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Effect of IL‐2R<i>β</i>‐binding cytokines on costimulatory properties of chronic lymphocytic leukaemia cells: implications for immunotherapy

2004· article· en· W2075128381 on OpenAlexaff
David Spaner, Caitlin Hammond, Jenny Mena, Yonghong Shi

Bibliographic record

VenueBritish Journal of Haematology · 2004
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsWomen's College HospitalUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsCD80CD86ImmunologyCD38ImmunotherapyCancer researchChronic lymphocytic leukemiaCD40BiologyT cellImmune systemCytotoxic T cellCell biologyLeukemiaStem cellIn vitro

Abstract

fetched live from OpenAlex

Weak immunogenicity of chronic lymphocytic leukaemia (CLL) cells may contribute to disease progression and inhibit the effectiveness of immunotherapies, such as vaccines. Agents that can enhance the antigen presenting capabilities of CLL cells might then help to improve the clinical results of immunotherapies. This study investigated the effects of the common gamma chain-binding cytokines, interleukin (IL)-2 and IL-15, on costimulatory properties of primary CLL cells from 51 patients. IL-2 improved the ability of CLL cells to stimulate T cell proliferation and increased the expression of costimulatory molecules (particularly CD80) in a dose-dependent fashion, especially in CLL cells with weak expression of CD38. CD80 and CD86 induction by IL-2 were positively regulated through the mitogen-activated protein kinase pathway, while CD86 expression was negatively regulated through Janus kinase pathways. However, further activation with protein kinase C agonists was required for IL-2 activated CLL cells to stimulate autologous T cells sufficiently to clear bystander CLL cells from mixed lymphocyte responses. IL-15 had similar effects on the costimulatory properties of CLL cells. These results suggest a role for IL-2, or IL-15, in immunotherapeutic strategies for CLL.

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)0.001
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 score0.093
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.020
GPT teacher head0.308
Teacher spread0.288 · 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

Citations12
Published2004
Admission routes1
Has abstractyes

Explore more

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