Title:	Prognostic factors and mechanisms of relapse of acute myeloid leukaemia
DNr:	NAISS 2025/22-76
Project Type:	NAISS Small Compute
Principal Investigator:	Ahmed Waraky <Ahmed.waraky@gu.se>
Affiliation:	Göteborgs universitet
Duration:	2025-01-23 – 2026-02-01
Classification:	10610
Keywords:

Abstract

Despite advancements in the clinical management of paediatric acute myeloid leukaemia (AML), approximately 30% of patients still face relapse. The assessment of measurable residual disease (MRD) has emerged as an essential tool for patient management and risk stratification. However, current MRD methods, including RT-PCR and multiparameter flow cytometry (MFC), face significant limitations. These include the absence of abnormal immunophenotypes in some patients, changes in immunophenotypes over time, and high inter- and intrapatient heterogeneity, leading to relapses in patients initially deemed MRD-negative. Moreover, the lack of universal genetic MRD targets in paediatric AML further complicates effective monitoring. To address these challenges, we aim to develop a computational framework using single-cell omics (epigenome, proteome, and transcriptome) and machine learning for precision characterization of leukaemia stem cells (LSCs) within subgroups of paediatric AML diagnostic categories. Our approach will identify LSC-specific genetic, epigenetic, and surface antigen markers that are tailored to the unique biology of each subgroup within the diagnostic categories of AML. The computational framework will be initiated using resources provided by SNIC, focusing on the development of the comprehensive pipeline. This pipeline will integrate techniques such as CITE-seq, scATAC-seq, scRNA-seq, and targeted cDNA sequencing for clonal tracing. Optimization of the framework will utilize non-clinical data from cell lines alongside clinical data from publicly available datasets.