AllergoOncology: the role of IgE‐mediated allergy in cancer
Bibliographic record
Abstract
Epidemiological studies have suggested inverse associations between allergic diseases and malignancies. As a proof of concept for the capability of immunoglobulin E (IgE) to destruct tumor cells, several experimental strategies have evolved to specifically target this antibody class towards relevant tumor antigens. It could be demonstrated that IgE antibodies specific to overexpressed tumor antigens have been superior to any other immunoglobulin class with respect to antibody-dependent cellular cytotoxicity (ADCC) and phagocytosis (ADCP) reactions. In an alternative approach, IgE nonspecifically attached to tumor cells proved to be a powerful adjuvant establishing tumor-specific immune memory. Active Th2 immunity could also be achieved by applying an oral immunization regimen using mimotopes, i.e. epitope mimics of tumor antigens. The induced IgE antibodies could be cross-linked by live tumor cells leading to tumoricidic mediator release. Thus, IgE antibodies may not only act in natural tumor surveillance, but could possibly also be exploited for tumor control in active and passive immunotherapy settings. Thereby, eosinophils, mast cells and macrophages can be armed with the cytophilic IgE and become potent anti-tumor effectors, able to trace viable tumor cells in the tissues. It is strongly suggested that the evolving new field AllergoOncology will give new insights into the role of IgE-mediated allergy in malignancies, possibly opening new avenues for tumor therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".