Mass spectrometry-based molecular investigation identifies key proteins involved in synergistic AXL and BCL-2 dual inhibition for Acute Myeloid Leukemia
Notice bibliographique
Résumé
Abstract Background The 5-year survival rate for AML patients remains around 30%, with drug resistance being a significant challenge. Overexpression of the AXL, a member of the TAM family of receptor tyrosine has been linked to poor outcomes in AML. Our recent studies have demonstrated that AXL activity is upregulated in AML stem/progenitor cells, particularly in MLL-mutant cells. A novel AXL inhibitor SLC-391 (SLC), combined with the BCL-2 inhibitor Venetoclax (VEN), sensitized these cells to VEN, with strong synergistic effects in vitro and PDX models. However, the underlying mechanism of this synergy remains unclear. Objectives To investigate the molecular mechanism and synergistic effect of AXL/BCL-2 dual inhibition in AML cells by identifying and validating critical proteins that mediate cell-killing, drug resistance, and signaling pathways. Methods LC-MS/MS-based proteomic analysis was performed on MLL-rearranged AML cell lines (MV411, Molm13) treated with DMSO (control), VEN (10nM), SLC (0.25 uM), and their combination (72 hours, n=3). Data were processed using FragPipe and analyzed with the R limma package to identify proteins that were significantly altered (p<0.05, log2FC>1), and were further prioritized based on pathway involvement, clinical relevance, and supporting literature. Candidate proteins will be functionally assessed via shRNA knockdown to evaluate their impact on cell survival and drug response. Results After a data quality control analysis, 1,611 proteins were consistently detected across all samples. Differential expression analysis using limma on both MV411 and Molm13 cell lines revealed that the combination of BCL-2 and AXL inhibition led to the highest number of differentially expressed proteins. Using an adjusted p-value threshold of < 0.05, 10 proteins were uniquely significant in VEN treatment, 3 in SLC treatment, and 437 in the combination treatment. Additionally, 12 proteins were significantly expressed across all three conditions, 49 were shared between VEN and the combination treatment, and 27 between SLC and the combination treatment. Among these, 26 proteins had a log2 fold change greater than 1. Gene set enrichment analysis (GSEA) revealed distinct alterations in pathway activity. The top significantly regulated pathways were identified by ranking the Normalized Enrichment Score (NES) and adjusted p-value (p< 0.1). In the combination treatment, analysis using the GO Biological Process database revealed that mRNA metabolism and cytoskeleton-related pathways were downregulated. In contrast, pathways associated with protein processing and energy signaling were upregulated. Hallmark gene set analysis further showed downregulation of the G2M checkpoint and upregulation of the Peroxisome and OXPHOS pathways. To investigate the intermediate proteins potentially contributing to the synergistic effect of combination treatment, HCLS1, LCP1, STMN1, and AHNAK are shortlisted based on the gene function, data variation, p-value, and fold change. The cBioPortal, Beat AML program, and UCSC analyses further confirmed that LCP1 and HCLS1 are overexpressed in AML, particularly in the MLL-mutated cells, and this overexpression correlates with poor patient survival. Conclusions These findings suggest that dual AXL and BCL-2 inhibition may activate or enhance apoptosis- and cell growth-associated pathways, including increased energy metabolism and cytoskeleton remodeling, thereby synergistically promoting leukemic cell death. Notably, four newly identified proteins emerge as potential biomarkers and therapeutic targets in AML, especially for patients exhibiting resistance to existing treatment options.
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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 | 0,001 | 0,001 |
| 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) | 0,002 | 0,001 |
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 ».