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Record W4405374417 · doi:10.1016/j.iotech.2024.100929

176MO ORCA-010 oncolytic therapy: Inducing tumor-specific immune responses and activation of tumor microenvironment in treatment-naïve prostate cancer

2024· article· en· W4405374417 on OpenAlexaff
T.D. de Gruijl, Reza Nadafi, T. Brachtlová, Jonathan Giddens, Kenneth Jansz, Peter Incze, Amitai Abramovitch, B. Shayegan, Richard Casey, Wanli Dong, Victor W. van Beusechem

Bibliographic record

VenueImmuno-Oncology Technology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsSt. Joseph’s Healthcare HamiltonOakville-Trafalgar Memorial HospitalJoseph Brant HospitalThe Scarborough HospitalBrampton Civic Hospital
FundersNovo NordiskNational Institute for Health and Care ResearchH. Lundbeck A/SCancer Research UKGordon and Rose McAlpine Foundation for Neuroscience ResearchFrancis Crick Institute
KeywordsProstate cancerOncolytic virusImmune systemTumor microenvironmentCancerMedicineCancer researchOncologyImmune escapeInternal medicineImmunology

Abstract

fetched live from OpenAlex

interactions.WES analysis showed LOH of one HLA-II isotype in 10/76 non-acral cutaneous melanoma samples (2/9 patients), and only in 1/52 acral/mucosal samples (1/5 patients), suggesting immune editing may drive HLA-II loss.Conclusions: Our findings indicate a link between melanoma dedifferentiation, CD4+ TIL recognition via HLA-DR, and HLA-II loss.Recognition of HLA-II+ melanomas is preferentially mediated via HLA-DR, but LOH might impact neoantigen presentation, leading to immune evasion.Clinical trial identification: PEACE study: NCT03004755, study start at 2014/03.Legal entity responsible for the study: The authors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.015
GPT teacher head0.309
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations0
Published2024
Admission routes1
Has abstractyes

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