Abstract PR002: Global proteomic profiling of endometrial carcinomas identify prognostic markers
Bibliographic record
Abstract
Abstract While endometrial cancer (EC) has an overall good prognosis, some patients do poorly and there is room for refinement within current classification systems. Using the TCGA prognostic grouping of EC, our group developed the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE), which reliably and reproducibly stratifies ECs into four prognostic groups: POLEmut, p53wt/NSMP, MMRd (mismatch repair deficient), and p53abn, with the best prognosis for POLEmut to worst prognosis for p53abn. In the current study, global proteomic analysis was performed using the clinical SP3-CTP workflow on archival tissues from 151 patients including 40 MMRd, 34 POLEmut, 34 p53abn and 43 p53wt/NSMP, with clinical follow up data. Included in the cohort were 11 replicate samples (different parts of the same tumor) to examine spatial heterogeneity in the proteomic profiles. Replicate samples were highly correlated to each other, with the exception of three POLEmut cases with very poor correlation in the proteome in different parts of the tumor. As POLEmut tumors have an exceedingly high mutation burden, it is not surprising that this translates to heterogeneity at the proteomic level. Disease specific survival was examined to determine prognostic significance within the whole cohort and within individual molecular subgroups. High TOMM34, PLTP or TSFM expression was correlated to poor disease specific survival in the whole cohort and independently prognostic when molecular subtype, grade and histotype are considered. High MGST, NCL or XPNPEP3 were associated with poor outcomes within the p53wt/NSMP group. POLD2 and ENAH were prognostic within the MMRd group. Within the p53abn group, ACADVL and BABAM1 were found to be prognostic, and GRB7 was found to be enriched in the p53abn group compared to other molecular subtypes. As the group with the worst prognosis, p53abn group could benefit from novel therapeutic avenues. ACADVL, BABAM1 and GRB7 all lie within pathways that are potentially targetable. Our proteomic analysis has identified prognostic markers that may be useful in further refining current molecular classification to help guide treatment decisions. Furthermore, new therapeutic interventions could be developed to target proteins and pathways identified by this proteomics screen. Citation Format: Dawn R. Cochrane, Gian Negri, Jutta Huvila, David A. Farnell, Emily Thompson, Winnie Yang, Genny Trigo-Gonzales, Amy Lum, Sandra Spencer, Ryan Riley, Samuel Leung, Christine Chow, Jamie Lim, Martin Koebel, Stefan Kommoss, Friedrich Kommoss, Lien Hoang, David G. Huntsman, Gregg Morin, Jessica N. McAlpine. Global proteomic profiling of endometrial carcinomas identify prognostic markers [abstract]. In: Proceedings of the AACR Virtual Special Conference: Endometrial Cancer: New Biology Driving Research and Treatment; 2020 Nov 9-10. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(3_Suppl):Abstract nr PR002.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".