ArticleProceedings of the National Academy of Sciences of the United States of America2015
Comprehensive assessment of cancer missense mutation clustering in protein structures.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 146 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
146 citing papers in PubMed, 246 citations in OpenAlex.
- Genomic and transcriptomic profiling reveals distinct molecular subsets associated with outcomes in mantle cell lymphoma.The Journal of clinical investigation · 2022Trial
- The structural context of mutations in proteins predicts their effect on antibiotic resistance.eLife · 2026Article
- Probabilistic mapping of sub-genic intolerance reveals functional and disease-critical protein regions.bioRxiv : the preprint server for biology · 2026Article
- Interpretable deep learning framework for mapping E3-substrate binding interfaces.Nature communications · 2026Article
- Neo-Cysteine Molecular Glues for Targeting Mutated SMAD4 Protein.Angewandte Chemie (International ed. in English) · 2026Article
- Exploratory identification of coding and splicing-related SNV variants in A1A1 and A2A2 β-casein Holstein dairy cows.Journal of animal science · 2026Article
- Classification of driver and passenger mutations in different cancer types using deep neural networks.Bioinformatics advances · 2026Article
- Integrative analysis of lung adenocarcinoma across diverse ethnicities and exposures.Cancer cell · 2025Article
- Target Mapping in Cancer: Ligandable Protein Pockets on 3D OncoPPI Networks.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Deep structure-function analysis of the endonuclease Mus81 with dominant mutational scanning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cancer gene identification from RNA variant allelic frequencies using RVdriver.Genome biology · 2025Article
- Accurate identification and mechanistic evaluation of pathogenic missense variants withProceedings of the National Academy of Sciences of the United States of America · 2025Article
- A series of reviews in familial cancer: genetic cancer risk in context variants of uncertain significance in MMR genes: which procedures should be followed?Familial cancer · 2025Review
- Article
- Antigen selection reflected in the subclonal architecture of the B-cell receptor immunoglobulin gene repertoire in splenic marginal zone lymphoma.HemaSphere · 2025Article
- Proteome-wide assessment of differential missense variant clustering in neurodevelopmental disorders and cancer.Cell genomics · 2025Article
- Decoding the functional impact of the cancer genome through protein-protein interactions.Nature reviews. Cancer · 2025Review
- A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology.Nature communications · 2025Article
- Specificity of mRNA binding to proteins within the NMD machinery is influenced in cancer.Frontiers in molecular biosciences · 2025Article
- Analysis of a cancer-associated mutation in the budding yeast Nuf2 kinetochore protein.microPublication biology · 2025Article
86 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Large-scale tumor sequencing projects enabled the identification of many new cancer gene candidates through computational approaches. Here, we describe a general method to detect cancer genes based on significant 3D clustering of mutations relative to the structure of the encoded protein products. The approach can also be used to search for proteins with an enrichment of mutations at binding interfaces with a protein, nucleic acid, or small molecule partner. We applied this approach to systematically analyze the PanCancer compendium of somatic mutations from 4,742 tumors relative to all known 3D structures of human proteins in the Protein Data Bank. We detected significant 3D clustering of missense mutations in several previously known oncoproteins including HRAS, EGFR, and PIK3CA. Although clustering of missense mutations is often regarded as a hallmark of oncoproteins, we observed that a number of tumor suppressors, including FBXW7, VHL, and STK11, also showed such clustering. Beside these known cases, we also identified significant 3D clustering of missense mutations in NUF2, which encodes a component of the kinetochore, that could affect chromosome segregation and lead to aneuploidy. Analysis of interaction interfaces revealed enrichment of mutations in the interfaces between FBXW7-CCNE1, HRAS-RASA1, CUL4B-CAND1, OGT-HCFC1, PPP2R1A-PPP2R5C/PPP2R2A, DICER1-Mg2+, MAX-DNA, SRSF2-RNA, and others. Together, our results indicate that systematic consideration of 3D structure can assist in the identification of cancer genes and in the understanding of the functional role of their mutations.
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.