A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces

Porta-Pardo, Eduard; Garcia‐Alonso, Luz; Hrabe, Thomas; Dopazo, Joaquín; Godzik, Adam

doi:10.1371/journal.pcbi.1004518

Cited by 134 publications

(124 citation statements)

References 80 publications

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“…Benign SNPs are significantly underrepresented in interfaces (odds ratio of 0.5). Taken together, these results add to the growing evidence for the importance of PPIs in disease (Wang et al, 2012, Kamburov et al, 2015, Porta-Pardo et al, 2015) and demonstrate that the PrePPI-predicted interfaces are accurate enough to identify disease-related mutations.…”

Section: Resultssupporting

confidence: 66%

A computational interactome and functional annotation for the human proteome

Garzón

Deng

Murray

et al. 2016

eLife

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We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein’s function. We provide annotations for most human proteins, including many annotated as having unknown function.DOI: http://dx.doi.org/10.7554/eLife.18715.001

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Section: Resultssupporting

confidence: 66%

A computational interactome and functional annotation for the human proteome

Garzón

Deng

Murray

et al. 2016

eLife

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“…We analyzed several different protein interaction databases to identify physical or functional interactions either between these proteins themselves or between them and proteins known to modulate the tumor immune infiltrate (13). A tight interaction cluster was formed by 33 proteins (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Also, the functionality of domainXplorer and similar analysis can be extended by including functional regions that are defined in three dimensions, such as protein interaction interfaces. Just as in the case of cancer-driver genes (13), analyses focusing on three-dimensional regions will likely reveal more features that influence the immune infiltrate in cancer. Finally, these 122 associations between protein domains and the amount of immune infiltration, as well as the algorithm used to discover them, show that domainXplorer could become a valuable resource to improve our understanding of the complex relationships between cancer and immune cells.…”

Section: Discussionmentioning

confidence: 99%

“…The reason is that genes are not monolithic entities; they consist of different regions, coding for specific protein domains that are usually responsible for different functions. Accounting for this fact in the analysis of cancer-driving mutations and drug sensitivity biomarkers led to discovery of many “domain drivers” or “domain biomarkers” (13, 14). …”

Section: Introductionmentioning

confidence: 99%

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Mutation Drivers of Immunological Responses to Cancer

Porta-Pardo

Godzik

2016

Cancer Immunology Research

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In cancer immunology, somatic missense mutations have been mostly studied regarding their role in the generation of neoantigens. However, growing evidence suggests that mutations in certain genes, such as CASP8 or TP53, influence the immune response against a tumor by other mechanisms. Identifying these genes and mechanisms is important because, just as the identification of cancer driver genes led to the development of personalized cancer therapies, a comprehensive catalog of such cancer immunity drivers will aid in the development of therapies aimed at restoring antitumor immunity. Here we present an algorithm, domainXplorer, that can be used to identify potential cancer immunity drivers. To demonstrate its potential, we used it to analyze a dataset of 5,164 tumor samples from TCGA and to identify protein domains whose mutation status correlates with the presence of immune cells in cancer tissue (immune infiltrate). We identified 122 such protein regions including several that belong to proteins with known roles in immune response, such as C2, CD163L1, or FCγR2A. In several cases we show that mutations within the same protein can be associated with more or less immune cell infiltration, depending on the specific domain mutated. These results expand the catalog of potential cancer immunity drivers and highlight the importance of taking into account the structural context of somatic mutations when analyzing their potential association with immune phenotypes.

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“…In a recent paper [24], patient level information such as mutation profiles is incorporated to identify protein-protein interaction (PPI) interfaces enriched in somatic mutations. It will be interesting to explore how to incorporate patient heterogeneity information into our approach.…”

Section: Resultsmentioning

confidence: 99%

Exploring the importance of cancer pathways by meta-analysis of differential protein expression networks in three different cancers

Sikdar

Datta

2016

Biol Direct

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BackgroundIt is believed that all cancers occur due to the mutation or change in one or more genes. In order to investigate the significance of the biological pathways which are interrupted by these genetic mutations, we pursue an integrated analysis using multiple cancer datasets released by the International Cancer Genome Consortium (ICGC). This dataset consists of expression profiles for genes/proteins of patients receiving treatment, for three types of cancer - Head and Neck Squamous Cell Carcinoma (HNSC), Lung Adenocarcinoma (LUAD) and Kidney Renal Clear Cell Carcinoma (KIRC). We consider pathway analysis to identify all the biological pathways which are active among the patients and investigate the roles of the significant pathways using a differential network analysis of the protein expression datasets for the three cancers separately. We then integrate the pathway based results of all the three cancers which provide a more comprehensive picture of the three cancers.ResultsFrom our analysis of the protein expression data, overall, RAS and PI3K signaling pathways appear to play the most significant roles in the three cancers - Head and Neck Squamous Cell Carcinoma (HNSC), Lung Adenocarcinoma (LUAD) and Kidney Renal Clear Cell Carcinoma (KIRC).ConclusionThis analysis suggests that the RAS and PI3K signaling pathways are the two most important pathways in all the three cancers and should be investigated further for their potential roles in cancers.ReviewersThis article was reviewed by Joaquin Dopazo and Samiran Ghosh.

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A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces

Cited by 134 publications

References 80 publications

A computational interactome and functional annotation for the human proteome

A computational interactome and functional annotation for the human proteome

Mutation Drivers of Immunological Responses to Cancer

Exploring the importance of cancer pathways by meta-analysis of differential protein expression networks in three different cancers

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