<> "The repository administrator has not yet configured an RDF license."^^ . <> . . "Multi-omics of AML"^^ . "Acute myeloid leukemia (AML) is one of the most aggressive hematopoietic malignancies and has been\r\nrecognized as a heterogeneous disease due to a lack of unifying characteristics. It is driven by different\r\ngenome aberrations, gene expression changes, and epigenomic dysregulations. Therefore a multi-omics\r\napproach is needed to unravel the complex biology of this disease. This thesis deals with the challenges of\r\nidentifying driver events that account for differences in clinical phenotypes and responses to treatment.\r\nThe work presented here investigates the driver events of AML and epigenetics drug response profiles.\r\nThe thesis consists of three main projects. The first study identifies recurrent mutations in AML carrying\r\nt(8;16)(p11;p13), a rare abnormality. The second project is identifying prospective drivers of mutation-\r\nnegative nkAML. The third project concentrates on epigenetic changes after AML drugs.\r\nt(8;16) AML is a rare and distinguishable clinicopathological entity. Some previous reports that rep-\r\nresented the characteristics of patients with this type of AML suggest that the t(8;16) translocation\r\ncould be sufficient to induce hematopoietic cell transformation to AML without acquiring other genetic\r\nalterations. Therefore here I evaluate the frequently mutated genes and compare them with the most\r\nfrequent mutated genes in AML in general and AML carrying t(8;16) translocation. FLT3 mutation was\r\nfound in 3 patients of my cohort, a potential target for therapy with tyrosine kinase inhibitors. However,\r\nexciting finding was the mutations in EYS, KRTAP9-1, PSIP1, and SPTBN5 that were depicted earlier\r\nin AML.\r\nElucidating different layers of aberrations in normal karyotype no-driver acute myeloid leukemia pro-\r\nvides better biology insight and may impact risk-group stratification and new potential driver events.\r\nTherefore, this study aimed to detect such anomalies in samples without known driver genetic abnor-\r\nmalities using multi-omic molecular profiling. Samples were analyzed using RNA sequencing (n=43),\r\nwhole genome sequencing (n=43), and EPIC DNA methylation array (n=42). In 33 of 43 patients, all\r\nthree layers of data were available. I developed a pipeline looking for a driver in any layer of data by\r\nconnecting the information of all layers of data and utilizing public genomic, transcriptomic, and clinical\r\ndata available from TCGA. Genetic alterations of somatic cells can drive malignant clone formation\r\nand promote leukemogenesis. Therefore I first built a mutation prioritization workflow that checks each\r\npatient’s genomic mutation drivers. Here I use the information on the allele frequency of the specific mu-\r\ntation combining information from WGS and RNA sequencing data. Finally, I compared each mutation\r\non a positional level with AML and other TCGA cancer cohorts to assess the causative genomic muta-\r\ntions. I found potential driver stopgain mutation in genes implicated in chromosome segregation during\r\nmitosis and some tumor suppressor genes. I found new stopgain mutations in cancer genes (NIPBL\r\nand NF1). Since fusions are increasingly acknowledged as oncology therapeutic targets, I investigated\r\npotential driver fusion events by evaluating high-confidence and in-frame cancer-related fusion findings.\r\nAs a result, I found specific gene fusion patterns. Kinases activated by gene fusions define a meaningful\r\nclass of oncogenes associated with hematopoietic malignancies. I identify several novel and recurrent\r\nfusions involving kinases that potentially play a role in leukemogenesis. I detected previously unreported\r\nfusions involving known cancer-related genes, such as PIM3- RAC2 and PROK2- EIF4E3. In addition,\r\noutliers, such as gene expression levels, can pinpoint potential pathogenic events. Therefore, combining\r\nmy AML cohort with a healthy control group, I determined aberrant gene expression levels as possible\r\npathogenic events using the deep learning method. Finally, I combined the data and looked for a com-\r\nparison to the methylation pattern of each patient. Overall, the analysis uncovered a rich landscape of\r\npotential drivers. In different data layers, I found an altered genomic and transcriptomic signature of\r\ndifferent GTPases, which are known to be involved in many stages of tumorigenesis. My methods and\r\nresults demonstrate the power of integrating multi-omics data to study complex driver alterations in\r\nAML and point to future directions of research that aim to bridge gaps in research and clinical applications. Furthermore, I provide in vitro evidence for antileukemic cooperativity and epigenetic activity\r\nbetween DAC and ATRA. I performed differential methylation analysis on CpG resolution and across\r\ngenomic and transposable elements regions, enhancing the results’ statistical power and interpretabil-\r\nity. I demonstrated that single-agent ATRA caused no global demethylation, nor did ATRA improve\r\nthe demethylation mediated by DAC. In summary, combining multi-omics profiling is a powerful tool\r\nfor studying dysregulated patterns in AML. Furthermore, multi-omics profiling performed on mutation-\r\nnegative nkAML reveals several promising drivers. My findings not only go beyond augmenting my\r\nunderstanding of the heterogeneity landscape of AML but also may have immediate implications for new\r\ntargeted therapy studies."^^ . "2023" . . . . . . . "Ralitsa"^^ . "Langova"^^ . "Ralitsa Langova"^^ . . . . . . "Multi-omics of AML (PDF)"^^ . . . "PhD_thesis.pdf"^^ . . . "Multi-omics of AML (Other)"^^ . . . . . . "indexcodes.txt"^^ . . . "Multi-omics of AML (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "Multi-omics of AML (Other)"^^ . . . . . . "preview.jpg"^^ . . . "Multi-omics of AML (Other)"^^ . . . . . . "medium.jpg"^^ . . . "Multi-omics of AML (Other)"^^ . . . . . . "small.jpg"^^ . . "HTML Summary of #33419 \n\nMulti-omics of AML\n\n" . "text/html" . .