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Molecular landscape of acute myeloid leukemia in younger adults and its clinical relevance

髓系白血病 医学 白血病 疾病 内科学 计算生物学 生物 生物信息学 癌症研究 遗传学
作者
David Grimwade,Adam Ivey,Brian J.P. Huntly
出处
期刊:Blood [Elsevier BV]
卷期号:127 (1): 29-41 被引量:398
标识
DOI:10.1182/blood-2015-07-604496
摘要

Abstract Recent major advances in understanding the molecular basis of acute myeloid leukemia (AML) provide a double-edged sword. Although defining the topology and key features of the molecular landscape are fundamental to development of novel treatment approaches and provide opportunities for greater individualization of therapy, confirmation of the genetic complexity presents a huge challenge to successful translation into routine clinical practice. It is now clear that many genes are recurrently mutated in AML; moreover, individual leukemias harbor multiple mutations and are potentially composed of subclones with differing mutational composition, rendering each patient’s AML genetically unique. In order to make sense of the overwhelming mutational data and capitalize on this clinically, it is important to identify (1) critical AML-defining molecular abnormalities that distinguish biological disease entities; (2) mutations, typically arising in subclones, that may influence prognosis but are unlikely to be ideal therapeutic targets; (3) mutations associated with preleukemic clones; and (4) mutations that have been robustly shown to confer independent prognostic information or are therapeutically relevant. The reward of identifying AML-defining molecular lesions present in all leukemic populations (including subclones) has been exemplified by acute promyelocytic leukemia, where successful targeting of the underlying PML-RARα oncoprotein has eliminated the need for chemotherapy for disease cure. Despite the molecular heterogeneity and recognizing that treatment options for other forms of AML are limited, this review will consider the scope for using novel molecular information to improve diagnosis, identify subsets of patients eligible for targeted therapies, refine outcome prediction, and track treatment response.

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