T‐cell analysis in identical twins reveals an impaired anti‐follicular lymphoma immune response in the patient but not in the healthy twin
Bibliographic record
Abstract
In lymphomas an innate defect in the T-cell repertoire could account for the impaired tumour-specific immune response; alternatively, the tumour itself could exert an inhibitory effect on the immune system. To address this issue we analysed the T-cell responses against follicular lymphoma (FL) in identical twins as it can be postulated that their overall T-cell repertoire is identical. While differences between the T-cell response of the patient and the healthy twin would point to a tumour-induced T-cell unresponsiveness, impaired responses in both would point to a defective T-cell repertoire. We demonstrated an impaired tumour-specific proliferation (P = 0.035 and P = 0.013) and cytokine release (P = 0.004 and P = 0.0008) of both peripheral blood and tumour-derived T-cells, respectively, in the FL patient compared with the T-cell response of the healthy twin. Moreover, only syngeneic primed T cells were able to directly lyse unmodified FL cells of the patient. These data support previous findings in murine lymphomas and suggest that inhibitory mechanisms during tumour growth, rather than a defective T-cell repertoire, are responsible for the insufficient T-cell response in lymphoma.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".