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Technological and computational advances in genomics and interactomics have made it possible to identify how disease mutations perturb protein-protein interaction (PPI) networks within human cells. Here, we show that disease-associated germline variants are significantly enriched in sequences encoding PPI interfaces compared to variants identified in healthy participants from the projects 1000Genomes and ExAC. Somatic missense mutations are also significantly enriched in PPI interfaces compared to noninterfaces in 10,861 tumor exomes. We computationally identified 470 putative oncoPPIs in a pan-cancer analysis and demonstrate that oncoPPIs are highly correlated with patient survival and drug resistance/sensitivity. We experimentally validate the network effects of 13oncoPPIs using a systematic binary interaction assay, and also demonstrate the functional consequences of two of these on tumor cell growth. In summary, this human interactome network framework provides a powerful tool for prioritization of alleles with PPI-perturbing mutations to inform pathobiological mechanism- and genotype-based therapeutic discovery.
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Interpretation of the clinical pathogenic effects of variants is crucial for the advancement of precision medicine. However, our ability to understand the functional and biological consequences of genetic variants identified by human genome sequencing projects is limited. Many computational approaches can identify only a small proportion of pathogenic variants with the high confidence required in clinical settings. Human genome sequencing studies have reported potential mutation-disease associations with the functional regions altered by somatic mutations, such as molecular drivers in cancers1,2. However, many important issues in the field remain unclear, including the phenotypic consequences of different mutations within the same gene and the same mutation across different cell types.
Recent efforts using systematic analyses of 1,000-3,000 missense mutations in Mendelian disorders3,4 and ~2,000 de novo missense mutations in developmental disorders5 demonstrate that disease-associated alleles commonly alter distinct PPIs rather than grossly affecting the folding and stability of proteins3,4. Networkbased approaches provide new insights into disease-disease6 and drug-disease7-9 relationships within the human interactome. Nevertheless, the functional consequences of disease mutations on the comprehensive human interactome and their implications for therapeutic development remain understudied. Several studies have suggested that protein structure-based mutation enrichment analysis offers a potential tool for identification of possible cancer driver genes10, such as hotspot mutation regions in three-dimensional (3D) protein structures11-14. Development of new computational and experimental approaches for the study...