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Kolokotroni E, Abler D, Ghosh A, Tzamali E, Grogan J, Georgiadi E, Büchler P, Radhakrishnan R, Byrne H, Sakkalis V, Nikiforaki K, Karatzanis I, McFarlane NJB, Kaba D, Dong F, Bohle RM, Meese E, Graf N, Stamatakos G. A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin. J Pers Med 2024; 14:475. [PMID: 38793058 PMCID: PMC11122096 DOI: 10.3390/jpm14050475] [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: 03/03/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/26/2024] Open
Abstract
The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary.
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Affiliation(s)
- Eleni Kolokotroni
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
| | - Daniel Abler
- Department of Oncology, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland;
- Department of Oncology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Alokendra Ghosh
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; (A.G.); (R.R.)
| | - Eleftheria Tzamali
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - James Grogan
- Irish Centre for High End Computing, University of Galway, H91 TK33 Galway, Ireland;
| | - Eleni Georgiadi
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
- Biomedical Engineering Department, University of West Attica, 12243 Egaleo, Greece
| | | | - Ravi Radhakrishnan
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; (A.G.); (R.R.)
| | - Helen Byrne
- Mathematical Institute, University of Oxford, Oxford OX1 2JD, UK;
| | - Vangelis Sakkalis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - Katerina Nikiforaki
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - Ioannis Karatzanis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | | | - Djibril Kaba
- Department of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK;
| | - Feng Dong
- Department of Computer & Information Sciences, University of Strathclyde, Glasgow G1 1XH, UK;
| | - Rainer M. Bohle
- Department of Pathology, Saarland University, 66421 Homburg, Germany;
| | - Eckart Meese
- Department of Human Genetics, Saarland University, 66421 Homburg, Germany;
| | - Norbert Graf
- Department of Paediatric Oncology and Haematology, Saarland University, 66421 Homburg, Germany;
| | - Georgios Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
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Fernandez ME, Martinez-Romero J, Aon MA, Bernier M, Price NL, de Cabo R. How is Big Data reshaping preclinical aging research? Lab Anim (NY) 2023; 52:289-314. [PMID: 38017182 DOI: 10.1038/s41684-023-01286-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 10/10/2023] [Indexed: 11/30/2023]
Abstract
The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.
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Affiliation(s)
- Maria Emilia Fernandez
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jorge Martinez-Romero
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Miguel A Aon
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Michel Bernier
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Nathan L Price
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Rafael de Cabo
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
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Goh WWB, Hui HWH, Wong L. How missing value imputation is confounded with batch effects and what you can do about it. Drug Discov Today 2023; 28:103661. [PMID: 37301250 DOI: 10.1016/j.drudis.2023.103661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/12/2023]
Abstract
In data-processing pipelines, upstream steps can influence downstream processes because of their sequential nature. Among these data-processing steps, batch effect (BE) correction (BEC) and missing value imputation (MVI) are crucial for ensuring data suitability for advanced modeling and reducing the likelihood of false discoveries. Although BEC-MVI interactions are not well studied, they are ultimately interdependent. Batch sensitization can improve the quality of MVI. Conversely, accounting for missingness also improves proper BE estimation in BEC. Here, we discuss how BEC and MVI are interconnected and interdependent. We show how batch sensitization can improve any MVI and bring attention to the idea of BE-associated missing values (BEAMs). Finally, we discuss how batch-class imbalance problems can be mitigated by borrowing ideas from machine learning.
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Affiliation(s)
- Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore; Center for Biomedical Informatics, Nanyang Technological University, Singapore.
| | - Harvard Wai Hann Hui
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore; Department of Pathology, National University of Singapore, Singapore.
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Bas TG. Biosimilars for the next decade in Latin America: a window of opportunity. Expert Opin Biol Ther 2023; 23:659-669. [PMID: 37542714 DOI: 10.1080/14712598.2023.2245780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/31/2023] [Accepted: 08/04/2023] [Indexed: 08/07/2023]
Abstract
INTRODUCTION Biosimilars are gaining popularity in Latin America (LA). The biosimilars market is expected to grow rapidly over the next decade as a cost-effective alternative to expensive patented biologics. The drivers for the growing demand include needs for affordable health care, the prevalence of chronic diseases, expiration of patents for numerous biologic medicines and the advent of artificial intelligence (AI). Countries such as Argentina, Brazil and Mexico have implemented regulatory frameworks for the approval of biosimilars as well as for investment in local manufacturing capacity, sale, and distribution. Some LA countries face challenges related to low quality institutional frameworks and deficient public policies for regulatory harmonization of these medicines. AREAS COVERED The aim of this article is to analyze the broad window of opportunity for biosimilars in LA (Brazil, Mexico and Argentina) in the next decade, considering their regulations and institutional quality, as well as an affordable cost for patients with chronic diseases and highlight the biosimilars approved in the three countries studied. Likewise, the future contribution of AI in the drug R&D process is considered. EXPERT OPINION Preparing the next decade of biosimilars in LA will involve improving international regulatory frameworks, institutional quality, investments and capacity in R&D (competencies, infrastructure, and AI).
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Affiliation(s)
- Tomas Gabriel Bas
- Universidad Catolica del Norte (Chile), Escuela de Ciencias Empresariales, Coquimbo, Chile
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Are batch effects still relevant in the age of big data? Trends Biotechnol 2022; 40:1029-1040. [DOI: 10.1016/j.tibtech.2022.02.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/13/2022] [Accepted: 02/18/2022] [Indexed: 12/30/2022]
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Allain F, Roméjon J, La Rosa P, Jarlier F, Servant N, Hupé P. Geniac: Automatic Configuration GENerator and Installer for nextflow pipelines. OPEN RESEARCH EUROPE 2022; 1:76. [PMID: 37645091 PMCID: PMC10445886 DOI: 10.12688/openreseurope.13861.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/11/2022] [Indexed: 08/31/2023]
Abstract
With the advent of high-throughput biotechnological platforms and their ever-growing capacity, life science has turned into a digitized, computational and data-intensive discipline. As a consequence, standard analysis with a bioinformatics pipeline in the context of routine production has become a challenge such that the data can be processed in real-time and delivered to the end-users as fast as possible. The usage of workflow management systems along with packaging systems and containerization technologies offer an opportunity to tackle this challenge. While very powerful, they can be used and combined in many multiple ways which may differ from one developer to another. Therefore, promoting the homogeneity of the workflow implementation requires guidelines and protocols which detail how the source code of the bioinformatics pipeline should be written and organized to ensure its usability, maintainability, interoperability, sustainability, portability, reproducibility, scalability and efficiency. Capitalizing on Nextflow, Conda, Docker, Singularity and the nf-core initiative, we propose a set of best practices along the development life cycle of the bioinformatics pipeline and deployment for production operations which target different expert communities including i) the bioinformaticians and statisticians ii) the software engineers and iii) the data managers and core facility engineers. We implemented Geniac (Automatic Configuration GENerator and Installer for nextflow pipelines) which consists of a toolbox with three components: i) a technical documentation available at https://geniac.readthedocs.io to detail coding guidelines for the bioinformatics pipeline with Nextflow, ii) a command line interface with a linter to check that the code respects the guidelines, and iii) an add-on to generate configuration files, build the containers and deploy the pipeline. The Geniac toolbox aims at the harmonization of development practices across developers and automation of the generation of configuration files and containers by parsing the source code of the Nextflow pipeline.
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Affiliation(s)
- Fabrice Allain
- Mines Paris Tech, Fontainebleau, F-77305, France
- Institut Curie, Paris, F-75005, France
- U900, Inserm, Paris, F-75005, France
- PSL Research University, Paris, F-75005, France
| | - Julien Roméjon
- Mines Paris Tech, Fontainebleau, F-77305, France
- Institut Curie, Paris, F-75005, France
- U900, Inserm, Paris, F-75005, France
- PSL Research University, Paris, F-75005, France
| | - Philippe La Rosa
- Mines Paris Tech, Fontainebleau, F-77305, France
- Institut Curie, Paris, F-75005, France
- U900, Inserm, Paris, F-75005, France
- PSL Research University, Paris, F-75005, France
| | - Frédéric Jarlier
- Mines Paris Tech, Fontainebleau, F-77305, France
- Institut Curie, Paris, F-75005, France
- U900, Inserm, Paris, F-75005, France
- PSL Research University, Paris, F-75005, France
| | - Nicolas Servant
- Mines Paris Tech, Fontainebleau, F-77305, France
- Institut Curie, Paris, F-75005, France
- U900, Inserm, Paris, F-75005, France
- PSL Research University, Paris, F-75005, France
| | - Philippe Hupé
- Mines Paris Tech, Fontainebleau, F-77305, France
- Institut Curie, Paris, F-75005, France
- U900, Inserm, Paris, F-75005, France
- PSL Research University, Paris, F-75005, France
- UMR144, CNRS, Paris, F-75005, France
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Wei J, Zhou T, Zhang X, Tian T. DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation. GENOMICS, PROTEOMICS & BIOINFORMATICS 2021; 19:306-318. [PMID: 33662626 PMCID: PMC8602766 DOI: 10.1016/j.gpb.2020.08.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 05/26/2020] [Accepted: 10/29/2020] [Indexed: 12/13/2022]
Abstract
One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data. Although substantial studies have been conducted in recent years, more effective methods are still strongly needed to infer the developmental processes accurately. This work devises a new method, named DTFLOW, for determining the pseudo-temporal trajectories with multiple branches. DTFLOW consists of two major steps: a new method called Bhattacharyya kernel feature decomposition (BKFD) to reduce the data dimensions, and a novel approach named Reverse Searching on k-nearest neighbor graph (RSKG) to identify the multi-branching processes of cellular differentiation. In BKFD, we first establish a stationary distribution for each cell to represent the transition of cellular developmental states based on the random walk with restart algorithm, and then propose a new distance metric for calculating pseudotime of single cells by introducing the Bhattacharyya kernel matrix. The effectiveness of DTFLOW is rigorously examined by using four single-cell datasets. We compare the efficiency of DTFLOW with the published state-of-the-art methods. Simulation results suggest that DTFLOW has superior accuracy and strong robustness properties for constructing pseudotime trajectories. The Python source code of DTFLOW can be freely accessed at https://github.com/statway/DTFLOW.
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Affiliation(s)
- Jiangyong Wei
- College of Science, Huazhong Agricultural University, Wuhan 430070, China; School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, China
| | - Tianshou Zhou
- School of Mathematics and Statistics, Sun Yat-sen University, Guangzhou 510275, China
| | - Xinan Zhang
- School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China
| | - Tianhai Tian
- School of Mathematics, Monash University, Melbourne, VIC 3800, Australia.
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Ho SY, Tan S, Sze CC, Wong L, Goh WWB. What can Venn diagrams teach us about doing data science better? INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2020. [DOI: 10.1007/s41060-020-00230-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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