Evidence map›Paper›PMID 30111589›Full record

ReviewOpen biology2018

Mouse models in the era of large human tumour sequencing studies.

J R de Ruiter, L F A Wessels, J Jonkers

Open access · goldAbstract readReview
In one paragraph

Review in Open biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.8field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. What sets the mutation rate of a cell type in an animal species?bioRxiv : the preprint server for biology · 2025
    Article
  4. Review
  5. Current methods in translational cancer research.Cancer metastasis reviews · 2021
    Review
  6. Mouse Models of Myeloid Malignancies.Cold Spring Harbor perspectives in medicine · 2021
    Review
  7. Investigating Tumor Heterogeneity in Mouse Models.Annual review of cancer biology · 2020
    Article
  8. Article
  9. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

J R de RuiterDivision of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
L F A WesselsDivision of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands l.wessels@nki.nl.
J JonkersDivision of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands j.jonkers@nki.nl.
Oncode Institute · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AnimalsDisease ProgressionEpigenesis, GeneticGene Expression ProfilingGene Regulatory NetworksGenetic Predisposition to DiseaseHumansMiceNeoplasms, ExperimentalSequence Analysis, DNASequence Analysis, RNAcancerdriver genesGEMMsgenetic screeningmouse modelsoncogenomics

Identifiers

PMID30111589
PMCPMC6119864
OpenAlexW2886865065

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.