Molecular subtyping of mammary‐like adenocarcinoma of the vulva shows molecular similarity to breast carcinomas
Why this work is in the frame
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Bibliographic record
Abstract
AIMS: Mammary-like adenocarcinoma (MLA) of the vulva is thought to be derived from vulvar mammary-like glands. The aim of this study was to characterize a series of MLAs by using an immunohistochemical algorithm that identifies the major molecular subtypes of breast cancer. METHODS AND RESULTS: Seven cases of vulval MLA were stained for oestrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), Ki67, epidermal growth factor receptor (EGFR), cytokeratin (CK) 5, nestin, and inositol polyphosphate-4-phosphatase (INPP4b). Seventeen cases of vulval extramammary Paget disease (EMPD), seven with invasion, were studied for comparison. The median age of patients with MLA was 72 years. All tumours except one were early-stage tumours. On the basis of an immunohistochemical panel, three of seven tumours were classified as luminal B, two of seven as HER2-enriched, one of seven as luminal A, and one of seven as basal-like. ER was expressed in four of seven tumours, PR in three of seven, HER2 in three of seven, EGFR in two of seven, and CK5 in one of seven, and the Ki67 index was >15% in six of seven cases. Nestin and INPP4b were, respectively, negative and positive in all cases. Of the seven cases of invasive EMPD, two showed a luminal A profile, three a luminal B profile (two of three with HER2 amplification), one a HER2-enriched profile, and one a basal-like profile. Three of seven were HER2-amplified. Among the 10 cases of EMPD without invasion, seven showed a luminal A profile and three showed a luminal B profile (all HER2-amplified); no HER2-enriched or basal-like subtypes were identified. CONCLUSIONS: Breast cancer subtyping can be applied to vulvar MLAs. All four intrinsic molecular subtypes are seen, with frequencies similar to those in breast carcinoma. Our results support the potential use of breast cancer molecular profiling algorithms to guide treatment for these cancers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it