Abstract 4542: Development of a one-step molecular classifier for endometrial carcinoma using an amplicon-based gene panel and next generation sequencing technology
Bibliographic record
Abstract
Abstract Objectives: Molecular classification of endometrial carcinoma (EC) is now recommended by the WHO, ESGO/ESTRO/ESP and NCCN guidelines. The pragmatic molecular classification tool, ProMisE, identifies four molecular subtypes based on next generation sequencing (NGS) for the detection of somatic pathogenic POLE mutations, and immunohistochemistry for mismatch repair and p53 proteins. ProMisE provides valuable prognostic and predictive information to direct care, however, multiple molecular results are often received from different time periods and/or from different centers which can cause delays. Therefore, we developed a classifier that relies on a single DNA-based NGS test with the goal of recapitulating the prognostic value of ProMisE by producing concordant results. Methods: Formalin fixed paraffin embedded (FFPE) EC tumor DNA was sequenced using the clinically validated Imagia Canexia Health Find ItTM amplicon-based next generation sequencing gene panel assay. The 38 gene panel assessed single nucleotide variants (SNVs), indels (insertions and deletions), gene amplification and microsatellite instability (MSI) using 21 MSI loci. To compare to the original ProMisE classifier, we identified somatic mutations in POLE, TP53 and MSI-High or MS-Stable for molecular classification. Molecular subtypes assigned by both classifiers were assessed for concordance metrics and Kaplan-Meier survival curves. Results: The one-step NGS molecular classifier assessed 165 unique patient FFPE EC samples that had previously been assessed by ProMisE. There were 152/165 cases that were concordant between molecular subtype assignment from the original ProMisE and the one-step NGS assay with a kappa statistic of 0.88 and accuracy of 0.92. There were 13 samples that were discordant (original classifications: 3 POLE, 3 MMRd, 2 p53abn, 5 NSMP/p53wt), which will be reviewed in detail. Results were concordant in 14 of 15 cases (93%) when both diagnostic biopsy and hysterectomy specimens were tested. Molecular subtypes maintained their associations with clinical outcomes (progression free survival, disease specific survival, overall survival). Conclusion: The one-step NGS molecular classifier demonstrates high concordance with the original ProMisE classifier including between biopsy and hysterectomy samples. This shows that reliable molecular testing could be obtained from time of first diagnosis. Prognostic value of this new one-step classification tool is maintained. Appropriate interpretation of results is critical, including limiting POLE mutation assignment to a confirmed list of pathogenic mutations and correct order of segregation for ECs with more than one molecular feature. Further validation is needed in a larger cohort to implement into standard of care testing. Citation Format: Melissa K. McConechy, Amy Jamieson, Amy Lum, Samuel Leung, Adrian Kense, Sonal Brahmbhatt, Ka Mun Nip, Ebru Baran, Dilmi Perera, Kurt Yakimovich, Ruth Miller, C. Blake Gilks, David Huntsman, Jessica N. McAlpine. Development of a one-step molecular classifier for endometrial carcinoma using an amplicon-based gene panel and next generation sequencing technology. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4542.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".