T-cell receptor affinity and avidity defines antitumor response and autoimmunity in T-cell immunotherapy
Bibliographic record
Abstract
T cells expressing antigen-specific T-cell receptors (TCRs) can mediate effective tumor regression, but they often also are accompanied by autoimmune responses. To determine the TCR affinity threshold defining the optimal balance between effective antitumor activity and autoimmunity in vivo, we used a unique self-antigen system comprising seven human melanoma gp100(209-217)-specific TCRs spanning physiological affinities (1-100 μM). We found that in vitro and in vivo T-cell responses are determined by TCR affinity, except in one case that was compensated by substantial CD8 involvement. Strikingly, we found that T-cell antitumor activity and autoimmunity are closely coupled but plateau at a defined TCR affinity of 10 µM, likely due to diminished contribution of TCR affinity to avidity above the threshold. Together, these results suggest that a relatively low-affinity threshold is necessary for the immune system to avoid self-damage, given the close relationship between antitumor activity and autoimmunity. The low threshold, in turn, indicates that adoptive T-cell therapy treatment strategies using in vitro-generated high-affinity TCRs do not necessarily improve efficacy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".