Chromosomal imbalances detected via RNA-sequencing in 28 cancers

Zuhal Ozcan, Francis A. San Lucas, Justin W. Wong, Kyle Chang, Konrad H. Stopsack, Jerry Fowler, Yasminka A. Jakubek, Paul Scheet

Research output: Contribution to journalArticlepeer-review

Abstract

Motivation: RNA-sequencing (RNA-seq) of tumor tissue is typically only used to measure gene expression. Here, we present a statistical approach that leverages existing RNA-seq data to also detect somatic copy number alterations (SCNAs), a pervasive phenomenon in human cancers, without a need to sequence the corresponding DNA. Results: We present an analysis of 4942 participant samples from 28 cancers in The Cancer Genome Atlas (TCGA), demonstrating robust detection of SCNAs from RNA-seq. Using genotype imputation and haplotype information, our RNA-based method had a median sensitivity of 85% to detect SCNAs defined by DNA analysis, at high specificity (∼95%). As an example of translational potential, we successfully replicated SCNA features associated with breast cancer subtypes. Our results credential haplotype-based inference based on RNA-seq to detect SCNAs in clinical and population-based settings.

Original languageEnglish
Pages (from-to)1483-1490
Number of pages8
JournalBioinformatics
Volume38
Issue number6
DOIs
StatePublished - Mar 15 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s) 2022. Published by Oxford University Press.

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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