Abstract B026: Substratification of mismatch repair deficient (MMRd) endometrial cancers can provide prognostic and predictive refinement
Bibliographic record
Abstract
Abstract Background: Mismatch repair deficient (MMRd) endometrial cancer (EC) represents one third of all ECs. Identifying patients with MMRd EC enables testing for Lynch Syndrome (LS) and access to FDA-approved immune checkpoint blockade (ICB) therapy. Recent data have highlighted the diversity within MMRd tumors, suggesting worse outcomes and lower response to ICB in patients with MLH1 loss. Our aim was to characterize a cohort of MMRd ECs to elucidate if clinically meaningful substratification of this molecular subtype (MLH1 loss vs. other MMRd) could be achieved. Methods: MMRd ECs were identified from retrospective institutional and population-based cohorts (1994-2016) with clinicopathologic data, immunohistochemistry (IHC) assessment of estrogen receptor (ER) and L1CAM and CTNNB1 mutation status recorded. Multiplex IHC for immune markers (CD3, CD8, CD79a, CD138, PD-1, PD-L1, FoxP3, IDO-1) was assessed and compared in patients with MLH1 loss or PMS2/MLH1 loss (when 2 antibody MMRd testing performed) vs. isolated MSH2, MSH6, PMS2 or MSH2/MSH6 loss (remainder of MMRd). Results: 655 MMRd ECs were identified, 52% of cases assessed with 4 antibody IHC (MLH1, PMS2, MSH2, MSH6) and 48% with 2 (PMS2 and MSH6). This included 32 (5%) patients with MLH1 loss and 488 (75%) with PMS2/MLH1 loss (together 80% of MMRd cohort), 1% (n= 9) MSH2, 11% (n=75) MSH6, 4% (n=24) PMS2, and 4% (n=24) with loss of MSH2/MSH6). 76 cases were confirmed to have MLH1 hypermethylation but 67% of cases with loss of MLH1 or PMS2/MLH1 did not have methylation testing performed. The majority MMRd ECs were FIGO stage I (75%) and endometrioid histotype (90%). Patients with loss of MLH1 or PMS2/MLH1 were older (p<0.001), had higher BMI (p<0.001) and had tumors with more LVI (p=0.031), deep myoinvasion (p<0.001) and grade 3 (p=0.046) compared to remainder of MMRd EC. 9% of the MMRd cohort were ER negative, 8% with loss of MLH1 or PMS2/MLH1 and 16% in remainder of MMRd. Inferior outcomes were observed for progression-free survival and overall survival in patients with MLH1 or PMS2/MLH1 loss compared with the remainder of MMRd. ER, L1CAM and CTNNB1 status were not associated with clinical outcomes across the total MMRd cohort, nor within MLH1 or PMS2/MLH1 loss. There was diversity in the immune landscape within MMRd EC with significantly lower CD8 levels in MLH1 or PMS2/MLH1 loss compared to the remainder of MMRd EC. Furthermore, 25% of the MLH1 or PMS2/MLH1 loss group were tumor infiltrating lymphocyte (TIL) ‘low’/immune cold by cluster analysis compared to 11% in the remainder of MMRd EC. Only 16 patients (2.4%) were identified as having LS but 82% of these MMRd patients had not been tested (31% non-LS testing attributable to identification of hypermethylation of MLH1). Conclusion: Substratification within MMRd ECs can provide prognostic and predictive information, with loss of MLH1 and/or its dimer partner PMS2 identifying a subset of MMRd EC with inferior outcomes that may be attributed to lower TIL. ER, L1CAM, and CTNNB1 mutation status do not add prognostic refinement within MMRd EC. Citation Format: Amy Jamieson, Jennifer Pors, Samuel Leung, Derek Chiu, Stefan Kommoss, Aline Talhouk, David G. Huntsman, Naveena Singh, Blake Gilks, Jessica N. McAlpine. Substratification of mismatch repair deficient (MMRd) endometrial cancers can provide prognostic and predictive refinement [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr B026.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".