Retinoblastoma Protein Loss in p53 Abnormal Endometrial Carcinoma: Histologic and Clinicopathological Correlates
Bibliographic record
Abstract
Of the 4 molecular subtypes of endometrial cancer (EC), p53-abnormal (p53abn) EC is associated with abundant copy number alterations and the worst clinical outcome. Patients with p53abn EC have the highest risk of disease recurrence and death, independent of tumor grade and histologic subtype. Currently, all invasive p53abn ECs are considered high risk, and no prognostic biomarkers have yet been found that can aid in clinical management. Here, we aimed to test whether loss of retinoblastoma (RB) protein expression using immunohistochemistry has the potential for prognostic refinement of p53abn EC. A large cohort of 227 p53abn ECs collected from the PORTEC-1/2/3 clinical trials and the Medisch Spectrum Twente cohort study was investigated, and RB loss was identified in 7.0% (n = 16/227). RB-lost p53abn ECs were predominantly high-grade endometrioid ECs (n = 6, 37.5%) and carcinosarcomas with endometrioid-type epithelial component (n = 5, 31.3%). Histologically, RB-lost p53abn ECs were typified by high-grade nuclear atypia (n = 16, 100%), predominantly solid growth pattern (n = 15/16, 93.8%), and polypoid growth (n = 9/16, 56.3%). Copy number loss involving the RB1 locus was identified in the majority of RB-lost p53abn EC (n = 13/14, 92.9%), explaining the loss of RB expression. Comparative analysis also showed that RB-lost p53abn ECs were diagnosed at earlier stages than RB-retained p53abn EC (P = .014). Interestingly, RB-lost p53abn EC showed prolonged time to overall recurrence (P = .038), even within stage I alone (P = .040). These findings highlight distinct morphomolecular features in RB-lost p53abn ECs and confirm the utility of RB immunohistochemistry as a surrogate for underlying molecular RB1 alterations. To our knowledge, this is the first study to show the potential use of RB in prognostic refinement of p53abn EC, although validation is warranted.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".