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

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.

Figure 1

Distribution of cytogenetically and molecularly defined subsets of AML presenting in younger adults. Based on analysis of large cohorts of patients and patterns of mutual exclusivity between cytogenetic and molecular genetic features, the majority of AML cases can be segregated into a number of biologically and prognostically distinct subgroups. In approximately a third of cases, AML is characterized by the presence of balanced chromosomal rearrangements, which lead to the generation of chimeric oncoproteins, considered to be initiating events in disease pathogenesis. These chromosomal abnormalities are mutually exclusive of mutations in the nucleophosmin gene (NPM1) and biallelic CEBPA (biCEBPA) mutations, which are recognized as recurrent AML-defining genetic abnormalities and typically associated with a normal karyotype. Recent studies have established a close correlation between complex karyotype/monosomal karyotype and underlying mutation in the TP53 gene, which defines a biological subgroup with very poor prognosis. Recent studies have distinguished a mutational profile involving alterations to a panel of genes including those encoding ASXL1 and spliceosome components associated with secondary AML arising on a background of myelodysplasia (MDS). For each cytogenetically and genetically defined subset of AML denoted in the pie chart, frequent associated cooperating mutations are shown in the respective boxes. Mutational frequencies are derived from integration of data from previous studies.^-

Figure 2

Progress in defining the molecular landscape of AML. Timing of the identification of leukemic fusion genes and mutations underlying the pathogenesis of AML.

Figure 3

Clonal architecture, patterns of relapse, and the existence of preleukemic stem cells. (A) Clonal evolution and clonal heterogeneity of AML. Evolution of AML in a hypothetical patient whose tumor carries cooperating DNMT3A, NPM1c, and FLT3-ITD mutations. Mutation of DNMT3A is the earliest event, and although facilitating clonal expansion, occurs prior to overt disease development. Subsequently, NPM1c occurs as the disease-defining mutation in the founding clone, with further acquisition of a FLT3-ITD mutation in a hyperproliferative clone during leukemia expansion, which becomes dominant at diagnosis. Quantification of the variant allele frequency (VAF) of each mutation (by VAF, right) allows a demonstration of the temporal acquisition of mutations and the clonal hierarchy of the bulk tumor. For simplicity, a linear evolutionary pattern is shown, although commonly branching evolution can be demonstrated. (B) Heterogeneous clonal pattern of relapse in AML. Potential patterns of relapse from the hypothetical tumor in panel A are shown in the Fish plots: 1, relapse of the dominant clone at diagnosis; 2, relapse of a subclone present at diagnosis; 3, relapse from an ancestrally related clone; 4, “apparent relapse,” where the new tumor is not clonally related to the initial leukemia, such as might happen in therapy-related AML. (C) Existence of preleukemic clones that precede overt AML and may persist through treatment. Recently, the existence of preleukemic stem cells has been demonstrated.^- These harbor AML-associated mutations such as DNMT3A, TET2, and IDH2 that permit multipotent differentiation, but also facilitate clonal expansion within the stem and progenitor compartment (panel, second right). Upon the acquisition of further mutations (again NPM1c and FLT3-ITD are shown in the third panel), overt disease develops. However, treatment, although successful in eradicating the AML blasts, does not eradicate the preleukemic stem cells. Evidence suggests that these cells form the reservoir for relapse and resistance (left panel), although further study is required to characterize their biology and prognostic significance.

Figure 4

Clinical relevance of the mutational landscape in AML. A longitudinal schematic of various phases of disease are shown for AML. For each time point, possible clinical applications relating to knowledge of the specific mutational complement of the tumor, or the presence of specific mutations, are highlighted. At diagnosis, where in this example DNMT3A, NPM1c, and FLT3-ITD mutations drive disease, WES or panel-based next-generation sequencing (NGS) analysis could optimize prognostication and therapeutic choice, identifying mutations with an existing therapy. Moreover, quantitation of mutation frequency by the VAF could prioritize mutations that exist within every cell as critical targets, and the mutational complement could identify/validate prospective associations between specific mutational genotypes and sensitivity to individual or combined agents. In remission, perhaps using sensitive techniques such as digital polymerase chain reaction (dPCR), identification of the optimal markers for prospective response monitoring could allow early and robust adaptive postinduction therapy such as identifying candidates for stem cell transplant. In the example shown, this would relate to persistence of the DNMT3A mutation, as illustrated by dPCR and the occurrence of an occult FLT3 resistance mutation D835I (yellow clone). At clinical relapse, further WES or panel-based analysis may inform potential mechanisms of chemoresistance and relapse (FLT3 mutation) and in doing so may inform treatment decisions for reinduction and/or further therapy. In this example, the patient has relapsed with a FLT3 mutation (D835I) that would confer therapeutic resistance to some but not all FLT3 inhibitors. The patient has also developed a further subclonal NPM1c mutation in addition to the DNMT3A and FLT3 mutation (purple clone).

Figure 5

Molecular heterogeneity of AML exemplified by mutational profiling in NPM1c AML. Summary of targeted sequencing data using a published panel covering 14 mutational groups conducted in diagnostic samples from a cohort of 223 patients with NPM1c AML. Each spoke radiating from the central NPM1c hub represents the mutation pattern of a single patient. Cooperating mutations are grouped into 4 tiers according to function and color coded according to the figure key, and white space indicates no mutation. For example, in the patient displayed at 12 o’clock, mutations were detected in NPM1, DNMT3A, TET2, FLT3 (ITD), and STAG2. Overall, based on mutational combination, patients segregated into >75 different subgroups.