Unveiling the Molecular Complexity of AML through Advanced Multi-Omics Analysis and Machine Learning

The HemAtlas 2.0 project embarks on a groundbreaking multi-omics journey to unravel the complexities of pediatric acute myeloid leukemia (AML), incorporating a diverse range of 241 pediatric cases plus 158 healthy donor samples, representing the normal hematopoiesis. Integrating genomic, transcriptomic, and epigenetic data with clinical insights, our work advances AML subtype classification and elucidates the impact of somatic mutations on prognosis, especially impacting overall and event-free survival. This integration highlights the importance of mutational networks over individual aberrations in disease progression and therapeutic response. Through Multi-Omics Factor Analysis (MOFA), we uncover intricate molecular interactions that define AML's heterogeneity. This provides deep insights into AML's pathogenesis identifying therapy targets. Our findings underscore the benefits of multi-omics integration in enhancing disease understanding, improving classification and prognosis, and paving the way for precision oncology in AML. The HemAtlas 2.0 project highlights the potential of combining diverse omic layers and clinical data to refine patient care strategies.

 

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Artikel online veröffentlicht:
10. Mai 2024