Tumoroid-on-a-Plate (ToP): Physiologically Relevant Cancer Model Generation and Therapeutic Screening
Bibliographic record
Abstract
Abstract Employing three-dimensional (3D) in vitro models, including tumor organoids and spheroids, stands pivotal in enhancing cancer therapy. These models bridge the gap between 2D cell cultures and complex in vivo environments, effectively mimicking the intricate cellular interplay and microenvironmental factors found in solid tumors. Consequently, they offer versatile tools for comprehensive studies into cancer progression, drug responses, and tailored therapies. In this study, we present a novel open-surface microfluidic-integrated platform called the Tumoroid-on-a-Plate (ToP) device, designed for generating intricate predictive 3D solid tumor models. By incorporating a tumor mass, stromal cells, and extracellular matrix components, we successfully replicate the complexity of glioblastoma (GBM) and pancreatic adenocarcinoma (PDAC) within our system. Using our advanced ToP model, we were able to successfully screen the effect of various GBM extracellular matrix compositions, such as Collagen and Reelin, on the invasiveness of the GBM cells with the ToP model. The ToP in vitro model also allowed for the screening of chemotherapeutic drugs such as temozolomide and iron-chelators in a single and binary treatment setting on the complex ECM-embedded tumoroids. This helped to investigate the toxic effect of different therapeutics on the viability and apoptosis of our in vitro GBM and PDAC cancer models. Additionally, by co-culturing human-derived fibroblast cells with PDAC tumoroids, the pro-invasive impact of the stromal component of the tumor microenvironment on growth behaviour and drug response of the tumoroids was revealed. This study underscores the transformative role of predictive 3D models in deciphering cancer intricacies and highlights the promise of ToP in advancing therapeutic understanding.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".