LG-02MYB-QKI REARRANGEMENTS IN ANGIOCENTRIC GLIOMA DRIVE TUMORIGENICITY THROUGH A TRIPARTITE MECHANISM
Bibliographic record
Abstract
INTRODUCTION: Pediatric low-grade gliomas (PLGGs) are among the most common solid tumors in children, encompassing multiple histological subtypes of WHO Grade I and II gliomas. While BRAF mutations and MYBL1 rearrangements have recently been identified as oncogenic drivers in pediatric gangliogliomas and diffuse astrocytomas, respectively, the oncogenic drivers for the majority of diffuse PLGGs remain unknown. Angiocentric gliomas (AGs) are pediatric low-grade gliomas (PLGGs) without known recurrent genetic drivers. METHODS: Prior genomic studies were insufficiently powered to determine the true frequency of driver alterations in rare PLGG subtypes, to identify recurrent driver alterations that occur less frequently, or to associate specific alterations with specific histological subtypes. To address this, we performed a genomic analysis of the PLGG landscape by combining newly generated and previously published sequencing datasets. Our combined cohort included 249 PLGGs including 19 AGs. RESULTS: We identified MYB-QKI fusions as a specific and single candidate driver event in AGs. In vitro and in vivo functional studies show MYB-QKI rearrangements promote tumorigenesis through three mechanisms: expression of the oncogenic MYB-QKI fusion protein, H3K27ac enhancer translocation that contributes to aberrant MYB-QKI expression, and hemizygous loss of the tumor suppressor QKI that co-operates with MYB-QKI expression to promote cell proliferation. CONCLUSIONS: We have identified MYB-QKI fusions to be a specific and single candidate driver event in AGs. This finding has diagnostic and therapeutic significance. In addition, we present the first example of a single driver rearrangement simultaneously transforming cells via three genetic and epigenetic mechanisms in a cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".