HGG-34. EARLY PRECLINICAL OUTCOMES OF INTRATUMORAL MODULATION THERAPY FOR DIFFUSE INTRINSIC PONTINE GLIOMA AND GLIOBLASTOMA
Bibliographic record
Abstract
Diffuse intrinsic pontine glioma (DIPG) is a common, malignant childhood central nervous system (CNS) cancer with dismal treatment outcomes. Glioblastoma (GBM) is another rapidly fatal glial tumor that occurs throughout the CNS in children and adults. We are pioneering a novel treatment strategy, called Intratumoral Modulation Therapy (IMT), to exploit the vulnerability of glioma cells to non-ablative, locoregional electrotherapy. IMT delivers low intensity, titratable therapeutic electric fields across the tumor-infiltrated CNS using an implantable bioelectrode system. We previously reported the in vitro efficacy of IMT in adult GBM and now speculate that this innovative approach may also be effective in vivo and offer new treatment potential for DIPG. This study evaluated the efficacy of IMT in patient-derived DIPG and GBM cells using customized in vitro and in vivo preclinical treatment models. DIPG and GBM cells were treated with a standardized regime of sham, IMT, temozolomide (TMZ) chemotherapy or IMT plus TMZ, followed by metabolic and immunocytological viability analyses. A custom-designed rodent IMT model was used for electric field mapping in the brain and evaluating the in vivo efficacy of IMT against GBM. Patient DIPG cells exhibited a robust ~40% viability loss with IMT monotherapy and synergistic response of 60-80% reduction using the combination of IMT and TMZ. The rodent model demonstrated in vivo IMT efficacy, showing tumor volume reductions of 22-40% using IMT monotherapy delivered with a pilot treatment system. IMT is a novel locoregional electrotherapeutic strategy that shows significant preclinical efficacy against DIPG and GBM.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".