Hybrid approach to classify pediatric cases with acute myeloid leukemia

Abstract

Pediatric acute myeloid leukemia (AML) is a rare heterogeneous disease with an unfavorable prognosis. It is characterized by a high risk of relapse, which higlights the importance of accurately identifying AML and its subtypes in order to implement appropriate treatments. Differences in blast morphology, immunophenotype, as well as in cytogenetic and molecular aberrations make AML a heterogeneous disease. The identification of structural chromosomal abnormalities (SCAs) could help to classify AML patients into different subtypes with different outcomes, which would allow the application of more targeted treatments. However, SCAs do not always allow for the classification of all AML patients, because not all samples present fusions related to a characterized AML subtype, either because the fusion is not yet well studied or because the characterized fusion could not be detected with the used technique. These cases are grouped together by not having a characterized subtype, although they may have a different prognosis. Therefore, there is a need to better characterize those patients to offer them more tailored treatments. In this study, we propose a hybrid approach to identify diverse AML subtypes. This method integrates the initial identification of specific gene fusions with a second classification based on the gene expression profiles of the samples, using RNAseq data. We have developed a pipeline using Nextflow, to establish a bioinformatic workflow that permits the execution of multiple tools in a specific order to execute data processing. In this particular case, the integration of multiple tools is used to identify gene fusions and quantify the reads of each gene in the sample. This hybrid approach, provides a gene set that enables differentiation between healthy controls and AML patients, as well as the identification of SCAs and a gene expression profile to classify the different AML subgroups. This pipeline could potentially improve the classification of AML patients and help to better stratify patients into different risk groups.