Date

2022-8-29

Author

Gerekci Yeşilyurt, Selin

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Acute Myeloid Leukemia is a hematological cancer with high phenotypic and genotypic heterogeneity. Patients diagnosed with AML are categorized into risk groups based on cytogenetic and molecular abnormality tests, which determine the specific treatment regimes. Since risk status determination takes significant amount of time and some emergency patients require immediate treatment, a method to provide fast clinical data that will be the basis for initial treatment regime is needed in the medical community. Objective of the proposed work is to discover patterns associated with FLT3-ITD and NPM1 mutations in Acute Myeloid Leukemia patients, which will provide a fast clinical method for the proper first-response treatment. The rationale for the hypothesis is based on the previous studies which indicate a link between NPM1 and FLT3-ITD mutations with glucose and amino acid metabolism. NPM1 and FLT3 mutations were chosen based on their high frequency in AML patients and their essential role in risk group determination. Metabolic pattern determination of the mutations was achieved by LC-MS/MS measurements of amino acid and acyl carnitine panels that are highly associated with amino acid and glucose metabolism. After preprocessing of the raw data, univariate (ANOVA) and multivariate analyses (PCA and PLS-DA) were performed to define class discrimination between sample groups. The remarkable analytes that have significant power in the discrimination were determined by VIP analysis. The developed model was validated with K-Fold cross validation method and permutation test. The most significant pathways in class discrimination were identified with pathway analysis. Visualization was accomplished via Metaboanalyst 5.0 software. Principal Component Analysis (PCA) showed that 79% of the total variance of the sample groups was explained by the model. In order to increase class discrimination, Partial Least Squares-Discriminant Analysis (PLS-DA) was performed. R2Y and Q2 were found as 0.845 and 0.619, respectively. PLS-DA model was validated with K-fold analysis and permutation test. In all the validation experiments carried out, a low cross-validation error was observed. In VIP analysis, the most significant analytes that cause the class discrimination were found as C0 carnitine, glutamic acid, aspartic acid, tryptophan, and histidine, respectively. In the pathway enrichment analysis performed with these analytes, aminoacyl t-RNA biosynthesis, arginine biosynthesis, valine-leucine-isoleucine biosynthesis, alanine-aspartate-glutamate metabolism, histidine metabolism and arginine-proline metabolism were found as statistically significant pathways responsible for the class discrimination. In conclusion, a preliminary model based on the targeted metabolomics approach was developed for the prediction of mutation status of NPM1 and FLT3 proteins in AML patients. Proposed model has a high fit value, validity, and strong predictive power. The reliability and validity of the model can be further increased by future multicenter studies.

Subject Keywords

Hematological Malignancies, Acute Myeloid Leukemia, Metabolomics, Multivariate Analysis, Mass Spectrometry

URI

https://hdl.handle.net/11511/99539

Collections

Graduate School of Natural and Applied Sciences, Thesis