Evidence map›Paper›PMID 34316057›Full record

ReviewNature reviews. Cancer2021

Mutational signatures: emerging concepts, caveats and clinical applications.

Gene Koh, Andrea Degasperi, Xueqing Zou, Sophie Momen, Serena Nik-Zainal

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 192 papers.

0numbers the graph read from it
0cells of the map it votes in
192citing papers in PubMed
–field-weighted citation impact
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

192 citing papers in PubMed.

  1. Review
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  15. Topographical archetypes of somatic mutagenesis in cancer.bioRxiv : the preprint server for biology · 2026
    Article
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  17. Article
  18. Review
  19. Review
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132 more citing papers are in PubMed but not listed here.

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

5 authors.

Gene KohDepartment of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0002-3840-4954
Andrea DegasperiDepartment of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0001-6879-0596
Xueqing ZouDepartment of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Sophie MomenDepartment of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Serena Nik-ZainalDepartment of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK. sn206@cam.ac.uk.ORCID http://orcid.org/0000-0001-5054-1727

Funding

Cancer Research UK 23916Cancer Research UK C60100/A23916Medical Research CouncilWellcome Trust WT101126
6 · The paper itself

Abstract

Whole-genome sequencing has brought the cancer genomics community into new territory. Thanks to the sheer power provided by the thousands of mutations present in each patient's cancer, we have been able to discern generic patterns of mutations, termed 'mutational signatures', that arise during tumorigenesis. These mutational signatures provide new insights into the causes of individual cancers, revealing both endogenous and exogenous factors that have influenced cancer development. This Review brings readers up to date in a field that is expanding in computational, experimental and clinical directions. We focus on recent conceptual advances, underscoring some of the caveats associated with using the mutational signature frameworks and highlighting the latest experimental insights. We conclude by bringing attention to areas that are likely to see advancements in clinical applications.

Indexed as

DNA Mutational AnalysisGenomicsMutationCarcinogenesisHumansNeoplasms

Identifiers

What OpenQuestion holds

Textmetadata
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.