Immunohistochemistry and Next-generation Sequencing Are Complementary Tests in Identifying PTEN Abnormality in Endometrial Carcinoma Biopsies
Bibliographic record
Abstract
PTEN plays a central role in the pathogenesis of endometrial carcinoma. Previous studies reported a high interobserver reproducibility for the interpretation of PTEN immunohistochemistry (IHC). However, PTEN IHC and its interpretation remain challenging during laboratory practice. The purpose of this study was to reevaluate PTEN IHC pattern in direct comparison to next-generation sequencing in identifying PTEN abnormality. IHC and tagged-amplicon next-generation sequencing PTEN sequencing was performed on 182 endometrial carcinoma biopsy/curetting samples from five centers (Barts, Calgary, Cambridge, Leiden, and Vancouver). Sensitivity, specificity and accuracy of PTEN IHC to predict loss of function PTEN mutations were calculated. Abnormalities of PTEN in association with histotype and molecular subtype were assessed. A total of 5 PTEN IHC patterns were recorded: absent, subclonal loss, equivocal, reduced (relative to internal control) and retained. The absence of PTEN IHC has a sensitivity of 75.4% (95% confidence interval: 62.7-85.5%), a specificity of 84.6% (95% confidence interval: 76.2%-90.9%), and accuracy of 81.2% (95% confidence interval: 74.4%-86.9%) in predicting loss of function PTEN mutation. PTEN abnormality by complementary interpretation of both assays was present in 91.9% of endometrial endometrioid carcinoma, grade 1, and significantly higher in endometrial endometrioid carcinomas of all grades compared with endometrial serous carcinoma (80.0% vs. 19.4%, P<0.0001). PTEN abnormalities are common across all molecular subtypes of endometrioid carcinomas. Our data support the use of ancillary PTEN IHC for diagnostic purposes in endometrial neoplasms. However, for clinical trial design complementary testing of both IHC and sequencing of PTEN should be considered to assess the PTEN status in endometrial carcinomas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.002 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".