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Protein-protein interaction reveals synergistic discrimination of cancer phenotype.

ABSTRACT: Cancer is a disease associated with the deregulation of multiple gene networks. Microarray data has permitted researchers to identify gene panel markers for diagnosis or prognosis of cancer but these are not sufficient to make specific mechanistic assertions about phenotype switches. We propose a strategy to identify putative mechanisms of cancer phenotypes by protein-protein interactions (PPI). We first extracted the logic status of a PPI via the relative expression of the corresponding gene pair. The joint association of a gene pair on a cancer phenotype was calculated by entropy minimization and assessed using a support vector machine. A typical predictor is "If Src high-expression, and Cav-1 low-expression, then cancer." We achieved 90% accuracy on test data with a majority of predictions associated with the MAPK pathway, focal adhesion, apoptosis and cell cycle. Our results can aid in the development of phenotype discrimination biomarkers and identification of putative therapeutic interference targets for drug development.

SUBMITTER: Xiong J

PROVIDER: S-EPMC2865773 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Protein-protein interaction reveals synergistic discrimination of cancer phenotype.

Xiong Jianghui J Liu Juan J Rayner Simon S Li Yinghui Y Chen Shanguang S

Cancer informatics 20100326

Cancer is a disease associated with the deregulation of multiple gene networks. Microarray data has permitted researchers to identify gene panel markers for diagnosis or prognosis of cancer but these are not sufficient to make specific mechanistic assertions about phenotype switches. We propose a strategy to identify putative mechanisms of cancer phenotypes by protein-protein interactions (PPI). We first extracted the logic status of a PPI via the relative expression of the corresponding gene pa ...[more]

PMID: 20458363

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Project description:Recently it has been shown that cancer mutations selectively target protein-protein interactions. We hypothesized that mutations affecting distinct protein interactions involving established cancer genes could contribute to tumor heterogeneity, and that novel mechanistic insights might be gained into tumorigenesis by investigating protein interactions under positive selection in cancer. To identify protein interactions under positive selection in cancer, we mapped over 1.2 million nonsynonymous somatic cancer mutations onto 4,896 experimentally determined protein structures and analyzed their spatial distribution. In total, 20% of mutations on the surface of known cancer genes perturbed protein-protein interactions (PPIs), and this enrichment for PPI interfaces was observed for both tumor suppressors (Odds Ratio 1.28, P-value < 10(-4)) and oncogenes (Odds Ratio 1.17, P-value < 10(-3)). To study this further, we constructed a bipartite network representing structurally resolved PPIs from all available human complexes in the Protein Data Bank (2,864 proteins, 3,072 PPIs). Analysis of frequently mutated cancer genes within this network revealed that tumor-suppressors, but not oncogenes, are significantly enriched with functional mutations in homo-oligomerization regions (Odds Ratio 3.68, P-Value < 10(-8)). We present two important examples, TP53 and beta-2-microglobulin, for which the patterns of somatic mutations at interfaces provide insights into specifically perturbed biological circuits. In patients with TP53 mutations, patient survival correlated with the specific interactions that were perturbed. Moreover, we investigated mutations at the interface of protein-nucleotide interactions and observed an unexpected number of missense mutations but not silent mutations occurring within DNA and RNA binding sites. Finally, we provide a resource of 3,072 PPI interfaces ranked according to their mutation rates. Analysis of this list highlights 282 novel candidate cancer genes that encode proteins participating in interactions that are perturbed recurrently across tumors. In summary, mutation of specific protein interactions is an important contributor to tumor heterogeneity and may have important implications for clinical outcomes.

Project description:Amyotrophic lateral sclerosis (ALS) is underpinned by an oligogenic rare variant architecture. Identified genetic variants of ALS include RNA-binding proteins containing prion-like domains (PrLDs). We hypothesized that screening genes encoding additional similar proteins will yield novel genetic causes of ALS. The most common genetic variant of ALS patients is a G4C2-repeat expansion within C9ORF72. We have shown that G4C2-repeat RNA sequesters RNA-binding proteins. A logical consequence of this is that loss-of-function mutations in G4C2-binding partners might contribute to ALS pathogenesis independently of and/or synergistically with C9ORF72 expansions. Targeted sequencing of genomic DNA encoding either RNA-binding proteins or known ALS genes (n = 274 genes) was performed in ALS patients to identify rare deleterious genetic variants and explore genotype-phenotype relationships. Genomic DNA was extracted from 103 ALS patients including 42 familial ALS patients and 61 young-onset (average age of onset 41 years) sporadic ALS patients; patients were chosen to maximize the probability of identifying genetic causes of ALS. Thirteen patients carried a G4C2-repeat expansion of C9ORF72. We identified 42 patients with rare deleterious variants; 6 patients carried more than one variant. Twelve mutations were discovered in known ALS genes which served as a validation of our strategy. Rare deleterious variants in RNA-binding proteins were significantly enriched in ALS patients compared to control frequencies (p = 5.31E-18). Nineteen patients featured at least one variant in a RNA-binding protein containing a PrLD. The number of variants per patient correlated with rate of disease progression (t-test, p = 0.033). We identified eighteen patients with a single variant in a G4C2-repeat binding protein. Patients with a G4C2-binding protein variant in combination with a C9ORF72 expansion had a significantly faster disease course (t-test, p = 0.025). Our data are consistent with an oligogenic model of ALS. We provide evidence for a number of entirely novel genetic variants of ALS caused by mutations in RNA-binding proteins. Moreover we show that these mutations act synergistically with each other and with C9ORF72 expansions to modify the clinical phenotype of ALS. A key finding is that this synergy is present only between functionally interacting variants. This work has significant implications for ALS therapy development.

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Protein-protein interaction reveals synergistic discrimination of cancer phenotype.

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Protein-protein interaction reveals synergistic discrimination of cancer phenotype.

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