Prognostic mutation constellations in acute myeloid leukaemia and myelodysplastic syndrome

In the past decade, numerous studies analysing the genome and transcriptome of large cohorts of acute myeloid leukaemia (AML) and myelodysplastic syndrome (MDS) patients have substantially improved our knowledge of the genetic landscape of these diseases with the identification of heterogeneous constellations of germline and somatic mutations with prognostic and therapeutic relevance. However, inclusion of integrated genetic data into classification schema is still far from a reality. The purpose of this review is to summarize recent insights into the prevalence, pathogenic role, clonal architecture, prognostic impact and therapeutic management of genetic alterations across the spectrum of myeloid malignancies.

Recent multiomic-studies, including analysis of genetic alterations at the single-cell resolution, have revealed a high heterogeneity of lesions in over 200 recurrently mutated genes affecting disease initiation, clonal evolution and clinical outcome. Artificial intelligence and specifically machine learning approaches have been applied to large cohorts of AML and MDS patients to define in an unbiased manner clinically meaningful disease patterns including, disease classification, prognostication and therapeutic vulnerability, paving the way for future use in clinical practice.

Integration of genomic, transcriptomic, epigenomic and clinical data coupled to conventional and machine learning approaches will allow refined leukaemia classification and risk prognostication and will identify novel therapeutic targets for these still high-risk leukaemia subtypes.