Chemogenomic Landscape of <i>RUNX1</i> -mutated AML Reveals Importance of <i>RUNX1</i> Allele Dosage in Genetics and Glucocorticoid Sensitivity
Bibliographic record
Abstract
Abstract Purpose: RUNX1-mutated (RUNX1mut) acute myeloid leukemia (AML) is associated with adverse outcome, highlighting the urgent need for a better genetic characterization of this AML subgroup and for the design of efficient therapeutic strategies for this disease. Toward this goal, we further dissected the mutational spectrum and gene expression profile of RUNX1mut AML and correlated these results to drug sensitivity to identify novel compounds targeting this AML subgroup. Experimental Design: RNA-sequencing of 47 RUNX1mut primary AML specimens was performed and sequencing results were compared to those of RUNX1 wild-type samples. Chemical screens were also conducted using RUNX1mut specimens to identify compounds selectively affecting the viability of RUNX1mut AML. Results: We show that samples with no remaining RUNX1 wild-type allele are clinically and genetically distinct and display a more homogeneous gene expression profile. Chemical screening revealed that most RUNX1mut specimens are sensitive to glucocorticoids (GCs) and we confirmed that GCs inhibit AML cell proliferation through their interaction with the glucocorticoid receptor (GR). We observed that specimens harboring RUNX1 mutations expected to result in low residual RUNX1 activity are most sensitive to GCs, and that coassociating mutations as well as GR levels contribute to GC sensitivity. Accordingly, acquired glucocorticoid sensitivity was achieved by negatively regulating RUNX1 expression in human AML cells. Conclusions: Our findings show the profound impact of RUNX1 allele dosage on gene expression profile and glucocorticoid sensitivity in AML, thereby opening opportunities for preclinical testing which may lead to drug repurposing and improved disease characterization. Clin Cancer Res; 23(22); 6969–81. ©2017 AACR.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".