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Record W4380365659 · doi:10.1093/neuonc/noad073.214

LGG-04. CLINICALLY RELEVANT MODELING OF FUSION-DRIVEN PEDIATRIC LOW GRADE GLIOMAS USING DROSOPHILA MELANOGASTER

2023· article· en· W4380365659 on OpenAlexaff
Geena Jung, Payal Jain, Zizhuo Liang, Feng Li, Komal S. Rathi, Xin Chen, Joshua Straka, Mateusz Koptyra, Stephanie Sanchez-maldonado, Anahita Fathi-Kazerooni, Ariana Familiar, Ali Nabavizadeh, Angela J. Waanders, Xi Huang, Adam Resnick, Yuanquan Song

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsDrosophila melanogasterBiologyModel organismZebrafishPhenotypeMelanogasterGliomaFusion proteinFusion geneMdm2Signal transductionCancer researchGeneComputational biologyGenetics

Abstract

fetched live from OpenAlex

Abstract Pediatric low-grade gliomas (PLGGs) are the most common brain tumors in children, with varying degrees of brain invasion. Recent whole-genome sequencing has identified a rare gene fusion involving RAF1, a RAF isoform. Unlike other RAF fusions, RAF1 fusions are resistant to existing RAF inhibitors. Therefore, aside from surgical resection with adjuvant chemotherapy and radiation therapy, there are few targeted therapeutic alternatives for RAF1-fusion-driven PLGGs. Despite the prevalence and challenges this disease presented, our understanding of PLGGs was limited by a lack of genetic models. We ultimately picked Drosophila melanogaster as our model organism due to the conservation of major signaling pathways between flies and humans. Furthermore, this connection between humans and flies, coupled with other technical advantages associated with this model organism, like short generation cycle and its powerful genetic toolbox, makes Drosophila melanogaster an ideal organism to study the genesis and progression of PLGGs. With the help of the GAL4/UAS system, we established four fusion-driven PLGG fly genetic models and found that glial overexpression of QKI-RAF1, a fusion gene in pilocytic astrocytomas, induces an invasion-like phenotype with aberrant glial migration. This migration defect was suppressed by glial overexpression of repulsive guidance signaling receptors Robo2 or PlexA/B, indicating the dysregulation of repulsive guidance signaling pathways. Immunostaining coupled with quantitative analysis revealed that Robo2 expression is downregulated in migrating tumor cells in flies, which is recapitulated in mouse astrocytes overexpressing QKI-RAF1 and PLGG patients with RAF fusions. We further broaden our findings by profiling the tumor transcriptomes, revealing potential downstream effectors, including the G protein-coupled receptor GPR180/CG9304, and inhibition of which suppresses tumor invasion in flies. Taken together, we present the PLGG fly model system, leading to the discovery of Robo2, Plexins, and GPR180/CG9304 as potential therapeutic targets.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.060
GPT teacher head0.344
Teacher spread0.284 · 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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