Evidence map›Paper›PMID 38890488›Full record

ArticleNature genetics2024

Analysis of 10,478 cancer genomes identifies candidate driver genes and opportunities for precision oncology.

Ben Kinnersley, Amit Sud, Andrew Everall, Alex J Cornish, Daniel Chubb, Richard Culliford, Andreas J Gruber, Adrian Lärkeryd, Costas Mitsopoulos, David Wedge and 1 more

Abstract read
In one paragraph

Article in Nature genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing 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

39 citing papers in PubMed.

  1. Trial
  2. Structural and functional divergence of cancer hotspot mutations in CREBBP.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  3. Article
  4. Ultra-rare TP53 and KRAS variants predict survival in ICI-treated solid tumours.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026
    Article
  15. Article
  16. Article
  17. Article
  18. Canadian oncology nursing journal = Revue canadienne de nursing oncologique · 2026
    Article
  19. Translating evidence into practice: Developing Canada's first position statement on genomics-informed oncology nursing.Canadian oncology nursing journal = Revue canadienne de nursing oncologique · 2026
    Article
  20. 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

11 authors.

Ben Kinnersley *Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.ORCID http://orcid.org/0000-0003-1783-6296
Amit Sud *Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.ORCID http://orcid.org/0000-0002-6133-0164
Andrew Everall *Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
Alex J CornishDivision of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
Daniel ChubbDivision of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
Richard CullifordDivision of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
Andreas J GruberSystems Biology & Biomedical Data Science Laboratory, University of Konstanz, Konstanz, Germany.ORCID http://orcid.org/0000-0001-7664-4257
Adrian LärkerydDivision of Molecular Pathology, The Institute of Cancer Research, London, UK.
Costas MitsopoulosDivision of Cancer Therapeutics, The Institute of Cancer Research, London, UK.
David WedgeManchester Cancer Research Centre, University of Manchester, Manchester, UK.ORCID http://orcid.org/0000-0002-7572-3196
Richard HoulstonDivision of Genetics and Epidemiology, The Institute of Cancer Research, London, UK. richard.houlston@icr.ac.uk.ORCID http://orcid.org/0000-0002-5268-0242

Funding

Cancer Research UK (CRUK) C1298/A8362Wellcome Trust 214388Wellcome Trust 227000Wellcome Trust (Wellcome) 227000/Z/23/Z
6 · The paper itself

Abstract

Tumor genomic profiling is increasingly seen as a prerequisite to guide the treatment of patients with cancer. To explore the value of whole-genome sequencing (WGS) in broadening the scope of cancers potentially amenable to a precision therapy, we analysed whole-genome sequencing data on 10,478 patients spanning 35 cancer types recruited to the UK 100,000 Genomes Project. We identified 330 candidate driver genes, including 74 that are new to any cancer. We estimate that approximately 55% of patients studied harbor at least one clinically relevant mutation, predicting either sensitivity or resistance to certain treatments or clinical trial eligibility. By performing computational chemogenomic analysis of cancer mutations we identify additional targets for compounds that represent attractive candidates for future clinical trials. This study represents one of the most comprehensive efforts thus far to identify cancer driver genes in the real world setting and assess their impact on informing precision oncology.

Indexed as

MutationNeoplasmsPrecision MedicineWhole Genome SequencingGenome, HumanGenomicsHumansMedical Oncology

Identifiers

PMID38890488
PMCPMC11387197

What OpenQuestion holds

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Registered trials

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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.