Biomarker In Acute Myeloid Leukemia Research Articles

Comprehensive characterization of immunogenic cell death in acute myeloid leukemia revealing the association with prognosis and tumor immune microenvironment

BackgroundThis study aimed to explore the clinical significance of immunogenic cell death (ICD) in acute myeloid leukemia (AML) and its relationship with the tumor immune microenvironment characteristics. It also aimed to provide a potential perspective for bridging the pathogenesis of AML and immunological research, and to provide a theoretical basis for precise individualized treatment of AML patients.MethodsFirstly, we identified two subtypes associated with ICD by consensus clustering and explored the biological enrichment pathways, somatic mutations, and tumor microenvironment landscape between the ICD subtypes. Additionally, we developed and validated a prognostic model associated with ICD-related genes. Finally, we conducted a preliminary exploration of the construction of disease regulatory networks and prediction of small molecule drugs based on five signature genes.ResultsDifferentially expressed ICD-related genes can distinguish AML into subgroups with significant differences in clinical characteristics and survival prognosis. The relationship between the ICD- high subgroup and the immune microenvironment was tight, showing significant enrichment in immune-related pathways such as antibody production in the intestinal immune environment, allograft rejection, and Leishmaniasis infection. Additionally, the ICD- high subtype showed significant upregulation in a variety of immune cells such as B_cells, Macrophages_M2, Monocytes, and T_cells_CD4. We constructed a prognostic risk feature based on five signature genes (TNF, CXCR3, CD4, PIK3CA and CALR), and the time-dependent ROC curve confirmed the high accuracy in predicting the clinical outcomes.ConclusionThere is a strong close relationship between the ICD- high subgroup and the immune microenvironment. Immunogenicity-related genes have the potential to be a prognostic biomarker for AML.

Prognostic impact of ARHGAP43(SH3BP1) in acute myeloid leukemia

BackgroundAcute myeloid leukemia (AML) is a hematological malignancy with a heterogeneous prognosis. Novel markers are required to accurately assess the prognosis and formulate treatment plans. MethodsThe association of ARHGAP family genes with prognostic value in acute myeloid leukemia (AML) was assessed using public databases (CCLE, GEPIA, TCGA, and GEO). ResultsElevated expression of ARHGAP43 (SH3BP1) was associated with poor prognosis in patients with acute myeloid leukemia. ARHGAP43 (SH3BP1) expression was higher in the poor/adverse prognosis (P < 0.001) and TP53 mutation groups (P = 0.0093). Higher ARHGAP43 (SH3BP1) expression was found to be an independent prognostic predictor in multivariate COX regression analysis (HR = 1.317, 95% CI: 1.008–1.720, P = 0.044). Higher ARHGAP43 (SH3BP1) expression who did not receive hematopoietic stem cell transplantation (HSCT) had shorter overall survival (OS) and progression-free survival (PFS) (OS: median: 7.60 vs. 24.90 months; P = 0.006; PFS: median: 11.40 vs. 27.22 months; P = 0.0096), whereas OS and PFS of patients who received HSCT were unaffected, suggesting that HSCT is a better treatment option for patients with higher ARHGAP43 (SH3BP1) expression. KEGG and GSEA analyses revealed that high-expression ARHGAP43 (SH3BP1) was related to inflammation and immune response. Additionally, down-regulation of ARHGAP43 (SH3BP1) expression inhibited AML cell proliferation. ConclusionThese findings highlight the clinical potential of ARHGAP43 (SH3BP1) as a novel biomarker of AML, with higher levels indicating a poor prognosis.

Transcriptome free energy can serve as a dynamic patient-specific biomarker in acute myeloid leukemia

Acute myeloid leukemia (AML) is prevalent in both adult and pediatric patients. Despite advances in patient categorization, the heterogeneity of AML remains a challenge. Recent studies have explored the use of gene expression data to enhance AML diagnosis and prognosis, however, alternative approaches rooted in physics and chemistry may provide another level of insight into AML transformation. Utilizing publicly available databases, we analyze 884 human and mouse blood and bone marrow samples. We employ a personalized medicine strategy, combining state-transition theory and surprisal analysis, to assess the RNA transcriptome of individual patients. The transcriptome is transformed into physical parameters that represent each sample’s steady state and the free energy change (FEC) from that steady state, which is the state with the lowest free energy.We found the transcriptome steady state was invariant across normal and AML samples. FEC, representing active molecular processes, varied significantly between samples and was used to create patient-specific barcodes to characterize the biology of the disease. We discovered that AML samples that were in a transition state had the highest FEC. This disease state may be characterized as the most unstable and hence the most therapeutically targetable since a change in free energy is a thermodynamic requirement for disease progression. We also found that distinct sets of ongoing processes may be at the root of otherwise similar clinical phenotypes, implying that our integrated analysis of transcriptome profiles may facilitate a personalized medicine approach to cure AML and restore a steady state in each patient.

Landscape of FLT3 Variations Associated with Structural and Functional Impact on Acute Myeloid Leukemia: A Computational Study.

Acute myeloid leukemia (AML) is hallmarked by the clonal proliferation of myeloid blasts. Mutations that result in the constitutive activation of the fms-like tyrosine kinase 3 (FLT3) gene, coding for a class III receptor tyrosine kinase, are significantly associated with this heterogeneous hematologic malignancy. The fms-related tyrosine kinase 3 ligand binds to the extracellular domain of the FLT3 receptor, inducing homodimer formation in the plasma membrane, leading to autophosphorylation and activation of apoptosis, proliferation, and differentiation of hematopoietic cells in bone marrow. In the present study, we evaluated the association of FLT3 as a significant biomarker for AML and tried to comprehend the effects of specific variations on the FLT3 protein's structure and function. We also examined the effects of I836 variants on binding affinity to sorafenib using molecular docking. We integrated multiple bioinformatics tools, databases, and resources such as OncoDB, UniProt, COSMIC, UALCAN, PyMOL, ProSA, Missense3D, InterProScan, SIFT, PolyPhen, and PredictSNP to annotate the structural, functional, and phenotypic impact of the known variations associated with FLT3. Twenty-nine FLT3 variants were analyzed using in silico approaches such as DynaMut, CUPSAT, AutoDock, and Discovery Studio for their impact on protein stability, flexibility, function, and binding affinity. The OncoDB and UALCAN portals confirmed the association of FLT3 gene expression and its mutational status with AML. A computational structural analysis of the deleterious variants of FLT3 revealed I863F mutants as destabilizers of the protein structure, possibly leading to functional changes. Many single-nucleotide variations in FLT3 have an impact on its structure and function. Thus, the annotation of FLT3 SNVs and the prediction of their deleterious pathogenic impact will facilitate an insight into the tumorigenesis process and guide experimental studies and clinical implications.

Comprehensive analysis of m6A methylome alterations after azacytidine plus venetoclax treatment for acute myeloid leukemia by nanopore sequencing

N6 adenosine methylation (m6A), one of the most prevalent internal modifications on mammalian RNAs, regulates RNA transcription, stabilization, and splicing. Growing evidence has focused on the functional role of m6A regulators on acute myeloid leukemia (AML). However, the global m6A levels after azacytidine (AZA) plus venetoclax (VEN) treatment in AML patients remain unclear. In our present study, bone marrow (BM) sample pairs (including pre-treatment [AML] and post-treatment [complete remission (CR)] samples) were harvested from three AML patients who had achieved CR after AZA plus VEN treatment for Nanopore direct RNA sequencing. Notably, the amount of m6A sites and the m6A levels in CR BMs was significantly lower than those in the AML BMs. Such a significant reduction in the m6A levels was also detected in AZA-treated HL-60 cells. Thirteen genes with decreased m6A and expression levels were identified, among which three genes (HPRT1, SNRPC, and ANP32B) were closely related to the prognosis of AML. Finally, we speculated the mechanism via which m6A modifications affected the mRNA stability of these three genes. In conclusion, we illustrated for the first time the global landscape of m6A levels in AZA plus VEN treated AML (CR) patients and revealed that AZA had a significant demethylation effect at the RNA level in AML patients. In addition, we identified new biomarkers for AZA plus VEN-treated AML via Nanopore sequencing technology in RNA epigenetics.

Identification and validation of STAT4 as a prognostic biomarker in acute myeloid leukemia.

Acute myelogenous leukemia (AML) is a common malignancy and is supposed to have the ability to escape host immune surveillance. The present study aimed to identify key genes in AML that may affect tumor immunity and to provide prognosis biomarkers of AML. The Cancer Genome Atlas (TCGA) dataset was screened for transcription factors (TFs) involved in immunity and influencing survival, combining Gene Expression Omnibus (GEO) data to validate the impact on patient survival. A prognostic signature was established using four transcription factors, and these genes play an important role in the immune system, with higher regulatory T cell (Treg) scores in high-risk patients compared with the low-risk group. Analysis of individual genes showed that STAT4 and Treg are closely related, which may be due to STAT4 transcribing related genes that affect immunity. STAT4 expression was positively correlated with the proportion of abnormal cells and promoted AML recurrence as verified by AML clinical patient samples. In addition, silencing of STAT4 significantly slowed down the proliferation capacity of HL60 cells. In conclusion, these findings suggest that STAT4 may be a potential biomarker for AML prognosis. As a key gene affecting the prognosis of AML patients, STAT4 has the potential to be a candidate diagnostic and prognostic biomarker for AML.

Therapeutic biomarkers in acute myeloid leukemia: functional and genomic approaches.

Acute myeloid leukemia (AML) is clinically and genetically a heterogeneous disease characterized by clonal expansion of abnormal hematopoietic progenitors. Genomic approaches to precision medicine have been implemented to direct targeted therapy for subgroups of AML patients, for instance, IDH inhibitors for IDH1/2 mutated patients, and FLT3 inhibitors with FLT3 mutated patients. While next generation sequencing for genetic mutations has improved treatment outcomes, only a fraction of AML patients benefit due to the low prevalence of actionable targets. In recent years, the adoption of newer functional technologies for quantitative phenotypic analysis and patient-derived avatar models has strengthened the potential for generalized functional precision medicine approach. However, functional approach requires robust standardization for multiple variables such as functional parameters, time of drug exposure and drug concentration for making in vitro predictions. In this review, we first summarize genomic and functional therapeutic biomarkers adopted for AML therapy, followed by challenges associated with these approaches, and finally, the future strategies to enhance the implementation of precision medicine.

Research Progress of Circular RNA in Acute Myeloid Leukemia --Review

Acute myeloid leukemia(AML) is a malignant tumor of the blood system that is highly heterogeneous in terms of pathogenesis, genetic background and prognostic outcome, with an extremely high fatality rate and recurrence rate. Therefore, exploring new treatment methods and diagnostic strategies is one of the ways to improve the survival rate of patients with acute myeloid leukemia. Circular RNA (circRNA) is a special kind of noncoding RNA, which plays an important role in gene transcription, translation and epigenetic modification, and participates in the disease progression and prognosis of multiple solid tumors. At present, it has found that the abnormal expression of circRNA is closely related to the occurrence, development, drug resistance and prognosis of acute myeloid leukemia.Clinically, it can be used as a new biomarker and potential therapeutic target for AML. This article briefly reviews the research progress of circRNA in acute myeloid leukemia, aiming to provide new strategies for optimizing the diagnosis, treatment and prognosis of leukemia.

Integrated analysis and validation reveal CYTH4 as a potential prognostic biomarker in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a clonal hematological malignancy with high mortality rates. The identification of novel markers is urgent for AML. Cytohesins are a subfamily of guanine nucleotide exchange factors activating the ADP-ribosylation factor family GTPases. While the important roles of cytohesins have been reported in various cancers, their function in AML remains unclear. The present study aimed to explore the prognostic impact of cytohesin-4 (CYTH4) and the underlying molecular functions. RNA sequencing and AML clinical data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases to investigate gene expression and survival. Using the R software, differentially expressed genes were identified between the high- and the low-CYTH4 group. Functional enrichment analysis was conducted by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analyses. The CIBERSORTx tool was used to explore the proportions of different immune cell types. The molecular function of CYTH4 was also validated in vitro by examining cell growth, cell cycle, apoptosis and colony-forming ability. CYTH4 was significantly upregulated in AML compared with other cancers and normal tissues. High CYTH4 expression was associated with high white blood count (P=0.004) and higher risk status (P<0.001). Patients with high CYTH4 expression had poor overall survival (OS; HR=2.19; 95% CI, 1.40-3.44; P=0.0006; high vs. low) and event-free survival (EFS; HR=2.32; 95% CI, 1.43-3.75; P=0.0006; high vs. low), and these patients could benefit from transplantation (HR=0.29; 95% CI, 0.18-0.47; P<0.0001; transplantation vs. chemotherapy). Multivariate analysis showed that high CYTH4 expression was independently associated with inferior OS (HR=2.49; 95% CI, 1.28-4.83; P=0.007) and EFS (HR=2.56; 95% CI, 1.48-4.42; P=0.001). Functional analysis showed that CYTH4 was involved in immunoregulation. In vitro validation showed knockdown of CYTH4 adversely affected cell growth and induced cell apoptosis, while overexpression of CYTH4 enhanced cell growth. Taken together, CYTH4 is expressed at high levels in AML and can potentially function as a prognostic biomarker.

Endogenous retroviruses Suppressyn and Syncytin-2 as innovative prognostic biomarkers in Acute Myeloid Leukemia.

Emerging evidence has proven that human endogenous retroviruses (HERVs) play a critical role in the pathogenesis of Acute Myeloid Leukemia (AML), whereas the specific HERVs influencing the prognosis of AML patients have yet to be fully understood. In this study, a systematic exploration was achieved to identify potential prognostic HERVs for AML, sourced from TCGA and GTEx database. Differential analysis and functional enrichment studies were conducted using GO, KEGG, GSEA, and GSVA. The ESTIMATE algorithm was applied to explore the immune infiltration of HERVs in AML. A prognostic risk-score model was evaluated with predicted yearly accuracy using ROC analysis. Two HERVs Suppressyn and Syncytin-2, were identified as promising prognostic biomarkers, with high discrimination ability based on ROC analysis between AML and healthy cohorts from TCGA. Their expression was notably higher in AML patients compared to those in healthy individuals but correlates with favorable clinical outcomes in sub-groups such as white race, lower WBC counts, favorable and intermediate risks, and NPM1 or IDH1 mutation. Suppressyn and Syncytin-2 participated in immune-related pathways and exhibited correlations with multiple immune infiltration cells, such as T cells, mast cells, and tumor-associated macrophages. Finally, we developed a prognostic risk-scoring model combining Suppressyn and Syncytin-2, where a high risk-score is associated with better prognosis. Collectively, our findings revealed that Suppressyn and Syncytin-2 may act as valuable diagnostic and prognostic biomarkers for individuals with AML, while highlighting links between HERV activation, immunogenicity, and future therapeutic targets.

Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia

BackgroundAcute Myeloid Leukemia (AML) generally has a relatively low survival rate after treatment. There is an urgent need to find new biomarkers that may improve the survival prognosis of patients. Machine-learning tools are more and more widely used in the screening of biomarkers.MethodsLeast Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), lrFuncs, IdaProfile, caretFuncs, and nbFuncs models were used to screen key genes closely associated with AML. Then, based on the Cancer Genome Atlas (TCGA), pan-cancer analysis was performed to determine the correlation between important genes and AML or other cancers. Finally, the diagnostic value of important genes for AML was verified in different data sets.ResultsThe survival analysis results of the training set showed 26 genes with survival differences. After the intersection of the results of each machine learning method, DNM1, MEIS1, and SUSD3 were selected as key genes for subsequent analysis. The results of the pan-cancer analysis showed that MEIS1 and DNM1 were significantly highly expressed in AML; MEIS1 and SUSD3 are potential risk factors for the prognosis of AML, and DNM1 is a potential protective factor. Three key genes were significantly associated with AML immune subtypes and multiple immune checkpoints in AML. The results of the verification analysis show that DNM1, MEIS1, and SUSD3 have potential diagnostic value for AML.ConclusionMultiple machine learning methods identified DNM1, MEIS1, and SUSD3 can be regarded as prognostic biomarkers for AML.

Exploring the relationship between immune heterogeneity characteristic genes of rheumatoid arthritis and acute myeloid leukemia.

People with autoimmune diseases are prone to cancer, and there is a close relationship between rheumatoid arthritis (RA) and acute myeloid leukemia (AML). The bone marrow (BM) is affected throughout the course of RA, with a variety of hematologic involvement. Hopes are pinned on rheumatoid arthritis research to obtain BM biomarkers for AML. Synovial transcriptome sequencing data for RA and osteoarthritis (OA), and single-cell sequencing data for RA and controls were obtained from the GEO database.Bone marrow sequencing data for AML patients and normal subjects were obtained from the UCSC Xena database. The final immune heterogeneity characteristics of RA were determined through ssGSEA analysis, gene differential expression analysis, fuzzy c-means clustering algorithm, and XGboost algorithm. Random Ferns classifiers (RFs) are used to identify new bone marrow markers for AML. SELL, PTPRC, IL7R, CCR7, and KLRB1 were able to distinguish leukemia cells from normal cells well, with AUC values higher than 0.970. Genes characterizing the immune heterogeneity of RA are associated with AML, and KLRBA may be a potential target for AML treatment.

NF-α, IL6, IL1-α, IL-10 and TGF-β1 Gene Expression in Iranian Acute Myeloid Leukemia Patients

Acute myeloid leukemia is a heterogeneous clonal disorder of blood-producing cells that are among the most common malignant disorders in adults. The purpose of this study was to examine the gene expression of TNF-α, IL6, IL1-α, IL-10, and TGF-β1 at diagnosis in acute myeloid leukemia. The current study included 50 patients with acute myeloid leukemia and 50 control subjects. ELISA was used to determine the serum concentrations of TNF-α, IL6, IL1-α, IL-10, and TGF-β1. After cDNA synthesis and RNA extraction, the expression of TNF-α, IL6, IL1-α, IL-10, and TGF-β1 genes was determined using real-time PCR (ΔΔCT computational). Statistical analysis was conducted with Statistical Package for the Social Sciences 19, and P&lt;0.05 was deemed statistically significant. The mean serum levels of TNF-α, IL6, IL1-α, IL-10, and TGF-β1 were lower in patients with acute myeloid leukemia than in the control group. In addition, cytokine mRNA expression in peripheral blood mononuclear cells was significantly lower in AML patients compared to the control group (p&lt;0.0001). Changes in TNF-α, IL6, IL1-α, IL-10, and TGF-β1 were associated with acute myeloid leukemia, according to the findings. The current study supports the use of cytokines as diagnostic biomarkers for acute myeloid leukemia.

Single-Cell Metabolic Profiling Integrated with Extracellular Vesicle Analysis Reveals Novel Metabolic Vulnerabilities and Prognostic Biomarkers in Acute Myeloid Leukemia

Introduction Acute myeloid leukemia (AML) is an aggressive disease for which less invasive tools are needed to explore disease dynamics. Changes in cell metabolism and metabolic adaptation are hallmark features for AML, thus emerging as promising therapeutic targets. However, it is still poorly elucidated whether and which extrinsic microenvironment signals may regulate the metabolic reprogramming occurring in AML. In this regard, Extracellular Vesicles (EVs) represent a promising tool for non-invasive liquid biopsy and may reveal a novel role in energy metabolism as well as clinically relevant biomarkers for AML. Along with EV profiling from peripheral blood (PB), we mapped the metabolic signature of leukemic stem cells (LSCs) and immune cell subsets to identify metabolic vulnerabilities and novel prognostic biomarkers. Methods PB (and paired bone marrow) were collected from AML patients at diagnosis (n=98) and healthy donors (HD, n=12). EVs were purified from platelet-free PB plasma of AML patients by size-exclusion chromatography followed by ultrafiltration. We have submitted all relevant data of our EV experiments to the EV-TRACK knowledgebase (EV-TRACK ID: EV230004). MACSPlex Exosome Kit was used for screening up to 37 surface EV markers. Western blot analysis was also performed. Energy cell metabolism was explored by Single Cell ENergetic metabolism by profilIng Translation inHibition (SCENITH TM) in LSC-like cells (CD34 +CD38 low/-)and immune cell subsets (CD3 +/CD4 +/CD8 +/T regulatory cells) using conventional/spectral flow-cytometry. Results Here, we developed a novel method exploiting both LSCs and EVs from AML patients using fresh whole blood for metabolic exploration. Firstly, we observed that LSC-like cells display low reactive oxygen species (ROS) levels with both high glutathione (GSH, as antioxidant molecule) levels and mitochondrial functionality suggesting a more resistant phenotype. Consistently, poor clinical outcomes were experienced by AML patients treated with conventional chemotherapy and with high GSH levels and mitochondrial potential in LSC-like cells (log-rank p=0.04). We also found high dependence on glucose and a preferential skewing toward glycolysis in LSC-like cells revealing a distinct metabolic status for LSCs in circulation. However, according to European LeukemiaNet (ELN) 2022 risk classification, a more prominent mitochondrial dependence on LSC-like cells was reported for intermediate- or adverse-risk AML patients revealing a prognostic relevance. By contrast, exploring the immunometabolic reprogramming in circulation, we revealed a significant decrease in glucose dependence for immune cells (CD4 + and CD8 + T cells) in comparison to LSCs proving the presence of a complex metabolic network in AML. Interestingly, we investigated the potential metabolic effects of EVs on LSCs. Indeed, CD34 + LSC-like cells co-cultured with circulating EVs enhanced their GSH levels and mitochondrial functionality towards a more aggressive phenotype. Accordingly, we discovered the presence of the antioxidant enzyme, glutathione peroxidases 3 (GPx-3), in AML EVs. Then, we profiled the surface proteins of isolated EVs to detect their cell origins and provide a reference map for clinical prognosis. We found that circulating AML EVs were significantly enriched in CD4 (helper T cell marker), CD14 (monocyte marker), CD40 (B-cell associated marker), CD44 and CD133-1 (stem cell markers), CD105 (stromal cell marker) and HLA-II (antigen-presenting cell marker) compared to HD EVs. In particular, we found that intermediate-risk AML patients showed a strong depletion in the expression of EV markers including T cell markers (CD2 and CD8) and mesenchymal markers (CD49e and CD146) in comparison to favorable and adverse-risk patients. Interestingly, AML patients who had been treated not intensively and showed low expression of CD40 (HR=0.23, 95% CI, 0.05−0,99, p=0.04) or CD62P (platelet activation marker, HR=0.24, 95% CI, 0.05−1.02, p=0.05) on EVs experienced poor outcomes suggesting that dedifferentiated phenotype on EVs might be associated with worse prognosis. Conclusion Overall, towards a new redefined ‘metabolic’ stratification for AML patients, we identified novel prognostic biomarkers for AML patients that might reveal novel metabolic vulnerabilities by exploiting in parallel single-cell metabolic profile and EV-based liquid biopsy.

Free Energy Changes at State-Transition Critical Points As a Patient-Specific Biomarker in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive hematopoietic malignancy with a poor overall survival rate. AML is a highly heterogeneous disease driven by combinations of genomic mutations, epigenetic alterations, and biochemical signaling. The heterogeneity underlying AML needs novel approaches for individualized clinical assessment and treatment selection. However, approaches that rely on differential expression or correlation analysis lack an underlying theoretical model of the disease to inform the interpretation of ongoing processes in each individual which is crucial for personalized diagnostics and treatment selection. We have previously shown that the state-transition (ST) theory, state-space could predict AML disease evolution using the mRNA of a conditional Cbfb-MYH11 (CM) knock-in mouse model. The ST model represents AML evolution from health to disease as a state transition of the transcriptome state described as a particle undergoing Brownian motion in a double-well quasi-potential, where critical points define states of healthy, c1, transition to AML, c2, and overt AML, c3. We integrated two approaches, the ST model and surprisal analysis (SA) from thermodynamics, to create a patient-specific characterization that can be used to identify the individualized state of the disease. The publicly available datasets of human blood including normal samples, AML samples of bone marrow (BM), and peripheral blood (PB) were utilized for the methods. A total of 858 samples from three publicly available RNA-seq datasets were used. The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) dataset is a pediatric study, from which we analyzed 84 normal samples and 126 AML inv(16) samples, consisting of 105 BM and 21 PB samples with ages from 0.3 to 28 years. Two additional datasets from the BEATAML study and the Cancer Genome Atlas (TCGA), were downloaded from the Genomic Data Commons (GDC) portal using GDCRNATools. The BEATAML dataset included 21 normal and 476 AML samples consisting of 226 PB and 243 BM samples with ages from 2 to 87, and TCGA dataset contained 151 AML PB samples, with ages 21 to 88 years. We used a pseudotime approach to infer disease dynamics for ST using KIT and CD33. We found via t-test analysis that KIT expression between normal and AML samples was significantly different for both TARGET (p&amp;lt;0.01) and BEATAML (p&amp;lt;0.01). CD33 expression between normal and AML samples was significant in BEATAML (p=0.012), but not TARGET (p=0.30). We combined the ST with SA to analyze free energy changes (FEC) that occurred at ST critical points, which predicted AML progression as normal hematopoiesis, c1, transition to AML, c2, and overt AML, c3. Significant differences in FEC at the critical points c1, c2, and c3 were observed by t-test comparisons as c1 vs. c2 (p &amp;lt;0.001) and c1 vs. c3 (p &amp;lt;0.001) in TARGET dataset, and c2 vs. c3 (p &amp;lt; 0.01) in the BEATAML dataset. Our results showed that the transition disease state, c2, had higher FEC as compared to normal states, c1, suggesting that samples were less stable from a thermodynamic perspective. Certain samples at the more advanced state, c3, exhibited a trend toward decreasing FEC levels, suggesting a process of state stabilization at more advanced stages of the disease. SA and Gene Ontology analysis suggested that interleukin pathways such as IL1, IL2, IL8 and IL10, MAPK, NFkB, and migration-related pathways, which are known to be involved in AML progression, were found in multiple unbalanced processes in SA characterizing AML across all three datasets. The IL1 pathway was found in 3 unbalanced processes characterizing TARGET dataset and in four processes characterizing TCGA and BEATAML. IL2 related categories were found among induced transcripts in 4 unbalanced processes characterizing TARGET dataset, in 6 characterizing TCGA, and in 4 processes characterizing BEATAML. We observed that AML samples with comparable FEC levels or clinical characteristics could be defined by different sets of unbalanced processes, implying that this information might be used to determine tailored therapies in subgroups of individuals with similar clinical or thermodynamic characteristics. We showed that mapping FEC into an AML state-space given by ST critical points could provide a high resolution, patient-specific disease characterization, and these methods could be used in personalized diagnostics and individualized treatment procedures.

A Novel Approach to Epigenetic Profiling in Acute Myeloid Leukemia: Mapping Methylation and Hydroxymethylation in Genomic and Cell-Free DNA

Introduction: Acute Myeloid Leukemia (AML) is marked by uncontrolled growth of undifferentiated myeloid cells. DNA methylation patterns in AML have been thoroughly researched, however, studies on hydroxy methylation patterns, indicating active DNA methylation erasure, are still evolving. Our principal goal was to detect differential methylation and hydroxy methylation regions between AML and non-malignant samples. We proposed that simultaneous profiling of blood cells and circulating cell-free DNA would offer more insights into the disease mechanism. Therefore, we combined these data sources to provide a comprehensive epigenetic picture, assuming that this dual approach could reveal new AML biomarkers and enhance our understanding regarding AML pathobiology. Methods: We collected peripheral blood samples from 16 newly diagnosed AML patients (median age 64, age range 36-85) treated at Tel Aviv Sourasky Medical Center, Israel, and 33 age-matched healthy volunteers (NCT05735704). We utilized novel microarray technology to examine methylation and hydroxy methylation patterns in both genomic and cell-free DNA. Following differential methylation region extraction, we used DAVID functional annotation tool to interpret our results' biological significance. Results: This report presents the preliminary methylation data results. Methylation analysis unveiled 474 and 475 differentially methylated regions between AML and controls in genomic and cell-free DNA analysis, respectively, with only 18 common regions. The clear AML-control distinction is displayed in hierarchical clustering heatmaps (Figure 1a, 1b). Further DAVID tool analysis revealed enrichment in biological processes such as aging, cell cycle regulation, cell morphogenesis, and MAP kinase activity - processes closely linked with AML. Notably, genomic data analysis showed significant enrichment of the ‘transcriptional misregulation in cancer’ pathway, involving genes encoding vital transcription factors for cell development and differentiation. Figure 2 demonstrates the biological pathways for genomic and cell free data analysis. Conclusions: This research underscores the importance of combined epigenetic data from malignant tissue and its microenvironment, obtained by examining genomic and cell-free DNA. Both datasets exhibit striking differences between AML and control samples. We detected enrichment of AML-related biological pathways within differential regions. Our findings of enriched biological pathways align with existing literature, reinforce our analysis validity, and support our combined view of methylation patterns in genomic and cell-free DNA. By suggesting a combined view of these two types of DNA, we provide a more comprehensive perspective on AML's complex epigenetic landscape. It underscores the value of such integrative epigenetic profiling in shedding new light on the intricacies of AML development and progression, which may eventually guide the discovery of novel therapeutic targets.

ALDH3A2 Regulates Ferroptosis By Participating in Fatty Acid Synthesis and Finally Lead to Chemotherapy Resistance in Acute Myeloid Leukemia

Chemotherapeutic resistance is a major challenge in the treatment of acute myeloid leukemia (AML). Although increasing the dose of anthracycline drugs improve chemotherapy efficacy and reduce drug resistance, the side effects limit the increase of their dosage. One strategy to improve the therapeutic effects is to improve the efficacy in their limited doses. Ferroptosis is one important way in which Adriamycin exerts cytotoxic effects on AML cells, which was also associated with resistance to Adriamycin. ALDH3A2 is a fatty aldehyde dehydrogenase related to the conversion of fatty aldehydes and fatty acids. Here, we found that ALDH3A2 is a biomarker for chemotherapy-resistant AML, increased expression of ALDH3A2 reduced ferroptosis and cytotoxicity induced by Adriamycin in AML cells. In our studies, we demonstrated that ALDH3A2 regulated ferroptosis and AML drug resistance by participating in fatty acid synthesis. By analyzing the data from TCGA and GTEx databases, we found that ALDH3A2 expression varied in different types of tumor cells. AML patients had higher ALDH3A2 expression than normal tissues. We analyzed the RNA-seq results of 88 leukemia cell lines from the HPA database, and found that 15 out of the top 25% highest ALDH3A2 expression cell lines were AML cell lines, indicating that AML cells had higher ALDH3A2 expression than other types of leukemia cells. We plotted survival curves based on the gene data and clinical survival data from TCGA and GTEx databases. It showed that AML patients with high ALDH3A2 expression had shorter overall survival (OS) than those with low expression. We detected the expression of ALDH3A2 in AML cell lines and primary cells from patients using Western blot and RT-qPCR, respectively. Our data showed that drug-resistant AML cell lines had higher ALDH3A2 expression than their parental cells. The expression of ALDH3A2 in primary cells from AML patients achieving continuous complete remission (CR) within two years was significantly lower than that in the first-line non-remission(C1NR) group and the relapsed/refractory (R/R) group. Besides, the expression of ALDH3A2 is related to certain types of mutation such as CEBPα, FLT3-ITD and RUNX1. To investigate the function of ALDH3A2, we constructed AML cell lines with ALDH3A2 knockdown or overexpression. We found that the increasing ferroptosis level and decreasing cell activity after treatment with the same concentration of Adriamycin was observed in the knockdown group, and overexpression group had the opposite effect. By ELISA and GC-MS, we discovered that there were differences in 4-HNE and fatty acid content between the overexpression group and the control group. It suggested that ALDH3A2 overexpression reduced the cytotoxic 4-HNE and increased the content of heptadecanoic acid, oleic acid, and linoleic acid. We further pretreated AML cells with heptadecanoic acid, oleic acid, and linoleic acid, and found that pretreatment with heptadecanoic acid and oleic acid reduced ferroptosis level and cytotoxicity induced by Adriamycin in AML cells. We also tried to find the regulatory mechanism of ALDH3A2 expression and found that acetylation plays a certain role in it. In conclusion, our studies found that increased expression of ALDH3A2 was related to poor chemotherapy efficacy in AML patients and clarifies the mechanism of ALDH3A2 regulating chemosensitivity in AML. ALDH3A2 degraded the cell toxic product 4-HNE of lipid peroxidation and regulated the proportion and content of heptadecanoic acid, oleic acid, and linoleic acid in cells, which related to ferroptosis and resistance to anthracycline drugs. Downregulating ALDH3A2 could increase AML's susceptibility to chemotherapy which may make it a promising therapy.

Serum circRNA_100199 is a Prognostic Biomarker in Acute Myeloid Leukemia.

An aberrant level of serum microRNA expression has been demonstrated to be a prognostic marker for acute myeloid leukemia (AML). The therapeutic relevance of serum circRNA 100199 remained unknown, however. This research aimed to investigate the probable prognostic significance of serum circRNA_100199 for AML. This study included a total of 200 participants consisting of 114 AML-diagnosed patients and 86 healthy people. Blood samples were taken, and the level of circRNA_100199 in the serum was measured using quantitative reverse transcription polymerase chain reaction (qRT-PCR) to explore its potential clinical significance. Our study demonstrated that circRNA_100199 expression in the serum was substantially higher in AML subjects than in healthy persons. This increase in serum circRNA_100199 levels was particularly noticeable in M4/M5 subtype AML patients, and those with poor cytogenetic risk or higher white blood cell counts. Using receiver operating characteristic (ROC) analysis, AML cases were effectively differentiated from healthy persons based on the level of serum circRNA_100199. Furthermore, it was found that high serum circRNA_100199 expression was strongly linked with shorter survival times and more severe clinical features. Our study also confirmed that high serum circRNA_100199 expression was an independent predictor of relapse-free survival (RFS) and overall survival (OS) in AML patients. Interestingly, the serum expression level of circRNA_100199 was significantly reduced following treatment, and its levels were substantially lower in AML patients who achieved complete remission (CR) than those who did not. Overall, these findings suggest that serum circRNA_100199 has the potential to be a favorable prognostic biomarker for AML.

Heterogenous lung inflammation CT patterns distinguish pneumonia and immune checkpoint inhibitor pneumonitis and complement blood biomarkers in acute myeloid leukemia: proof of concept.

Immune checkpoint inhibitors (ICI) may cause pneumonitis, resulting in potentially fatal lung inflammation. However, distinguishing pneumonitis from pneumonia is time-consuming and challenging. To fill this gap, we build an image-based tool, and further evaluate it clinically alongside relevant blood biomarkers. We studied CT images from 97 patients with pneumonia and 29 patients with pneumonitis from acute myeloid leukemia treated with ICIs. We developed a CT-derived signature using a habitat imaging algorithm, whereby infected lungs are segregated into clusters ("habitats"). We validated the model and compared it with a clinical-blood model to determine whether imaging can add diagnostic value. Habitat imaging revealed intrinsic lung inflammation patterns by identifying 5 distinct subregions, correlating to lung parenchyma, consolidation, heterogenous ground-glass opacity (GGO), and GGO-consolidation transition. Consequently, our proposed habitat model (accuracy of 79%, sensitivity of 48%, and specificity of 88%) outperformed the clinical-blood model (accuracy of 68%, sensitivity of 14%, and specificity of 85%) for classifying pneumonia versus pneumonitis. Integrating imaging and blood achieved the optimal performance (accuracy of 81%, sensitivity of 52% and specificity of 90%). Using this imaging-blood composite model, the post-test probability for detecting pneumonitis increased from 23% to 61%, significantly (p = 1.5E - 9) higher than the clinical and blood model (post-test probability of 22%). Habitat imaging represents a step forward in the image-based detection of pneumonia and pneumonitis, which can complement known blood biomarkers. Further work is needed to validate and fine tune this imaging-blood composite model and further improve its sensitivity to detect pneumonitis.

Cell-free DNA 5-hydroxymethylcytosine is highly sensitive for MRD assessment in acute myeloid leukemia

Measurable residual disease (MRD) is an important biomarker in acute myeloid leukemia (AML). However, MRD cannot be detected in many patients using current methods. We developed a highly sensitive 5-hydroxymethylcytosine (5hmC) signature in cell-free DNA by analyzing 115 AML patients and 86 controls. The 5hmC method detected MRD in 20 of 29 patients with negative MRD by multiparameter flow cytometry and 11 of 14 patients with negative MRD by molecular methods. MRD detection by the 5hmC method was significantly associated with relapse-free survival. This novel method can be used in most AML patients and may significantly impact AML patient management.