Abstract B001: Comparative exploration of cultured spheroids and organoids as models to study epithelial ovarian cancer pathobiology
Bibliographic record
Abstract
Abstract Epithelial ovarian cancer (EOC) is a devastating disease with a unique metastatic progression involving spheroids. Our extensive research into spheroid pathobiology has provided insight into several molecular and cellular changes implicated during EOC metastasis. We speculate that EOC cells change between tumor and spheroid states to withstand stress and promote cell survival during metastasis, yet this switching behavior is not fully understood. Established high-grade serous (OVCAR3, OVCAR4, OVCAR8) and new ascites-derived iOvCa cell lines (n=7) were used to generate spheroids in suspension on Ultra-Low Attachment® plates and organoids using modified patient-derived organoid (PDO) culture conditions. Brightfield microscopy with our Incucyte® S3 Organoid Module, immunohistochemistry (IHC), and immunofluorescence (IF) were conducted for morphological comparisons between spheroids and organoids. Immunoblotting was performed to evaluate bioenergetic stress and autophagy, which are both altered processes in spheroids from our previous studies. OVCAR8 CRISPR knockout cell lines (for STK11, CAMKKβ, and ULK1) were also used for a more in depth look at these pathways. Bulk RNA-sequencing was completed on the seven iOvCa cell lines and three recently developed PDOs, with subsequent bioinformatics analyses, to discover new implicated pathways in EOC disease progression. Organoids appeared heterogeneous in morphology with dense, cystic, or mixed phenotypes, whereas spheroids often existed as compact, grape-like clusters, or sparse structures. There were also clear differences in growth dynamics, proliferative capacity based on IHC of Ki67, and fibronectin deposition based on IF staining within each 3D spheroid and organoid structure. Interestingly, established EOC cell lines and patient ascites-derived iOvCa182 and iOvCa246 organoids have higher AMP-activated protein kinase (AMPK) T172 phosphorylation as compared with spheroids, however the remaining iOvCa cell lines exhibited increased AMPK activity in spheroids only. This indicates reliance of bioenergetic stress mainly in our spheroids and increased use of patient-derived samples in current EOC research. Lastly, transcriptomic analyses of our seven iOvCa cell lines showed elevated pathways for G2M checkpoint and E2F targets in organoids compared to spheroids, which could provide a new avenue for targeted therapeutics on EOC cells. The EOC cellular adaptations during disease progression have become more apparent as the phenotypic and molecular differences of spheroids and organoids are uncovered in this study, as well as the patient’s heterogeneity. Our results show that being able to target both proliferative and dormant cells is very important in developing new treatment options. Therefore, parallel assays of spheroid and organoid models will be crucial experimental systems to discover new therapeutic vulnerabilities in advanced EOC disease. Citation Format: Emily Tomas, Yudith Ramos-Valdes, Jennifer Davis, Bartlomiej Kolendowski, Gabriel E. DiMattia, Trevor G. Shepherd. Comparative exploration of cultured spheroids and organoids as models to study epithelial ovarian cancer pathobiology [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B001.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".