Molecular Classification of Endometrial Cancers Using an Integrative DNA Sequencing Panel
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Adoption of molecular classification in endometrial cancer (EC) into clinical practice remains challenging due to complexity in coordination of multiple assays. We aimed to develop a simple molecular technique to classify ECs into four subgroups using our custom-designed targeted sequencing panel. METHODS: Patients with newly diagnosed ECs were prospectively recruited from three cancer centres in Ontario, Canada. Using our panel, 181 ECs were sequenced. Variants were analysed for pathogenicity and clinicopathologic information was collected through medical records retrospectively. RESULTS: Of 181, 86 (48%) were mismatch repair deficient (MMRd), of which 62 (72%) harboured MLH1 promoter methylation and 24 (28%) had pathogenic variants in MMR genes. Of single classifiers, three (1.8%) had pathogenic POLE (POLEmut), 15 (9%) had TP53 mutations (p53abn) and 61 (37%) had no specific molecular profile subtype (NSMP). Sixteen (9%) had more than one molecular classifying feature, with eight (4%) MMRd-p53abn, six (3%) POLEmut-MMRd, one (0.5%) POLEmut-MMRd-p53abn and one (0.5%) POLEmut-p53abn. When MMRd group was further subclassified according to mechanism of MMR loss, MLH1 promoter methylated group had worse outcomes than those with somatic MMR pathogenic variants. CONCLUSIONS: Our panel can classify ECs into four subgroups through a simplified process and can be implemented reflexively in clinical practice.
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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 | 0.002 | 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 | 0.000 |
| 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".