Mass spectrometry-based molecular investigation identifies key proteins involved in synergistic AXL and BCL-2 dual inhibition for Acute Myeloid Leukemia
Bibliographic record
Abstract
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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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".