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Record W7132934017

A Designer Peptide to Treat Glioblastoma by Targeting Cancer-neuron Interaction

2023· dissertation· W7132934017 on OpenAlexaff
Weifan Dong

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsAmgen (Canada)
Fundersnot available
KeywordsTemozolomideGene knockdownGliomaGlioblastomaDrugBrain tumorPeptideChemotherapy
DOInot available

Abstract

fetched live from OpenAlex

Glioblastoma (GBM) is the most common and malignant adult primary brain tumor with patient survival of around 15 months. Temozolomide (TMZ) is the only chemotherapy drug approved by U.S. Food and Drug Administration (FDA) to treat early-diagnosed GBM. It only lengthens patient survival by 2.5 months with more than 50% of patients displaying intrinsic or adaptive resistance. Thus, there is an urgent need to identify novel therapeutic targets to treat GBM. Recent studies demonstrated that neuronal activities promote GBM initiation, proliferation, and invasion. Reciprocally, GBM cells remodel the synaptic constituency and induce hyperactivity of surrounding neurons, thereby creating a pro-tumorigenic microenvironment. These interactions between neurons and glioma cells underlie tumor progression. Thus, targeting cancer-neuron interactions to identify therapeutic opportunities is emerging as an area of intense study.I identified that EAG2 and Kvβ2 are highly expressed at the GBM-brain interface conducive to GBM cell-neuron interaction. Knockdown of EAG2 and Kvβ2 significantly reduced the infiltrative behaviour of GBM cells in xenograft tumor models, resulting in reduced tumor microtube formation and neuron-GBM interaction, collectively leading to a less infiltrative GBM-brain interface. EAG2 localizes at GBM cell-neuron contacts in a Kvβ2-dependent manner. Knockdown of the EAG2-Kvβ2 complex decreased calcium transients of tumor cells, suppressed tumor growth and invasion, and extended the survival of tumor-bearing mice. EAG2 and Kvβ2 physically interact to form a potassium channel complex, their interaction is enriched in GBM due to a specific Kvβ2 isoform. EAG2-Kvβ2 interaction domain-derived designer peptide blocked EAG2-Kvβ2 interaction, prolonged survival of tumor-bearing mice with no detectable toxicity on normal tissues. Single-cell RNA sequencing revealed that a group of GBM cells that closely associate with neurons are highly sensitive to the designer peptide. Neurons upregulate chemoresistant genes in GBM cells to promote TMZ resistance in an EAG2-Kvβ2-dependent manner. Moreover, designer peptide possesses robust efficacy in treating TMZ-resistant GBM.My study uncovered the EAG2-Kvβ2 complex as a unique GBM vulnerability and developed a first-in-class designer peptide to disrupt this protein-protein interaction in GBM treatment. These findings may have profound implications to develop next-generation therapeutics to benefit GBM patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.040
GPT teacher head0.356
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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