Trametinib Sensitivity is Defined by a Myeloid Differentiation Profile in Acute Myeloid Leukemia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Acute myelogenous leukemia (AML) is a common blood cancer marked by heterogeneity in disease and diverse genetic abnormalities. Additional therapies are needed as the 5-year survival remains below 30%. Trametinib is a mitogen-activated extracellular signal-regulated kinase (MEK) inhibitor that is widely used in solid tumors and also in tumors with activating RAS mutations. A subset of patients with AML carry activating RAS mutations; however, a small-scale clinical trial with trametinib showed little efficacy. Here, we sought to identify transcriptomic determinants of trametinib sensitivity in AML. METHODS: We tested the activity of trametinib against a panel of tumor cells from patients with AML ex vivo and compared this with RNA sequencing (RNA-Seq) data from untreated blasts from the same patient samples. We then used a correlation analysis between gene expression and trametinib sensitivity to identify potential biomarkers predictive of drug response. RESULTS: We found that a subset of AML tumor cells were sensitive to trametinib ex vivo, only a fraction of which (3/10) carried RAS mutations. On the basis of our RNA-Seq analysis we found that markers of trametinib sensitivity are associated with a myeloid differentiation profile that includes high expression of CD14 and CLEC7A (Dectin-1), similar to the gene expression profile of monocytes. Further characterization confirmed that trametinib-sensitive samples display features of monocytic differentiation with high CD14 surface expression and were enriched for the M4 subtypes of the FAB classification. CONCLUSIONS: Our study identifies additional molecular markers that can be used with molecular features including RAS status to identify patients with AML that may benefit from trametinib treatment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".