SC-15 * ISOLATING GLIOBLASTOMA TUMOR INITIATING PROGENITOR CELLS FROM THE SUBVENTRICULAR ZONE USING A NOVEL MINIMALLY INVASIVE APPROACH
Bibliographic record
Abstract
Over 50% of brain tumor patients do not have effective therapeutic options, partly due to the delicate location of the brain tumor in the brain, suggesting the need for improved neurosurgical approaches. Furthermore, for those patients who do receive standard care, although tumor shrinkage is often observed, 90% of patients with Glioblastoma Multiforme (GBM) exhibit tumor relapse. Mechanistically, this could be attributed to residual GBM tumor initiating cells (GTICs). One important variable in the isolation of GTICs from patients may be the anatomic site from where they are harvested. We report a novel minimally invasive corridor-based approach to resect the primary GBM, but also move beyond the Magnetic Resonance Image (MRI)-visible GBM in a more accurate, efficient, and targeted manner towards the subventricular zone (SVZ). Most importantly, the reported approach preserves the sample biology and integrity while respecting surrounding healthy tissues. Through this approach, we harvested samples from four consented GBM patients from two locations, the MRI-visible GBM and the SVZ. We demonstrate that GTICs can be isolated with a 100% success rate from the SVZ as oppose to 50% from the MRI-visible GBM. SVZ-GTICs were tumorigenic in xenografts and contained GBM-associated mutations EGFR and PTEN confirming that they were not endogenous neural stem cells residing in the SVZ. The high harvest rate of GTICs can be attributed to the closed-loop system that we employed which allowed the harvested samples to be kept in physiologic conditions, preserving sample biology and integrity. Furthermore, it could also be attributed to our ability to isolate GTICs directly from their niche in the SVZ. In conclusion, we demonstrate here the directed and targeted isolation of GTICs from their SVZ niche in human GBM patients intraoperatively.
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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 | 0.000 |
| 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".