Using singscore to predict mutations in acute myeloid leukemia from transcriptomic signatures.
AML mutations
NPM1c mutation
TCGA
gene set scoring
mutation prediction
signature scoring
single sample
Journal
F1000Research
ISSN: 2046-1402
Titre abrégé: F1000Res
Pays: England
ID NLM: 101594320
Informations de publication
Date de publication:
Historique:
accepted:
23
05
2019
entrez:
15
11
2019
pubmed:
15
11
2019
medline:
15
11
2019
Statut:
epublish
Résumé
Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.
Identifiants
pubmed: 31723419
doi: 10.12688/f1000research.19236.1
pmc: PMC6844140
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
776Informations de copyright
Copyright: © 2019 Bhuva DD et al.
Déclaration de conflit d'intérêts
No competing interests were disclosed.
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