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Biomarkers in Acute Myeloid Leukemia: Leveraging Next Generation Sequencing Data for Optimal Therapeutic Strategies
Next generation sequencing (NGS) is routinely used for mutation profiling of acute myeloid leukemia. The extensive application of NGS in hematologic malignancies, and its significant association with the outcomes in multiple large cohorts constituted a proof of concept that AML phenotype is driven b...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8514876/ https://www.ncbi.nlm.nih.gov/pubmed/34660311 http://dx.doi.org/10.3389/fonc.2021.748250 |
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author | El Achi, Hanadi Kanagal-Shamanna, Rashmi |
author_facet | El Achi, Hanadi Kanagal-Shamanna, Rashmi |
author_sort | El Achi, Hanadi |
collection | PubMed |
description | Next generation sequencing (NGS) is routinely used for mutation profiling of acute myeloid leukemia. The extensive application of NGS in hematologic malignancies, and its significant association with the outcomes in multiple large cohorts constituted a proof of concept that AML phenotype is driven by underlying mutational signature and is amenable for targeted therapies. These findings urged incorporation of molecular results into the latest World Health Organization (WHO) sub-classification and integration into risk-stratification and treatment guidelines by the European Leukemia Net. NGS mutation profiling provides a large amount of information that guides diagnosis and management, dependent on the type and number of gene mutations, variant allele frequency and amenability to targeted therapeutics. Hence, molecular mutational profiling is an integral component for work-up of AML and multiple leukemic entities. In addition, there is a vast amount of informative data that can be obtained from routine clinical NGS sequencing beyond diagnosis, prognostication and therapeutic targeting. These include identification of evidence regarding the ontogeny of the disease, underlying germline predisposition and clonal hematopoiesis, serial monitoring to assess the effectiveness of therapy and resistance mutations, which have broader implications for management. In this review, using a few prototypic genes in AML, we will summarize the clinical applications of NGS generated data for optimal AML management, with emphasis on the recently described entities and Food and Drug Administration approved target therapies. |
format | Online Article Text |
id | pubmed-8514876 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85148762021-10-15 Biomarkers in Acute Myeloid Leukemia: Leveraging Next Generation Sequencing Data for Optimal Therapeutic Strategies El Achi, Hanadi Kanagal-Shamanna, Rashmi Front Oncol Oncology Next generation sequencing (NGS) is routinely used for mutation profiling of acute myeloid leukemia. The extensive application of NGS in hematologic malignancies, and its significant association with the outcomes in multiple large cohorts constituted a proof of concept that AML phenotype is driven by underlying mutational signature and is amenable for targeted therapies. These findings urged incorporation of molecular results into the latest World Health Organization (WHO) sub-classification and integration into risk-stratification and treatment guidelines by the European Leukemia Net. NGS mutation profiling provides a large amount of information that guides diagnosis and management, dependent on the type and number of gene mutations, variant allele frequency and amenability to targeted therapeutics. Hence, molecular mutational profiling is an integral component for work-up of AML and multiple leukemic entities. In addition, there is a vast amount of informative data that can be obtained from routine clinical NGS sequencing beyond diagnosis, prognostication and therapeutic targeting. These include identification of evidence regarding the ontogeny of the disease, underlying germline predisposition and clonal hematopoiesis, serial monitoring to assess the effectiveness of therapy and resistance mutations, which have broader implications for management. In this review, using a few prototypic genes in AML, we will summarize the clinical applications of NGS generated data for optimal AML management, with emphasis on the recently described entities and Food and Drug Administration approved target therapies. Frontiers Media S.A. 2021-09-30 /pmc/articles/PMC8514876/ /pubmed/34660311 http://dx.doi.org/10.3389/fonc.2021.748250 Text en Copyright © 2021 El Achi and Kanagal-Shamanna https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology El Achi, Hanadi Kanagal-Shamanna, Rashmi Biomarkers in Acute Myeloid Leukemia: Leveraging Next Generation Sequencing Data for Optimal Therapeutic Strategies |
title | Biomarkers in Acute Myeloid Leukemia: Leveraging Next Generation Sequencing Data for Optimal Therapeutic Strategies |
title_full | Biomarkers in Acute Myeloid Leukemia: Leveraging Next Generation Sequencing Data for Optimal Therapeutic Strategies |
title_fullStr | Biomarkers in Acute Myeloid Leukemia: Leveraging Next Generation Sequencing Data for Optimal Therapeutic Strategies |
title_full_unstemmed | Biomarkers in Acute Myeloid Leukemia: Leveraging Next Generation Sequencing Data for Optimal Therapeutic Strategies |
title_short | Biomarkers in Acute Myeloid Leukemia: Leveraging Next Generation Sequencing Data for Optimal Therapeutic Strategies |
title_sort | biomarkers in acute myeloid leukemia: leveraging next generation sequencing data for optimal therapeutic strategies |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8514876/ https://www.ncbi.nlm.nih.gov/pubmed/34660311 http://dx.doi.org/10.3389/fonc.2021.748250 |
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