Evidence map›Paper›PMID 27135912›Full record

ArticleCell systems2015

Pan-Cancer Analysis of Mutation Hotspots in Protein Domains.

Martin L Miller, Ed Reznik, Nicholas P Gauthier, Bülent Arman Aksoy, Anil Korkut, Jianjiong Gao, Giovanni Ciriello, Nikolaus Schultz, Chris Sander

Open access · bronzeAbstract read
In one paragraph

Article in Cell systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed
5.7field-weighted citation impact, top 3% 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

76 citing papers in PubMed, 124 citations in OpenAlex.

  1. Article
  2. Article
  3. Convergent Evolution in Tumor Genomes Targets Functional Domains.bioRxiv : the preprint server for biology · 2026
    Article
  4. Article
  5. Integrating evidence from protein domains to identify cancer driver mutations.Protein science : a publication of the Protein Society · 2026
    Article
  6. Article
  7. Article
  8. MutationAssessor in cBioPortal.bioRxiv : the preprint server for biology · 2025
    Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Unraveling Extremely DamagingJournal of personalized medicine · 2023
    Article
  20. Pathogenic mutation hotspots in protein kinase domain structure.Protein science : a publication of the Protein Society · 2023
    Article

16 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

9 authors at 2 institutions in 2 countries.

Martin L MillerComputational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK. Electronic address: mutalign@gmail.com.
Ed ReznikComputational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
Nicholas P GauthierComputational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
Bülent Arman AksoyComputational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
Anil KorkutComputational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
Jianjiong GaoComputational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
Giovanni CirielloComputational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
Nikolaus SchultzComputational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
Chris SanderComputational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA. Electronic address: mutalign@gmail.com.
Memorial Sloan Kettering Cancer Center · USUniversity of Cambridge · GB

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
TR&D 3 - Network Guided Machine LearningP41GM103504 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI IDEKER, TREY · 2012 to 2024
$17.3M
MSKCC Center for Translational Cancer Genomic AnalysisU24CA143840 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI LADANYI, MARC, SANDER, CHRIS · 2009 to 2016
$11.5M
Cancer Research UK 21141NCI NIH HHS P30 CA008748NCI NIH HHS U24 CA143840NIGMS NIH HHS P41 GM103504
6 · The paper itself

Abstract

In cancer genomics, recurrence of mutations in independent tumor samples is a strong indicator of functional impact. However, rare functional mutations can escape detection by recurrence analysis owing to lack of statistical power. We enhance statistical power by extending the notion of recurrence of mutations from single genes to gene families that share homologous protein domains. Domain mutation analysis also sharpens the functional interpretation of the impact of mutations, as domains more succinctly embody function than entire genes. By mapping mutations in 22 different tumor types to equivalent positions in multiple sequence alignments of domains, we confirm well-known functional mutation hotspots, identify uncharacterized rare variants in one gene that are equivalent to well-characterized mutations in another gene, detect previously unknown mutation hotspots, and provide hypotheses about molecular mechanisms and downstream effects of domain mutations. With the rapid expansion of cancer genomics projects, protein domain hotspot analysis will likely provide many more leads linking mutations in proteins to the cancer phenotype.

Identifiers

PMID27135912
PMCPMC4982675
OpenAlexW2160578982

What OpenQuestion holds

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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.