Chronic lymphocytic leukemia-reactive T cells during disease progression and after autologous tumor cell vaccines.
Bibliographic record
Abstract
PURPOSE: Tumor-reactive T cells were measured in patients with chronic lymphocytic leukemia (CLL) because vaccines that increase the activity of these cells might lead to better disease control. EXPERIMENTAL DESIGN: Proliferation and ELISPOT assays (for T cells producing IFN-gamma after stimulation by CD40-activated CLL cells) were used to determine the prevalence of tumor-reactive T cells in 25 CLL patients at various stages of disease progression. The effects of vaccines, composed of autologous-oxidized tumor cells, on both the clinical course and tumor-reactive T-cell numbers were then determined in 2 patients. RESULTS: CLL-reactive T cells were found at frequencies of > or =10(-3) in 6 of 11 patients. Significant proliferation was found in 15 of 25 patients and correlated with clinical stage. The inability to measure CLL-reactive T cells in the remaining patients was not uniformly a result of generalized T-cell dysfunction or defective antigen presentation by CD40-activated CLL cells. CLL-reactive T-cell frequencies increased in response to vaccination with oxidized autologous tumor cells in a patient with preexisting CLL-reactive T cells but not in a patient where tumor-reactive T cells were undetectable in the ELISPOT assay. CONCLUSIONS: Tumor-reactive T cells exist in some CLL patients (mainly during earlier stages of disease) and may potentially mediate therapeutic responses if their numbers and activation states can be sufficiently increased by tumor vaccines.
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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 | 0.001 |
| 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".