Quantitative TCR:pMHC Dissociation Rate Assessment by NTAmers Reveals Antimelanoma T Cell Repertoires Enriched for High Functional Competence
Bibliographic record
Abstract
Experimental models demonstrated that therapeutic induction of CD8 T cell responses may offer protection against tumors or infectious diseases providing that T cells have sufficiently high TCR/CD8:pMHC avidity for efficient Ag recognition and consequently strong immune functions. However, comprehensive characterization of TCR/CD8:pMHC avidity in clinically relevant situations has remained elusive. In this study, using the novel NTA-His tag-containing multimer technology, we quantified the TCR:pMHC dissociation rates (koff) of tumor-specific vaccine-induced CD8 T cell clones (n = 139) derived from seven melanoma patients vaccinated with IFA, CpG, and the native/EAA or analog/ELA Melan-A(MART-1)(26-35) peptide, binding with low or high affinity to MHC, respectively. We observed substantial correlations between koff and Ca(2+) mobilization (p = 0.016) and target cell recognition (p < 0.0001), with the latter independently of the T cell differentiation state. Our strategy was successful in demonstrating that the type of peptide impacted on TCR/CD8:pMHC avidity, as tumor-reactive T cell clones derived from patients vaccinated with the low-affinity (native) peptide expressed slower koff rates than those derived from patients vaccinated with the high-affinity (analog) peptide (p < 0.0001). Furthermore, we observed that the low-affinity peptide promoted the selective differentiation of tumor-specific T cells bearing TCRs with high TCR/CD8:pMHC avidity (p < 0.0001). Altogether, TCR:pMHC interaction kinetics correlated strongly with T cell functions. Our study demonstrates the feasibility and usefulness of TCR/CD8:pMHC avidity assessment by NTA-His tag-containing multimers of naturally occurring polyclonal T cell responses, which represents a strong asset for the development of 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 | 0.000 |
| 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".