L1CAM to further stratify endometrial carcinoma patients with nonspecific molecular risk profile
Bibliographic record
Abstract
Background: The newly developed and validated Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) has consistently been shown to be prognostically significant in endometrial carcinomas (EC). Recently we have demonstrated L1 cell-adhesion molecule (L1CAM) to be a significant indicator of high risk disease in EC. In the current study, it was our aim to determine the prognostic and biological significance of aberrant L1CAM expression in ProMisE subgroups in a large single center EC cohort. Methods: ProMisE (POLE/MMR-D/p53 wt/p53 abn) classification results from a previously characterized large population-based cohort of EC were available for further analysis. L1CAM expression was studied by immunohistochemistry (IHC). Association between ProMisE molecular subtypes and L1CAM expression was evaluated using the log-rank test, survival analyses were calculated using cox-regression models. Results: 97/452 (21%) of EC were L1CAM positive. The distribution of L1CAM positive tumors among ProMisE subtypes was as follows: 7/42 (17%) POLE, 26/127 (21%) of MMR-D, 20/229 (8%) p53 wt and 45/55 (80%) p53 abn. Upon ProMisE subtype analysis, L1CAM status was predictive of worse DSS among p53 wt tumors with a HR of 6.53 (CI 2.26 – 16.85; p < 0.0001). In a multivariate model within the p53 wt subtype subgroup, L1CAM remained a significant prognosticator for DSS [HR 3.54 (CI 1.01 – 11.32)]. Conclusion: Our study confirms a highly significant correlation between mutation-type p53 immunostaining and abnormal L1CAM expression in EC. Furthermore, L1CAM status may help to further stratify the large p53 wt ProMisE subgroup into tumors with lower and higher malignant potential.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".