Spatial Cancer-Immune Phenotypes Predict Shorter Recurrence-Free Survival in the No Specific Molecular Profile Molecular Subtype of Endometrial Carcinoma
Bibliographic record
Abstract
Compartmentation of the immune response into 3 main spatial cancer-immune phenotypes (SCIs) - inflamed, excluded, and desert - has been proposed as the main predictor of response to immune checkpoint inhibitors in solid tumors. The objective of the study was to define and characterize the SCI in a consecutive series of 213 endometrial carcinomas (ECs) by correlating it with molecular subtypes, clinicopathologic features, and prognosis. Immunohistochemistry (IHC) and next-generation sequencing were used to assign surrogate molecular EC subtypes: POLE mutant (POLE), mismatch repair deficient (MMRd), TP53 mutant (p53abn), and no specific molecular profile (NSMP). Immune cell markers (CD20, CD3, CD8, CD68, PD-L1) were assessed by IHC on whole sections and quantified by digital image analysis to define the 3 SCIs. ECs were stratified into 4 molecular subtypes: 17 (8.0%) POLE, 68 (31.9%) MMRd, 42 (19.7%) p53abn, and 86 (40.4%) NSMP. SCI determination showed 105 (49.3%) inflamed, 62 (29.1%) desert, and 46 (25.6%) excluded tumors. The inflamed phenotype was more prevalent in MMRd (64.7%) and POLE (76.5%) subtypes compared with NSMP (45.3%) and p53abn (21.4%). SCI revealed a strong correlation with disease-free survival in NSMP tumors: inflamed 96.2%, desert 83.2%, and excluded 40.5%. The SCI prognostic impact was also maintained in NSMP cases treated with adjuvant therapy resulting in a significant difference in recurrence between the inflamed and excluded phenotypes. To simplify SCI determination, a subset of immune cell markers was selected as appropriate to define the 3 SCI patterns: high intraepithelial CD8 for the inflamed phenotype; CD68, CD20, and PD-L1 to discriminate between desert and excluded tumors. The integration of SCI into molecular classification could be a promising opportunity to improve the prognostic risk stratification of patients and may guide the therapeutic approach, particularly in the NSMP subtype. Thus, the different patterns of immune response are a new prognostic parameter in the NSMP subtype.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".