Spontaneous high clonal expansion of Wilms’ tumor gene 1-specific cytotoxic T-lymphocytes in patients with Wilms’ tumor gene 1-expressing solid tumor
Bibliographic record
Abstract
Abstract Wilms’ tumor protein 1 (WT1)-targeted immunotherapy has been used in patients with leukemia and solid tumors. However, the spontaneous WT1-specific immune response before WT1 peptide vaccination in patients with WT1-expressing tumors (PTs) remains unclear. Therefore, we investigated whether WT1-specific cytotoxic CD8 + T-lymphocytes (CTLs) are clonally expanded in the peripheral blood outside of tumor sites. Clonal expansion of WT1 126 peptide (a.a.126–134)-specific CTLs (WT1 126 -CTLs) was compared between seven PTs and five healthy volunteers (HVs), and their T-cell receptors (TCRs) were analyzed at the single-cell level. Overall, 433 and 351 TCR β-chains of WT1 126 -CTLs were detected from PTs and HVs, respectively, and complementarity-determining region 3 was sequenced for clonality analysis. The frequencies of WT1 126 -CTLs were higher in human leukocyte antigen (HLA)-A*02:01 + PTs than in HLA-A*02:01 + HVs, although the difference was not statistically significant. WT1 126 -CTLs of differentiated types, including memory and effector, were higher in PTs than in HVs; whereas, those of the naïve type were higher in HVs than in PTs. WT1 126 -CTL clonality was significantly higher in PTs than in HVs. Furthermore, the frequency of effector WT1 126 -CTLs positively correlated with WT1 126 -CTL clonality in PTs; whereas, the frequency of naïve phenotype WT1 126 -CTLs tended to be negatively correlated with clonality. In conclusion, these results suggest that the WT1 protein in tumor cells is highly immunogenic, thereby stimulating endogenous naïve-type WT1 126 -CTLs and enabling them to clonally expand and differentiate into effector-type WT1 126 -CTLs.
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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".