Identification of molecular checkpoints in patients with prostate cancer immunized with PSA146-154 peptide vaccine (41.24)
Bibliographic record
Abstract
Abstract The objective of the current study is to delineate genes that may be differentially expressed in responding versus non responding patients in the context of a completed PSA146-154 peptide vaccine protocol. 28 HLA-A2+ prostate cancer patients with high risk, local disease or metastatic, hormone sensitive disease in remission were vaccinated on weeks 1, 4, and 10 with peptide plus GM-CSF or peptide loaded autologous DC. Before and after PBMC were assessed for specific tetramer response and gene expression profiles per Affymetrix whole human genome arrays. Clinical status was evaluated per serial serum PSA. 13 of 27 patients achieved stable serum PSA (1 not evaluable), of whom 9 patients developed specific tetramer responses at six months post first vaccination. Thus the detectable immunity was associated with lower risk of PSA progression (p=0.02). Comparison of array data between responders and non responders indicated that 114 of 54,675 genes were differentially expressed (p<0.001). Notably VEGF-A, LIF, TNFRSF21 and IFNK, pertaining to the cytokine-cytokine receptor interaction pathway were predicted to be impacted (Impact factor=5.19). Taqman gene assays indicated that the expression of VEGF-A relative to GAPDH was greater in non responding compared to responding patients. Hence, VEGF-A is one of the likely candidates to impede vaccine efficacy, possibly by mediating its effects on antigen presenting cells.
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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".