Immunotherapy for metastatic prostate cancer: where are we at with sipuleucel-T?
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Prostate cancer is the leading malignancy in North American men and despite improvements in treatments 20 - 30% of patients will relapse. Immunotherapy using activated mononuclear cells is a way to harness the body's adaptive immune response to fight metastatic prostate cancer. AREAS COVERED IN THIS REVIEW: In 2005, at least 10 therapeutic cancer vaccines, designed to confer active, specific immunotherapy against tumor-associated antigens, were in clinical trials. These covered potential fields of immunological strategy to overcome castration-resistant prostate cancer. WHAT THE READER WILL GAIN: A literature review was performed using the search terms sipuleucel-T, Provenge and APC8015 or APC-8015, and restricted to English language articles from 2000 to 2010. The immunological design and development of sipuleucel-T are summarized. The efficacy and safety of sipuleucel-T are discussed based on current data from clinical trials. Ongoing clinical trials involving sipuleucel-T are summarized. TAKE HOME MESSAGE: Efficacy and safety with sipuleucel-T has been demonstrated in Phase I/II trials. The latest data from a Phase III trial shows that sipuleucel-T has met the primary endpoint of survival benefit. Further work is needed to understand the mechanisms behind cancer vaccine failure and elucidate the population for whom this vaccine will be suitable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".