Evidence map›Paper›PMID 37076339›Full record

ReviewTrends in molecular medicine2023

Cancer driver mutations: predictions and reality.

Daria Ostroverkhova, Teresa M Przytycka, Anna R Panchenko

Open access · greenAbstract readReview
In one paragraph

Review in Trends in molecular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.

0numbers the graph read from it
0cells of the map it votes in
79citing papers in PubMed
29.9field-weighted citation impact, top 1% 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

79 citing papers in PubMed, 125 citations in OpenAlex.

  1. Article
  2. Convergent Evolution in Tumor Genomes Targets Functional Domains.bioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Telomere Crisis Shapes Cancer Evolution.Cold Spring Harbor perspectives in biology · 2026
    Review
  9. Article
  10. An interpretable molecular framework for predicting cancer driver missense mutations.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  11. Review
  12. Integrating evidence from protein domains to identify cancer driver mutations.Protein science : a publication of the Protein Society · 2026
    Article
  13. Article
  14. The functional imperative in high-grade glioma.Experimental & molecular medicine · 2026
    Review
  15. Review
  16. Article
  17. Review
  18. Food & nutrition research · 2026
    Article
  19. Article
  20. Article

19 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

3 authors at 3 institutions in 3 countries.

Daria OstroverkhovaDepartment of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada.
Teresa M PrzytyckaNational Library of Medicine, National Institutes of Health (NIH), Bethesda, MD, USA. Electronic address: przytyck@ncbi.nlm.nih.gov.
Anna R PanchenkoDepartment of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada; Department of Biology and Molecular Sciences, Queen's University, Kingston, ON, Canada; School of Computing, Queen's University, Kingston, ON, Canada; Ontario Institute of Cancer Research, Toronto, ON, Canada. Electronic address: anna.panchenko@queensu.ca.
National Institutes of Health · USOntario Institute for Cancer Research · CAQueen's University · CA

Funding

Combinatorial and graph theoretical approach to systems biology and mol. evo.ZIALM200887 · NLM · NATIONAL LIBRARY OF MEDICINE · PI PRZYTYCKA, TERESA · 2009 to 2025
$24.4M
Intramural NIH HHS ZIA LM200887
6 · The paper itself

Abstract

Cancer cells accumulate many genetic alterations throughout their lifetime, but only a few of them drive cancer progression, termed driver mutations. Driver mutations may vary between cancer types and patients, can remain latent for a long time and become drivers at particular cancer stages, or may drive oncogenesis only in conjunction with other mutations. The high mutational, biochemical, and histological tumor heterogeneity makes driver mutation identification very challenging. In this review we summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA). We also report on the boundaries of their applicability in clinical research.

Indexed as

NeoplasmsBiomarkers, TumorCarcinogenesisHumansMutationBiomarkers, Tumorcancercancer biomarkersdriver mutation prediction methodsdriver mutationsmutational motifsmutational signatures

Identifiers

PMID37076339
PMCPMC12232956
OpenAlexW4366144498

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.