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Record W2944427520 · doi:10.1073/pnas.1819004116

TLR1/2 ligand enhances antitumor efficacy of CTLA-4 blockade by increasing intratumoral Treg depletion

2019· article· en· W2944427520 on OpenAlexafffund
Naveen Sharma, Jean Vacher, James P. Allison

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversité de MontréalMontreal Clinical Research Institute
FundersNational Cancer InstituteCanadian Institutes of Health ResearchGenentechGovernment of CanadaCancer Prevention and Research Institute of Texas
KeywordsBlockadeIpilimumabCancer researchLigand (biochemistry)MedicineCancerImmune checkpointCTLA-4Cancer immunotherapyAntibodyReceptorMelanomaImmunotherapyImmunologyPharmacologyInternal medicineImmune systemT cell

Abstract

fetched live from OpenAlex

Immune checkpoint inhibitors such as anti-CTLA-4 antibody are widely accepted therapeutic options for many cancers, but there is still a considerable gap in achieving their full potential. We explored the potential of activating the innate and adaptive immune pathways together to improve tumor reduction and survival outcomes. We treated a mouse model of melanoma with intratumoral injections of Toll-like receptor 1/2 (TLR1/2) ligand Pam3CSK4 plus i.p. injections of anti-CTLA-4 antibody. This combination treatment enhanced antitumor immune responses both qualitatively and quantitatively over anti-CTLA-4 alone, and its efficacy depended on CD4 T cells, CD8 T cells, Fcγ receptor IV, and macrophages. Interestingly, our results suggest a unique mechanism by which TLR1/2 ligand increased Fcγ receptor IV expression on macrophages, leading to antibody-dependent macrophage-mediated depletion of regulatory T cells in the tumor microenvironment and increasing efficacy of anti-CTLA-4 antibody in the combination treatment. This mechanism could be harnessed to modulate the clinical outcome of anti-CTLA-4 antibodies and possibly other antibody-based immunotherapies.

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)0.001
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 score0.141
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.020
GPT teacher head0.297
Teacher spread0.278 · 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

Citations71
Published2019
Admission routes2
Has abstractyes

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