Sensitive molecular detection of small nodal metastasis in uterine cervical cancer using HPV16-E6/CK19/MUC1 cancer biomarkers
Bibliographic record
Abstract
// Vanessa Samouëlian 1, 2, 3 , Nawel Mechtouf 1 , Eric Leblanc 4 , Guillaume B. Cardin 1 , Valérie Lhotellier 5 , Denis Querleu 6 , Françoise Révillion 5, * and Francis Rodier 1, 7, * 1 CRCHUM et Institut du cancer de Montréal, Montreal, QC, Canada 2 Université de Montréal, Département d’Obstétrique Gynécologie, Montreal, QC, Canada 3 CHUM, Service de Gynécologie oncologique, Montreal, QC, Canada 4 Department of Surgery - Centre Oscar Lambret, Lille Cedex, France 5 Laboratory of Human Molecular Oncology - Centre Oscar Lambret, Lille Cedex, France 6 Institut Bergonie, Bordeaux, France 7 Université de Montréal, Département de Radiologie, Radio-Oncologie et Médicine Nucléaire, Montreal, QC, Canada * These authors contributed equally to this work Correspondence to: Francis Rodier, email: rodierf@mac.com , francis.rodier@umontreal.ca Françoise Révillion, email: f-revillion@o-lambret.fr Vanessa Samouëlian, email: vanessa.samouelian.chum@ssss.gouv.qc.ca Keywords: diagnostic of lymph node metastasis; HPV viral oncogenes; intraoperative pcr; pretherapeutic evaluation; RT-PCR; Pathology Received: May 01, 2016 Accepted: March 15, 2018 Published: April 24, 2018 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 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 | 0.001 | 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".