RAF-independent MEK mutations drive refractory histiocytic neoplasms but respond to ERK inhibition
Bibliographic record
Abstract
Histiocytic neoplasms are clonal disorders of the monocyte/macrophage lineage defined by mutations activating mitogen-activated protein kinase (MAPK) signaling. Recently, the MEK1/2 inhibitor cobimetinib was FDA-approved for patients with adult histiocytoses. Here, aided by a prospective registry of patients with histiocytoses (NCT03329274), we identify that MEK1/2 mutations which constitutively activate MEK independently of RAF are associated with worse progression-free survival with MEK1/2 inhibition as compared to patients with other MEK1/2 mutational classes. The most common RAF-independent MEK1 mutation (MEK1 E102_I103del ) drove a lethal histiocytic-like neoplasm in mice, which was sensitive to the ERK1/2 inhibitor ulixertinib. We subsequently treated five MEK1 E102_I103del -mutant patients with ulixertinib on prospective protocols, four of whom were refractory to MEK inhibition. Four of five patients experienced objective responses to ulixertinib. These data reveal the impact of oncogenic MEK mutations in vivo , identify patients with likelihood of resistance to MEK inhibition, and nominate ERK inhibition to overcome resistance to MEK inhibition in histiocytoses. • RAF-independent (class III) MEK1 mutations are common in histiocytoses patients • Class III MEK1/2 mutations are associated with MEK inhibitor disease progression • Class III MEK1 E102_I103del mutation drives aggressive myeloid neoplasms in mice • Class III MEK1-mutant histiocytoses patients and mice respond to ERK inhibition Diamond et al. find RAF-independent (“class III”) mutations in MEK1/2 kinases are common in patients with systemic histiocytic neoplasms, drive disease in a conditional knock-in mouse model, and associate with progression on FDA-approved MEK inhibitors. Patients with these mutations who progress on MEK inhibitors respond to the ERK inhibitor ulixertinib.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".