Generation of<i>in vitro</i>B-CLL specific HLA class I restricted CTL responses using autologous dendritic cells pulsed with necrotictumor lysate
Bibliographic record
Abstract
New approaches in the treatment of chronic B lymphocytic leukemia (B-CLL) have led to improved clinical response rates. In this setting there is a need to evaluate novel therapeutic approaches that aim to eradicate minimal residual B-CLL cells following an initial favorable response. The use of tumor lysate-pulsed dendritic cells (DC) represents a potentially important development in the field of cancer vaccination. B-CLL is ideally suited for DC-based vaccination since tumor cells are readily available (peripheral blood) and both known (tumor idiotype) and unknown antigens can be exploited to stimulate immune responses. In the current study we have evaluated the ability to stimulate in vitro autologous immune reactivity against target B-CLL cells using autologous DCs pulsed with B-CLL tumor lysate. Enhanced specific T cell IFN-gamma expression was detected in 9 of 14 patients evaluated. These responses were specific with increased levels of IFN-gamma mRNA measurable in T-cells stimulated with NC-DCs and not unpulsed DCs or DCs pulsed with normal B cell lysate. CTLs demonstrating increased levels of IFN-gamma mRNA also lysed autologous B-CLL targets cells in an MHC class 1-restricted manner by (51)chromium release assay. Priming target leukemic cells with CD40 ligand and IL-4 enhanced CTL killing. The effector CTL displayed negligible toxicity against NK susceptible target cells K-562 and spared CD19(+)CD5(-) normal B cells in cytotoxicity assays. The specificity of the CTL response was confirmed by blocking HLA class I molecules and cold target inhibition assays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".