Prognostic value of kallikrein‐related peptidase 6 protein expression levels in advanced ovarian cancer evaluated by automated quantitative analysis (AQUA)
Bibliographic record
Abstract
Kallikrein-related peptidases, a subgroup of the serine protease enzyme family, are considered important prognostic biomarkers in cancer. In the present study, we sought to determine the prognostic value of kallikrein-related peptidase 6 (KLK6) in ovarian cancer using a novel method of compartmentalized in situ protein analysis. A tissue array composed of 150 advanced stage ovarian cancers, uniformly treated with surgical debulking followed by platinum-paclitaxel combination chemotherapy, was constructed. For evaluation of KLK6 protein expression, we used an immunofluorescence-based method of automated in situ quantitative measurement of protein analysis (AQUA). Mean follow-up time of the cohort was 34.35 months. One hundred and thirty-five of 150 cases had sufficient tissue for AQUA analysis. In univariate survival analysis, low tumor KLK6 expression was associated with better outcome for overall survival over 3 years (P = 0.019). There was no association between tumor KLK6 expression and progression-free survival (P = 0.128). In multivariate survival analysis, adjusting for well-characterized prognostic variables, low tumor KLK6 expression level was one of the most significant predictor variable for overall survival (95% confidence interval, 1.19-3.50; P = 0.009). High tumor KLK6 protein expression is associated with inferior patient outcome in ovarian cancer. KLK6 may represent a promising disease biomarker and therapeutic target in ovarian 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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".