Dataset Information

Transposable Element Expression in Acute Myeloid Leukemia Transcriptome and Prognosis.

ABSTRACT: Over half of the human genome is comprised of transposable elements (TE). Despite large-scale studies of the transcriptome in cancer, a comprehensive look at TE expression and its relationship to various mutations or prognosis has not been performed. We characterized the expression of TE in 178 adult acute myeloid leukemia (AML) patients using transcriptome data from The Cancer Genome Atlas. We characterized mutation specific dysregulation of TE expression using a multivariate linear model. We identified distinct patterns of TE expression associated with specific mutations and transcriptional networks. Genes regulating methylation was not associated with significant change in TE expression. Using an unpenalized cox regression analysis we identified a TE expression signature that predicted prognosis in AML. We identified 14 candidate prognostic TE transcripts (TEP) that classified AML as high/low-risk and this was independent of mutation-based and coding-gene expression based risk-stratification. TEP was able to predict prognosis in independent cohorts of 284 pediatric AML patients and 19 relapsed adult AML patients. This first comprehensive study of TE expression in AML demonstrates that TE expression can serve as a biomarker for prognosis in AML, and provides novel insights into the biology of AML. Studies characterizing its role in other cancers are warranted.

SUBMITTER: Colombo AR

PROVIDER: S-EPMC6219593 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Transposable Element Expression in Acute Myeloid Leukemia Transcriptome and Prognosis.

Colombo Anthony R AR Triche Timothy T Ramsingh Giridharan G

Scientific reports 20181106 1

Over half of the human genome is comprised of transposable elements (TE). Despite large-scale studies of the transcriptome in cancer, a comprehensive look at TE expression and its relationship to various mutations or prognosis has not been performed. We characterized the expression of TE in 178 adult acute myeloid leukemia (AML) patients using transcriptome data from The Cancer Genome Atlas. We characterized mutation specific dysregulation of TE expression using a multivariate linear model. We i ...[more]

PMID: 30401833

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Project description:BackgroundRecent 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.MethodsWe 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.ResultsWe 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.ConclusionsThe 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.).

Dataset Information

Transposable Element Expression in Acute Myeloid Leukemia Transcriptome and Prognosis.

Publications

Transposable Element Expression in Acute Myeloid Leukemia Transcriptome and Prognosis.

Similar Datasets

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