Custom 3D Brain Cancer Model to Test a Novel Anti‐Cancer Electrotherapy
Bibliographic record
Abstract
Introduction High‐Grade Gliomas (HGG) are primary malignant brain tumors with dismal treatment outcomes. Our team has been pioneering a novel implantable biotechnology called Intratumoral modulation therapy (IMT) that distributes non‐ablative electric fields across tumor‐affected brain regions to induce tumor cell death. A systematic means to define the mechanism and optimal treatment parameters for various forms of HGG is needed to advance this promising technology towards clinical application. The aim of this study was to develop a custom, high throughput, patient‐derived 3D HGG model to investigate the therapeutic effects of IMT. Methods Primary patient HGG cells (1×10 4 ) were implanted into wells of a 96‐well plate containing 200 uL of Matrigel®. The resultant tumor spheroids were characterized over 14 days by observing growth, invasion and viability with MTT assays, confocal microscopy and bioluminescence imaging (BLI). The culture plates were custom adapted to house implantable IMT devices to broadly distribute IMT fields across HGG tumors. Computer‐generated IMT field modeling was performed using COMSOL software to predict field strength and distribution, and to reconstruct IMT fields in the 3D system. Spheroids received 72‐hours of IMT using a spectrum of treatment parameters based upon a continuous, low amplitude sinusoidal waveform. Results Patient‐derived HGG spheroids exhibited multi‐layered, progressive growth and invasion of peritumoral matrix over the 14‐day study period. Computer simulation predicted electric field distribution and intensity across patient HGG spheroids for a spectrum of defined IMT parameters. Using the simulation plans to guide treatment settings, IMT produced a significant reduction (>60%) of metabolic viability in HGG spheroids treated with IMT compared to sham conditions. This data was corroborated by BLI which revealed >65% signal loss associated with IMT. Conclusion IMT is a highly promising innovation designed to combat HGGs, the most devastating of primary brain cancers. Our custom in vitro IMT model permits high throughput testing of treatment parameters in 3D patient HGG spheroids. This innovative preclinical strategy will be instrumental in defining the potential and optimizing treatment response across a spectrum of high fatality HGG cancers. Support or Funding Information This work was supported by the Cancer Research Society and preformed at Schulich School of Medicine and Dentistry and Lawson Health Research Institute.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".