Advances in Vulvar Cancer Biology and Management
Bibliographic record
Abstract
PURPOSE: Vulvar squamous cell carcinoma (VSCC), a rare gynecologic malignancy, has been rising in incidence. Molecular classification on the basis of human papilloma virus (HPV) and tumor protein 53 (p53) status has identified three clinically distinct subtypes, but we still treat all VSCCs the same. Here, we review molecular classification of VSCC, outline treatment landscape, and highlight potential for targeted therapies in advanced VSCC. DESIGN: We conducted a comprehensive review of the literature on treatment of advanced VSCC with particular focus on the implications of molecular stratification on the basis of HPV and p53 status on the treatment landscape of advanced VSCC. RESULTS: Incorporation of HPV and p53 status in locoregional treatment decision making has the potential to advise (de)escalation strategies. The role of immunotherapy, alone and in combination, requires further exploration particularly earlier in the course of the disease. In advanced stages, potential for targeted therapies in VSCCs include inhibitors of vascular endothelial growth factor, endothelial growth factor receptor, cell cycle, and DNA damage response, particularly in HPV-negative (HPV-) VSCCs. Targeting the phosphoinositide 3 kinase/mammalian target of rapamycin pathway is attractive in HPV-positive and HPV-/p53 wildtype VSCCs. Trials incorporating antibody-drug conjugates (eg, trophoblast cell-surface antigen 2, human epidermal growth factor receptor 2) should be considered, and basket trials in perineal squamous cell cancers are warranted. Preclinical models are limited and should be expanded to inform trial design. CONCLUSION: Like other rare cancers, vulvar cancer lags behind in the identification and optimization of precision medicine strategies. Molecular-based preclinical models and rationally designed clinical trials, incorporating high-quality translational studies, are urgently required. These trials will require international collaboration to ensure feasibility and improvement of outcomes for women diagnosed with this disease.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".