OS09.5.A RECURRENT COPY NUMBER ALTERATIONS DRIVE IDH-MUTANT ASTROCYTOMA DEVELOPMENT AND PROGRESSION
Bibliographic record
Abstract
Abstract BACKGROUND IDH-mutant gliomas are diffusely infiltrating, slow-growing brain tumors that inevitably undergo malignant progression to grade IV astrocytoma - a highly aggressive and uniformly fatal disease with dismal prognosis. Despite extensive research, the molecular mechanisms underlying this transformation remain poorly understood. While alterations in canonical driver genes such as CDKN2A, PTEN, and PDGFRA have been implicated, the broader landscape of genetic drivers involved in IDH-mutant glioma progression is largely uncharacterized. In contrast to many other malignancies, IDH-mutant gliomas acquire relatively few somatic point mutations during progression - typically in the range of 30 to 40 - suggesting that alternative mechanisms may be driving tumor evolution. Notably, these tumors exhibit widespread chromosomal instability, resulting in a heterogeneous and highly prevalent pattern of copy number alterations (CNAs). This observation raises the possibility that gene dosage changes due to CNAs play a critical role in tumor progression. MATERIAL AND METHODS To investigate this hypothesis, we performed integrative analysis of IDH-mutant glioma cohorts from the Mayo Clinic and TCGA datasets (~1,500 patients), identifying twelve highly recurrent CNAs significantly associated with patient outcomes. We refer to these as malignant IDH-associated CNAs (mIDH CNAs). We hypothesized that these recurrent CNAs harbor driver genes that confer selective growth advantages, thereby contributing to their recurrence and prognostic relevance. To systematically assess the functional impact of genes within these regions, we developed a novel in vivo CRISPR-based screening platform, termed CRISPR-Knock Out and Activation Linked Assay (CRISPR-KOALA). This approach utilizes stereotaxic delivery of lentiviral gRNA libraries - targeting mouse orthologs of ~1,800 genes within recurrently deleted regions and ~1,200 genes within amplified regions - into the brains of a genetically engineered mouse model of IDH-mutant glioma (rs557mut;IDHmut;P53KO;AtrxKO;Cas9GFP: Yanchus et. al Science 2022). RESULTS Mice injected with mIDH CNA-targeting libraries developed tumors significantly more rapidly than control cohorts. Deconvolution of the CRISPR-KOALA screen identified several putative driver genes, including regulators of the NOTCH signaling pathway, lipid metabolism modulators such as THEM6, and the NMDA receptor modulator GRINA - implicating dysregulation of metabolism, immune response, and glutamatergic signaling in the malignant transformation of IDH-mutant gliomas. CONCLUSION Together, these findings provide critical insights into the genetic mechanisms driving the progression of IDH-mutant gliomas and identify new candidate genes and pathways for potential therapeutic targeting.
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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 | 0.001 | 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) | 0.003 | 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".