Combined TP53 status in tumor-free resection margins and circulating microRNA profiling predicts the risk of locoregional recurrence in head and neck cancer
Bibliographic record
Abstract
Abstract Locoregional recurrences represent a frequently unexpected problem in head and neck squamous cell carcinoma (HNSCC). Relapse often (10–30%) occurs in patients with histologically negative resection margins (RMs), probably due to residual tumor cells or hidden pre-cancerous lesions in normal mucosa, both missed by histopathological examination. Therefore, definition of a ‘clean’ or tumor-negative RM is controversial, demanding for novel approaches to be accurately explored. Here, we evaluated next generation sequencing (NGS) and digital PCR (dPCR) as tools to profile TP53 mutational status and circulating microRNA expression aiming at scoring the locoregional risk of recurrence by means of molecular analyses. Serial monitoring of these biomarkers allowed identifying patients at high risk, laying the ground for accurate tracking of disease evolution and potential intensification of post-operative treatments. Additionally, our pipeline demonstrated its applicability into the clinical routine, being cost-effective and feasible in terms of patient sampling, holding promise to accurately (re)-stage RMs in the era of precision medicine.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".