Chemogenomic Approach Unveils the Increased Susceptibility of RUNX1-Mutated AML to Glucocorticoids
Bibliographic record
Abstract
Abstract RUNX1 is an essential transcription factor for definite hematopoiesis and plays important roles in immune function. Mutations in RUNX1 occur in 5-13% of Acute Myeloid Leukemia (AML) patients (RUNX1mut ) and are associated with adverse outcome, highlighting the need for better genetic characterization of this AML subgroup and for the design of efficient therapeutic strategies for patients with RUNX1mut AML. Toward this goal, we performed RNA-sequencing of a cohort of RUNX1mut primary AML specimens and used a chemogenomic approach combining mutational and gene expression analysis of these specimens with assessment of their drug sensitivity profile through chemical screening. Sequencing data analysis demonstrated that samples with no remaining RUNX1 wild-type allele are clinically and genetically distinct and display a more homogeneous gene expression profile. Interestingly, these studies also unveiled the increased susceptibility of RUNX1-mutated specimens to Glucocorticoids (GCs) and revealed that RUNX1 allele dosage dictates sensitivity to these compounds in AML patient cells, unravelling a new role for RUNX1 in the Glucocorticoid Receptor (GR) pathway. GR is a nuclear receptor that modulates the expression of thousands of genes involved in several biological processes such as metabolism, immune function, skeletal growth, etc. GCs are commonly used to treat cancers of the lymphoid system, however their potential benefits for AML treatment have never been assessed formally and the mechanism of action of these drugs is not fully understood. Transcriptome analyses identified NR3C1 (encoding the GR) as one of the genes whose expression is determined by RUNX1 allele dosage, with increased expression in RUNX1mut specimens, indicating that RUNX1 inactivation could lead to GR upregulation, which might explain the increased sensitivity to GCs. We previously showed that RUNX1 silencing sensitizes human AML cell lines to GCs and that this acquired sensitivity is accompanied by the upregulation of the GR both at the transcript and protein levels. However, basal levels of GR could not explain GC sensitivity in all cases, indicating that other mechanisms are involved in the GC response. By performing co-immunoprecipitations (co-IP), we demonstrated that RUNX1 and GR physically interact in AML cells. Overexpression of FLAG-tagged RUNX1 mutants in HEK293 cells followed by co-IP identified the C-terminal inhibitory domain of RUNX1 as essential for the interaction with GR. Our results suggest that RUNX1 could be part of the GR transcriptional complex and could modulate the transcription of genes involved in the response to GCs. To identify regulators of the GC response, we are currently performing genome-wide CRISPR-Cas9-based genetic screens in AML cell lines. We generated RUNX1-deficient (GCs sensitive) AML cell lines that uniformly express Cas9 under a doxycycline-inducible promoter. These cell lines, along with parental cell lines (GC resistant) were used to conduct screens in GCs-supplemented media for the identification of genes that confer sensitivity or resistance to GCs. Mechanistic insights gained from these experiments will allow the design of additional therapeutic strategies to potentiate the effect of GCs on poor outcome AML. Disclosures No relevant conflicts of interest to declare.
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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 | 0.000 |
| 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 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".