Mesonephric‐like adenocarcinoma harbours characteristic copy number variations and a distinct <scp>DNA</scp> methylation signature closely related to mesonephric adenocarcinoma of the cervix
Bibliographic record
Abstract
Mesonephric-like adenocarcinoma (MLA) of the female genital tract is an uncommon histotype that can arise in both the endometrium and the ovary. The exact cell of origin and histogenesis currently remain unknown. Here, we investigated whole genome DNA methylation patterns and copy number variations (CNVs) in a series of MLAs in the context of a large cohort of various gynaecological carcinoma types. CNV analysis of 19 MLAs uncovered gains of chromosomes 1q (18/19, 95%), 10 (15/19, 79%), 12 (14/19, 74%), and 2 (10/19, 53%), as well as loss of chromosome 1p (7/19, 37%). Gains of chromosomes 1q, 10, and 12 were also identified in the majority of mesonephric adenocarcinomas of the uterine cervix (MAs) as well as subsets of endometrioid carcinomas (ECs) and low-grade serous carcinomas of the ovary (LGSCs) but only in a minority of serous carcinomas of the uterine corpus (USCs), clear cell carcinomas (CCCs), and tubo-ovarian high-grade serous carcinomas (HGSCs). While losses of chromosome 1p together with gains of chromosome 1q were also identified in both MA and LGSC, gains of chromosome 2 were almost exclusively identified in MLA and MA. Unsupervised hierarchical clustering and t-SNE analysis of DNA methylation data (Illumina EPIC array) identified a co-clustering for MLAs and MAs, which was distinct from clusters of ECs, USCs, CCCs, LGSCs, and HGSCs. Group-wise comparisons confirmed a close epigenetic relationship between MLA and MA. These findings, in conjunction with the established histological and immunophenotypical overlap, suggest bona fide mesonephric differentiation, and support a more precise terminology of mesonephric-type adenocarcinoma instead of MLA in these tumours. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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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) | 0.001 | 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".