Abstract PR002: Proteomic profiling of endometrial carcinomas
Bibliographic record
Abstract
Abstract While endometrial cancer (EC) has an overall favorable prognosis, some patients do poorly and may benefit from refinements of current classification systems. The TCGA-inspired pragmatic molecular classification tool Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) has been integrated into international guidelines for EC risk stratification and management. ProMisE stratifies ECs into four prognostic groups: POLEmut, NSMP (no specific molecular profile), MMRd (mismatch repair deficient), and p53abn, where POLEmut has the best prognosis and p53abn has the worst prognosis. Our objective was to determine if proteomic profiling could provide additional prognostic or predictive information for EC patients, across or within ProMisE molecular subtypes. Global proteome profiling of FFPE samples, that had clinicopathologic and outcome data, was performed on 184 ECs encompassing all four ProMisE subtypes, including replicate samples of the same tumor, and both biopsy and final hysterectomy specimens. To ensure representation of each subtype, we aimed for an approximately equal distribution; 40 (27 %) MMRd, 33 (22 %) POLEmut, 42 (28 %) NSMP and 33 (22 %) p53abn, 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 (Pearson’s correlation >0.9). Consensus clustering generated four clusters, named ‘Adhesion’, ‘Immune’, ‘Proliferation’, and ‘Metabolic’ based on proteins enriched in each cluster. The Proliferation Cluster had the worst outcomes and the highest proportion of stage III/IV, serous, and p53abn tumors than the other clusters. The Immune Cluster had the most favorable outcomes, despite having a relatively substantial proportion of stage III/IV, serous, and p53abn tumors. We correlated protein expression with common mutations, including ARID1A mutations that cause loss of ARID1A protein expression, found in up to 60% ECs. ARID1A positive tumors also express proteins in the retinoic acid signaling pathway, while ARID1A-deficient tumors express proteins indicative of neutrophil infiltration. Comparing molecular subtypes, we found p53abn ECs were enriched in proteins associated with poor outcomes in many tumor types, such as GRB7. We validated elevated GRB7 expression in p53abn tumors using immunohistochemistry and found an association with worse disease specific survival (DSS) across the whole cohort (HR=2.39). Nucleolin expression was associated with worse prognosis in the NSMP subtype (DSS HR=9.88), while BABAM1 expression was associated with better prognosis within p53abn tumors (DSS HR=2.43). Knockout of BABAM1 (part of the BRCA complex) in cell lines resulted in increased sensitivity to PARP inhibition. Proteomic analysis of EC identifies candidate prognostic markers that may further refine current molecular classification and help guide treatment decisions. New therapeutic interventions could be developed to target proteins and pathways identified by EC proteomic profiling. Citation Format: Dawn R. Cochrane, Gian Luca Negri, Jutta Huvila, Juliana Sobral de Barros, Forouh Kalantari, Nissreen Mohammad, David Farnell, Emily Thompson, Amy Lum, Sandra E. Spencer, Amy Jamieson, Samuel Leung, Derek Chiu, Martin Koebel, Stefan Kommoss, Friedrich Kommoss, Blake Gilks, Lien Hoang, David Huntsman, Gregg B. Morin, Jessica N. McAlpine. Proteomic profiling of endometrial carcinomas [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 PR002.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".