Selective Activation of Intracellular Signalling Pathways in Dendritic Cells for Cancer Immunotherapy
Bibliographic record
Abstract
One of the major challenges in achieving effective anti-cancer immunotherapy is to counteract immunological tolerance. Most tumor-associated antigens (TAAs) are sensed as self. Hence, naturally occurring tolerance towards them has to be overcome. Fortunately, there is increasing evidence that anti-tumor immune responses occur and play a crucial role in the success of well-established anti-neoplastic therapies such as radiotherapy and chemotherapy. In fact, their effectiveness relies on signalling by pattern recognition receptors such as Toll-like receptors (TLRs). TLR signal transduction involves activation of a few well-known pathways, of which nuclear factor κB (NF-κB) and mitogen activated protein kinases (MAPKs) are possibly the best characterized. Therefore, constitutive activation of these pathways in immune cells can potentially enhance anti-tumor immunity, especially when targeted to professional antigen presenting cells (APCs) such as dendritic cells (DCs). Several strategies have been devised to test this hypothesis, including constitutive activation of TLRs, NF-κB and MAPKs (extracellular-signal regulated kinase (ERK), p38 and c-Jun kinase 1 (JNK1)). Activation of these pathways in mouse and human DCs has differential effects in immunogenicity and in many cases, enhanced antitumor immunity in pre-clinical models, establishing the basis for future clinical applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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".