Using singscore to predict mutations in acute myeloid leukemia from transcriptomic signatures

Dharmesh D. Bhuva, Momeneh Foroutan, Yi Xie, Ruqian Lyu, Joseph Cursons, Melissa J. Davis

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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.

Original languageEnglish
Article number776
Number of pages42
JournalF1000Research
Volume8
DOIs
Publication statusPublished - 3 Jun 2019
Externally publishedYes

Keywords

  • AML mutations
  • gene set scoring
  • mutation prediction
  • NPM1c mutation
  • signature scoring
  • single sample
  • TCGA

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