Targeted Molecular Sequencing of Recurrent and Multifocal Non–HPV-associated Squamous Cell Carcinoma of the Vulva
Bibliographic record
Abstract
Recurrent vulvar squamous cell carcinomas (SCCs) are a poorly understood and aggressive group of treatment-resistant neoplasms. Currently, it remains unclear whether these are in fact recurrences of the same primary tumor, or the development of entirely new tumors. Here, to address this question, we examined the mutational profile of a series of patients with recurrent or multifocal non-human papilloma virus (HPV)-associated vulvar SCC. We performed a targeted 33-gene next-generation sequencing panel on a series of 14 patients with recurrent or multifocal non-HPV-associated vulvar SCC and precursor neoplasms. This amounted to 54 cases (33 SCC, 1 verrucous carcinoma, 13 differentiated vulvar intraepithelial neoplasia, and 7 differentiated exophytic vulvar intraepithelial lesion), with 79 mutations detected altogether. TP53 [51/79 (65%)] was the most frequently mutated gene. Mutations in PIK3CA [16/79 (20%)), HRAS [6/79 (8%)], PTEN [4/79 (5%)], EGFR [1/79 (1%)], and GNAS [1/79 (1%)] were occasionally seen. Most patients with SCC [5/9 (56%)] recurrent, 4/5 (80%) multifocal] demonstrated a clonal relationship, and harbored the same mutations in the same genes in metachronous or synchronous tumors. A subset of the recurrent tumors [2/5 (40%)] recurred with additional mutations. These clonal relationships were shared between SCC and differentiated vulvar intraepithelial neoplasia in each case. By contrast, a small number of recurrent tumors [3/9 (33%)] demonstrated novel mutations, entirely different from the primary tumor. Thus, our findings suggest that recurrent non-HPV-associated vulvar SCC can arise from 2 mechanisms.
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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 | 0.001 |
| 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) | 0.001 | 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".