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Gene networks in cancer are biased by aneuploidies and sample impurities

Michael Schubert1, Maria Colomé-Tatché2, Floris Foijer3

  • 1European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV, Groningen, the Netherlands; Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany.

Biochimica Et Biophysica Acta. Gene Regulatory Mechanisms
|October 27, 2019
PubMed

Insights

Inferring gene regulatory networks from cancer data is challenging. Transcription factor annotations are crucial for accuracy, as copy number changes and impurities significantly impact network inference results.

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Genomics

Background:

  • Gene regulatory network inference aims to elucidate gene expression control from biological data.
  • Existing methods are well-evaluated on synthetic data but face challenges with real-world cancer datasets due to limited samples and confounding factors.

Purpose of the Study:

  • To assess the reliability of gene regulatory network inference methods when applied to human cancer data.
  • To identify key factors that compromise the accuracy of inferred networks in cancer studies.

Main Methods:

  • Inference of gene regulatory networks using established algorithms on The Cancer Genome Atlas (TCGA) cohort data.
  • Evaluation of network reliability by considering transcription factor annotations and potential biases from genomic alterations and sample composition.

Main Results:

  • Transcription factor annotations are essential for constructing dependable gene regulatory networks.
  • A significant proportion (20-80%) of inferred network edges in cancer data may arise from copy number alterations and cellular heterogeneity, not direct transcription factor regulation.

Conclusions:

  • Current gene regulatory network inference methods require careful validation when applied to complex cancer genomics data.
  • Future research should focus on developing methods robust to copy number variations and sample impurities for more accurate cancer network reconstruction.
Keywords:
AneuploidyCancerGene regulatory networksMethod comparison

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