Systematic Interpretation of Comutated Genes in Large-Scale Cancer Mutation Profiles

Gu, Yunyan; Yang, Da; Zou, Jinfeng; Ma, Wencai; Wu, Ruihong; Zhao, Wenyuan; Zhang, Yuannv; Xiao, Hui; Gong, Xue; Zhang, Min; Zhu, Jing; Guo, Zheng

doi:10.1158/1535-7163.mct-10-0022

Cited by 18 publications

(16 citation statements)

References 48 publications

(62 reference statements)

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“…proposed a stratified FDR control approach which combined with the traditional statistical test for identifying co-mutated gene pairs in cancer genome ( 14 ). Furthermore, by considering biological pathways, some studies focused on co-occurring pairs between/within pathways ( 15 , 16 ).…”

Section: Introductionmentioning

confidence: 99%

Cooperative genomic alteration network reveals molecular classification across 12 major cancer types

Zhang¹,

Deng²,

Zhang³

et al. 2016

Nucleic Acids Res

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The accumulation of somatic genomic alterations that enables cells to gradually acquire growth advantage contributes to tumor development. This has the important implication of the widespread existence of cooperative genomic alterations in the accumulation process. Here, we proposed a computational method HCOC that simultaneously consider genetic context and downstream functional effects on cancer hallmarks to uncover somatic cooperative events in human cancers. Applying our method to 12 TCGA cancer types, we totally identified 1199 cooperative events with high heterogeneity across human cancers, and then constructed a pan-cancer cooperative alteration network. These cooperative events are associated with genomic alterations of some high-confident cancer drivers, and can trigger the dysfunction of hallmark associated pathways in a co-defect way rather than single alterations. We found that these cooperative events can be used to produce a prognostic classification that can provide complementary information with tissue-of-origin. In a further case study of glioblastoma, using 23 cooperative events identified, we stratified patients into molecularly relevant subtypes with a prognostic significance independent of the Glioma-CpG Island Methylator Phenotype (GCIMP). In summary, our method can be effectively used to discover cancer-driving cooperative events that can be valuable clinical markers for patient stratification.

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Section: Introductionmentioning

confidence: 99%

Cooperative genomic alteration network reveals molecular classification across 12 major cancer types

Zhang¹,

Deng²,

Zhang³

et al. 2016

Nucleic Acids Res

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“…Based on these premises, the emergence of combinatorial properties among patterns of genomic events has been investigated in a number of recent studies, through the application of novel statistical measures quantifying, for example, the ‘mutual exclusivity’ or the ‘co-occurrence’ of different genomic lesions (Ciriello et al, 2012; Cui, 2010; Gu et al , 2010; Miller et al , 2011; Vandin et al , 2012; Yeang et al , 2008). Among these studies, those aimed at identifying groups of genes whose mutation patterns tend to ME are based on the same principle and are conceptually similar (Ciriello et al , 2012; Miller et al , 2011; Vandin et al , 2012), although they differ in two crucial methodological aspects:

The way sets of genes to be tested for ME are selected.

The way ME of a gene set is assessed and its statistical significance is quantified.

In (Ciriello et al , 2012), for example, the authors designed MEMo, a computational framework in which gene sets to be tested for ME are derived from cliques (i.e.…”

Section: Introductionmentioning

confidence: 99%

Fast randomization of large genomic datasets while preserving alteration counts

et al. 2014

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Motivation: Studying combinatorial patterns in cancer genomic datasets has recently emerged as a tool for identifying novel cancer driver networks. Approaches have been devised to quantify, for example, the tendency of a set of genes to be mutated in a ‘mutually exclusive’ manner. The significance of the proposed metrics is usually evaluated by computing P-values under appropriate null models. To this end, a Monte Carlo method (the switching-algorithm) is used to sample simulated datasets under a null model that preserves patient- and gene-wise mutation rates. In this method, a genomic dataset is represented as a bipartite network, to which Markov chain updates (switching-steps) are applied. These steps modify the network topology, and a minimal number of them must be executed to draw simulated datasets independently under the null model. This number has previously been deducted empirically to be a linear function of the total number of variants, making this process computationally expensive.Results: We present a novel approximate lower bound for the number of switching-steps, derived analytically. Additionally, we have developed the R package BiRewire, including new efficient implementations of the switching-algorithm. We illustrate the performances of BiRewire by applying it to large real cancer genomics datasets. We report vast reductions in time requirement, with respect to existing implementations/bounds and equivalent P-value computations. Thus, we propose BiRewire to study statistical properties in genomic datasets, and other data that can be modeled as bipartite networks.Availability and implementation: BiRewire is available on BioConductor at http://www.bioconductor.org/packages/2.13/bioc/html/BiRewire.htmlContact: iorio@ebi.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

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“…GOLGB1, the top-ranked gene both with ¼ 3 and 4, has been reported as a gene that is strongly correlated to the in°ammatory carcinoma of breast. 9,17 It also interacts with gene BRMS 1, a breast cancer metastasis suppressor, and gene ACBD3 that plays an important role in asymmetric cell division and breast cancer. 18 The second and third top-ranked gene markers from our MRT-test ( ¼ 3) are CCNT 2 and CBY 1, both of which were also reported to have tissue expression in breast tumors.…”

Section: Biomarker Discovery For Binary-class Datamentioning

confidence: 99%

Multi-Resolution-Test for Consistent Phenotype Discrimination and Biomarker Discovery in Translational Bioinformatics

Han

et al. 2013

J. Bioinform. Comput. Biol.

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While high-throughput technologies are expected to play a critical role in clinical translational research for complex disease diagnosis, the ability to accurately and consistently discriminate disease phenotypes by determining the gene and protein expression patterns as signatures of different clinical conditions remains a challenge in translational bioinformatics. In this study, we propose a novel feature selection algorithm: Multi-Resolution-Test (MRT-test) that can produce significantly accurate and consistent phenotype discrimination across a series of omics data. Our algorithm can capture those features contributing to subtle data behaviors instead of selecting the features contributing to global data behaviors, which seems to be essential in achieving clinical level diagnosis for different expression data. Furthermore, as an effective biomarker discovery algorithm, it can achieve linear separation for high-dimensional omics data with few biomarkers. We apply our MRT-test to complex disease phenotype diagnosis by combining it with state-of-the-art classifiers and attain exceptional diagnostic results, which suggests that our method's advantage in molecular diagnostics. Experimental evaluation showed that MRT-test based diagnosis is able to generate consistent and robust clinical-level phenotype separation for various diseases. In addition, based on the seed biomarkers detected by the MRT-test, we design a novel network marker synthesis (NMS) algorithm to decipher the underlying molecular mechanisms of tumorigenesis from a systems viewpoint. Unlike existing top-down gene network building approaches, our network marker synthesis method has a less dependence on the global network and enables it to capture the gene regulators for different subnetwork markers, which will provide biologically meaningful insights for understanding the genetic basis of complex diseases.

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Systematic Interpretation of Comutated Genes in Large-Scale Cancer Mutation Profiles

Cited by 18 publications

References 48 publications

Cooperative genomic alteration network reveals molecular classification across 12 major cancer types

Cooperative genomic alteration network reveals molecular classification across 12 major cancer types

Fast randomization of large genomic datasets while preserving alteration counts

Multi-Resolution-Test for Consistent Phenotype Discrimination and Biomarker Discovery in Translational Bioinformatics

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