Proteomic analysis uncovers biological diversity in molecularly defined endometrial carcinomas
Bibliographic record
Abstract
While endometrial cancer has an overall favorable prognosis, some patients have poor outcomes and may benefit from further refinements of the current classification systems. Molecular classification stratifies endometrial cancer patients into four prognostic subtypes: POLEmut, MMRd (mismatch repair deficient), p53abn, and NSMP (no specific molecular profile), where patients with POLEmut have the best prognosis and p53abn has the worst prognosis. We used proteomic profiling to assess if additional prognostic or predictive information could be identified across or within molecular subtypes. Global proteome profiling of formalin fixed, paraffin embedded samples, that had clinicopathologic and outcome data, was performed on 184 endometrial cancers encompassing all four molecular subtypes, including replicate samples of the same tumor, and both biopsy and final hysterectomy specimens. To ensure representation of each subtype, we profiled an approximately equal distribution in the 148 unique tumors; 34 (23%) POLEmut, 40 (27%) MMRd, 35 (24%) p53abn and 39 (26%) NSMP, rather than the population-based distributions. There was high reproducibility in the proteomic profiles of intra-tumor replicate samples, and between matched biopsy and hysterectomy tumor samples. Consensus clustering identified four clusters with different prognosis, named 'Adhesion', 'Immune', 'Proliferation', and 'Metabolic' based on the functional characteristics of the enriched proteins. We associated protein expression features with common mutations, molecular subtype, and outcomes. These results demonstrate the biologic diversity within endometrial cancers, both between and within molecular subtypes, and provide candidate features for functional and clinical investigation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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".