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Accession IconGSE70124

Genomic structure, evolution and molecular classification of acute myeloid leukemia

Organism Icon Homo sapiens
Sample Icon 36 Downloadable Samples
Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

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Background: Acute myeloid leukemia (AML) is driven by somatic mutations and genomic rearrangements affecting >20 genes. Many of these are recent discoveries and how this molecular heterogeneity dictates AML pathophysiology and clinical outcome remains unclear. Methods: We sequenced 111 leukemia genes for driver mutations in 1540 AML patients with cytogenetic and clinical data. We modeled AMLs genomic structure, defining genetic interactions, patterns of temporal evolution and clinical correlations. Results: We identified 5,236 driver mutations involving 77 loci, including hotspot mutations in MYC. We found 1 driver mutation in 96% patients, and 2 in 85%. Gene mutations implicated in age related clonal hematopoiesis (DNMT3A, ASXL1, TET2) were the earliest in AML evolution, followed by highly specific and ordered patterns of co-mutation in chromatin, transcription and splicing regulators, NPM1 and signaling genes. The patterns of co-mutation compartmentalize AML into 12 discrete molecular classes, each presenting with distinct clinical manifestation. Amongst these, mutations in chromatin and spliceosome genes demarcate a molecularly heterogeneous subgroup enriched for older AML patients currently classified as intermediate risk and results in adverse prognosis. Two- and three-way genetic interactions often implicating rare genes/mutation-hotspots, markedly redefined clinical response and long-term curability, with the NPM1:DNMT3A:FLT3ITD genotype (6% patients) identifying poor prognosis disease, whereas within the same class NPM1:DNMT3A:NRASG12/13 (3%) associated with favorable outlooks. Conclusions: 79% of AML is molecularly classified in 12 genomic subgroups. These represent distinct molecular phylogenies, implicating complex genotypes. Delineation of higher-order genomic relationships, guide the development of personally tailored classification, prognostication and clinical protocols. Similar studies across cancer types are warranted.
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