DIPG Harbors Alterations Targetable by MEK Inhibitors, with Acquired Resistance Mechanisms Overcome by Combinatorial Inhibition
Bibliographic record
Abstract
The survival of children with diffuse intrinsic pontine glioma (DIPG) remains dismal, with new treatments desperately needed. In a prospective biopsy-stratified clinical trial, we combined detailed molecular profiling and drug screening in newly established patient-derived models in vitro and in vivo. We identified in vitro sensitivity to MEK inhibitors in DIPGs harboring MAPK pathway alterations, but treatment of patient-derived xenograft models and a patient at relapse failed to elicit a significant response. We generated trametinib-resistant clones in a BRAFG469V model through continuous drug exposure and identified acquired mutations in MEK1/2 with sustained pathway upregulation. These cells showed hallmarks of mesenchymal transition and expression signatures overlapping with inherently trametinib-insensitive patient-derived cells, predicting sensitivity to dasatinib. Combined trametinib and dasatinib showed highly synergistic effects in vitro and on ex vivo brain slices. We highlight the MAPK pathway as a therapeutic target in DIPG and show the importance of parallel resistance modeling and combinatorial treatments for meaningful clinical translation. SIGNIFICANCE: We report alterations in the MAPK pathway in DIPGs to confer initial sensitivity to targeted MEK inhibition. We further identify for the first time the mechanism of resistance to single-agent targeted therapy in these tumors and suggest a novel combinatorial treatment strategy to overcome it in the clinic. This article is highlighted in the In This Issue feature, p. 587.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".