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Cazzato G, Ingravallo G, Ribatti D. Angiogenesis Still Plays a Crucial Role in Human Melanoma Progression. Cancers (Basel) 2024; 16:1794. [PMID: 38791873 PMCID: PMC11120419 DOI: 10.3390/cancers16101794] [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: 04/17/2024] [Revised: 05/03/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
Angiogenesis plays a pivotal role in tumor progression, particularly in melanoma, the deadliest form of skin cancer. This review synthesizes current knowledge on the intricate interplay between angiogenesis and tumor microenvironment (TME) in melanoma progression. Pro-angiogenic factors, including VEGF, PlGF, FGF-2, IL-8, Ang, TGF-β, PDGF, integrins, MMPs, and PAF, modulate angiogenesis and contribute to melanoma metastasis. Additionally, cells within the TME, such as cancer-associated fibroblasts, mast cells, and melanoma-associated macrophages, influence tumor angiogenesis and progression. Anti-angiogenic therapies, while showing promise, face challenges such as drug resistance and tumor-induced activation of alternative angiogenic pathways. Rational combinations of anti-angiogenic agents and immunotherapies are being explored to overcome resistance. Biomarker identification for treatment response remains crucial for personalized therapies. This review highlights the complexity of angiogenesis in melanoma and underscores the need for innovative therapeutic approaches tailored to the dynamic TME.
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Affiliation(s)
- Gerardo Cazzato
- Section of Molecular Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari “Aldo Moro”, 70124 Bari, Italy;
| | - Giuseppe Ingravallo
- Section of Molecular Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari “Aldo Moro”, 70124 Bari, Italy;
| | - Domenico Ribatti
- Department of Translational Biomedicine and Neuroscience, University of Bari Medical School, 70124 Bari, Italy;
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2
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Lim SY, Rizos H. Single-cell RNA sequencing in melanoma: what have we learned so far? EBioMedicine 2024; 100:104969. [PMID: 38241976 PMCID: PMC10831183 DOI: 10.1016/j.ebiom.2024.104969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 12/18/2023] [Accepted: 01/03/2024] [Indexed: 01/21/2024] Open
Abstract
Over the past decade, there have been remarkable improvements in the treatment and survival rates of melanoma patients. Treatment resistance remains a persistent challenge, however, and is partly attributable to intratumoural heterogeneity. Melanoma cells can transition through a series of phenotypic and transcriptional cell states that vary in invasiveness and treatment responsiveness. The diverse stromal and immune contexture of the tumour microenvironment also contributes to intratumoural heterogeneity and disparities in treatment response in melanoma patients. Recent advances in single-cell sequencing technologies have enabled a more detailed understanding of melanoma heterogeneity and the underlying transcriptional programs that regulate melanoma cell diversity and behaviour. In this review, we examine the concept of intratumoural heterogeneity and the challenges it poses to achieving long-lasting treatment responses. We focus on the significance of next generation single-cell sequencing in advancing our understanding of melanoma diversity and the unique insights gained from single-cell studies.
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Affiliation(s)
- Su Yin Lim
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Australia; Melanoma Institute Australia, Sydney, Australia.
| | - Helen Rizos
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Australia; Melanoma Institute Australia, Sydney, Australia
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3
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Vijayaragavan K, Cannon BJ, Tebaykin D, Bossé M, Baranski A, Oliveria JP, Bukhari SA, Mrdjen D, Corces MR, McCaffrey EF, Greenwald NF, Sigal Y, Marquez D, Khair Z, Bruce T, Goldston M, Bharadwaj A, Montine KS, Angelo RM, Montine TJ, Bendall SC. Single-cell spatial proteomic imaging for human neuropathology. Acta Neuropathol Commun 2022; 10:158. [PMID: 36333818 PMCID: PMC9636771 DOI: 10.1186/s40478-022-01465-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
Neurodegenerative disorders are characterized by phenotypic changes and hallmark proteopathies. Quantifying these in archival human brain tissues remains indispensable for validating animal models and understanding disease mechanisms. We present a framework for nanometer-scale, spatial proteomics with multiplex ion beam imaging (MIBI) for capturing neuropathological features. MIBI facilitated simultaneous, quantitative imaging of 36 proteins on archival human hippocampus from individuals spanning cognitively normal to dementia. Customized analysis strategies identified cell types and proteopathies in the hippocampus across stages of Alzheimer's disease (AD) neuropathologic change. We show microglia-pathologic tau interactions in hippocampal CA1 subfield in AD dementia. Data driven, sample independent creation of spatial proteomic regions identified persistent neurons in pathologic tau neighborhoods expressing mitochondrial protein MFN2, regardless of cognitive status, suggesting a survival advantage. Our study revealed unique insights from multiplexed imaging and data-driven approaches for neuropathologic analysis and serves broadly as a methodology for spatial proteomic analysis of archival human neuropathology. TEASER: Multiplex Ion beam Imaging enables deep spatial phenotyping of human neuropathology-associated cellular and disease features.
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Affiliation(s)
| | - Bryan J Cannon
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Dmitry Tebaykin
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Marc Bossé
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Alex Baranski
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - J P Oliveria
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Syed A Bukhari
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Dunja Mrdjen
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Erin F McCaffrey
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Noah F Greenwald
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Diana Marquez
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Zumana Khair
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Trevor Bruce
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Mako Goldston
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Anusha Bharadwaj
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Kathleen S Montine
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - R Michael Angelo
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Thomas J Montine
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Sean C Bendall
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA.
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4
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Loeffler-Wirth H, Rade M, Arakelyan A, Kreuz M, Loeffler M, Koehl U, Reiche K, Binder H. Transcriptional states of CAR-T infusion relate to neurotoxicity – lessons from high-resolution single-cell SOM expression portraying. Front Immunol 2022; 13:994885. [PMID: 36248848 PMCID: PMC9558919 DOI: 10.3389/fimmu.2022.994885] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 08/29/2022] [Indexed: 11/26/2022] Open
Abstract
Anti-CD19 CAR-T cell immunotherapy is a hopeful treatment option for patients with B cell lymphomas, however it copes with partly severe adverse effects like neurotoxicity. Single-cell resolved molecular data sets in combination with clinical parametrization allow for comprehensive characterization of cellular subpopulations, their transcriptomic states, and their relation to the adverse effects. We here present a re-analysis of single-cell RNA sequencing data of 24 patients comprising more than 130,000 cells with focus on cellular states and their association to immune cell related neurotoxicity. For this, we developed a single-cell data portraying workflow to disentangle the transcriptional state space with single-cell resolution and its analysis in terms of modularly-composed cellular programs. We demonstrated capabilities of single-cell data portraying to disentangle transcriptional states using intuitive visualization, functional mining, molecular cell stratification, and variability analyses. Our analysis revealed that the T cell composition of the patient’s infusion product as well as the spectrum of their transcriptional states of cells derived from patients with low ICANS grade do not markedly differ from those of cells from high ICANS patients, while the relative abundancies, particularly that of cycling cells, of LAG3-mediated exhaustion and of CAR positive cells, vary. Our study provides molecular details of the transcriptomic landscape with possible impact to overcome neurotoxicity.
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Affiliation(s)
- Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics (IZBI), Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
- *Correspondence: Henry Loeffler-Wirth,
| | - Michael Rade
- Bioinformatics Unit, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany
| | - Arsen Arakelyan
- Armenian Bioinformatics Institute (ABI), Yerevan, Armenia
- Research Group of Bioinformatics, Institute of Molecular Biology of the National Academy of Sciences of the Republic of Armenia, Yerevan, Armenia
| | - Markus Kreuz
- Bioinformatics Unit, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany
| | - Markus Loeffler
- Interdisciplinary Centre for Bioinformatics (IZBI), Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
| | - Ulrike Koehl
- Bioinformatics Unit, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany
| | - Kristin Reiche
- Bioinformatics Unit, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics (IZBI), Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
- Armenian Bioinformatics Institute (ABI), Yerevan, Armenia
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5
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The Transcriptome and Methylome of the Developing and Aging Brain and Their Relations to Gliomas and Psychological Disorders. Cells 2022; 11:cells11030362. [PMID: 35159171 PMCID: PMC8834030 DOI: 10.3390/cells11030362] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/15/2022] [Accepted: 01/18/2022] [Indexed: 02/01/2023] Open
Abstract
Mutually linked expression and methylation dynamics in the brain govern genome regulation over the whole lifetime with an impact on cognition, psychological disorders, and cancer. We performed a joint study of gene expression and DNA methylation of brain tissue originating from the human prefrontal cortex of individuals across the lifespan to describe changes in cellular programs and their regulation by epigenetic mechanisms. The analysis considers previous knowledge in terms of functional gene signatures and chromatin states derived from independent studies, aging profiles of a battery of chromatin modifying enzymes, and data of gliomas and neuropsychological disorders for a holistic view on the development and aging of the brain. Expression and methylation changes from babies to elderly adults decompose into different modes associated with the serial activation of (brain) developmental, learning, metabolic and inflammatory functions, where methylation in gene promoters mostly represses transcription. Expression of genes encoding methylome modifying enzymes is very diverse reflecting complex regulations during lifetime which also associates with the marked remodeling of chromatin between permissive and restrictive states. Data of brain cancer and psychotic disorders reveal footprints of pathophysiologies related to brain development and aging. Comparison of aging brains with gliomas supports the view that glioblastoma-like and astrocytoma-like tumors exhibit higher cellular plasticity activated in the developing healthy brain while oligodendrogliomas have a more stable differentiation hierarchy more resembling the aged brain. The balance and specific shifts between volatile and stable and between more irreversible and more plastic epigenomic networks govern the development and aging of healthy and diseased brain.
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6
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Single-Cell Transcriptomics Reveals the Expression of Aging- and Senescence-Associated Genes in Distinct Cancer Cell Populations. Cells 2021; 10:cells10113126. [PMID: 34831349 PMCID: PMC8623328 DOI: 10.3390/cells10113126] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/31/2021] [Accepted: 11/09/2021] [Indexed: 12/12/2022] Open
Abstract
The human aging process is associated with molecular changes and cellular degeneration, resulting in a significant increase in cancer incidence with age. Despite their potential correlation, the relationship between cancer- and ageing-related transcriptional changes is largely unknown. In this study, we aimed to analyze aging-associated transcriptional patterns in publicly available bulk mRNA-seq and single-cell RNA-seq (scRNA-seq) datasets for chronic myelogenous leukemia (CML), colorectal cancer (CRC), hepatocellular carcinoma (HCC), lung cancer (LC), and pancreatic ductal adenocarcinoma (PDAC). Indeed, we detected that various aging/senescence-induced genes (ASIGs) were upregulated in malignant diseases compared to healthy control samples. To elucidate the importance of ASIGs during cell development, pseudotime analyses were performed, which revealed a late enrichment of distinct cancer-specific ASIG signatures. Notably, we were able to demonstrate that all cancer entities analyzed in this study comprised cell populations expressing ASIGs. While only minor correlations were detected between ASIGs and transcriptome-wide changes in PDAC, a high proportion of ASIGs was induced in CML, CRC, HCC, and LC samples. These unique cellular subpopulations could serve as a basis for future studies on the role of aging and senescence in human malignancies.
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7
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Wang MM, Chen C, Lynn MN, Figueiredo CR, Tan WJ, Lim TS, Coupland SE, Chan ASY. Applying Single-Cell Technology in Uveal Melanomas: Current Trends and Perspectives for Improving Uveal Melanoma Metastasis Surveillance and Tumor Profiling. Front Mol Biosci 2021; 7:611584. [PMID: 33585560 PMCID: PMC7874218 DOI: 10.3389/fmolb.2020.611584] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 11/25/2020] [Indexed: 12/21/2022] Open
Abstract
Uveal melanoma (UM) is the most common primary adult intraocular malignancy. This rare but devastating cancer causes vision loss and confers a poor survival rate due to distant metastases. Identifying clinical and molecular features that portend a metastatic risk is an important part of UM workup and prognostication. Current UM prognostication tools are based on determining the tumor size, gene expression profile, and chromosomal rearrangements. Although we can predict the risk of metastasis fairly accurately, we cannot obtain preclinical evidence of metastasis or identify biomarkers that might form the basis of targeted therapy. These gaps in UM research might be addressed by single-cell research. Indeed, single-cell technologies are being increasingly used to identify circulating tumor cells and profile transcriptomic signatures in single, drug-resistant tumor cells. Such advances have led to the identification of suitable biomarkers for targeted treatment. Here, we review the approaches used in cutaneous melanomas and other cancers to isolate single cells and profile them at the transcriptomic and/or genomic level. We discuss how these approaches might enhance our current approach to UM management and review the emerging data from single-cell analyses in UM.
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Affiliation(s)
- Mona Meng Wang
- Singapore National Eye Centre and Singapore Eye Research Institute, Singapore, Singapore
| | - Chuanfei Chen
- Cytogenetics Laboratory, Department of Molecular Pathology, Singapore General Hospital, Singapore, Singapore
| | - Myoe Naing Lynn
- Singapore National Eye Centre and Singapore Eye Research Institute, Singapore, Singapore
| | - Carlos R. Figueiredo
- MediCity Research Laboratory and Institute of Biomedicine, University of Turku, Turku, Finland
| | - Wei Jian Tan
- A. Menarini Biomarkers Singapore Pte Ltd, Singapore, Singapore
| | - Tong Seng Lim
- A. Menarini Biomarkers Singapore Pte Ltd, Singapore, Singapore
| | - Sarah E. Coupland
- Department of Molecular and Clinical Cancer Medicine, ITM, University of Liverpool, Liverpool, United Kingdom
- Liverpool Clinical Laboratories, Royal Liverpool University Hospital, Liverpool, United Kingdom
| | - Anita Sook Yee Chan
- Singapore National Eye Centre and Singapore Eye Research Institute, Singapore, Singapore
- Duke-Nus Medical School, Singapore, Singapore
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8
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Schmidt M, Loeffler-Wirth H, Binder H. Developmental scRNAseq Trajectories in Gene- and Cell-State Space-The Flatworm Example. Genes (Basel) 2020; 11:E1214. [PMID: 33081343 PMCID: PMC7603055 DOI: 10.3390/genes11101214] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 10/13/2020] [Accepted: 10/14/2020] [Indexed: 12/19/2022] Open
Abstract
Single-cell RNA sequencing has become a standard technique to characterize tissue development. Hereby, cross-sectional snapshots of the diversity of cell transcriptomes were transformed into (pseudo-) longitudinal trajectories of cell differentiation using computational methods, which are based on similarity measures distinguishing cell phenotypes. Cell development is driven by alterations of transcriptional programs e.g., by differentiation from stem cells into various tissues or by adapting to micro-environmental requirements. We here complement developmental trajectories in cell-state space by trajectories in gene-state space to more clearly address this latter aspect. Such trajectories can be generated using self-organizing maps machine learning. The method transforms multidimensional gene expression patterns into two dimensional data landscapes, which resemble the metaphoric Waddington epigenetic landscape. Trajectories in this landscape visualize transcriptional programs passed by cells along their developmental paths from stem cells to differentiated tissues. In addition, we generated developmental "vector fields" using RNA-velocities to forecast changes of RNA abundance in the expression landscapes. We applied the method to tissue development of planarian as an illustrative example. Gene-state space trajectories complement our data portrayal approach by (pseudo-)temporal information about changing transcriptional programs of the cells. Future applications can be seen in the fields of tissue and cell differentiation, ageing and tumor progression and also, using other data types such as genome, methylome, and also clinical and epidemiological phenotype data.
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Affiliation(s)
- Maria Schmidt
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany; (H.L.-W.); (H.B.)
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9
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Lee D, Park Y, Kim S. Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches. Brief Bioinform 2020; 22:5896573. [PMID: 34020548 DOI: 10.1093/bib/bbaa188] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/29/2020] [Accepted: 07/21/2020] [Indexed: 12/19/2022] Open
Abstract
The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.
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Affiliation(s)
- Dohoon Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Youngjune Park
- Department of Computer Science and Engineering, Institute of Engineering Research, Seoul National University, Seoul 08826, Korea
| | - Sun Kim
- Bioinformatics Institute, Seoul National University, Seoul 08826, Korea
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10
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Nikoghosyan M, Schmidt M, Margaryan K, Loeffler-Wirth H, Arakelyan A, Binder H. SOMmelier-Intuitive Visualization of the Topology of Grapevine Genome Landscapes Using Artificial Neural Networks. Genes (Basel) 2020; 11:genes11070817. [PMID: 32709105 PMCID: PMC7397337 DOI: 10.3390/genes11070817] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 06/26/2020] [Accepted: 07/15/2020] [Indexed: 01/02/2023] Open
Abstract
Background: Whole-genome studies of vine cultivars have brought novel knowledge about the diversity, geographical relatedness, historical origin and dissemination, phenotype associations and genetic markers. Method: We applied SOM (self-organizing maps) portrayal, a neural network-based machine learning method, to re-analyze the genome-wide Single Nucleotide Polymorphism (SNP) data of nearly eight hundred grapevine cultivars. The method generates genome-specific data landscapes. Their topology reflects the geographical distribution of cultivars, indicates paths of cultivar dissemination in history and genome-phenotype associations about grape utilization. Results: The landscape of vine genomes resembles the geographic map of the Mediterranean world, reflecting two major dissemination paths from South Caucasus along a northern route via Balkan towards Western Europe and along a southern route via Palestine and Maghreb towards Iberian Peninsula. The Mediterranean and Black Sea, as well as the Pyrenees, constitute barriers for genetic exchange. On the coarsest level of stratification, cultivars divide into three major groups: Western Europe and Italian grapes, Iberian grapes and vine cultivars from Near East and Maghreb regions. Genetic landmarks were associated with agronomic traits, referring to their utilization as table and wine grapes. Pseudotime analysis describes the dissemination of grapevines in an East to West direction in different waves of cultivation. Conclusion: In analogy to the tasks of the wine waiter in gastronomy, the sommelier, our ‘SOMmelier’-approach supports understanding the diversity of grapevine genomes in the context of their geographic and historical background, using SOM portrayal. It offers an option to supplement vine cultivar passports by genome fingerprint portraits.
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Affiliation(s)
- Maria Nikoghosyan
- Research Group of Bioinformatics, Institute of Molecular Biology of National Academy of Sciences RA, Yerevan 0014, Armenia; (M.N.); (A.A.)
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, Yerevan 0051, Armenia
| | - Maria Schmidt
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany; (M.S.); (H.L.-W.)
| | - Kristina Margaryan
- Research Group of Plant Genetics and Immunology, Institute of Molecular Biology of National Academy of Sciences RA, Yerevan 0014, Armenia;
- Department of Genetics and Cytology, Yerevan State University, Yerevan 0025, Armenia
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany; (M.S.); (H.L.-W.)
| | - Arsen Arakelyan
- Research Group of Bioinformatics, Institute of Molecular Biology of National Academy of Sciences RA, Yerevan 0014, Armenia; (M.N.); (A.A.)
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, Yerevan 0051, Armenia
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany; (M.S.); (H.L.-W.)
- Correspondence:
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11
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Single cell analysis to dissect molecular heterogeneity and disease evolution in metastatic melanoma. Cell Death Dis 2019; 10:827. [PMID: 31672982 PMCID: PMC6823362 DOI: 10.1038/s41419-019-2048-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 09/17/2019] [Accepted: 10/07/2019] [Indexed: 02/07/2023]
Abstract
Originally described as interpatient variability, tumour heterogeneity has now been demonstrated to occur intrapatiently, within the same lesion, or in different lesions of the same patient. Tumour heterogeneity involves both genetic and epigenetic changes. Intrapatient heterogeneity is responsible for generating subpopulations of cancer cells which undergo clonal evolution with time. Tumour heterogeneity develops also as a consequence of the selective pressure imposed by the immune system. It has been demonstrated that tumour heterogeneity and different spatiotemporal interactions between all the cellular compontents within the tumour microenvironment lead to cancer adaptation and to therapeutic pressure. In this context, the recent advent of single cell analysis approaches which are able to better study tumour heterogeneity from the genomic, transcriptomic and proteomic standpoint represent a major technological breakthrough. In this review, using metastatic melanoma as a prototypical example, we will focus on applying single cell analyses to the study of clonal trajectories which guide the evolution of drug resistance to targeted therapy.
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12
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Abstract
An incomplete view of the mechanisms that drive metastasis, the primary cause of cancer-related death, has been a major barrier to development of effective therapeutics and prognostic diagnostics. Increasing evidence indicates that the interplay between microenvironment, genetic lesions, and cellular plasticity drives the metastatic cascade and resistance to therapies. Here, using melanoma as a model, we outline the diversity and trajectories of cell states during metastatic dissemination and therapy exposure, and highlight how understanding the magnitude and dynamics of nongenetic reprogramming in space and time at single-cell resolution can be exploited to develop therapeutic strategies that capitalize on nongenetic tumor evolution.
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Affiliation(s)
- Florian Rambow
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, Vlaams Instituut voor Biotechnologie (VIB), Herestraat 49, 3000 Leuven, Belgium
- Laboratory for Molecular Cancer Biology, Department of Oncology, KULeuven, Herestraat 49, B-3000 Leuven, Belgium
| | - Jean-Christophe Marine
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, Vlaams Instituut voor Biotechnologie (VIB), Herestraat 49, 3000 Leuven, Belgium
- Laboratory for Molecular Cancer Biology, Department of Oncology, KULeuven, Herestraat 49, B-3000 Leuven, Belgium
| | - Colin R Goding
- Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Headington, Oxford OX3 7DQ, United Kingdom
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