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Stivala A, Lomi A. Testing biological network motif significance with exponential random graph models. APPLIED NETWORK SCIENCE 2021; 6:91. [PMID: 34841042 PMCID: PMC8608783 DOI: 10.1007/s41109-021-00434-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 11/08/2021] [Indexed: 06/13/2023]
Abstract
UNLABELLED Analysis of the structure of biological networks often uses statistical tests to establish the over-representation of motifs, which are thought to be important building blocks of such networks, related to their biological functions. However, there is disagreement as to the statistical significance of these motifs, and there are potential problems with standard methods for estimating this significance. Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs. ERGMs were first introduced into the bioinformatics literature over 10 years ago but have had limited application to biological networks, possibly due to the practical difficulty of estimating model parameters. Advances in estimation algorithms now afford analysis of much larger networks in practical time. We illustrate the application of ERGM to both an undirected protein-protein interaction (PPI) network and directed gene regulatory networks. ERGM models indicate over-representation of triangles in the PPI network, and confirm results from previous research as to over-representation of transitive triangles (feed-forward loop) in an E. coli and a yeast regulatory network. We also confirm, using ERGMs, previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s41109-021-00434-y.
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Affiliation(s)
- Alex Stivala
- Institute of Computational Science, Università della Svizzera italiana, Via Giuseppe Buffi 13, 6900 Lugano, Switzerland
| | - Alessandro Lomi
- Institute of Computational Science, Università della Svizzera italiana, Via Giuseppe Buffi 13, 6900 Lugano, Switzerland
- The University of Exeter Business School, Rennes Drive, Exeter, EX4 4PU UK
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Abbas-Aghababazadeh F, Mo Q, Fridley BL. Statistical genomics in rare cancer. Semin Cancer Biol 2019; 61:1-10. [PMID: 31437624 DOI: 10.1016/j.semcancer.2019.08.021] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 08/14/2019] [Accepted: 08/17/2019] [Indexed: 12/26/2022]
Abstract
Rare cancers make of more than 20% of cancer cases. Due to the rare nature, less research has been conducted on rare cancers resulting in worse outcomes for patients with rare cancers compared to common cancers. The ability to study rare cancers is impaired by the ability to collect a large enough set of patients to complete an adequately powered genomic study. In this manuscript we outline analytical approaches and public genomic datasets that have been used in genomic studies of rare cancers. These statistical analysis approaches and study designs include: gene set / pathway analyses, pedigree and consortium studies, meta-analysis or horizontal integration, and integration of multiple types of genomic information or vertical integration. We also discuss some of the publicly available resources that can be leveraged in rare cancer genomic studies.
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Affiliation(s)
| | - Qianxing Mo
- Department of Biostatistics & Bioinformatics, Moffitt Cancer Center, Tampa, FL, 33612, USA.
| | - Brooke L Fridley
- Department of Biostatistics & Bioinformatics, Moffitt Cancer Center, Tampa, FL, 33612, USA.
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Xiao Q, Luo P, Li M, Wang J, Wu FX. A Novel Core-Attachment-Based Method to Identify Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks. Proteomics 2019; 19:e1800129. [PMID: 30650262 DOI: 10.1002/pmic.201800129] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 12/09/2018] [Indexed: 11/06/2022]
Abstract
Cellular functions are always performed by protein complexes. At present, many approaches have been proposed to identify protein complexes from protein-protein interaction (PPI) networks. Some approaches focus on detecting local dense subgraphs in PPI networks which are regarded as protein-complex cores, then identify protein complexes by including local neighbors. However, from gene expression profiles at different time points or tissues it is known that proteins are dynamic. Therefore, identifying dynamic protein complexes should become very important and meaningful. In this study, a novel core-attachment-based method named CO-DPC to detect dynamic protein complexes is presented. First, CO-DPC selects active proteins according to gene expression profiles and the 3-sigma principle, and constructs dynamic PPI networks based on the co-expression principle and PPI networks. Second, CO-DPC detects local dense subgraphs as the cores of protein complexes and then attach close neighbors of these cores to form protein complexes. In order to evaluate the method, the method and the existing algorithms are applied to yeast PPI networks. The experimental results show that CO-DPC performs much better than the existing methods. In addition, the identified dynamic protein complexes can match very well and thus become more meaningful for future biological study.
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Affiliation(s)
- Qianghua Xiao
- School of Mathematics and Physics, University of South China, Hengyang, 421001, P. R. China
| | - Ping Luo
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada
| | - Min Li
- School of Information Science and Engineering, Central South University, Changsha, 410083, P. R. China
| | - Jianxin Wang
- School of Information Science and Engineering, Central South University, Changsha, 410083, P. R. China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada
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Azad AKM, Lawen A, Keith JM. Bayesian model of signal rewiring reveals mechanisms of gene dysregulation in acquired drug resistance in breast cancer. PLoS One 2017; 12:e0173331. [PMID: 28288164 PMCID: PMC5348014 DOI: 10.1371/journal.pone.0173331] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 02/20/2017] [Indexed: 11/24/2022] Open
Abstract
Small molecule inhibitors, such as lapatinib, are effective against breast cancer in clinical trials, but tumor cells ultimately acquire resistance to the drug. Maintaining sensitization to drug action is essential for durable growth inhibition. Recently, adaptive reprogramming of signaling circuitry has been identified as a major cause of acquired resistance. We developed a computational framework using a Bayesian statistical approach to model signal rewiring in acquired resistance. We used the p1-model to infer potential aberrant gene-pairs with differential posterior probabilities of appearing in resistant-vs-parental networks. Results were obtained using matched gene expression profiles under resistant and parental conditions. Using two lapatinib-treated ErbB2-positive breast cancer cell-lines: SKBR3 and BT474, our method identified similar dysregulated signaling pathways including EGFR-related pathways as well as other receptor-related pathways, many of which were reported previously as compensatory pathways of EGFR-inhibition via signaling cross-talk. A manual literature survey provided strong evidence that aberrant signaling activities in dysregulated pathways are closely related to acquired resistance in EGFR tyrosine kinase inhibitors. Our approach predicted literature-supported dysregulated pathways complementary to both node-centric (SPIA, DAVID, and GATHER) and edge-centric (ESEA and PAGI) methods. Moreover, by proposing a novel pattern of aberrant signaling called V-structures, we observed that genes were dysregulated in resistant-vs-sensitive conditions when they were involved in the switch of dependencies from targeted to bypass signaling events. A literature survey of some important V-structures suggested they play a role in breast cancer metastasis and/or acquired resistance to EGFR-TKIs, where the mRNA changes of TGFBR2, LEF1 and TP53 in resistant-vs-sensitive conditions were related to the dependency switch from targeted to bypass signaling links. Our results suggest many signaling pathway structures are compromised in acquired resistance, and V-structures of aberrant signaling within/among those pathways may provide further insights into the bypass mechanism of targeted inhibition.
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Affiliation(s)
- A. K. M. Azad
- School of Mathematical Sciences, Monash University, Clayton, VIC, Australia
- * E-mail:
| | - Alfons Lawen
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, VIC, Australia
| | - Jonathan M. Keith
- School of Mathematical Sciences, Monash University, Clayton, VIC, Australia
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Azad AKM, Lawen A, Keith JM. Prediction of signaling cross-talks contributing to acquired drug resistance in breast cancer cells by Bayesian statistical modeling. BMC SYSTEMS BIOLOGY 2015; 9:2. [PMID: 25599599 PMCID: PMC4307189 DOI: 10.1186/s12918-014-0135-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2014] [Accepted: 12/11/2014] [Indexed: 01/21/2023]
Abstract
BACKGROUND Initial success of inhibitors targeting oncogenes is often followed by tumor relapse due to acquired resistance. In addition to mutations in targeted oncogenes, signaling cross-talks among pathways play a vital role in such drug inefficacy. These include activation of compensatory pathways and altered activities of key effectors in other cell survival and growth-associated pathways. RESULTS We propose a computational framework using Bayesian modeling to systematically characterize potential cross-talks among breast cancer signaling pathways. We employed a fully Bayesian approach known as the p 1-model to infer posterior probabilities of gene-pairs in networks derived from the gene expression datasets of ErbB2-positive breast cancer cell-lines (parental, lapatinib-sensitive cell-line SKBR3 and the lapatinib-resistant cell-line SKBR3-R, derived from SKBR3). Using this computational framework, we searched for cross-talks between EGFR/ErbB and other signaling pathways from Reactome, KEGG and WikiPathway databases that contribute to lapatinib resistance. We identified 104, 188 and 299 gene-pairs as putative drug-resistant cross-talks, respectively, each comprised of a gene in the EGFR/ErbB signaling pathway and a gene from another signaling pathway, that appear to be interacting in resistant cells but not in parental cells. In 168 of these (distinct) gene-pairs, both of the interacting partners are up-regulated in resistant conditions relative to parental conditions. These gene-pairs are prime candidates for novel cross-talks contributing to lapatinib resistance. They associate EGFR/ErbB signaling with six other signaling pathways: Notch, Wnt, GPCR, hedgehog, insulin receptor/IGF1R and TGF- β receptor signaling. We conducted a literature survey to validate these cross-talks, and found evidence supporting a role for many of them in contributing to drug resistance. We also analyzed an independent study of lapatinib resistance in the BT474 breast cancer cell-line and found the same signaling pathways making cross-talks with the EGFR/ErbB signaling pathway as in the primary dataset. CONCLUSIONS Our results indicate that the activation of compensatory pathways can potentially cause up-regulation of EGFR/ErbB pathway genes (counteracting the inhibiting effect of lapatinib) via signaling cross-talk. Thus, the up-regulated members of these compensatory pathways along with the members of the EGFR/ErbB signaling pathway are interesting as potential targets for designing novel anti-cancer therapeutics.
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Affiliation(s)
- A K M Azad
- School of Mathematical Science, Monash University, Wellington Road, Clayton, VIC, Australia.
| | - Alfons Lawen
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Wellington Road, Clayton, VIC, Australia.
| | - Jonathan M Keith
- School of Mathematical Science, Monash University, Wellington Road, Clayton, VIC, Australia.
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Skreti G, Bei ES, Kalantzaki K, Zervakis M. Temporal and Spatial Patterns of Gene Profiles during Chondrogenic Differentiation. IEEE J Biomed Health Inform 2014; 18:799-809. [DOI: 10.1109/jbhi.2014.2305770] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Kalantzaki K, Bei ES, Exarchos KP, Zervakis M, Garofalakis M, Fotiadis DI. Nonparametric network design and analysis of disease genes in oral cancer progression. IEEE J Biomed Health Inform 2014; 18:562-73. [PMID: 24608056 DOI: 10.1109/jbhi.2013.2274643] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Biological networks in living organisms can be seen as the ultimate means of understanding the underlying mechanisms in complex diseases, such as oral cancer. During the last decade, many algorithms based on high-throughput genomic data have been developed to unravel the complexity of gene network construction and their progression in time. However, the small size of samples compared to the number of observed genes makes the inference of the network structure quite challenging. In this study, we propose a framework for constructing and analyzing gene networks from sparse experimental temporal data and investigate its potential in oral cancer. We use two network models based on partial correlations and kernel density estimation, in order to capture the genetic interactions. Using this network construction framework on real clinical data of the tissue and blood at different time stages, we identified common disease-related structures that may decipher the association between disease state and biological processes in oral cancer. Our study emphasizes an altered MET (hepatocyte growth factor receptor) network during oral cancer progression. In addition, we demonstrate that the functional changes of gene interactions during oral cancer progression might be particularly useful for patient categorization at the time of diagnosis and/or at follow-up periods.
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Xiao Q, Wang J, Peng X, Wu FX. Detecting protein complexes from active protein interaction networks constructed with dynamic gene expression profiles. Proteome Sci 2013; 11:S20. [PMID: 24565281 PMCID: PMC3908890 DOI: 10.1186/1477-5956-11-s1-s20] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Background Protein interaction networks (PINs) are known to be useful to detect protein complexes. However, most available PINs are static, which cannot reflect the dynamic changes in real networks. At present, some researchers have tried to construct dynamic networks by incorporating time-course (dynamic) gene expression data with PINs. However, the inevitable background noise exists in the gene expression array, which could degrade the quality of dynamic networkds. Therefore, it is needed to filter out contaminated gene expression data before further data integration and analysis. Results Firstly, we adopt a dynamic model-based method to filter noisy data from dynamic expression profiles. Then a new method is proposed for identifying active proteins from dynamic gene expression profiles. An active protein at a time point is defined as the protein the expression level of whose corresponding gene at that time point is higher than a threshold determined by a standard variance involved threshold function. Furthermore, a noise-filtered active protein interaction network (NF-APIN) is constructed. To demonstrate the efficiency of our method, we detect protein complexes from the NF-APIN, compared with those from other dynamic PINs. Conclusion A dynamic model based method can effectively filter out noises in dynamic gene expression data. Our method to compute a threshold for determining the active time points of noise-filtered genes can make the dynamic construction more accuracy and provide a high quality framework for network analysis, such as protein complex prediction.
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