Endometrial carcinoma and immune escape: prognostic relevance of <scp>HLA</scp> class I loss in <scp>NSMP</scp> subtype
Bibliographic record
Abstract
AIMS: This study aims to define and characterize human leukocyte antigen class I (HLA-I) expression in a consecutive series of molecularly classified endometrial carcinomas (ECs), and to evaluate its association with clinicopathologic features, spatial cancer-immune phenotypes and patient prognosis, with a focus on the NSMP (no specific molecular profile) subtype. METHODS AND RESULTS: HLA-I expression was assessed by immunohistochemistry on whole tissue sections from 208 ECs, classified into POLE-mutated, MMR-deficient (MMRd), p53-abnormal (p53abn) and NSMP subtypes. Loss of HLA-I was identified in 31% of cases and was associated with adverse features including high-grade, aggressive histotypes, deep myometrial invasion, substantial lymphovascular space invasion (LVSI), extensive tumour necrosis and an 'excluded' immune phenotype. While HLA-I loss showed no significant prognostic impact in POLE, MMRd or p53abn tumours, it significantly correlated with worse disease-free survival in NSMP tumours (P < 0.001). Multivariate analysis confirmed HLA-I loss as an independent prognostic factor in early-stage NSMP ECs, in addition to substantial LVSI, presence of lymph node metastases and spatial cancer-immune phenotypes. Integration of HLA-I status improved the performance of predictive models over time. CONCLUSIONS: HLA-I loss defines a biologically aggressive subgroup within NSMP ECs and is associated with adverse clinicopathologic and immune features. Assessment of HLA-I expression could refine risk stratification in NSMP ECs, a group traditionally lacking robust prognostic markers and may help identify patients who could benefit from intensified clinical surveillance and future immunomodulatory treatment strategies.
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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 | 0.000 | 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) | 0.001 | 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".