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Patil SS, Roberts SA, Gebremedhin AH. Network analysis of driver genes in human cancers. FRONTIERS IN BIOINFORMATICS 2024; 4:1365200. [PMID: 39040139 PMCID: PMC11260686 DOI: 10.3389/fbinf.2024.1365200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 06/14/2024] [Indexed: 07/24/2024] Open
Abstract
Cancer is a heterogeneous disease that results from genetic alteration of cell cycle and proliferation controls. Identifying mutations that drive cancer, understanding cancer type specificities, and delineating how driver mutations interact with each other to establish disease is vital for identifying therapeutic vulnerabilities. Such cancer specific patterns and gene co-occurrences can be identified by studying tumor genome sequences, and networks have proven effective in uncovering relationships between sequences. We present two network-based approaches to identify driver gene patterns among tumor samples. The first approach relies on analysis using the Directed Weighted All Nearest Neighbors (DiWANN) model, which is a variant of sequence similarity network, and the second approach uses bipartite network analysis. A data reduction framework was implemented to extract the minimal relevant information for the sequence similarity network analysis, where a transformed reference sequence is generated for constructing the driver gene network. This data reduction process combined with the efficiency of the DiWANN network model, greatly lowered the computational cost (in terms of execution time and memory usage) of generating the networks enabling us to work at a much larger scale than previously possible. The DiWANN network helped us identify cancer types in which samples were more closely connected to each other suggesting they are less heterogeneous and potentially susceptible to a common drug. The bipartite network analysis provided insight into gene associations and co-occurrences. We identified genes that were broadly mutated in multiple cancer types and mutations exclusive to only a few. Additionally, weighted one-mode gene projections of the bipartite networks revealed a pattern of occurrence of driver genes in different cancers. Our study demonstrates that network-based approaches can be an effective tool in cancer genomics. The analysis identifies co-occurring and exclusive driver genes and mutations for specific cancer types, providing a better understanding of the driver genes that lead to tumor initiation and evolution.
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Affiliation(s)
- Shruti S. Patil
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States
| | - Steven A. Roberts
- School of Molecular Biosciences, Washington State University, Pullman, WA, United States
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT, United States
- UVM’s Larner College of Medicine, University of Vermont Cancer Center, Burlington, VT, United States
| | - Assefaw H. Gebremedhin
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States
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2
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Velazquez-Caldelas TE, Zamora-Fuentes JM, Hernandez-Lemus E. Coordinated inflammation and immune response transcriptional regulation in breast cancer molecular subtypes. Front Immunol 2024; 15:1357726. [PMID: 38983850 PMCID: PMC11231215 DOI: 10.3389/fimmu.2024.1357726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 06/03/2024] [Indexed: 07/11/2024] Open
Abstract
Breast cancer, characterized by its complexity and diversity, presents significant challenges in understanding its underlying biology. In this study, we employed gene co-expression network analysis to investigate the gene composition and functional patterns in breast cancer subtypes and normal breast tissue. Our objective was to elucidate the detailed immunological features distinguishing these tumors at the transcriptional level and to explore their implications for diagnosis and treatment. The analysis identified nine distinct gene module clusters, each representing unique transcriptional signatures within breast cancer subtypes and normal tissue. Interestingly, while some clusters exhibited high similarity in gene composition between normal tissue and certain subtypes, others showed lower similarity and shared traits. These clusters provided insights into the immune responses within breast cancer subtypes, revealing diverse immunological functions, including innate and adaptive immune responses. Our findings contribute to a deeper understanding of the molecular mechanisms underlying breast cancer subtypes and highlight their unique characteristics. The immunological signatures identified in this study hold potential implications for diagnostic and therapeutic strategies. Additionally, the network-based approach introduced herein presents a valuable framework for understanding the complexities of other diseases and elucidating their underlying biology.
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Affiliation(s)
| | | | - Enrique Hernandez-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
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Deschildre J, Vandemoortele B, Loers JU, De Preter K, Vermeirssen V. Evaluation of single-sample network inference methods for precision oncology. NPJ Syst Biol Appl 2024; 10:18. [PMID: 38360881 PMCID: PMC10869342 DOI: 10.1038/s41540-024-00340-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 01/17/2024] [Indexed: 02/17/2024] Open
Abstract
A major challenge in precision oncology is to detect targetable cancer vulnerabilities in individual patients. Modeling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning, resulting in aggregate networks. However, to implement patient-tailored approaches in precision oncology, we need to interpret omics data at the level of individual patients. Several single-sample network inference methods have been developed that infer biological networks for an individual sample from bulk RNA-seq data. However, only a limited comparison of these methods has been made and many methods rely on 'normal tissue' samples as reference, which are not always available. Here, we conducted an evaluation of the single-sample network inference methods SSN, LIONESS, SWEET, iENA, CSN and SSPGI using transcriptomic profiles of lung and brain cancer cell lines from the CCLE database. The methods constructed functional gene networks with distinct network characteristics. Hub gene analyses revealed different degrees of subtype-specificity across methods. Single-sample networks were able to distinguish between tumor subtypes, as exemplified by node strength clustering, enrichment of known subtype-specific driver genes among hubs and differential node strength. We also showed that single-sample networks correlated better to other omics data from the same cell line as compared to aggregate networks. We conclude that single-sample network inference methods can reflect sample-specific biology when 'normal tissue' samples are absent and we point out peculiarities of each method.
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Affiliation(s)
- Joke Deschildre
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Boris Vandemoortele
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Jens Uwe Loers
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Katleen De Preter
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Lab of Translational Onco-genomics and Bio-informatics, Center for Medical Biotechnology (VIB-UGent), Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Vanessa Vermeirssen
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium.
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium.
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium.
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4
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Su D, Xiong Y, Wang S, Wei H, Ke J, Li H, Wang T, Zuo Y, Yang L. Structural deep clustering network for stratification of breast cancer patients through integration of somatic mutation profiles. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107808. [PMID: 37716222 DOI: 10.1016/j.cmpb.2023.107808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/15/2023] [Accepted: 09/10/2023] [Indexed: 09/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Breast cancer is among of the most malignant tumor that occurs in women and is one of the leading causes of death from gynecologic malignancy worldwide. The high degree of heterogeneity that characterizes breast cancer makes it challenging to devise effective therapeutic strategies. Accumulating evidence highlights the crucial role of stratifying breast cancer patients into clinically significant subtypes to achieve better prognoses and treatments. The structural deep clustering network is a graph convolutional network-based clustering algorithm that integrates structural information and has achieved state-of-the-art performance in various applications. METHODS In this study, we employed structural deep clustering network to integrate somatic mutation profiles for stratifying 2526 breast cancer patients from the Memorial Sloan Kettering Cancer Center into two clinically differentiable subtypes. RESULTS Breast cancer patients in cluster 1 exhibited better prognosis than breast cancer patients in cluster 2, and the difference between them was statistically significant. The immunogenomic landscape further demonstrated that cluster 1 was associated with remarkable infiltration of the tumor infiltrating lymphocytes. The clustering subtype could be used to evaluate the therapeutic benefit of immunotherapy and chemotherapy in breast cancer patients. Furthermore, our approach effectively classified patients from eight different cancer types, demonstrating its generalizability. CONCLUSIONS Our study represents a step towards a generic methodology for classifying cancer patients using only somatic mutation data and structural deep clustering network approaches. Employing structural deep clustering network to identify breast cancer subtypes is promising and can inform the development of more accurate and personalized therapies.
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Affiliation(s)
- Dongqing Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yuqiang Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Shiyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Haodong Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Jiawei Ke
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Honghao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Tao Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China; Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd. Hohhot, 010010, China; Inner Mongolia International Mongolian Hospital, Hohhot 010065, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
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Melograna F, Li Z, Galazzo G, van Best N, Mommers M, Penders J, Stella F, Van Steen K. Edge and modular significance assessment in individual-specific networks. Sci Rep 2023; 13:7868. [PMID: 37188794 PMCID: PMC10185658 DOI: 10.1038/s41598-023-34759-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 05/07/2023] [Indexed: 05/17/2023] Open
Abstract
Individual-specific networks, defined as networks of nodes and connecting edges that are specific to an individual, are promising tools for precision medicine. When such networks are biological, interpretation of functional modules at an individual level becomes possible. An under-investigated problem is relevance or "significance" assessment of each individual-specific network. This paper proposes novel edge and module significance assessment procedures for weighted and unweighted individual-specific networks. Specifically, we propose a modular Cook's distance using a method that involves iterative modeling of one edge versus all the others within a module. Two procedures assessing changes between using all individuals and using all individuals but leaving one individual out (LOO) are proposed as well (LOO-ISN, MultiLOO-ISN), relying on empirically derived edges. We compare our proposals to competitors, including adaptions of OPTICS, kNN, and Spoutlier methods, by an extensive simulation study, templated on real-life scenarios for gene co-expression and microbial interaction networks. Results show the advantages of performing modular versus edge-wise significance assessments for individual-specific networks. Furthermore, modular Cook's distance is among the top performers across all considered simulation settings. Finally, the identification of outlying individuals regarding their individual-specific networks, is meaningful for precision medicine purposes, as confirmed by network analysis of microbiome abundance profiles.
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Affiliation(s)
- Federico Melograna
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium.
| | - Zuqi Li
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Gianluca Galazzo
- School of Nutrition and Translational Research in Metabolism (NUTRIM), Department of Medical Microbiology Infectious Diseases and Infection Prevention, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Niels van Best
- Institute of Medical Microbiology, RWTH University Hospital Aachen, RWTH University, Aachen, Germany
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Monique Mommers
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - John Penders
- School of Nutrition and Translational Research in Metabolism (NUTRIM), Department of Medical Microbiology Infectious Diseases and Infection Prevention, Maastricht University Medical Center+, Maastricht, The Netherlands
- Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Fabio Stella
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126, Milan, Italy
| | - Kristel Van Steen
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
- BIO3 - Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
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6
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Tojo T, Yamaoka-Tojo M. Molecular Mechanisms Underlying the Progression of Aortic Valve Stenosis: Bioinformatic Analysis of Signal Pathways and Hub Genes. Int J Mol Sci 2023; 24:ijms24097964. [PMID: 37175670 PMCID: PMC10177913 DOI: 10.3390/ijms24097964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
The calcification of the aortic valve causes increased leaflet stiffness and leads to the development and progression of stenotic aortic valve disease. However, the molecular and cellular mechanisms underlying stenotic calcification remain poorly understood. Herein, we examined the gene expression associated with valve calcification and the progression of calcific aortic valve stenosis. We downloaded two publicly available gene expression profiles (GSE83453 and GSE51472) from NCBI-Gene Expression Omnibus database for the combined analysis of samples from human aortic stenosis and normal aortic valve tissue. After identifying the differentially expressed genes (DEGs) using the GEO2R online tool, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We also analyzed the protein-protein interactions (PPIs) of the DEGs using the NetworkAnalyst online tool. We identified 4603 upregulated and 6272 downregulated DEGs, which were enriched in the positive regulation of cell adhesion, leukocyte-mediated immunity, response to hormones, cytokine signaling in the immune system, lymphocyte activation, and growth hormone receptor signaling. PPI network analysis identified 10 hub genes: VCAM1, FHL2, RUNX1, TNFSF10, PLAU, SPOCK1, CD74, SIPA1L2, TRIB1, and CXCL12. Through bioinformatic analysis, we identified potential biomarkers and therapeutic targets for aortic stenosis, providing a theoretical basis for future studies.
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Affiliation(s)
- Taiki Tojo
- Department of Cardiovascular Medicine, Kitasato University School of Medicine, Sagamihara 252-0374, Japan
| | - Minako Yamaoka-Tojo
- Department of Cardiovascular Medicine, Kitasato University School of Medicine, Sagamihara 252-0374, Japan
- Department of Rehabilitation, Kitasato University School of Allied Health Sciences, Sagamihara 252-0373, Japan
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7
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Zeng L, Huang H, Liu Y, Ruan C, Fan S, Xia Y, Zhou J. The core symptom in multiple myeloma patients undergoing chemotherapy: a network analysis. Support Care Cancer 2023; 31:297. [PMID: 37097532 PMCID: PMC10126563 DOI: 10.1007/s00520-023-07759-7] [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: 11/23/2022] [Accepted: 04/16/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND During chemotherapy for multiple myeloma, symptoms include those related to the disease, as well as adverse effects of the treatment. Few studies have explored the relationships between these symptoms. Network analysis could identify the core symptom in the symptom network. OBJECTIVE The aim of this study was to explore the core symptom in multiple myeloma patients undergoing chemotherapy. METHODS This was a cross-sectional study in which sequential sampling was used to recruit 177 participants from Hunan, China. Demographic and clinical characteristics were surveyed using a self-developed instrument. The symptoms of chemotherapy-treated multiple myeloma, including pain, fatigue, worry, nausea, and vomiting, were measured using a questionnaire with good reliability and validity. The mean ± SD, frequency, and percentages were used as descriptive statistics. Network analysis was used to estimate the correlation between symptoms. RESULTS The results showed that 70% of multiple myeloma patients using chemotherapy exhibited pain. In the network analysis, worrying was the dominant symptom, and the strongest relationship was between nausea and vomiting in chemotherapy-treated multiple myeloma patients' symptoms. CONCLUSION Worrying is the core symptom of multiple myeloma patients. Interventions could be most effective if there is a symptom management focus on worrying when providing care to chemotherapy-treated multiple myeloma patients. Nausea combined with vomiting could be better managed, which would decrease the cost of health care. Understanding the relationship between the symptoms of multiple myeloma patients undergoing chemotherapy is beneficial for precise symptom management. IMPLICATIONS FOR PRACTICE Nurses and health care teams should be a priority to intervene in the worrying for chemotherapy-treated multiple myeloma patients to maximize the effectiveness of an intervention. Except, nausea and vomiting should be managed together in a clinical setting.
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Affiliation(s)
- Lihong Zeng
- Xiangya School of Nursing, Central South University, Changsha, China
| | - Hui Huang
- The Third Xiangya Hospital, Central South University, Changsha, China.
| | - Yaqi Liu
- The Third Xiangya Hospital, Central South University, Changsha, China
| | - Chunhong Ruan
- The Third Xiangya Hospital, Central South University, Changsha, China
| | - Sisi Fan
- Xiangya School of Nursing, Central South University, Changsha, China
| | - Yuting Xia
- Xiangya School of Nursing, Central South University, Changsha, China
| | - Jiandang Zhou
- The Third Xiangya Hospital, Central South University, Changsha, China.
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Wang X, Chan YS, Wong K, Yoshitake R, Sadava D, Synold TW, Frankel P, Twardowski PW, Lau C, Chen S. Mechanism-Driven and Clinically Focused Development of Botanical Foods as Multitarget Anticancer Medicine: Collective Perspectives and Insights from Preclinical Studies, IND Applications and Early-Phase Clinical Trials. Cancers (Basel) 2023; 15:701. [PMID: 36765659 PMCID: PMC9913787 DOI: 10.3390/cancers15030701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/13/2023] [Accepted: 01/19/2023] [Indexed: 01/25/2023] Open
Abstract
Cancer progression and mortality remain challenging because of current obstacles and limitations in cancer treatment. Continuous efforts are being made to explore complementary and alternative approaches to alleviate the suffering of cancer patients. Epidemiological and nutritional studies have indicated that consuming botanical foods is linked to a lower risk of cancer incidence and/or improved cancer prognosis after diagnosis. From these observations, a variety of preclinical and clinical studies have been carried out to evaluate the potential of botanical food products as anticancer medicines. Unfortunately, many investigations have been poorly designed, and encouraging preclinical results have not been translated into clinical success. Botanical products contain a wide variety of chemicals, making them more difficult to study than traditional drugs. In this review, with the consideration of the regulatory framework of the USFDA, we share our collective experiences and lessons learned from 20 years of defining anticancer foods, focusing on the critical aspects of preclinical studies that are required for an IND application, as well as the checkpoints needed for early-phase clinical trials. We recommend a developmental pipeline that is based on mechanisms and clinical considerations.
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Affiliation(s)
- Xiaoqiang Wang
- Department of Cancer Biology & Molecular Medicine, Beckman Research Institute, City of Hope, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - Yin S. Chan
- Department of Cancer Biology & Molecular Medicine, Beckman Research Institute, City of Hope, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - Kelly Wong
- Department of Cancer Biology & Molecular Medicine, Beckman Research Institute, City of Hope, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - Ryohei Yoshitake
- Department of Cancer Biology & Molecular Medicine, Beckman Research Institute, City of Hope, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - David Sadava
- Department of Cancer Biology & Molecular Medicine, Beckman Research Institute, City of Hope, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - Timothy W. Synold
- Department of Medical Oncology & Therapeutics Research, City of Hope Comprehensive Cancer Center, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - Paul Frankel
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - Przemyslaw W. Twardowski
- Department of Urologic Oncology, Saint John’s Cancer Institute, 2200 Santa Monica Blvd, Santa Monica, CA 90404, USA
| | - Clayton Lau
- Department of Surgery, City of Hope Comprehensive Cancer Center, 1500 E. Duarte Rd., Duarte, CA 91010, USA
| | - Shiuan Chen
- Department of Cancer Biology & Molecular Medicine, Beckman Research Institute, City of Hope, 1500 E. Duarte Rd., Duarte, CA 91010, USA
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9
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Peixoto C, Lopes MB, Martins M, Casimiro S, Sobral D, Grosso AR, Abreu C, Macedo D, Costa AL, Pais H, Alvim C, Mansinho A, Filipe P, Costa PMD, Fernandes A, Borralho P, Ferreira C, Malaquias J, Quintela A, Kaplan S, Golkaram M, Salmans M, Khan N, Vijayaraghavan R, Zhang S, Pawlowski T, Godsey J, So A, Liu L, Costa L, Vinga S. Identification of biomarkers predictive of metastasis development in early-stage colorectal cancer using network-based regularization. BMC Bioinformatics 2023; 24:17. [PMID: 36647008 PMCID: PMC9841719 DOI: 10.1186/s12859-022-05104-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 12/07/2022] [Indexed: 01/18/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common cancer and the second most deathly worldwide. It is a very heterogeneous disease that can develop via distinct pathways where metastasis is the primary cause of death. Therefore, it is crucial to understand the molecular mechanisms underlying metastasis. RNA-sequencing is an essential tool used for studying the transcriptional landscape. However, the high-dimensionality of gene expression data makes selecting novel metastatic biomarkers problematic. To distinguish early-stage CRC patients at risk of developing metastasis from those that are not, three types of binary classification approaches were used: (1) classification methods (decision trees, linear and radial kernel support vector machines, logistic regression, and random forest) using differentially expressed genes (DEGs) as input features; (2) regularized logistic regression based on the Elastic Net penalty and the proposed iTwiner-a network-based regularizer accounting for gene correlation information; and (3) classification methods based on the genes pre-selected using regularized logistic regression. Classifiers using the DEGs as features showed similar results, with random forest showing the highest accuracy. Using regularized logistic regression on the full dataset yielded no improvement in the methods' accuracy. Further classification using the pre-selected genes found by different penalty factors, instead of the DEGs, significantly improved the accuracy of the binary classifiers. Moreover, the use of network-based correlation information (iTwiner) for gene selection produced the best classification results and the identification of more stable and robust gene sets. Some are known to be tumor suppressor genes (OPCML-IT2), to be related to resistance to cancer therapies (RAC1P3), or to be involved in several cancer processes such as genome stability (XRCC6P2), tumor growth and metastasis (MIR602) and regulation of gene transcription (NME2P2). We show that the classification of CRC patients based on pre-selected features by regularized logistic regression is a valuable alternative to using DEGs, significantly increasing the models' predictive performance. Moreover, the use of correlation-based penalization for biomarker selection stands as a promising strategy for predicting patients' groups based on RNA-seq data.
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Affiliation(s)
- Carolina Peixoto
- grid.9983.b0000 0001 2181 4263INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, 1000-029 Lisbon, Portugal
| | - Marta B. Lopes
- NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), NOVA School of Science and Technology, 2829-516 Caparica, Portugal ,Center for Mathematics and Applications (NOVA MATH), NOVA School of Science and Technology (FCT NOVA), 2829-516 Caparica, Portugal
| | - Marta Martins
- grid.9983.b0000 0001 2181 4263Instituto de Medicina Molecular - João Lobo Antunes, Faculdade de Medicina de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisbon, Portugal
| | - Sandra Casimiro
- grid.9983.b0000 0001 2181 4263Instituto de Medicina Molecular - João Lobo Antunes, Faculdade de Medicina de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisbon, Portugal
| | - Daniel Sobral
- grid.10772.330000000121511713Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal ,grid.10772.330000000121511713UCIBIO - Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Ana Rita Grosso
- grid.10772.330000000121511713Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal ,grid.10772.330000000121511713UCIBIO - Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Catarina Abreu
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Daniela Macedo
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Ana Lúcia Costa
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Helena Pais
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Cecília Alvim
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - André Mansinho
- grid.9983.b0000 0001 2181 4263Instituto de Medicina Molecular - João Lobo Antunes, Faculdade de Medicina de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisbon, Portugal ,grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Pedro Filipe
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Pedro Marques da Costa
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Afonso Fernandes
- grid.9983.b0000 0001 2181 4263Instituto de Medicina Molecular - João Lobo Antunes, Faculdade de Medicina de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisbon, Portugal
| | - Paula Borralho
- grid.9983.b0000 0001 2181 4263Instituto de Medicina Molecular - João Lobo Antunes, Faculdade de Medicina de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisbon, Portugal
| | - Cristina Ferreira
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - João Malaquias
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - António Quintela
- grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Shannon Kaplan
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Mahdi Golkaram
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Michael Salmans
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Nafeesa Khan
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Raakhee Vijayaraghavan
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Shile Zhang
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Traci Pawlowski
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Jim Godsey
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Alex So
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Li Liu
- grid.185669.50000 0004 0507 3954Illumina Inc., 5200 Illumina Way, San Diego, CA 92122 USA
| | - Luís Costa
- grid.9983.b0000 0001 2181 4263Instituto de Medicina Molecular - João Lobo Antunes, Faculdade de Medicina de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisbon, Portugal ,grid.418341.b0000 0004 0474 1607Oncology Division, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | - Susana Vinga
- grid.9983.b0000 0001 2181 4263INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, 1000-029 Lisbon, Portugal ,grid.9983.b0000 0001 2181 4263IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal
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10
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Pacheco Pachado M, Casas AI, Elbatreek MH, Nogales C, Guney E, Espay AJ, Schmidt HH. Re-Addressing Dementia by Network Medicine and Mechanism-Based Molecular Endotypes. J Alzheimers Dis 2023; 96:47-56. [PMID: 37742653 PMCID: PMC10657714 DOI: 10.3233/jad-230694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/22/2023] [Indexed: 09/26/2023]
Abstract
Alzheimer's disease (AD) and other forms of dementia are together a leading cause of disability and death in the aging global population, imposing a high personal, societal, and economic burden. They are also among the most prominent examples of failed drug developments. Indeed, after more than 40 AD trials of anti-amyloid interventions, reduction of amyloid-β (Aβ) has never translated into clinically relevant benefits, and in several cases yielded harm. The fundamental problem is the century-old, brain-centric phenotype-based definitions of diseases that ignore causal mechanisms and comorbidities. In this hypothesis article, we discuss how such current outdated nosology of dementia is a key roadblock to precision medicine and articulate how Network Medicine enables the substitution of clinicopathologic phenotypes with molecular endotypes and propose a new framework to achieve precision and curative medicine for patients with neurodegenerative disorders.
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Affiliation(s)
- Mayra Pacheco Pachado
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Ana I. Casas
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Universitätsklinikum Essen, Klinik für Neurologie, Essen, Germany
| | - Mahmoud H. Elbatreek
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Zagazig University, Zagazig, Egypt
| | - Cristian Nogales
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Emre Guney
- Discovery and Data Science (DDS) Unit, STALICLA R&D SL, Barcelona, Spain
| | - Alberto J. Espay
- James J. and Joan A. Gardner Family Center for Parkinson’s Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, OH, USA
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
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11
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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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12
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Gentili M, Martini L, Sponziello M, Becchetti L. Biological Random Walks: multi-omics integration for disease gene prioritization. Bioinformatics 2022; 38:4145-4152. [PMID: 35792834 DOI: 10.1093/bioinformatics/btac446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 06/22/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Over the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets. This is all the more important, since experimental investigation of potential gene candidates is an expensive task, thus not always a feasible option. On the other hand, many sources of biological information exist beyond the interactome and an important research direction is the design of effective techniques for their integration. RESULTS In this work, we introduce the Biological Random Walks (BRW) approach for disease gene prioritization in the human interactome. The proposed framework leverages multiple biological sources within an integrated framework. We perform an extensive, comparative study of BRW's performance against well-established baselines. AVAILABILITY AND IMPLEMENTATION All codes are publicly available and can be downloaded at https://github.com/LeoM93/BiologicalRandomWalks. We used publicly available datasets, details on their retrieval and preprocessing are provided in the Supplementary Material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Michele Gentili
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Leonardo Martini
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Marialuisa Sponziello
- Translational and Precision Medicine Department, Sapienza University of Rome, Rome, Italy
| | - Luca Becchetti
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
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13
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Identification of Signal Pathways and Hub Genes of Pulmonary Arterial Hypertension by Bioinformatic Analysis. Can Respir J 2022; 2022:1394088. [PMID: 36072642 PMCID: PMC9444450 DOI: 10.1155/2022/1394088] [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: 05/26/2022] [Accepted: 07/19/2022] [Indexed: 11/30/2022] Open
Abstract
Pulmonary arterial hypertension (PAH) is a progressive and complex pulmonary vascular disease with poor prognosis. The aim of this study was to provide a new understanding of the pathogenesis of disease and potential treatment targets for patients with PAH based on multiple-microarray analysis.Two microarray datasets (GSE53408 and GSE113439) downloaded from the Gene Expression Omnibus (GEO) database were analysed. All the raw data were processed by R, and differentially expressed genes (DEGs) were screened out by the “limma” package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed and visualized by R and Cytoscape software. Protein-protein interactions (PPI) of DEGs were analysed based on the NetworkAnalyst online tool. A total of 442 upregulated DEGs and 84 downregulated DEGs were identified. GO enrichment analysis showed that these DEGs were mainly enriched in mitotic nuclear division, organelle fission, chromosome segregation, nuclear division, and sister chromatid segregation. Significant KEGG pathway enrichment included ribosome biogenesis in eukaryotes, RNA transport, proteoglycans in cancer, dilated cardiomyopathy, rheumatoid arthritis, vascular smooth muscle contraction, focal adhesion, regulation of the actin cytoskeleton, and hypertrophic cardiomyopathy. The PPI network identified 10 hub genes including HSP90AA1, CDC5L, MDM2, LRRK2, CFTR, IQGAP1, CAND1, TOP2A, DDX21, and HIF1A. We elucidated potential biomarkers and therapeutic targets for PAH by bioinformatic analysis, which provides a theoretical basis for future study.
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14
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Patient-level proteomic network prediction by explainable artificial intelligence. NPJ Precis Oncol 2022; 6:35. [PMID: 35672443 PMCID: PMC9174200 DOI: 10.1038/s41698-022-00278-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 04/15/2022] [Indexed: 11/08/2022] Open
Abstract
Understanding the pathological properties of dysregulated protein networks in individual patients’ tumors is the basis for precision therapy. Functional experiments are commonly used, but cover only parts of the oncogenic signaling networks, whereas methods that reconstruct networks from omics data usually only predict average network features across tumors. Here, we show that the explainable AI method layer-wise relevance propagation (LRP) can infer protein interaction networks for individual patients from proteomic profiling data. LRP reconstructs average and individual interaction networks with an AUC of 0.99 and 0.93, respectively, and outperforms state-of-the-art network prediction methods for individual tumors. Using data from The Cancer Proteome Atlas, we identify known and potentially novel oncogenic network features, among which some are cancer-type specific and show only minor variation among patients, while others are present across certain tumor types but differ among individual patients. Our approach may therefore support predictive diagnostics in precision oncology by inferring “patient-level” oncogenic mechanisms.
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15
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Petrov I, Alexeyenko A. Individualized discovery of rare cancer drivers in global network context. eLife 2022; 11:74010. [PMID: 35593700 PMCID: PMC9159755 DOI: 10.7554/elife.74010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 05/20/2022] [Indexed: 11/13/2022] Open
Abstract
Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become ‘hard currency’ of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of global interaction network and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations’ co-occurrence in each tumour genome – as an alternative to summarizing information over the whole cancer patient cohorts. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to 10 largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. This revealed driver genes with individually sparse mutation patterns that would not be detectable by other computational methods and related to cancer biology domains poorly covered by previous analyses. In particular, recurrent mutations of collagen, laminin, and integrin genes were observed in the adenocarcinoma and glioblastoma cancers. Considering constellation patterns of candidate drivers in individual cancer genomes opens a novel avenue for personalized cancer medicine.
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Affiliation(s)
- Iurii Petrov
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.,Science for Life Laboratory, Solna, Sweden
| | - Andrey Alexeyenko
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.,Science for Life Laboratory, Solna, Sweden.,Evi-networks, enskild konsultföretag, Huddinge, Sweden
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16
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Sun P, Wu Y, Yin C, Jiang H, Xu Y, Sun H. Molecular Subtyping of Cancer Based on Distinguishing Co-Expression Modules and Machine Learning. Front Genet 2022; 13:866005. [PMID: 35586568 PMCID: PMC9108363 DOI: 10.3389/fgene.2022.866005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/07/2022] [Indexed: 02/05/2023] Open
Abstract
Molecular subtyping of cancer is recognized as a critical and challenging step towards individualized therapy. Most existing computational methods solve this problem via multi-classification of gene-expressions of cancer samples. Although these methods, especially deep learning, perform well in data classification, they usually require large amounts of data for model training and have limitations in interpretability. Besides, as cancer is a complex systemic disease, the phenotypic difference between cancer samples can hardly be fully understood by only analyzing single molecules, and differential expression-based molecular subtyping methods are reportedly not conserved. To address the above issues, we present here a new framework for molecular subtyping of cancer through identifying a robust specific co-expression module for each subtype of cancer, generating network features for each sample by perturbing correlation levels of specific edges, and then training a deep neural network for multi-class classification. When applied to breast cancer (BRCA) and stomach adenocarcinoma (STAD) molecular subtyping, it has superior classification performance over existing methods. In addition to improving classification performance, we consider the specific co-expressed modules selected for subtyping to be biologically meaningful, which potentially offers new insight for diagnostic biomarker design, mechanistic studies of cancer, and individualized treatment plan selection.
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Affiliation(s)
- Peishuo Sun
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Ying Wu
- Phase I Clinical Trails Center, The First Affiliated Hospital, China Medical University, Shenyang, China
| | - Chaoyi Yin
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Hongyang Jiang
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Ying Xu
- Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics University of Georgia, Athens, GA, United States
- *Correspondence: Huiyan Sun, ; Ying Xu,
| | - Huiyan Sun
- School of Artificial Intelligence, Jilin University, Changchun, China
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
- *Correspondence: Huiyan Sun, ; Ying Xu,
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17
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Kaczmarczyk R, Sitaru S, Wecker H, Zink A. [Network analyses as a valuable extension of conventional statistics]. Hautarzt 2022; 73:735-739. [PMID: 35482044 PMCID: PMC9046710 DOI: 10.1007/s00105-022-04998-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/05/2022] [Indexed: 11/25/2022]
Affiliation(s)
- Robert Kaczmarczyk
- Klinik und Poliklinik für Dermatologie und Allergologie, München Rechts der Isar, Technische Universität München, Biedersteiner Str. 29, 80802, München, Deutschland.
| | - Sebastian Sitaru
- Klinik und Poliklinik für Dermatologie und Allergologie, München Rechts der Isar, Technische Universität München, Biedersteiner Str. 29, 80802, München, Deutschland
| | - Hannah Wecker
- Klinik und Poliklinik für Dermatologie und Allergologie, München Rechts der Isar, Technische Universität München, Biedersteiner Str. 29, 80802, München, Deutschland
| | - Alexander Zink
- Klinik und Poliklinik für Dermatologie und Allergologie, München Rechts der Isar, Technische Universität München, Biedersteiner Str. 29, 80802, München, Deutschland
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18
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Panditrao G, Bhowmick R, Meena C, Sarkar RR. Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects. J Biosci 2022. [PMID: 36210749 PMCID: PMC9018971 DOI: 10.1007/s12038-022-00253-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.
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Affiliation(s)
- Gauri Panditrao
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
| | - Rupa Bhowmick
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
| | - Chandrakala Meena
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
| | - Ram Rup Sarkar
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
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19
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Redefining the hypotheses driving Parkinson's diseases research. NPJ Parkinsons Dis 2022; 8:45. [PMID: 35440633 PMCID: PMC9018840 DOI: 10.1038/s41531-022-00307-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 03/04/2022] [Indexed: 12/20/2022] Open
Abstract
Parkinson’s disease (PD) research has largely focused on the disease as a single entity centred on the development of neuronal pathology within the central nervous system. However, there is growing recognition that PD is not a single entity but instead reflects multiple diseases, in which different combinations of environmental, genetic and potential comorbid factors interact to direct individual disease trajectories. Moreover, an increasing body of recent research implicates peripheral tissues and non-neuronal cell types in the development of PD. These observations are consistent with the hypothesis that the initial causative changes for PD development need not occur in the central nervous system. Here, we discuss how the use of neuronal pathology as a shared, qualitative phenotype minimises insights into the possibility of multiple origins and aetiologies of PD. Furthermore, we discuss how considering PD as a single entity potentially impairs our understanding of the causative molecular mechanisms, approaches for patient stratification, identification of biomarkers, and the development of therapeutic approaches to PD. The clear consequence of there being distinct diseases that collectively form PD, is that there is no single biomarker or treatment for PD development or progression. We propose that diagnosis should shift away from the clinical definitions, towards biologically defined diseases that collectively form PD, to enable informative patient stratification. N-of-one type, clinical designs offer an unbiased, and agnostic approach to re-defining PD in terms of a group of many individual diseases.
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20
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Gan SKE, Phua SX, Yeo JY. Sagacious epitope selection for vaccines, and both antibody-based therapeutics and diagnostics: tips from virology and oncology. Antib Ther 2022; 5:63-72. [PMID: 35372784 PMCID: PMC8972324 DOI: 10.1093/abt/tbac005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/24/2022] [Accepted: 02/12/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
The target of an antibody plays a significant role in the success of antibody-based therapeutics and diagnostics, and vaccine development. This importance is focused on the target binding site—epitope, where epitope selection as a part of design thinking beyond traditional antigen selection using whole cell or whole protein immunization can positively impact success. With purified recombinant protein production and peptide synthesis to display limited/selected epitopes, intrinsic factors that can affect the functioning of resulting antibodies can be more easily selected for. Many of these factors stem from the location of the epitope that can impact accessibility of the antibody to the epitope at a cellular or molecular level, direct inhibition of target antigen activity, conservation of function despite escape mutations, and even non-competitive inhibition sites. By incorporating novel computational methods for predicting antigen changes to model-informed drug discovery and development, superior vaccines and antibody-based therapeutics or diagnostics can be easily designed to mitigate failures. With detailed examples, this review highlights the new opportunities, factors and methods of predicting antigenic changes for consideration in sagacious epitope selection.
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Affiliation(s)
- Samuel Ken-En Gan
- Antibody & Product Development Lab, EDDC-BII, Agency for Science, Technology and Research (A*STAR), Singapore 138672, Singapore
- APD SKEG Pte Ltd, Singapore 439444, Singapore
| | - Ser-Xian Phua
- Antibody & Product Development Lab, EDDC-BII, Agency for Science, Technology and Research (A*STAR), Singapore 138672, Singapore
| | - Joshua Yi Yeo
- Antibody & Product Development Lab, EDDC-BII, Agency for Science, Technology and Research (A*STAR), Singapore 138672, Singapore
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21
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Abstract
DNA microarrays are widely used to investigate gene expression. Even though the classical analysis of microarray data is based on the study of differentially expressed genes, it is well known that genes do not act individually. Network analysis can be applied to study association patterns of the genes in a biological system. Moreover, it finds wide application in differential coexpression analysis between different systems. Network based coexpression studies have for example been used in (complex) disease gene prioritization, disease subtyping, and patient stratification.In this chapter we provide an overview of the methods and tools used to create networks from microarray data and describe multiple methods on how to analyze a single network or a group of networks. The described methods range from topological metrics, functional group identification to data integration strategies, topological pathway analysis as well as graphical models.
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Affiliation(s)
- Alisa Pavel
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland
| | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland
| | - Luca Cattelani
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- BioMediTech Institute, Tampere University, Tampere, Finland.
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland.
- Institute of Biotechnology , University of Helsinki, Helsinki, Finland.
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22
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Cutigi JF, Evangelista AF, Reis RM, Simao A. A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks. Sci Rep 2021; 11:23551. [PMID: 34876593 PMCID: PMC8651746 DOI: 10.1038/s41598-021-02671-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 10/26/2021] [Indexed: 11/25/2022] Open
Abstract
Identifying significantly mutated genes in cancer is essential for understanding the mechanisms of tumor initiation and progression. This task is a key challenge since large-scale genomic studies have reported an endless number of genes mutated at a shallow frequency. Towards uncovering infrequently mutated genes, gene interaction networks combined with mutation data have been explored. This work proposes Discovering Significant Cancer Genes (DiSCaGe), a computational method for discovering significant genes for cancer. DiSCaGe computes a mutation score for the genes based on the type of mutations they have. The influence received for their neighbors in the network is also considered and obtained through an asymmetric spreading strength applied to a consensus gene network. DiSCaGe produces a ranking of prioritized possible cancer genes. An experimental evaluation with six types of cancer revealed the potential of DiSCaGe for discovering known and possible novel significant cancer genes.
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Affiliation(s)
- Jorge Francisco Cutigi
- Federal Institute of Sao Paulo, Sao Carlos, SP, Brazil.
- University of Sao Paulo, Sao Carlos, SP, Brazil.
| | | | - Rui Manuel Reis
- Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, SP, Brazil
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23
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Kan Y, Jiang L, Guo Y, Tang J, Guo F. Two-stage-vote ensemble framework based on integration of mutation data and gene interaction network for uncovering driver genes. Brief Bioinform 2021; 23:6426028. [PMID: 34791034 DOI: 10.1093/bib/bbab429] [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: 07/25/2021] [Revised: 08/30/2021] [Accepted: 09/18/2021] [Indexed: 11/14/2022] Open
Abstract
Identifying driver genes, exactly from massive genes with mutations, promotes accurate diagnosis and treatment of cancer. In recent years, a lot of works about uncovering driver genes based on integration of mutation data and gene interaction networks is gaining more attention. However, it is in suspense if it is more effective for prioritizing driver genes when integrating various types of mutation information (frequency and functional impact) and gene networks. Hence, we build a two-stage-vote ensemble framework based on somatic mutations and mutual interactions. Specifically, we first represent and combine various kinds of mutation information, which are propagated through networks by an improved iterative framework. The first vote is conducted on iteration results by voting methods, and the second vote is performed to get ensemble results of the first poll for the final driver gene list. Compared with four excellent previous approaches, our method has better performance in identifying driver genes on $33$ types of cancer from The Cancer Genome Atlas. Meanwhile, we also conduct a comparative analysis about two kinds of mutation information, five gene interaction networks and four voting strategies. Our framework offers a new view for data integration and promotes more latent cancer genes to be admitted.
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Affiliation(s)
- Yingxin Kan
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Limin Jiang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.,Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yan Guo
- Comprehensive cancer center, Department of Internal Medicine, University of New Mexico, Albuquerque, U.S
| | - Jijun Tang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,School of Computational Science and Engineering, University of South Carolina, Columbia, U.S
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, China
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24
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Demirel HC, Arici MK, Tuncbag N. Computational approaches leveraging integrated connections of multi-omic data toward clinical applications. Mol Omics 2021; 18:7-18. [PMID: 34734935 DOI: 10.1039/d1mo00158b] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing cellular activity. The complexity of the biological processes arises from the interactions between omic entities such as genes, proteins, and metabolites. Therefore, multi-omic data integration is crucial but challenging. The impact of the molecular alterations in multi-omic data is not local in the neighborhood of the altered gene or protein; rather, the impact diffuses in the network and changes the functionality of multiple signaling pathways and regulation of the gene expression. Additionally, multi-omic data is high-dimensional and has background noise. Several integrative approaches have been developed to accurately interpret the multi-omic datasets, including machine learning, network-based methods, and their combination. In this review, we overview the most recent integrative approaches and tools with a focus on network-based methods. We then discuss these approaches according to their specific applications, from disease-network and biomarker identification to patient stratification, drug discovery, and repurposing.
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Affiliation(s)
- Habibe Cansu Demirel
- Graduate School of Informatics, Middle East Technical University, Ankara, 06800, Turkey
| | - Muslum Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, 06800, Turkey.,Foot and Mouth Diseases Institute, Ministry of Agriculture and Forestry, Ankara, 06044, Turkey
| | - Nurcan Tuncbag
- Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul, 34450, Turkey.,School of Medicine, Koc University, Istanbul, 34450, Turkey.,Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, Turkey.
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25
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Chowdhury S, Hofree M, Lin K, Maru D, Kopetz S, Shen JP. Implications of Intratumor Heterogeneity on Consensus Molecular Subtype (CMS) in Colorectal Cancer. Cancers (Basel) 2021; 13:4923. [PMID: 34638407 PMCID: PMC8507736 DOI: 10.3390/cancers13194923] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 09/21/2021] [Accepted: 09/25/2021] [Indexed: 01/04/2023] Open
Abstract
The implications of intratumor heterogeneity on the four consensus molecular subtypes (CMS) of colorectal cancer (CRC) are not well known. Here, we use single-cell RNA sequencing (scRNASeq) to build an algorithm to assign CMS classification to individual cells, which we use to explore the distributions of CMSs in tumor and non-tumor cells. A dataset of colorectal tumors with bulk RNAseq (n = 3232) was used to identify CMS specific-marker gene sets. These gene sets were then applied to a discovery dataset of scRNASeq profiles (n = 10) to develop an algorithm for single-cell CMS (scCMS) assignment, which recapitulated the intrinsic biology of all four CMSs. The single-cell CMS assignment algorithm was used to explore the scRNASeq profiles of two prospective CRC tumors with mixed CMS via bulk sequencing. We find that every CRC tumor contains individual cells of each scCMS, as well as many individual cells that have enrichment for features of more than one scCMS (called mixed cells). scCMS4 and scCMS1 cells dominate stroma and immune cell clusters, respectively, but account for less than 3% epithelial cells. These data imply that CMS1 and CMS4 are driven by the transcriptomic contribution of immune and stromal cells, respectively, not tumor cells.
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Affiliation(s)
- Saikat Chowdhury
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (S.C.); (K.L.); (S.K.)
| | - Matan Hofree
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA;
- Department of Biological Regulation, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Kangyu Lin
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (S.C.); (K.L.); (S.K.)
| | - Dipen Maru
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Scott Kopetz
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (S.C.); (K.L.); (S.K.)
| | - John Paul Shen
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (S.C.); (K.L.); (S.K.)
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26
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Guo WF, Zhang SW, Zeng T, Akutsu T, Chen L. Network control principles for identifying personalized driver genes in cancer. Brief Bioinform 2021; 21:1641-1662. [PMID: 31711128 DOI: 10.1093/bib/bbz089] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 06/26/2019] [Accepted: 06/27/2019] [Indexed: 02/02/2023] Open
Abstract
To understand tumor heterogeneity in cancer, personalized driver genes (PDGs) need to be identified for unraveling the genotype-phenotype associations corresponding to particular patients. However, most of the existing driver-focus methods mainly pay attention on the cohort information rather than on individual information. Recent developing computational approaches based on network control principles are opening a new way to discover driver genes in cancer, particularly at an individual level. To provide comprehensive perspectives of network control methods on this timely topic, we first considered the cancer progression as a network control problem, in which the expected PDGs are altered genes by oncogene activation signals that can change the individual molecular network from one health state to the other disease state. Then, we reviewed the network reconstruction methods on single samples and introduced novel network control methods on single-sample networks to identify PDGs in cancer. Particularly, we gave a performance assessment of the network structure control-based PDGs identification methods on multiple cancer datasets from TCGA, for which the data and evaluation package also are publicly available. Finally, we discussed future directions for the application of network control methods to identify PDGs in cancer and diverse biological processes.
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Affiliation(s)
- Wei-Feng Guo
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan
| | - Luonan Chen
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.,Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, 200031, China.,School of Life Science and Technology, ShanghaiTech University, 201210 Shanghai, China.,Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
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27
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Karimi MR, Karimi AH, Abolmaali S, Sadeghi M, Schmitz U. Prospects and challenges of cancer systems medicine: from genes to disease networks. Brief Bioinform 2021; 23:6361045. [PMID: 34471925 PMCID: PMC8769701 DOI: 10.1093/bib/bbab343] [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: 05/04/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 12/20/2022] Open
Abstract
It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.
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Affiliation(s)
| | | | | | - Mehdi Sadeghi
- Department of Cell & Molecular Biology, Semnan University, Semnan, Iran
| | - Ulf Schmitz
- Department of Molecular & Cell Biology, James Cook University, Townsville, QLD 4811, Australia
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28
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Ozturk K, Carter H. Predicting functional consequences of mutations using molecular interaction network features. Hum Genet 2021; 141:1195-1210. [PMID: 34432150 PMCID: PMC8873243 DOI: 10.1007/s00439-021-02329-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 07/31/2021] [Indexed: 12/13/2022]
Abstract
Variant interpretation remains a central challenge for precision medicine. Missense variants are particularly difficult to understand as they change only a single amino acid in a protein sequence yet can have large and varied effects on protein activity. Numerous tools have been developed to identify missense variants with putative disease consequences from protein sequence and structure. However, biological function arises through higher order interactions among proteins and molecules within cells. We therefore sought to capture information about the potential of missense mutations to perturb protein interaction networks by integrating protein structure and interaction data. We developed 16 network-based annotations for missense mutations that provide orthogonal information to features classically used to prioritize variants. We then evaluated them in the context of a proven machine-learning framework for variant effect prediction across multiple benchmark datasets to demonstrate their potential to improve variant classification. Interestingly, network features resulted in larger performance gains for classifying somatic mutations than for germline variants, possibly due to different constraints on what mutations are tolerated at the cellular versus organismal level. Our results suggest that modeling variant potential to perturb context-specific interactome networks is a fruitful strategy to advance in silico variant effect prediction.
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Affiliation(s)
- Kivilcim Ozturk
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA.,Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - Hannah Carter
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA. .,Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA. .,Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.
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29
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Qi X, Lin Y, Chen J, Shen B. Decoding competing endogenous RNA networks for cancer biomarker discovery. Brief Bioinform 2021; 21:441-457. [PMID: 30715152 DOI: 10.1093/bib/bbz006] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 12/13/2018] [Accepted: 12/25/2018] [Indexed: 02/05/2023] Open
Abstract
Crosstalk between competing endogenous RNAs (ceRNAs) is mediated by shared microRNAs (miRNAs) and plays important roles both in normal physiology and tumorigenesis; thus, it is attractive for systems-level decoding of gene regulation. As ceRNA networks link the function of miRNAs with that of transcripts sharing the same miRNA response elements (MREs), e.g. pseudogenes, competing mRNAs, long non-coding RNAs, and circular RNAs, the perturbation of crucial interactions in ceRNA networks may contribute to carcinogenesis by affecting the balance of cellular regulatory system. Therefore, discovering biomarkers that indicate cancer initiation, development, and/or therapeutic responses via reconstructing and analyzing ceRNA networks is of clinical significance. In this review, the regulatory function of ceRNAs in cancer and crucial determinants of ceRNA crosstalk are firstly discussed to gain a global understanding of ceRNA-mediated carcinogenesis. Then, computational and experimental approaches for ceRNA network reconstruction and ceRNA validation, respectively, are described from a systems biology perspective. We focus on strategies for biomarker identification based on analyzing ceRNA networks and highlight the translational applications of ceRNA biomarkers for cancer management. This article will shed light on the significance of miRNA-mediated ceRNA interactions and provide important clues for discovering ceRNA network-based biomarker in cancer biology, thereby accelerating the pace of precision medicine and healthcare for cancer patients.
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Affiliation(s)
- Xin Qi
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Yuxin Lin
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Jiajia Chen
- School of Chemistry, Biology and Material Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Bairong Shen
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
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30
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Aberasturi D, Pouladi N, Zaim SR, Kenost C, Berghout J, Piegorsch WW, Lussier YA. 'Single-subject studies'-derived analyses unveil altered biomechanisms between very small cohorts: implications for rare diseases. Bioinformatics 2021; 37:i67-i75. [PMID: 34252934 PMCID: PMC8336591 DOI: 10.1093/bioinformatics/btab290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/26/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Identifying altered transcripts between very small human cohorts is particularly challenging and is compounded by the low accrual rate of human subjects in rare diseases or sub-stratified common disorders. Yet, single-subject studies (S3) can compare paired transcriptome samples drawn from the same patient under two conditions (e.g. treated versus pre-treatment) and suggest patient-specific responsive biomechanisms based on the overrepresentation of functionally defined gene sets. These improve statistical power by: (i) reducing the total features tested and (ii) relaxing the requirement of within-cohort uniformity at the transcript level. We propose Inter-N-of-1, a novel method, to identify meaningful differences between very small cohorts by using the effect size of 'single-subject-study'-derived responsive biological mechanisms. RESULTS In each subject, Inter-N-of-1 requires applying previously published S3-type N-of-1-pathways MixEnrich to two paired samples (e.g. diseased versus unaffected tissues) for determining patient-specific enriched genes sets: Odds Ratios (S3-OR) and S3-variance using Gene Ontology Biological Processes. To evaluate small cohorts, we calculated the precision and recall of Inter-N-of-1 and that of a control method (GLM+EGS) when comparing two cohorts of decreasing sizes (from 20 versus 20 to 2 versus 2) in a comprehensive six-parameter simulation and in a proof-of-concept clinical dataset. In simulations, the Inter-N-of-1 median precision and recall are > 90% and >75% in cohorts of 3 versus 3 distinct subjects (regardless of the parameter values), whereas conventional methods outperform Inter-N-of-1 at sample sizes 9 versus 9 and larger. Similar results were obtained in the clinical proof-of-concept dataset. AVAILABILITY AND IMPLEMENTATION R software is available at Lussierlab.net/BSSD.
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Affiliation(s)
- Dillon Aberasturi
- Center for Biomedical Informatics and Biostatistics (CB2), University of Arizona Health Sciences, University of Arizona, Tucson, AZ, USA 85721.,Department of Medicine, University of Arizona, Tucson, AZ, USA 85724-5035.,Graduate Interdisciplinary Program in Statistics & Data Science, Graduate Interdisciplinary Program, University of Arizona, Tucson, AZ, USA 85721
| | - Nima Pouladi
- Department of Medicine, University of Arizona, Tucson, AZ, USA 85724-5035.,Department of Biomedical Informatics, University of Utah, UT, USA 84108
| | - Samir Rachid Zaim
- Center for Biomedical Informatics and Biostatistics (CB2), University of Arizona Health Sciences, University of Arizona, Tucson, AZ, USA 85721.,Department of Medicine, University of Arizona, Tucson, AZ, USA 85724-5035.,Graduate Interdisciplinary Program in Statistics & Data Science, Graduate Interdisciplinary Program, University of Arizona, Tucson, AZ, USA 85721
| | - Colleen Kenost
- Center for Biomedical Informatics and Biostatistics (CB2), University of Arizona Health Sciences, University of Arizona, Tucson, AZ, USA 85721.,Department of Medicine, University of Arizona, Tucson, AZ, USA 85724-5035.,Graduate Interdisciplinary Program in Statistics & Data Science, Graduate Interdisciplinary Program, University of Arizona, Tucson, AZ, USA 85721.,Department of Biomedical Informatics, University of Utah, UT, USA 84108
| | - Joanne Berghout
- Center for Biomedical Informatics and Biostatistics (CB2), University of Arizona Health Sciences, University of Arizona, Tucson, AZ, USA 85721.,Department of Medicine, University of Arizona, Tucson, AZ, USA 85724-5035.,Ctr for Appl. Genetics and Genomic Medic, University of Arizona, Tucson, AZ, USA 85721
| | - Walter W Piegorsch
- Center for Biomedical Informatics and Biostatistics (CB2), University of Arizona Health Sciences, University of Arizona, Tucson, AZ, USA 85721.,Graduate Interdisciplinary Program in Statistics & Data Science, Graduate Interdisciplinary Program, University of Arizona, Tucson, AZ, USA 85721.,Bio5 Institute, University of Arizona, Tucson, AZ, USA 85721
| | - Yves A Lussier
- Center for Biomedical Informatics and Biostatistics (CB2), University of Arizona Health Sciences, University of Arizona, Tucson, AZ, USA 85721.,Department of Medicine, University of Arizona, Tucson, AZ, USA 85724-5035.,Graduate Interdisciplinary Program in Statistics & Data Science, Graduate Interdisciplinary Program, University of Arizona, Tucson, AZ, USA 85721.,Department of Biomedical Informatics, University of Utah, UT, USA 84108.,Ctr for Appl. Genetics and Genomic Medic, University of Arizona, Tucson, AZ, USA 85721.,Bio5 Institute, University of Arizona, Tucson, AZ, USA 85721
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31
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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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32
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Tan X, Yu Y, Duan K, Zhang J, Sun P, Sun H. Current Advances and Limitations of Deep Learning in Anticancer Drug Sensitivity Prediction. Curr Top Med Chem 2021; 20:1858-1867. [PMID: 32648840 DOI: 10.2174/1568026620666200710101307] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 04/02/2020] [Accepted: 04/14/2020] [Indexed: 02/06/2023]
Abstract
Anticancer drug screening can accelerate drug discovery to save the lives of cancer patients, but cancer heterogeneity makes this screening challenging. The prediction of anticancer drug sensitivity is useful for anticancer drug development and the identification of biomarkers of drug sensitivity. Deep learning, as a branch of machine learning, is an important aspect of in silico research. Its outstanding computational performance means that it has been used for many biomedical purposes, such as medical image interpretation, biological sequence analysis, and drug discovery. Several studies have predicted anticancer drug sensitivity based on deep learning algorithms. The field of deep learning has made progress regarding model performance and multi-omics data integration. However, deep learning is limited by the number of studies performed and data sources available, so it is not perfect as a pre-clinical approach for use in the anticancer drug screening process. Improving the performance of deep learning models is a pressing issue for researchers. In this review, we introduce the research of anticancer drug sensitivity prediction and the use of deep learning in this research area. To provide a reference for future research, we also review some common data sources and machine learning methods. Lastly, we discuss the advantages and disadvantages of deep learning, as well as the limitations and future perspectives regarding this approach.
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Affiliation(s)
- Xian Tan
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Yang Yu
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Kaiwen Duan
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Jingbo Zhang
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Pingping Sun
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Hui Sun
- College of Humanities and Sciences of Northeast Normal University, Changchun 130117, China
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33
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Guo WF, Yu X, Shi QQ, Liang J, Zhang SW, Zeng T. Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis. PLoS Comput Biol 2021; 17:e1008962. [PMID: 33956788 PMCID: PMC8130943 DOI: 10.1371/journal.pcbi.1008962] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 05/18/2021] [Accepted: 04/12/2021] [Indexed: 11/29/2022] Open
Abstract
In the past few years, a wealth of sample-specific network construction methods and structural network control methods has been proposed to identify sample-specific driver nodes for supporting the Sample-Specific network Control (SSC) analysis of biological networked systems. However, there is no comprehensive evaluation for these state-of-the-art methods. Here, we conducted a performance assessment for 16 SSC analysis workflows by using the combination of 4 sample-specific network reconstruction methods and 4 representative structural control methods. This study includes simulation evaluation of representative biological networks, personalized driver genes prioritization on multiple cancer bulk expression datasets with matched patient samples from TCGA, and cell marker genes and key time point identification related to cell differentiation on single-cell RNA-seq datasets. By widely comparing analysis of existing SSC analysis workflows, we provided the following recommendations and banchmarking workflows. (i) The performance of a network control method is strongly dependent on the up-stream sample-specific network method, and Cell-Specific Network construction (CSN) method and Single-Sample Network (SSN) method are the preferred sample-specific network construction methods. (ii) After constructing the sample-specific networks, the undirected network-based control methods are more effective than the directed network-based control methods. In addition, these data and evaluation pipeline are freely available on https://github.com/WilfongGuo/Benchmark_control.
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Affiliation(s)
- Wei-Feng Guo
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian, China
| | - Xiangtian Yu
- Clinical Research Center, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Qian-Qian Shi
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Jing Liang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xian, China
| | - Tao Zeng
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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34
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A roadmap of six different pathways to improve survival in laryngeal cancer patients. Curr Opin Otolaryngol Head Neck Surg 2021; 29:65-78. [PMID: 33337612 DOI: 10.1097/moo.0000000000000684] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
PURPOSE OF REVIEW Laryngeal cancer continues to require improvement in earlier stage diagnosis and better imaging delineation of disease, and hence 'more evidence-based' selection of treatment, as recent evidence suggests that related mortality, in the last decades, has not significantly decreased worldwide. Even though the reasons are not fully understood, there persists an urgency for a review and development of future strategies to embrace such clinical and diagnostic challenges from a political, societal, as well as scientific and clinical points of view. RECENT FINDINGS This review of the published literature suggests that survival improvement in laryngeal cancer may be achieved by fuelling and combining at least some or all of six targeted agendas: documentation of disease global incidence and national burden monitoring; development and implementation of high-quality cancer registries; education on risk factors and hazardous habits associated with laryngeal cancer for the general population; active modification of proven at-risk population lifestyles; centralization of treatment; and use of machine learning of gathered 'big data' and their integration into approaches for the optimization of prevention and treatments strategies. SUMMARY Laryngeal cancer should be tackled on several fronts, commencing with disease monitoring and prevention, up to treatment optimisation. Available modern resources offer the possibility to generate significant advances in laryngeal cancer management. However, each nation needs to develop a comprehensive approach, which is an essential prerequisite to obtain meaningful improvement on results.
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Valent P, Orfao A, Kubicek S, Staber P, Haferlach T, Deininger M, Kollmann K, Lion T, Virgolini I, Winter G, Hantschel O, Kenner L, Zuber J, Grebien F, Moriggl R, Hoermann G, Hermine O, Andreeff M, Bock C, Mughal T, Constantinescu SN, Kralovics R, Sexl V, Skoda R, Superti-Furga G, Jäger U. Precision Medicine in Hematology 2021: Definitions, Tools, Perspectives, and Open Questions. Hemasphere 2021; 5:e536. [PMID: 33623882 PMCID: PMC7892291 DOI: 10.1097/hs9.0000000000000536] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 12/16/2020] [Indexed: 12/20/2022] Open
Abstract
During the past few years, our understanding of molecular mechanisms and cellular interactions relevant to malignant blood cell disorders has improved substantially. New insights include a detailed knowledge about disease-initiating exogenous factors, endogenous (genetic, somatic, epigenetic) elicitors or facilitators of disease evolution, and drug actions and interactions that underlie efficacy and adverse event profiles in defined cohorts of patients. As a result, precision medicine and personalized medicine are rapidly growing new disciplines that support the clinician in making the correct diagnosis, in predicting outcomes, and in optimally selecting patients for interventional therapies. In addition, precision medicine tools are greatly facilitating the development of new drugs, therapeutic approaches, and new multiparametric prognostic scoring models. However, although the emerging roles of precision medicine and personalized medicine in hematology and oncology are clearly visible, several questions remain. For example, it remains unknown how precision medicine tools can be implemented in healthcare systems and whether all possible approaches are also affordable. In addition, there is a need to define terminologies and to relate these to specific and context-related tools and strategies in basic and applied science. To discuss these issues, a working conference was organized in September 2019. The outcomes of this conference are summarized herein and include a proposal for definitions, terminologies, and applications of precision and personalized medicine concepts and tools in hematologic neoplasms. We also provide proposals aimed at reducing costs, thereby making these applications affordable in daily practice.
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Affiliation(s)
- Peter Valent
- Department of Medicine I, Division of Hematology & Hemostaseology, Medical University of Vienna, Austria
- Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Austria
| | - Alberto Orfao
- Servicio Central de Citometria, Centro de Investigacion del Cancer (IBMCC; CSIC/USAL), IBSAL, CIBERONC and Department of Medicine, University of Salamanca, Spain
| | - Stefan Kubicek
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Philipp Staber
- Department of Medicine I, Division of Hematology & Hemostaseology, Medical University of Vienna, Austria
- Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Austria
| | | | - Michael Deininger
- Division of Hematology and Hematologic Malignancies, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah, USA
| | - Karoline Kollmann
- Institute of Pharmacology and Toxicology, University of Veterinary Medicine Vienna, Austria
| | - Thomas Lion
- Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Austria
- Children’s Cancer Research Institute, Vienna, Austria
| | - Irene Virgolini
- Department of Nuclear Medicine, Medical University of Innsbruck, Austria
| | - Georg Winter
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Oliver Hantschel
- Institute of Physiological Chemistry, Faculty of Medicine, Philipps-University of Marburg, Germany
| | - Lukas Kenner
- Pathology of Laboratory Animals, University of Veterinary Medicine, Vienna, Austria
| | - Johannes Zuber
- Research Institute of Molecular Pathology (IMP), Vienna, Austria
| | - Florian Grebien
- Institute for Medical Biochemistry, University of Veterinary Medicine Vienna, Austria
| | - Richard Moriggl
- Institute of Animal Breeding and Genetics, Unit for Functional Cancer Genomics, University of Veterinary Medicine Vienna, Austria
| | - Gregor Hoermann
- Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Austria
- MLL Munich Leukemia Laboratory, Munich, Germany
| | - Olivier Hermine
- Imagine Institute Université Paris Descartes, Sorbonne, Paris Cité, Paris, France
- Department of Hematology, Necker Hospital, Paris, France
| | - Michael Andreeff
- University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Tariq Mughal
- Division of Hematology & Oncology, Tufts University Medical Center, Boston, Massachusetts, USA
| | - Stefan N. Constantinescu
- de Duve Institute and Ludwig Cancer Research Brussels, Université catholique de Louvain, Brussels, Belgium
| | - Robert Kralovics
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Veronika Sexl
- Institute of Pharmacology and Toxicology, University of Veterinary Medicine Vienna, Austria
| | - Radek Skoda
- Departement of Biomedicine, University of Basel, Switzerland
| | - Giulio Superti-Furga
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Ulrich Jäger
- Department of Medicine I, Division of Hematology & Hemostaseology, Medical University of Vienna, Austria
- Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Austria
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36
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Amirmahani F, Ebrahimi N, Molaei F, Faghihkhorasani F, Jamshidi Goharrizi K, Mirtaghi SM, Borjian‐Boroujeni M, Hamblin MR. Approaches for the integration of big data in translational medicine: single‐cell and computational methods. Ann N Y Acad Sci 2021; 1493:3-28. [DOI: 10.1111/nyas.14544] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/31/2020] [Accepted: 11/12/2020] [Indexed: 12/11/2022]
Affiliation(s)
- Farzane Amirmahani
- Genetics Division, Department of Cell and Molecular Biology and Microbiology, Faculty of Science and Technology University of Isfahan Isfahan Iran
| | - Nasim Ebrahimi
- Genetics Division, Department of Cell and Molecular Biology and Microbiology, Faculty of Science and Technology University of Isfahan Isfahan Iran
| | - Fatemeh Molaei
- Department of Anesthesiology, Faculty of Paramedical Jahrom University of Medical Sciences Jahrom Iran
| | | | | | | | | | - Michael R. Hamblin
- Laser Research Centre, Faculty of Health Science University of Johannesburg South Africa
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37
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Kowalski LP, Coletta RD, Salo T, Maschietto M, Chojniak R, Lima JM, Mlynarek A, Hier MP, Alaoui-Jamali MA, Silva SD. Head and neck cancer: Emerging concepts in biomarker discovery and opportunities for clinical translation. Clin Transl Med 2020; 10:e209. [PMID: 33252865 PMCID: PMC7668191 DOI: 10.1002/ctm2.209] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 10/03/2020] [Indexed: 11/12/2022] Open
Affiliation(s)
- Luiz Paulo Kowalski
- Department of Head and Neck Surgery and Otorhinolaryngology, A.C. Camargo Cancer Center and National Institute of Science and Technology on Oncogenomics (INCITO), Sao Paulo, Brazil
| | - Ricardo Della Coletta
- Department of Oral Diagnosis, School of Dentistry, University of Campinas, Sao Paulo, Brazil
| | - Tuula Salo
- Cancer and Translational Medicine Research Unit, University of Oulu, Oulu, Finland.,Department of Oral and Maxillofacial Diseases, University of Helsinki, Helsinki, Finland
| | | | - Rubens Chojniak
- Department of Head and Neck Surgery and Otorhinolaryngology, A.C. Camargo Cancer Center and National Institute of Science and Technology on Oncogenomics (INCITO), Sao Paulo, Brazil
| | - Jefferson Muniz Lima
- Otolaryngology-Head and Neck Surgery Department, Jewish General Hospital-Segal Cancer Center, McGill University, Montreal, Canada
| | - Alex Mlynarek
- Otolaryngology-Head and Neck Surgery Department, Jewish General Hospital-Segal Cancer Center, McGill University, Montreal, Canada
| | - Michael P Hier
- Otolaryngology-Head and Neck Surgery Department, Jewish General Hospital-Segal Cancer Center, McGill University, Montreal, Canada
| | - Moulay A Alaoui-Jamali
- Segal Cancer Centre, Lady Davis Institute for Medical Research, McGill University, Montreal, Canada
| | - Sabrina Daniela Silva
- Otolaryngology-Head and Neck Surgery Department, Jewish General Hospital-Segal Cancer Center, McGill University, Montreal, Canada.,Segal Cancer Centre, Lady Davis Institute for Medical Research, McGill University, Montreal, Canada
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38
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Petti M, Bizzarri D, Verrienti A, Falcone R, Farina L. Connectivity Significance for Disease Gene Prioritization in an Expanding Universe. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:2155-2161. [PMID: 31484130 DOI: 10.1109/tcbb.2019.2938512] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A fundamental topic in network medicine is disease genes prioritization. The underlying hypothesis is that disease genes are organized as modules confined within the interactome. Here, we propose a novel algorithm called DiaBLE (DIAMOnD Background Local Expansion) which is a modified version of DIAMOnD, a successful algorithm based on the concept of connectivity significance. Instead of taking the whole interactome as the background model, DiaBLE considers as gene universe the smallest local expansion of the current seeds set at each iteration step. We show that DiaBLE significantly increases the overall DIAMOnD ranking quality of genes prioritization both in terms of cross-validation and biological consistency. Here, we focus on the two algorithms only since a comparative analysis among gene prioritization methods is beyond the scope of this study. Finally, we briefly discuss the improvement of biological insight provided by DiaBLE for two cancers (head and neck squamous cell carcinoma and kidney renal clear cell carcinoma).
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Abstract
Network theory provides one of the most potent analysis tools for the study of complex systems. In this paper, we illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. We first present data sources from which networks are built, then show some examples of how the networks can be used to investigate drug-related systems. A section is devoted to network-based inference applications, i.e., prediction methods based on interactomes, that can be used to identify putative drug-target interactions without resorting to 3D modeling. Finally, we present some aspects of Boolean networks dynamics, anticipating that it might become a very potent modeling framework to develop in silico screening protocols able to simulate phenotypic screening experiments. We conclude that network applications integrated with machine learning and 3D modeling methods will become an indispensable tool for computational drug discovery in the next years.
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Affiliation(s)
- Maurizio Recanatini
- Department of Pharmacy and
Biotechnology, Alma Mater Studiorum—University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
| | - Chiara Cabrelle
- Department of Pharmacy and
Biotechnology, Alma Mater Studiorum—University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
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40
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Cutigi JF, Evangelista AF, Simao A. Approaches for the identification of driver mutations in cancer: A tutorial from a computational perspective. J Bioinform Comput Biol 2020; 18:2050016. [PMID: 32698724 DOI: 10.1142/s021972002050016x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Cancer is a complex disease caused by the accumulation of genetic alterations during the individual's life. Such alterations are called genetic mutations and can be divided into two groups: (1) Passenger mutations, which are not responsible for cancer and (2) Driver mutations, which are significant for cancer and responsible for its initiation and progression. Cancer cells undergo a large number of mutations, of which most are passengers, and few are drivers. The identification of driver mutations is a key point and one of the biggest challenges in Cancer Genomics. Many computational methods for such a purpose have been developed in Cancer Bioinformatics. Such computational methods are complex and are usually described in a high level of abstraction. This tutorial details some classical computational methods, from a computational perspective, with the transcription in an algorithmic format towards an easy access by researchers.
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Affiliation(s)
- Jorge Francisco Cutigi
- Federal Institute of São Paulo (IFSP), São Carlos, SP, Brazil.,University of São Paulo (USP), São Carlos, SP, Brazil
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41
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Vinga S. Structured sparsity regularization for analyzing high-dimensional omics data. Brief Bioinform 2020; 22:77-87. [PMID: 32597465 DOI: 10.1093/bib/bbaa122] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 05/15/2020] [Accepted: 05/18/2020] [Indexed: 12/18/2022] Open
Abstract
The development of new molecular and cell technologies is having a significant impact on the quantity of data generated nowadays. The growth of omics databases is creating a considerable potential for knowledge discovery and, concomitantly, is bringing new challenges to statistical learning and computational biology for health applications. Indeed, the high dimensionality of these data may hamper the use of traditional regression methods and parameter estimation algorithms due to the intrinsic non-identifiability of the inherent optimization problem. Regularized optimization has been rising as a promising and useful strategy to solve these ill-posed problems by imposing additional constraints in the solution parameter space. In particular, the field of statistical learning with sparsity has been significantly contributing to building accurate models that also bring interpretability to biological observations and phenomena. Beyond the now-classic elastic net, one of the best-known methods that combine lasso with ridge penalizations, we briefly overview recent literature on structured regularizers and penalty functions that have been applied in biomedical data to build parsimonious models in a variety of underlying contexts, from survival to generalized linear models. These methods include functions of $\ell _k$-norms and network-based penalties that take into account the inherent relationships between the features. The successful application to omics data illustrates the potential of sparse structured regularization for identifying disease's molecular signatures and for creating high-performance clinical decision support systems towards more personalized healthcare. Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.
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Affiliation(s)
- Susana Vinga
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
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42
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Hristov BH, Chazelle B, Singh M. uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes. Cell Syst 2020; 10:470-479.e3. [PMID: 32684276 PMCID: PMC7821437 DOI: 10.1016/j.cels.2020.05.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 04/24/2020] [Accepted: 05/19/2020] [Indexed: 12/23/2022]
Abstract
Protein interaction networks provide a powerful framework for identifying genes causal for complex genetic diseases. Here, we introduce a general framework, uKIN, that uses prior knowledge of disease-associated genes to guide, within known protein-protein interaction networks, random walks that are initiated from newly identified candidate genes. In large-scale testing across 24 cancer types, we demonstrate that our network propagation approach for integrating both prior and new information not only better identifies cancer driver genes than using either source of information alone but also readily outperforms other state-of-the-art network-based approaches. We also apply our approach to genome-wide association data to identify genes functionally relevant for several complex diseases. Overall, our work suggests that guided network propagation approaches that utilize both prior and new data are a powerful means to identify disease genes. uKIN is freely available for download at: https://github.com/Singh-Lab/uKIN.
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Affiliation(s)
- Borislav H Hristov
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Bernard Chazelle
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
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43
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Moradi A, Pourseif MM, Jafari B, Parvizpour S, Omidi Y. Nanobody-based therapeutics against colorectal cancer: Precision therapies based on the personal mutanome profile and tumor neoantigens. Pharmacol Res 2020; 156:104790. [DOI: 10.1016/j.phrs.2020.104790] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 03/07/2020] [Accepted: 03/31/2020] [Indexed: 12/19/2022]
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44
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Panebianco V, Pecoraro M, Fiscon G, Paci P, Farina L, Catalano C. Prostate cancer screening research can benefit from network medicine: an emerging awareness. NPJ Syst Biol Appl 2020; 6:13. [PMID: 32382028 PMCID: PMC7206063 DOI: 10.1038/s41540-020-0133-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 04/09/2020] [Indexed: 01/03/2023] Open
Abstract
Up to date, screening for prostate cancer (PCa) remains one of the most appealing but also a very controversial topics in the urological community. PCa is the second most common cancer in men worldwide and it is universally acknowledged as a complex disease, with a multi-factorial etiology. The pathway of PCa diagnosis has changed dramatically in the last few years, with the multiparametric magnetic resonance (mpMRI) playing a starring role with the introduction of the “MRI Pathway”. In this scenario the basic tenet of network medicine (NM) that sees the disease as perturbation of a network of interconnected molecules and pathways, seems to fit perfectly with the challenges that PCa early detection must face to advance towards a more reliable technique. Integration of tests on body fluids, tissue samples, grading/staging classification, physiological parameters, MR multiparametric imaging and molecular profiling technologies must be integrated in a broader vision of “disease” and its complexity with a focus on early signs. PCa screening research can greatly benefit from NM vision since it provides a sound interpretation of data and a common language, facilitating exchange of ideas between clinicians and data analysts for exploring new research pathways in a rational, highly reliable, and reproducible way.
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Affiliation(s)
- Valeria Panebianco
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I of Rome, Rome, Italy.
| | - Martina Pecoraro
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I of Rome, Rome, Italy
| | - Giulia Fiscon
- Institute for System Analysis and Computer Science (IASI), National Research Council, Rome, Italy
| | - Paola Paci
- Institute for System Analysis and Computer Science (IASI), National Research Council, Rome, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Carlo Catalano
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I of Rome, Rome, Italy
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45
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Raiter A, Lipovetzki J, Lubin I, Yerushalmi R. GRP78 expression in peripheral blood mononuclear cells is a new predictive marker for the benefit of taxanes in breast cancer neoadjuvant treatment. BMC Cancer 2020; 20:333. [PMID: 32306920 PMCID: PMC7168854 DOI: 10.1186/s12885-020-06835-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Accepted: 04/06/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Breast cancer treatment is tailored to the specific cancer subtype. Often, systemic treatment is given prior to surgery. Chemotherapy induces significant endoplasmic reticulum (ER) stress-mediated cell death and upregulation of 78-kDa glucose-regulated protein (GRP78). We hypothesized that chemotherapy induces ER stress not only in the tumor tissue but also in immune cells, which may affect the response to anti-cancer treatment. METHODS We determined the surface expression of GRP78 on 15 different peripheral blood mononuclear cell (PBMC) subpopulations in 20 breast cancer patients at three time points of the neoadjuvant treatment, i.e., at baseline, after anthracycline treatment, and after taxanes treatment. For this purpose, we performed flow cytometric analyses and analyzed the data using ANOVA and the Tukey test. Serum cytokine levels were also evaluated, and their levels were correlated with response to treatment using the t-test after log transformation and Mann-Whitney U Wilcoxon W test. RESULTS A significant increase in GRP78 expression in PBMCs was documented during the taxane phase, only in patients who achieved pathological complete response (pCR). GRP78-positive clones correlated with increased serum levels of interferon gamma (IFNγ). CONCLUSIONS The presence of GRP78-positive clones in certain PBMC subpopulations in pCR patients suggests a dynamic interaction between ER stress and immune responsiveness. The correlation of GRP78-positive clones with increased levels of IFNγ supports the idea that GRP78 expression in PBMCs might serve as a new predictive marker to identify the possible benefits of taxanes in the neoadjuvant setting.
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Affiliation(s)
- Annat Raiter
- Felsenstein Medical Research Center, Sackler School of Medicine, Tel Aviv University, Rabin Medical Center, Beilinson Campus, 49100, Petach Tikva, Israel.
| | - Julia Lipovetzki
- Felsenstein Medical Research Center, Sackler School of Medicine, Tel Aviv University, Rabin Medical Center, Beilinson Campus, 49100, Petach Tikva, Israel
| | - Ido Lubin
- Felsenstein Medical Research Center, Sackler School of Medicine, Tel Aviv University, Rabin Medical Center, Beilinson Campus, 49100, Petach Tikva, Israel
| | - Rinat Yerushalmi
- Felsenstein Medical Research Center, Sackler School of Medicine, Tel Aviv University, Rabin Medical Center, Beilinson Campus, 49100, Petach Tikva, Israel.
- Davidoff Cancer Center, Rabin Medical Center, Beilinson Campus, 49100, Petach Tikva, Israel.
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46
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Capobianco E. Imprecise Data and Their Impact on Translational Research in Medicine. Front Med (Lausanne) 2020; 7:82. [PMID: 32266273 PMCID: PMC7096475 DOI: 10.3389/fmed.2020.00082] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 03/02/2020] [Indexed: 11/13/2022] Open
Abstract
The medical field expects from big data essentially two main results: the ability to build predictive models and the possibility of applying them to obtain accurate patient risk profiles and/or health trajectories. Note that the paradigm of precision has determined that similar challenges need to be faced in both population and individualized studies, namely the need of assembling, integrating, modeling, and interpreting data from a variety of information sources and scales potentially influencing disease from onset to progression. In many cases, data require computational treatment through solutions for otherwise intractable problems. However, as precision medicine remains subject to a substantial amount of data imprecision and lack of translational impact, a revision of methodological inference approaches is needed. Both the relevance and the usefulness of such revision crucially deal with the assimilation of data features dynamically interconnected.
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Affiliation(s)
- Enrico Capobianco
- Institute of Data Science and Computing, University of Miami, Miami, FL, United States
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47
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Pham TD, Fan C, Pfeifer D, Zhang H, Sun XF. Image-Based Network Analysis of DNp73 Expression by Immunohistochemistry in Rectal Cancer Patients. Front Physiol 2020; 10:1551. [PMID: 31969833 PMCID: PMC6960186 DOI: 10.3389/fphys.2019.01551] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 12/09/2019] [Indexed: 02/05/2023] Open
Abstract
Background: Rectal cancer is a disease characterized with tumor heterogeneity. The combination of surgery, radiotherapy, and chemotherapy can reduce the risk of local recurrence. However, there is a significant difference in the response to radiotherapy among rectal cancer patients even they have the same tumor stage. Despite rapid advances in knowledge of cellular functions affecting radiosensitivity, there is still a lack of predictive factors for local recurrence and normal tissue damage. The tumor protein DNp73 is thought as a biomarker in colorectal cancer, but its clinical significance is still not sufficiently investigated, mainly due to the limitation of human-based pathology analysis. In this study, we investigated the predictive value of DNp73 in patients with rectal adenocarcinoma using image-based network analysis. Methods: The fuzzy weighted recurrence network of time series was extended to handle multi-channel image data, and applied to the analysis of immunohistochemistry images of DNp73 expression obtained from a cohort of 25 rectal cancer patients who underwent radiotherapy before surgery. Two mathematical weighted network properties, which are the clustering coefficient and characteristic path length, were computed for the image-based networks of the primary tumor (obtained after operation) and biopsy (obtained before operation) of each cancer patient. Results: The ratios of two weighted recurrence network properties of the primary tumors to biopsies reveal the correlation of DNp73 expression and long survival time, and discover the non-effective radiotherapy to a cohort of rectal cancer patients who had short survival time. Conclusion: Our work contributes to the elucidation of the predictive value of DNp73 expression in rectal cancer patients who were given preoperative radiotherapy. Mathematical properties of fuzzy weighted recurrence networks of immunohistochemistry images are not only able to show the predictive factor of DNp73 expression in the patients, but also reveal the identification of non-effective application of radiotherapy to those who had poor overall survival outcome.
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Affiliation(s)
- Tuan D Pham
- Department of Biomedical Engineering, Linkoping University, Linkoping, Sweden.,The Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia
| | - Chuanwen Fan
- Department of Oncology, Clinical and Experimental Medicine, Linkoping University, Linkoping, Sweden.,Institute of Digestive Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Daniella Pfeifer
- Department of Oncology, Clinical and Experimental Medicine, Linkoping University, Linkoping, Sweden
| | - Hong Zhang
- Department of Medical Sciences, Orebro University, Orebro, Sweden
| | - Xiao-Feng Sun
- Department of Oncology, Clinical and Experimental Medicine, Linkoping University, Linkoping, Sweden
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48
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Cao TH, Jones DJ, Voors AA, Quinn PA, Sandhu JK, Chan DC, Parry HM, Mohan M, Mordi IR, Sama IE, Anker SD, Cleland JG, Dickstein K, Filippatos G, Hillege HL, Metra M, Ponikowski P, Samani NJ, Van Veldhuisen DJ, Zannad F, Lang CC, Ng LL. Plasma proteomic approach in patients with heart failure: insights into pathogenesis of disease progression and potential novel treatment targets. Eur J Heart Fail 2020; 22:70-80. [PMID: 31692186 PMCID: PMC7028019 DOI: 10.1002/ejhf.1608] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 08/13/2019] [Accepted: 08/19/2019] [Indexed: 12/17/2022] Open
Abstract
AIMS To provide insights into pathogenesis of disease progression and potential novel treatment targets for patients with heart failure by investigation of the plasma proteome using network analysis. METHODS AND RESULTS The plasma proteome of 50 patients with heart failure who died or were rehospitalised were compared with 50 patients with heart failure, matched for age and sex, who did not have an event. Peptides were analysed on two-dimensional liquid chromatography coupled to tandem mass spectrometry (2D LC ESI-MS/MS) in high definition mode (HDMSE). We identified and quantified 3001 proteins, of which 51 were significantly up-regulated and 46 down-regulated with more than two-fold expression changes in those who experienced death or rehospitalisation. Gene ontology enrichment analysis and protein-protein interaction networks of significant differentially expressed proteins discovered the central role of metabolic processes in clinical outcomes of patients with heart failure. The findings revealed that a cluster of proteins related to glutathione metabolism, arginine and proline metabolism, and pyruvate metabolism in the pathogenesis of poor outcome in patients with heart failure who died or were rehospitalised. CONCLUSIONS Our findings show that in patients with heart failure who died or were rehospitalised, the glutathione, arginine and proline, and pyruvate pathways were activated. These pathways might be potential targets for therapies to improve poor outcomes in patients with heart failure.
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Affiliation(s)
- Thong H. Cao
- Department of Cardiovascular SciencesUniversity of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalLeicesterUK
| | - Donald J.L. Jones
- Department of Cardiovascular SciencesUniversity of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalLeicesterUK
- Leicester Cancer Research Centre, Leicester Royal InfirmaryUniversity of LeicesterLeicesterUK
| | - Adriaan A. Voors
- Department of CardiologyUniversity of GroningenGroningenThe Netherlands
| | - Paulene A. Quinn
- Department of Cardiovascular SciencesUniversity of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalLeicesterUK
| | - Jatinderpal K. Sandhu
- Department of Cardiovascular SciencesUniversity of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalLeicesterUK
| | - Daniel C.S. Chan
- Department of Cardiovascular SciencesUniversity of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalLeicesterUK
| | - Helen M. Parry
- Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical SchoolUniversity of DundeeDundeeUK
| | - Mohapradeep Mohan
- Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical SchoolUniversity of DundeeDundeeUK
| | - Ify R. Mordi
- Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical SchoolUniversity of DundeeDundeeUK
| | - Iziah E. Sama
- Department of CardiologyUniversity of GroningenGroningenThe Netherlands
| | - Stefan D. Anker
- Division of Cardiology and Metabolism; Department of Cardiology (CVK)Center for Regenerative Therapies (BCRT); German Centre for Cardiovascular Research (DZHK) partner site Berlin; Charité Universitätsmedizin BerlinBerlinGermany
| | - John G. Cleland
- Robertson Centre for BiostatisticsInstitute of Health and Wellbeing, University of Glasgow, Glasgow Royal InfirmaryGlasgowUK
| | | | - Gerasimos Filippatos
- Department of Cardiology, Heart Failure Unit, Athens University Hospital Attikon, School of MedicineNational and Kapodistrian University of AthensAthensGreece
| | - Hans L. Hillege
- Department of CardiologyUniversity of GroningenGroningenThe Netherlands
| | - Marco Metra
- Institute of Cardiology, Department of Medical and Surgical Specialties, Radiological Sciences and Public HealthUniversity of BresciaBresciaItaly
| | - Piotr Ponikowski
- Department of Heart DiseasesWroclaw Medical University and Cardiology Department, Military HospitalWroclawPoland
| | - Nilesh J. Samani
- Department of Cardiovascular SciencesUniversity of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalLeicesterUK
| | | | - Faiez Zannad
- Inserm CIC 1433Université de LorraineNancyFrance
| | - Chim C. Lang
- Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical SchoolUniversity of DundeeDundeeUK
| | - Leong L. Ng
- Department of Cardiovascular SciencesUniversity of Leicester and National Institute for Health Research Leicester Biomedical Research Centre, Glenfield HospitalLeicesterUK
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Rachid Zaim S, Kenost C, Berghout J, Vitali F, Zhang HH, Lussier YA. Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine. BMC Med Genomics 2019; 12:96. [PMID: 31296218 PMCID: PMC6624180 DOI: 10.1186/s12920-019-0513-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Background Gene expression profiling has benefited medicine by providing clinically relevant insights at the molecular candidate and systems levels. However, to adopt a more ‘precision’ approach that integrates individual variability including ‘omics data into risk assessments, diagnoses, and therapeutic decision making, whole transcriptome expression needs to be interpreted meaningfully for single subjects. We propose an “all-against-one” framework that uses biological replicates in isogenic conditions for testing differentially expressed genes (DEGs) in a single subject (ss) in the absence of an appropriate external reference standard or replicates. To evaluate our proposed “all-against-one” framework, we construct reference standards (RSs) with five conventional replicate-anchored analyses (NOISeq, DEGseq, edgeR, DESeq, DESeq2) and the remainder were treated separately as single-subject sample pairs for ss analyses (without replicates). Results Eight ss methods (NOISeq, DEGseq, edgeR, mixture model, DESeq, DESeq2, iDEG, and ensemble) for identifying genes with differential expression were compared in Yeast (parental line versus snf2 deletion mutant; n = 42/condition) and a MCF7 breast-cancer cell line (baseline versus stimulated with estradiol; n = 7/condition). Receiver-operator characteristic (ROC) and precision-recall plots were determined for eight ss methods against each of the five RSs in both datasets. Consistent with prior analyses of these data, ~ 50% and ~ 15% DEGs were obtained in Yeast and MCF7 datasets respectively, regardless of the RSs method. NOISeq, edgeR, and DESeq were the most concordant for creating a RS. Single-subject versions of NOISeq, DEGseq, and an ensemble learner achieved the best median ROC-area-under-the-curve to compare two transcriptomes without replicates regardless of the RS method and dataset (> 90% in Yeast, > 0.75 in MCF7). Further, distinct specific single-subject methods perform better according to different proportions of DEGs. Conclusions The “all-against-one” framework provides a honest evaluation framework for single-subject DEG studies since these methods are evaluated, by design, against reference standards produced by unrelated DEG methods. The ss-ensemble method was the only one to reliably produce higher accuracies in all conditions tested in this conservative evaluation framework. However, single-subject methods for identifying DEGs from paired samples need improvement, as no method performed with precision> 90% and obtained moderate levels of recall. http://www.lussiergroup.org/publications/EnsembleBiomarker
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Affiliation(s)
- Samir Rachid Zaim
- The Center for Biomedical Informatics & Biostatistics of the University of Arizona Health Sciences, 1230 N. Cherry Ave, Tucson, AZ, 85721, USA.,The Department of Medicine, College of Medicine Tucson, 1501 N. Campbell Ave, Tucson, AZ, 85724-5035, USA.,The Graduate Interdisciplinary Program in Statistics, The University of Arizona, 617 N. Santa Rita Ave, Tucson, AZ, 85721, USA
| | - Colleen Kenost
- The Center for Biomedical Informatics & Biostatistics of the University of Arizona Health Sciences, 1230 N. Cherry Ave, Tucson, AZ, 85721, USA.,The Department of Medicine, College of Medicine Tucson, 1501 N. Campbell Ave, Tucson, AZ, 85724-5035, USA
| | - Joanne Berghout
- The Center for Biomedical Informatics & Biostatistics of the University of Arizona Health Sciences, 1230 N. Cherry Ave, Tucson, AZ, 85721, USA.,The Department of Medicine, College of Medicine Tucson, 1501 N. Campbell Ave, Tucson, AZ, 85724-5035, USA.,The Center for Applied Genetic and Genomic Medicine, 1295 N. Martin, Tucson, AZ, 85721, USA
| | - Francesca Vitali
- The Center for Biomedical Informatics & Biostatistics of the University of Arizona Health Sciences, 1230 N. Cherry Ave, Tucson, AZ, 85721, USA.,The Department of Medicine, College of Medicine Tucson, 1501 N. Campbell Ave, Tucson, AZ, 85724-5035, USA
| | - Helen Hao Zhang
- The Graduate Interdisciplinary Program in Statistics, The University of Arizona, 617 N. Santa Rita Ave, Tucson, AZ, 85721, USA.,The Department of Mathematics, College of Sciences, The University of Arizona, 617 N. Santa Rita Ave, Tucson, AZ, 85721, USA
| | - Yves A Lussier
- The Center for Biomedical Informatics & Biostatistics of the University of Arizona Health Sciences, 1230 N. Cherry Ave, Tucson, AZ, 85721, USA. .,The Department of Medicine, College of Medicine Tucson, 1501 N. Campbell Ave, Tucson, AZ, 85724-5035, USA. .,The Graduate Interdisciplinary Program in Statistics, The University of Arizona, 617 N. Santa Rita Ave, Tucson, AZ, 85721, USA. .,The Center for Applied Genetic and Genomic Medicine, 1295 N. Martin, Tucson, AZ, 85721, USA. .,The University of Arizona Cancer Center, 3838 N. Campbell Ave, Tucson, AZ, 85719-1454, USA.
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Cabezas-Cruz A, Estrada-Peña A, de la Fuente J. The Good, the Bad and the Tick. Front Cell Dev Biol 2019; 7:79. [PMID: 31157221 PMCID: PMC6529820 DOI: 10.3389/fcell.2019.00079] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 04/30/2019] [Indexed: 12/17/2022] Open
Affiliation(s)
- Alejandro Cabezas-Cruz
- UMR BIPAR, INRA, ANSES, Ecole Nationale Vétérinaire d'Alfort, Université Paris-Est, Maisons-Alfort, France
| | | | - Jose de la Fuente
- SaBio, Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ciudad Real, Spain.,Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, OK, United States
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