The role of next generation sequencing in predicting treatment outcome in AML patients: Paving the road to better individualized treatment

Elhaddad, Heba Ahmed 2024. The role of next generation sequencing in predicting treatment outcome in AML patients: Paving the road to better individualized treatment. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

Our research evaluates the risk assessment of two types of myeloid haematological malignancies: Acute Myeloid leukaemia (AML) and Myelodysplastic Syndrome (MDS). Recent advances in understanding the genetic basis of these malignancies have provided significant insights into their pathogenesis and prognosis, leading to the development of novel therapeutic approaches. The emergence of next-generation sequencing has significantly transformed the landscape of cancer research over the past decade. Therefore, we based our research on using Bioinformatics, Mutational Signatures (MSs), and Machine Learning (ML) to analyse patients' genomic data, aiming to unveil hidden genomic features that can predict the patient's outcomes. The use of MS analysis in predicting patients' outcomes proved that the sequencing data obtained at the time of diagnosis includes the necessary genomic information to predict patients' outcomes irrespective of treatment strategies. ML analysis proved that the Random Forest algorithm and SMOTE (Synthetic Minority Oversampling Technique) balancing can be used to predict AML patients' response to induction chemotherapy prospectively. Comparing our results to the currently used AML risk stratification score revealed the value of integrating these algorithms in future approaches to improve the risk stratification system. The second part of this research explored the role of non-coding RNAs in the pathogenesis of t(6;9)-DEK::CAN (D/C) AML. Several studies have indicated that the HOTAIRM1 RNA is highly expressed in AML, and its presence is correlated with poor prognosis. Other researchers have reported that the expression patterns of microRNAs at the time of diagnosis can provide relevant prognostic information in AML patients. Our research revealed a complex relationship between D/C, HOTAIRM1 and microRNA expression in t(6;9) AML. We proved that the D/C fusion protein is the main culprit behind HOTAIRM1 upregulation in t(6;9) AML. The microRNA expression pattern was found to be significantly influenced by both HOTAIRM1 and D/C independently from each other.

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Schools > Medicine
Date of First Compliant Deposit:	27 February 2025
Last Modified:	27 Feb 2025 10:50
URI:	https://orca.cardiff.ac.uk/id/eprint/176506

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