Evaluation of the prognostic significance of human kallikrein 8 protein expression levels in advanced ovarian cancer by using automated quantitative protein analysis
Bibliographic record
Abstract
5581 Background: Kallikreins, a subgroup of the serine protease enzyme family, are considered important prognostic biomarkers in cancer. Here, we sought to determine the prognostic value of kallikrein 8 (hkl8) in ovarian cancer using a novel method of compartmentalized in situ protein analysis. Materials and Methods: 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 kallikrein 8 protein expression, we used an immunofluorescence-based method of automated in situ quantitative measurement of protein analysis (AQUA). Results: Mean follow-up time of the cohort was 34.35 months. One hundred twenty six of 150 cases had sufficient tissue for AQUA analysis. There was association between tumor mask hk8 protein expression levels and clinicopathological variables including grade (p=0.0011), residual disease (p=0.0063),clinical response to chemotherapy (p=0.0346). In univariate survival analysis there was a correlation between hk8 tumor mask expression and 5 years progression-free survival. Low hk8 expression correlated with better outcome (top vs. bottom quartile, p = 0.0319). In multivariate survival analysis, adjusting for well-characterized prognostic variables, tumor hkl8 expression level retained its prognostic significance for disease free survival (95%CI: 0.341–1.027, p=0.0468). Conclusions: Human Kallikrein 8 is an adverse prognostic factor in patients with ovarian cancer.The possibilities that hK8 may be suitable candidate as diagnostic, prognostic marker and therapeutic target merit further investigation. No significant financial relationships to disclose.
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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) | 0.000 | 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".