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Genomic landscape in acute myeloid leukemia and its implications in risk classification and targeted therapies.

ABSTRACT: Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy in terms of clinical features, underlying pathogenesis and treatment outcomes. Recent advances in genomic techniques have unraveled the molecular complexity of AML leukemogenesis, which in turn have led to refinement of risk stratification and personalized therapeutic strategies for patients with AML. Incorporation of prognostic and druggable genetic biomarkers into clinical practice to guide patient-specific treatment is going to be the mainstay in AML therapeutics. Since 2017 there has been an explosion of novel treatment options to tailor personalized therapy for AML patients. In the past 3 years, the U.S. Food and Drug Administration approved a total of eight drugs for the treatment of AML; most specifically target certain gene mutations, biological pathways, or surface antigen. These novel agents are especially beneficial for older patients or those with comorbidities, in whom the treatment choice is limited and the clinical outcome is very poor. How to balance efficacy and toxicity to further improve patient outcome is clinically relevant. In this review article, we give an overview of the most relevant genetic markers in AML with special focus on the therapeutic implications of these aberrations.

SUBMITTER: Hou HA

PROVIDER: S-EPMC7372828 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Genomic landscape in acute myeloid leukemia and its implications in risk classification and targeted therapies.

Hou Hsin-An HA Tien Hwei-Fang HF

Journal of biomedical science 20200721 1

Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy in terms of clinical features, underlying pathogenesis and treatment outcomes. Recent advances in genomic techniques have unraveled the molecular complexity of AML leukemogenesis, which in turn have led to refinement of risk stratification and personalized therapeutic strategies for patients with AML. Incorporation of prognostic and druggable genetic biomarkers into clinical practice to guide patient-specific treatment is goi ...[more]

PMID: 32690020

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Project description:BackgroundRecent studies have provided a detailed census of genes that are mutated in acute myeloid leukemia (AML). Our next challenge is to understand how this genetic diversity defines the pathophysiology of AML and informs clinical practice.MethodsWe enrolled a total of 1540 patients in three prospective trials of intensive therapy. Combining driver mutations in 111 cancer genes with cytogenetic and clinical data, we defined AML genomic subgroups and their relevance to clinical outcomes.ResultsWe identified 5234 driver mutations across 76 genes or genomic regions, with 2 or more drivers identified in 86% of the patients. Patterns of co-mutation compartmentalized the cohort into 11 classes, each with distinct diagnostic features and clinical outcomes. In addition to currently defined AML subgroups, three heterogeneous genomic categories emerged: AML with mutations in genes encoding chromatin, RNA-splicing regulators, or both (in 18% of patients); AML with TP53 mutations, chromosomal aneuploidies, or both (in 13%); and, provisionally, AML with IDH2(R172) mutations (in 1%). Patients with chromatin-spliceosome and TP53-aneuploidy AML had poor outcomes, with the various class-defining mutations contributing independently and additively to the outcome. In addition to class-defining lesions, other co-occurring driver mutations also had a substantial effect on overall survival. The prognostic effects of individual mutations were often significantly altered by the presence or absence of other driver mutations. Such gene-gene interactions were especially pronounced for NPM1-mutated AML, in which patterns of co-mutation identified groups with a favorable or adverse prognosis. These predictions require validation in prospective clinical trials.ConclusionsThe driver landscape in AML reveals distinct molecular subgroups that reflect discrete paths in the evolution of AML, informing disease classification and prognostic stratification. (Funded by the Wellcome Trust and others; ClinicalTrials.gov number, NCT00146120.).

Dataset Information

Genomic landscape in acute myeloid leukemia and its implications in risk classification and targeted therapies.

Publications

Genomic landscape in acute myeloid leukemia and its implications in risk classification and targeted therapies.

Similar Datasets

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