Trop2 Expression in Correlation to the Molecular Subtype in Vulvar Squamous Cell Carcinomas
Bibliographic record
Abstract
INTRODUCTION: Targeted therapy with antibody-drug conjugates (ADCs) has achieved promising results in the treatment of different solid tumors. Sacituzumab-Govitecan (SG), a humanized anti-Trop2 monoclonal antibody linked with the cytotoxic topoisomerase I inhibitor SN-38, has been approved for the treatment of metastatic triple-negative breast cancer. The treatment approach with SG requires the expression of Trop2 within the tumor cells. Trop2 is overexpressed in many other cancer types, suggesting a broader therapeutic application beyond breast cancer to these ADCs. We explore expression of Trop2 vulvar squamous cell carcinomas (VSCCs) and how this relates to molecular classification. METHODS: Immunohistochemical Trop2 expression was evaluated on diagnostic biopsies of VSCC using an immunoreactive score. Staining results were compared to the molecular subtype of VSCC. RESULTS: Fifty-seven cases were included in the study. 63.2% of VSCC were p16-ve/p53abn (HPV-independent (p53abn)) molecular subtype, 29.8% p16+ve/p53wt (HPV-associated) and 1.4% p16-ve/p53wt (HPV-independent (p53wt)) tumors. All diagnostic biopsies (N = 57) showed at least a weak Trop2 expression. Moderate and strong expression was seen in 15/17 (88.2%) of the p16-ve/p53abn, 32/36 (88.8%) of the p16+ve/p53wt and 3/4 (75%) of the p16-ve/p53wt molecular subtype. Expression was significantly higher, as assessed by H score, in the HPV-associated VSCC, compared to HPV-independent. CONCLUSION: VSCCs have high expression of Trop2 and represents a promising therapeutic target. Clinical trials exploring Trop2-directed ADCs such as SG are warranted in this rare cancer type, including in the prognostically poor HPV-independent VSCC with a TP53-mutation (p16-ve/p53abn molecular subtype). The targetable molecule, Trop2, can be easily assessed by immunohistochemistry on diagnostic biopsies from VSCC.
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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) | 0.003 | 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".