Abstract 5015: Drug screening and phenotypic analysis in a microwell-based 3D cell culture system
Bibliographic record
Abstract
Abstract Three-dimensional (3D) cell culture models provide more physiologically relevant drug and toxicity screening platforms than traditional 2D platforms (1). Phenotypic analysis is an informative endpoint for high-throughput screens; however, variability in 3D culture systems can make statistically sound identification of good candidate compounds from a small number of replicates difficult. To minimize this variability we tested AggreWell™, a microwell-based 3D cell culture device that is a prototype system for 3D culture including drug and toxicity screening. The geometry of this system provides precise localization, segmentation and uniformity of spheroids derived from various cell types, and is predicted to minimize experimental variability. To test this directly, estrogen receptor-expressing MCF-7 breast cancer cells were seeded into AggreWell™400 plates in MammoCult™ medium in triplicate, and 24 hours after seeding, the resulting spheroids were treated with a single drug or combination of tamoxifen (TMX), the HER2 and EGFR inhibitor lapatinib (LTB), and the γ-secretase and Notch inhibitor DAPT. Three days later, the spheroids were imaged for the following parameters: size, brightness, shape, and specific morphologic features. Spheroids were also dissociated for viable cell counts. The IC50 values for drug effects for TMX and LTB are 21.6 and 6.3 uM, respectively, whereas DAPT had no effect on the spheroids at doses up to 100 uM. These results are consistent with previous published results and allow for correlation of observed changes in morphologic and cell viability post-treatment. For combinatorial drug treatments, concentrations of 20 uM TMX, 5 uM LTB, and 10 uM DAPT were used. TMX treatment exhibited the greatest reduction of cell reduction (52%). Combination of TMX with DAPT, LTB, or both, resulted in synergistic reductions of viability of 64%, 74% and 83%, respectively. Morphologic features of these cultures analyzed by principal component analysis reveal that parameters including circularity and gray values are associated with untreated cultures, whereas spheroid perimeter and area are associated with treated cultures. Graphing combinations of these parameters, for example, area vs. integrated density, delineates a clear decision plane for classifying treated versus untreated conditions. Analysis of variability in aggregate morphology demonstrates that measurement of multiple spheroids per well in AggreWell™ increased experimental power (e.g., power of 0.8 for 16 spheroids versus 0.3 for 4 spheroids, respectively; α = 0.05) and reduced experimental variance. These results confirm that morphologic analysis of spheroids grown on AggreWell™ plates is highly suited for high-throughput 3D drug screening.Reference: 1. Lee GY et al. Nat. Methods 2007. Citation Format: Michael Hiatt, Marta Mroczek, Eric Jervis, Terry E. Thomas, Allen C. Eaves, Sharon Louis. Drug screening and phenotypic analysis in a microwell-based 3D cell culture system [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5015.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".