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Choubey J, Wolkenhauer O, Chatterjee T. Systems Biology Approach to Analyze Microarray Datasets for Identification of Disease-Causing Genes: Case Study of Oral Squamous Cell Carcinoma. Methods Mol Biol 2024; 2719:13-31. [PMID: 37803110 DOI: 10.1007/978-1-0716-3461-5_2] [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] [Indexed: 10/08/2023]
Abstract
The discovery of potential disease-causing genes can aid medical progress. The post-genomic era has made this a more difficult task. Modern high-throughput methods have not solved the problem of identifying disease genes. Conventional methods cannot be used to investigate many rare or lethal diseases. Monitoring gene expression values in different samples using microarray technology is one of the best and most accurate ways to identify disease-causing genes. One of the most recent advances in experimental molecular biology is microarrays, which allow researchers to simultaneously monitor the expression levels of thousands of genes. Statistical analysis of microarray data might aid gene discovery by revealing pathways related to the target gene and facilitating identification of candidate genes. Systems biology, an interdisciplinary approach, has emerged as a crucial analytic tool with the potential to reveal previously unidentified causes and consequences of human illness. Genetic, environmental, immunological, or neurological factors have been implicated in the developing complex disorders like cancer. Because of this, it is important to approach the study of such disease from a novel perspective. The system biology approach allows us to rapidly identify disease-causing genes and assess their viability as therapeutic targets. This chapter demonstrates systems biology approaches to identify candidate genes using public database. Oral squamous cell carcinoma (OSCC) is used as a model disease to show how systems biology can be used successfully to identify and prioritize disease genes.
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Affiliation(s)
| | - Olaf Wolkenhauer
- Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany
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Singh N, Khan FM, Bala L, Vera J, Wolkenhauer O, Pützer B, Logotheti S, Gupta SK. Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression. BMC Chem 2023; 17:161. [PMID: 37993971 PMCID: PMC10666365 DOI: 10.1186/s13065-023-01082-2] [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: 06/12/2023] [Accepted: 11/08/2023] [Indexed: 11/24/2023] Open
Abstract
Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.
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Affiliation(s)
- Nivedita Singh
- Department of Biochemistry, BBDCODS, BBD University, Lucknow, Uttar Pradesh, India
- MRC Laboratory for Molecular Cell Biology, University College London, London, UK
| | - Faiz M Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Lakshmi Bala
- Department of Biochemistry, BBDCODS, BBD University, Lucknow, Uttar Pradesh, India
| | - Julio Vera
- Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Comprehensive Cancer Center Erlangen-European Metropolitan Area of Nuremberg (CCC ER-EMN), Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), Erlangen, Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
- Leibniz Institute for Food Systems Biology, Technical University of Munich, Munich, Germany
- Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India
- Stellenbosch Institute of Advanced Study, Wallenberg Research Centre, Stellenbosch University, Stellenbosch, South Africa
| | - Brigitte Pützer
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Stella Logotheti
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
- DNA Damage Laboratory, Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens (NTUA), Zografou, Athens, Greece
| | - Shailendra K Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.
- Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India.
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Song L, Yang J, Qin Z, Ou C, Luo R, Yang W, Wang L, Wang N, Ma S, Wu Q, Gong C. Multi-Targeted and On-Demand Non-Coding RNA Regulation Nanoplatform against Metastasis and Recurrence of Triple-Negative Breast Cancer. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2207576. [PMID: 36905244 DOI: 10.1002/smll.202207576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/06/2023] [Indexed: 06/08/2023]
Abstract
Dysregulation of microRNAs (miRs) is the hallmark of triple-negative breast cancer (TNBC), which is closely involved with its growth, metastasis, and recurrence. Dysregulated miRs are promising targets for TNBC therapy, however, targeted and accurate regulation of multiple disordered miRs in tumors is still a great challenge. Here, a multi-targeting and on-demand non-coding RNA regulation nanoplatform (MTOR) is reported to precisely regulate disordered miRs, leading to dramatical suppression of TNBC growth, metastasis, and recurrence. With the assistance of long blood circulation, ligands of urokinase-type plasminogen activator peptide and hyaluronan located in multi-functional shells enable MTOR to actively target TNBC cells and breast cancer stem cell-like cells (BrCSCs). After entering TNBC cells and BrCSCs, MTOR is subjected to lysosomal hyaluronidase-induced shell detachment, leading to an explosion of the TAT-enriched core, thereby enhancing nuclear targeting. Subsequently, MTOR could precisely and simultaneously downregulate microRNA-21 expression and upregulate microRNA-205 expression in TNBC. In subcutaneous xenograft, orthotopic xenograft, pulmonary metastasis, and recurrence TNBC mouse models, MTOR shows remarkably synergetic effects on the inhibition of tumor growth, metastasis, and recurrence due to its on-demand regulation of disordered miRs. This MTOR system opens a new avenue for on-demand regulation of disordered miRs against growth, metastasis, and recurrence of TNBC.
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Affiliation(s)
- Linjiang Song
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Jin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Zeyi Qin
- Department of Biology, Brandeis University, Waltham, MA, 02453, USA
| | - Chunqing Ou
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Rui Luo
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Wen Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Li Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Ning Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Shuang Ma
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Qinjie Wu
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Changyang Gong
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
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Retzlaff J, Lai X, Berking C, Vera J. Integration of transcriptomics data into agent-based models of solid tumor metastasis. Comput Struct Biotechnol J 2023; 21:1930-1941. [PMID: 36942106 PMCID: PMC10024179 DOI: 10.1016/j.csbj.2023.02.014] [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: 10/06/2022] [Revised: 02/06/2023] [Accepted: 02/06/2023] [Indexed: 03/06/2023] Open
Abstract
Recent progress in our understanding of cancer mostly relies on the systematic profiling of patient samples with high-throughput techniques like transcriptomics. With this approach, one can find gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, omics data alone cannot generate insights into the spatiotemporal aspects of tumor progression. Here, multi-level computational modeling is a promising approach that would benefit from protocols to integrate the data generated by the high-throughput profiling of patient samples. We present a computational workflow to integrate transcriptomics data from tumor patients into hybrid, multi-scale cancer models. In the method, we conduct transcriptomics analysis to select key differentially regulated pathways in therapy responders and non-responders and link them to agent-based model parameters. We then determine global and local sensitivity through systematic model simulations that assess the relevance of parameter variations in triggering therapy resistance. We illustrate the methodology with a de novo generated agent-based model accounting for the interplay between tumor and immune cells in a melanoma micrometastasis. The application of the workflow identifies three distinct scenarios of therapy resistance.
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Affiliation(s)
- Jimmy Retzlaff
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
- BioMediTech, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Carola Berking
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
- Corresponding author at: Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
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5
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Drug Repurposing at the Interface of Melanoma Immunotherapy and Autoimmune Disease. Pharmaceutics 2022; 15:pharmaceutics15010083. [PMID: 36678712 PMCID: PMC9865219 DOI: 10.3390/pharmaceutics15010083] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/06/2022] [Accepted: 12/21/2022] [Indexed: 12/29/2022] Open
Abstract
Cancer cells have a remarkable ability to evade recognition and destruction by the immune system. At the same time, cancer has been associated with chronic inflammation, while certain autoimmune diseases predispose to the development of neoplasia. Although cancer immunotherapy has revolutionized antitumor treatment, immune-related toxicities and adverse events detract from the clinical utility of even the most advanced drugs, especially in patients with both, metastatic cancer and pre-existing autoimmune diseases. Here, the combination of multi-omics, data-driven computational approaches with the application of network concepts enables in-depth analyses of the dynamic links between cancer, autoimmune diseases, and drugs. In this review, we focus on molecular and epigenetic metastasis-related processes within cancer cells and the immune microenvironment. With melanoma as a model, we uncover vulnerabilities for drug development to control cancer progression and immune responses. Thereby, drug repurposing allows taking advantage of existing safety profiles and established pharmacokinetic properties of approved agents. These procedures promise faster access and optimal management for cancer treatment. Together, these approaches provide new disease-based and data-driven opportunities for the prediction and application of targeted and clinically used drugs at the interface of immune-mediated diseases and cancer towards next-generation immunotherapies.
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Vera J, Lai X, Baur A, Erdmann M, Gupta S, Guttà C, Heinzerling L, Heppt MV, Kazmierczak PM, Kunz M, Lischer C, Pützer BM, Rehm M, Ostalecki C, Retzlaff J, Witt S, Wolkenhauer O, Berking C. Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence. Brief Bioinform 2022; 23:6761961. [PMID: 36252807 DOI: 10.1093/bib/bbac433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/28/2022] [Accepted: 09/08/2022] [Indexed: 12/19/2022] Open
Abstract
We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.
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Affiliation(s)
- Julio Vera
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Xin Lai
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Andreas Baur
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Michael Erdmann
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock 18051, Germany
| | - Cristiano Guttà
- Institute of Cell Biology and Immunology, University of Stuttgart, 70569 Stuttgart, Germany
| | - Lucie Heinzerling
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany.,Department of Dermatology, LMU University Hospital, Munich, Germany
| | - Markus V Heppt
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | | | - Manfred Kunz
- Department of Dermatology, Venereology and Allergology, University of Leipzig, 04103 Leipzig, Germany
| | - Christopher Lischer
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Brigitte M Pützer
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, 18057 Rostock, Germany
| | - Markus Rehm
- Institute of Cell Biology and Immunology, University of Stuttgart, 70569 Stuttgart, Germany.,Stuttgart Research Center Systems Biology, University of Stuttgart, 70569 Stuttgart, Germany
| | - Christian Ostalecki
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Jimmy Retzlaff
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | | | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock 18051, Germany
| | - Carola Berking
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
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Pützer BM, Sabapathy K. Editorial: Multidisciplinary Approaches in Exploring Cancer Heterogeneity, TME and Therapy Resistance: Perspectives for Systems Medicine. Front Cell Dev Biol 2022; 10:842596. [PMID: 35198561 PMCID: PMC8859833 DOI: 10.3389/fcell.2022.842596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 01/12/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Brigitte M. Pützer
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
- Department Life, Light & Matter, University of Rostock, Rostock, Germany
- *Correspondence: Brigitte M. Pützer,
| | - Kanaga Sabapathy
- Division of Cellular and Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre Singapore, Singapore, Singapore
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, Singapore
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Lai X, Keller C, Santos G, Schaft N, Dörrie J, Vera J. Multi-Level Computational Modeling of Anti-Cancer Dendritic Cell Vaccination Utilized to Select Molecular Targets for Therapy Optimization. Front Cell Dev Biol 2022; 9:746359. [PMID: 35186943 PMCID: PMC8847669 DOI: 10.3389/fcell.2021.746359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 12/23/2021] [Indexed: 01/18/2023] Open
Abstract
Dendritic cells (DCs) can be used for therapeutic vaccination against cancer. The success of this therapy depends on efficient tumor-antigen presentation to cytotoxic T lymphocytes (CTLs) and the induction of durable CTL responses by the DCs. Therefore, simulation of such a biological system by computational modeling is appealing because it can improve our understanding of the molecular mechanisms underlying CTL induction by DCs and help identify new strategies to improve therapeutic DC vaccination for cancer. Here, we developed a multi-level model accounting for the life cycle of DCs during anti-cancer immunotherapy. Specifically, the model is composed of three parts representing different stages of DC immunotherapy - the spreading and bio-distribution of intravenously injected DCs in human organs, the biochemical reactions regulating the DCs' maturation and activation, and DC-mediated activation of CTLs. We calibrated the model using quantitative experimental data that account for the activation of key molecular circuits within DCs, the bio-distribution of DCs in the body, and the interaction between DCs and T cells. We showed how such a data-driven model can be exploited in combination with sensitivity analysis and model simulations to identify targets for enhancing anti-cancer DC vaccination. Since other previous works show how modeling improves therapy schedules and DC dosage, we here focused on the molecular optimization of the therapy. In line with this, we simulated the effect in DC vaccination of the concerted modulation of combined intracellular regulatory processes and proposed several possibilities that can enhance DC-mediated immunogenicity. Taken together, we present a comprehensive time-resolved multi-level model for studying DC vaccination in melanoma. Although the model is not intended for personalized patient therapy, it could be used as a tool for identifying molecular targets for optimizing DC-based therapy for cancer, which ultimately should be tested in in vitro and in vivo experiments.
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Affiliation(s)
- Xin Lai
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum Immuntherapie and Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Christine Keller
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Guido Santos
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Departament of Biochemistry, Microbiology, Cell Biology and Genetics, Faculty of Sciences, University of La Laguna, San Cristóbal de La Laguna, Spain
| | - Niels Schaft
- Deutsches Zentrum Immuntherapie and Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
- RNA Group, Department of Dermatology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Jan Dörrie
- Deutsches Zentrum Immuntherapie and Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
- RNA Group, Department of Dermatology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum Immuntherapie and Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
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Lai X, Schmitz U, Vera J. The Role of MicroRNAs in Cancer Biology and Therapy from a Systems Biology Perspective. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1385:1-22. [DOI: 10.1007/978-3-031-08356-3_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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10
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Logotheti S, Richter C, Murr N, Spitschak A, Marquardt S, Pützer BM. Mechanisms of Functional Pleiotropy of p73 in Cancer and Beyond. Front Cell Dev Biol 2021; 9:737735. [PMID: 34650986 PMCID: PMC8506118 DOI: 10.3389/fcell.2021.737735] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 09/10/2021] [Indexed: 01/21/2023] Open
Abstract
The transcription factor p73 is a structural and functional homolog of TP53, the most famous and frequently mutated tumor-suppressor gene. The TP73 gene can synthesize an overwhelming number of isoforms via splicing events in 5′ and 3′ ends and alternative promoter usage. Although it originally came into the spotlight due to the potential of several of these isoforms to mimic p53 functions, it is now clear that TP73 has its own unique identity as a master regulator of multifaceted processes in embryonic development, tissue homeostasis, and cancer. This remarkable functional pleiotropy is supported by a high degree of mechanistic heterogeneity, which extends far-beyond the typical mode of action by transactivation and largely relies on the ability of p73 isoforms to form protein–protein interactions (PPIs) with a variety of nuclear and cytoplasmic proteins. Importantly, each p73 isoform carries a unique combination of functional domains and residues that facilitates the establishment of PPIs in a highly selective manner. Herein, we summarize the expanding functional repertoire of TP73 in physiological and oncogenic processes. We emphasize how TP73’s ability to control neurodevelopment and neurodifferentiation is co-opted in cancer cells toward neoneurogenesis, an emerging cancer hallmark, whereby tumors promote their own innervation. By further exploring the canonical and non-canonical mechanistic patterns of p73, we apprehend its functional diversity as the result of a sophisticated and coordinated interplay of: (a) the type of p73 isoforms (b) the presence of p73 interaction partners in the cell milieu, and (c) the architecture of target gene promoters. We suppose that dysregulation of one or more of these parameters in tumors may lead to cancer initiation and progression by reactivating p73 isoforms and/or p73-regulated differentiation programs thereof in a spatiotemporally inappropriate manner. A thorough understanding of the mechanisms supporting p73 functional diversity is of paramount importance for the efficient and precise p73 targeting not only in cancer, but also in other pathological conditions where TP73 dysregulation is causally involved.
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Affiliation(s)
- Stella Logotheti
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Christin Richter
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Nico Murr
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Alf Spitschak
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Stephan Marquardt
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Brigitte M Pützer
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany.,Department Life, Light & Matter, University of Rostock, Rostock, Germany
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Choudhari JK, Chatterjee T, Gupta S, Garcia-Garcia JG, Vera-González J. Network Biology Approaches in Ophthalmological Diseases: A Case Study of Glaucoma. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11586-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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12
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Vera-González J, Cantone M, Blume C. Network and Systems Biology Approaches in Glial Cells. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11614-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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13
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Qi FF, Yang Y, Zhang H, Chen H. Long non-coding RNAs: Key regulators in oxaliplatin resistance of colorectal cancer. Biomed Pharmacother 2020; 128:110329. [PMID: 32502843 DOI: 10.1016/j.biopha.2020.110329] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/22/2020] [Accepted: 05/23/2020] [Indexed: 12/19/2022] Open
Abstract
Colorectal cancer (CRC) is one of the most commonly diagnosed malignancies in the world with high relapse and mortality rates. Although oxaliplatin (OXA), a platinum-based anticancer drug, is widely used in CRC treatment, the resulting chemoresistance dramatically attenuates the drug efficacy and increases the failure rate of this therapy. Thus, the study on OXA-induced chemoresistance is extremely urgent. In recent years, emerging evidence has shown that lncRNAs play irreplaceable roles in drug resistance. However, we only have a limited knowledge of the lncRNAs that are closely related to oxaliplatin resistance in CRC. In present study, we identify and characterize these lncRNAs, including their functions, underlying mechanisms and possible applications.
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Affiliation(s)
- Fang-Fang Qi
- Department of Histology and Embryology, Medical College of Nanchang University, Nanchang, Jiangxi 330006, PR China; Queen Mary School, Medical Department, Nanchang University, Nanchang, Jiangxi 330006, PR China
| | - Yunyao Yang
- Department of Histology and Embryology, Medical College of Nanchang University, Nanchang, Jiangxi 330006, PR China; Queen Mary School, Medical Department, Nanchang University, Nanchang, Jiangxi 330006, PR China
| | - Haowen Zhang
- Department of Histology and Embryology, Medical College of Nanchang University, Nanchang, Jiangxi 330006, PR China; Queen Mary School, Medical Department, Nanchang University, Nanchang, Jiangxi 330006, PR China
| | - Hongping Chen
- Department of Histology and Embryology, Medical College of Nanchang University, Nanchang, Jiangxi 330006, PR China; Jiangxi Key Laboratory of Experimental Animals, Nanchang University, Nanchang, Jiangxi 330006, PR China.
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14
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Lai X, Eberhardt M, Schmitz U, Vera J. Systems biology-based investigation of cooperating microRNAs as monotherapy or adjuvant therapy in cancer. Nucleic Acids Res 2019; 47:7753-7766. [PMID: 31340025 PMCID: PMC6735922 DOI: 10.1093/nar/gkz638] [Citation(s) in RCA: 118] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 07/05/2019] [Accepted: 07/13/2019] [Indexed: 12/22/2022] Open
Abstract
MicroRNAs (miRNAs) are short, noncoding RNAs that regulate gene expression by suppressing mRNA translation and reducing mRNA stability. A miRNA can potentially bind many mRNAs, thereby affecting the expression of oncogenes and tumor suppressor genes as well as the activity of whole pathways. The promise of miRNA therapeutics in cancer is to harness this evolutionarily conserved mechanism for the coordinated regulation of gene expression, and thus restoring a normal cell phenotype. However, the promiscuous binding of miRNAs can provoke unwanted off-target effects, which are usually caused by high-dose single-miRNA treatments. Thus, it is desirable to develop miRNA therapeutics with increased specificity and efficacy. To achieve that, we propose the concept of miRNA cooperativity in order to exert synergistic repression on target genes, thus lowering the required total amount of miRNAs. We first review miRNA therapies in clinical application. Next, we summarize the knowledge on the molecular mechanism and biological function of miRNA cooperativity and discuss its application in cancer therapies. We then propose and discuss a systems biology approach to investigate miRNA cooperativity for the clinical setting. Altogether, we point out the potential of miRNA cooperativity to reduce off-target effects and to complement conventional, targeted, or immune-based therapies for cancer.
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Affiliation(s)
- Xin Lai
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen, 91052 Erlangen, Germany
- Faculty of Medicine, Friedrich-Alexander University Erlangen-Nürnberg, 91052 Erlangen, Germany
| | - Martin Eberhardt
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen, 91052 Erlangen, Germany
- Faculty of Medicine, Friedrich-Alexander University Erlangen-Nürnberg, 91052 Erlangen, Germany
| | - Ulf Schmitz
- Computational BioMedicine Laboratory Centenary Institute, The University of Sydney, 2006 Camperdown, Australia
- Gene & Stem Cell Therapy Program Centenary Institute, The University of Sydney, 2006 Camperdown, Australia
- Sydney Medical School, The University of Sydney, 2006 Camperdown, Australia
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen, 91052 Erlangen, Germany
- Faculty of Medicine, Friedrich-Alexander University Erlangen-Nürnberg, 91052 Erlangen, Germany
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15
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Modelling of Protein Kinase Signaling Pathways in Melanoma and Other Cancers. Cancers (Basel) 2019; 11:cancers11040465. [PMID: 30987166 PMCID: PMC6520749 DOI: 10.3390/cancers11040465] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Revised: 03/26/2019] [Accepted: 03/30/2019] [Indexed: 12/18/2022] Open
Abstract
Melanoma is a highly aggressive tumor with a strong dependence on intracellular signaling pathways. Almost half of all melanomas are driven by mutations in the v-Raf murine sarcoma viral oncogene homolog B (BRAF) with BRAFV600E being the most prevalent mutation. Recently developed targeted treatment directed against mutant BRAF and downstream mitogen-activated protein kinase (MAPK) MAP2K1 (also termed MEK1) have improved overall survival of melanoma patients. However, the MAPK signaling pathway is far more complex than a single chain of consecutively activated MAPK enzymes and it contains nested-, inherent feedback mechanisms, crosstalk with other signaling pathways, epigenetic regulatory mechanisms, and interacting small non-coding RNAs. A more complete understanding of this pathway is needed to better understand melanoma development and mechanisms of treatment resistance. Network reconstruction, analysis, and modelling under the systems biology paradigm have been used recently in different malignant tumors including melanoma to analyze and integrate 'omics' data, formulate mechanistic hypotheses on tumorigenesis, assess and personalize anticancer therapy, and propose new drug targets. Here we review the current knowledge of network modelling approaches in cancer with a special emphasis on melanoma.
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16
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Zhao C, Zhang Y, Popel AS. Mechanistic Computational Models of MicroRNA-Mediated Signaling Networks in Human Diseases. Int J Mol Sci 2019; 20:E421. [PMID: 30669429 PMCID: PMC6358731 DOI: 10.3390/ijms20020421] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Revised: 01/14/2019] [Accepted: 01/15/2019] [Indexed: 12/17/2022] Open
Abstract
MicroRNAs (miRs) are endogenous non-coding RNA molecules that play important roles in human health and disease by regulating gene expression and cellular processes. In recent years, with the increasing scientific knowledge and new discovery of miRs and their gene targets, as well as the plentiful experimental evidence that shows dysregulation of miRs in a wide variety of human diseases, the computational modeling approach has emerged as an effective tool to help researchers identify novel functional associations between differential miR expression and diseases, dissect the phenotypic expression patterns of miRs in gene regulatory networks, and elucidate the critical roles of miRs in the modulation of disease pathways from mechanistic and quantitative perspectives. Here we will review the recent systems biology studies that employed different kinetic modeling techniques to provide mechanistic insights relating to the regulatory function and therapeutic potential of miRs in human diseases. Some of the key computational aspects to be discussed in detail in this review include (i) models of miR-mediated network motifs in the regulation of gene expression, (ii) models of miR biogenesis and miR⁻target interactions, and (iii) the incorporation of such models into complex disease pathways in order to generate mechanistic, molecular- and systems-level understanding of pathophysiology. Other related bioinformatics tools such as computational platforms that predict miR-disease associations will also be discussed, and we will provide perspectives on the challenges and opportunities in the future development and translational application of data-driven systems biology models that involve miRs and their regulatory pathways in human diseases.
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Affiliation(s)
- Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
| | - Yu Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
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17
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Emerging functional markers for cancer stem cell-based therapies: Understanding signaling networks for targeting metastasis. Semin Cancer Biol 2018; 53:90-109. [PMID: 29966677 DOI: 10.1016/j.semcancer.2018.06.006] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 06/20/2018] [Accepted: 06/28/2018] [Indexed: 12/18/2022]
Abstract
Metastasis is one of the most challenging issues in cancer patient management, and effective therapies to specifically target disease progression are missing, emphasizing the urgent need for developing novel anti-metastatic therapeutics. Cancer stem cells (CSCs) gained fast attention as a minor population of highly malignant cells within liquid and solid tumors that are responsible for tumor onset, self-renewal, resistance to radio- and chemotherapies, and evasion of immune surveillance accelerating recurrence and metastasis. Recent progress in the identification of their phenotypic and molecular characteristics and interactions with the tumor microenvironment provides great potential for the development of CSC-based targeted therapies and radical improvement in metastasis prevention and cancer patient prognosis. Here, we report on newly uncovered signaling mechanisms controlling CSC's aggressiveness and treatment resistance, and CSC-specific agents and molecular therapeutics, some of which are currently under investigation in clinical trials, gearing towards decisive functional CSC intrinsic or surface markers. One special research focus rests upon subverted regulatory pathways such as insulin-like growth factor 1 receptor signaling and its interactors in metastasis-initiating cell populations directly related to the gain of stem cell- and EMT-associated properties, as well as key components of the E2F transcription factor network regulating metastatic progression, microenvironmental changes, and chemoresistance. In addition, the study provides insight into systems biology tools to establish complex molecular relationships behind the emergence of aggressive phenotypes from high-throughput data that rely on network-based analysis and their use to investigate immune escape mechanisms or predict clinical outcome-relevant CSC receptor signaling signatures. We further propose that customized vector technologies could drastically enhance systemic drug delivery to target sites, and summarize recent progress and remaining challenges. This review integrates available knowledge on CSC biology, computational modeling approaches, molecular targeting strategies, and delivery techniques to envision future clinical therapies designed to conquer metastasis-initiating cells.
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18
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Santos G, Lai X, Eberhardt M, Vera J. Bacterial Adherence and Dwelling Probability: Two Drivers of Early Alveolar Infection by Streptococcus pneumoniae Identified in Multi-Level Mathematical Modeling. Front Cell Infect Microbiol 2018; 8:159. [PMID: 29868515 PMCID: PMC5962665 DOI: 10.3389/fcimb.2018.00159] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Accepted: 04/25/2018] [Indexed: 01/31/2023] Open
Abstract
Pneumococcal infection is the most frequent cause of pneumonia, and one of the most prevalent diseases worldwide. The population groups at high risk of death from bacterial pneumonia are infants, elderly and immunosuppressed people. These groups are more vulnerable because they have immature or impaired immune systems, the efficacy of their response to vaccines is lower, and antibiotic treatment often does not take place until the inflammatory response triggered is already overwhelming. The immune response to bacterial lung infections involves dynamic interactions between several types of cells whose activation is driven by intracellular molecular networks. A feasible approach to the integration of knowledge and data linking tissue, cellular and intracellular events and the construction of hypotheses in this area is the use of mathematical modeling. For this paper, we used a multi-level computational model to analyse the role of cellular and molecular interactions during the first 10 h after alveolar invasion of Streptococcus pneumoniae bacteria. By “multi-level” we mean that we simulated the interplay between different temporal and spatial scales in a single computational model. In this instance, we included the intracellular scale of processes driving lung epithelial cell activation together with the scale of cell-to-cell interactions at the alveolar tissue. In our analysis, we combined systematic model simulations with logistic regression analysis and decision trees to find genotypic-phenotypic signatures that explain differences in bacteria strain infectivity. According to our simulations, pneumococci benefit from a high dwelling probability and a high proliferation rate during the first stages of infection. In addition to this, the model predicts that during the very early phases of infection the bacterial capsule could be an impediment to the establishment of the alveolar infection because it impairs bacterial colonization.
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Affiliation(s)
- Guido Santos
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Faculty of Medicine, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Faculty of Medicine, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Martin Eberhardt
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Faculty of Medicine, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Faculty of Medicine, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
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19
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Dreyer FS, Cantone M, Eberhardt M, Jaitly T, Walter L, Wittmann J, Gupta SK, Khan FM, Wolkenhauer O, Pützer BM, Jäck HM, Heinzerling L, Vera J. A web platform for the network analysis of high-throughput data in melanoma and its use to investigate mechanisms of resistance to anti-PD1 immunotherapy. Biochim Biophys Acta Mol Basis Dis 2018; 1864:2315-2328. [PMID: 29410200 DOI: 10.1016/j.bbadis.2018.01.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 01/12/2018] [Accepted: 01/16/2018] [Indexed: 01/11/2023]
Abstract
Cellular phenotypes are established and controlled by complex and precisely orchestrated molecular networks. In cancer, mutations and dysregulations of multiple molecular factors perturb the regulation of these networks and lead to malignant transformation. High-throughput technologies are a valuable source of information to establish the complex molecular relationships behind the emergence of malignancy, but full exploitation of this massive amount of data requires bioinformatics tools that rely on network-based analyses. In this report we present the Virtual Melanoma Cell, an online tool developed to facilitate the mining and interpretation of high-throughput data on melanoma by biomedical researches. The platform is based on a comprehensive, manually generated and expert-validated regulatory map composed of signaling pathways important in malignant melanoma. The Virtual Melanoma Cell is a tool designed to accept, visualize and analyze user-generated datasets. It is available at: https://www.vcells.net/melanoma. To illustrate the utilization of the web platform and the regulatory map, we have analyzed a large publicly available dataset accounting for anti-PD1 immunotherapy treatment of malignant melanoma patients.
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20
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Lai X, Gupta SK, Schmitz U, Marquardt S, Knoll S, Spitschak A, Wolkenhauer O, Pützer BM, Vera J. MiR-205-5p and miR-342-3p cooperate in the repression of the E2F1 transcription factor in the context of anticancer chemotherapy resistance. Theranostics 2018; 8:1106-1120. [PMID: 29464002 PMCID: PMC5817113 DOI: 10.7150/thno.19904] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 11/14/2017] [Indexed: 01/05/2023] Open
Abstract
High rates of lethal outcome in tumour metastasis are associated with the acquisition of invasiveness and chemoresistance. Several clinical studies indicate that E2F1 overexpression across high-grade tumours culminates in unfavourable prognosis and chemoresistance in patients. Thus, fine-tuning the expression of E2F1 could be a promising approach for treating patients showing chemoresistance. Methods: We integrated bioinformatics, structural and kinetic modelling, and experiments to study cooperative regulation of E2F1 by microRNA (miRNA) pairs in the context of anticancer chemotherapy resistance. Results: We showed that an enhanced E2F1 repression efficiency can be achieved in chemoresistant tumour cells through two cooperating miRNAs. Sequence and structural information were used to identify potential miRNA pairs that can form tertiary structures with E2F1 mRNA. We then employed molecular dynamics simulations to show that among the identified triplexes, miR-205-5p and miR-342-3p can form the most stable triplex with E2F1 mRNA. A mathematical model simulating the E2F1 regulation by the cooperative miRNAs predicted enhanced E2F1 repression, a feature that was verified by in vitro experiments. Finally, we integrated this cooperative miRNA regulation into a more comprehensive network to account for E2F1-related chemoresistance in tumour cells. The network model simulations and experimental data indicate the ability of enhanced expression of both miR-205-5p and miR-342-3p to decrease tumour chemoresistance by cooperatively repressing E2F1. Conclusions: Our results suggest that pairs of cooperating miRNAs could be used as potential RNA therapeutics to reduce E2F1-related chemoresistance.
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21
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Khan FM, Sadeghi M, Gupta SK, Wolkenhauer O. A Network-Based Integrative Workflow to Unravel Mechanisms Underlying Disease Progression. Methods Mol Biol 2018; 1702:247-276. [PMID: 29119509 DOI: 10.1007/978-1-4939-7456-6_12] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Unraveling mechanisms underlying diseases has motivated the development of systems biology approaches. The key challenges for the development of mathematical models and computational tool are (1) the size of molecular networks, (2) the nonlinear nature of spatio-temporal interactions, and (3) feedback loops in the structure of interaction networks. We here propose an integrative workflow that combines structural analyses of networks, high-throughput data, and mechanistic modeling. As an illustration of the workflow, we use prostate cancer as a case study with the aim of identifying key functional components associated with primary to metastasis transitions. Analysis carried out by the workflow revealed that HOXD10, BCL2, and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN, and JUNB are playing a central role. The identified key elements of each network are validated using patient survival analysis. The workflow presented here allows experimentalists to use heterogeneous data sources for the identification of diagnostic and prognostic signatures.
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Affiliation(s)
- Faiz M Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Mehdi Sadeghi
- Research Institute for Fundamental Sciences (RIFS), University of Tabriz, Tabriz, Iran
| | - Shailendra K Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany.,Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany. .,Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India. .,Stellenbosch Institute of Advanced Study (STIAS), Wallenberg Research Centre, Stellenbosch University, Stellenbosch, South Africa.
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22
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Nikolov S, Santos G, Wolkenhauer O, Vera J. Model-Based Phenotypic Signatures Governing the Dynamics of the Stem and Semi-differentiated Cell Populations in Dysplastic Colonic Crypts. Bull Math Biol 2017; 80:360-384. [PMID: 29218591 DOI: 10.1007/s11538-017-0378-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 12/01/2017] [Indexed: 01/09/2023]
Abstract
Mathematical modeling of cell differentiated in colonic crypts can contribute to a better understanding of basic mechanisms underlying colonic tissue organization, but also its deregulation during carcinogenesis and tumor progression. Here, we combined bifurcation analysis to assess the effect that time delay has in the complex interplay of stem cells and semi-differentiated cells at the niche of colonic crypts, and systematic model perturbation and simulation to find model-based phenotypes linked to cancer progression. The models suggest that stem cell and semi-differentiated cell population dynamics in colonic crypts can display chaotic behavior. In addition, we found that clinical profiling of colorectal cancer correlates with the in silico phenotypes proposed by the mathematical model. Further, potential therapeutic targets for chemotherapy resistant phenotypes are proposed, which in any case will require experimental validation.
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Affiliation(s)
- Svetoslav Nikolov
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany. .,Institute of Mechanics and Biomechanics-BAS, Acad. G. Bonchev Str., Bl. 4, 1113, Sofia, Bulgaria. .,University of Transport, Geo Milev Str., 158, 1574, Sofia, Bulgaria. .,Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen, Erlangen, Germany.
| | - Guido Santos
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen, Erlangen, Germany.,Systems Biology and Mathematical Modelling Group, Departamento de Bioquímica, Microbiología, Biología Celular y Genética, Instituto de Tecnología Biomédica, CIBICAN, Universidad de La Laguna, Campus Ciencias de La Salud, 38071, La Laguna (Tenerife), Spain
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany.,Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen, Erlangen, Germany.
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23
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Pützer BM, Solanki M, Herchenröder O. Advances in cancer stem cell targeting: How to strike the evil at its root. Adv Drug Deliv Rev 2017; 120:89-107. [PMID: 28736304 DOI: 10.1016/j.addr.2017.07.013] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 07/10/2017] [Accepted: 07/16/2017] [Indexed: 12/18/2022]
Abstract
Cancer progression to metastatic stages is still unmanageable and the promise of effective anti-metastatic therapy remains largely unmet, emphasizing the need to develop novel therapeutics. The special focus here is on cancer stem cells (CSC) as the seed of tumor initiation, epithelial-mesenchymal transition, chemoresistance and, as a consequence, drivers of metastatic dissemination. We report on targeted therapies gearing towards the CSC's internal and membrane-anchored markers using agents such as antibody derivatives, nucleic therapeutics, small molecules and genetic payloads. Another emphasis lies on novel proceedings envisaged to deliver current and prospective therapies to the target sites using newest viral and non-viral vector technologies. In this review, we summarize recent progress and remaining challenges in therapeutic strategies to combat CSC.
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Affiliation(s)
- Brigitte M Pützer
- Institute of Experimental Gene Therapy and Cancer Research, Biomedical Research Center (BMFZ), Rostock University Medical School, Germany.
| | - Manish Solanki
- Institute of Experimental Gene Therapy and Cancer Research, Biomedical Research Center (BMFZ), Rostock University Medical School, Germany
| | - Ottmar Herchenröder
- Institute of Experimental Gene Therapy and Cancer Research, Biomedical Research Center (BMFZ), Rostock University Medical School, Germany
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24
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Unraveling a tumor type-specific regulatory core underlying E2F1-mediated epithelial-mesenchymal transition to predict receptor protein signatures. Nat Commun 2017; 8:198. [PMID: 28775339 PMCID: PMC5543083 DOI: 10.1038/s41467-017-00268-2] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Accepted: 06/15/2017] [Indexed: 12/18/2022] Open
Abstract
Cancer is a disease of subverted regulatory pathways. In this paper, we reconstruct the regulatory network around E2F, a family of transcription factors whose deregulation has been associated to cancer progression, chemoresistance, invasiveness, and metastasis. We integrate gene expression profiles of cancer cell lines from two E2F1-driven highly aggressive bladder and breast tumors, and use network analysis methods to identify the tumor type-specific core of the network. By combining logic-based network modeling, in vitro experimentation, and gene expression profiles from patient cohorts displaying tumor aggressiveness, we identify and experimentally validate distinctive, tumor type-specific signatures of receptor proteins associated to epithelial-mesenchymal transition in bladder and breast cancer. Our integrative network-based methodology, exemplified in the case of E2F1-induced aggressive tumors, has the potential to support the design of cohort- as well as tumor type-specific treatments and ultimately, to fight metastasis and therapy resistance.Deregulation of E2F family transcription factors is associated with cancer progression and metastasis. Here, the authors construct a map of the regulatory network around the E2F family, and using gene expression profiles, identify tumour type-specific regulatory cores and receptor expression signatures associated with epithelial-mesenchymal transition in bladder and breast cancer.
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25
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Sadeghi M, Ranjbar B, Ganjalikhany MR, M. Khan F, Schmitz U, Wolkenhauer O, Gupta SK. MicroRNA and Transcription Factor Gene Regulatory Network Analysis Reveals Key Regulatory Elements Associated with Prostate Cancer Progression. PLoS One 2016; 11:e0168760. [PMID: 28005952 PMCID: PMC5179129 DOI: 10.1371/journal.pone.0168760] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2016] [Accepted: 11/21/2016] [Indexed: 11/18/2022] Open
Abstract
Technological and methodological advances in multi-omics data generation and integration approaches help elucidate genetic features of complex biological traits and diseases such as prostate cancer. Due to its heterogeneity, the identification of key functional components involved in the regulation and progression of prostate cancer is a methodological challenge. In this study, we identified key regulatory interactions responsible for primary to metastasis transitions in prostate cancer using network inference approaches by integrating patient derived transcriptomic and miRomics data into gene/miRNA/transcription factor regulatory networks. One such network was derived for each of the clinical states of prostate cancer based on differentially expressed and significantly correlated gene, miRNA and TF pairs from the patient data. We identified key elements of each network using a network analysis approach and validated our results using patient survival analysis. We observed that HOXD10, BCL2 and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN and JUNB are playing a central role. Benefiting integrative networks our analysis suggests that some of these molecules were targeted by several overexpressed miRNAs which may have a major effect on the dysregulation of these molecules. For example, in the metastatic tumors five miRNAs (miR-671-5p, miR-665, miR-663, miR-512-3p and miR-371-5p) are mainly responsible for the dysregulation of STAT3 and hence can provide an opportunity for early detection of metastasis and development of alternative therapeutic approaches. Our findings deliver new details on key functional components in prostate cancer progression and provide opportunities for the development of alternative therapeutic approaches.
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Affiliation(s)
- Mehdi Sadeghi
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Bijan Ranjbar
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | | | - Faiz M. Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Ulf Schmitz
- Gene and Stem Cell Therapy Program, Centenary Institute, University of Sydney, Camperdown, Australia
- Sydney Medical School, University of Sydney, Camperdown, Australia
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
- Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa
| | - Shailendra K. Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
- Department of Bioinformatics, CSIR-Indian Institute of Toxicology Research, Lucknow, India
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26
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Amirkhah R, Farazmand A, Wolkenhauer O, Schmitz U. RNA Systems Biology for Cancer: From Diagnosis to Therapy. Methods Mol Biol 2016; 1386:305-30. [PMID: 26677189 DOI: 10.1007/978-1-4939-3283-2_14] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
It is due to the advances in high-throughput omics data generation that RNA species have re-entered the focus of biomedical research. International collaborate efforts, like the ENCODE and GENCODE projects, have spawned thousands of previously unknown functional non-coding RNAs (ncRNAs) with various but primarily regulatory roles. Many of these are linked to the emergence and progression of human diseases. In particular, interdisciplinary studies integrating bioinformatics, systems biology, and biotechnological approaches have successfully characterized the role of ncRNAs in different human cancers. These efforts led to the identification of a new tool-kit for cancer diagnosis, monitoring, and treatment, which is now starting to enter and impact on clinical practice. This chapter is to elaborate on the state of the art in RNA systems biology, including a review and perspective on clinical applications toward an integrative RNA systems medicine approach. The focus is on the role of ncRNAs in cancer.
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Affiliation(s)
- Raheleh Amirkhah
- Department of Cell and Molecular Biology, School of Biology, College of Science, University of Tehran, Tehran, Iran
| | - Ali Farazmand
- Department of Cell and Molecular Biology, School of Biology, College of Science, University of Tehran, Tehran, Iran
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.,Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa
| | - Ulf Schmitz
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.
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Lai X, Wolkenhauer O, Vera J. Understanding microRNA-mediated gene regulatory networks through mathematical modelling. Nucleic Acids Res 2016; 44:6019-35. [PMID: 27317695 PMCID: PMC5291278 DOI: 10.1093/nar/gkw550] [Citation(s) in RCA: 114] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2015] [Accepted: 06/06/2016] [Indexed: 12/19/2022] Open
Abstract
The discovery of microRNAs (miRNAs) has added a new player to the regulation of gene expression. With the increasing number of molecular species involved in gene regulatory networks, it is hard to obtain an intuitive understanding of network dynamics. Mathematical modelling can help dissecting the role of miRNAs in gene regulatory networks, and we shall here review the most recent developments that utilise different mathematical modelling approaches to provide quantitative insights into the function of miRNAs in the regulation of gene expression. Key miRNA regulation features that have been elucidated via modelling include: (i) the role of miRNA-mediated feedback and feedforward loops in fine-tuning of gene expression; (ii) the miRNA–target interaction properties determining the effectiveness of miRNA-mediated gene repression; and (iii) the competition for shared miRNAs leading to the cross-regulation of genes. However, there is still lack of mechanistic understanding of many other properties of miRNA regulation like unconventional miRNA–target interactions, miRNA regulation at different sub-cellular locations and functional miRNA variant, which will need future modelling efforts to deal with. This review provides an overview of recent developments and challenges in this field.
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Affiliation(s)
- Xin Lai
- Laboratory of Systems Tumour Immunology, Department of Dermatology, Erlangen University Hospital and Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, 91054, Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, 18051, Germany Stellenbosch Institute for Advanced Study, Wallenberg Research Centre at Stellenbosch University, 7600, South Africa
| | - Julio Vera
- Laboratory of Systems Tumour Immunology, Department of Dermatology, Erlangen University Hospital and Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, 91054, Germany
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Santos G, Nikolov S, Lai X, Eberhardt M, Dreyer FS, Paul S, Schuler G, Vera J. Model-based genotype-phenotype mapping used to investigate gene signatures of immune sensitivity and resistance in melanoma micrometastasis. Sci Rep 2016; 6:24967. [PMID: 27113331 PMCID: PMC4844979 DOI: 10.1038/srep24967] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Accepted: 04/08/2016] [Indexed: 02/07/2023] Open
Abstract
In this paper, we combine kinetic modelling and patient gene expression data analysis to elucidate biological mechanisms by which melanoma becomes resistant to the immune system and to immunotherapy. To this end, we systematically perturbed the parameters in a kinetic model and performed a mathematical analysis of their impact, thereby obtaining signatures associated with the emergence of phenotypes of melanoma immune sensitivity and resistance. Our phenotypic signatures were compared with published clinical data on pretreatment tumor gene expression in patients subjected to immunotherapy against metastatic melanoma. To this end, the differentially expressed genes were annotated with standard gene ontology terms and aggregated into metagenes. Our method sheds light on putative mechanisms by which melanoma may develop immunoresistance. Precisely, our results and the clinical data point to the existence of a signature of intermediate expression levels for genes related to antigen presentation that constitutes an intriguing resistance mechanism, whereby micrometastases are able to minimize the combined anti-tumor activity of complementary responses mediated by cytotoxic T cells and natural killer cells, respectively. Finally, we computationally explored the efficacy of cytokines used as low-dose co-adjuvants for the therapeutic anticancer vaccine to overcome tumor immunoresistance.
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Affiliation(s)
- Guido Santos
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander University of Erlangen-Nuremberg, Germany
- Department of Dermatology and Erlangen University Hospital and Faculty of Medicine, Friedrich-Alexander University of Erlangen-Nuremberg, Germany
- Systems Biology and Mathematical Modelling Group, University of La Laguna, Spain
| | - Svetoslav Nikolov
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander University of Erlangen-Nuremberg, Germany
- Institute of Mechanics, Bulgarian Academy of Science, Sofia, Bulgaria
- University of Transport, Sofia, Bulgaria
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander University of Erlangen-Nuremberg, Germany
- Department of Dermatology and Erlangen University Hospital and Faculty of Medicine, Friedrich-Alexander University of Erlangen-Nuremberg, Germany
| | - Martin Eberhardt
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander University of Erlangen-Nuremberg, Germany
- Department of Dermatology and Erlangen University Hospital and Faculty of Medicine, Friedrich-Alexander University of Erlangen-Nuremberg, Germany
| | - Florian S. Dreyer
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander University of Erlangen-Nuremberg, Germany
- Department of Dermatology and Erlangen University Hospital and Faculty of Medicine, Friedrich-Alexander University of Erlangen-Nuremberg, Germany
| | - Sushmita Paul
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander University of Erlangen-Nuremberg, Germany
- Department of Dermatology and Erlangen University Hospital and Faculty of Medicine, Friedrich-Alexander University of Erlangen-Nuremberg, Germany
| | - Gerold Schuler
- Department of Dermatology and Erlangen University Hospital and Faculty of Medicine, Friedrich-Alexander University of Erlangen-Nuremberg, Germany
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander University of Erlangen-Nuremberg, Germany
- Department of Dermatology and Erlangen University Hospital and Faculty of Medicine, Friedrich-Alexander University of Erlangen-Nuremberg, Germany
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Eberhardt M, Lai X, Tomar N, Gupta S, Schmeck B, Steinkasserer A, Schuler G, Vera J. Third-Kind Encounters in Biomedicine: Immunology Meets Mathematics and Informatics to Become Quantitative and Predictive. Methods Mol Biol 2016; 1386:135-179. [PMID: 26677184 DOI: 10.1007/978-1-4939-3283-2_9] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The understanding of the immune response is right now at the center of biomedical research. There are growing expectations that immune-based interventions will in the midterm provide new, personalized, and targeted therapeutic options for many severe and highly prevalent diseases, from aggressive cancers to infectious and autoimmune diseases. To this end, immunology should surpass its current descriptive and phenomenological nature, and become quantitative, and thereby predictive.Immunology is an ideal field for deploying the tools, methodologies, and philosophy of systems biology, an approach that combines quantitative experimental data, computational biology, and mathematical modeling. This is because, from an organism-wide perspective, the immunity is a biological system of systems, a paradigmatic instance of a multi-scale system. At the molecular scale, the critical phenotypic responses of immune cells are governed by large biochemical networks, enriched in nested regulatory motifs such as feedback and feedforward loops. This network complexity confers them the ability of highly nonlinear behavior, including remarkable examples of homeostasis, ultra-sensitivity, hysteresis, and bistability. Moving from the cellular level, different immune cell populations communicate with each other by direct physical contact or receiving and secreting signaling molecules such as cytokines. Moreover, the interaction of the immune system with its potential targets (e.g., pathogens or tumor cells) is far from simple, as it involves a number of attack and counterattack mechanisms that ultimately constitute a tightly regulated multi-feedback loop system. From a more practical perspective, this leads to the consequence that today's immunologists are facing an ever-increasing challenge of integrating massive quantities from multi-platforms.In this chapter, we support the idea that the analysis of the immune system demands the use of systems-level approaches to ensure the success in the search for more effective and personalized immune-based therapies.
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Affiliation(s)
- Martin Eberhardt
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Namrata Tomar
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Bernd Schmeck
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Marburg, Philipps University, Marburg, Germany
- Systems Biology Platform, Institute for Lung Research/iLung, German Center for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps University Marburg, Marburg, Germany
| | - Alexander Steinkasserer
- Department of Immune Modulation at the Department of Dermatology, University Hospital Erlangen, Erlangen, Germany
| | - Gerold Schuler
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
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Bhattacharya A, Schmitz U, Raatz Y, Schönherr M, Kottek T, Schauer M, Franz S, Saalbach A, Anderegg U, Wolkenhauer O, Schadendorf D, Simon JC, Magin T, Vera J, Kunz M. miR-638 promotes melanoma metastasis and protects melanoma cells from apoptosis and autophagy. Oncotarget 2015; 6:2966-80. [PMID: 25650662 PMCID: PMC4413631 DOI: 10.18632/oncotarget.3070] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2014] [Accepted: 12/19/2014] [Indexed: 12/27/2022] Open
Abstract
The present study identified miR-638 as one of the most significantly overexpressed miRNAs in metastatic lesions of melanomas compared with primary melanomas. miR-638 enhanced the tumorigenic properties of melanoma cells in vitro and lung colonization in vivo. mRNA expression profiling identified new candidate genes including TP53INP2 as miR-638 targets, the majority of which are involved in p53 signalling. Overexpression of TP53INP2 severely attenuated proliferative and invasive capacity of melanoma cells which was reversed by miR-638. Depletion of miR-638 stimulated expression of p53 and p53 downstream target genes and induced apoptosis and autophagy. miR-638 promoter analysis identified the miR-638 target transcription factor associated protein 2α (TFAP2A/AP-2α) as a direct negative regulator of miR-638, suggestive for a double-negative regulatory feedback loop. Taken together, miR-638 supports melanoma progression and suppresses p53-mediated apoptosis pathways, autophagy and expression of the transcriptional repressor TFAP2A/AP-2α.
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Affiliation(s)
- Animesh Bhattacharya
- Department of Dermatology, Venereology and Allergology, University of Leipzig, Leipzig, Germany
| | - Ulf Schmitz
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Yvonne Raatz
- Department of Dermatology, Venereology and Allergology, University of Leipzig, Leipzig, Germany
| | - Madeleine Schönherr
- Department of Dermatology, Venereology and Allergology, University of Leipzig, Leipzig, Germany
| | - Tina Kottek
- Department of Dermatology, Venereology and Allergology, University of Leipzig, Leipzig, Germany
| | - Marianne Schauer
- Department of Dermatology, Venereology and Allergology, University of Leipzig, Leipzig, Germany
| | - Sandra Franz
- Department of Dermatology, Venereology and Allergology, University of Leipzig, Leipzig, Germany
| | - Anja Saalbach
- Department of Dermatology, Venereology and Allergology, University of Leipzig, Leipzig, Germany
| | - Ulf Anderegg
- Department of Dermatology, Venereology and Allergology, University of Leipzig, Leipzig, Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Dirk Schadendorf
- Department of Dermatology, Venereology and Allergology, University Hospital Essen, Essen, Germany
| | - Jan C Simon
- Department of Dermatology, Venereology and Allergology, University of Leipzig, Leipzig, Germany
| | - Thomas Magin
- Institute of Biology and Translational Centre for Regenerative Medicine (TRM), University of Leipzig, Leipzig, Germany
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Faculty of Medicine, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Manfred Kunz
- Department of Dermatology, Venereology and Allergology, University of Leipzig, Leipzig, Germany
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Schipper H, Alla V, Meier C, Nettelbeck DM, Herchenröder O, Pützer BM. Eradication of metastatic melanoma through cooperative expression of RNA-based HDAC1 inhibitor and p73 by oncolytic adenovirus. Oncotarget 2015; 5:5893-907. [PMID: 25071017 PMCID: PMC4171600 DOI: 10.18632/oncotarget.1839] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Malignant melanoma is a highly aggressive cancer that retains functional p53 and p73, and drug unresponsiveness largely depends on defects in death pathways after epigenetic gene silencing in conjunction with an imbalanced p73/DNp73 ratio. We constructed oncolytic viruses armed with an inhibitor of deacetylation and/or p73 to specifically target metastatic cancer. Arming of the viruses is aimed at lifting epigenetic blockage and re-opening apoptotic programs in a staggered manner enabling both, efficient virus replication and balanced destruction of target cells through apoptosis. Our results showed that cooperative expression of shHDAC1 and p73 efficiently enhances apoptosis induction and autophagy of infected cells which reinforces progeny production. In vitro analyses revealed 100% cytotoxicity after infecting cells with OV.shHDAC1.p73 at a lower virus dose compared to control viruses. Intriguingly, OV.shHDAC1.p73 acts as a potent inhibitor of highly metastatic xenograft tumors in vivo. Tumor expansion was significantly reduced after intratumoral injection of 3 × 108 PFU of either OV.shHDAC1 or OV.p73 and, most important, complete regression could be achieved in 100% of tumors treated with OV.shHDAC1.p73. Our results point out that the combination of high replication capacity and simultaneous restoration of cell death routes significantly enhance antitumor activity.
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Affiliation(s)
- Holger Schipper
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany; These authors contributed equally to the work
| | - Vijay Alla
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany; These authors contributed equally to the work
| | - Claudia Meier
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Dirk M Nettelbeck
- Helmholtz University Group Oncolytic Adenoviruses, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ottmar Herchenröder
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Brigitte M Pützer
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
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Meier C, Hardtstock P, Joost S, Alla V, Pützer BM. p73 and IGF1R Regulate Emergence of Aggressive Cancer Stem-like Features via miR-885-5p Control. Cancer Res 2015; 76:197-205. [PMID: 26554827 DOI: 10.1158/0008-5472.can-15-1228] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Accepted: 10/19/2015] [Indexed: 11/16/2022]
Abstract
Cancer stem-like cells (CSC) have been proposed to promote cancer progression by initiating tumor growth at distant sites, suggesting that stem-like cell features can support metastatic efficiency. Here, we demonstrate that oncogenic DNp73, a dominant-negative variant of the tumor-suppressor p73, confers cancer cells with enhanced stem-like properties. DNp73 overexpression in noninvasive melanoma and lung cancer cells increased anchorage-independent growth and elevated the expression of the pluripotency factors CD133, Nanog, and Oct4. Conversely, DNp73 depletion in metastatic cells downregulated stemness genes, attenuated sphere formation and reduced the tumor-initiating capability of spheroids in tumor xenograft models. Mechanistic investigations indicated that DNp73 acted by attenuating expression of miR-885-5p, a direct regulator of the IGF1 receptor (IGF1R) responsible for stemness marker expression. Modulating this pathway was sufficient to enhance chemosensitivity, overcoming DNp73-mediated drug resistance. Clinically, we established a correlation between low p73 function and high IGF1R/CD133/Nanog/Oct4 levels in melanoma specimens that associated with reduced patient survival. Our work shows how DNp73 promotes cancer stem-like features and provides a mechanistic rationale to target the DNp73-IGF1R cascade as a therapeutic strategy to eradicate CSC.
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Affiliation(s)
- Claudia Meier
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Philip Hardtstock
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Sophie Joost
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Vijay Alla
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Brigitte M Pützer
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany.
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Schmitz U, Naderi-Meshkin H, Gupta SK, Wolkenhauer O, Vera J. The RNA world in the 21st century-a systems approach to finding non-coding keys to clinical questions. Brief Bioinform 2015; 17:380-92. [PMID: 26330575 DOI: 10.1093/bib/bbv061] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Indexed: 02/01/2023] Open
Abstract
There was evidence that RNAs are a functionally rich class of molecules not only since the arrival of the next-generation sequencing technology. Non-coding RNAs (ncRNA) could be the key to accelerated diagnosis and enhanced prediction of disease and therapy outcomes as well as the design of advanced therapeutic strategies to overcome yet unsatisfactory approaches.In this review, we discuss the state of the art in RNA systems biology with focus on the application in the systems biomedicine field. We propose guidelines for analysing the role of microRNAs and long non-coding RNAs in human pathologies. We introduce RNA expression profiling and network approaches for the identification of stable and effective RNomics-based biomarkers, providing insights into the role of ncRNAs in disease regulation. Towards this, we discuss ways to model the dynamics of gene regulatory networks and signalling pathways that involve ncRNAs. We also describe data resources and computational methods for finding putative mechanisms of action of ncRNAs. Finally, we discuss avenues for the computer-aided design of novel RNA-based therapeutics.
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Gupta SK, Jaitly T, Schmitz U, Schuler G, Wolkenhauer O, Vera J. Personalized cancer immunotherapy using Systems Medicine approaches. Brief Bioinform 2015; 17:453-67. [DOI: 10.1093/bib/bbv046] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2015] [Indexed: 12/27/2022] Open
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Nair S, Kong ANT. Architecture of Signature miRNA Regulatory Networks in Cancer Chemoprevention. ACTA ACUST UNITED AC 2015. [DOI: 10.1007/s40495-014-0014-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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Day RS. What Tumor Dynamics Modeling Can Teach us About Exploiting the Stem-Cell View for Better Cancer Treatment. Cancer Inform 2015; 14:25-36. [PMID: 25780337 PMCID: PMC4345852 DOI: 10.4137/cin.s17294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2014] [Revised: 01/19/2015] [Accepted: 01/22/2015] [Indexed: 12/26/2022] Open
Abstract
The cancer stem cell hypothesis is that in human solid cancers, only a small proportion of the cells, the cancer stem cells (CSCs), are self-renewing; the vast majority of the cancer cells are unable to sustain tumor growth indefinitely on their own. In recent years, discoveries have led to the concentration, if not isolation, of putative CSCs. The evidence has mounted that CSCs do exist and are important. This knowledge may promote better understanding of treatment resistance, create opportunities to test agents against CSCs, and open up promise for a fresh approach to cancer treatment. The first clinical trials of new anti-CSC agents are completed, and many others follow. Excitement is mounting that this knowledge will lead to major improvements, even breakthroughs, in treating cancer. However, exploitation of this phenomenon may be more successful if informed by insights into the population dynamics of tumor development. We revive some ideas in tumor dynamics modeling to extract some guidance in designing anti-CSC treatment regimens and the clinical trials that test them.
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Affiliation(s)
- Roger S Day
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
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37
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Affiliation(s)
- Brigitte M Pützer
- Institure of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
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38
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Engelmann D, Meier C, Alla V, Pützer BM. A balancing act: orchestrating amino-truncated and full-length p73 variants as decisive factors in cancer progression. Oncogene 2014; 34:4287-99. [PMID: 25381823 DOI: 10.1038/onc.2014.365] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Revised: 09/24/2014] [Accepted: 09/29/2014] [Indexed: 12/23/2022]
Abstract
p73 is the older sibling of p53 and mimics most of its tumor-suppressor functions. Through alternative promoter usage and splicing, the TP73 gene generates more than two dozen isoforms of which N-terminal truncated DNp73 variants have a decisive role in cancer pathogenesis as they outweigh the positive effects of full-length TAp73 and p53 in acting as a barrier to tumor development. Beyond the prevailing view that DNp73 predominantly counteract cell cycle arrest and apoptosis, latest progress indicates that these isoforms acquire novel functions in epithelial-to-mesenchymal transition, metastasis and therapy resistance. New insight into the mechanisms underlying this behavior reinforced the expectation that DNp73 variants contribute to aggressive cellular traits through both loss of wild-type tumor-suppressor activity and gain-of-function, suggesting an equally important role in cancer progression as mutant p53. In this review, we describe the novel properties of DNp73 in the invasion metastasis cascade and outline the comprehensive p73 regulatome with an emphasis on molecular processes putting TAp73 out of action in advanced tumors. These intriguing insights provoke a new understanding of the acquisition of aggressive traits by cancer cells and may help to set novel therapies for a broad range of metastatic tumors.
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Affiliation(s)
- D Engelmann
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - C Meier
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - V Alla
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - B M Pützer
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
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Brocato T, Dogra P, Koay EJ, Day A, Chuang YL, Wang Z, Cristini V. Understanding Drug Resistance in Breast Cancer with Mathematical Oncology. CURRENT BREAST CANCER REPORTS 2014; 6:110-120. [PMID: 24891927 PMCID: PMC4039558 DOI: 10.1007/s12609-014-0143-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Chemotherapy is mainstay of treatment for the majority of patients with breast cancer, but results in only 26% of patients with distant metastasis living 5 years past treatment in the United States, largely due to drug resistance. The complexity of drug resistance calls for an integrated approach of mathematical modeling and experimental investigation to develop quantitative tools that reveal insights into drug resistance mechanisms, predict chemotherapy efficacy, and identify novel treatment approaches. This paper reviews recent modeling work for understanding cancer drug resistance through the use of computer simulations of molecular signaling networks and cancerous tissues, with a particular focus on breast cancer. These mathematical models are developed by drawing on current advances in molecular biology, physical characterization of tumors, and emerging drug delivery methods (e.g., nanotherapeutics). We focus our discussion on representative modeling works that have provided quantitative insight into chemotherapy resistance in breast cancer and how drug resistance can be overcome or minimized to optimize chemotherapy treatment. We also discuss future directions of mathematical modeling in understanding drug resistance.
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Affiliation(s)
- Terisse Brocato
- Department of Chemical and Nuclear Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131
| | - Prashant Dogra
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131
| | - Eugene J. Koay
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX 77030
| | - Armin Day
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131
| | - Yao-Li Chuang
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131
| | - Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131
| | - Vittorio Cristini
- Department of Chemical and Nuclear Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131
- Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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MicroRNAs: master regulators of drug resistance, stemness, and metastasis. J Mol Med (Berl) 2014; 92:321-36. [PMID: 24509937 DOI: 10.1007/s00109-014-1129-2] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2013] [Revised: 01/21/2014] [Accepted: 01/23/2014] [Indexed: 12/13/2022]
Abstract
MicroRNAs (miRNAs) are 20-22 nucleotides long small non-coding RNAs that regulate gene expression post-transcriptionally. Last decade has witnessed emerging evidences of active roles of miRNAs in tumor development, progression, metastasis, and drug resistance. Many factors contribute to their dysregulation in cancer, such as chromosomal aberrations, differential methylation of their own or host genes' promoters and alterations in miRNA biogenesis pathways. miRNAs have been shown to act as tumor suppressors or oncogenes depending on the targets they regulate and the tissue where they are expressed. Because miRNAs can regulate dozens of genes simultaneously and they can function as tumor suppressors or oncogenes, they have been proposed as promising targets for cancer therapy. In this review, we focus on the role of miRNAs in driving drug resistance and metastasis which are associated with stem cell properties of cancer cells. Furthermore, we discuss systems biology approaches to combine experimental and computational methods to study effects of miRNAs on gene or protein networks regulating these processes. Finally, we describe methods to target oncogenic or replace tumor suppressor miRNAs and current delivery strategies to sensitize refractory cells and to prevent metastasis. A holistic understanding of miRNAs' functions in drug resistance and metastasis, which are major causes of cancer-related deaths, and the development of novel strategies to target them efficiently will pave the way towards better translation of miRNAs into clinics and management of cancer therapy.
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Di C, Yang L, Zhang H, Ma X, Zhang X, Sun C, Li H, Xu S, An L, Li X, Bai Z. Mechanisms, function and clinical applications of DNp73. Cell Cycle 2013; 12:1861-7. [PMID: 23708520 DOI: 10.4161/cc.24967] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
p73, has two distinct promoters, which allow the formation of two protein isoforms: full-length transactivating (TA) p73 and an N-terminally truncated p73 species (referred to as DNp73) that lacks the N-terminal transactivating domain. Although the exact cellular function of DNp73 is unclear, the high expression levels of the genes have been observed in a variety of human cancers and cancer cell lines and have been connected to pro-tumor activities. Hence the aim of this review is to summarize DNp73 expression status in cancer in the current literature. Furthermore, we also focused on recent findings of DNp73 related to the biological functions from apoptosis, chemosensitivity, radiosensitibity, differentiation, development, etc. Thus this review highlights the significance of DNp73 as a marker for disease severity in patients and as target for cancer therapy.
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Affiliation(s)
- Cuixia Di
- Department of Heavy Ion Radiation Medicine, Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
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