The role of next-generation sequencing in acute myeloid leukemia

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Autores de IIS La Fe

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Abstract

Purpose of reviewThe 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).Recent findingsNGS 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.SummaryNGS 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. © 2022 Lippincott Williams and Wilkins. All rights reserved.

Datos de la publicación

ISSN/ISSNe:
1040-8746, 1531-703X

CURRENT OPINION IN ONCOLOGY  LIPPINCOTT WILLIAMS & WILKINS

Tipo:
Review
Páginas:
723-728
PubMed:
36102349
Enlace a otro recurso:
www.scopus.com
Factor de Impacto:
1,022 SCImago ℠
Cuartil:
Q2 SCImago ℠

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Keywords

  • Genomics; High-Throughput Nucleotide Sequencing; Humans; Leukemia, Myeloid, Acute; Mutation; Neoplasm, Residual; RNA; DNA; RNA; RNA; acute myeloid leukemia; artificial intelligence; cancer diagnosis; cancer genetics; cancer patient; cancer risk; DNA sequencing; follow up; high throughput sequencing; human; machine learning; minimal residual disease; molecular diagnosis; oncogenomics; Review; RNA sequencing; tumor gene; genetics; genomics; high throughput sequencing; mutation; procedures

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