Assessment of Nectin4-expression in vulvar squamous cell carcinomas (VSCC): correlation with HPV-associated and HPV-independent molecular subtypes
Bibliographic record
Abstract
BACKGROUND: The development of antibody-drug conjugates (ADC) for cancer treatment has achieved promising results in different solid tumors and targets. Enfortumab-Vedotin (EV), a humanised anti-Nectin4-IgG1 monoclonal antibody linked to the microtubule-disrupting agent monomethyl auristatin E (MMAE), is an FDA-approved Nectin4-directed ADC for the treatment of locally advanced or metastatic pre-treated urothelial cancer. Targeted therapy with EV requires the expression of Nectin4 within the tumor cells. The present study evaluates Nectin4 expression in vulvar squamous cell carcinomas (VSCC) and its correlation to different VSCC molecular subtypes. METHODS: Immunohistochemical Nectin4-expression was evaluated semiquantitatively on diagnostic biopsies of VSCC using an immunoreactive score (IRS). There was a correlation between IRS and different molecular subtypes of VSCC. RESULTS: ) VSCC, a difference that is not statistically significant. CONCLUSION: molecular subtype), that are associated with the worst prognosis. Therefore, Nectin4-directed ADC such as Enfortumab-Vedotin (EV) may present a potential treatment option in VSCC. Nectin4-expression can easily be assessed by immunohistochemistry on diagnostic biopsies. Clinical trials exploring Nectin4-directed ADCs such as EV are necessary.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".