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Wang V, Geybels MS, Jordahl KM, Gerke T, Hamid A, Penney KL, Markt SC, Freedman M, Pomerantz M, Lee GSM, Rana H, Börnigen D, Rebbeck TR, Huttenhower C, Eeles RA, Stanford JL, Consortium P, Berndt SI, Claessens F, Sørensen KD, Park JY, Vega A, Usmani N, Mucci L, Sweeney CJ. A polymorphism in the promoter of FRAS1 is a candidate SNP associated with metastatic prostate cancer. Prostate 2021; 81:683-693. [PMID: 33956343 PMCID: PMC8491321 DOI: 10.1002/pros.24148] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 02/19/2021] [Accepted: 04/22/2021] [Indexed: 01/04/2023]
Abstract
BACKGROUND Inflammation and one of its mediators, NF-kappa B (NFκB), have been implicated in prostate cancer carcinogenesis. We assessed whether germline polymorphisms associated with NFκB are associated with the risk of developing lethal disease (metastases or death from prostate cancer). METHODS Using a Bayesian approach leveraging NFκB biology with integration of publicly available datasets we used a previously defined genome-wide functional association network specific to NFκB and lethal prostate cancer. A dense-module-searching method identified modules enriched with significant genes from a genome-wide association study (GWAS) study in a discovery data set, Physicians' Health Study and Health Professionals Follow-up Study (PHS/HPFS). The top 48 candidate single nucleotide polymorphisms (SNPs) from the dense-module-searching method were then assessed in an independent prostate cancer cohort and the one SNP reproducibly associated with lethality was tested in a third cohort. Logistic regression models evaluated the association between each SNP and lethal prostate cancer. The candidate SNP was assessed for association with lethal prostate cancer in 6 of 28 studies in the prostate cancer association group to investigate cancer associated alterations in the genome (PRACTICAL) Consortium where there was some medical record review for death ascertainment which also had SNP data from the ONCOARRAY platform. All men self-identified as Caucasian. RESULTS The rs1910301 SNP which was reproducibly associated with lethal disease was nominally associated with lethal disease (odds ratio [OR] = 1.40; p = .02) in the discovery cohort and the minor allele was also associated with lethal disease in two independent cohorts (OR = 1.35; p = .04 and OR = 1.35; p = .07). Fixed effects meta-analysis of all three cohorts found an association: OR = 1.37 (95% confidence interval [CI]: 1.15-1.62, p = .0003). This SNP is in the promoter region of FRAS1, a gene involved in epidermal-basement membrane adhesion and is present at a higher frequency in men with African ancestry. No association was found in the subset of studies from the PRACTICAL consortium studies which had a total of 106 deaths out total of 3263 patients and a median follow-up of 4.4 years. CONCLUSIONS Through its connection with the NFκB pathway, a candidate SNP with a higher frequency in men of African ancestry without cancer was found to be associated with lethal prostate cancer across three well-annotated independent cohorts of Caucasian men.
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Affiliation(s)
- Victoria Wang
- Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Milan S Geybels
- Division of Public Health Sciences, Fred Hutchison Cancer Research Center, Seattle, Washington, USA
- Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Kristina M Jordahl
- Division of Public Health Sciences, Fred Hutchison Cancer Research Center, Seattle, Washington, USA
| | - Travis Gerke
- Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Anis Hamid
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Kathryn L Penney
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Sarah C Markt
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Matthew Freedman
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts, USA
| | - Mark Pomerantz
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts, USA
| | - Gwo-Shu M Lee
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts, USA
| | - Huma Rana
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts, USA
| | - Daniela Börnigen
- University Medical Center Hamburg-Eppendorf, Bioinformatics Core, Hamburg, Germany
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Timothy R Rebbeck
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Curtis Huttenhower
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Ros A Eeles
- Oncogenetics, Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
- The Royal Marsden NHS Foundation Trust, London, UK
| | - Janet L Stanford
- Division of Public Health Sciences, Fred Hutchison Cancer Research Center, Seattle, Washington, USA
| | - Practical Consortium
- Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland, USA
| | - Frank Claessens
- Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Karina D Sørensen
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus N, Denmark
| | - Jong Y Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida, USA
| | - Ana Vega
- Fundación Pública Galega Medicina Xenómica, Santiago de Compostela, Spain
| | - Nawaid Usmani
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Lorelei Mucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Christopher J Sweeney
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts, USA
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2
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McIver LJ, Abu-Ali G, Franzosa EA, Schwager R, Morgan XC, Waldron L, Segata N, Huttenhower C. bioBakery: a meta'omic analysis environment. Bioinformatics 2018; 34:1235-1237. [PMID: 29194469 PMCID: PMC6030947 DOI: 10.1093/bioinformatics/btx754] [Citation(s) in RCA: 193] [Impact Index Per Article: 32.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 11/28/2017] [Indexed: 11/13/2022] Open
Abstract
Summary bioBakery is a meta'omic analysis environment and collection of individual software tools with the capacity to process raw shotgun sequencing data into actionable microbial community feature profiles, summary reports, and publication-ready figures. It includes a collection of pre-configured analysis modules also joined into workflows for reproducibility. Availability and implementation bioBakery (http://huttenhower.sph.harvard.edu/biobakery) is publicly available for local installation as individual modules and as a virtual machine image. Each individual module has been developed to perform a particular task (e.g. quantitative taxonomic profiling or statistical analysis), and they are provided with source code, tutorials, demonstration data, and validation results; the bioBakery virtual image includes the entire suite of modules and their dependencies pre-installed. Images are available for both Amazon EC2 and Google Compute Engine. All software is open source under the MIT license. bioBakery is actively maintained with a support group at biobakery-users@googlegroups.com and new tools being added upon their release. Contact chuttenh@hsph.harvard.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lauren J McIver
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Galeb Abu-Ali
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Eric A Franzosa
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Randall Schwager
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Xochitl C Morgan
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Department of Microbiology and Immunology, University of Otago, Dunedin 9054, New Zealand
| | - Levi Waldron
- Graduate School of Public Health and Health Policy, City University of New York, New York, NY 10027, USA
| | - Nicola Segata
- Centre for Integrative Biology, University of Trento, Trento 38123, Italy
| | - Curtis Huttenhower
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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3
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Gerke T, Beltran H, Wang X, Lee GSM, Sboner A, Karnes RJ, Klein EA, Davicioni E, Yousefi K, Ross AE, Börnigen D, Huttenhower C, Mucci LA, Trock BJ, Sweeney CJ. Low Tristetraprolin Expression Is Associated with Lethal Prostate Cancer. Cancer Epidemiol Biomarkers Prev 2018; 28:584-590. [PMID: 30420441 DOI: 10.1158/1055-9965.epi-18-0667] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 09/07/2018] [Accepted: 11/05/2018] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Inflammation is linked to prostate cancer progression and is mediated by NF-κB. Tristetraprolin is a key node of NF-κB activation and we investigated its biological and prognostic role in lethal prostate cancer. METHODS In vitro assays assessed the function of tristetraprolin and the association between low mRNA tristetraprolin levels and lethal prostate cancer (metastatic disease or death) was assessed across independent prostatectomy cohorts: (i) nested case-control studies from Health Professionals Follow-up Study and Physicians' Health Study, and (ii) prostatectomy samples from Cleveland Clinic, Mayo Clinic, Johns Hopkins and Memorial Sloan Kettering Cancer Center. Tristetraprolin expression levels in prostatectomy samples from patients with localized disease and biopsies of metastatic castration-resistant prostate cancer (mCRPC) were assessed in a Cornell University cohort. RESULTS In vitro tristetraprolin expression was inversely associated with NF-κB-controlled genes, proliferation, and enzalutamide sensitivity. Men with localized prostate cancer and lower quartile of tumor tristetraprolin expression had a significant, nearly two-fold higher risk of lethal prostate cancer after adjusting for known clinical and histologic prognostic features (age, RP Gleason score, T-stage). Tristetraprolin expression was also significantly lower in mCRPC compared with localized prostate cancer. CONCLUSIONS Lower levels of tristetraprolin in human prostate cancer prostatectomy tissue are associated with more aggressive prostate cancer and may serve as an actionable prognostic and predictive biomarker. IMPACT There is a clear need for improved biomarkers to identify patients with localized prostate cancer in need of treatment intensification, such as adjuvant testosterone suppression, or treatment de-intensification, such as active surveillance. Tristetraprolin levels may serve as informative biomarkers in localized prostate cancer.
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Affiliation(s)
| | | | | | | | | | | | - Eric A Klein
- Cleveland Clinic Glickman Urological and Kidney Institute, Cleveland, Ohio
| | | | | | - Ashley E Ross
- James Buchanan Brady Urological Institute, Department of Urology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Daniela Börnigen
- University Heart Center Hamburg, Clinic for General and Interventional Cardiology, Hamburg, Germany
| | | | - Lorelei A Mucci
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Bruce J Trock
- James Buchanan Brady Urological Institute, Department of Urology, Johns Hopkins University School of Medicine, Baltimore, Maryland
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4
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Lee JE, Kim YY. How Should Biobanks Prioritize and Diversify Biosample Collections? A 40-Year Scientific Publication Trend Analysis by the Type of Biosample. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2018; 22:255-263. [PMID: 29584577 DOI: 10.1089/omi.2017.0197] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Biobanks are infrastructures for large-scale biology innovation. Governance of biobanks can be usefully informed by studies of publication trends, for example, on the types of biosamples employed in scientific publications. We examined trends in each of the serum, plasma, peripheral blood mononuclear cell (PBMC), buffy coat, tissue, and gut microbiome biosample-related scientific publications over the past 40 years, using data between 1977 and 2016 from the Scopus database. We found that the number of tissue-related publications was the highest in each year of our analysis than other biosamples, but was generally less than the sum of serum- and plasma-related publications. Importantly, the microbiome publications increased greatly starting in the 2010s, and currently overtook the number of publications on PBMC and buffy coat. Among serum-, plasma-, and tissue-related publications, the number of protein- and RNA-related publications was generally higher than cell-free DNA-, DNA-, and metabolite-related publications for the past 40 years. Mass spectrometry- and next-generation sequencing-related publications have increased dramatically since the 2000s and 2010s, respectively. Microbiome- and metabolite-related biosamples can help diversify future biosample collections, while tissue collections appear to maintain their importance in scientific publications. We also report here our observations on the countries that use biosample research (e.g., China, United Kingdom, United States, and others). These publication trends by the type of biosamples illuminate roadmaps by which biobanks might establish and diversify their biosample collections in the future. In addition, we note that biobanks need to secure biosamples appropriate for integrated analysis of multi-omics research data.
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Affiliation(s)
- Jae-Eun Lee
- Division of Biobank for Health Sciences, Center for Genome Science, Korea National Institute of Health , Korea Centers for Disease Control and Prevention, Cheongju-si, Korea
| | - Young-Youl Kim
- Division of Biobank for Health Sciences, Center for Genome Science, Korea National Institute of Health , Korea Centers for Disease Control and Prevention, Cheongju-si, Korea
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5
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Lakiotaki K, Vorniotakis N, Tsagris M, Georgakopoulos G, Tsamardinos I. BioDataome: a collection of uniformly preprocessed and automatically annotated datasets for data-driven biology. Database (Oxford) 2018; 2018:4917852. [PMID: 29688366 PMCID: PMC5836265 DOI: 10.1093/database/bay011] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 01/12/2018] [Accepted: 01/15/2018] [Indexed: 01/12/2023]
Abstract
Biotechnology revolution generates a plethora of omics data with an exponential growth pace. Therefore, biological data mining demands automatic, 'high quality' curation efforts to organize biomedical knowledge into online databases. BioDataome is a database of uniformly preprocessed and disease-annotated omics data with the aim to promote and accelerate the reuse of public data. We followed the same preprocessing pipeline for each biological mart (microarray gene expression, RNA-Seq gene expression and DNA methylation) to produce ready for downstream analysis datasets and automatically annotated them with disease-ontology terms. We also designate datasets that share common samples and automatically discover control samples in case-control studies. Currently, BioDataome includes ∼5600 datasets, ∼260 000 samples spanning ∼500 diseases and can be easily used in large-scale massive experiments and meta-analysis. All datasets are publicly available for querying and downloading via BioDataome web application. We demonstrate BioDataome's utility by presenting exploratory data analysis examples. We have also developed BioDataome R package found in: https://github.com/mensxmachina/BioDataome/.Database URL: http://dataome.mensxmachina.org/.
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Affiliation(s)
- Kleanthi Lakiotaki
- Computer Science Department, University of Crete, Voutes Campus, 70013 Heraklion, Crete, Greece
| | - Nikolaos Vorniotakis
- Computer Science Department, University of Crete, Voutes Campus, 70013 Heraklion, Crete, Greece
| | - Michail Tsagris
- Computer Science Department, University of Crete, Voutes Campus, 70013 Heraklion, Crete, Greece
| | - Georgios Georgakopoulos
- Computer Science Department, University of Crete, Voutes Campus, 70013 Heraklion, Crete, Greece
| | - Ioannis Tsamardinos
- Computer Science Department, University of Crete, Voutes Campus, 70013 Heraklion, Crete, Greece
- Gnosis Data Analysis PC, Palaiokapa 64, 71305 Heraklion, Crete, Greece
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6
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Nayfach S, Pollard KS. Toward Accurate and Quantitative Comparative Metagenomics. Cell 2016; 166:1103-1116. [PMID: 27565341 DOI: 10.1016/j.cell.2016.08.007] [Citation(s) in RCA: 164] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Revised: 04/11/2016] [Accepted: 08/03/2016] [Indexed: 01/08/2023]
Abstract
Shotgun metagenomics and computational analysis are used to compare the taxonomic and functional profiles of microbial communities. Leveraging this approach to understand roles of microbes in human biology and other environments requires quantitative data summaries whose values are comparable across samples and studies. Comparability is currently hampered by the use of abundance statistics that do not estimate a meaningful parameter of the microbial community and biases introduced by experimental protocols and data-cleaning approaches. Addressing these challenges, along with improving study design, data access, metadata standardization, and analysis tools, will enable accurate comparative metagenomics. We envision a future in which microbiome studies are replicable and new metagenomes are easily and rapidly integrated with existing data. Only then can the potential of metagenomics for predictive ecological modeling, well-powered association studies, and effective microbiome medicine be fully realized.
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Affiliation(s)
- Stephen Nayfach
- Integrative Program in Quantitative Biology, University of California, San Francisco, CA 94158, USA; Gladstone Institutes, San Francisco, CA 94158, USA
| | - Katherine S Pollard
- Gladstone Institutes, San Francisco, CA 94158, USA; Division of Biostatistics, Institute for Human Genetics, and Institute for Computational Health Sciences, University of California, San Francisco, CA 94158, USA.
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7
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Vassilev B, Louhimo R, Ikonen E, Hautaniemi S. Language-Agnostic Reproducible Data Analysis Using Literate Programming. PLoS One 2016; 11:e0164023. [PMID: 27711123 PMCID: PMC5053501 DOI: 10.1371/journal.pone.0164023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Accepted: 09/19/2016] [Indexed: 01/15/2023] Open
Abstract
A modern biomedical research project can easily contain hundreds of analysis steps and lack of reproducibility of the analyses has been recognized as a severe issue. While thorough documentation enables reproducibility, the number of analysis programs used can be so large that in reality reproducibility cannot be easily achieved. Literate programming is an approach to present computer programs to human readers. The code is rearranged to follow the logic of the program, and to explain that logic in a natural language. The code executed by the computer is extracted from the literate source code. As such, literate programming is an ideal formalism for systematizing analysis steps in biomedical research. We have developed the reproducible computing tool Lir (literate, reproducible computing) that allows a tool-agnostic approach to biomedical data analysis. We demonstrate the utility of Lir by applying it to a case study. Our aim was to investigate the role of endosomal trafficking regulators to the progression of breast cancer. In this analysis, a variety of tools were combined to interpret the available data: a relational database, standard command-line tools, and a statistical computing environment. The analysis revealed that the lipid transport related genes LAPTM4B and NDRG1 are coamplified in breast cancer patients, and identified genes potentially cooperating with LAPTM4B in breast cancer progression. Our case study demonstrates that with Lir, an array of tools can be combined in the same data analysis to improve efficiency, reproducibility, and ease of understanding. Lir is an open-source software available at github.com/borisvassilev/lir.
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Affiliation(s)
- Boris Vassilev
- Department of Anatomy, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- * E-mail:
| | - Riku Louhimo
- Research Programs Unit, Genome-Scale Biology, University of Helsinki, Helsinki, Finland
| | - Elina Ikonen
- Department of Anatomy, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Sampsa Hautaniemi
- Research Programs Unit, Genome-Scale Biology, University of Helsinki, Helsinki, Finland
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8
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Haase T, Börnigen D, Müller C, Zeller T. Systems Medicine as an Emerging Tool for Cardiovascular Genetics. Front Cardiovasc Med 2016; 3:27. [PMID: 27626034 PMCID: PMC5003874 DOI: 10.3389/fcvm.2016.00027] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Accepted: 08/16/2016] [Indexed: 01/11/2023] Open
Abstract
Cardiovascular disease (CVD) is a major contributor to morbidity and mortality worldwide. However, the pathogenesis of CVD is complex and remains elusive. Within the last years, systems medicine has emerged as a novel tool to study the complex genetic, molecular, and physiological interactions leading to diseases. In this review, we provide an overview about the current approaches for systems medicine in CVD. They include bioinformatical and experimental tools such as cell and animal models, omics technologies, network, and pathway analyses. Additionally, we discuss challenges and current literature examples where systems medicine has been successfully applied for the study of CVD.
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Affiliation(s)
- Tina Haase
- Clinic for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany; Partner Site Hamburg/Kiel/Lübeck, German Center for Cardiovascular Research (DZHK e.V.), Hamburg, Germany
| | - Daniela Börnigen
- Clinic for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany; Partner Site Hamburg/Kiel/Lübeck, German Center for Cardiovascular Research (DZHK e.V.), Hamburg, Germany
| | - Christian Müller
- Clinic for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany; Partner Site Hamburg/Kiel/Lübeck, German Center for Cardiovascular Research (DZHK e.V.), Hamburg, Germany
| | - Tanja Zeller
- Clinic for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany; Partner Site Hamburg/Kiel/Lübeck, German Center for Cardiovascular Research (DZHK e.V.), Hamburg, Germany
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9
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Börnigen D, Tyekucheva S, Wang X, Rider JR, Lee GS, Mucci LA, Sweeney C, Huttenhower C. Computational Reconstruction of NFκB Pathway Interaction Mechanisms during Prostate Cancer. PLoS Comput Biol 2016; 12:e1004820. [PMID: 27078000 PMCID: PMC4831844 DOI: 10.1371/journal.pcbi.1004820] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Accepted: 02/19/2016] [Indexed: 12/21/2022] Open
Abstract
Molecular research in cancer is one of the largest areas of bioinformatic investigation, but it remains a challenge to understand biomolecular mechanisms in cancer-related pathways from high-throughput genomic data. This includes the Nuclear-factor-kappa-B (NFκB) pathway, which is central to the inflammatory response and cell proliferation in prostate cancer development and progression. Despite close scrutiny and a deep understanding of many of its members’ biomolecular activities, the current list of pathway members and a systems-level understanding of their interactions remains incomplete. Here, we provide the first steps toward computational reconstruction of interaction mechanisms of the NFκB pathway in prostate cancer. We identified novel roles for ATF3, CXCL2, DUSP5, JUNB, NEDD9, SELE, TRIB1, and ZFP36 in this pathway, in addition to new mechanistic interactions between these genes and 10 known NFκB pathway members. A newly predicted interaction between NEDD9 and ZFP36 in particular was validated by co-immunoprecipitation, as was NEDD9's potential biological role in prostate cancer cell growth regulation. We combined 651 gene expression datasets with 1.4M gene product interactions to predict the inclusion of 40 additional genes in the pathway. Molecular mechanisms of interaction among pathway members were inferred using recent advances in Bayesian data integration to simultaneously provide information specific to biological contexts and individual biomolecular activities, resulting in a total of 112 interactions in the fully reconstructed NFκB pathway: 13 (11%) previously known, 29 (26%) supported by existing literature, and 70 (63%) novel. This method is generalizable to other tissue types, cancers, and organisms, and this new information about the NFκB pathway will allow us to further understand prostate cancer and to develop more effective prevention and treatment strategies. In molecular research in cancer it remains challenging to uncover biomolecular mechanisms in cancer-related pathways from high-throughput genomic data, including the Nuclear-factor-kappa-B (NFκB) pathway. Despite close scrutiny and a deep understanding of many of the NFκB pathway members’ biomolecular activities, the current list of pathway members and a systems-level understanding of their interactions remains incomplete. In this study, we provide the first steps toward computational reconstruction of interaction mechanisms of the NFκB pathway in prostate cancer. We identified novel roles for 8 genes in this pathway and new mechanistic interactions between these genes and 10 known pathway members. We combined 651 gene expression datasets with 1.4M interactions to predict the inclusion of 40 additional genes in the pathway. Molecular mechanisms of interaction were inferred using recent advances in Bayesian data integration to simultaneously provide information specific to biological contexts and individual biomolecular activities, resulting in 112 interactions in the fully reconstructed NFκB pathway. This method is generalizable, and this new information about the NFκB pathway will allow us to further understand prostate cancer.
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Affiliation(s)
- Daniela Börnigen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America.,The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Svitlana Tyekucheva
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
| | - Xiaodong Wang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jennifer R Rider
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
| | - Gwo-Shu Lee
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Lorelei A Mucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
| | - Christopher Sweeney
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Curtis Huttenhower
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America.,The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
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