Survival Advantage in Patients with Metastatic Breast Cancer Receiving Endocrine Therapy plus Sialyl Tn-KLH Vaccine: Post Hoc Analysis of a Large Randomized Trial
Bibliographic record
Abstract
BACKGROUND: A multicenter, double blinded, randomized phase III trial of the therapeutic cancer vaccine sialy1-Tn (STn) conjugated to keyhole-limpet Hemocyanin (KLH) was completed in an international cohort of 1,028 women with metastatic breast cancer who had nonprogressive disease or no evidence of disease after first-line chemotherapy (ClinicalTrials.gov, (NCT00003638). STn-KLH was safe and relatively well tolerated but did not affect time to progression (TTP) or overall survival (OS) duration. The purpose of this post hoc analysis was to explore whether patients who received concurrent endocrine therapy and STn-KLH had a TTP or OS benefit. METHODS: A retrospective, blinded review of the data from the phase III trial of STn-KLH was performed to ensure that strata assignments were appropriate. We then studied the effect of concomitant endocrine therapy and STn-KLH or KLH on TTP and OS in the cohort described above. We also assessed the TTP and OS by antibody responses in patients who received endocrine therapy. RESULTS: The women treated with concomitant endocrine therapy, a pre-stratified subset comprising approximately one-third of the original study population, and STn-KLH had longer TTP and OS than the control group of women who received KLH alone. Moreover, of the women who received endocrine therapy, those who had a median or greater antibody response (titer >1:320 toward ovine sub maxillary mucin) to the STn-KLH vaccine had significantly longer median OS than those who had a below-median antibody response. CONCLUSION: Adding STn-KLH to endocrine therapy may improve clinical outcomes with few adverse effects for women with metastatic breast cancer.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".