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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".