Molecular analysis suggests oligoclonality and metastasis of endometriosis lesions across anatomically defined subtypes
Bibliographic record
Abstract
OBJECTIVE: To investigate the heterogeneity of somatic cancer-driver mutations within patients and across endometriosis types. DESIGN: A single-center cohort, retrospective study. SETTING: Tertiary specialist-care center at a university hospital. PATIENT(S): Patients with surgically and histologically confirmed endometriosis of at least 2 anatomically distinct types (ovarian, deep infiltrating, and superficial). INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Specimens were analyzed for the presence or absence of somatic cancer-driver mutations using targeted panel sequencing with orthogonal validation using droplet digital polymerase chain reaction and mutation-surrogate immunohistochemistry. RESULT(S): It was found that 13 of 27 patients had informative somatic driver mutations in endometriosis lesions; of these 13 patients, 9 had identical mutations across distinct lesions. Endometriomas showed a higher mutational complexity, with functionally redundant driver mutations in the same gene and within the same lesions. CONCLUSION(S): Our data are consistent with clonality across endometriosis lesions, regardless of subtype. Further, the finding of redundancy in mutations within the same gene and lesions is consistent with endometriosis representing an oligoclonal disease with dissemination likely to consist of multiple epithelial clones traveling together. This suggests that the current anatomically defined classification of endometriosis does not fully recognize the etiology of the disease. A novel classification should consider genomic and other molecular features to promote personalized endometriosis diagnosis and care.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".