High concordance of molecular subtyping between pre-surgical biopsy and surgical resection specimen (matched-pair analysis) in patients with vulvar squamous cell carcinoma using p16- and p53-immunostaining
Bibliographic record
Abstract
OBJECTIVE: Vulvar squamous cell carcinoma (VSCC) can be stratified into three molecular subtypes based on the immunoexpression of p16 and p53: HPV-independent p53-abnormal (p53abn) (most common, biologically aggressive), HPV-associated, with p16-overexpression (second most common, prognostically more favourable) and more recently recognised HPV-independent p53-wildtype (p53wt) (rarest subtype, prognostically intermediate). Our aim was to determine whether molecular subtypes can be reliably identified in pre-operative biopsies and whether these correspond to the subsequent vulvectomy specimen. METHODS: Matched-paired pre-surgical biopsies and subsequent resection specimen of 57 patients with VSCC were analysed for the immunohistochemical expression of p16 and p53 by performing a three-tiered molecular subtyping to test the accuracy rate. RESULTS: Most cases 36/57 (63.2%) belonged to the HPV-independent (p53-abn) molecular subtype, followed by HPV-associated 17/57 (29.8%) and HPV-independent (p53wt) 4/57 (7.0%). The overall accuracy rate on biopsy was 91.2% (52/57): 97.3% for p53-abnormal, 94.1% for p16-overexpression and 50% for p16-neg/p53-wt VSCC. Incorrect interpretation of immunohistochemical p53 staining pattern was the reason for discordant results in molecular subtyping in all five cases. In one case there was an underestimation of p53 pattern (wildtype instead of abnormal/aberrant) and in one case an overestimation of the p53 staining pattern (abnormal/aberrant instead of wildtype). In 3/5 there was a "double positive" staining result (p16 overexpression and abnormal/aberrant p53 staining pattern). In that cases additional molecular workup is required for correct molecular subtyping, resulting in an overall need for molecular examination of 3/57 (3.5%). CONCLUSIONS: Compared to the final resections specimen, the three-tiered molecular classification of VSCC can be determined on pre-surgical biopsies with a high accuracy rate. This enables more precise surgical planning, prediction of the response to (chemo) radiation, selection of targeted therapies and planning of the optimal follow-up strategy for patients in the age of personalised medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".