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Record W2012872684 · doi:10.1182/blood-2006-02-002055

In vivo control of acute lymphoblastic leukemia by immunostimulatory CpG oligonucleotides

2006· article· en· W2012872684 on OpenAlexaff
Hisaki Fujii, Jacqueline D. Trudeau, David T. Teachey, Jonathan D. Fish, Stephan A. Grupp, Kirk R. Schultz, Gregor S. D. Reid

Bibliographic record

VenueBlood · 2006
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsIn vivoImmunologyCpG OligodeoxynucleotidePeripheral blood mononuclear cellCpG siteCancer researchNodLeukemiaImmune systemImmunotherapyEx vivoMedicineBiologyIn vitroDNA methylation

Abstract

fetched live from OpenAlex

Despite considerable success in treating newly diagnosed childhood acute lymphoblastic leukemia (ALL), relapsed disease remains a significant clinical challenge. Using a NOD/SCID mouse xenograft model, we report that immunostimulatory DNA oligonucleotides containing CpG motifs (CpG ODNs) stimulate significant immune activity against primary human ALL cells in vivo. The administration of CpG ODNs induced a significant reduction in systemic leukemia burden, mediated continued disease control, and significantly improved survival of mice with established human ALL. The death of leukemia cells in vivo was independent of the ability of ALL cells to respond directly to CpG ODNs and correlated with the production of IL-12p70, IFN-alpha, and IFN-gamma by the host. In addition, depletion of natural killer cells by anti-asialo-GM1 treatment significantly reduced the in vivo antileukemic activity of CpG ODN. This antileukemia effect was not limited to the xenograft model because natural killer cell-dependent killing of ALL by human peripheral blood mononuclear cells (PBMCs) was also increased by CpG ODN stimulation. These results suggest that CpG ODNs have potential as therapeutic agents for the treatment of ALL.

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 categoriesInsufficient payload (model declined to judge)
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 score0.010
Threshold uncertainty score0.999

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)0.0020.000

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 head0.003
GPT teacher head0.191
Teacher spread0.188 · 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.

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

Citations42
Published2006
Admission routes1
Has abstractyes

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