Abstract B007: Parallel epigenetic and transcriptomic profiling of carcinoma and stroma compartments across major ovarian cancer histotypes
Bibliographic record
Abstract
Abstract Introduction: The molecular landscapes of epithelial ovarian cancers (EOCs) have largely been profiled using bulk sequencing techniques. Consequently, biologically relevant signals from the tumor and its microenvironment (hereafter stroma) are often conflated, dampening both tumor and stroma-specific observations. Methods: We profiled the carcinoma and stroma compartments separately across clear cell (CCOC), endometrioid (ENOC), and high-grade serous ovarian cancer (HGSC) using laser capture microdissection followed by sample-matched whole genome bisulfite sequencing and RNA-seq. Results: Histotype designations drove sample segregation in the carcinoma compartment yet were not sufficient to explain top expression or methylation variations in the stroma. Unsupervised clustering of the top variable genes in the stroma identified four stromal subtypes (S1-S4) shared across the three histotypes. Of these, S1, S2, and S4 were the most distinguishable and could be recovered in external datasets. S1 was enriched for RNA splicing processes (SFSWAP, RBM25), S2 for metabolism (GATM, ALDH1A1) and S4 for remodeling of the extracellular matrix (FN1, POSTN, SFRP2). While the characteristics of S4 echo previous reports of the desmoplastic/highly fibrous stroma reported in HGSC, we show that S4 is also present in CCOC and ENOC tumors. Using HGSC samples from TCGA, we observed a strong correlation of S4 markers with the Mesenchymal subtype and an inverse correlation of S4 and S2 markers, suggesting mutual exclusivity of these stromal subtypes. Examining the TCGA-derived subtype markers in our microdissected data revealed that the canonical Immunoreactive and Mesenchymal subtypes were driven by signals from the stroma, rather than the carcinoma. Conversely, Differentiated and Proliferative markers were enriched in the carcinoma. Clustering of pure carcinomas revealed a Proliferative versus Differentiated dichotomy within the carcinomas, particularly within ENOC and HGSC tumors. CCOC tumors were systematically depleted of the Proliferative subtype, consistent with their indolent nature. We next investigated if genetic, epigenetic or transcriptional cues in the carcinoma compartment are associated with the characteristic of its TME but failed to detect differences at 5% FDR. This suggests that the stromal subtype may not be shaped solely by instructions from the cancer cells and that a more complex, reciprocal cross-talk exists between the carcinoma and its TME. Conclusions: Epigenetic and transcriptional differences in CCOC, ENOC and HGSC carcinomas can be largely attributed to histotype, yet this is not the case for their associated TMEs. With the growing importance of TME in influencing patient outcomes, and response to treatment, understanding the nature of a tumor’s TME is crucial. Thus, coupling the stromal subtypes (S1-S4) with the well-established histotype designations of EOCs may provide a more robust way to classify EOCs and inform optimal treatment strategies that account for the characteristic of the tumor stroma. Citation Format: Svetlana Djirackor, Karolin Heinze, Ian Beddows, David Sokol, Bianca Ribeiro de Souza, Martin Koebel, Marie Adams, Michael Anglesio, Hui Shen. Parallel epigenetic and transcriptomic profiling of carcinoma and stroma compartments across major ovarian cancer histotypes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr B007.
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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 | 0.000 |
| 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) | 0.002 | 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".