DDDR-35. TARGETING GBM INVASION BY INHIBITING KCNA1 WITH 4-AMINOPYRIDINE: AN FDA APPROVED DRUG THAT EASILY PASS THROUGH THE BBB
Bibliographic record
Abstract
Abstract Diffuse invasion is a hall mark of glioblastoma (GB) and one of the primary causes of poor clinical outcomes in GBM patients. Tumor cells migrate deep into the normal brain tissues are frequently protected by the BBB, making them particularly difficult to treat. New therapeutic targets are needed. Our previous studies using spatially dissected and functionally validated matching pairs of invasive and tumor core GBM cells identified KCNA1 as a shared gene that is selectively over-expressed in the invasive GBM cells in 6 patient derived orthotopic xenograft (PDOX) mouse models of pediatric GBM (Huang YL et al, Adv Science 2021). A subsequent analysis of adult GBM RNAseq data from IVY Atlas revealed a significantly elevated expression of KCNA1 (4.9 fold) in the invasive edges of patient GBM tumors. It is also one of the 11 core molecules identified in GEO and TCGA databases through an integrated bioinformatic analysis (Yang J, Front Onc 2021). To determine the anti-invasive activities of targeting KCNA1, we treated three highly invasive adult GBM PDOX models with 4-aminopyridine (4-AP), an old lipid soluble drug that easily penetrate the BBB, at 5 mg/kg, i.p., 5 days/week for 8 weeks acing alone or in combination with fractionated radiation (at 2 Gy/day x days). As single agent, 4-AP significantly extended median animal survival times in 1/3 GBM models (69 to 77 days, P = 0.033). Combination with XRT did not significantly improve the animal survival times in the three models. Systematic analysis of GBM invasion in mouse brains of the 3 PDOX models before, during and after 4-AP treatment revealed remarkable inhibition of tumor invasion. Our data highlighted the role of KCNA1 in promoting GBM invasion and support the fine tuning of 4-AP dose, schedule, and length of treatment to serve as a novel component of anti-GBM invasion therapies.
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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) | 0.002 | 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".