Molecular therapy for acute myeloid leukaemia

CEBPA公司 医学 净现值1 疾病 体细胞 肿瘤科 生物信息学 个性化医疗 内科学 髓样 突变 计算生物学 遗传学 基因 生物 核型 染色体
作者
Catherine C. Coombs,Martin S. Tallman,Ross L. Levine
出处
期刊:Nature Reviews Clinical Oncology [Nature Portfolio]
卷期号:13 (5): 305-318 被引量:113
标识
DOI:10.1038/nrclinonc.2015.210
摘要

Acute myeloid leukaemia (AML) is a heterogeneous disease that is typically associated with a very poor prognosis; however, cytogenetic and molecular abnormalities that characterize different forms of AML have been used to better prognosticate patients and inform treatment strategies, which might enable better outcomes to be achieved. Moreover, in the era of next-generation sequencing and molecularly targeted therapy, genetic profiling of patients with AML could open new avenues of treatment. Herein, the authors discuss the evidence-base for integrating mutational data into treatment decisions for patients with AML, and propose novel therapeutic algorithms aimed at improving outcomes of this dismal disease by promoting clinical research. Acute myeloid leukaemia (AML) is a heterogeneous disease that is, in general, associated with a very poor prognosis. Multiple cytogenetic and molecular abnormalities that characterize different forms of AML have been used to better prognosticate patients and inform treatment decisions. Indeed, risk status in patients with this disease has classically been based on cytogenetic findings; however, additional molecular characteristics have been shown to inform risk assessment, including FLT3, NPM1, KIT, and CEBPA mutation status. Advances in sequencing technology have led to the discovery of novel somatic mutations in tissue samples from patients with AML, providing deeper insight into the mutational landscape of the disease. The majority of patients with AML (>97%) are found to have a clonal somatic abnormality on mutational profiling. Nevertheless, our understanding of the utility of mutation profiling in clinical practice remains incomplete and is continually evolving, and evidence-based approaches to application of these data are needed. In this Review, we discuss the evidence-base for integrating mutational data into treatment decisions for patients with AML, and propose novel therapeutic algorithms in the era of molecular medicine.
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