TMOD-18. DIRECT IN VIVO CRISPR SCREEN IDENTIFIES COOPERATING TUMOR SUPPRESSORS THAT DRIVE PROGRESSION OF IDH1-MUTANT LOW-GRADE GLIOMA TO AGGRESSIVE GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Low-grade glioma (LGG) are generally slowly growing brain cancers, that frequently undergo malignant progression to aggressive, secondary glioblastoma with a dismal prognosis. By combining genetically engineered Idh1-mutant mice with in vivo CRISPR gene editing we generated a mouse model faithfully recapitulating the founder mutations of LGG. Clonal activation of the neomorphic Idh1 R132H mutation cooperates with Trp53 and Atrx mutations to trigger development of brain tumors but only with ~30% penetrance and very long latency. To elucidate the molecular mechanisms underlying the malignant progression of IDH1-mutant LGG, we devised and deployed a direct in vivo CRISPR screen targeting genes commonly mutated in human IDH-mutant secondary glioblastoma. Stereotaxic delivery of a lentiviral sgRNA library targeting the mouse orthologs of these genes into the brain of Idh1 R132H ;Trp53;Atrx;Cas9 and control Idh1 wt ;Trp53;Atrx;Cas9 compound mutant mice resulted in rapid formation of tumors that recapitulate human Idh1-mutant glioblastoma. Deconvoluting the screen showed that PI3K pathway members Pten and Pik3ca as well as Notch1, Smarca4 and Fat1 are preferentially enriched in Idh1 R132H-tumors, while Rb1 and NF2 were enriched in Idh1 wt tumors. Co-mutation analysis further identified additional co-occurring driver combinations such as Bcor-Met, Olig2-Met, Olig2-Med12 or Bcor-Olig2. We validated the tumor suppressive function of Notch1 and Pten using conventional floxed knock-out alleles and found that Notch1 functions in a haploinsufficient manner. Interestingly, Idh1 R132H did not alter tumor latency or pathology in a high grade p53;Pten;Rb1 mutant background, indicating that the neomorphic IDH-mutations can drive low but not high grade glioma development. Our study provides a functional landscape of gliomagenesis suppressors in vivo.
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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 | 0.000 |
| 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".