Data mining in acute myeloid leukemia: identification of disease biomarkers, prognostic factors, novel targets, and potential drugs.
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ABSTRACT:
SUBMITTER: Machado-Neto JA
PROVIDER: S-EPMC10583012 | biostudies-literature | 2023 Sep
REPOSITORIES: biostudies-literature
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Data mining in acute myeloid leukemia: identification of disease biomarkers, prognostic factors, novel targets, and potential drugs.
Machado-Neto João Agostinho JA do Nascimento Mariane Cristina MC Garnique Anali Del Milagro Bernabe ADMB Lima Keli K
Translational cancer research 20230815 9
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Project description:Three mutation-specific targeted therapies have recently been approved by the FDA for the treatment of acute myeloid leukemia (AML): midostaurin for FLT3 mutations, enasidenib for relapsed or refractory cases with IDH2 mutations, and ivosidenib for cases with an IDH1 mutation. Together, these agents offer a mutation-directed treatment approach for up to 45% of de novo adult AML cases, a welcome deluge after a prolonged drought. At the same time, a number of computational tools have recently been developed that promise to further accelerate progress in mutation-specific therapy for AML and other cancers. Technical advances together with comprehensively annotated AML tissue banks have resulted in the availability of large and complex data sets for exploration by the end-user, including (i) microarray gene expression, (ii) exome sequencing, (iii) deep sequencing data of sub-clone heterogeneity, (iv) RNA sequencing of gene expression (bulk and single cell), (v) DNA methylation and chromatin, (vi) and germline quantitative trait loci. Yet few clinicians or experimental hematologists have the time or the training to access or analyze these repositories. This review summarizes the data sets and bioinformatic tools currently available to further the discovery of mutation-specific targets with an emphasis on web-based applications that are open, accessible, user-friendly, and do not require coding experience to navigate. We show examples of how available data can be mined to identify potential targets using synthetic lethality, drug repurposing, epigenetic sub-grouping, and proteomic networks while also highlighting strengths and limitations and the need for superior models for validation.
| S-EPMC7250137 | biostudies-literature
Project description:BackgroundIncreasing studies showed that miR-200 family (miR-200s) clusters are aberrantly expressed in multiple human cancers, and miR-200s clusters function as tumor suppressor genes by affecting cell proliferation, self-renewal, differentiation, division and apoptosis. Herein, we aimed to investigate the expression and clinical implication of miR-200s clusters in acute myeloid leukemia (AML).MethodsRT-qPCR was performed to detect expression of miR-200s clusters in 19 healthy donors, 98 newly diagnosed AML patients, and 35 AML patients achieved complete remission (CR).ResultsExpression of miR-200a/200b/429 cluster but not miR-200c/141 cluster was decreased in newly diagnosed AML patients as compared to healthy donors and AML patients achieved CR. Although no significant differences were observed between miR-200s clusters and most of the features, low expression of miR-200s clusters seems to be associated with higher white blood cells especially for miR-200a/200b. Of the five members of miR-200s clusters, low expression of miR-200b/429/200c was found to be associated with lower CR rate. Logistic regression analysis further revealed that low expression of miR-429 acted as an independent risk factor for CR in AML. Based on Kaplan-Meier analysis, low expression of miR-200b/429/200c was associated with shorter OS, whereas miR-200a/141 had a trend. Moreover, multivariate analysis of Cox regression models confirmed the independently prognostic value of miR-200b expression for OS in AML.ConclusionsExpression of miR-200a/200b/429 cluster was frequently down-regulated in AML, and low expression of miR-429 as an independent risk factor for CR, whereas low expression of miR-200b as an independent prognostic biomarker for OS.
| S-EPMC5963159 | biostudies-literature
Project description:BackgroundDespite significant advances in comprehending its tumorigenic role, the prognostic and therapeutic potential of targeting oxidative phosphorylation (OXPHOS) in acute myeloid leukemia (AML) remain obscure.MethodsThe prognostic value of ~ 200 mitochondrial/OXPHOS genes as candidate biomarkers was examined in AML patients over ~ 10 years follow-up using Kaplan-Meier and Cox regression analyses. Furthermore, the transcript levels of the assessed markers were inspected in healthy bone marrow tissues and the dependencies of AML cells on the assessed genes were examined.ResultsElevated levels of NADH:ubiquinone oxidoreductase subunit A6 (NDUFA6), succinate dehydrogenase complex flavoprotein subunit A (SDHA), solute carrier family 25 member 12 (SLC25A12), electron transfer flavoprotein subunit beta (ETFB), carnitine palmitoyltransferase 1A (CPT1A) and glutathione peroxidase 4 (GPX4) were associated with poor overall survival of AML patients. SLC25A12, ETFB and CPT1A were overexpressed in AML compared to healthy tissues. Cytochrome B5 type A (CYB5A)high, SLC25A12high and GPX4high AML patients displayed higher levels of circulating and engrafted blasts compared to low-expressing cohorts. NPM1 and SRSF2 mutations were frequent in SDHAlow and CPT1Alow AML patients respectively. FLT3-ITD, NPM1 and IDH1 mutations were prevalent in CPT1Ahigh AML patients. FLT3-ITD AMLs were more dependent on OXPHOS.ConclusionsThis study identifies NDUFA6 and SDHA as novel companion prognostic biomarkers which might present a rational strategy for personalized therapy of AML patients.
| S-EPMC11559054 | biostudies-literature
Project description:AIMS:The solute carrier family 2 (SLC2) genes are comprised of 14 members which are essential for the maintenance of glucose uptake and survival of tumour cells. This study was performed to investigate the associations of SLC2 family gene expression with mortality in acute myeloid leukemia (AML). METHODS:Clinical features and SLC2 family gene expression data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus database. The associations between SLC2 family gene expression and clinicopathologic features were analyzed using linear regression model. Kaplan-Meier survival, univariate, multivariate survival analyses and validation analysis were performed to analyze the associations between SLC2 family gene expression and patients' overall survival. RESULTS:Patient mortality was positively associated with age and cytogenetic risk in AML patients. Kaplan-Meier survival analysis suggested that patients with high SLC2A5 and SLC2A10 expression showed poorer survival than those with low SLC2A5 and SLC2A10 expression. In contrast, patients with high SLC2A13 expression exhibited better prognosis than those with low SLC2A13 expression (P < 0.05 for all cases, log rank test). Multivariate survival analysis and validation analysis confirmed that high expression of SLC2A5 and SLC2A10 and low expression of SLC2A13 were associated with increased mortality (P = 0.00, Odd ratio [OR]:4.05, 95% Confidence Interval [CI]: 1.73-10.22; P = 0.00, OR: 3.66, 95% CI: 1.54-9.25; and P = 0.01, OR: 0.26, 95% CI: 0.09-0.68, respectively). CONCLUSION:SLC family gene expression, such as SLC2A5, SLC2A10 and SLC2A13, was significantly associated with prognosis of AML patients, their expression levels might become useful prognostic biomarkers in AML.
| S-EPMC7099669 | biostudies-literature
Project description:BackgroundAcute myeloid leukemia (AML) is a heterogeneous clonal disease that prevents normal myeloid differentiation with its common features. Its incidence increases with age and has a poor prognosis. Studies have shown that DNA methylation and abnormal gene expression are closely related to AML.MethodsThe methylation array data and mRNA array data are from the Gene Expression Omnibus (GEO) database. Through the GEO data, we identified differential genes from tumors and normal samples. Then we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses on these differential genes. Protein-protein interaction (PPI) network construction and module analysis were performed to screen the highest-scoring modules. Next, we used SurvExpress software to analyze the genes in the highest-scoring module and selected potential prognostic genes by univariate and multivariate Cox analysis. Finally, the three genes screened by SurvExpress software were analyzed using the methylation analysis site MethSurv to explore AML associated methylation biomarkers.ResultsWe found three genes that can be used as independent prognostic factors for AML. These three genes are the low expression/methylation genes ATP11A and ITGAM, and the high expression/low methylation gene ZNRF2.ConclusionsIn this study, we performed a comprehensive analysis of DNA methylation and gene expression to identify key epigenetic genes in AML.
| S-EPMC6989983 | biostudies-literature
Project description:The role of Notch signaling in acute myeloid leukemia (AML) is still under investigation. We have previously shown that high levels of Notch receptors and ligands could interfere with drug response. In this study, the protein expression of 79 AML blast samples collected from newly diagnosed patients was examined through flow cytometry. Gamma-secretase inhibitors were used in AML mouse xenograft models to evaluate the contribution of Notch pharmacological inhibition to mouse survival. We used univariate analysis for testing the correlation and/or association between protein expression and well-known prognostics markers. All the four receptors (Notch1-4) and some ligands (Jagged2, DLL-3) were highly expressed in less mature subtypes (M0-M1). Notch3, Notch4, and Jagged2 were overexpressed in an adverse cytogenetic risk group compared to good cytogenetic risk patients. Chi-square analysis revealed a positive association between the complete remission rate after induction therapy and weak expression of Notch2 and Notch3. We also found an association between low levels of Notch4 and Jagged2 and three-year remission following allogeneic stem cell transplantation (HSCT). Accordingly, Kaplan-Meier analysis showed improved OS for patients lacking significant expression of Notch4, Jagged2, and DLL3. In vivo experiments in an AML mouse model highlighted both improved survival and a significant reduction of leukemia cell burden in the bone marrow of mice treated with the combination of Notch pan-inhibitors (GSIs) plus chemotherapy (Ara-C). Our results suggest that Notch can be useful as a prognostic marker and therapeutic target in AML.
| S-EPMC6966525 | biostudies-literature
Project description:Acute myeloid leukemia (AML) is a highly heterogeneous malignancy characterized by the clonal expansion of myeloid stem and progenitor cells in the bone marrow, peripheral blood, and other tissues. AML results from the acquisition of gene mutations or chromosomal abnormalities that induce proliferation or block differentiation of hematopoietic progenitors. A combination of cytogenetic profiling and gene mutation analyses are essential for the proper diagnosis, classification, prognosis, and treatment of AML. In the present review, we provide a summary of genomic abnormalities in AML that have emerged as both markers of disease and therapeutic targets. We discuss the abnormalities of RARA, FLT3, BCL2, IDH1, and IDH2, their significance as therapeutic targets in AML, and how various mechanisms cause resistance to the currently FDA-approved inhibitors. We also discuss the limitations of current genomic approaches for producing a comprehensive picture of the activated signaling pathways at diagnosis or at relapse in AML patients, and how innovative technologies combining genomic and functional methods will improve the discovery of novel therapeutic targets in AML. The ultimate goal is to optimize a personalized medicine approach for AML patients and possibly those with other types of cancers.
| S-EPMC8533805 | biostudies-literature
Project description:Chromosomal abnormalities are detected in 50-60% of patients with acute myeloid leukemia (AML) and are important predictors of prognosis and risk of relapse. The remaining patients, those with cytogenetically normal AML, are a seemingly homogeneous group that in fact consists of subsets of patients with distinct clinical outcomes. This heterogeneity is likely related to acquired gene mutations, as well as altered miRNA and gene-expression profiles, which occur within the group. The identification of recurrent molecular abnormalities has improved prognostication and provided insight into mechanisms of leukemogenesis for patients with cytogenetically normal AML, as well as led to the discovery of novel therapeutic targets. As the number of mutations continues to expand, bioinformatic algorithms that allow for integration of multiple markers will be necessary to provide optimal care for patients with this disease.
| S-EPMC3582378 | biostudies-literature
Project description:Acute myeloid leukemia (AML), caused by an interplay of genetic and epigenetic alterations, is a heterogeneous hematological malignancy characterized by accumulation of proliferative clonal abnormally myeloblasts in the bone marrow and blood. Chemotherapy resistance is common, and relapses occur frequently for the majority of AML patients, indicating a strong need for better therapeutic strategies. The molecular complexity of acute myeloid leukemia (AML) presents a considerable challenge to implementation of clinical genetic testing for accurate risk stratification. Identification of better biomarkers and alternative targeted therapeutic strategies therefore remain a high priority to enable improving established stratification and guiding risk-adapted therapy decisions. RNA-sequencing offers the greatest diagnostic return, enabling identification of whole-transcriptome expression, expressed gene fusions, single nucleotide and short insertion/deletion variants, and alternative splicing information. Therefore, a total of 157 patient bone marrow samples were collected as diagnosis and subjected to RNA-seq.
2022-11-23 | GSE201492 | GEO
Project description: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.
| S-EPMC10759623 | biostudies-literature