Purpose of review
The development of high-throughput techniques like next-generation sequencing (NGS) has unraveled the genetic profile of cancer. In this review, we discuss the role of NGS on the diagnostic, risk stratification, and follow-up of patients with acute myeloid leukemia (AML).
NGS has become an essential tool in clinical practice for AML management. Therefore, efforts are being made to improve its applications, automation, and turnaround time. Other high-throughput techniques, such as whole genome sequencing or RNA-sequencing, can be also used to this end. However, not all institutions may be able to implement these approaches. NGS is being investigated for measurable residual disease (MRD) assessment, especially with the development of error-correction NGS. New data analysis approaches like machine learning are being investigated in order to integrate genomic and clinical data and develop comprehensive classifications and risk scores.
NGS has proven to be a useful approach for the analysis of genomic alterations in patients with AML, which aids patient management. Current research is being directed at reducing turnaround time and simplifying processes so that these techniques can be universally integrated into clinical practice.