Sensitive molecular detection of small nodal metastasis in uterine cervical cancer using HPV16-E6/CK19/MUC1 cancer biomarkers
Bibliographic record
Abstract
Metastatic nodal involvement is a critical prognostic factor in uterine cervical cancer (UCC). To improve current methods of detecting UCC metastases in lymph nodes (LNs), we used quantitative PCR (qPCR) to assess mRNA expression of potential metastatic biomarkers. We found that expression of HPV16-E6, cytokeratin19 (CK19), and mucin1 (MUC1) is consistently upregulated in tumors and metastatic tissues, supporting a role for these genes in UCC progression. These putative biomarkers were able to predict the presence of histologically positive metastatic LNs with respective sensitivities and specificities of 82% and 99% (CK19), 76% and 95% (HPV16-E6), and 76% and 78% (MUC1). While the biomarkers failed to detect 1.7% to 2.2% of the histologically positive LNs when used individually, combining CK19 and HPV16-E6 enhanced sensitivity and specificity to 100% and 94%, respectively. To explore the sensitivity of qPCR-based detection of varying proportions of invading HPV16-positive UCC cells, we designed a LN metastasis model that achieved a fresh cell detection limit of 0.008% (1:12500 HPV16-positive to HPV16-negative cells), and a paraffin-embedded, formalin-fixed (PEFF) detection limit of 0.02% (1:5000 HPV16-positive to HPV16-negative cells), both of which are within the theoretical detection limit for micrometastasis. Thus, HPV E6/E7 oncogenes may be useful targets for the ultrasensitive detection of nodal involvements like micrometastases in fresh or archived tissue samples. Moreover, our results suggest that the biomarker combination of CK19/HPV-E6 could support a real-time intraoperative strategy for the detection of small, but potentially lethal, metastatic nodal involvements in fresh UCC tissues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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 teacher head, 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".