ArticlePLoS computational biology2017
Oncodomains: A protein domain-centric framework for analyzing rare variants in tumor samples.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Integrating evidence from protein domains to identify cancer driver mutations.Protein science : a publication of the Protein Society · 2026Article
- Article
- Singleton mutations in large-scale cancer genome studies: uncovering the tail of cancer genome.NAR cancer · 2024Article
- Most protein domains exist as variants with distinct functions across cells, tissues and diseases.NAR genomics and bioinformatics · 2023Article
- Protein domains provide a new layer of information for classifying human variations in rare diseases.Frontiers in bioinformatics · 2023Article
- Recurrent high-impact mutations at cognate structural positions in class A G protein-coupled receptors expressed in tumors.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- A domain damage index to prioritizing the pathogenicity of missense variants.Human mutation · 2021Article
- Structural and functional analysis of somatic coding and UTR indels in breast and lung cancer genomes.Scientific reports · 2021Article
- dSPRINT: predicting DNA, RNA, ion, peptide and small molecule interaction sites within protein domains.Nucleic acids research · 2021Article
- PertInInt: An Integrative, Analytical Approach to Rapidly Uncover Cancer Driver Genes with Perturbed Interactions and Functionalities.Cell systems · 2020Article
- A linear-time algorithm that avoids inverses and computes Jackknife (leave-one-out) products like convolutions or other operators in commutative semigroups.Algorithms for molecular biology : AMB · 2020Article
- 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
- Finding driver mutations in cancer: Elucidating the role of background mutational processes.PLoS computational biology · 2019Article
- A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations.Scientific reports · 2019Article
- PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data.Genome medicine · 2018Article
- A new twist in the coil: functions of the coiled-coil domain of structural maintenance of chromosome (SMC) proteins.Current genetics · 2018Review
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5 authors.
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Abstract
The fight against cancer is hindered by its highly heterogeneous nature. Genome-wide sequencing studies have shown that individual malignancies contain many mutations that range from those commonly found in tumor genomes to rare somatic variants present only in a small fraction of lesions. Such rare somatic variants dominate the landscape of genomic mutations in cancer, yet efforts to correlate somatic mutations found in one or few individuals with functional roles have been largely unsuccessful. Traditional methods for identifying somatic variants that drive cancer are 'gene-centric' in that they consider only somatic variants within a particular gene and make no comparison to other similar genes in the same family that may play a similar role in cancer. In this work, we present oncodomain hotspots, a new 'domain-centric' method for identifying clusters of somatic mutations across entire gene families using protein domain models. Our analysis confirms that our approach creates a framework for leveraging structural and functional information encapsulated by protein domains into the analysis of somatic variants in cancer, enabling the assessment of even rare somatic variants by comparison to similar genes. Our results reveal a vast landscape of somatic variants that act at the level of domain families altering pathways known to be involved with cancer such as protein phosphorylation, signaling, gene regulation, and cell metabolism. Due to oncodomain hotspots' unique ability to assess rare variants, we expect our method to become an important tool for the analysis of sequenced tumor genomes, complementing existing methods.
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