Estrogen Receptor Expression in DICER1-related Lesions is Associated With the Presence of Cystic Components
Bibliographic record
Abstract
DICER1 tumor predisposition syndrome results from pathogenic variants in DICER1 and is associated with a variety of benign and malignant lesions, typically involving kidney, lung, and female reproductive system. Over 70% of sarcomas in DICER1 tumor predisposition syndrome occur in females. Notably, pediatric cystic nephroma (pCN), a classic DICER1 tumor predisposition syndrome lesion, shows estrogen receptor (ER) expression in stromal cells. There are also renal, hepatic, and pancreatic lesions unassociated with DICER1 tumor predisposition syndrome that have an adult female predominance and are characterized/defined by ER-positive stromal cells. Except for pCN, the expression of ER in DICER1-associated lesions remains uninvestigated. In the present study, ER expression was assessed by immunohistochemistry in 89 cases of DICER1-related lesions and 44 lesions lacking DICER1 pathogenic variants. Expression was seen in stromal cells in pCN and pleuropulmonary blastoma (PPB) types I and Ir, whereas anaplastic sarcoma of kidney and PPB types II and III were typically negative, as were other solid tumors of non-Müllerian origin. ER expression was unrelated to the sex or age of the patient. Expression of ER showed an inverse relationship to preferentially expressed antigen in melanoma (PRAME) expression; as lesions progressed from cystic to solid (pCN/anaplastic sarcoma of kidney, and PPB types I to III), ER expression was lost and (PRAME) expression increased. Thus, in DICER1 tumor predisposition syndrome, there is no evidence that non-Müllerian tumors are hormonally driven and antiestrogen therapy is not predicted to be beneficial. Lesions not associated with DICER1 pathogenic variants also showed ER-positive stromal cells, including cystic pulmonary airway malformations, cystic renal dysplasia, and simple renal cysts in adult kidneys. ER expression in stromal cells is not a feature of DICER1 perturbation but rather is related to the presence of cystic components.
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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".