ArticleBMC bioinformatics2010
Statistical method on nonrandom clustering with application to somatic mutations in cancer.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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.
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.
Who cites it
35 citing papers in PubMed, 78 citations in OpenAlex.
- Cancer treatment alters mutant selection in normal esophagus.Nature genetics · 2026Article
- Adaptive genetics reveals constraints on protein structure/function by evolving E. coli under constant nutrient limitation.BMC biology · 2025Article
- Gene-level analysis of core carbohydrate metabolism across the Enterobacteriaceae pan-genome.Communications biology · 2025Article
- Predicted functional consequences of WNT ligand mutations in colorectal cancer.Biophysical journal · 2025Article
- Proteome-wide assessment of differential missense variant clustering in neurodevelopmental disorders and cancer.Cell genomics · 2025Article
- Proteome-Wide Assessment of Clustering of Missense Variants in Neurodevelopmental Disorders Versus Cancer.medRxiv : the preprint server for health sciences · 2024Article
- KCNQ potassium channels modulate Wnt activity in gastro-oesophageal adenocarcinomas.Life science alliance · 2023Article
- Delineation of functionally essential protein regions for 242 neurodevelopmental genes.Brain : a journal of neurology · 2023Article
- Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs.Journal of molecular biology · 2022Review
- Predictive functional, statistical and structural analysis ofFrontiers in molecular biosciences · 2022Article
- Identification of a signature of evolutionarily conserved stress-induced mutagenesis in cancer.Frontiers in genetics · 2022Article
- PertInInt: An Integrative, Analytical Approach to Rapidly Uncover Cancer Driver Genes with Perturbed Interactions and Functionalities.Cell systems · 2020Article
- Identifying mutation hotspots reveals pathogenetic mechanisms of KCNQ2 epileptic encephalopathy.Scientific reports · 2020Article
- Computational methods for detecting cancer hotspots.Computational and structural biotechnology journal · 2020Review
- Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers.PLoS computational biology · 2019Review
- Distribution based Fuzzy Estimate Spectral Clustering for Cancer Detection with Protein Sequence and Structural MotifsAsian Pacific journal of cancer prevention : APJCP · 2018Article
- No major role for rare plectin variants in arrhythmogenic right ventricular cardiomyopathy.PloS one · 2018Article
- Spatial statistical tools for genome-wide mutation cluster detection under a microarray probe sampling system.PloS one · 2018Article
- Comparison of algorithms for the detection of cancer drivers at subgene resolution.Nature methods · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHuman cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention.
resultsWe propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC) database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and beta-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors.
conclusionsOur proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.