1
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Luo XG, Kuipers J, Beerenwinkel N. Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees. Nat Commun 2023; 14:3676. [PMID: 37344522 DOI: 10.1038/s41467-023-39400-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 06/12/2023] [Indexed: 06/23/2023] Open
Abstract
Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors enable high-resolution reconstruction of the mutational history of each tumor and highlight the extensive diversity across tumors and patients. Resolving the interactions among mutations and recovering recurrent evolutionary processes may offer greater opportunities for successful therapeutic strategies. To this end, we present a novel probabilistic framework, called TreeMHN, for the joint inference of exclusivity patterns and recurrent trajectories from a cohort of intra-tumor phylogenetic trees. Through simulations, we show that TreeMHN outperforms existing alternatives that can only focus on one aspect of the task. By analyzing datasets of blood, lung, and breast cancers, we find the most likely evolutionary trajectories and mutational patterns, consistent with and enriching our current understanding of tumorigenesis. Moreover, TreeMHN facilitates the prediction of tumor evolution and provides probabilistic measures on the next mutational events given a tumor tree, a prerequisite for evolution-guided treatment strategies.
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Affiliation(s)
- Xiang Ge Luo
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland.
- SIB Swiss Institute of Bioinformatics, Mattenstrasse 26, 4058, Basel, Switzerland.
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2
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Galindez G, Sadegh S, Baumbach J, Kacprowski T, List M. Network-based approaches for modeling disease regulation and progression. Comput Struct Biotechnol J 2022; 21:780-795. [PMID: 36698974 PMCID: PMC9841310 DOI: 10.1016/j.csbj.2022.12.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.
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Affiliation(s)
- Gihanna Galindez
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.,Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
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3
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Suter P, Dazert E, Kuipers J, Ng CKY, Boldanova T, Hall MN, Heim MH, Beerenwinkel N. Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model. PLoS Comput Biol 2022; 18:e1009767. [PMID: 36067230 PMCID: PMC9481159 DOI: 10.1371/journal.pcbi.1009767] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 09/16/2022] [Accepted: 07/18/2022] [Indexed: 11/18/2022] Open
Abstract
Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments.
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Affiliation(s)
- Polina Suter
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Eva Dazert
- Biozentrum, University of Basel, Basel, Switzerland
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Charlotte K. Y. Ng
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department for BioMedical Research (DBMR), University of Bern, Bern, Switzerland
- Department of Biomedicine, University Hospital Basel, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Tuyana Boldanova
- Department of Biomedicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | | | - Markus H. Heim
- Department of Biomedicine, University Hospital Basel, University of Basel, Basel, Switzerland
- Department of Gastroenterology and Hepatology, Clarunis, University Center for Gastrointestinal and Liver Diseases, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- * E-mail:
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4
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Dirmeier S, Beerenwinkel N. Structured hierarchical models for probabilistic inference from perturbation screening data. Ann Appl Stat 2022. [DOI: 10.1214/21-aoas1580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Simon Dirmeier
- Department of Biosystems Science and Engineering, ETH Zurich
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5
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Suter P, Kuipers J, Beerenwinkel N. Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks. Brief Bioinform 2022; 23:6604993. [PMID: 35679575 PMCID: PMC9294428 DOI: 10.1093/bib/bbac219] [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: 12/16/2021] [Revised: 04/29/2022] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
Dynamic Bayesian networks (DBNs) can be used for the discovery of gene regulatory networks (GRNs) from time series gene expression data. Here, we suggest a strategy for learning DBNs from gene expression data by employing a Bayesian approach that is scalable to large networks and is targeted at learning models with high predictive accuracy. Our framework can be used to learn DBNs for multiple groups of samples and highlight differences and similarities in their GRNs. We learn these DBN models based on different structural and parametric assumptions and select the optimal model based on the cross-validated predictive accuracy. We show in simulation studies that our approach is better equipped to prevent overfitting than techniques used in previous studies. We applied the proposed DBN-based approach to two time series transcriptomic datasets from the Gene Expression Omnibus database, each comprising data from distinct phenotypic groups of the same tissue type. In the first case, we used DBNs to characterize responders and non-responders to anti-cancer therapy. In the second case, we compared normal to tumor cells of colorectal tissue. The classification accuracy reached by the DBN-based classifier for both datasets was higher than reported previously. For the colorectal cancer dataset, our analysis suggested that GRNs for cancer and normal tissues have a lot of differences, which are most pronounced in the neighborhoods of oncogenes and known cancer tissue markers. The identified differences in gene networks of cancer and normal cells may be used for the discovery of targeted therapies.
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Affiliation(s)
- Polina Suter
- Department of Biosystems Science and Engineering, ETH Zurich, Matternstrasse 26, 4058 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Switzerland
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Matternstrasse 26, 4058 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Matternstrasse 26, 4058 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Switzerland
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6
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Chen C, Bao H, Lin W, Chen X, Huang Y, Wang H, Yang Y, Liu J, Lv X, Teng L. ASF1b is a novel prognostic predictor associated with cell cycle signaling pathway in gastric cancer. J Cancer 2022; 13:1985-2000. [PMID: 35399734 PMCID: PMC8990430 DOI: 10.7150/jca.69544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 03/17/2022] [Indexed: 12/16/2022] Open
Abstract
Gastric cancer (GC) is one of the most common malignant tumors with poor outcomes. Identification of new therapeutic targets is urgently needed. Accumulating evidence has shown that anti-silencing function 1b (ASF1b) contributes to the progression in multiple cancer types. However, detailed mechanisms of ASF1b tumorigenesis in gastric cancer remain elusive. This study showed that ASF1b was upregulated in GC tissues and remarkably correlated with TNM stage, histological grade and poor prognosis of GC. We induced down and up-regulation of ASF1b in GC cell lines and monitored the changes in their biological behavior. Furthermore, loss of ASF1b was efficient to suppress subcutaneous xenograft tumor growth in vivo. We demonstrate that ASF1b is involved in regulation of cell cycle and PI3K/AKT/mTOR signaling through experiments and database analysis. Mechanistically, ASF1b promoted the proliferation, migration and invasion of GC cells. Taken together, this study highlights the role of ASF1b, which provided new insights into the underlying mechanism of progression and metastasis in GC for the first time.
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Affiliation(s)
- Chuanzhi Chen
- Department of Surgical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Haili Bao
- Department of Organ Transplantation, Shanghai Changzheng Hospital, Navy Military Medical University, Shanghai, 200003, China
| | - Wu Lin
- Department of Surgical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Xiangliu Chen
- Department of Surgical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Yingying Huang
- Department of Surgical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Haohao Wang
- Department of Surgical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Yan Yang
- Department of Surgical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Jin Liu
- Department of Surgical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Xiadong Lv
- Department of Surgical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Lisong Teng
- Department of Surgical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
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7
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Angelopoulos N, Chatzipli A, Nangalia J, Maura F, Campbell PJ. Bayesian networks elucidate complex genomic landscapes in cancer. Commun Biol 2022; 5:306. [PMID: 35379892 PMCID: PMC8980036 DOI: 10.1038/s42003-022-03243-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 03/09/2022] [Indexed: 11/27/2022] Open
Abstract
Bayesian networks (BNs) are disciplined, explainable Artificial Intelligence models that can describe structured joint probability spaces. In the context of understanding complex relations between a number of variables in biological settings, they can be constructed from observed data and can provide a guiding, graphical tool in exploring such relations. Here we propose BNs for elucidating the relations between driver events in large cancer genomic datasets. We present a methodology that is specifically tailored to biologists and clinicians as they are the main producers of such datasets. We achieve this by using an optimal BN learning algorithm based on well established likelihood functions and by utilising just two tuning parameters, both of which are easy to set and have intuitive readings. To enhance value to clinicians, we introduce (a) the use of heatmaps for families in each network, and (b) visualising pairwise co-occurrence statistics on the network. For binary data, an optional step of fitting logic gates can be employed. We show how our methodology enhances pairwise testing and how biologists and clinicians can use BNs for discussing the main relations among driver events in large genomic cohorts. We demonstrate the utility of our methodology by applying it to 5 cancer datasets revealing complex genomic landscapes. Our networks identify central patterns in all datasets including a central 4-way mutual exclusivity between HDR, t(4,14), t(11,14) and t(14,16) in myeloma, and a 3-way mutual exclusivity of three major players: CALR, JAK2 and MPL, in myeloproliferative neoplasms. These analyses demonstrate that our methodology can play a central role in the study of large genomic cancer datasets. Bayesian network inference on several blood and solid cancer genomic datasets provides more accessible ways to explore driver events in cancer.
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Affiliation(s)
- Nicos Angelopoulos
- The Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK. .,Systems Immunity Research Institute, Medical School, Cardiff University, Cardiff, CF14 4XN, UK.
| | - Aikaterini Chatzipli
- The Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK
| | - Jyoti Nangalia
- The Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK
| | - Francesco Maura
- Myeloma Program, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA
| | - Peter J Campbell
- The Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK
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8
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Kuipers J, Suter P, Moffa G. Efficient Sampling and Structure Learning of Bayesian Networks. J Comput Graph Stat 2021. [DOI: 10.1080/10618600.2021.2020127] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Jack Kuipers
- D-BSSE, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Polina Suter
- D-BSSE, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Giusi Moffa
- Division of Psychiatry, University College London, London, UK
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
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9
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Kuipers J, Moore AL, Jahn K, Schraml P, Wang F, Morita K, Futreal PA, Takahashi K, Beisel C, Moch H, Beerenwinkel N. Statistical tests for intra-tumour clonal co-occurrence and exclusivity. PLoS Comput Biol 2021; 17:e1009036. [PMID: 34910733 PMCID: PMC8716063 DOI: 10.1371/journal.pcbi.1009036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 12/29/2021] [Accepted: 11/19/2021] [Indexed: 12/31/2022] Open
Abstract
Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Intra-tumoural pairs of mutations that are overrepresented in a co-occurring or clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical testing framework, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our framework for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 1 clonally co-occurring and 8 clonally exclusive gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways. Tumours typically display high levels of heterogeneity, not only between different tumours but also within a single one. Intra-tumour heterogeneity results from an evolutionary process, giving rise to different populations of cancer cells known as clones. How clones interact may affect tumour evolution, which in turn determines disease progression and treatment outcome. In practice, we may observe pairs of mutations that co-occur in clones or exclude each other more often than we would expect for a given cohort of patient samples. Exclusive pairs are suggestive that clones carrying one or the other mutation may cooperate in the evolutionary process. Targeting only one of them may then suffice to alter the tumour evolution. Therefore it is critical to have statistical methods which allow us to identify such pairs. GeneAccord is a novel statistical testing framework we developed especially to identify pairs of genes altered in distinct clones of the same tumour. Accounting for the evolutionary dependencies among clones emerged as critical to adequately control testing errors. In a cohort of 123 acute myeloid leukaemia patients, GeneAccord identified one clonally co-occurring and eight clonally exclusive gene pairs. The latter predominantly involved genes of key signalling pathways.
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Affiliation(s)
- Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Ariane L. Moore
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Peter Schraml
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Feng Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Kiyomi Morita
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - P. Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Koichi Takahashi
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Christian Beisel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
- * E-mail:
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10
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Ghareyazi A, Mohseni A, Dashti H, Beheshti A, Dehzangi A, Rabiee HR, Alinejad-Rokny H. Whole-Genome Analysis of De Novo Somatic Point Mutations Reveals Novel Mutational Biomarkers in Pancreatic Cancer. Cancers (Basel) 2021; 13:4376. [PMID: 34503185 PMCID: PMC8431675 DOI: 10.3390/cancers13174376] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/16/2021] [Accepted: 08/16/2021] [Indexed: 12/15/2022] Open
Abstract
It is now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.
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Affiliation(s)
- Amin Ghareyazi
- Bioinformatics and Computational Biology Laboratory, Sharif University of Technology, Tehran 11365, Iran; (A.G.); (A.M.); (H.D.)
| | - Amir Mohseni
- Bioinformatics and Computational Biology Laboratory, Sharif University of Technology, Tehran 11365, Iran; (A.G.); (A.M.); (H.D.)
| | - Hamed Dashti
- Bioinformatics and Computational Biology Laboratory, Sharif University of Technology, Tehran 11365, Iran; (A.G.); (A.M.); (H.D.)
| | - Amin Beheshti
- Department of Computing, Macquarie University, Sydney, NSW 2109, Australia;
| | - Abdollah Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ 08102, USA;
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
| | - Hamid R. Rabiee
- Bioinformatics and Computational Biology Laboratory, Sharif University of Technology, Tehran 11365, Iran; (A.G.); (A.M.); (H.D.)
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
- UNSW Data Science Hub, The University of New South Wales, Sydney, NSW 2052, Australia
- Health Data Analytics Program, AI-Enabled Processes (AIP) Research Centre, Macquarie University, Sydney, NSW 2109, Australia
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11
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12
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Nourse J, Spada S, Danckwardt S. Emerging Roles of RNA 3'-end Cleavage and Polyadenylation in Pathogenesis, Diagnosis and Therapy of Human Disorders. Biomolecules 2020; 10:biom10060915. [PMID: 32560344 PMCID: PMC7356254 DOI: 10.3390/biom10060915] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 06/10/2020] [Accepted: 06/13/2020] [Indexed: 12/11/2022] Open
Abstract
A crucial feature of gene expression involves RNA processing to produce 3′ ends through a process termed 3′ end cleavage and polyadenylation (CPA). This ensures the nascent RNA molecule can exit the nucleus and be translated to ultimately give rise to a protein which can execute a function. Further, alternative polyadenylation (APA) can produce distinct transcript isoforms, profoundly expanding the complexity of the transcriptome. CPA is carried out by multi-component protein complexes interacting with multiple RNA motifs and is tightly coupled to transcription, other steps of RNA processing, and even epigenetic modifications. CPA and APA contribute to the maintenance of a multitude of diverse physiological processes. It is therefore not surprising that disruptions of CPA and APA can lead to devastating disorders. Here, we review potential CPA and APA mechanisms involving both loss and gain of function that can have tremendous impacts on health and disease. Ultimately we highlight the emerging diagnostic and therapeutic potential CPA and APA offer.
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Affiliation(s)
- Jamie Nourse
- Institute for Clinical Chemistry and Laboratory Medicine, University Medical Center of the Johannes Gutenberg University, 55131 Mainz, Germany; (J.N.); (S.S.)
- Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University, 55131 Mainz, Germany
| | - Stefano Spada
- Institute for Clinical Chemistry and Laboratory Medicine, University Medical Center of the Johannes Gutenberg University, 55131 Mainz, Germany; (J.N.); (S.S.)
- Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University, 55131 Mainz, Germany
| | - Sven Danckwardt
- Institute for Clinical Chemistry and Laboratory Medicine, University Medical Center of the Johannes Gutenberg University, 55131 Mainz, Germany; (J.N.); (S.S.)
- Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University, 55131 Mainz, Germany
- German Center for Cardiovascular Research (DZHK), Rhine-Main, Germany
- Correspondence:
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13
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Cancer Classification at the Crossroads. Cancers (Basel) 2020; 12:cancers12040980. [PMID: 32326638 PMCID: PMC7226085 DOI: 10.3390/cancers12040980] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 04/13/2020] [Accepted: 04/14/2020] [Indexed: 01/24/2023] Open
Abstract
Internationally accepted classifications of malignant tumors, developed by the World Health Organization (WHO) and the Union for International Cancer Control (UICC), are based on the histotype, site of origin, morphologic grade, and spread of cancer throughout the body. The WHO classifications are the foundation of cancer diagnosis and the starting point for cancer management. Starting in 2000, the WHO classifications began to include biologic and molecular–genetic features. These developments are having a strong impact on cancer diagnosis and treatment, and this impact is amplifying, given the advances in cancer genomics. Molecular–genetic profiling can be used to refine existing classifications of tumors and, for a small but increasing number of cancers, even determine the treatment irrespective of histotype. Here I discuss how cancer classifications may change in the era of cancer genomics.
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14
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Al-Harazi O, El Allali A, Colak D. Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2020; 23:138-151. [PMID: 30883301 DOI: 10.1089/omi.2018.0205] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Next-generation sequencing approaches and genome-wide studies have become essential for characterizing the mechanisms of human diseases. Consequently, many researchers have applied these approaches to discover the genetic/genomic causes of common complex and rare human diseases, generating multiomics big data that span the continuum of genomics, proteomics, metabolomics, and many other system science fields. Therefore, there is a significant and unmet need for biological databases and tools that enable and empower the researchers to analyze, integrate, and make sense of big data. There are currently large number of databases that offer different types of biological information. In particular, the integration of gene expression profiles and protein-protein interaction networks provides a deeper understanding of the complex multilayered molecular architecture of human diseases. Therefore, there has been a growing interest in developing methodologies that integrate and contextualize big data from molecular interaction networks to identify biomarkers of human diseases at a subnetwork resolution as well. In this expert review, we provide a comprehensive summary of most popular biomolecular databases for molecular interactions (e.g., Biological General Repository for Interaction Datasets, Kyoto Encyclopedia of Genes and Genomes and Search Tool for The Retrieval of Interacting Genes/Proteins), gene-disease associations (e.g., Online Mendelian Inheritance in Man, Disease-Gene Network, MalaCards), and population-specific databases (e.g., Human Genetic Variation Database), and describe some examples of their usage and potential applications. We also present the most recent subnetwork identification approaches and discuss their main advantages and limitations. As the field of data science continues to emerge, the present analysis offers a deeper and contextualized understanding of the available databases in molecular biomedicine.
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Affiliation(s)
- Olfat Al-Harazi
- 1 Department of Biostatistics, Epidemiology, and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.,2 Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Achraf El Allali
- 2 Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Dilek Colak
- 1 Department of Biostatistics, Epidemiology, and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
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15
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Kratzer G, Lewis FI, Willi B, Meli ML, Boretti FS, Hofmann-Lehmann R, Torgerson P, Furrer R, Hartnack S. Bayesian Network Modeling Applied to Feline Calicivirus Infection Among Cats in Switzerland. Front Vet Sci 2020; 7:73. [PMID: 32175337 PMCID: PMC7055399 DOI: 10.3389/fvets.2020.00073] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 01/28/2020] [Indexed: 11/29/2022] Open
Abstract
Bayesian network (BN) modeling is a rich and flexible analytical framework capable of elucidating complex veterinary epidemiological data. It is a graphical modeling technique that enables the visual presentation of multi-dimensional results while retaining statistical rigor in population-level inference. Using previously published case study data about feline calicivirus (FCV) and other respiratory pathogens in cats in Switzerland, a full BN modeling analysis is presented. The analysis shows that reducing the group size and vaccinating animals are the two actionable factors directly associated with FCV status and are primary targets to control FCV infection. The presence of gingivostomatitis and Mycoplasma felis is also associated with FCV status, but signs of upper respiratory tract disease (URTD) are not. FCV data is particularly well-suited to a network modeling approach, as both multiple pathogens and multiple clinical signs per pathogen are involved, along with multiple potentially interrelated risk factors. BN modeling is a holistic approach—all variables of interest may be mutually interdependent—which may help to address issues, such as confounding and collinear factors, as well as to disentangle directly vs. indirectly related variables. We introduce the BN methodology as an alternative to the classical uni- and multivariable regression approaches commonly used for risk factor analyses. We advise and guide researchers about how to use BNs as an exploratory data tool and demonstrate the limitations and practical issues. We present a step-by-step case study using FCV data along with all code necessary to reproduce our analyses in the open-source R environment. We compare and contrast the findings of the current case study using BN modeling with previous results that used classical regression techniques, and we highlight new potential insights. Finally, we discuss advanced methods, such as Bayesian model averaging, a common way of accounting for model uncertainty in a Bayesian network context.
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Affiliation(s)
- Gilles Kratzer
- Department of Mathematics, University of Zurich, Zurich, Switzerland
| | | | - Barbara Willi
- Clinic for Small Animal Internal Medicine, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland
| | - Marina L Meli
- Clinical Laboratory, Department of Clinical Diagnostics and Services, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland.,Center for Clinical Studies, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland
| | - Felicitas S Boretti
- Clinic for Small Animal Internal Medicine, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland
| | - Regina Hofmann-Lehmann
- Clinical Laboratory, Department of Clinical Diagnostics and Services, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland.,Center for Clinical Studies, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland
| | - Paul Torgerson
- Section of Epidemiology, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland
| | - Reinhard Furrer
- Department of Mathematics, University of Zurich, Zurich, Switzerland.,Department of Computational Science, University of Zurich, Zurich, Switzerland
| | - Sonja Hartnack
- Section of Epidemiology, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland
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16
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Kamieniarz-Gdula K, Gdula MR, Panser K, Nojima T, Monks J, Wiśniewski JR, Riepsaame J, Brockdorff N, Pauli A, Proudfoot NJ. Selective Roles of Vertebrate PCF11 in Premature and Full-Length Transcript Termination. Mol Cell 2019; 74:158-172.e9. [PMID: 30819644 PMCID: PMC6458999 DOI: 10.1016/j.molcel.2019.01.027] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 12/07/2018] [Accepted: 01/17/2019] [Indexed: 12/02/2022]
Abstract
The pervasive nature of RNA polymerase II (Pol II) transcription requires efficient termination. A key player in this process is the cleavage and polyadenylation (CPA) factor PCF11, which directly binds to the Pol II C-terminal domain and dismantles elongating Pol II from DNA in vitro. We demonstrate that PCF11-mediated termination is essential for vertebrate development. A range of genomic analyses, including mNET-seq, 3′ mRNA-seq, chromatin RNA-seq, and ChIP-seq, reveals that PCF11 enhances transcription termination and stimulates early polyadenylation genome-wide. PCF11 binds preferentially between closely spaced genes, where it prevents transcriptional interference and consequent gene downregulation. Notably, PCF11 is sub-stoichiometric to the CPA complex. Low levels of PCF11 are maintained by an auto-regulatory mechanism involving premature termination of its own transcript and are important for normal development. Both in human cell culture and during zebrafish development, PCF11 selectively attenuates the expression of other transcriptional regulators by premature CPA and termination. Human PCF11 enhances transcription termination and 3′ end processing, genome-wide PCF11 is substoichiometric to CPA complex due to autoregulation of its transcription PCF11 stimulates expression of closely spaced genes but attenuates other genes PCF11-mediated functions are conserved in vertebrates and essential in development
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Affiliation(s)
- Kinga Kamieniarz-Gdula
- Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK.
| | - Michal R Gdula
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK
| | - Karin Panser
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria
| | - Takayuki Nojima
- Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK
| | - Joan Monks
- Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK
| | - Jacek R Wiśniewski
- Biochemical Proteomics Group, Department of Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Joey Riepsaame
- Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK
| | - Neil Brockdorff
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK
| | - Andrea Pauli
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria.
| | - Nick J Proudfoot
- Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK.
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