Mouse models in the era of large human tumour sequencing studies

J. R. De Ruiter, L.F.A. Wessels, J. Jonkers

Research output: Contribution to journalReview articleScientificpeer-review

5 Citations (Scopus)
21 Downloads (Pure)

Abstract

Cancer is a complex disease in which cells progressively accumulate mutations disrupting their cellular processes. A fraction of these mutations drive tumourigenesis by affecting oncogenes or tumour suppressor genes, but many mutations are passengers with no clear contribution to tumour development. The advancement of DNA and RNA sequencing technologies has enabled in-depth analysis of thousands of human tumours from various tissues to perform systematic characterization of their (epi)genomes and transcriptomes in order to identify (epi)genetic changes associated with cancer. Combined with considerable progress in algorithmic development, this expansion in scale has resulted in the identification of many cancer-associated mutations, genes and pathways that are considered to be potential drivers of tumour development. However, it remains challenging to systematically identify drivers affected by complex genomic rearrangements and drivers residing in non-coding regions of the genome or in complex amplicons or deletions of copy-number driven tumours. Furthermore, functional characterization is challenging in the human context due to the lack of genetically tractable experimental model systems in which the effects of mutations can be studied in the context of their tumour microenvironment. In this respect, mouse models of human cancer provide unique opportunities for pinpointing novel driver genes and their detailed characterization. In this review, we provide an overview of approaches for complementing human studies with data from mouse models. We also discuss state-of-the-art technological developments for cancer gene discovery and validation in mice.
Original languageEnglish
Article number180080
Pages (from-to)1-16
Number of pages16
JournalOpen Biology
Volume8
Issue number8
DOIs
Publication statusPublished - 2018

Keywords

  • cancer
  • driver genes
  • GEMMs
  • genetic screening
  • mouse models
  • oncogenomics

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