LGG-35. FUNCTIONAL GENOMIC APPROACHES TO IDENTIFY THERAPEUTIC TARGETS IN <i>MYB</i> AND <i>MYBL1</i> EXPRESSING PEDIATRIC LOW-GRADE GLIOMAS
Bibliographic record
Abstract
Abstract AIM Recurrent structural variants involving MYB and MYBL1 transcription factors were recently identified in pediatric low-grade gliomas (pLGGs), such as the MYB-QKI rearrangement in Angiocentric Gliomas and truncations of MYBL1 (MYBL1tr) in Diffuse Astrocytomas. However, therapeutic dependencies induced by these alterations remain unexplored. METHODOLOGY We have generated in vitro pLGG mouse neural stem cell (NSCs) models engineered to harbor distinct MYB/MYBL1 genomic alterations. We used single cell RNA sequencing approaches to determine the transcriptional profile and dissect the central regulatory networks of our in vitro pLGG models over time. To identify specific genetic dependencies associated with MYB/MYBL1 mutations, we employed the Brie genome-wide mouse CRISPR lentiviral knockout pooled library, consisting of 78,637 single guide RNAs (sgRNAs) targeting 19,674 mouse genes. RESULTS MYB/MYBL1 expression in neural stem cells induced activation of cell-cycle related, glioma-related and senescence-related pathways that are involved in normal development, including activation of MAPK and mTOR signaling which are also activated in human pLGG samples. Genome-scale CRISPR-cas9 screens in isogenic NSCs expressing MYB-QKI or MYBL1tr identified differential genetic dependencies relative to GFP controls. These included regulators of cell-cycle progression and several modulators of the ubiquitin-proteasome degradation pathway. Analysis of RNA-sequencing data from human tumors revealed several of these dependencies identified in the cell line model to be differentially expressed in MYB-altered pLGG tumors relative to normal brain. CONCLUSION Expression of MYB family alterations induces expression of key developmental and oncogenic pathways and genetic dependencies that represent potential therapeutic targets for MYB or MYBL1 rearranged pLGGs.
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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 | 0.001 |
| 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".