Abstract 3997: Advancing precision cancer medicine with patient-derived organoids: an endometrial cancer case study
Notice bibliographique
Résumé
Abstract Background and Purpose: Endometrial cancer (EC) is one of the most common gynecologic cancer, comprising a group of complex, heterogeneous subtypes with distinct features and varying outcomes. While advancements in EC treatment have progressed rapidly, managing mixed endometrial carcinomas remains challenging due to the variability of subtypes and their impact on clinical and biological behavior. This study highlights the potential of patient-derived organoid (PDO) models through the case of a young patient initially diagnosed with grade 1 endometrioid endometrial cancer, treated with endocrine therapy. The disease later progressed into a biologically aggressive mixed carcinoma exhibiting three distinct patterns: grade 1 endometrioid, large-cell neuroendocrine, and undifferentiated carcinoma. Methods: A PDO model (OPTO.85) and a corresponding organoid-derived xenograft (ODX) model were generated from the patient’s surgical specimen. Patient tissue and OPTO.85 models underwent WES, RNAseq and ATACseq to profile genomic and epigenomic alterations. A high-throughput drug screen was performed using the ApexBio FDA-Approved and Epigenetic Drug Libraries, as well as the OICR Kinase Inhibitor and Tool Compound Libraries. Organoids were plated on 1536-well drug plates, drugs were added at 2.5 µM concentration, and cell viability was measured after 6 days using Alamar Blue. Drug sensitivity curves were performed on individual compounds, using a 21-point dose-response. To assess in vivo drug response, OPTO.85 was implanted into NOD SCID mice, and mice were treated with BKM120 (50 mg/kg) +/- Cediranib (6 mg/kg) daily via oral gavage. Results: Histopathological and genomic analyses confirmed that the PDO model accurately reflected the tumor’s biology. Sequencing revealed oncogenic alterations in PIK3CA, ARID1A, and CTNNB1 genes across patient tissue, PDO, and ODX models. OPTO.85 PDO demonstrated sensitivity to PI3K inhibitors. RNAseq and ATACseq analyses revealed enrichment in VEGF and Wnt signaling pathways, suggesting potential therapeutic vulnerabilities. High-throughput drug screening identified sensitivity to VEGF inhibition. The VEGF inhibitor Cediranib demonstrated synergy with BKM120, significantly reducing OPTO.85 organoid growth. This combination also showed in vivo efficacy in the OPTO.85 ODX model, where it significantly suppressed tumor growth. Conclusion: We demonstrate the potential of PDO models in cancer research. By leveraging RNAseq, ATACseq, and high-throughput drug screening, this study identified actionable targets in the VEGF and PI3K pathways and validated the synergistic effects of Cediranib and BKM120 in an endometrial cancer with complex histology and genomic profile. These findings highlight the value of PDO models as innovative tools for personalizing cancer treatment and developing effective therapies for complex endometrial malignancies. Citation Format: Nikolina Radulovich, Pamela P. Soberanis, Molly Udaskin, Quan Li, Kevin C. Nixon, Irene Xie, Nhu-An Pham, Ming S. Tsao, Stephanie Lheureux. Advancing precision cancer medicine with patient-derived organoids: an endometrial cancer case study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3997.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| 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 ».