ArticlePloS one2014
Deriving a mutation index of carcinogenicity using protein structure and protein interfaces.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 24 citations in OpenAlex.
- Dynamic Allostery: Evolution's Double-Edged Sword in Protein Function and Disease.Journal of molecular biology · 2025Review
- 3DVizSNP: a tool for rapidly visualizing missense mutations identified in high throughput experiments in iCn3D.BMC bioinformatics · 2023Article
- Dynamic coupling of residues within proteins as a mechanistic foundation of many enigmatic pathogenic missense variants.PLoS computational biology · 2022Article
- Proteogenomic Analysis of Protein Sequence Alterations in Breast Cancer Cells.Scientific reports · 2019Article
- Protein interaction disruption in cancer.BMC cancer · 2019Article
- A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations.Scientific reports · 2019Article
- Identification and analysis of mutational hotspots in oncogenes and tumour suppressors.Oncotarget · 2017Article
- PinSnps: structural and functional analysis of SNPs in the context of protein interaction networks.Bioinformatics (Oxford, England) · 2016Article
- Balancing Protein Stability and Activity in Cancer: A New Approach for Identifying Driver Mutations Affecting CBL Ubiquitin Ligase Activation.Cancer research · 2016Article
- Structure-Based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces.PloS one · 2016Article
- Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine.Current opinion in structural biology · 2015Review
- A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces.PLoS computational biology · 2015Article
- The Contribution of Missense Mutations in Core and Rim Residues of Protein-Protein Interfaces to Human Disease.Journal of molecular biology · 2015Article
- Therapeutic opportunities within the DNA damage response.Nature reviews. Cancer · 2015Review
- SNP2Structure: A Public and Versatile Resource for Mapping and Three-Dimensional Modeling of Missense SNPs on Human Protein Structures.Computational and structural biotechnology journal · 2015Article
- Functional consequences of somatic mutations in cancer using protein pocket-based prioritization approach.Genome medicine · 2014Article
- MSEA: detection and quantification of mutation hotspots through mutation set enrichment analysis.Genome biology · 2014Article
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 2 institutions in 1 country.
Funding
Abstract
With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/.
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.