Incorporating molecular and functional context into the analysis and prioritization of human variants associated with cancer

Peterson, Thomas; Nehrt, Nathan L.; Park, DoHwan; Kann, Maricel G.

doi:10.1136/amiajnl-2011-000655

Cited by 24 publications

(33 citation statements)

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“…We demonstrated that significant DS-Scores indicate that a mutation at a specific position is highly likely to be a contributor to disease in any protein containing the domain in which the mutations are located. In particular, we have shown that Mendelian disease mutations form clusters at protein domain sites [18]. In addition, results from Yue et al [19], Nehrt et al [20], and Peterson et al [18] have further shown that inherited and somatic cancer mutations cluster at specific sites at the protein domain level.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, we have developed a statistical approach, the domain significance score (or DS-Score), for finding significantly mutated positions for individual protein domains [18]. We demonstrated that significant DS-Scores indicate that a mutation at a specific position is highly likely to be a contributor to disease in any protein containing the domain in which the mutations are located.…”

Section: Introductionmentioning

confidence: 99%

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A protein domain-centric approach for the comparative analysis of human and yeast phenotypically relevant mutations

2013

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BackgroundThe body of disease mutations with known phenotypic relevance continues to increase and is expected to do so even faster with the advent of new experimental techniques such as whole-genome sequencing coupled with disease association studies. However, genomic association studies are limited by the molecular complexity of the phenotype being studied and the population size needed to have adequate statistical power. One way to circumvent this problem, which is critical for the study of rare diseases, is to study the molecular patterns emerging from functional studies of existing disease mutations. Current gene-centric analyses to study mutations in coding regions are limited by their inability to account for the functional modularity of the protein. Previous studies of the functional patterns of known human disease mutations have shown a significant tendency to cluster at protein domain positions, namely position-based domain hotspots of disease mutations. However, the limited number of known disease mutations remains the main factor hindering the advancement of mutation studies at a functional level. In this paper, we address this problem by incorporating mutations known to be disruptive of phenotypes in other species. Focusing on two evolutionarily distant organisms, human and yeast, we describe the first inter-species analysis of mutations of phenotypic relevance at the protein domain level.ResultsThe results of this analysis reveal that phenotypic mutations from yeast cluster at specific positions on protein domains, a characteristic previously revealed to be displayed by human disease mutations. We found over one hundred domain hotspots in yeast with approximately 50% in the exact same domain position as known human disease mutations.ConclusionsWe describe an analysis using protein domains as a framework for transferring functional information by studying domain hotspots in human and yeast and relating phenotypic changes in yeast to diseases in human. This first-of-a-kind study of phenotypically relevant yeast mutations in relation to human disease mutations demonstrates the utility of a multi-species analysis for advancing the understanding of the relationship between genetic mutations and phenotypic changes at the organismal level.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A protein domain-centric approach for the comparative analysis of human and yeast phenotypically relevant mutations

2013

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“…For example, some of the most frequent oncogenic mutations in human cancer affect analogous residues of the activation segment of the kinase domain and cause constitutive activation of several oncogenes, including FLT3 D835 mutations in acute myeloid leukemia, KIT D816 mutations in gastrointestinal stromal tumors, and BRAF V600 mutations in melanoma (Dibb et al, 2004; Greenman et al, 2007). Proteome-wide bioinformatics analysis of mutations in domains have been performed to identify domains enriched for alterations (Nehrt et al, 2012; Peterson et al, 2012; Yang et al, 2015) as well as to detect significantly mutated domain hotspots using multiple sequence analysis (Peterson et al, 2010; Yue et al, 2010). We here extend upon these analyses by performing a systematic pan-cancer analysis of recurrence of mutations in protein domains (hotspots and enrichment of mutations across the domain body) and identify dozes of unreported cancer-associated mutations that are not detection using standard gene-based approaches.…”

Section: Introductionmentioning

confidence: 99%

Pan-Cancer Analysis of Mutation Hotspots in Protein Domains

et al. 2015

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SUMMARY 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.

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“…Proteome-wide analyses have been performed to identify domains enriched in missense mutations [45,47] [50] and to identify domain-centric positions of hotspot missense mutations [48,49] [50]. These studies focused exclusively on missense mutation and as yet, little attempt was to use these data to distinguish between activating and loss of function mutations in the majority of cases.…”

Section: Domain-based Approaches At Identifying Mutational Hotspotsmentioning

confidence: 99%

Mutational patterns in oncogenes and tumour suppressors

Baeissa

Benstead-Hume

Richardson

et al. 2016

Biochemical Society Transactions

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All cancers depend upon mutations in critical genes, which confer a selective advantage to the tumour cell. Knowledge of these mutations is crucial to understanding the biology of cancer initiation and progression, and to the development of targeted therapeutic strategies. The key to understanding the contribution of a disease-associated mutation to the development and progression of cancer, comes from an understanding of the consequences of that mutation on the function of the affected protein, and the impact on the pathways in which that protein is involved.In this paper we examine the mutation patterns observed in oncogenes and tumour suppressors, and discuss different approaches that have been developed to identify driver mutations within cancers that contribute to the disease progress. We also discuss the MOKCa database where we have developed an automatic pipeline that structurally and functionally annotates all proteins from the human proteome that are mutated in cancer.

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Incorporating molecular and functional context into the analysis and prioritization of human variants associated with cancer

Cited by 24 publications

References 55 publications

A protein domain-centric approach for the comparative analysis of human and yeast phenotypically relevant mutations

A protein domain-centric approach for the comparative analysis of human and yeast phenotypically relevant mutations

Pan-Cancer Analysis of Mutation Hotspots in Protein Domains

Mutational patterns in oncogenes and tumour suppressors

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