Sensitization of IL-2 Signaling through TLR-7 Enhances B Lymphoma Cell Immunogenicity
Bibliographic record
Abstract
The innate ability of B lymphoma cells to escape control by tumor-reactive T cells must be overcome to develop effective immunotherapies for these diseases. Because signals from both the innate and adaptive immune systems direct the acquisition of strong immunogenicity by professional APCs, the effects of IL-2 and the TLR-7 agonist, S28690, on the immunogenic properties of chronic lymphocytic leukemia (CLL) B cells were studied. IL-2 with S28690 caused CLL cells to proliferate and increased their expression of B7-family members, production of TNF-alpha and IL-10, and levels of tyrosine-phosphorylated STAT-1 and STAT-3 proteins. S28690 increased CD25 expression on CLL cells and sensitized them to IL-2 signaling. However, IL-2 did not change TLR-7 expression or signaling in CLL cells. The ability to stimulate T cell proliferation required additional activation of protein kinase C, which inhibited tumor cell proliferation, "switched off" IL-10 production, and caused essentially all CLL cells (regardless of clinical stage) to acquire a CD83(high)CD80(high)CD86(high)CD54(high) surface phenotype marked by the activation of STAT-1 without STAT-3. These findings suggest that TLR-7 "licenses" human B cells to respond to cytokines of the adaptive immune system (such as IL-2) and provide a strategy to increase the immunogenicity of lymphoma cells for therapeutic purposes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".