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Zhang T, Zhang SW, Xie MY, Li Y. Identifying cooperating cancer driver genes in individual patients through hypergraph random walk. J Biomed Inform 2024; 157:104710. [PMID: 39159864 DOI: 10.1016/j.jbi.2024.104710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 07/30/2024] [Accepted: 08/14/2024] [Indexed: 08/21/2024]
Abstract
OBJECTIVE Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies. METHODS Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients. RESULTS The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments. CONCLUSION We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.
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Affiliation(s)
- Tong Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China; School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan 467000, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Ming-Yu Xie
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yan Li
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
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2
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Agrawal P, Jain N, Gopalan V, Timon A, Singh A, Rajagopal PS, Hannenhalli S. Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in triple negative breast cancer. iScience 2024; 27:109752. [PMID: 38699227 PMCID: PMC11063905 DOI: 10.1016/j.isci.2024.109752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 03/18/2024] [Accepted: 04/12/2024] [Indexed: 05/05/2024] Open
Abstract
Breast cancers (BRCA) exhibit substantial transcriptional heterogeneity, posing a significant clinical challenge. The global transcriptional changes in a disease context, however, are likely mediated by few key genes which reflect disease etiology better than the differentially expressed genes (DEGs). We apply our network-based tool PathExt to 1,059 BRCA tumors across 4 subtypes to identify key mediator genes in each subtype. Compared to conventional differential expression analysis, PathExt-identified genes exhibit greater concordance across tumors, revealing shared and subtype-specific biological processes; better recapitulate BRCA-associated genes in multiple benchmarks, and are more essential in BRCA subtype-specific cell lines. Single-cell transcriptomic analysis reveals a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target key genes potentially mediating drug resistance.
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Affiliation(s)
- Piyush Agrawal
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
| | | | - Vishaka Gopalan
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Annan Timon
- University of Pennsylvania, Philadelphia, PA, USA
| | - Arashdeep Singh
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Padma S. Rajagopal
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
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3
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Gillman R, Field MA, Schmitz U, Karamatic R, Hebbard L. Identifying cancer driver genes in individual tumours. Comput Struct Biotechnol J 2023; 21:5028-5038. [PMID: 37867967 PMCID: PMC10589724 DOI: 10.1016/j.csbj.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/24/2023] Open
Abstract
Cancer is a heterogeneous disease with a strong genetic component making it suitable for precision medicine approaches aimed at identifying the underlying molecular drivers within a tumour. Large scale population-level cancer sequencing consortia have identified many actionable mutations common across both cancer types and sub-types, resulting in an increasing number of successful precision medicine programs. Nonetheless, such approaches fail to consider the effects of mutations unique to an individual patient and may miss rare driver mutations, necessitating personalised approaches to driver-gene prioritisation. One approach is to quantify the functional importance of individual mutations in a single tumour based on how they affect the expression of genes in a gene interaction network (GIN). These GIN-based approaches can be broadly divided into those that utilise an existing reference GIN and those that construct de novo patient-specific GINs. These single-tumour approaches have several limitations that likely influence their results, such as use of reference cohort data, network choice, and approaches to mathematical approximation, and more research is required to evaluate the in vitro and in vivo applicability of their predictions. This review examines the current state of the art methods that identify driver genes in single tumours with a focus on GIN-based driver prioritisation.
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Affiliation(s)
- Rhys Gillman
- Department of Biomedical Sciences and Molecular and Cell Biology, College of Public Health, Medical, and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns, Queensland, Australia
| | - Matt A. Field
- Department of Biomedical Sciences and Molecular and Cell Biology, College of Public Health, Medical, and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns, Queensland, Australia
- Immunogenomics Lab, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
- Menzies School of Health Research, Charles Darwin University, Darwin, Northern Territory, Australia
| | - Ulf Schmitz
- Department of Biomedical Sciences and Molecular and Cell Biology, College of Public Health, Medical, and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns, Queensland, Australia
| | - Rozemary Karamatic
- Gastroenterology and Hepatology, Townsville University Hospital, PO Box 670, Townsville, Queensland 4810, Australia
- College of Medicine and Dentistry, Division of Tropical Health and Medicine, James Cook University, Townsville, Queensland, Australia
| | - Lionel Hebbard
- Department of Biomedical Sciences and Molecular and Cell Biology, College of Public Health, Medical, and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns, Queensland, Australia
- Storr Liver Centre, Westmead Institute for Medical Research, Westmead Hospital and University of Sydney, Sydney, New South Wales, Australia
- Australian Institute for Tropical Health and Medicine, Townsville, Queensland, Australia
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4
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Agrawal P, Jain N, Gopalan V, Timon A, Singh A, Rajagopal PS, Hannenhalli S. Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in Triple Negative Breast Cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.21.541618. [PMID: 37425784 PMCID: PMC10327220 DOI: 10.1101/2023.05.21.541618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Breast cancers exhibit substantial transcriptional heterogeneity, posing a significant challenge to the prediction of treatment response and prognostication of outcomes. Especially, translation of TNBC subtypes to the clinic remains a work in progress, in part because of a lack of clear transcriptional signatures distinguishing the subtypes. Our recent network-based approach, PathExt, demonstrates that global transcriptional changes in a disease context are likely mediated by a small number of key genes, and these mediators may better reflect functional or translationally relevant heterogeneity. We apply PathExt to 1059 BRCA tumors and 112 healthy control samples across 4 subtypes to identify frequent, key-mediator genes in each BRCA subtype. Compared to conventional differential expression analysis, PathExt-identified genes (1) exhibit greater concordance across tumors, revealing shared as well as BRCA subtype-specific biological processes, (2) better recapitulate BRCA-associated genes in multiple benchmarks, and (3) exhibit greater dependency scores in BRCA subtype-specific cancer cell lines. Single cell transcriptomes of BRCA subtype tumors reveal a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified TNBC subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target top novel genes potentially mediating drug resistance. Overall, PathExt applied to breast cancer refines previous views of gene expression heterogeneity and identifies potential mediators of TNBC subtypes, including potential therapeutic targets.
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Affiliation(s)
- Piyush Agrawal
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
| | | | - Vishaka Gopalan
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Annan Timon
- University of Pennsylvania, Philadelphia, PA, USA
| | - Arashdeep Singh
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Padma S Rajagopal
- Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA
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5
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Zhao N, Quicksall Z, Asmann YW, Ren Y. Network approaches for omics studies of neurodegenerative diseases. Front Genet 2022; 13:984338. [PMID: 36186441 PMCID: PMC9523597 DOI: 10.3389/fgene.2022.984338] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
The recent methodological advances in multi-omics approaches, including genomic, transcriptomic, metabolomic, lipidomic, and proteomic, have revolutionized the research field by generating “big data” which greatly enhanced our understanding of the molecular complexity of the brain and disease states. Network approaches have been routinely applied to single-omics data to provide critical insight into disease biology. Furthermore, multi-omics integration has emerged as both a vital need and a new direction to connect the different layers of information underlying disease mechanisms. In this review article, we summarize popular network analytic approaches for single-omics data and multi-omics integration and discuss how these approaches have been utilized in studying neurodegenerative diseases.
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Affiliation(s)
- Na Zhao
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, United States
| | - Zachary Quicksall
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States
| | - Yan W. Asmann
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States
| | - Yingxue Ren
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States
- *Correspondence: Yingxue Ren,
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Liu C, Dai Y, Yu K, Zhang ZK. Enhancing Cancer Driver Gene Prediction by Protein-Protein Interaction Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2231-2240. [PMID: 33656997 DOI: 10.1109/tcbb.2021.3063532] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
With the advances in gene sequencing technologies, millions of somatic mutations have been reported in the past decades, but mining cancer driver genes with oncogenic mutations from these data remains a critical and challenging area of research. In this study, we proposed a network-based classification method for identifying cancer driver genes with merging the multi-biological information. In this method, we construct a cancer specific genetic network from the human protein-protein interactome (PPI) to mine the network structure attributes, and combine biological information such as mutation frequency and differential expression of genes to achieve accurate prediction of cancer driver genes. Across seven different cancer types, the proposed algorithm always achieves high prediction accuracy, which is superior to the existing advanced methods. In the analysis of the predicted results, about 40 percent of the top 10 candidate genes overlap with the Cancer Gene Census database. Interestingly, the feature comparison indicates that the network based features are still more important than the biological features, including the mutation frequency and genetic differential expression. Further analyses also show that the integration of network structure attributes and biological information is valuable for predicting new cancer driver genes.
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7
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Prokop A. Towards the First Principles in Biology and Cancer: New Vistas in Computational Systems Biology of Cancer. Life (Basel) 2021; 12:21. [PMID: 35054414 PMCID: PMC8778485 DOI: 10.3390/life12010021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 12/06/2021] [Accepted: 12/15/2021] [Indexed: 01/02/2023] Open
Abstract
These days many leading scientists argue for a new paradigm for cancer research and propose a complex systems-view of cancer supported by empirical evidence. As an example, Thea Newman (2021) has applied "the lessons learned from physical systems to a critique of reductionism in medical research, with an emphasis on cancer". It is the understanding of this author that the mesoscale constructs that combine the bottom-up as well as top-down approaches, are very close to the concept of emergence. The mesoscale constructs can be said to be those effective components through which the system allows itself to be understood. A short list of basic concepts related to life/biology fundamentals are first introduced to demonstrate a lack of emphasis on these matters in literature. It is imperative that physical and chemical approaches are introduced and incorporated in biology to make it more conceptually sound, quantitative, and based on the first principles. Non-equilibrium thermodynamics is the only tool currently available for making progress in this direction. A brief outline of systems biology, the discovery of emergent properties, and metabolic modeling are introduced in the second part. Then, different cancer initiation concepts are reviewed, followed by application of non-equilibrium thermodynamics in the metabolic and genomic analysis of initiation and development of cancer, stressing the endogenous network hypothesis (ENH). Finally, extension of the ENH is suggested to include a cancer niche (exogenous network hypothesis). It is expected that this will lead to a unifying systems-biology approach for a future combination of the analytical and synthetic arms of two major hypotheses of cancer models (SMT and TOFT).
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Affiliation(s)
- Aleš Prokop
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN 37235-1826, USA
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8
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Ha MJ, Stingo FC, Baladandayuthapani V. Bayesian Structure Learning in Multi-layered Genomic Networks. J Am Stat Assoc 2021; 116:605-618. [PMID: 34239216 DOI: 10.1080/01621459.2020.1775611] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers, dictated by multiple platforms, and exhibit both undirected and directed relationships. We propose a multi-layered Gaussian graphical model (mlGGM) to investigate conditional independence structures in such multi-level genomic networks in human cancers. We implement a Bayesian node-wise selection (BANS) approach based on variable selection techniques that coherently accounts for the multiple types of dependencies in mlGGM; this flexible strategy exploits edge-specific prior knowledge and selects sparse and interpretable models. Through simulated data generated under various scenarios, we demonstrate that BANS outperforms other existing multivariate regression-based methodologies. Our integrative genomic network analysis for key signaling pathways across multiple cancer types highlights commonalities and differences of p53 integrative networks and epigenetic effects of BRCA2 on p53 and its interaction with T68 phosphorylated CHK2, that may have translational utilities of finding biomarkers and therapeutic targets.
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Affiliation(s)
- Min Jin Ha
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
| | - Francesco Claudio Stingo
- Department of Statistics, Computer Science, Applications "G. Parenti", The University of Florence
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9
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Zhang T, Zhang SW, Li Y. Identifying Driver Genes for Individual Patients through Inductive Matrix Completion. Bioinformatics 2021; 37:4477-4484. [PMID: 34175939 DOI: 10.1093/bioinformatics/btab477] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 04/30/2021] [Accepted: 06/25/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The driver genes play a key role in the evolutionary process of cancer. Effectively identifying these driver genes is crucial to cancer diagnosis and treatment. However, due to the high heterogeneity of cancers, it remains challenging to identify the driver genes for individual patients. Although some computational methods have been proposed to tackle this problem, they seldom consider the fact that the genes functionally similar to the well-established driver genes may likely play similar roles in cancer process, which potentially promotes the driver gene identification. Thus, here we developed a novel approach of IMCDriver to promote the driver gene identification both for cohorts and individual patients. RESULTS IMCDriver first considers the well-established driver genes as prior information, and adopts the using multi-omics data (e.g., somatic mutation, gene expression and protein-protein interaction) to compute the similarity between patients/genes. Then, IMCDriver prioritizes the personalized mutated genes according to their functional similarity to the well-established driver genes via Inductive Matrix Completion. Finally, IMCDriver identifies the highly rank-ordered genes as the personalized driver genes. The results on five cancer datasets from TCGA show that our IMCDriver outperforms other existing state-of-the-art methods both in the cohort and patient-specific driver gene identification. IMCDriver also reveals some novel driver genes that potentially drive cancer development. In addition, even for the driver genes rarely mutated among a population, IMCDriver can still identify them and prioritize them with high priorities. AVAILABILITY Code available at https://github.com/NWPU-903PR/IMCDriver. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tong Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, China Xi'an.,School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, China Xi'an
| | - Yan Li
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, China Xi'an
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10
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Cesareni G, Sacco F, Perfetto L. Assembling Disease Networks From Causal Interaction Resources. Front Genet 2021; 12:694468. [PMID: 34178043 PMCID: PMC8226215 DOI: 10.3389/fgene.2021.694468] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 05/19/2021] [Indexed: 12/27/2022] Open
Abstract
The development of high-throughput high-content technologies and the increased ease in their application in clinical settings has raised the expectation of an important impact of these technologies on diagnosis and personalized therapy. Patient genomic and expression profiles yield lists of genes that are mutated or whose expression is modulated in specific disease conditions. The challenge remains of extracting from these lists functional information that may help to shed light on the mechanisms that are perturbed in the disease, thus setting a rational framework that may help clinical decisions. Network approaches are playing an increasing role in the organization and interpretation of patients' data. Biological networks are generated by connecting genes or gene products according to experimental evidence that demonstrates their interactions. Till recently most approaches have relied on networks based on physical interactions between proteins. Such networks miss an important piece of information as they lack details on the functional consequences of the interactions. Over the past few years, a number of resources have started collecting causal information of the type protein A activates/inactivates protein B, in a structured format. This information may be represented as signed directed graphs where physiological and pathological signaling can be conveniently inspected. In this review we will (i) present and compare these resources and discuss the different scope in comparison with pathway resources; (ii) compare resources that explicitly capture causality in terms of data content and proteome coverage (iii) review how causal-graphs can be used to extract disease-specific Boolean networks.
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Affiliation(s)
- Gianni Cesareni
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Francesca Sacco
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Livia Perfetto
- Department of Biology, Fondazione Human Technopole, Milan, Italy
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11
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Feng L, Sun YD, Li C, Li YX, Chen LN, Zeng R. Pan-cancer Network Disorders Revealed by Overall and Local Signaling Entropy. J Mol Cell Biol 2021; 13:622-635. [PMID: 34097054 PMCID: PMC8648393 DOI: 10.1093/jmcb/mjab031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 01/27/2021] [Accepted: 02/05/2021] [Indexed: 11/15/2022] Open
Abstract
Tumor development is a process involving loss of the differentiation phenotype and acquisition of stem-like characteristics, which is driven by intracellular rewiring of signaling network. The measurement of network reprogramming and disorder would be challenging due to the complexity and heterogeneity of tumors. Here, we proposed signaling entropy (SR) to assess the degree of tumor network disorder. We calculated SR for 33 tumor types in The Cancer Genome Atlas database based on transcriptomic and proteomic data. The SR of tumors was significantly higher than that of normal samples and was highly correlated with cell stemness, cancer type, tumor grade, and metastasis. We further demonstrated the sensitivity and accuracy of using local SR in prognosis prediction and drug response evaluation. Overall, SR could reveal cancer network disorders related to tumor malignant potency, clinical prognosis, and drug response.
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Affiliation(s)
- Li Feng
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Yi-Di Sun
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chen Li
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yi-Xue Li
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Luo-Nan Chen
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,University of Chinese Academy of Sciences, Shanghai 200031, China.,CAS Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - Rong Zeng
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,University of Chinese Academy of Sciences, Shanghai 200031, China.,CAS Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
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12
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Integrating Patient-Specific Information into Logic Models of Complex Diseases: Application to Acute Myeloid Leukemia. J Pers Med 2021; 11:jpm11020117. [PMID: 33578936 PMCID: PMC7916657 DOI: 10.3390/jpm11020117] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/05/2021] [Accepted: 02/05/2021] [Indexed: 12/12/2022] Open
Abstract
High throughput technologies such as deep sequencing and proteomics are increasingly becoming mainstream in clinical practice and support diagnosis and patient stratification. Developing computational models that recapitulate cell physiology and its perturbations in disease is a required step to help with the interpretation of results of high content experiments and to devise personalized treatments. As complete cell-models are difficult to achieve, given limited experimental information and insurmountable computational problems, approximate approaches should be considered. We present here a general approach to modeling complex diseases by embedding patient-specific genomics data into actionable logic models that take into account prior knowledge. We apply the strategy to acute myeloid leukemia (AML) and assemble a network of logical relationships linking most of the genes that are found frequently mutated in AML patients. We derive Boolean models from this network and we show that by priming the model with genomic data we can infer relevant patient-specific clinical features. Here we propose that the integration of literature-derived causal networks with patient-specific data should be explored to help bedside decisions.
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13
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Comandante-Lou N, Fallahi-Sichani M. Models of Cancer Drug Discovery and Response to Therapy. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11356-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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14
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Fang J, Pian C, Xu M, Kong L, Li Z, Ji J, Zhang L, Chen Y. Revealing Prognosis-Related Pathways at the Individual Level by a Comprehensive Analysis of Different Cancer Transcription Data. Genes (Basel) 2020; 11:genes11111281. [PMID: 33138076 PMCID: PMC7692404 DOI: 10.3390/genes11111281] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 10/26/2020] [Accepted: 10/26/2020] [Indexed: 02/07/2023] Open
Abstract
Identifying perturbed pathways at an individual level is important to discover the causes of cancer and develop individualized custom therapeutic strategies. Though prognostic gene lists have had success in prognosis prediction, using single genes that are related to the relevant system or specific network cannot fully reveal the process of tumorigenesis. We hypothesize that in individual samples, the disruption of transcription homeostasis can influence the occurrence, development, and metastasis of tumors and has implications for patient survival outcomes. Here, we introduced the individual-level pathway score, which can measure the correlation perturbation of the pathways in a single sample well. We applied this method to the expression data of 16 different cancer types from The Cancer Genome Atlas (TCGA) database. Our results indicate that different cancer types as well as their tumor-adjacent tissues can be clearly distinguished by the individual-level pathway score. Additionally, we found that there was strong heterogeneity among different cancer types and the percentage of perturbed pathways as well as the perturbation proportions of tumor samples in each pathway were significantly different. Finally, the prognosis-related pathways of different cancer types were obtained by survival analysis. We demonstrated that the individual-level pathway score (iPS) is capable of classifying cancer types and identifying some key prognosis-related pathways.
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Affiliation(s)
- Jingya Fang
- College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.F.); (M.X.); (L.K.); (Z.L.); (J.J.)
| | - Cong Pian
- Department of Mathematics, College of Science, Nanjing Agricultural University, Nanjing 210095, China;
| | - Mingmin Xu
- College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.F.); (M.X.); (L.K.); (Z.L.); (J.J.)
| | - Lingpeng Kong
- College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.F.); (M.X.); (L.K.); (Z.L.); (J.J.)
| | - Zutan Li
- College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.F.); (M.X.); (L.K.); (Z.L.); (J.J.)
| | - Jinwen Ji
- College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.F.); (M.X.); (L.K.); (Z.L.); (J.J.)
| | - Liangyun Zhang
- College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China; (J.F.); (M.X.); (L.K.); (Z.L.); (J.J.)
- Correspondence: (L.Z.); (Y.C.)
| | - Yuanyuan Chen
- Department of Mathematics, College of Science, Nanjing Agricultural University, Nanjing 210095, China;
- Correspondence: (L.Z.); (Y.C.)
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15
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Kim KJ, Moon SJ, Park KS, Tagkopoulos I. Network-based modeling of drug effects on disease module in systemic sclerosis. Sci Rep 2020; 10:13393. [PMID: 32770109 PMCID: PMC7414841 DOI: 10.1038/s41598-020-70280-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 07/10/2020] [Indexed: 01/13/2023] Open
Abstract
The network-based proximity between drug targets and disease genes can provide novel insights regarding the repercussions, interplay, and repositioning of drugs in the context of disease. Current understanding and treatment for reversing of the fibrotic process is limited in systemic sclerosis (SSc). We have developed a network-based analysis for drug effects that takes into account the human interactome network, proximity measures between drug targets and disease-associated genes, genome-wide gene expression and disease modules that emerge through pertinent analysis. Currently used and potential drugs showed a wide variation in proximity to SSc-associated genes and distinctive proximity to the SSc-relevant pathways, depending on their class and targets. Tyrosine kinase inhibitors (TyKIs) approach disease gene through multiple pathways, including both inflammatory and fibrosing processes. The SSc disease module includes the emerging molecular targets and is in better accord with the current knowledge of the pathophysiology of the disease. In the disease-module network, the greatest perturbing activity was shown by nintedanib, followed by imatinib, dasatinib, and acetylcysteine. Suppression of the SSc-relevant pathways and alleviation of the skin fibrosis was remarkable in the inflammatory subsets of the SSc patients receiving TyKI therapy. Our results show that network-based drug-disease proximity offers a novel perspective into a drug’s therapeutic effect in the SSc disease module. This could be applied to drug combinations or drug repositioning, and be helpful guiding clinical trial design and subgroup analysis.
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Affiliation(s)
- Ki-Jo Kim
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. .,St. Vincent's Hospital, 93 Jungbu-daero, Paldal-gu, Suwon, Gyeonggi-do, 16247, Republic of Korea.
| | - Su-Jin Moon
- Division of Rheumatology, Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Kyung-Su Park
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Ilias Tagkopoulos
- Department of Computer Science, University of California, Davis, CA, USA. .,Genome Center, University of California, Davis, CA, USA. .,AI Institute for Next-Generation Food Systems, AIFS, Davis, CA, USA.
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16
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Sun L, Jiang L, Grant CN, Wang HG, Gragnoli C, Liu Z, Wu R. Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk. Cancers (Basel) 2020; 12:cancers12082086. [PMID: 32731407 PMCID: PMC7465094 DOI: 10.3390/cancers12082086] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 07/24/2020] [Accepted: 07/25/2020] [Indexed: 01/03/2023] Open
Abstract
Neuroblastoma is a common cancer in children, affected by a number of genes that interact with each other through intricate but coordinated networks. Traditional approaches can only reconstruct a single regulatory network that is topologically not informative enough to explain the complexity of neuroblastoma risk. We implemented and modified an advanced model for recovering informative, omnidirectional, dynamic, and personalized networks (idopNetworks) from static gene expression data for neuroblastoma risk. We analyzed 3439 immune genes of neuroblastoma for 217 high-risk patients and 30 low-risk patients by which to reconstruct large patient-specific idopNetworks. By converting these networks into risk-specific representations, we found that the shift in patients from a low to high risk or from a high to low risk might be due to the reciprocal change of hub regulators. By altering the directions of regulation exerted by these hubs, it may be possible to reduce a high risk to a low risk. Results from a holistic, systems-oriented paradigm through idopNetworks can potentially enable oncologists to experimentally identify the biomarkers of neuroblastoma and other cancers.
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Affiliation(s)
- Lidan Sun
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China; (L.S.); (L.J.)
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA;
| | - Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China; (L.S.); (L.J.)
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Christa N. Grant
- Division of Pediatric Surgery, Department of Surgery, Penn State College of Medicine, Hershey, PA 17033, USA;
| | - Hong-Gang Wang
- Division of Pediatric Hematology and Oncology, Department of Pediatrics, Penn State College of Medicine, Hershey, PA 17022, USA;
| | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA;
- Division of Endocrinology, Diabetes, and Metabolic Disease, Translational Medicine, Department of Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19144, USA
- Molecular Biology Laboratory, Bios Biotech Multi Diagnostic Health Center, 00197 Rome, Italy
| | - Zhenqiu Liu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA;
- Division of Pediatric Hematology and Oncology, Department of Pediatrics, Penn State College of Medicine, Hershey, PA 17022, USA;
- Correspondence: (Z.L.); (R.W.); Tel.: +1-717-531-0003 (Z.L.); +1-717-531-2037 (R.W.)
| | - Rongling Wu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA;
- Correspondence: (Z.L.); (R.W.); Tel.: +1-717-531-0003 (Z.L.); +1-717-531-2037 (R.W.)
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17
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Bhuva DD, Cursons J, Smyth GK, Davis MJ. Differential co-expression-based detection of conditional relationships in transcriptional data: comparative analysis and application to breast cancer. Genome Biol 2019; 20:236. [PMID: 31727119 PMCID: PMC6857226 DOI: 10.1186/s13059-019-1851-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 10/02/2019] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Elucidation of regulatory networks, including identification of regulatory mechanisms specific to a given biological context, is a key aim in systems biology. This has motivated the move from co-expression to differential co-expression analysis and numerous methods have been developed subsequently to address this task; however, evaluation of methods and interpretation of the resulting networks has been hindered by the lack of known context-specific regulatory interactions. RESULTS In this study, we develop a simulator based on dynamical systems modelling capable of simulating differential co-expression patterns. With the simulator and an evaluation framework, we benchmark and characterise the performance of inference methods. Defining three different levels of "true" networks for each simulation, we show that accurate inference of causation is difficult for all methods, compared to inference of associations. We show that a z-score-based method has the best general performance. Further, analysis of simulation parameters reveals five network and simulation properties that explained the performance of methods. The evaluation framework and inference methods used in this study are available in the dcanr R/Bioconductor package. CONCLUSIONS Our analysis of networks inferred from simulated data show that hub nodes are more likely to be differentially regulated targets than transcription factors. Based on this observation, we propose an interpretation of the inferred differential network that can reconstruct a putative causal network.
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Affiliation(s)
- Dharmesh D Bhuva
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Joseph Cursons
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Gordon K Smyth
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Melissa J Davis
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia. .,Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, 3010, Australia. .,Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, 3010, Australia.
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18
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Vasan N, Baselga J, Hyman DM. A view on drug resistance in cancer. Nature 2019; 575:299-309. [PMID: 31723286 DOI: 10.1038/s41586-019-1730-1] [Citation(s) in RCA: 1302] [Impact Index Per Article: 260.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 09/23/2019] [Indexed: 12/11/2022]
Abstract
The problem of resistance to therapy in cancer is multifaceted. Here we take a reductionist approach to define and separate the key determinants of drug resistance, which include tumour burden and growth kinetics; tumour heterogeneity; physical barriers; the immune system and the microenvironment; undruggable cancer drivers; and the many consequences of applying therapeutic pressures. We propose four general solutions to drug resistance that are based on earlier detection of tumours permitting cancer interception; adaptive monitoring during therapy; the addition of novel drugs and improved pharmacological principles that result in deeper responses; and the identification of cancer cell dependencies by high-throughput synthetic lethality screens, integration of clinico-genomic data and computational modelling. These different approaches could eventually be synthesized for each tumour at any decision point and used to inform the choice of therapy.
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Affiliation(s)
- Neil Vasan
- Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Weill Cornell Medical College, New York, NY, USA
| | - José Baselga
- Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Weill Cornell Medical College, New York, NY, USA.,AstraZeneca, Gaithersburg, MD, USA
| | - David M Hyman
- Memorial Sloan Kettering Cancer Center, New York, NY, USA. .,Weill Cornell Medical College, New York, NY, USA.
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19
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Wang Y, Liu Q, Huang S, Yuan B. Learning a Structural and Functional Representation for Gene Expressions: To Systematically Dissect Complex Cancer Phenotypes. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1729-1742. [PMID: 28489545 DOI: 10.1109/tcbb.2017.2702161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Cancer is a heterogeneous disease, thus one of the central problems is how to dissect the resulting complex phenotypes in terms of their biological building blocks. Computationally, this is to represent and interpret high dimensional observations through a structural and conceptual abstraction into the most influential determinants underlying the problem. The working hypothesis of this report is to consider gene interaction to be largely responsible for the manifestation of complex cancer phenotypes, thus where the representation is to be conceptualized. Here, we report a representation learning strategy combined with regularizations, in which gene expressions are described in terms of a regularized product of meta-genes and their expression levels. The meta-genes are constrained by gene interactions thus representing their original topological contexts. The expression levels are supervised by their conditional dependencies among the observations thus providing a cluster-specific constraint. We obtain both of these structural constraints using a node-based graphical model. Our representation allows the selection of more influential modules, thus implicating their possible roles in neoplastic transformations. We validate our representation strategy by its robust recognitions of various cancer phenotypes comparing with various classical methods. The modules discovered are either shared or specify for different types or stages of human cancers, all of which are consistent with literature and biology.
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20
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Seshachalam VP, Sekar K, Hui KM. Insights into the etiology-associated gene regulatory networks in hepatocellular carcinoma from The Cancer Genome Atlas. J Gastroenterol Hepatol 2018; 33:2037-2047. [PMID: 29672926 DOI: 10.1111/jgh.14262] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 04/04/2018] [Accepted: 04/10/2018] [Indexed: 12/30/2022]
Abstract
BACKGROUND AND AIM Hepatitis B virus (HBV), hepatitis C virus, alcohol consumption, and non-alcoholic fatty liver disease are the major known risk factors for hepatocellular carcinoma (HCC). There have been very few studies comparing the underlying biological mechanisms associated with the different etiologies of HCC. In this study, we hypothesized the existence of different regulatory networks associated with different liver disease etiologies involved in hepatocarcinogenesis. METHODS Using upstream regulatory analysis tool in ingenuity pathway analysis software, upstream regulators (URs) were predicted using differential expressed genes for HCC to facilitate the interrogation of global gene regulation. RESULTS Analysis of regulatory networks for HBV HCC revealed E2F1 as activated UR, regulating genes involved in cell cycle and DNA replication, and HNF4A and HNF1A as inhibited UR. In hepatitis C virus HCC, interferon-γ, involved in cellular movement and signaling, was activated, while IL1RN, mitogen-activated protein kinase 1 involved in interleukin 22 signaling and immune response, was inhibited. In alcohol consumption HCC, ERBB2 involved in inflammatory response and cellular movement was activated, whereas HNF4A and NUPR1 were inhibited. For HCC derived from non-alcoholic fatty liver disease, miR-1249-5p was activated, and NUPR1 involved in cell cycle and apoptosis was inhibited. The prognostic value of representative genes identified in the regulatory networks for HBV HCC can be further validated by an independent HBV HCC dataset established in our laboratory with survival data. CONCLUSIONS Our study identified functionally distinct candidate URs for HCC developed from different etiologic risk factors. Further functional validation studies of these regulatory networks could facilitate the management of HCC towards personalized medicine.
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Affiliation(s)
| | - Karthik Sekar
- Laboratory of Cancer Genomics, Division of Cellular and Molecular Research, National Cancer Center Singapore, Singapore
| | - Kam M Hui
- Laboratory of Cancer Genomics, Division of Cellular and Molecular Research, National Cancer Center Singapore, Singapore.,Institute of Molecular and Cell Biology, A*STAR, Singapore.,Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore.,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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21
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Typing tumors using pathways selected by somatic evolution. Nat Commun 2018; 9:4159. [PMID: 30297789 PMCID: PMC6175900 DOI: 10.1038/s41467-018-06464-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 09/03/2018] [Indexed: 01/01/2023] Open
Abstract
Many recent efforts to analyze cancer genomes involve aggregation of mutations within reference maps of molecular pathways and protein networks. Here, we find these pathway studies are impeded by molecular interactions that are functionally irrelevant to cancer or the patient’s tumor type, as these interactions diminish the contrast of driver pathways relative to individual frequently mutated genes. This problem can be addressed by creating stringent tumor-specific networks of biophysical protein interactions, identified by signatures of epistatic selection during tumor evolution. Using such an evolutionarily selected pathway (ESP) map, we analyze the major cancer genome atlases to derive a hierarchical classification of tumor subtypes linked to characteristic mutated pathways. These pathways are clinically prognostic and predictive, including the TP53-AXIN-ARHGEF17 combination in liver and CYLC2-STK11-STK11IP in lung cancer, which we validate in independent cohorts. This ESP framework substantially improves the definition of cancer pathways and subtypes from tumor genome data. Informative pathways driving cancer pathogenesis and subtypes can be difficult to identify in the presence of many gene interactions irrelevant to cancer. Here, the authors describe an approach for cancer gene pathway analysis based on key molecular interactions that drive cancer in relevant tissue types, and they assemble a focused map of Evolutionarily Selected Pathways (ESP) with interactions supported by both protein–protein binding and genetic epistasis during somatic tumor evolution.
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22
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Engelmann BW, Hsiao CJ, Blischak JD, Fourne Y, Khan Z, Ford M, Gilad Y. A Methodological Assessment and Characterization of Genetically-Driven Variation in Three Human Phosphoproteomes. Sci Rep 2018; 8:12106. [PMID: 30108239 PMCID: PMC6092387 DOI: 10.1038/s41598-018-30587-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 07/17/2018] [Indexed: 11/12/2022] Open
Abstract
Phosphorylation of proteins on serine, threonine, and tyrosine residues is a ubiquitous post-translational modification that plays a key part of essentially every cell signaling process. It is reasonable to assume that inter-individual variation in protein phosphorylation may underlie phenotypic differences, as has been observed for practically any other molecular regulatory phenotype. However, we do not know much about the extent of inter-individual variation in phosphorylation because it is quite challenging to perform a quantitative high throughput study to assess inter-individual variation in any post-translational modification. To test our ability to address this challenge with SILAC-based mass spectrometry, we quantified phosphorylation levels for three genotyped human cell lines within a nested experimental framework, and found that genetic background is the primary determinant of phosphoproteome variation. We uncovered multiple functional, biophysical, and genetic associations with germline driven phosphopeptide variation. Variants affecting protein levels or structure were among these associations, with the latter presenting, on average, a stronger effect. Interestingly, we found evidence that is consistent with a phosphopeptide variability buffering effect endowed from properties enriched within longer proteins. Because the small sample size in this 'pilot' study may limit the applicability of our genetic observations, we also undertook a thorough technical assessment of our experimental workflow to aid further efforts. Taken together, these results provide the foundation for future work to characterize inter-individual variation in post-translational modification levels and reveal novel insights into the nature of inter-individual variation in phosphorylation.
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Affiliation(s)
- Brett W Engelmann
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA.
- AbbVie, North Chicago, Illinois, USA.
| | | | - John D Blischak
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
| | - Yannick Fourne
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
| | - Zia Khan
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
- Genentech, South San Francisco, California, USA
| | - Michael Ford
- MS Bioworks, LLC, 3950, Varsity Drive, Ann Arbor, Michigan, USA
| | - Yoav Gilad
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA.
- Department of Medicine, University of Chicago, Chicago, Illinois, USA.
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23
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Kikkawa A. Random Matrix Analysis for Gene Interaction Networks in Cancer Cells. Sci Rep 2018; 8:10607. [PMID: 30006574 PMCID: PMC6045654 DOI: 10.1038/s41598-018-28954-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 07/03/2018] [Indexed: 01/12/2023] Open
Abstract
Investigations of topological uniqueness of gene interaction networks in cancer cells are essential for understanding the disease. Although cancer is considered to originate from the topological alteration of a huge molecular interaction network in cellular systems, the theoretical study to investigate such complex networks is still insufficient. It is necessary to predict the behavior of a huge complex interaction network from the behavior of a finite size network. Based on the random matrix theory, we study the distribution of the nearest neighbor level spacings P(s) of interaction matrices of gene networks in human cancer cells. The interaction matrices are computed using the Cancer Network Galaxy (TCNG) database which is a repository of gene interactions inferred by a Bayesian network model. 256 NCBI GEO entries regarding gene expressions in human cancer cells have been used for the inference. We observe the Wigner distribution of P(s) when the gene networks are dense networks that have more than ~38,000 edges. In the opposite case, when the networks have smaller numbers of edges, the distribution P(s) becomes the Poisson distribution. We investigate relevance of P(s) both to the sparseness of the networks and to edge frequency factor which is the reliance (likelihood) of the inferred gene interactions.
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Affiliation(s)
- Ayumi Kikkawa
- Mathematical and Theoretical Physics Unit, Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Kunigami-gun, Okinawa, 904-0495, Japan.
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24
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Chen X, Li X, Zhao W, Li T, Ouyang Q. Parameter sensitivity analysis for a stochastic model of mitochondrial apoptosis pathway. PLoS One 2018; 13:e0198579. [PMID: 29912904 PMCID: PMC6005494 DOI: 10.1371/journal.pone.0198579] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 05/22/2018] [Indexed: 11/18/2022] Open
Abstract
Understanding how gene alterations induce oncogenesis plays an important role in cancer research and may be instructive for cancer prevention and treatment. We conducted a parameter sensitivity analysis to the mitochondrial apoptosis model. Both a nonlinear bifurcation analysis of the deterministic dynamics and energy barrier analysis of the corresponding stochastic models were performed. We found that the parameter sensitivity ranking according to the change of the bifurcation-point locations in deterministic models and the change of the barrier heights from a living to death state of the cell in stochastic models are highly correlated. For the model we considered, in combination with previous knowledge that the parameters significantly affecting the system’s bifurcation point are strongly associated with frequently mutated oncogenic genes, we conclude that the energy barrier height can be used as indicator of oncogenesis as well as bifurcation point. We provide a possible mechanism that may help elucidate the logic of cancer initiation from the view of stochastic dynamics and energy landscape. And we show the equivalence of energy barrier height and bifurcation-point location in determining the parameter sensitivity spectrum for the first time.
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Affiliation(s)
- Xianli Chen
- The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, Department of Physics, Peking University, Beijing, China
| | - Xiaoguang Li
- College of Mathematics and Compute Science, Hunan Normal University, Changsha, China
- Beijing Computational Science Research Center, Beijing, China
| | - Wei Zhao
- Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Tiejun Li
- LMAM and School of Mathematical Sciences, Peking University, Beijing, China
- * E-mail: (TL); (QO)
| | - Qi Ouyang
- The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, Department of Physics, Peking University, Beijing, China
- Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
- * E-mail: (TL); (QO)
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25
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Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ. Next-Generation Machine Learning for Biological Networks. Cell 2018; 173:1581-1592. [PMID: 29887378 DOI: 10.1016/j.cell.2018.05.015] [Citation(s) in RCA: 444] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 03/10/2018] [Accepted: 05/07/2018] [Indexed: 02/07/2023]
Abstract
Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.
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Affiliation(s)
- Diogo M Camacho
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Katherine M Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Brain & Cognitive Sciences and Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Rani K Powers
- Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - James C Costello
- Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
| | - James J Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Biological Engineering and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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26
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Iorio F, Garcia-Alonso L, Brammeld JS, Martincorena I, Wille DR, McDermott U, Saez-Rodriguez J. Pathway-based dissection of the genomic heterogeneity of cancer hallmarks' acquisition with SLAPenrich. Sci Rep 2018; 8:6713. [PMID: 29713020 PMCID: PMC5928049 DOI: 10.1038/s41598-018-25076-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 04/16/2018] [Indexed: 12/31/2022] Open
Abstract
Cancer hallmarks are evolutionary traits required by a tumour to develop. While extensively characterised, the way these traits are achieved through the accumulation of somatic mutations in key biological pathways is not fully understood. To shed light on this subject, we characterised the landscape of pathway alterations associated with somatic mutations observed in 4,415 patients across ten cancer types, using 374 orthogonal pathway gene-sets mapped onto canonical cancer hallmarks. Towards this end, we developed SLAPenrich: a computational method based on population-level statistics, freely available as an open source R package. Assembling the identified pathway alterations into sets of hallmark signatures allowed us to connect somatic mutations to clinically interpretable cancer mechanisms. Further, we explored the heterogeneity of these signatures, in terms of ratio of altered pathways associated with each individual hallmark, assuming that this is reflective of the extent of selective advantage provided to the cancer type under consideration. Our analysis revealed the predominance of certain hallmarks in specific cancer types, thus suggesting different evolutionary trajectories across cancer lineages. Finally, although many pathway alteration enrichments are guided by somatic mutations in frequently altered high-confidence cancer genes, excluding these driver mutations preserves the hallmark heterogeneity signatures, thus the detected hallmarks' predominance across cancer types. As a consequence, we propose the hallmark signatures as a ground truth to characterise tails of infrequent genomic alterations and identify potential novel cancer driver genes and networks.
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Affiliation(s)
- Francesco Iorio
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK.
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK.
- Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SD, UK.
| | - Luz Garcia-Alonso
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SD, UK
| | - Jonathan S Brammeld
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK
| | - Iňigo Martincorena
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK
| | - David R Wille
- GlaxoSmithKline, Gunnels Wood Rd, Stevenage Herts, SG1 2NY, UK
- Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SD, UK
| | - Ultan McDermott
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK.
- Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Faculty of Medicine, MTZ Pauwelstrasse 19, Aachen, 52074, Germany.
- Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SD, UK.
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27
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Wang Y, Qian W, Yuan B. A Graphical Model of Smoking-Induced Global Instability in Lung Cancer. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1-14. [PMID: 27542180 DOI: 10.1109/tcbb.2016.2599867] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Smoking is the major cause of lung cancer and the leading cause of cancer-related death in the world. The most current view about lung cancer is no longer limited to individual genes being mutated by any carcinogenic insults from smoking. Instead, tumorigenesis is a phenotype conferred by many systematic and global alterations, leading to extensive heterogeneity and variation for both the genotypes and phenotypes of individual cancer cells. Thus, strategically it is foremost important to develop a methodology to capture any consistent and global alterations presumably shared by most of the cancerous cells for a given population. This is particularly true that almost all of the data collected from solid cancers (including lung cancers) are usually distant apart over a large span of temporal or even spatial contexts. Here, we report a multiple non-Gaussian graphical model to reconstruct the gene interaction network using two previously published gene expression datasets. Our graphical model aims to selectively detect gross structural changes at the level of gene interaction networks. Our methodology is extensively validated, demonstrating good robustness, as well as the selectivity and specificity expected based on our biological insights. In summary, gene regulatory networks are still relatively stable during presumably the early stage of neoplastic transformation. But drastic structural differences can be found between lung cancer and its normal control, including the gain of functional modules for cellular proliferations such as EGFR and PDGFRA, as well as the lost of the important IL6 module, supporting their roles as potential drug targets. Interestingly, our method can also detect early modular changes, with the ALDH3A1 and its associated interactions being strongly implicated as a potential early marker, whose activations appear to alter LCN2 module as well as its interactions with the important TP53-MDM2 circuitry. Our strategy using the graphical model to reconstruct gene interaction work with biologically-inspired constraints exemplifies the importance and beauty of biology in developing any bio-computational approach.
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Bellazzi R, Engel F, Ferrazzi F. Gene network analysis: from heart development to cardiac therapy. Thromb Haemost 2017; 113:522-31. [DOI: 10.1160/th14-06-0483] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2014] [Accepted: 08/14/2014] [Indexed: 12/31/2022]
Abstract
SummaryNetworks offer a flexible framework to represent and analyse the complex interactions between components of cellular systems. In particular gene networks inferred from expression data can support the identification of novel hypotheses on regulatory processes. In this review we focus on the use of gene network analysis in the study of heart development. Understanding heart development will promote the elucidation of the aetiology of congenital heart disease and thus possibly improve diagnostics. Moreover, it will help to establish cardiac therapies. For example, understanding cardiac differentiation during development will help to guide stem cell differentiation required for cardiac tissue engineering or to enhance endogenous repair mechanisms. We introduce different methodological frameworks to infer networks from expression data such as Boolean and Bayesian networks. Then we present currently available temporal expression data in heart development and discuss the use of network-based approaches in published studies. Collectively, our literature-based analysis indicates that gene network analysis constitutes a promising opportunity to infer therapy-relevant regulatory processes in heart development. However, the use of network-based approaches has so far been limited by the small amount of samples in available datasets. Thus, we propose to acquire high-resolution temporal expression data to improve the mathematical descriptions of regulatory processes obtained with gene network inference methodologies. Especially probabilistic methods that accommodate the intrinsic variability of biological systems have the potential to contribute to a deeper understanding of heart development.
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Xu SC, Ning P. Predicting pathogenic genes for primary myelofibrosis based on a system‑network approach. Mol Med Rep 2017; 17:186-192. [PMID: 29115418 PMCID: PMC5780125 DOI: 10.3892/mmr.2017.7847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 08/11/2017] [Indexed: 11/06/2022] Open
Abstract
The aim of the present study was to predict pathogenic genes for primary myelofibrosis (PMF) using a system‑network approach by combining protein‑protein interaction (PPI) network and gene expression data with known pathogenic genes. PMF gene expression profiles, known pathogenic genes and protein‑protein interactions were obtained. Using these data, differentially expressed genes (DEGs) were identified between PMF and normal conditions using significance analysis of microarrays, and seed genes were determined based on the intersection of known pathogenic genes and the PMF gene expression profile. A new network was constructed using the seed genes and their adjacent DEGs within the PPI network. Subsequently, a pathogenic network was extracted from the new network, and contained genes that interacted with at least two seed genes, and the candidate pathogenic genes were predicted based on the cohesion with seed genes. Cluster analysis was performed to mine the pathogenic modules from the pathogenic network, and functional analysis was performed to identify the putative biological processes of the candidate pathogenic genes. Results from the present study identified 845 DEGs between PMF and normal conditions, and 45 seed genes in PMF were screened. Subsequently, a pathogenic network comprising 103 nodes and 265 interactions was constructed, and 4 pathogenic modules (modules A‑D) were mined from the pathogenic network. There were nine candidate pathogenic genes contained within Module A and four potential pathogenic genes, including E1A‑binding protein p300, RAS‑like proto‑oncogene A, von Willebrand factor and RAF‑1 proto‑oncogene, serine/threonine kinase, were identified that may be involved in the same biological process with the seed genes. This study predicted 10 candidate pathogenic genes and several signaling pathways that may be related to the pathogenesis of PMF using a system‑network approach. These predictions may shed light on the PMF pathogenesis and may provide guidelines for future experimental verification.
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Affiliation(s)
- Shu-Cai Xu
- Department of Oncology and Hematology, Hubei Provincial Hospital of Integrated Chinese and Western Medicine, Wuhan, Hubei 430015, P.R. China
| | - Peng Ning
- Department of Traumatic Hand and Foot Surgery, Taian City Central Hospital, Taian, Shandong 271000, P.R. China
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Isik Z, Ercan ME. Integration of RNA-Seq and RPPA data for survival time prediction in cancer patients. Comput Biol Med 2017; 89:397-404. [PMID: 28869900 DOI: 10.1016/j.compbiomed.2017.08.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Revised: 08/20/2017] [Accepted: 08/25/2017] [Indexed: 10/19/2022]
Abstract
Integration of several types of patient data in a computational framework can accelerate the identification of more reliable biomarkers, especially for prognostic purposes. This study aims to identify biomarkers that can successfully predict the potential survival time of a cancer patient by integrating the transcriptomic (RNA-Seq), proteomic (RPPA), and protein-protein interaction (PPI) data. The proposed method -RPBioNet- employs a random walk-based algorithm that works on a PPI network to identify a limited number of protein biomarkers. Later, the method uses gene expression measurements of the selected biomarkers to train a classifier for the survival time prediction of patients. RPBioNet was applied to classify kidney renal clear cell carcinoma (KIRC), glioblastoma multiforme (GBM), and lung squamous cell carcinoma (LUSC) patients based on their survival time classes (long- or short-term). The RPBioNet method correctly identified the survival time classes of patients with between 66% and 78% average accuracy for three data sets. RPBioNet operates with only 20 to 50 biomarkers and can achieve on average 6% higher accuracy compared to the closest alternative method, which uses only RNA-Seq data in the biomarker selection. Further analysis of the most predictive biomarkers highlighted genes that are common for both cancer types, as they may be driver proteins responsible for cancer progression. The novelty of this study is the integration of a PPI network with mRNA and protein expression data to identify more accurate prognostic biomarkers that can be used for clinical purposes in the future.
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Affiliation(s)
- Zerrin Isik
- Computer Engineering Department, Dokuz Eylul Universitesi, 35160, Izmir, Turkey.
| | - Muserref Ece Ercan
- Computer Engineering Department, Dokuz Eylul Universitesi, 35160, Izmir, Turkey
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31
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Bioinformatics in translational drug discovery. Biosci Rep 2017; 37:BSR20160180. [PMID: 28487472 PMCID: PMC6448364 DOI: 10.1042/bsr20160180] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 05/04/2017] [Accepted: 05/08/2017] [Indexed: 12/31/2022] Open
Abstract
Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications.
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Abstract
Recent studies across multiple tumour types are starting to reveal a recurrent regulatory architecture in which genomic alterations cluster upstream of functional master regulator (MR) proteins, the aberrant activity of which is both necessary and sufficient to maintain tumour cell state. These proteins form small, hyperconnected and autoregulated modules (termed tumour checkpoints) that are increasingly emerging as optimal biomarkers and therapeutic targets. Crucially, as their activity is mostly dysregulated in a post-translational manner, rather than by mutations in their corresponding genes or by differential expression, the identification of MR proteins by conventional methods is challenging. In this Opinion article, we discuss novel methods for the systematic analysis of MR proteins and of the modular regulatory architecture they implement, including their use as a valuable reductionist framework to study the genetic heterogeneity of human disease and to drive key translational applications.
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Affiliation(s)
- Andrea Califano
- Department of Systems Biology, Columbia University, and the Departments of Biomedical Informatics, Biochemistry and Molecular Biophysics, JP Sulzberger Columbia Genome Center, Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York 10032, USA
| | - Mariano J Alvarez
- DarwinHealth, Inc., 3960 Broadway, Suite 540, New York, New York 10032, USA
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Proteome-Scale Investigation of Protein Allosteric Regulation Perturbed by Somatic Mutations in 7,000 Cancer Genomes. Am J Hum Genet 2017; 100:5-20. [PMID: 27939638 DOI: 10.1016/j.ajhg.2016.09.020] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Accepted: 09/27/2016] [Indexed: 02/05/2023] Open
Abstract
The allosteric regulation triggering the protein's functional activity via conformational changes is an intrinsic function of protein under many physiological and pathological conditions, including cancer. Identification of the biological effects of specific somatic variants on allosteric proteins and the phenotypes that they alter during tumor initiation and progression is a central challenge for cancer genomes in the post-genomic era. Here, we mapped more than 47,000 somatic missense mutations observed in approximately 7,000 tumor-normal matched samples across 33 cancer types into protein allosteric sites to prioritize the mutated allosteric proteins and we tested our prediction in cancer cell lines. We found that the deleterious mutations identified in cancer genomes were more significantly enriched at protein allosteric sites than tolerated mutations, suggesting a critical role for protein allosteric variants in cancer. Next, we developed a statistical approach, namely AlloDriver, and further identified 15 potential mutated allosteric proteins during pan-cancer and individual cancer-type analyses. More importantly, we experimentally confirmed that p.Pro360Ala on PDE10A played a potential oncogenic role in mediating tumorigenesis in non-small cell lung cancer (NSCLC). In summary, these findings shed light on the role of allosteric regulation during tumorigenesis and provide a useful tool for the timely development of targeted cancer therapies.
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Abstract
A long-standing paradigm in drug discovery has been the concept of designing maximally selective drugs to act on individual targets considered to underlie a disease of interest. Nonetheless, although some drugs have proven to be successful, many more potential drugs identified by the "one gene, one drug, one disease" approach have been found to be less effective than expected or to cause notable side effects. Advances in systems biology and high-throughput in-depth genomic profiling technologies along with an analysis of the successful and failed drugs uncovered that the prominent factor to determine drug sensitivity is the intrinsic robustness of the response of biological systems in the face of perturbations. The complexity of the molecular and cellular bases of systems responses to drug interventions has fostered an increased interest in systems-oriented approaches to drug discovery. Consonant with this knowledge of the multifactorial mechanistic basis of drug sensitivity and resistance is the application of network-based approaches for the identification of molecular (multi-)feature signatures associated with desired (multi-)drug phenotypic profiles. This chapter illustrates the principal network analysis and inference techniques which have found application in systems-oriented drug design and considers their benefits and drawbacks in relation to the nature of the data produced by network pharmacology.
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de Anda-Jáuregui G, Velázquez-Caldelas TE, Espinal-Enríquez J, Hernández-Lemus E. Transcriptional Network Architecture of Breast Cancer Molecular Subtypes. Front Physiol 2016; 7:568. [PMID: 27920729 PMCID: PMC5118907 DOI: 10.3389/fphys.2016.00568] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Accepted: 11/08/2016] [Indexed: 12/22/2022] Open
Abstract
Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.
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Affiliation(s)
| | | | - Jesús Espinal-Enríquez
- Computational Genomics, National Institute of Genomic MedicineMexico City, Mexico
- Complejidad en Biología de Sistemas, Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de MéxicoMexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics, National Institute of Genomic MedicineMexico City, Mexico
- Complejidad en Biología de Sistemas, Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de MéxicoMexico City, Mexico
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Yambartsev A, Perlin MA, Kovchegov Y, Shulzhenko N, Mine KL, Dong X, Morgun A. Unexpected links reflect the noise in networks. Biol Direct 2016; 11:52. [PMID: 27737689 PMCID: PMC5480421 DOI: 10.1186/s13062-016-0155-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2016] [Accepted: 10/01/2016] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Gene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis. RESULTS We propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches. This method is based on a special relationship between sign of correlation (positive/negative) and directionality (up/down) of gene regulation, and allows for the identification and removal of approximately half of all erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we establish a mathematical foundation for our method. Analyzing existing biological datasets, we find a strong correlation between the results of our method and false discovery rate (FDR). Furthermore, simulation analysis demonstrates that our method provides a more accurate estimate of network error than FDR. CONCLUSIONS Thus, our study provides a new robust approach for improving reconstruction of covariation networks. REVIEWERS This article was reviewed by Eugene Koonin, Sergei Maslov, Daniel Yasumasa Takahashi.
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Affiliation(s)
- Anatoly Yambartsev
- Department of Statistics, Institute of Mathematics and Statistics, University of Sao Paulo, Sao Paulo, SP, Brazil
| | - Michael A Perlin
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Yevgeniy Kovchegov
- Department of Mathematics, College of Science, Oregon State University, Corvallis, OR, USA
| | - Natalia Shulzhenko
- College of Veterinary Medicine, Oregon State University, Corvallis, OR, USA
| | - Karina L Mine
- Instituto de Imunogenética - Associação Fundo de Incentivo à Pesquisa (IGEN-AFIP), São Paulo, SP, Brazil
| | - Xiaoxi Dong
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Andrey Morgun
- College of Pharmacy, Oregon State University, Corvallis, OR, USA.
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Csermely P, Korcsmáros T, Nussinov R. Intracellular and intercellular signaling networks in cancer initiation, development and precision anti-cancer therapy: RAS acts as contextual signaling hub. Semin Cell Dev Biol 2016; 58:55-9. [PMID: 27395026 PMCID: PMC5028272 DOI: 10.1016/j.semcdb.2016.07.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Accepted: 07/05/2016] [Indexed: 12/31/2022]
Abstract
Cancer initiation and development are increasingly perceived as systems-level phenomena, where intra- and inter-cellular signaling networks of the ecosystem of cancer and stromal cells offer efficient methodologies for outcome prediction and intervention design. Within this framework, RAS emerges as a 'contextual signaling hub', i.e. the final result of RAS activation or inhibition is determined by the signaling network context. Current therapies often 'train' cancer cells shifting them to a novel attractor, which has increased metastatic potential and drug resistance. The few therapy-surviving cancer cells are surrounded by massive cell death triggering a primordial adaptive and reparative general wound healing response. Overall, dynamic analysis of patient- and disease-stage specific intracellular and intercellular signaling networks may open new areas of anticancer therapy using multitarget drugs, drugs combinations, edgetic drugs, as well as help design 'gentler', differentiation and maintenance therapies.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 2, H-1428 Budapest, Hungary.
| | - Tamás Korcsmáros
- Gut Health and Food Safety Programme, Institute of Food Research, Norwich Research Park, Norwich NR4 7UA, UK; Earlham Institute/TGAC, The Genome Analysis Centre, Norwich Research Park, Norwich NR4 7UH, UK
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
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Information theoretic approaches for inference of biological networks from continuous-valued data. BMC SYSTEMS BIOLOGY 2016; 10:89. [PMID: 27599566 PMCID: PMC5013667 DOI: 10.1186/s12918-016-0331-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Accepted: 08/23/2016] [Indexed: 01/30/2023]
Abstract
Background Characterising programs of gene regulation by studying individual protein-DNA and protein-protein interactions would require a large volume of high-resolution proteomics data, and such data are not yet available. Instead, many gene regulatory network (GRN) techniques have been developed, which leverage the wealth of transcriptomic data generated by recent consortia to study indirect, gene-level relationships between transcriptional regulators. Despite the popularity of such methods, previous methods of GRN inference exhibit limitations that we highlight and address through the lens of information theory. Results We introduce new model-free and non-linear information theoretic measures for the inference of GRNs and other biological networks from continuous-valued data. Although previous tools have implemented mutual information as a means of inferring pairwise associations, they either introduce statistical bias through discretisation or are limited to modelling undirected relationships. Our approach overcomes both of these limitations, as demonstrated by a substantial improvement in empirical performance for a set of 160 GRNs of varying size and topology. Conclusions The information theoretic measures described in this study yield substantial improvements over previous approaches (e.g. ARACNE) and have been implemented in the latest release of NAIL (Network Analysis and Inference Library). However, despite the theoretical and empirical advantages of these new measures, they do not circumvent the fundamental limitation of indeterminacy exhibited across this class of biological networks. These methods have presently found value in computational neurobiology, and will likely gain traction for GRN analysis as the volume and quality of temporal transcriptomics data continues to improve.
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Cheng F, Liu C, Shen B, Zhao Z. Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach. BMC SYSTEMS BIOLOGY 2016; 10 Suppl 3:65. [PMID: 27585651 PMCID: PMC5009528 DOI: 10.1186/s12918-016-0309-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Cancer is increasingly recognized as a cellular system phenomenon that is attributed to the accumulation of genetic or epigenetic alterations leading to the perturbation of the molecular network architecture. Elucidation of network properties that can characterize tumor initiation and progression, or pinpoint the molecular targets related to the drug sensitivity or resistance, is therefore of critical importance for providing systems-level insights into tumorigenesis and clinical outcome in the molecularly targeted cancer therapy. RESULTS In this study, we developed a network-based framework to quantitatively examine cellular network heterogeneity and modularity in cancer. Specifically, we constructed gene co-expressed protein interaction networks derived from large-scale RNA-Seq data across 8 cancer types generated in The Cancer Genome Atlas (TCGA) project. We performed gene network entropy and balanced versus unbalanced motif analysis to investigate cellular network heterogeneity and modularity in tumor versus normal tissues, different stages of progression, and drug resistant versus sensitive cancer cell lines. We found that tumorigenesis could be characterized by a significant increase of gene network entropy in all of the 8 cancer types. The ratio of the balanced motifs in normal tissues is higher than that of tumors, while the ratio of unbalanced motifs in tumors is higher than that of normal tissues in all of the 8 cancer types. Furthermore, we showed that network entropy could be used to characterize tumor progression and anticancer drug responses. For example, we found that kinase inhibitor resistant cancer cell lines had higher entropy compared to that of sensitive cell lines using the integrative analysis of microarray gene expression and drug pharmacological data collected from the Genomics of Drug Sensitivity in Cancer database. In addition, we provided potential network-level evidence that smoking might increase cancer cellular network heterogeneity and further contribute to tyrosine kinase inhibitor (e.g., gefitinib) resistance. CONCLUSION In summary, we demonstrated that network properties such as network entropy and unbalanced motifs associated with tumor initiation, progression, and anticancer drug responses, suggesting new potential network-based prognostic and predictive measure in cancer.
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Affiliation(s)
- Feixiong Cheng
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Chuang Liu
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Bairong Shen
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Zhongming Zhao
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA. .,Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN, USA. .,Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN, USA. .,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
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Abstract
Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.
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41
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Hou JP, Emad A, Puleo GJ, Ma J, Milenkovic O. A new correlation clustering method for cancer mutation analysis. Bioinformatics 2016; 32:3717-3728. [PMID: 27540270 DOI: 10.1093/bioinformatics/btw546] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Revised: 06/14/2016] [Accepted: 08/16/2016] [Indexed: 01/17/2023] Open
Abstract
MOTIVATION Cancer genomes exhibit a large number of different alterations that affect many genes in a diverse manner. An improved understanding of the generative mechanisms behind the mutation rules and their influence on gene community behavior is of great importance for the study of cancer. RESULTS To expand our capability to analyze combinatorial patterns of cancer alterations, we developed a rigorous methodology for cancer mutation pattern discovery based on a new, constrained form of correlation clustering. Our new algorithm, named C3 (Cancer Correlation Clustering), leverages mutual exclusivity of mutations, patient coverage and driver network concentration principles. To test C3, we performed a detailed analysis on TCGA breast cancer and glioblastoma data and showed that our algorithm outperforms the state-of-the-art CoMEt method in terms of discovering mutually exclusive gene modules and identifying biologically relevant driver genes. The proposed agnostic clustering method represents a unique tool for efficient and reliable identification of mutation patterns and driver pathways in large-scale cancer genomics studies, and it may also be used for other clustering problems on biological graphs. AVAILABILITY AND IMPLEMENTATION The source code for the C3 method can be found at https://github.com/jackhou2/C3 CONTACTS: jianma@cs.cmu.edu or milenkov@illinois.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jack P Hou
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.,Medical Scholars Program, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Amin Emad
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.,Coordinated Science Lab, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Gregory J Puleo
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.,Coordinated Science Lab, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Jian Ma
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.,Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Olgica Milenkovic
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.,Coordinated Science Lab, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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Sun Z, Chen X, Wang G, Li L, Fu G, Kuruc M, Wang X. Identification of functional metabolic biomarkers from lung cancer patient serum using PEP technology. Biomark Res 2016; 4:11. [PMID: 27252855 PMCID: PMC4888258 DOI: 10.1186/s40364-016-0065-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 05/12/2016] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Reprogrammed metabolism is a new hallmark of cancer. In many types of cancer, most of the genes in the glycolytic pathway are overexpressed, reflecting an essential shift of metabolism during cancer development. The reprogrammed metabolism contributes to cancer development in multiple ways, from supplying the elevated energy requirement to creating a microenvironment suitable for tumor growth and suppressing the human immune surveillance system. METHOD In this study, a functional proteomics top-down approach was used to systematically monitor metabolic enzyme activities in resolved serum proteins produced by a modified 2-D gel separation and subsequent Protein Elution Plate, a method collectively called PEP. RESULTS We found that the enrichment of low abundance proteins with a bead based product called AlbuVoid™(,) is important to increase the number of observable features and to increase the level of signal achievable from the assay used. From our methods, significant metabolic enzyme activities were detected in both normal and lung cancer patient sera in many fractions after the elution of the 2-D gel separated proteins to the Protein Elution Plate (PEP). Eighteen fractions with the most dramatic metabolic enzyme activity difference between the normal and lung cancer patient sera were submitted for mass spectrometry protein identification. Proteins from the glycolytic metabolic pathway, such as GAPDH along with other proteins not previously annotated to the glycolytic pathway were identified. Further verification with commercially purified GAPDH showed that the addition of purified GAPDH to the metabolic enzyme assay system employed enhanced the enzyme activity, demonstrating that proteins identified from the PEP technology and mass spectrometry could be further verified with biological assay. CONCLUSION This study identified several potential functional enzyme biomarkers from lung cancer patient serum, it provides an alternative and complementary approach to sequence annotation for the discovery of biomarkers in human diseases.
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Affiliation(s)
- Zhenyu Sun
- />The Third Hospital Affiliated to Nantong University School of Medicine, Wuxi, China
| | - Xiaofeng Chen
- />Shanghai Huashan Hospital, Fudan University School of Medicine, Shanghai, China
| | - Gan. Wang
- />Institute of Environmental Health Sciences, Wayne State University, 259 Mack Avenue, Detroit, MI 48201 USA
| | - Liang Li
- />Zibo Central Hospital, Zibo, China
| | | | - Matthew Kuruc
- />Biotech Support Group, LLC, Monmouth Junction, NJ USA
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43
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Li S, Zhu X, Liu B, Wang G, Ao P. Endogenous molecular network reveals two mechanisms of heterogeneity within gastric cancer. Oncotarget 2016; 6:13607-27. [PMID: 25962957 PMCID: PMC4537037 DOI: 10.18632/oncotarget.3633] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Accepted: 04/10/2015] [Indexed: 12/20/2022] Open
Abstract
Intratumor heterogeneity is a common phenomenon and impedes cancer therapy and research. Gastric cancer (GC) cells have generally been classified into two heterogeneous cellular phenotypes, the gastric and intestinal types, yet the mechanisms of maintaining two phenotypes and controlling phenotypic transition are largely unknown. A qualitative systematic framework, the endogenous molecular network hypothesis, has recently been proposed to understand cancer genesis and progression. Here, a minimal network corresponding to such framework was found for GC and was quantified via a stochastic nonlinear dynamical system. We then further extended the framework to address the important question of intratumor heterogeneity quantitatively. The working network characterized main known features of normal gastric epithelial and GC cell phenotypes. Our results demonstrated that four positive feedback loops in the network are critical for GC cell phenotypes. Moreover, two mechanisms that contribute to GC cell heterogeneity were identified: particular positive feedback loops are responsible for the maintenance of intestinal and gastric phenotypes; GC cell progression routes that were revealed by the dynamical behaviors of individual key components are heterogeneous. In this work, we constructed an endogenous molecular network of GC that can be expanded in the future and would broaden the known mechanisms of intratumor heterogeneity.
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Affiliation(s)
- Site Li
- Shanghai Center for Systems Biomedicine, Ministry of Education Key Laboratory of Systems Biomedicine, Collaborative Innovation Center of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
| | | | - Bingya Liu
- Shanghai Center for Systems Biomedicine, Ministry of Education Key Laboratory of Systems Biomedicine, Collaborative Innovation Center of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.,Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Gaowei Wang
- Shanghai Center for Systems Biomedicine, Ministry of Education Key Laboratory of Systems Biomedicine, Collaborative Innovation Center of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ping Ao
- Shanghai Center for Systems Biomedicine, Ministry of Education Key Laboratory of Systems Biomedicine, Collaborative Innovation Center of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.,State Key Laboratory for Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200032, China.,Department of Physics, Shanghai Jiao Tong University, Shanghai 200240, China
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44
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Yu MK, Kramer M, Dutkowski J, Srivas R, Licon K, Kreisberg J, Ng CT, Krogan N, Sharan R, Ideker T. Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems. Cell Syst 2016; 2:77-88. [PMID: 26949740 PMCID: PMC4772745 DOI: 10.1016/j.cels.2016.02.003] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Accurately translating genotype to phenotype requires accounting for the functional impact of genetic variation at many biological scales. Here we present a strategy for genotype-phenotype reasoning based on existing knowledge of cellular subsystems. These subsystems and their hierarchical organization are defined by the Gene Ontology or a complementary ontology inferred directly from previously published datasets. Guided by the ontology's hierarchical structure, we organize genotype data into an "ontotype," that is, a hierarchy of perturbations representing the effects of genetic variation at multiple cellular scales. The ontotype is then interpreted using logical rules generated by machine learning to predict phenotype. This approach substantially outperforms previous, non-hierarchical methods for translating yeast genotype to cell growth phenotype, and it accurately predicts the growth outcomes of two new screens of 2,503 double gene knockouts impacting DNA repair or nuclear lumen. Ontotypes also generalize to larger knockout combinations, setting the stage for interpreting the complex genetics of disease.
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Affiliation(s)
- Michael Ku Yu
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla CA 92093, USA
- Department of Medicine, University of California San Diego, La Jolla CA 92093, USA
| | - Michael Kramer
- Department of Medicine, University of California San Diego, La Jolla CA 92093, USA
- Biomedical Sciences Program, University of California San Diego, La Jolla CA 92093, USA
| | - Janusz Dutkowski
- Department of Medicine, University of California San Diego, La Jolla CA 92093, USA
- Data4Cure, La Jolla, CA 92037, USA
| | - Rohith Srivas
- Department of Medicine, University of California San Diego, La Jolla CA 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla CA 92093, USA
| | - Katherine Licon
- Department of Medicine, University of California San Diego, La Jolla CA 92093, USA
| | - Jason Kreisberg
- Department of Medicine, University of California San Diego, La Jolla CA 92093, USA
| | | | - Nevan Krogan
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco 94143, USA
| | - Roded Sharan
- Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv 69978, Israel
| | - Trey Ideker
- Department of Medicine, University of California San Diego, La Jolla CA 92093, USA
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45
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Abstract
Over the past decade, rapid advances in genomics, proteomics and functional genomics technologies that enable in-depth interrogation of cancer genomes and proteomes and high-throughput analysis of gene function have enabled characterization of the kinome 'at large' in human cancers, providing crucial insights into how members of the protein kinase superfamily are dysregulated in malignancy, the context-dependent functional role of specific kinases in cancer and how kinome remodelling modulates sensitivity to anticancer drugs. The power of these complementary approaches, and the insights gained from them, form the basis of this Analysis article.
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Affiliation(s)
- Emmy D G Fleuren
- Department of Medical Oncology, Radboud University Medical Centre, Geert Grooteplein-Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Luxi Zhang
- Cancer Program, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
- University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Jianmin Wu
- Cancer Division, Kinghorn Cancer Centre, Garvan Institute of Medical Research, 370 Victoria Street, Sydney, New South Wales 2010, Australia
| | - Roger J Daly
- Cancer Program, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
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46
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Systematic identification of genes with a cancer-testis expression pattern in 19 cancer types. Nat Commun 2016; 7:10499. [PMID: 26813108 PMCID: PMC4737856 DOI: 10.1038/ncomms10499] [Citation(s) in RCA: 106] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2015] [Accepted: 12/17/2015] [Indexed: 02/06/2023] Open
Abstract
Cancer-testis (CT) genes represent the similarity between the processes of spermatogenesis and tumorigenesis. It is possible that their selective expression pattern can help identify driver genes in cancer. In this study, we integrate transcriptomics data from multiple databases and systematically identify 876 new CT genes in 19 cancer types. We explore their relationship with testis-specific regulatory elements. We propose that extremely highly expressed CT genes (EECTGs) are potential drivers activated through epigenetic mechanisms. We find mutually exclusive associations between EECTGs and somatic mutations in mutated genes, such as PIK3CA in breast cancer. We also provide evidence that promoter demethylation and close non-coding RNAs (namely, CT-ncRNAs) may be two mechanisms to reactivate EECTG gene expression. We show that the meiosis-related EECTG (MEIOB) and its nearby CT-ncRNA have a role in tumorigenesis in lung adenocarcinoma. Our findings provide methods for identifying epigenetic-driver genes of cancer, which could serve as targets of future cancer therapies. Genes normally expressed in the testis but aberrantly expressed in cancer are termed cancer testis antigens. In this study, the authors catalogue the expression of these genes in 19 different cancer types and correlate expression with some somatically mutated oncogenes.
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47
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Frieboes HB, Curtis LT, Wu M, Kani K, Mallick P. Simulation of the Protein-Shedding Kinetics of a Fully Vascularized Tumor. Cancer Inform 2015; 14:163-75. [PMID: 26715830 PMCID: PMC4687979 DOI: 10.4137/cin.s35374] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Revised: 11/09/2015] [Accepted: 11/15/2015] [Indexed: 12/12/2022] Open
Abstract
Circulating biomarkers are of significant interest for cancer detection and treatment personalization. However, the biophysical processes that determine how proteins are shed from cancer cells or their microenvironment, diffuse through tissue, enter blood vasculature, and persist in circulation remain poorly understood. Since approaches primarily focused on experimental evaluation are incapable of measuring the shedding and persistence for every possible marker candidate, we propose an interdisciplinary computational/experimental approach that includes computational modeling of tumor tissue heterogeneity. The model implements protein production, transport, and shedding based on tumor vascularization, cell proliferation, hypoxia, and necrosis, thus quantitatively relating the tumor and circulating proteomes. The results highlight the dynamics of shedding as a function of protein diffusivity and production. Linking the simulated tumor parameters to clinical tumor and vascularization measurements could potentially enable this approach to reveal the tumor-specific conditions based on the protein detected in circulation and thus help to more accurately manage cancer diagnosis and treatment.
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Affiliation(s)
- Hermann B Frieboes
- Department of Bioengineering, University of Louisville, Louisville, KY, USA. ; James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA
| | - Louis T Curtis
- Department of Bioengineering, University of Louisville, Louisville, KY, USA
| | - Min Wu
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Chicago, IL, USA
| | - Kian Kani
- Center for Applied Molecular Medicine, University of Southern California, Los Angeles, CA, USA
| | - Parag Mallick
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA, USA
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48
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Bead Based Proteome Enrichment Enhances Features of the Protein Elution Plate (PEP) for Functional Proteomic Profiling. Proteomes 2015; 3:454-466. [PMID: 28248280 PMCID: PMC5217392 DOI: 10.3390/proteomes3040454] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Revised: 11/19/2015] [Accepted: 11/30/2015] [Indexed: 01/23/2023] Open
Abstract
A novel functional proteomics technology called PEP(Protein Elution Plate) was developed to separate complex proteomes from natural sources and analyze protein functions systematically. The technology takes advantage of the powerful resolution of two-dimensional gel electrophoresis (2-D Gels). The modification of electrophoretic conditions in combination with a high-resolution protein elution plate supports the recovery of functionally active proteins. As 2DE(2-Dimensional Electrophoresis) resolution can be limited by protein load, we investigated the use of bead based enrichment technologies, called AlbuVoid™ and KinaSorb™ to determine their effect on the proteomic features which can be generated from the PEP platform. Using a variety of substrates and enzyme activity assays, we report on the benefits of combining bead based enrichment to improve the signal report and the features generated for Hexokinase, Protein Kinase, Protease, and Alkaline Phosphatase activities. As a result, the PEP technology allows systematic analysis of large enzyme families and can build a comprehensive picture of protein function from a complex proteome, providing biological insights that could otherwise not be observed if only protein abundances were analyzed.
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49
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Field SL, Dasgupta T, Cummings M, Savage RS, Adebayo J, McSara H, Gunawardena J, Orsi NM. Bayesian modeling suggests that IL-12 (p40), IL-13 and MCP-1 drive murine cytokine networks in vivo. BMC SYSTEMS BIOLOGY 2015; 9:76. [PMID: 26553024 PMCID: PMC4640223 DOI: 10.1186/s12918-015-0226-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 10/31/2015] [Indexed: 12/19/2022]
Abstract
Background Cytokine-hormone network deregulations underpin pathologies ranging from autoimmune disorders to cancer, but our understanding of these networks in physiological/pathophysiological states remains patchy. We employed Bayesian networks to analyze cytokine-hormone interactions in vivo using murine lactation as a dynamic, physiological model system. Results Circulatory levels of estrogen, progesterone, prolactin and twenty-three cytokines were profiled in post partum mice with/without pups. The resultant networks were very robust and assembled about structural hubs, with evidence that interleukin (IL)-12 (p40), IL-13 and monocyte chemoattractant protein (MCP)-1 were the primary drivers of network behavior. Network structural conservation across physiological scenarios coupled with the successful empirical validation of our approach suggested that in silico network perturbations can predict in vivo qualitative responses. In silico perturbation of network components also captured biological features of cytokine interactions (antagonism, synergy, redundancy). Conclusion These findings highlight the potential of network-based approaches in identifying novel cytokine pharmacological targets and in predicting the effects of their exogenous manipulation in inflammatory/immune disorders. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0226-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sarah L Field
- Women's Health Research Group, Section of Cancer & Pathology, Leeds Institute of Cancer & Pathology, St James's University Hospital, Leeds, LS9 7TF, UK.
| | - Tathagata Dasgupta
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, Massachusetts, 02115, USA.
| | - Michele Cummings
- Women's Health Research Group, Section of Cancer & Pathology, Leeds Institute of Cancer & Pathology, St James's University Hospital, Leeds, LS9 7TF, UK.
| | - Richard S Savage
- Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, UK.
| | - Julius Adebayo
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, Massachusetts, 02115, USA. .,IDeA Labs, Department of Computer Science, TMCB 1124, Brigham Young University, Provo, UT, 84602, USA.
| | - Hema McSara
- Women's Health Research Group, Section of Cancer & Pathology, Leeds Institute of Cancer & Pathology, St James's University Hospital, Leeds, LS9 7TF, UK.
| | - Jeremy Gunawardena
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, Massachusetts, 02115, USA.
| | - Nicolas M Orsi
- Women's Health Research Group, Section of Cancer & Pathology, Leeds Institute of Cancer & Pathology, St James's University Hospital, Leeds, LS9 7TF, UK.
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50
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Morgun A, Dzutsev A, Dong X, Greer RL, Sexton DJ, Ravel J, Schuster M, Hsiao W, Matzinger P, Shulzhenko N. Uncovering effects of antibiotics on the host and microbiota using transkingdom gene networks. Gut 2015; 64:1732-43. [PMID: 25614621 PMCID: PMC5166700 DOI: 10.1136/gutjnl-2014-308820] [Citation(s) in RCA: 190] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Accepted: 12/22/2014] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Despite widespread use of antibiotics for the treatment of life-threatening infections and for research on the role of commensal microbiota, our understanding of their effects on the host is still very limited. DESIGN Using a popular mouse model of microbiota depletion by a cocktail of antibiotics, we analysed the effects of antibiotics by combining intestinal transcriptome together with metagenomic analysis of the gut microbiota. In order to identify specific microbes and microbial genes that influence the host phenotype in antibiotic-treated mice, we developed and applied analysis of the transkingdom network. RESULTS We found that most antibiotic-induced alterations in the gut can be explained by three factors: depletion of the microbiota; direct effects of antibiotics on host tissues and the effects of remaining antibiotic-resistant microbes. Normal microbiota depletion mostly led to downregulation of different aspects of immunity. The two other factors (antibiotic direct effects on host tissues and antibiotic-resistant microbes) primarily inhibited mitochondrial gene expression and amounts of active mitochondria, increasing epithelial cell death. By reconstructing and analysing the transkingdom network, we discovered that these toxic effects were mediated by virulence/quorum sensing in antibiotic-resistant bacteria, a finding further validated using in vitro experiments. CONCLUSIONS In addition to revealing mechanisms of antibiotic-induced alterations, this study also describes a new bioinformatics approach that predicts microbial components that regulate host functions and establishes a comprehensive resource on what, why and how antibiotics affect the gut in a widely used mouse model of microbiota depletion by antibiotics.
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Affiliation(s)
- Andrey Morgun
- College of Pharmacy, Oregon State University, Corvallis, Oregon,
USA,Ghost Lab, National Institute of Allergy and Infectious Diseases,
National Institutes of Health, Bethesda, Maryland, USA
| | - Amiran Dzutsev
- Cancer and Inflammation Program, National Cancer Institute/Leidos
Biomedical Research, Inc., Frederick, Maryland, USA
| | - Xiaoxi Dong
- College of Pharmacy, Oregon State University, Corvallis, Oregon,
USA
| | - Renee L Greer
- College of Veterinary Medicine, Oregon State University, Corvallis,
Oregon, USA
| | - D Joseph Sexton
- Department of Microbiology, Oregon State University, Corvallis,
Oregon, USA
| | - Jacques Ravel
- Institute for Genome Sciences, University of Maryland School of
Medicine, Baltimore, Maryland, USA
| | - Martin Schuster
- Department of Microbiology, Oregon State University, Corvallis,
Oregon, USA
| | - William Hsiao
- University of British Columbia, Vancouver, British Columbia,
Canada
| | - Polly Matzinger
- Ghost Lab, National Institute of Allergy and Infectious Diseases,
National Institutes of Health, Bethesda, Maryland, USA
| | - Natalia Shulzhenko
- College of Veterinary Medicine, Oregon State University, Corvallis,
Oregon, USA,Ghost Lab, National Institute of Allergy and Infectious Diseases,
National Institutes of Health, Bethesda, Maryland, USA
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