Dominant Negative PTEN Alterations in Endometrial Carcinoma Are Associated With Retained Immunohistochemical PTEN Expression
Bibliographic record
Abstract
PTEN immunohistochemistry (IHC) is considered complimentary for assessment of PTEN abnormality in endometrial carcinoma (EC), since PTEN IHC staining pattern does not entirely correlate with the presence and absence of mutations on sequencing. A set of functionally defective PTEN variants with stable protein levels are known to act in a dominant-negative manner to suppress wild-type PTEN activity. Our objective was to evaluate PTEN IHC patterns in ECs with dominant-negative (DN) PTEN mutations. ECs with next-generation sequencing (NGS, using Oncomine Comprehensive Assay v3) over a 3-year period were enrolled. PTEN IHC was scored as loss, subclonal loss, reduced, and intact (the last 3 considered retained). Of 182 EC cases, 114 (62.6%) were identified to have PTEN mutation(s), the majority of endometrioid histotype (87.7%) from all EC molecular classes. Forty-seven cases (41.2%) harbored DN mutations which were of endometrioid (FIGO 1 [n=15, 31.9%], FIGO 2 [n=23, 48.9%], FIGO 3 [n=3, 6.4%]), dedifferentiated (n=2, 4%), carcinosarcoma (n=3, 6%), mixed endometrioid and clear cell carcinoma (n=1, 2%) histotype; with representatives from all molecular classes. PTEN IHC showed retained expression in 95.8% (45/47) of DN-mutated cases (intact staining in 36 [76.6%], reduced staining in 6 [12.5%], and subclonal loss in 3 [6.4%]) cases. Two cases showed loss of expression (4.2%). In the PTEN wild-type group, loss and subclonal loss of expression were seen in 12.5% and 9.4%, respectively. Our results indicate that DN PTEN mutations are common in EC, and are associated with retained IHC staining (intact, reduced, or subclonal loss). These results highlight that IHC and NGS are both required in capturing the full spectrum of PTEN-abnormal EC.
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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".