Aberrant Cytoplasmic p53 Staining in Oral Squamous Cell Carcinoma and Dysplasia
Bibliographic record
Abstract
p53 cytoplasmic sequestration has been shown to be a mechanism of carcinogenesis. However, until recently, an aberrant p53 cytoplasmic staining pattern by immunohistochemistry (IHC) was under-reported in oral squamous cell carcinoma (OSCC) and oral epithelial dysplasia (OED). Following the identification of a pilot case, the authors studied the clinicopathologic features of 4 OSCCs and 10 OEDs with p53 cytoplasmic staining pattern, 4 of which exhibited co-occurrence of nuclear null/overexpression patterns. Using next-generation sequencing (NGS), we demonstrate that this cytoplasmic staining pattern correlates with TP53 mutations that disrupt or truncate the C-terminal nuclear localization sequence (NLS) or nuclear exclusion sequence (NES). High-impact NLS-altering mutations in the same region are identified in 8.7% to 11.0% of TP53 -mutant samples in the TCGA-HNSC cohort. Our study provides a practical definition for aberrant p53 cytoplasmic staining in HNSCC and OED, a diagnostic pitfall with potential biological implication. This study proposes an updated p53 IHC interpretation algorithm to facilitate further data accrual.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".