Pattern-based p53 and p16 Immunohistochemistry as a Potential Alternative to Loss of Heterozygosity Testing for Progression Risk of Oral Epithelial Dysplasia
Bibliographic record
Abstract
Oral epithelial dysplasia (OED) is the precursor to oral squamous cell carcinoma, but histologic grading alone lacks reproducibility and prognostic power. This study evaluates whether pattern-based p53 and p16 immunohistochemistry (IHC) can serve as alternative markers to genomic loss of heterozygosity (LOH) testing in predicting OED progression. From a previously characterized LOH cohort, 64 patients were assessed with IHC for p53 and p16 using defined abnormal staining patterns (overexpression, cytoplasmic, or null). Abnormal p53 expression occurred in 19% of cases, with 93% specificity, and was significantly associated with reduced progression-free survival (PFS; 8-year PFS, 25% vs. 74%; P = 0.0011). Abnormal p16 expression was observed in 56% of cases with 95% sensitivity and was significantly associated with 8-year PFS (42% vs. 96%; P < 0.0001). Combined p53/p16-abnormal IHCs identified 95% of the progressing lesions and yielded superior risk discrimination (log-rank P < 0.0001), particularly at the 3-year follow-up mark. Concordance analysis revealed moderate agreement between p16 IHC and 9p LOH (κ = 0.39) and fair agreement between p53 IHC and 17p LOH (κ = 0.21), indicating that IHC and LOH detect related but distinct molecular disruptions. Chronologic evaluation of serial biopsies supported a sequential model in which p16 alteration precedes p53 alteration during malignant progression. Taken together, these findings highlight the potential of a pattern-based approach with combined p53/p16 IHC as a feasible, scalable, and clinically accessible tool to guide surveillance intensity and timely clinical intervention, thereby reducing progression risks. PREVENTION RELEVANCE: In this study, we demonstrate that p53/p16 pattern-based IHC provides a practical and sensitive tool for predicting progression in OED. Its clinical accessibility may facilitate early detection of high-risk lesions, optimizing triage, surveillance, and preventative treatment strategies to reduce the incidence of high-grade lesions or oral cancer.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".