Cytotoxic T-lymphocyte Immunotherapy for Ovarian Cancer
Bibliographic record
Abstract
The objective was to evaluate the toxicity and feasibility of intraperitoneal infusion of tumor-specific cytotoxic T lymphocytes (CTL) as therapy for recurrent ovarian cancer, and to determine if repetitive cycles of CTL generation and infusion measurably increases the host's ovarian cancer immune response. In this study, 7 subjects with recurrent ovarian cancer confined to the peritoneal cavity underwent up to 4 cycles, each cycle beginning with a leukapheresis for collection of precursor lymphocytes, which were stimulated in vitro with mucin 1, a tumor-specific antigen found commonly in ovarian cancer cells. The resulting new CTL for each cycle were reintroduced into the host by intraperitoneal infusion. Immunologic parameters (killer cells, cytokine production, memory T lymphocytes, and natural killer cells) were studied. Toxicity, CA-125, and survival data were also evaluated. The tumor marker CA-125 was nonstatistically significantly reduced after the first month of immunotherapy. However, after that it rose. Killer cells, cytokine production, and memory T lymphocytes increased after the first cycle of stimulation, but plateaued or reduced thereafter. The percent of natural killer cells inversely correlated with other immune parameters. Median survival was 11.5 months. One subject is free of disease since December, 2000. Multiple cycles, beyond 1 cycle, of T-cell stimulation followed by adoptive T-cell infusion, may not enhance the in vivo immune response.
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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".