Chemogenomic Approach Unveils the Increased Susceptibility of RUNX1-Mutated AML to Glucocorticoids
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».