Abstract 3997: Advancing precision cancer medicine with patient-derived organoids: an endometrial cancer case study
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".