CNSC-28. IDENTIFICATION OF EAG2-KVß2 POTASSIUM CHANNEL COMPLEX IN NEURON-GBM COMMUNICATIONS REVEALS GBM VULNERABILITY TO DESIGNER INTERFERENCE PEPTIDE
Bibliographic record
Abstract
Abstract Glioblastomas (GBMs) are the most aggressive brain tumors. GBM cells form extensive tumoral networks to communicate with each other and with surrounding neurons. Neuronal activity promotes GBM cell proliferation by secreting protumorigenic factors and triggering neuronal-activity-dependent Ca2+ transients, which are persisted and transmitted via tumor networks to promote overall tumor growth and therapy resistance. While how these processes are regulated is largely unknown. Here we show the GBM networks and Ca2+ transients are regulated by a voltage-gated potassium channel complex comprised of EAG2 and Kvβ2. GBM cells selectively overexpress Kvβ2 isoform 4 to facilitate tumor-specific Kvβ2-EAG2 interaction, which regulates neuron-GBM contact-dependent localization of EAG2. Rationally designed interfering peptide K90-114TAT blocks EAG2-Kvβ2 interaction, leading to reduced tumor cell proliferation, increased apoptosis, and prolonged survival of patient-derived xenograft mouse models with no evidence of toxicity to normal tissue. Single-cell RNA sequencing revealed a subgroup of GBM cells is highly sensitive to K90-114TAT treatment. These cells exhibit neuronal signatures and are highly associated with TMZ resistance and worse prognosis. In accordance with the notion, we found neurons promote TMZ resistance of GBM cells, which is regulated by the EAG2-Kvβ2 complex. Treating TMZ-resistant GBM xenograft mice with K90-114TAT yielded significant tumor burden reduction and prolonged survival. Together, our findings revealed the EAG2-Kvβ2 channel complex as a key regulator of neuron promoted GBM progression and designed an interfering peptide with anti-GBM efficacy and low general toxicity, which may have significant clinical impact.
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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) | 0.003 | 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".