EGFR as a biomarker of smoking status and survival in oropharyngeal squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: This study aims to investigate EGFR as a prognostic biomarker in oropharyngeal squamous cell carcinoma (OPSCC). METHODS: OPSCC patients from retrospective (1998-2009) and prospective cohorts (2014-2017) were included. Retrospectively collected tumors were used to construct tissue microarrays (TMAs), which were stained with EGFR, p16, DAPI and Pan-cytokeratin, and digitally quantified. EGFR, CDKN2A and HPV E6/7 levels from prospectively collected OPSCC was measured by droplet digital PCR (ddPCR). Biomarkers were compared to patient covariates, factors and survival outcomes. RESULTS: A total of 249 patients were included retrospectively and 64 patients were enrolled prospectively. p16 status (p < 0.001), smoking above 10 pack years (p = 0.04), smoking above 20 pack years (p < 0.001), total EGFR tumor levels (p = 0.016), and high EGFR within high or low Ki67 tumor nuclear staining (p = 0.03) were found to be significant predictors of 5-year disease specific survival (DSS). A Cox proportional hazard model of DSS showed smoking status and eGFR expression to be dependent of each other on predicting 5-year DSS. ddPCR analysis showed a significant association between smoking status and EGFR levels. CONCLUSIONS: Total EGFR tumor levels are predictive of 5-year DSS. EGFR levels correlate with. smoking and could be an objective marker for this disease etiology.
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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) | 0.001 | 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".