Fitness Screens Map State-Specific Glioblastoma Stem Cell Vulnerabilities
Bibliographic record
Abstract
Glioblastoma (GBM) is the most common and lethal primary brain tumor in adults and is driven by self-renewing glioblastoma stem cells (GSC) that persist after therapy and seed treatment-refractory recurrent tumors. GBM tumors display a high degree of intra- and intertumoral heterogeneity that is a prominent barrier to targeted treatment strategies. This heterogeneity extends to GSCs that exist on a gradient between two transcriptional states or subtypes termed developmental and injury response. Drug targets for each subtype are needed to effectively target GBM. To identify conserved and subtype-specific genetic dependencies across a large and heterogeneous panel of GSCs, we designed the GBM5K-targeted guide RNA library and performed fitness screens in a total of 30 patient-derived GSC cultures. The focused CRISPR screens identified the most conserved subtype-specific vulnerabilities in GSCs and elucidated the functional dependency gradient existing between the developmental and injury response states. Developmental-specific fitness genes were enriched for transcriptional regulators of neurodevelopment, whereas injury response-specific fitness genes were highlighted by several genes implicated in integrin and focal adhesion signaling. These context-specific vulnerabilities conferred differential sensitivity to inhibitors of β1 integrin, focal adhesion kinase, MEK, and OLIG2. Interestingly, the screens revealed that the subtype-specific signaling pathways drive differential cyclin D (CCND1 vs. CCND2) dependencies between subtypes. These data provide a biological insight and mechanistic understanding of GBM heterogeneity and point to opportunities for precision targeting of defined GBM and GSC subtypes to tackle heterogeneity. Significance: CRISPR-Cas9 screens in a panel of patient-derived glioblastoma stem cells reveal heterogeneity in genetic vulnerabilities across subtypes that have important implications for targeted and combination treatment strategies for glioblastoma.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".