Induction of antigen‐specific effector‐phase tolerance following vaccination against a previously ignored B‐cell lymphoma
Bibliographic record
Abstract
The mechanisms of immune evasion during haematological malignancies are poorly understood. As lymphomas grow in lymphoid organs, it would be expected that if these lymphomas express neo-antigens they should be readily detected by the immune system. To test this assumption, we generated a new non-Hodgkin B-cell lymphoma model expressing the model tumour neo-antigen Ovalbumin (OVA), and analysed the endogenous antigen-specific CD8(+) T-cell response that it elicited in recipient mice. The OVA+ lymphoma cells were eliminated by cytotoxic T lymphocytes (CTL) in mice that had been previously vaccinated against OVA. In contrast, the immune system of naïve mice ignored the malignant cells even though these continuously expressed and presented OVA on their MHC class I molecules. This state of ignorance could be overcome by therapeutic vaccination, which led to the expansion of endogenous anti-OVA-specific CD8(+) T cells. However, the cytotoxic and interferon-γ secretion capacity of these T cells were impaired. The tumour model that we describe thus reproduces several key aspects of human lymphoma; tumor ignorance can be broken by vaccination but the ensuing immune response remains ineffective. This model can be exploited to further understand the mechanisms of lymphoma immunoevasion and devise effective immunotherapy.
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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) | 0.001 | 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 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".