GENE-31. IDENTIFICATION OF CORE AND CONTEXT-SPECIFIC FITNESS GENES IN GLIOBLASTOMA STEM CELLS VIA GENOME-WIDE CRISPR-Cas9 SCREENS
Bibliographic record
Abstract
Abstract Advances in the characterization of the genetic, epigenetic and transcriptomic landscapes of glioblastoma (GBM) tumor samples and primary cell lines have yielded considerable insight into tumor initiation and progression. A major finding from these studies is the high degree of inter- and intra-tumoral variability displayed in GBM, which has increased interest in the identification of clinically relevant disease subtypes for precision targeting. While multiple groups have identified subtypes of GBM tumors and GBM stem cell lines, efforts to identify specific actionable targets have yet to bear fruit. To address this gap, we performed genome-wide TKO CRISPR-Cas9 fitness screens to functionally interrogate the genomes of a panel of 16 genetically/epigenetically characterized GBM stem cell lines. Our screens identify over 1,500 fitness genes per GBM stem cell line which collectively provide a functional landscape of genetic vulnerabilities in GBM. The size and heterogeneity of our screen panel provided us with the capability to identify both core (i.e. SOX9, Protein Ufmylation, JUN) and context specific fitness genes. Specifically, we have identified genes preferentially required for the proliferation of GBM stem cells based on transcriptionally defined subtypes (i.e. SOX2, OLIG2 in Proneural GSCs), genetic alterations (i.e. CDK6 in CDKN2A/B deleted GBM) and disease stage (i.e. ATP6AP2 in recurrent GBM). Experimental validation of key subtype-specific fitness genes is currently underway using genetic knockout and drug/inhibitor treatment. Our data provides biological insight and novel mechanistic understanding of heterogeneity in GBM and points to opportunities for precision targeting of defined GBM subtypes.
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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) | 0.002 | 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".