DDDR-26. IDENTIFICATION OF MODULATORS OF IDH INHIBITOR SENSITIVITY IN<i>IDH</i>-MUTATED DIFFUSE GLIOMAS
Bibliographic record
Abstract
Abstract Driver mutations in IDH1 and IDH2 characterize a substantial proportion of lower-grade diffuse gliomas in adults. Mutated IDH molecules cause the accumulation of D-2-hydroxyglutarate (D-2-HG), which perturbs many cellular functions, including metabolism, epigenetic regulation, and the ability to differentiate. This is notably associated with DNA and histone hyper-methylation. Recently, the newly developed IDH inhibitor, vorasidenib, was shown to delay tumor progression and the need for additional treatment in the groundbreaking INDIGO clinical trial. Nevertheless, the responses to vorasidenib were variable between patients, and so far have been established only for less aggressive disease. In addition, it is unclear to what extent vorasidenib can cause tumor regression. This highlights the need to identify molecules and pathways that control IDH inhibitor sensitivity. Towards this goal, we generated a new mouse model that combines Idh1R132H and Trp53 loss-of-function, targeted to oligodendrocyte progenitors. All the mutant mice develop diffuse gliomas that recapitulate the cardinal features of the corresponding human disease. We established multiple cell lines from these tumors, which retained expression of mutant IDH. The cells robustly produced D-2-HG, which was concentration-dependently suppressed by vorasidenib. Accordingly, vorasidenib could lower DNA methylation, and induce the expression of mature glial cell markers. To identify determinants of vorasidenib sensitivity, we performed genome-wide loss-of-function CRISPR/Cas9 functional genomics screens. In those experiments, targeting genes associated with astrocyte differentiation or Notch signaling enhanced cell fitness in the presence of vorasidenib, consistent with the anticipated effects of the drug on inducing cell differentiation. Conversely, vorasidenib-treated cells were more sensitive to the loss of regulators of cell growth and metabolism, and of some epigenetic regulators. These studies point to strategies that may enhance the efficacy of IDH inhibitors for the treatment of IDH mutated gliomas.
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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) | 0.002 | 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".