Tumoroid‐On‐a‐Plate (ToP): Physiologically Relevant Cancer Model Generation and Therapeutic Screening
Bibliographic record
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 two-dimensional (2D) cell cultures and complex in vivo environments and offer versatile tools for comprehensive studies into cancer progression, drug responses, and tailored therapies. This study introduces the Tumoroid-on-a-Plate (ToP) device, an innovative ope-surface microfluidic platform designed to create predictive 3D models of solid tumors. The ToP device combines tumor mass, stromal cells, and extracellular matrix (ECM) components, to closely replicate the microenvironment of glioblastoma (GBM) and pancreatic adenocarcinoma (PDAC). Using the advanced ToP model and testing various GBM ECM compositions such as collagen and Rreelin within the model, we can assess how specific elements affect GBM invasiveness. The ToP in vitro model also enables screening chemotherapeutics such as temozolomide and iron-chelators in a single and binary treatment setting on the complex ECM-embedded tumoroids to evaluate their toxicity on GBM and PDAC models viability and apoptosis. Furthermore, co-culturing PDAC tumoroids with human-derived fibroblasts reveals the pro-invasive influence of stromal elements on tumor growth and drug response. This research underscores the value of advanced 3D models like ToP in advancing the understanding of cancer complexity and therapy responses.
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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 | 0.001 |
| 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".