Abstract 6499: Towards a combined immunohistochemistry-based subtyping of ovarian endometrioid carcinoma
Bibliographic record
Abstract
Abstract Background: Ovarian endometrioid carcinoma (OEC) is an uncommon, understudied histotype that is distinguished from other histotypes by unique genomic features and high hormone receptor expression. There is growing interest in the biological relevance of subtyping using immunohistochemistry (IHC). Methods: Women with primary invasive OEC diagnosed from 1989 to 2010 were retrospectively identified from the population-based Alberta Cancer Registry, Canada. Pathology slide review and IHC staining for Napsin A, TP53, and WT1 were used to reduce histologic misclassification, resulting in N=166 participants (low-stage N=138, high-stage N=28), excluding those who received neo-adjuvant chemotherapy. The following markers were stained and scored on tissue microarrays: TP53 (normal/abnormal), PMS2/MSH6 (mismatch repair proficient [MMRp]/deficient [MMRd]), PGR (loss/retained), and CTNNB1 (nuclear/cytoplasmic). Using a hierarchical decision tree by stage group (low/high), we categorized tumors as TP53 abnormal, MMRd, PGR loss, nuclear CTNNB1, and no specific immunohistochemical profile (NSIP). Distributions and five-year disease specific survival (DSS) was estimated for each group; hazard ratios (HRs) adjusted for age were estimated using Cox regression with NSIP as referent group. Results: Among low-stage cases, five-year survival was as follows: 80% for TP53 abnormal (N=10, 7%), 87% for MMRd (N=15, 11%), 80% for PGR loss (N=10, 7%), 98% for nuclear CTNNB1 (N=63, 46%), and 95% for NSIP group (N=40, 29%). Overall, IHC group was associated with DSS among low stage cases (LRT p-value=0.006), and the group with nuclear CTNNB1 had a small but statistically significant DSS advantage (HR=0.089, 95% CI 0.011-0.742, p=0.025). TP53 and PGR loss suggested shorter DSS than the referent NSIP group, but not statistically significantly so (HR=1.26 and 3.08, respectively). Among high-stage cases, five-year DSS was as follows: 57% for TP53 abnormal (N=7, 25%), 75% for MMRd (N=8, 29%), 33% for nuclear CTNNB1 (N=3, 11%), and 17% for NSIP (N=10, 36%). There were no high-stage cases in the PGR-loss group. Nuclear CTNNB1 group had a shorter DSS among high-stage cases, but this was not statistically significantly so (HR=1.16, p=0.85). Conclusion: Pending replication in additional datasets, results suggest that OEC cases in nuclear CTNNB1 or NSIP IHC groups may have a favorable prognosis in which de-escalation of therapy can be considered. In addition, abnormal TP53 and loss of PGR may identify a high-risk subset of low-stage OEC. Citation Format: Brooke Jorgensen, Stacey Winham, Julie Cunningham, Sebastian Armasu, Chen Wang, Hunter Atkinson, Bryan McCauley, Linda Kelemen, Martin Koebel, Ellen Goode. Towards a combined immunohistochemistry-based subtyping of ovarian endometrioid carcinoma [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 6499.
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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 | 0.003 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".