clinical trials for approval as a new cancer treatment. Oncolytic viruses (OVs) selectively infect, replicate in, and kill tumor cells. For a long time, the therapeutic efficacy was thought to depend on the direct viral oncolysis (virocentric view). The host immune system was considered as a brake that impaired virus delivery and spread. Attention was paid primarily to approaches enhancing virus tumor selectivity and cytotoxicity and/or that limited antiviral responses. Thinking has changed over the past few years with the discovery that OV therapy was also inducing indirect oncolysis mechanisms. Among them, induction of an antitumor immunity following OV injection appeared to be a key factor for an efficient therapeutic activity (immunocentric view). Indeed, tumor-specific immune cells persist post-therapy and can search and destroy any tumor cells that escape the OVs, and thus immune memory may prevent relapse of the disease. Various strategies, which are summarized in this manuscript, have been developed to enhance the efficacy of OV therapy with a focus on its immunotherapeutic aspects. These include genetic engineering and combination with existing cancer treatments. Several are currently being evaluated in human patients and already display promising efficacy. Keywords: oncolytic virus, cancer immunotherapy, tumor antigen, cancer vaccine, combination strategies

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 score0.446
Threshold uncertainty score0.526

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 head0.069
GPT teacher head0.332
Teacher spread0.263 · 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

Citations31
Published2011
Admission routes1
Has abstractyes

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