Gene networks in cancer are biased by aneuploidies and sample impurities

Biochim Biophys Acta Gene Regul Mech. 2020 Jun;1863(6):194444. doi: 10.1016/j.bbagrm.2019.194444. Epub 2019 Oct 23.


Gene regulatory network inference is a standard technique for obtaining structured regulatory information from, for instance, gene expression measurements. Methods performing this task have been extensively evaluated on synthetic, and to a lesser extent real data sets. In contrast to these test evaluations, applications to gene expression data of human cancers are often limited by fewer samples and more potential regulatory links, and are biased by copy number aberrations as well as cell mixtures and sample impurities. Here, we take networks inferred from TCGA cohorts as an example to show that (1) transcription factor annotations are essential to obtain reliable networks, and (2) even for state of the art methods, we expect that between 20 and 80% of edges are caused by copy number changes and cell mixtures rather than transcription factor regulation.

Keywords: Aneuploidy; Cancer; Gene regulatory networks; Method comparison.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Aneuploidy
  • Bias
  • Gene Expression Regulation, Neoplastic*
  • Gene Regulatory Networks*
  • Genetic Techniques
  • Humans
  • Neoplasms / genetics*