Dataset Information

Genomic Classification and Prognosis in Acute Myeloid Leukemia.

ABSTRACT: Recent 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.We 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.We 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.The 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.).

SUBMITTER: Papaemmanuil E

PROVIDER: S-EPMC4979995 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Genomic Classification and Prognosis in Acute Myeloid Leukemia.

Papaemmanuil Elli E Gerstung Moritz M Bullinger Lars L Gaidzik Verena I VI Paschka Peter P Roberts Nicola D ND Potter Nicola E NE Heuser Michael M Thol Felicitas F Bolli Niccolo N Gundem Gunes G Van Loo Peter P Martincorena Inigo I Ganly Peter P Mudie Laura L McLaren Stuart S O'Meara Sarah S Raine Keiran K Jones David R DR Teague Jon W JW Butler Adam P AP Greaves Mel F MF Ganser Arnold A Döhner Konstanze K Schlenk Richard F RF Döhner Hartmut H Campbell Peter J PJ

The New England journal of medicine 20160601 23

<h4>Background</h4>Recent 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.<h4>Methods</h4>We 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 clini ...[more]

PMID: 27276561

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Project description:Acute myeloid leukemia (AML) is the most common childhood cancer and is a major cause of morbidity among adults with hematologic malignancies. Several novel genetic alterations, which target critical cellular pathways, including alterations in lymphoid development-regulating genes, tumor suppressors and oncogenes that contribute to leukemogenesis, have been identified. The present study aimed to identify molecular markers associated with the occurrence and poor prognosis of AML. Information on these molecular markers may facilitate prediction of clinical outcomes. Clinical data and RNA expression profiles of AML specimens from The Cancer Genome Atlas database were assessed. Mutation data were analyzed and mapped using the maftools package in R software. Kyoto Encyclopedia of Genes and Genomes, Reactome and Gene Ontology analyses were performed using the clusterProfiler package in R software. Furthermore, Kaplan-Meier survival analysis was performed using the survminer package in R software. The expression data of RNAs were subjected to univariate Cox regression analysis, which demonstrated that the mutation loads varied considerably among patients with AML. Subsequently, the expression data of mRNAs, microRNAs (miRNAs/miR) and long non-coding RNAs (lncRNAs) were subjected to univariate Cox regression analysis to determine the the 100 genes most associated with the survival of patients with AML, which revealed 48 mRNAs and 52 miRNAs. The top 1,900 mRNAs (P<0.05) were selected through enrichment analysis to determine their functional role in AML prognosis. The results demonstrated that these molecules were involved in the transforming growth factor-?, SMAD and fibroblast growth factor receptor-1 fusion mutant signaling pathways. Survival analysis indicated that patients with AML, with high MYH15, TREML2, ATP13A2, MMP7, hsa-let-7a-2-3p, hsa-miR-362-3p, hsa-miR-500a-5p, hsa-miR-500b-5p, hsa-miR-362-5p, LINC00987, LACAT143, THCAT393, THCAT531 and KHCAT230 expression levels had a shorter survival time compared with those without these factors. Conversely, a high KANSL1L expression level in patients was associated with a longer survival time. The present study determined genetic mutations, mRNAs, miRNAs, lncRNAs and signaling pathways involved in AML, in order to elucidate the underlying molecular mechanisms of the development and recurrence of this disease.

Dataset Information

Genomic Classification and Prognosis in Acute Myeloid Leukemia.

Publications

Genomic Classification and Prognosis in Acute Myeloid Leukemia.

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