Abstract B007: Parallel epigenetic and transcriptomic profiling of carcinoma and stroma compartments across major ovarian cancer histotypes
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».