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Sdeor E, Okada H, Saad R, Ben-Yishay T, Ben-David U. Aneuploidy as a driver of human cancer. Nat Genet 2024:10.1038/s41588-024-01916-2. [PMID: 39358600 DOI: 10.1038/s41588-024-01916-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 08/20/2024] [Indexed: 10/04/2024]
Abstract
Aneuploidy, an abnormal chromosome composition, is a major contributor to cancer development and progression and an important determinant of cancer therapeutic responses and clinical outcomes. Despite being recognized as a hallmark of human cancer, the exact role of aneuploidy as a 'driver' of cancer is still largely unknown. Identifying the specific genetic elements that underlie the recurrence of common aneuploidies remains a major challenge of cancer genetics. In this Review, we discuss recurrent aneuploidies and their function as drivers of tumor development. We then delve into the context-dependent identification and functional characterization of the driver genes underlying driver aneuploidies and examine emerging strategies to uncover these driver genes using cancer genomics data and cancer models. Lastly, we explore opportunities for targeting driver aneuploidies in cancer by leveraging the functional consequences of these common genetic alterations.
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Affiliation(s)
- Eran Sdeor
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Hajime Okada
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Ron Saad
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
- The Blavatnik School of Computer Science, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Tal Ben-Yishay
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
- The Blavatnik School of Computer Science, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Uri Ben-David
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
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2
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Laplane L, Maley CC. The evolutionary theory of cancer: challenges and potential solutions. Nat Rev Cancer 2024; 24:718-733. [PMID: 39256635 DOI: 10.1038/s41568-024-00734-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/24/2024] [Indexed: 09/12/2024]
Abstract
The clonal evolution model of cancer was developed in the 1950s-1970s and became central to cancer biology in the twenty-first century, largely through studies of cancer genetics. Although it has proven its worth, its structure has been challenged by observations of phenotypic plasticity, non-genetic forms of inheritance, non-genetic determinants of clone fitness and non-tree-like transmission of genes. There is even confusion about the definition of a clone, which we aim to resolve. The performance and value of the clonal evolution model depends on the empirical extent to which evolutionary processes are involved in cancer, and on its theoretical ability to account for those evolutionary processes. Here, we identify limits in the theoretical performance of the clonal evolution model and provide solutions to overcome those limits. Although we do not claim that clonal evolution can explain everything about cancer, we show how many of the complexities that have been identified in the dynamics of cancer can be integrated into the model to improve our current understanding of cancer.
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Affiliation(s)
- Lucie Laplane
- UMR 8590 Institut d'Histoire et Philosophie des Sciences et des Techniques, CNRS, University Paris I Pantheon-Sorbonne, Paris, France
- UMR 1287 Hematopoietic Tissue Aging, Gustave Roussy Cancer Campus, Villejuif, France
| | - Carlo C Maley
- Arizona Cancer Evolution Center, Arizona State University, Tempe, AZ, USA.
- School of Life Sciences, Arizona State University, Tempe, AZ, USA.
- Biodesign Center for Biocomputing, Security and Society, Arizona State University, Tempe, AZ, USA.
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA.
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3
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Li T, Zou Y, Li X, Wong TKF, Rodrigo AG. Mugen-UMAP: UMAP visualization and clustering of mutated genes in single-cell DNA sequencing data. BMC Bioinformatics 2024; 25:308. [PMID: 39333868 PMCID: PMC11437917 DOI: 10.1186/s12859-024-05928-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 09/13/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND The application of Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and visualization has revolutionized the analysis of single-cell RNA expression and population genetics. However, its potential in single-cell DNA sequencing data analysis, particularly for visualizing gene mutation information, has not been fully explored. RESULTS We introduce Mugen-UMAP, a novel Python-based program that extends UMAP's utility to single-cell DNA sequencing data. This innovative tool provides a comprehensive pipeline for processing gene annotation files of single-cell somatic single-nucleotide variants and metadata to the visualization of UMAP projections for identifying clusters, along with various statistical analyses. Employing Mugen-UMAP, we analyzed whole-exome sequencing data from 365 single-cell samples across 12 non-small cell lung cancer (NSCLC) patients, revealing distinct clusters associated with histological subtypes of NSCLC. Moreover, to demonstrate the general utility of Mugen-UMAP, we applied the program to 9 additional single-cell WES datasets from various cancer types, uncovering interesting patterns of cell clusters that warrant further investigation. In summary, Mugen-UMAP provides a quick and effective visualization method to uncover cell cluster patterns based on the gene mutation information from single-cell DNA sequencing data. CONCLUSIONS The application of Mugen-UMAP demonstrates its capacity to provide valuable insights into the visualization and interpretation of single-cell DNA sequencing data. Mugen-UMAP can be found at https://github.com/tengchn/Mugen-UMAP.
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Affiliation(s)
- Teng Li
- School of Biological Sciences, University of Auckland, Auckland, New Zealand.
- Research School of Biology, Australian National University, Canberra, ACT, Australia.
| | - Yiran Zou
- Research School of Biology, Australian National University, Canberra, ACT, Australia
| | - Xianghan Li
- Research School of Biology, Australian National University, Canberra, ACT, Australia
| | - Thomas K F Wong
- Research School of Biology, Australian National University, Canberra, ACT, Australia
- School of Computing, Australian National University, Canberra, ACT, Australia
| | - Allen G Rodrigo
- School of Biological Sciences, University of Auckland, Auckland, New Zealand.
- Research School of Biology, Australian National University, Canberra, ACT, Australia.
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4
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Hintzen DC, Schubert M, Soto M, Medema RH, Raaijmakers JA. Reduction of chromosomal instability and inflammation is a common aspect of adaptation to aneuploidy. EMBO Rep 2024:10.1038/s44319-024-00252-0. [PMID: 39294502 DOI: 10.1038/s44319-024-00252-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 08/20/2024] [Accepted: 08/28/2024] [Indexed: 09/20/2024] Open
Abstract
Aneuploidy, while detrimental to untransformed cells, is notably prevalent in cancer. Aneuploidy is found as an early event during tumorigenesis which indicates that cancer cells have the ability to surmount the initial stress responses associated with aneuploidy, enabling rapid proliferation despite aberrant karyotypes. To generate more insight into key cellular processes and requirements underlying adaptation to aneuploidy, we generated a panel of aneuploid clones in p53-deficient RPE-1 cells and studied their behavior over time. As expected, de novo-generated aneuploid clones initially display reduced fitness, enhanced levels of chromosomal instability (CIN), and an upregulated inflammatory response. Intriguingly, after prolonged culturing, aneuploid clones exhibit increased proliferation rates while maintaining aberrant karyotypes, indicative of an adaptive response to the aneuploid state. Interestingly, all adapted clones display reduced CIN and reduced inflammatory signaling, suggesting that these are common aspects of adaptation to aneuploidy. Collectively, our data suggests that CIN and concomitant inflammation are key processes that require correction to allow for fast proliferation in vitro. Finally, we provide evidence that amplification of oncogenic KRAS can promote adaptation.
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Affiliation(s)
- Dorine C Hintzen
- Oncode Institute, Division of Cell Biology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Michael Schubert
- Oncode Institute, Division of Cell Biology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Mar Soto
- Oncode Institute, Division of Cell Biology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - René H Medema
- Oncode Institute, Division of Cell Biology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- Oncode Institute, Princess Maxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, The Netherlands.
| | - Jonne A Raaijmakers
- Oncode Institute, Division of Cell Biology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
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5
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Seeler S, Arnarsson K, Dreßen M, Krane M, Doppler SA. Beyond the Heartbeat: Single-Cell Omics Redefining Cardiovascular Research. Curr Cardiol Rep 2024:10.1007/s11886-024-02117-3. [PMID: 39158785 DOI: 10.1007/s11886-024-02117-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/07/2024] [Indexed: 08/20/2024]
Abstract
PURPOSE OF REVIEW This review aims to explore recent advances in single-cell omics techniques as applied to various regions of the human heart, illuminating cellular diversity, regulatory networks, and disease mechanisms. We examine the contributions of single-cell transcriptomics, genomics, proteomics, epigenomics, and spatial transcriptomics in unraveling the complexity of cardiac tissues. RECENT FINDINGS Recent strides in single-cell omics technologies have revolutionized our understanding of the heart's cellular composition, cell type heterogeneity, and molecular dynamics. These advancements have elucidated pathological conditions as well as the cellular landscape in heart development. We highlight emerging applications of integrated single-cell omics, particularly for cardiac regeneration, disease modeling, and precision medicine, and emphasize the transformative potential of these technologies to advance cardiovascular research and clinical practice.
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Affiliation(s)
- Sabine Seeler
- Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Lazarettstr. 36, 80636, Munich, Germany
- Institute for Translational Cardiac Surgery (INSURE), Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Munich, Germany
| | - Kristjan Arnarsson
- Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Lazarettstr. 36, 80636, Munich, Germany
- Institute for Translational Cardiac Surgery (INSURE), Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Munich, Germany
| | - Martina Dreßen
- Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Lazarettstr. 36, 80636, Munich, Germany
- Institute for Translational Cardiac Surgery (INSURE), Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Munich, Germany
| | - Markus Krane
- Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Lazarettstr. 36, 80636, Munich, Germany
- Institute for Translational Cardiac Surgery (INSURE), Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Division of Cardiac Surgery, Department of Surgery, Yale School of Medicine, New Haven, CT, USA
| | - Stefanie A Doppler
- Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Lazarettstr. 36, 80636, Munich, Germany.
- Institute for Translational Cardiac Surgery (INSURE), Department of Cardiovascular Surgery, German Heart Center Munich, School of Medicine and Health, TUM University Hospital, Technical University Munich, Munich, Germany.
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6
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Liu H, Dong A, Rasteh AM, Wang P, Weng J. Identification of the novel exhausted T cell CD8 + markers in breast cancer. Sci Rep 2024; 14:19142. [PMID: 39160211 PMCID: PMC11333736 DOI: 10.1038/s41598-024-70184-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 08/13/2024] [Indexed: 08/21/2024] Open
Abstract
Cancer is one of the most concerning public health issues and breast cancer is one of the most common cancers in the world. The immune cells within the tumor microenvironment regulate cancer development. In this study, single immune cell data sets were used to identify marker gene sets for exhausted CD8 + T cells (CD8Tex) in breast cancer. Machine learning methods were used to cluster subtypes and establish the prognostic models with breast cancer bulk data using the gene sets to evaluate the impacts of CD8Tex. We analyzed breast cancer overexpressing and survival-associated marker genes and identified CD8Tex hub genes in the protein-protein-interaction network. The relevance of the hub genes for CD8 + T-cells in breast cancer was evaluated. The clinical associations of the hub genes were analyzed using bulk sequencing data and spatial sequencing data. The pan-cancer expression, survival, and immune association of the hub genes were analyzed. We identified biomarker gene sets for CD8Tex in breast cancer. CD8Tex-based subtyping systems and prognostic models performed well in the separation of patients with different immune relevance and survival. CRTAM, CLEC2D, and KLRB1 were identified as CD8Tex hub genes and were demonstrated to have potential clinical relevance and immune therapy impact. This study provides a unique view of the critical CD8Tex hub genes for cancer immune therapy.
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Affiliation(s)
- Hengrui Liu
- Cancer Research Institute, Jinan University, Guangzhou, China
| | | | | | - Panpan Wang
- The First Affiliated Hospital of Jinan University, Guangzhou, China.
| | - Jieling Weng
- Department of Pathology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
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7
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Shahrouzi P, Forouz F, Mathelier A, Kristensen VN, Duijf PHG. Copy number alterations: a catastrophic orchestration of the breast cancer genome. Trends Mol Med 2024; 30:750-764. [PMID: 38772764 DOI: 10.1016/j.molmed.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/12/2024] [Accepted: 04/26/2024] [Indexed: 05/23/2024]
Abstract
Breast cancer (BCa) is a prevalent malignancy that predominantly affects women around the world. Somatic copy number alterations (CNAs) are tumor-specific amplifications or deletions of DNA segments that often drive BCa development and therapy resistance. Hence, the complex patterns of CNAs complement BCa classification systems. In addition, understanding the precise contributions of CNAs is essential for tailoring personalized treatment approaches. This review highlights how tumor evolution drives the acquisition of CNAs, which in turn shape the genomic landscapes of BCas. It also discusses advanced methodologies for identifying recurrent CNAs, studying CNAs in BCa and their clinical impact.
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Affiliation(s)
- Parastoo Shahrouzi
- Department of Medical Genetics, Institute of Basic Medical Science, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway.
| | - Farzaneh Forouz
- School of Pharmacy, University of Queensland, Woolloongabba, Brisbane, Australia
| | - Anthony Mathelier
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway; Center for Bioinformatics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway; Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Vessela N Kristensen
- Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway; Division of Medicine, Department of Clinical Molecular Biology and Laboratory Science (EpiGen), Akershus University Hospital, Lørenskog, Norway; Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Pascal H G Duijf
- Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway; Centre for Cancer Biology, UniSA Clinical and Health Sciences, University of South Australia and SA Pathology, Adelaide, Australia.
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8
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Al-Ruwishan A, Amer B, Salem A, Abdi A, Chimpandu N, Esa A, Melemenis A, Saleem MZ, Mathew R, Gamallat Y. Advancements in Understanding the Hide-and-Seek Strategy of Hibernating Breast Cancer Cells and Their Implications in Oncology from a Broader Perspective: A Comprehensive Overview. Curr Issues Mol Biol 2024; 46:8340-8367. [PMID: 39194709 DOI: 10.3390/cimb46080492] [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: 06/10/2024] [Revised: 07/27/2024] [Accepted: 07/29/2024] [Indexed: 08/29/2024] Open
Abstract
Despite recent advancements in technology, breast cancer still poses a significant threat, often resulting in fatal consequences. While early detection and treatments have shown some promise, many breast cancer patients continue to struggle with the persistent fear of the disease returning. This fear is valid, as breast cancer cells can lay dormant for years before remerging, evading traditional treatments like a game of hide and seek. The biology of these dormant breast cancer cells presents a crucial yet poorly understood challenge in clinical settings. In this review, we aim to explore the mysterious world of dormant breast cancer cells and their significant impact on patient outcomes and prognosis. We shed light on the elusive role of the G9a enzyme and many other epigenetic factors in breast cancer recurrence, highlighting its potential as a target for eliminating dormant cancer cells and preventing disease relapse. Through this comprehensive review, we not only emphasise the urgency of unravelling the dynamics of dormant breast cancer cells to improve patient outcomes and advance personalised oncology but also provide a guide for fellow researchers. By clearly outlining the clinical and research gaps surrounding dormant breast cancer cells from a molecular perspective, we aim to inspire further exploration of this critical area, ultimately leading to improved patient care and treatment strategies.
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Affiliation(s)
- Aiman Al-Ruwishan
- Space for Research Initiative, Research Horizons, London NW10 2PU, UK
| | - Bushra Amer
- Department of Family Medicine, College of Human Medicine, Michigan State University, East Lansing, MI 48824, USA
| | - Ahmed Salem
- Department of Biological and Biochemical Sciences, Faculty of Chemical Technology, University of Pardubice, 53210 Pardubice, Czech Republic
| | - Ahmed Abdi
- Independent Researcher, Uxbridge UB9 6JH, UK
| | | | | | | | - Muhammad Zubair Saleem
- Department of Pharmacology and Systems Physiology, College of Medicine, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Roselit Mathew
- Department of Oncology, Biochemistry and Molecular Biology, and Laboratory Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Yaser Gamallat
- Department of Oncology, Biochemistry and Molecular Biology, and Laboratory Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
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9
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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10
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Ban GI, Puviindran V, Xiang Y, Nadesan P, Tang J, Ou J, Guardino N, Nakagawa M, Browne M, Wallace A, Ishikawa K, Shimada E, Martin JT, Diao Y, Kirsch DG, Alman BA. The COMPASS complex maintains the metastatic capacity imparted by a subpopulation of cells in UPS. iScience 2024; 27:110187. [PMID: 38989451 PMCID: PMC11233968 DOI: 10.1016/j.isci.2024.110187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 04/20/2024] [Accepted: 06/03/2024] [Indexed: 07/12/2024] Open
Abstract
Intratumoral heterogeneity is common in cancer, particularly in sarcomas like undifferentiated pleomorphic sarcoma (UPS), where individual cells demonstrate a high degree of cytogenic diversity. Previous studies showed that a small subset of cells within UPS, known as the metastatic clone (MC), as responsible for metastasis. Using a CRISPR-based genomic screen in-vivo, we identified the COMPASS complex member Setd1a as a key regulator maintaining the metastatic phenotype of the MC in murine UPS. Depletion of Setd1a inhibited metastasis development in the MC. Transcriptome and chromatin sequencing revealed COMPASS complex target genes in UPS, such as Cxcl10, downregulated in the MC. Deleting Cxcl10 in non-MC cells increased their metastatic potential. Treating mice with human UPS xenografts with a COMPASS complex inhibitor suppressed metastasis without affecting tumor growth in the primary tumor. Our data identified an epigenetic program in a subpopulation of sarcoma cells that maintains metastatic potential.
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Affiliation(s)
- Ga I. Ban
- Department of Orthopedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Vijitha Puviindran
- Department of Orthopedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Yu Xiang
- Department of Cell Biology and Duke Regeneration Center, Duke University School of Medicine, Durham, NC, USA
| | - Puvi Nadesan
- Department of Orthopedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Jackie Tang
- Department of Orthopedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Jianhong Ou
- Department of Cell Biology and Duke Regeneration Center, Duke University School of Medicine, Durham, NC, USA
| | - Nicholas Guardino
- Department of Orthopedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Makoto Nakagawa
- Department of Orthopedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - MaKenna Browne
- Department of Cell Biology and Duke Regeneration Center, Duke University School of Medicine, Durham, NC, USA
| | - Asjah Wallace
- Department of Orthopedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Koji Ishikawa
- Department of Orthopedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Eijiro Shimada
- Department of Orthopedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - John T. Martin
- Department of Orthopedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Yarui Diao
- Department of Cell Biology and Duke Regeneration Center, Duke University School of Medicine, Durham, NC, USA
| | - David G. Kirsch
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
- The Princes Margaret Cancer Centre, Department of Radiation Oncology, University Health Network and the University of Toronto, Toronto, ON, Canada
| | - Benjamin A. Alman
- Department of Orthopedic Surgery, Duke University School of Medicine, Durham, NC, USA
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11
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Kabeer F, Tran H, Andronescu M, Singh G, Lee H, Salehi S, Wang B, Biele J, Brimhall J, Gee D, Cerda V, O'Flanagan C, Algara T, Kono T, Beatty S, Zaikova E, Lai D, Lee E, Moore R, Mungall AJ, Williams MJ, Roth A, Campbell KR, Shah SP, Aparicio S. Single-cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers. Genome Biol 2024; 25:191. [PMID: 39026273 PMCID: PMC11256464 DOI: 10.1186/s13059-024-03318-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 06/20/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND The encoding of cell intrinsic drug resistance states in breast cancer reflects the contributions of genomic and non-genomic variations and requires accurate estimation of clonal fitness from co-measurement of transcriptomic and genomic data. Somatic copy number (CN) variation is the dominant mutational mechanism leading to transcriptional variation and notably contributes to platinum chemotherapy resistance cell states. Here, we deploy time series measurements of triple negative breast cancer (TNBC) single-cell transcriptomes, along with co-measured single-cell CN fitness, identifying genomic and transcriptomic mechanisms in drug-associated transcriptional cell states. RESULTS We present scRNA-seq data (53,641 filtered cells) from serial passaging TNBC patient-derived xenograft (PDX) experiments spanning 2.5 years, matched with genomic single-cell CN data from the same samples. Our findings reveal distinct clonal responses within TNBC tumors exposed to platinum. Clones with high drug fitness undergo clonal sweeps and show subtle transcriptional reversion, while those with weak fitness exhibit dynamic transcription upon drug withdrawal. Pathway analysis highlights convergence on epithelial-mesenchymal transition and cytokine signaling, associated with resistance. Furthermore, pseudotime analysis demonstrates hysteresis in transcriptional reversion, indicating generation of new intermediate transcriptional states upon platinum exposure. CONCLUSIONS Within a polyclonal tumor, clones with strong genotype-associated fitness under platinum remained fixed, minimizing transcriptional reversion upon drug withdrawal. Conversely, clones with weaker fitness display non-genomic transcriptional plasticity. This suggests CN-associated and CN-independent transcriptional states could both contribute to platinum resistance. The dominance of genomic or non-genomic mechanisms within polyclonal tumors has implications for drug sensitivity, restoration, and re-treatment strategies.
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Affiliation(s)
- Farhia Kabeer
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Hoa Tran
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Mirela Andronescu
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Gurdeep Singh
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Hakwoo Lee
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Sohrab Salehi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Beixi Wang
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Justina Biele
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Jazmine Brimhall
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - David Gee
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Viviana Cerda
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Ciara O'Flanagan
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Teresa Algara
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Takako Kono
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Sean Beatty
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Elena Zaikova
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Daniel Lai
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Eric Lee
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Richard Moore
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Andrew J Mungall
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Marc J Williams
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew Roth
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Kieran R Campbell
- Lunenfeld-Tanenbaum Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Samuel Aparicio
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada.
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12
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Qiao Y, Cheng T, Miao Z, Cui Y, Tu J. Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods. SMALL METHODS 2024:e2400789. [PMID: 38979872 DOI: 10.1002/smtd.202400789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Indexed: 07/10/2024]
Abstract
Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.
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Affiliation(s)
- Yi Qiao
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Tianguang Cheng
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Zikun Miao
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Yue Cui
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Jing Tu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
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13
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Guo CC, Lee S, Lee JG, Chen H, Zaleski M, Choi W, McConkey DJ, Wei P, Czerniak B. Molecular profile of bladder cancer progression to clinically aggressive subtypes. Nat Rev Urol 2024; 21:391-405. [PMID: 38321289 DOI: 10.1038/s41585-023-00847-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/15/2023] [Indexed: 02/08/2024]
Abstract
Bladder cancer is a histologically and clinically heterogenous disease. Most bladder cancers are urothelial carcinomas, which frequently develop distinct histological subtypes. Several urothelial carcinoma histological subtypes, such as micropapillary, plasmacytoid, small-cell carcinoma and sarcomatoid, show highly aggressive behaviour and pose unique challenges in diagnosis and treatment. Comprehensive genomic characterizations of the urothelial carcinoma subtypes have revealed that they probably arise from a precursor subset of conventional urothelial carcinomas that belong to different molecular subtypes - micropapillary and plasmacytoid subtypes develop along the luminal pathway, whereas small-cell and sarcomatoid subtypes evolve along the basal pathway. The subtypes exhibit distinct genomic alterations, but in most cases their biological properties seem to be primarily determined by specific gene expression profiles, including epithelial-mesenchymal transition, urothelial-to-neural lineage plasticity, and immune infiltration with distinct upregulation of immune regulatory genes. These breakthrough studies have transformed our view of bladder cancer histological subtype biology, generated new hypotheses for therapy and chemoresistance, and facilitated the discovery of new therapeutic targets.
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Affiliation(s)
- Charles C Guo
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sangkyou Lee
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - June G Lee
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Huiqin Chen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michael Zaleski
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Woonyoung Choi
- Johns Hopkins Greenberg Bladder Cancer Institute, Johns Hopkins University, Baltimore, MD, USA
| | - David J McConkey
- Johns Hopkins Greenberg Bladder Cancer Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bogdan Czerniak
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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14
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Liu S, Li X, Zhang Y, Deng Y, Li Z, Zhu Y, Li X, Shang Y, Yang G, Zhan X, Li Y, Ren H. A bibliometric study of the intellectual base and global research hotspots for single-cell sequencing [2009-2022] in breast cancer. Heliyon 2024; 10:e33219. [PMID: 39022007 PMCID: PMC11252796 DOI: 10.1016/j.heliyon.2024.e33219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 06/16/2024] [Accepted: 06/17/2024] [Indexed: 07/20/2024] Open
Abstract
Background Breast cancer is the most widespread malignant tumor worldwide. Single-cell sequencing technology offers novel insights and methods to understand the onset, progression, and treatment of tumors. Nevertheless, there is currently an absence of a thorough and unbiased report on the comprehensive research status of single-cell sequencing in breast cancer. This study seeks to summarize and quantify the dynamics and trends of research on breast cancer single-cell sequencing by bibliometric analysis. Methods Research articles and reviews related to breast cancer single-cell sequencing were selected from the WoSCC database. Visualization of data regarding countries, institutions, authors, references, and keywords was performed by CiteSpace and VOSviewer software. Results 583 articles and reviews were analyzed in this study. The quantity of publications related to breast cancer single-cell sequencing has been increasing annually. These studies originate from 302 institutions in 46 countries, with YMAX S WICHA producing the most publications and WANG Y being the most cited author. Nature Communications is the most researched journal, while Nature holds the highest number of citations. These journals predominantly cover topics in the molecular/biological/immunological fields. Moreover, an analysis of reference and keyword bursts revealed that current research trends in this area are primarily centered on "clonal evolution," "tumor microenvironment," and "immunotherapy." Conclusion Breast cancer single-cell sequencing is a rapidly growing area of scientific interest. Future research requires more frequent and in-depth collaborations among countries, institutions, and authors. Furthermore, "clonal evolution," "tumor microenvironment," and "immunotherapy" are likely to become major focal points in upcoming research on breast cancer single-cell sequencing.
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Affiliation(s)
- Shan Liu
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xudong Li
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Ying Zhang
- Department of Neurology, Air Force Medical Center, PLA, Beijing, China
| | - Yuhan Deng
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Zehao Li
- Jiamusi University School of Clinical Medicine, Jiamusi, China
| | - Yunan Zhu
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xue Li
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yuefeng Shang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Guang Yang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xiaolu Zhan
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yingpu Li
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - He Ren
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
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15
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Ji Y, An Q, Wen X, Xu Z, Xia Z, Xia Z, Hu Q, Lei S. Liver cancer from the perspective of single-cell sequencing: a review combined with bibliometric analysis. J Cancer Res Clin Oncol 2024; 150:316. [PMID: 38910204 PMCID: PMC11194221 DOI: 10.1007/s00432-024-05855-7] [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: 03/16/2024] [Accepted: 06/17/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND Liver cancer (LC) is a prevalent malignancy and a leading cause of cancer-related mortality worldwide. Extensive research has been conducted to enhance patient outcomes and develop effective prevention strategies, ranging from molecular mechanisms to clinical interventions. Single-cell sequencing, as a novel bioanalysis technology, has significantly contributed to the understanding of the global cognition and dynamic changes in liver cancer. However, there is a lack of bibliometric analysis in this specific research area. Therefore, the objective of this study is to provide a comprehensive overview of the knowledge structure and research hotspots in the field of single-cell sequencing in liver cancer research through the use of bibliometrics. METHOD Publications related to the application of single-cell sequencing technology to liver cancer research as of December 31, 2023, were searched on the web of science core collection (WoSCC) database. VOSviewers, CiteSpace, and R package "bibliometrix" were used to conduct this bibliometric analysis. RESULTS A total of 331 publications from 34 countries, primarily led by China and the United States, were included in this study. The research focuses on the application of single cell sequencing technology to liver cancer, and the number of related publications has been increasing year by year. The main research institutions involved in this field are Fudan University, Sun Yat-Sen University, and the Chinese Academy of Sciences. Frontiers in Immunology and Nature Communications is the most popular journal in this field, while Cell is the most frequently co-cited journal. These publications are authored by 2799 individuals, with Fan Jia and Zhou Jian having the most published papers, and Llovet Jm being the most frequently co-cited author. The use of single cell sequencing to explore the immune microenvironment of liver cancer, as well as its implications in immunotherapy and chemotherapy, remains the central focus of this field. The emerging research hotspots are characterized by keywords such as 'Gene-Expression', 'Prognosis', 'Tumor Heterogeneity', 'Immunoregulation', and 'Tumor Immune Microenvironment'. CONCLUSION This is the first bibliometric study that comprehensively summarizes the research trends and developments on the application of single cell sequencing in liver cancer. The study identifies recent research frontiers and hot directions, providing a valuable reference for researchers exploring the landscape of liver cancer, understanding the composition of the immune microenvironment, and utilizing single-cell sequencing technology to guide and enhance the prognosis of liver cancer patients.
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Affiliation(s)
- Yanwei Ji
- Department of Anesthesiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Qi An
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xinyu Wen
- Department of Anesthesiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhou Xu
- The Second Clinical Medical College, Jiangxi Medical College, Nanchang University, Jiangxi, Nanchang, China
| | - Zhengyuan Xia
- Department of Anesthesiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
- Faculty of Chinese Medicine, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao, China
| | - Zhongyuan Xia
- Department of Anesthesiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Qinyong Hu
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China.
| | - Shaoqing Lei
- Department of Anesthesiology, Renmin Hospital of Wuhan University, Wuhan, China.
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16
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Li L, Xie W, Zhan L, Wen S, Luo X, Xu S, Cai Y, Tang W, Wang Q, Li M, Xie Z, Deng L, Zhu H, Yu G. Resolving tumor evolution: a phylogenetic approach. JOURNAL OF THE NATIONAL CANCER CENTER 2024; 4:97-106. [PMID: 39282584 PMCID: PMC11390690 DOI: 10.1016/j.jncc.2024.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 02/28/2024] [Accepted: 03/20/2024] [Indexed: 09/19/2024] Open
Abstract
The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.
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Affiliation(s)
- Lin Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenqin Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Li Zhan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Shaodi Wen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Department of Oncology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital, Nanjing, China
| | - Xiao Luo
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Shuangbin Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Division of Laboratory Medicine, Microbiome Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yantong Cai
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Wenli Tang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Qianwen Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Ming Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Zijing Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Lin Deng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Hongyuan Zhu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
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17
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Yoon B, Kim H, Jung SW, Park J. Single-cell lineage tracing approaches to track kidney cell development and maintenance. Kidney Int 2024; 105:1186-1199. [PMID: 38554991 DOI: 10.1016/j.kint.2024.01.045] [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: 09/08/2023] [Revised: 12/06/2023] [Accepted: 01/09/2024] [Indexed: 04/02/2024]
Abstract
The kidney is a complex organ consisting of various cell types. Previous studies have aimed to elucidate the cellular relationships among these cell types in developing and mature kidneys using Cre-loxP-based lineage tracing. However, this methodology falls short of fully capturing the heterogeneous nature of the kidney, making it less than ideal for comprehensively tracing cellular progression during kidney development and maintenance. Recent technological advancements in single-cell genomics have revolutionized lineage tracing methods. Single-cell lineage tracing enables the simultaneous tracing of multiple cell types within complex tissues and their transcriptomic profiles, thereby allowing the reconstruction of their lineage tree with cell state information. Although single-cell lineage tracing has been successfully applied to investigate cellular hierarchies in various organs and tissues, its application in kidney research is currently lacking. This review comprehensively consolidates the single-cell lineage tracing methods, divided into 4 categories (clustered regularly interspaced short palindromic repeat [CRISPR]/CRISPR-associated protein 9 [Cas9]-based, transposon-based, Polylox-based, and native barcoding methods), and outlines their technical advantages and disadvantages. Furthermore, we propose potential future research topics in kidney research that could benefit from single-cell lineage tracing and suggest suitable technical strategies to apply to these topics.
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Affiliation(s)
- Baul Yoon
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Hayoung Kim
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Su Woong Jung
- Division of Nephrology, Department of Internal Medicine, College of Medicine, Kyung Hee University, Seoul, Republic of Korea; Division of Nephrology, Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea.
| | - Jihwan Park
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea.
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18
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Singhal U, Nallandhighal S, Tosoian JJ, Hu K, Pham TM, Stangl-Kremser J, Liu CJ, Karim R, Plouffe KR, Morgan TM, Cieslik M, Lucianò R, Shariat SF, Finocchio N, Dambrosio L, Doglioni C, Chinnaiyan AM, Tomlins SA, Briganti A, Palapattu GS, Udager AM, Salami SS. Integrative multi-region molecular profiling of primary prostate cancer in men with synchronous lymph node metastasis. Nat Commun 2024; 15:4341. [PMID: 38773085 PMCID: PMC11109137 DOI: 10.1038/s41467-024-48629-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 05/08/2024] [Indexed: 05/23/2024] Open
Abstract
Localized prostate cancer is frequently composed of multiple spatially distinct tumors with significant inter- and intra-tumoral molecular heterogeneity. This genomic diversity gives rise to many competing clones that may drive the biological trajectory of the disease. Previous large-scale sequencing efforts have focused on the evolutionary process in metastatic prostate cancer, revealing a potential clonal progression to castration resistance. However, the clonal origin of synchronous lymph node (LN) metastases in primary disease is still unknown. Here, we perform multi-region, targeted next generation sequencing and construct phylogenetic trees in men with prostate cancer with synchronous LN metastasis to better define the pathologic and molecular features of primary disease most likely to spread to the LNs. Collectively, we demonstrate that a combination of histopathologic and molecular factors, including tumor grade, presence of extra-prostatic extension, cellular morphology, and oncogenic genomic alterations are associated with synchronous LN metastasis.
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Affiliation(s)
- Udit Singhal
- Department of Urology, Michigan Medicine, Ann Arbor, MI, USA.
- Department of Urology, Mayo Clinic, Rochester, MN, USA.
- Rogel Cancer Center, Michigan Medicine, Ann Arbor, MI, USA.
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI, USA.
| | | | - Jeffrey J Tosoian
- Department of Urology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Nashville, TN, USA
| | - Kevin Hu
- Department of Pathology, Michigan Medicine, Ann Arbor, MI, USA
| | - Trinh M Pham
- Department of Urology, Michigan Medicine, Ann Arbor, MI, USA
| | - Judith Stangl-Kremser
- Department of Urology, Michigan Medicine, Ann Arbor, MI, USA
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Chia-Jen Liu
- College of Literature, Science, and Arts, University of Michigan, Ann Arbor, MI, USA
| | - Razeen Karim
- College of Literature, Science, and Arts, University of Michigan, Ann Arbor, MI, USA
| | - Komal R Plouffe
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI, USA
- Department of Pathology, Michigan Medicine, Ann Arbor, MI, USA
| | - Todd M Morgan
- Department of Urology, Michigan Medicine, Ann Arbor, MI, USA
- Rogel Cancer Center, Michigan Medicine, Ann Arbor, MI, USA
| | - Marcin Cieslik
- Rogel Cancer Center, Michigan Medicine, Ann Arbor, MI, USA
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI, USA
- Department of Pathology, Michigan Medicine, Ann Arbor, MI, USA
| | - Roberta Lucianò
- Department of Pathology, Universita Vita-Salute San Raffaele, Milan, Italy
| | | | - Nadia Finocchio
- Department of Urology, Universita Vita-Salute San Raffaele, Milan, Italy
| | - Lucia Dambrosio
- Department of Urology, Universita Vita-Salute San Raffaele, Milan, Italy
| | - Claudio Doglioni
- Department of Pathology, Universita Vita-Salute San Raffaele, Milan, Italy
| | - Arul M Chinnaiyan
- Department of Urology, Michigan Medicine, Ann Arbor, MI, USA
- Rogel Cancer Center, Michigan Medicine, Ann Arbor, MI, USA
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI, USA
- Department of Pathology, Michigan Medicine, Ann Arbor, MI, USA
- Howard Hughes Medical Institute, University of Michigan, Ann Arbor, MI, USA
| | - Scott A Tomlins
- Department of Pathology, Michigan Medicine, Ann Arbor, MI, USA
| | - Alberto Briganti
- Department of Urology, Universita Vita-Salute San Raffaele, Milan, Italy
| | - Ganesh S Palapattu
- Department of Urology, Michigan Medicine, Ann Arbor, MI, USA
- Rogel Cancer Center, Michigan Medicine, Ann Arbor, MI, USA
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Aaron M Udager
- Rogel Cancer Center, Michigan Medicine, Ann Arbor, MI, USA.
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI, USA.
- Department of Pathology, Michigan Medicine, Ann Arbor, MI, USA.
| | - Simpa S Salami
- Department of Urology, Michigan Medicine, Ann Arbor, MI, USA.
- Rogel Cancer Center, Michigan Medicine, Ann Arbor, MI, USA.
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI, USA.
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19
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Xu J, Gao H, Guan X, Meng J, Ding S, Long Q, Yi W. Circulating tumor DNA: from discovery to clinical application in breast cancer. Front Immunol 2024; 15:1355887. [PMID: 38745646 PMCID: PMC11091288 DOI: 10.3389/fimmu.2024.1355887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 04/16/2024] [Indexed: 05/16/2024] Open
Abstract
Breast cancer (BC) stands out as the cancer with the highest incidence of morbidity and mortality among women worldwide, and its incidence rate is currently trending upwards. Improving the efficiency of breast cancer diagnosis and treatment is crucial, as it can effectively reduce the disease burden. Circulating tumor DNA (ctDNA) originates from the release of tumor cells and plays a pivotal role in the occurrence, development, and metastasis of breast cancer. In recent years, the widespread application of high-throughput analytical technology has made ctDNA a promising biomarker for early cancer detection, monitoring minimal residual disease, early recurrence monitoring, and predicting treatment outcomes. ctDNA-based approaches can effectively compensate for the shortcomings of traditional screening and monitoring methods, which fail to provide real-time information and prospective guidance for breast cancer diagnosis and treatment. This review summarizes the applications of ctDNA in various aspects of breast cancer, including screening, diagnosis, prognosis, treatment, and follow-up. It highlights the current research status in this field and emphasizes the potential for future large-scale clinical applications of ctDNA-based approaches.
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Affiliation(s)
- Jiachi Xu
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center For Breast Disease In Hunan Province, Changsha, China
| | - Hongyu Gao
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center For Breast Disease In Hunan Province, Changsha, China
| | - Xinyu Guan
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center For Breast Disease In Hunan Province, Changsha, China
| | - Jiahao Meng
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center For Breast Disease In Hunan Province, Changsha, China
| | - Shirong Ding
- Department of Oncology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Qian Long
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center For Breast Disease In Hunan Province, Changsha, China
| | - Wenjun Yi
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center For Breast Disease In Hunan Province, Changsha, China
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20
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Liu X, Zhang K, Kaya NA, Jia Z, Wu D, Chen T, Liu Z, Zhu S, Hillmer AM, Wuestefeld T, Liu J, Chan YS, Hu Z, Ma L, Jiang L, Zhai W. Tumor phylogeography reveals block-shaped spatial heterogeneity and the mode of evolution in Hepatocellular Carcinoma. Nat Commun 2024; 15:3169. [PMID: 38609353 PMCID: PMC11015015 DOI: 10.1038/s41467-024-47541-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
Abstract
Solid tumors are complex ecosystems with heterogeneous 3D structures, but the spatial intra-tumor heterogeneity (sITH) at the macroscopic (i.e., whole tumor) level is under-explored. Using a phylogeographic approach, we sequence genomes and transcriptomes from 235 spatially informed sectors across 13 hepatocellular carcinomas (HCC), generating one of the largest datasets for studying sITH. We find that tumor heterogeneity in HCC segregates into spatially variegated blocks with large genotypic and phenotypic differences. By dissecting the transcriptomic heterogeneity, we discover that 30% of patients had a "spatially competing distribution" (SCD), where different spatial blocks have distinct transcriptomic subtypes co-existing within a tumor, capturing the critical transition period in disease progression. Interestingly, the tumor regions with more advanced transcriptomic subtypes (e.g., higher cell cycle) often take clonal dominance with a wider geographic range, rejecting neutral evolution for SCD patients. Extending the statistical tests for detecting natural selection to many non-SCD patients reveal varying levels of selective signal across different tumors, implying that many evolutionary forces including natural selection and geographic isolation can influence the overall pattern of sITH. Taken together, tumor phylogeography unravels a dynamic landscape of sITH, pinpointing important evolutionary and clinical consequences of spatial heterogeneity in cancer.
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Affiliation(s)
- Xiaodong Liu
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Ke Zhang
- Department of General Surgery, Beijing Ditan Hospital, Capital Medical University, No. 8, Jingshun East Street, Chaoyang District, Beijing, P.R. China
| | - Neslihan A Kaya
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Zhe Jia
- Department of General Surgery, Beijing Ditan Hospital, Capital Medical University, No. 8, Jingshun East Street, Chaoyang District, Beijing, P.R. China
| | - Dafei Wu
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Tingting Chen
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Zhiyuan Liu
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Sinan Zhu
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Axel M Hillmer
- Institute of Pathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Torsten Wuestefeld
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Jin Liu
- Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Yun Shen Chan
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Zheng Hu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Liang Ma
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
| | - Li Jiang
- Department of General Surgery, Beijing Ditan Hospital, Capital Medical University, No. 8, Jingshun East Street, Chaoyang District, Beijing, P.R. China.
| | - Weiwei Zhai
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
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21
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Wang J, Li B, Luo M, Huang J, Zhang K, Zheng S, Zhang S, Zhou J. Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance. Signal Transduct Target Ther 2024; 9:83. [PMID: 38570490 PMCID: PMC10991592 DOI: 10.1038/s41392-024-01779-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 04/05/2024] Open
Abstract
Ductal carcinoma in situ (DCIS) represents pre-invasive breast carcinoma. In untreated cases, 25-60% DCIS progress to invasive ductal carcinoma (IDC). The challenge lies in distinguishing between non-progressive and progressive DCIS, often resulting in over- or under-treatment in many cases. With increasing screen-detected DCIS in these years, the nature of DCIS has aroused worldwide attention. A deeper understanding of the biological nature of DCIS and the molecular journey of the DCIS-IDC transition is crucial for more effective clinical management. Here, we reviewed the key signaling pathways in breast cancer that may contribute to DCIS initiation and progression. We also explored the molecular features of DCIS and IDC, shedding light on the progression of DCIS through both inherent changes within tumor cells and alterations in the tumor microenvironment. In addition, valuable research tools utilized in studying DCIS including preclinical models and newer advanced technologies such as single-cell sequencing, spatial transcriptomics and artificial intelligence, have been systematically summarized. Further, we thoroughly discussed the clinical advancements in DCIS and IDC, including prognostic biomarkers and clinical managements, with the aim of facilitating more personalized treatment strategies in the future. Research on DCIS has already yielded significant insights into breast carcinogenesis and will continue to pave the way for practical clinical applications.
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Affiliation(s)
- Jing Wang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Baizhou Li
- Department of Pathology, the Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Meng Luo
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
- Department of Plastic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jia Huang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Kun Zhang
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shu Zheng
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Suzhan Zhang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China.
| | - Jiaojiao Zhou
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China.
- Cancer Center, Zhejiang University, Hangzhou, China.
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22
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Ludwik KA, Greathouse FR, Han S, Stauffer K, Brenin DR, Stricker TP, Lannigan DA. Identifying the effectiveness of 3D culture systems to recapitulate breast tumor tissue in situ. Cell Oncol (Dordr) 2024; 47:481-496. [PMID: 37776423 PMCID: PMC11090829 DOI: 10.1007/s13402-023-00877-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/16/2023] [Indexed: 10/02/2023] Open
Abstract
PURPOSE Breast cancer heterogeneity contributes to chemotherapy resistance and decreased patient survival. To improve patient outcomes it is essential to develop a technology that is able to rapidly select the most efficacious therapy that targets the diverse phenotypes present within the tumor. Breast cancer organoid technologies are proposed as an attractive approach for evaluating drug responses prior to patient therapy. However, there remain challenges in evaluating the effectiveness of organoid cultures to recapitulate the heterogeneity present in the patient tumor in situ. METHOD Organoids were generated from seven normal breast and nineteen breast cancer tissues diagnosed as estrogen receptor positive or triple negative. The Jensen-Shannon divergence index, a measure of the similarity between distributions, was used to compare and evaluate heterogeneity in starting tissue and their resultant organoids. Heterogeneity was analyzed using cytokeratin 8 and cytokeratin 14, which provided an easily scored readout. RESULTS In the in vitro culture system HER1 and FGFR were able to drive intra-tumor heterogeneity to generate divergent phenotypes that have different sensitivities to chemotherapies. CONCLUSION Our methodology, which focuses on quantifiable cellular phenotypes, provides a tractable system that complements omics approaches to provide an unprecedented view of heterogeneity and will enhance the identification of novel therapies and facilitate personalized medicine.
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Affiliation(s)
- Katarzyna A Ludwik
- Department Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Frances R Greathouse
- Department Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | | | - Kimberly Stauffer
- Department Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - David R Brenin
- Department Surgery, University of Virginia, Charlottesville, VA, 22908, USA
| | - Thomas P Stricker
- Department Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Deborah A Lannigan
- Department Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Department Biomedical Engineering, Vanderbilt University, Nashville, TN, 37235, USA.
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23
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Fang X, Zhang Y, Miao R, Zhang Y, Yin R, Guan H, Huang X, Tian J. Single-cell sequencing: A promising approach for uncovering the characteristic of pancreatic islet cells in type 2 diabetes. Biomed Pharmacother 2024; 173:116292. [PMID: 38394848 DOI: 10.1016/j.biopha.2024.116292] [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: 12/07/2023] [Revised: 02/03/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024] Open
Abstract
Single-cell sequencing is a novel and rapidly advancing high-throughput technique that can be used to investigating genomics, transcriptomics, and epigenetics at a single-cell level. Currently, single-cell sequencing can not only be used to draw the pancreatic islet cells map and uncover the characteristics of cellular heterogeneity in type 2 diabetes, but can also be used to label and purify functional beta cells in pancreatic stem cells, improving stem cells and islet organoids therapies. In addition, this technology helps to analyze islet cell dedifferentiation and can be applied to the treatment of type 2 diabetes. In this review, we summarize the development and process of single-cell sequencing, describe the potential applications of single-cell sequencing in the field of type 2 diabetes, and discuss the prospects and limitations of single-cell sequencing to provide a new direction for exploring the pathogenesis of type 2 diabetes and finding therapeutic targets.
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Affiliation(s)
- Xinyi Fang
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China; Graduate College, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yanjiao Zhang
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Runyu Miao
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China; Graduate College, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yuxin Zhang
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Ruiyang Yin
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Huifang Guan
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Jilin 130117, China
| | - Xinyue Huang
- First Clinical Medical College, Changzhi Medical College, Shanxi 046013, China
| | - Jiaxing Tian
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
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24
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Inayatullah M, Mahesh A, Turnbull AK, Dixon JM, Natrajan R, Tiwari VK. Basal-epithelial subpopulations underlie and predict chemotherapy resistance in triple-negative breast cancer. EMBO Mol Med 2024; 16:823-853. [PMID: 38480932 PMCID: PMC11018633 DOI: 10.1038/s44321-024-00050-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 03/18/2024] Open
Abstract
Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype, characterized by extensive intratumoral heterogeneity, high metastasis, and chemoresistance, leading to poor clinical outcomes. Despite progress, the mechanistic basis of these aggressive behaviors remains poorly understood. Using single-cell and spatial transcriptome analysis, here we discovered basal epithelial subpopulations located within the stroma that exhibit chemoresistance characteristics. The subpopulations are defined by distinct signature genes that show a frequent gain in copy number and exhibit an activated epithelial-to-mesenchymal transition program. A subset of these genes can accurately predict chemotherapy response and are associated with poor prognosis. Interestingly, among these genes, elevated ITGB1 participates in enhancing intercellular signaling while ACTN1 confers a survival advantage to foster chemoresistance. Furthermore, by subjecting the transcriptional signatures to drug repurposing analysis, we find that chemoresistant tumors may benefit from distinct inhibitors in treatment-naive versus post-NAC patients. These findings shed light on the mechanistic basis of chemoresistance while providing the best-in-class biomarker to predict chemotherapy response and alternate therapeutic avenues for improved management of TNBC patients resistant to chemotherapy.
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Affiliation(s)
- Mohammed Inayatullah
- Institute for Molecular Medicine, University of Southern Denmark, Odense M, Denmark
| | - Arun Mahesh
- Institute for Molecular Medicine, University of Southern Denmark, Odense M, Denmark
| | - Arran K Turnbull
- Edinburgh Breast Cancer Now Research Group, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK
| | - J Michael Dixon
- Edinburgh Breast Cancer Now Research Group, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK
| | - Rachael Natrajan
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, SW3 6JB, UK
| | - Vijay K Tiwari
- Institute for Molecular Medicine, University of Southern Denmark, Odense M, Denmark.
- Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry & Biomedical Science, Queens University Belfast, Belfast, BT9 7BL, UK.
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, BT9 7AE, UK.
- Danish Institute for Advanced Study (DIAS), Odense M, Denmark.
- Department of Clinical Genetics, Odense University Hospital, Odense C, Denmark.
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25
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Weideman AMK, Wang R, Ibrahim JG, Jiang Y. Canopy2: tumor phylogeny inference by bulk DNA and single-cell RNA sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585595. [PMID: 38562795 PMCID: PMC10983938 DOI: 10.1101/2024.03.18.585595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Tumors are comprised of a mixture of distinct cell populations that differ in terms of genetic makeup and function. Such heterogeneity plays a role in the development of drug resistance and the ineffectiveness of targeted cancer therapies. Insight into this complexity can be obtained through the construction of a phylogenetic tree, which illustrates the evolutionary lineage of tumor cells as they acquire mutations over time. We propose Canopy2, a Bayesian framework that uses single nucleotide variants derived from bulk DNA and single-cell RNA sequencing to infer tumor phylogeny and conduct mutational profiling of tumor subpopulations. Canopy2 uses Markov chain Monte Carlo methods to sample from a joint probability distribution involving a mixture of binomial and beta-binomial distributions, specifically chosen to account for the sparsity and stochasticity of the single-cell data. Canopy2 demystifies the sources of zeros in the single-cell data and separates zeros categorized as non-cancerous (cells without mutations), stochastic (mutations not expressed due to bursting), and technical (expressed mutations not picked up by sequencing). Simulations demonstrate that Canopy2 consistently outperforms competing methods and reconstructs the clonal tree with high fidelity, even in situations involving low sequencing depth, poor single-cell yield, and highly-advanced and polyclonal tumors. We further assess the performance of Canopy2 through application to breast cancer and glioblastoma data, benchmarking against existing methods. Canopy2 is an open-source R package available at https://github.com/annweideman/canopy2.
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Affiliation(s)
- Ann Marie K. Weideman
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rujin Wang
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Joseph G. Ibrahim
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yuchao Jiang
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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26
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Schneider MP, Cullen AE, Pangonyte J, Skelton J, Major H, Van Oudenhove E, Garcia MJ, Chaves Urbano B, Piskorz AM, Brenton JD, Macintyre G, Markowetz F. scAbsolute: measuring single-cell ploidy and replication status. Genome Biol 2024; 25:62. [PMID: 38438920 PMCID: PMC10910719 DOI: 10.1186/s13059-024-03204-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 02/22/2024] [Indexed: 03/06/2024] Open
Abstract
Cancer cells often exhibit DNA copy number aberrations and can vary widely in their ploidy. Correct estimation of the ploidy of single-cell genomes is paramount for downstream analysis. Based only on single-cell DNA sequencing information, scAbsolute achieves accurate and unbiased measurement of single-cell ploidy and replication status, including whole-genome duplications. We demonstrate scAbsolute's capabilities using experimental cell multiplets, a FUCCI cell cycle expression system, and a benchmark against state-of-the-art methods. scAbsolute provides a robust foundation for single-cell DNA sequencing analysis across different technologies and has the potential to enable improvements in a number of downstream analyses.
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Affiliation(s)
- Michael P Schneider
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Amy E Cullen
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Justina Pangonyte
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Jason Skelton
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Harvey Major
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Elke Van Oudenhove
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Maria J Garcia
- Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | | | - Anna M Piskorz
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - James D Brenton
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Geoff Macintyre
- Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Florian Markowetz
- University of Cambridge, Cambridge, UK.
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK.
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27
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Bai X, Duren Z, Wan L, Xia LC. Joint inference of clonal structure using single-cell genome and transcriptome sequencing data. NAR Genom Bioinform 2024; 6:lqae017. [PMID: 38486887 PMCID: PMC10939367 DOI: 10.1093/nargab/lqae017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/19/2023] [Accepted: 01/29/2024] [Indexed: 03/17/2024] Open
Abstract
Latest advancements in the high-throughput single-cell genome (scDNA) and transcriptome (scRNA) sequencing technologies enabled cell-resolved investigation of tissue clones. However, it remains challenging to cluster and couple single cells for heterogeneous scRNA and scDNA data generated from the same specimen. In this study, we present a computational framework called CCNMF, which employs a novel Coupled-Clone Non-negative Matrix Factorization technique to jointly infer clonal structure for matched scDNA and scRNA data. CCNMF couples multi-omics single cells by linking copy number and gene expression profiles through their general concordance. It successfully resolved the underlying coexisting clones with high correlations between the clonal genome and transcriptome from the same specimen. We validated that CCNMF can achieve high accuracy and robustness using both simulated benchmarks and real-world applications, including an ovarian cancer cell lines mixture, a gastric cancer cell line, and a primary gastric cancer. In summary, CCNMF provides a powerful tool for integrating multi-omics single-cell data, enabling simultaneous resolution of genomic and transcriptomic clonal architecture. This computational framework facilitates the understanding of how cellular gene expression changes in conjunction with clonal genome alternations, shedding light on the cellular genomic difference of subclones that contributes to tumor evolution.
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Affiliation(s)
- Xiangqi Bai
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Zhana Duren
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson University, Greenwood, SC 29646, USA
| | - Lin Wan
- NCMIS, LSC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Li C Xia
- Department of Statistics and Financial Mathematics, School of Mathematics, South China University of Technology, Guangzhou, 510006, China
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28
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Li R, Su P, Shi Y, Shi H, Ding S, Su X, Chen P, Wu D. Gene doping detection in the era of genomics. Drug Test Anal 2024. [PMID: 38403949 DOI: 10.1002/dta.3664] [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: 10/03/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 02/27/2024]
Abstract
Recent progress in gene editing has enabled development of gene therapies for many genetic diseases, but also made gene doping an emerging risk in sports and competitions. By delivery of exogenous transgenes into human body, gene doping not only challenges competition fairness but also places health risk on athletes. World Anti-Doping Agency (WADA) has clearly inhibited the use of gene and cell doping in sports, and many techniques have been developed for gene doping detection. In this review, we will summarize the main tools for gene doping detection at present, highlight the main challenges for current tools, and elaborate future utilizations of high-throughput sequencing for unbiased, sensitive, economic and large-scale gene doping detections. Quantitative real-time PCR assays are the widely used detection methods at present, which are useful for detection of known targets but are vulnerable to codon optimization at exon-exon junction sites of the transgenes. High-throughput sequencing has become a powerful tool for various applications in life and health research, and the era of genomics has made it possible for sensitive and large-scale gene doping detections. Non-biased genomic profiling could efficiently detect new doping targets, and low-input genomics amplification and long-read third-generation sequencing also have application potentials for more efficient and straightforward gene doping detection. By closely monitoring scientific advancements in gene editing and sport genetics, high-throughput sequencing could play a more and more important role in gene detection and hopefully contribute to doping-free sports in the future.
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Affiliation(s)
- Ruihong Li
- eHealth Program of Shanghai Anti-doping Laboratory, Shanghai University of Sport, Shanghai, China
- Shanghai Center of Agri-Products Quality and Safety, Shanghai, China
| | - Peipei Su
- Innovative Program of Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi Shi
- eHealth Program of Shanghai Anti-doping Laboratory, Shanghai University of Sport, Shanghai, China
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, Shanghai, China
| | - Hui Shi
- eHealth Program of Shanghai Anti-doping Laboratory, Shanghai University of Sport, Shanghai, China
- Department of Rheumatology and Immunology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shengqian Ding
- eHealth Program of Shanghai Anti-doping Laboratory, Shanghai University of Sport, Shanghai, China
| | - Xianbin Su
- eHealth Program of Shanghai Anti-doping Laboratory, Shanghai University of Sport, Shanghai, China
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Peijie Chen
- School of Exercise and Health, Shanghai University of Sport, Shanghai, China
| | - Die Wu
- eHealth Program of Shanghai Anti-doping Laboratory, Shanghai University of Sport, Shanghai, China
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29
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Lynch A, Bradford S, Burkard ME. The reckoning of chromosomal instability: past, present, future. Chromosome Res 2024; 32:2. [PMID: 38367036 DOI: 10.1007/s10577-024-09746-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 01/11/2024] [Accepted: 01/27/2024] [Indexed: 02/19/2024]
Abstract
Quantitative measures of CIN are crucial to our understanding of its role in cancer. Technological advances have changed the way CIN is quantified, offering increased accuracy and insight. Here, we review measures of CIN through its rise as a field, discuss considerations for its measurement, and look forward to future quantification of CIN.
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Affiliation(s)
- Andrew Lynch
- UW Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI, USA
- Division of Hematology/Oncology, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Shermineh Bradford
- UW Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI, USA
- Division of Hematology/Oncology, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Mark E Burkard
- UW Carbone Cancer Center, University of Wisconsin, Madison, WI, USA.
- McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI, USA.
- Division of Hematology/Oncology, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.
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30
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Qin F, Cai G, Amos CI, Xiao F. A statistical learning method for simultaneous copy number estimation and subclone clustering with single-cell sequencing data. Genome Res 2024; 34:85-93. [PMID: 38290978 PMCID: PMC10903939 DOI: 10.1101/gr.278098.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 01/08/2024] [Indexed: 02/01/2024]
Abstract
The availability of single-cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, as cells comprising a subpopulation are found to share a genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified variants) in the procedure of CNA detection, thereby diminishing the accuracy of subclone identification within a large, complex cell population. In this study, we developed a subclone clustering method based on a fused lasso model, referred to as FLCNA, which can simultaneously detect CNAs in single-cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA, benchmarking it against existing copy number estimation methods (SCOPE, HMMcopy) in combination with commonly used clustering methods. Application of FLCNA to a scDNA-seq data set of breast cancer revealed different genomic variation patterns in neoadjuvant chemotherapy-treated samples and pretreated samples. We show that FLCNA is a practical and powerful method for subclone identification and CNA detection with scDNA-seq data.
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Affiliation(s)
- Fei Qin
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina 29208, USA
| | - Guoshuai Cai
- Department of Environmental Health Science, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina 29208, USA
| | - Christopher I Amos
- Department of Quantitative Sciences, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Feifei Xiao
- Department of Biostatistics, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida 32603, USA
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31
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Mah JL, Dunn CW. Cell type evolution reconstruction across species through cell phylogenies of single-cell RNA sequencing data. Nat Ecol Evol 2024; 8:325-338. [PMID: 38182680 DOI: 10.1038/s41559-023-02281-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 11/16/2023] [Indexed: 01/07/2024]
Abstract
The origin and evolution of cell types has emerged as a key topic in evolutionary biology. Driven by rapidly accumulating single-cell datasets, recent attempts to infer cell type evolution have largely been limited to pairwise comparisons because we lack approaches to build cell phylogenies using model-based approaches. Here we approach the challenges of applying explicit phylogenetic methods to single-cell data by using principal components as phylogenetic characters. We infer a cell phylogeny from a large, comparative single-cell dataset of eye cells from five distantly related mammals. Robust cell type clades enable us to provide a phylogenetic, rather than phenetic, definition of cell type, allowing us to forgo marker genes and phylogenetically classify cells by topology. We further observe evolutionary relationships between diverse vessel endothelia and identify the myelinating and non-myelinating Schwann cells as sister cell types. Finally, we examine principal component loadings and describe the gene expression dynamics underlying the function and identity of cell type clades that have been conserved across the five species. A cell phylogeny provides a rigorous framework towards investigating the evolutionary history of cells and will be critical to interpret comparative single-cell datasets that aim to ask fundamental evolutionary questions.
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Affiliation(s)
- Jasmine L Mah
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA.
| | - Casey W Dunn
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
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32
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Li J, Li S, Shu M, Hu W. Unravelling the heterogeneity of oral squamous cell carcinoma by integrative analysis of single-cell and bulk transcriptome data. J Cell Mol Med 2024; 28:e18108. [PMID: 38279519 PMCID: PMC10844683 DOI: 10.1111/jcmm.18108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/16/2023] [Accepted: 11/22/2023] [Indexed: 01/28/2024] Open
Abstract
Oral squamous cell carcinoma (OSCC) is a prevalent malignancy of the head and neck with rising global incidence. Despite advances in treatment modalities, OSCC prognosis remains diverse due to the complex molecular and cellular heterogeneity within tumours, as well as the heterogeneity in tumour microenvironment (TME). In this study, we utilized single-cell RNA sequencing (scRNA-seq) analysis to explore distinct subpopulations of tumour cells in OSCC tissues and their interaction with components in TME. We identified four major tumour cell subpopulations (C0, C1, C2 and C3) with unique molecular characteristics and functional features. Pathway enrichment analysis revealed that C0 primarily expressed genes involved in extracellular matrix interactions and C1 showed higher proliferation levels, suggesting that the two cell subpopulations exhibited tumour aggressiveness. Conversely, C2 and C3 displayed features associated with keratinization and cornified envelope formation. Accordingly, C0 and C1 subpopulations were associated with shorter overall and disease-free survival times, while C2 and C3 were weakly correlated with longer survival. Genomic analysis showed that C1 demonstrated a positive correlation with tumour mutation burden. Furthermore, C0 exhibited resistant to cisplatin treatment, while C1 showed more sensitive to cisplatin treatment, indicating that C0 might exhibit more aggressive compared to C1. Additionally, C0 had a higher level of communication with fibroblasts and endothelial cells in TME via integrin-MAPK signalling, suggesting that the function of C0 was maintained by that pathway. In summary, this study provided critical insights into the molecular and cellular heterogeneity of OSCC, with potential implications for prognosis prediction and personalized therapeutic approaches.
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Affiliation(s)
- Jia Li
- Department of ProsthodonticsShanghai Engineering Research Center of Tooth Restoration and RegenerationStomatological Hospital and Dental School of Tongji UniversityShanghaiChina
| | - Shengjiao Li
- Department of Oral and Maxillofacial SurgeryShanghai Engineering Research Center of Tooth Restoration and RegenerationStomatological Hospital and Dental School of Tongji UniversityShanghaiChina
| | - Mingyang Shu
- Department of StomatologyHuai'an Second People's Hospital and The Affiliated Huai'an Hospital of Xuzhou Medical UniversityHuai'anChina
| | - Weiwei Hu
- Department of StomatologyHuai'an Second People's Hospital and The Affiliated Huai'an Hospital of Xuzhou Medical UniversityHuai'anChina
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33
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Xue Y, Su Z, Lin X, Ho MK, Yu KHO. Single-cell lineage tracing with endogenous markers. Biophys Rev 2024; 16:125-139. [PMID: 38495438 PMCID: PMC10937880 DOI: 10.1007/s12551-024-01179-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/18/2024] [Indexed: 03/19/2024] Open
Abstract
Resolving lineage relationships between cells in an organism provides key insights into the fate of individual cells and drives a fundamental understanding of the process of development and disease. A recent rapid increase in experimental and computational advances for detecting naturally occurring somatic nuclear and mitochondrial mutation at single-cell resolution has expanded lineage tracing from model organisms to humans. This review discusses the advantages and challenges of experimental and computational techniques for cell lineage tracing using somatic mutation as endogenous DNA barcodes to decipher the relationships between cells during development and tumour evolution. We outlook the advantages of spatial clonal evolution analysis and single-cell lineage tracing using endogenous genetic markers.
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Affiliation(s)
- Yan Xue
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Zezhuo Su
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
- Department of Orthopaedics and Traumatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xinyi Lin
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Mun Kay Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Ken H. O. Yu
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
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34
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Mustafa EH, Laven-Law G, Kikhtyak Z, Nguyen V, Ali S, Pace AA, Iggo R, Kebede A, Noll B, Wang S, Winter JM, Dwyer AR, Tilley WD, Hickey TE. Selective inhibition of CDK9 in triple negative breast cancer. Oncogene 2024; 43:202-215. [PMID: 38001268 PMCID: PMC10786725 DOI: 10.1038/s41388-023-02892-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 11/02/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023]
Abstract
Targeted therapy for triple-negative breast cancers (TNBC) remains a clinical challenge due to tumour heterogeneity. Since TNBC have key features of transcriptionally addicted cancers, targeting transcription via regulators such as cyclin-dependent kinase 9 (CDK9) has potential as a therapeutic strategy. Herein, we preclinically tested a new selective CDK9 inhibitor (CDDD11-8) in TNBC using cell line, patient-derived organoid, and patient-derived explant models. In vitro, CDDD11-8 dose-dependently inhibited proliferation (IC50 range: 281-734 nM), induced cell cycle arrest, and increased apoptosis of cell lines, which encompassed the three major molecular subtypes of TNBC. On target inhibition of CDK9 activity was demonstrated by reduced RNAPII phosphorylation at a CDK9 target peptide and down-regulation of the MYC and MCL1 oncogenes at the mRNA and protein levels in all cell line models. Drug induced RNAPII pausing was evident at gene promoters, with strongest pausing at MYC target genes. Growth of five distinct patient-derived organoid models was dose-dependently inhibited by CDDD11-8 (IC50 range: 272-771 nM), including three derived from MYC amplified, chemo-resistant TNBC metastatic lesions. Orally administered CDDD11-8 also inhibited growth of mammary intraductal TNBC xenograft tumours with no overt toxicity in vivo (mice) or ex vivo (human breast tissues). In conclusion, our studies indicate that CDK9 is a viable therapeutic target in TNBC and that CDDD11-8, a novel selective CDK9 inhibitor, has efficacy in TNBC without apparent toxicity to normal tissues.
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Affiliation(s)
- Ebtihal H Mustafa
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Geraldine Laven-Law
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Zoya Kikhtyak
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Van Nguyen
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Simak Ali
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Alex A Pace
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Richard Iggo
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- Institut Bergonié, University of Bordeaux, Bordeaux, France
| | - Alemwork Kebede
- Drug Discovery and Development, Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Ben Noll
- Drug Discovery and Development, Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Shudong Wang
- Drug Discovery and Development, Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Jean M Winter
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Amy R Dwyer
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Wayne D Tilley
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Theresa E Hickey
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia.
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35
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Tijhuis AE, Foijer F. Characterizing chromosomal instability-driven cancer evolution and cell fitness at a glance. J Cell Sci 2024; 137:jcs260199. [PMID: 38224461 DOI: 10.1242/jcs.260199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024] Open
Abstract
Chromosomal instability (CIN), an increased rate of chromosome segregation errors during mitosis, is a hallmark of cancer cells. CIN leads to karyotype differences between cells and thus large-scale heterogeneity among individual cancer cells; therefore, it plays an important role in cancer evolution. Studying CIN and its consequences is technically challenging, but various technologies have been developed to track karyotype dynamics during tumorigenesis, trace clonal lineages and link genomic changes to cancer phenotypes at single-cell resolution. These methods provide valuable insight not only into the role of CIN in cancer progression, but also into cancer cell fitness. In this Cell Science at a Glance article and the accompanying poster, we discuss the relationship between CIN, cancer cell fitness and evolution, and highlight techniques that can be used to study the relationship between these factors. To that end, we explore methods of assessing cancer cell fitness, particularly for chromosomally unstable cancer.
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Affiliation(s)
- Andréa E Tijhuis
- European Research Institute for the Biology of Ageing , University Medical Center Groningen, University of Groningen,9713 AV Groningen, The Netherlands
| | - Floris Foijer
- European Research Institute for the Biology of Ageing , University Medical Center Groningen, University of Groningen,9713 AV Groningen, The Netherlands
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36
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Ivanov RA, Lashin SA. Intratumor heterogeneity: models of malignancy emergence and evolution. Vavilovskii Zhurnal Genet Selektsii 2023; 27:815-819. [PMID: 38213707 PMCID: PMC10777286 DOI: 10.18699/vjgb-23-94] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/07/2023] [Accepted: 08/17/2023] [Indexed: 01/13/2024] Open
Abstract
Cancer is a complex and heterogeneous disease characterized by the accumulation of genetic alterations that drive uncontrolled cell growth and proliferation. Evolutionary dynamics plays a crucial role in the emergence and development of tumors, shaping the heterogeneity and adaptability of cancer cells. From the perspective of evolutionary theory, tumors are complex ecosystems that evolve through a process of microevolution influenced by genetic mutations, epigenetic changes, tumor microenvironment factors, and therapy-induced changes. This dynamic nature of tumors poses significant challenges for effective cancer treatment, and understanding it is essential for developing effective and personalized therapies. By uncovering the mechanisms that determine tumor heterogeneity, researchers can identify key genetic and epigenetic changes that contribute to tumor progression and resistance to treatment. This knowledge enables the development of innovative strategies for targeting specific tumor clones, minimizing the risk of recurrence and improving patient outcomes. To investigate the evolutionary dynamics of cancer, researchers employ a wide range of experimental and computational approaches. Traditional experimental methods involve genomic profiling techniques such as next-generation sequencing and fluorescence in situ hybridization. These techniques enable the identification of somatic mutations, copy number alterations, and structural rearrangements within cancer genomes. Furthermore, single-cell sequencing methods have emerged as powerful tools for dissecting intratumoral heterogeneity and tracing clonal evolution. In parallel, computational models and algorithms have been developed to simulate and analyze cancer evolution. These models integrate data from multiple sources to predict tumor growth patterns, identify driver mutations, and infer evolutionary trajectories. In this paper, we set out to describe the current approaches to address this evolutionary complexity and theories of its occurrence.
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Affiliation(s)
- R A Ivanov
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - S A Lashin
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
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37
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Zhang L, Bass HW, Irianto J, Mallory X. Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data. Genome Res 2023; 33:2002-2017. [PMID: 37993137 PMCID: PMC10760445 DOI: 10.1101/gr.277249.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 10/25/2023] [Indexed: 11/24/2023]
Abstract
Single-cell DNA sequencing enables the construction of evolutionary trees that can reveal how tumors gain mutations and grow. Different whole-genome amplification procedures render genomic materials of different characteristics, often suitable for the detection of either single-nucleotide variation or copy number aberration, but not ideally for both. Consequently, this hinders the inference of a comprehensive phylogenetic tree and limits opportunities to investigate the interplay of SNVs and CNAs. Existing methods such as SCARLET and COMPASS require that the SNVs and CNAs are detected from the same sets of cells, which is technically challenging. Here we present a novel computational tool, SCsnvcna, that places SNVs on a tree inferred from CNA signals, whereas the sets of cells rendering the SNVs and CNAs are independent, offering a more practical solution in terms of the technical challenges. SCsnvcna is a Bayesian probabilistic model using both the genotype constraints on the tree and the cellular prevalence to search the optimal solution. Comprehensive simulations and comparison with seven state-of-the-art methods show that SCsnvcna is robust and accurate in a variety of circumstances. Particularly, SCsnvcna most frequently produces the lowest error rates, with ability to scale to a wide range of numerical values for leaf nodes in the tree, SNVs, and SNV cells. The application of SCsnvcna to two published colorectal cancer data sets shows highly consistent placement of SNV cells and SNVs with the original study while also supporting a refined placement of ATP7B, illustrating SCsnvcna's value in analyzing complex multitumor samples.
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Affiliation(s)
- Liting Zhang
- Department of Computer Science, Florida State University, Tallahassee, Florida 32306, USA
| | - Hank W Bass
- Department of Biological Science, Florida State University, Tallahassee, Florida 32306, USA
| | - Jerome Irianto
- College of Medicine, Florida State University, Tallahassee, Florida 32306, USA
| | - Xian Mallory
- Department of Computer Science, Florida State University, Tallahassee, Florida 32306, USA;
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38
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Queitsch K, Moore TW, O'Connell BL, Nichols RV, Muschler JL, Keith D, Lopez C, Sears RC, Mills GB, Yardımcı GG, Adey AC. Accessible high-throughput single-cell whole-genome sequencing with paired chromatin accessibility. CELL REPORTS METHODS 2023; 3:100625. [PMID: 37918402 PMCID: PMC10694488 DOI: 10.1016/j.crmeth.2023.100625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/29/2023] [Accepted: 10/11/2023] [Indexed: 11/04/2023]
Abstract
Single-cell whole-genome sequencing (scWGS) enables the assessment of genome-level molecular differences between individual cells with particular relevance to genetically diverse systems like solid tumors. The application of scWGS was limited due to a dearth of accessible platforms capable of producing high-throughput profiles. We present a technique that leverages nucleosome disruption methodologies with the widely adopted 10× Genomics ATAC-seq workflow to produce scWGS profiles for high-throughput copy-number analysis without new equipment or custom reagents. We further demonstrate the use of commercially available indexed transposase complexes from ScaleBio for sample multiplexing, reducing the per-sample preparation costs. Finally, we demonstrate that sequential indexed tagmentation with an intervening nucleosome disruption step allows for the generation of both ATAC and WGS data from the same cell, producing comparable data to the unimodal assays. By exclusively utilizing accessible commercial reagents, we anticipate that these scWGS and scWGS+ATAC methods can be broadly adopted by the research community.
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Affiliation(s)
- Konstantin Queitsch
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Travis W Moore
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Brendan L O'Connell
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA; Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Ruth V Nichols
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - John L Muschler
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA; Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, OR, USA
| | - Dove Keith
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, OR, USA
| | - Charles Lopez
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Rosalie C Sears
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA; Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, OR, USA
| | - Gordon B Mills
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University, Portland, OR, USA
| | - Galip Gürkan Yardımcı
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Andrew C Adey
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA; Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA.
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39
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Chen S, Zhou Z, Li Y, Du Y, Chen G. Application of single-cell sequencing to the research of tumor microenvironment. Front Immunol 2023; 14:1285540. [PMID: 37965341 PMCID: PMC10641410 DOI: 10.3389/fimmu.2023.1285540] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 10/17/2023] [Indexed: 11/16/2023] Open
Abstract
Single-cell sequencing is a technique for detecting and analyzing genomes, transcriptomes, and epigenomes at the single-cell level, which can detect cellular heterogeneity lost in conventional sequencing hybrid samples, and it has revolutionized our understanding of the genetic heterogeneity and complexity of tumor progression. Moreover, the tumor microenvironment (TME) plays a crucial role in the formation, development and response to treatment of tumors. The application of single-cell sequencing has ushered in a new age for the TME analysis, revealing not only the blueprint of the pan-cancer immune microenvironment, but also the heterogeneity and differentiation routes of immune cells, as well as predicting tumor prognosis. Thus, the combination of single-cell sequencing and the TME analysis provides a unique opportunity to unravel the molecular mechanisms underlying tumor development and progression. In this review, we summarize the recent advances in single-cell sequencing and the TME analysis, highlighting their potential applications in cancer research and clinical translation.
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Affiliation(s)
| | | | | | | | - Guoan Chen
- Department of Human Cell Biology and Genetics, Joint Laboratory of Guangdong-Hong Kong Universities for Vascular Homeostasis and Diseases, School of Medicine, Southern University of Science and Technology, Shenzhen, China
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40
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Niu M, Zhang Y, Luo J, Sinson JC, Thompson AM, Zong C. Characterization of Cancer Evolution Landscape Based on Accurate Detection of Somatic Mutations in Single Tumor Cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.09.561356. [PMID: 37873375 PMCID: PMC10592685 DOI: 10.1101/2023.10.09.561356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Accurate detection of somatic mutations in single tumor cells is greatly desired as it allows us to quantify the single-cell mutation burden and construct the mutation-based phylogenetic tree. Here we developed scNanoSeq chemistry and profiled 842 single cells from 21 human breast cancer samples. The majority of the mutation-based phylogenetic trees comprise a characteristic stem evolution followed by the clonal sweep. We observed the subtype-dependent lengths in the stem evolution. To explain this phenomenon, we propose that the differences are related to different reprogramming required for different subtypes of breast cancer. Furthermore, we reason that the time that the tumor-initiating cell took to acquire the critical clonal-sweep-initiating mutation by random chance set the time limit for the reprogramming process. We refer to this model as a reprogramming and critical mutation co-timing (RCMC) subtype model. Next, in the sweeping clone, we observed that tumor cells undergo a branched evolution with rapidly decreasing selection. In the most recent clades, effectively neutral evolution has been reached, resulting in a substantially large number of mutational heterogeneities. Integrative analysis with 522-713X ultra-deep bulk whole genome sequencing (WGS) further validated this evolution mode. Mutation-based phylogenetic trees also allow us to identify the early branched cells in a few samples, whose phylogenetic trees support the gradual evolution of copy number variations (CNVs). Overall, the development of scNanoSeq allows us to unveil novel insights into breast cancer evolution.
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41
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Chen Y, Qi Y, Wang K. Neoadjuvant chemotherapy for breast cancer: an evaluation of its efficacy and research progress. Front Oncol 2023; 13:1169010. [PMID: 37854685 PMCID: PMC10579937 DOI: 10.3389/fonc.2023.1169010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 09/14/2023] [Indexed: 10/20/2023] Open
Abstract
Neoadjuvant chemotherapy (NAC) for breast cancer is widely used in the clinical setting to improve the chance of surgery, breast conservation and quality of life for patients with advanced breast cancer. A more accurate efficacy evaluation system is important for the decision of surgery timing and chemotherapy regimen implementation. However, current methods, encompassing imaging techniques such as ultrasound and MRI, along with non-imaging approaches like pathological evaluations, often fall short in accurately depicting the therapeutic effects of NAC. Imaging techniques are subjective and only reflect macroscopic morphological changes, while pathological evaluation is the gold standard for efficacy assessment but has the disadvantage of delayed results. In an effort to identify assessment methods that align more closely with real-world clinical demands, this paper provides an in-depth exploration of the principles and clinical applications of various assessment approaches in the neoadjuvant chemotherapy process.
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Affiliation(s)
- Yushi Chen
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
| | - Yu Qi
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
| | - Kuansong Wang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
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42
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Khan R, Mallory X. Assessing the performance of methods for cell clustering from single-cell DNA sequencing data. PLoS Comput Biol 2023; 19:e1010480. [PMID: 37824596 PMCID: PMC10597505 DOI: 10.1371/journal.pcbi.1010480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/24/2023] [Accepted: 09/20/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Many cancer genomes have been known to contain more than one subclone inside one tumor, the phenomenon of which is called intra-tumor heterogeneity (ITH). Characterizing ITH is essential in designing treatment plans, prognosis as well as the study of cancer progression. Single-cell DNA sequencing (scDNAseq) has been proven effective in deciphering ITH. Cells corresponding to each subclone are supposed to carry a unique set of mutations such as single nucleotide variations (SNV). While there have been many studies on the cancer evolutionary tree reconstruction, not many have been proposed that simply characterize the subclonality without tree reconstruction. While tree reconstruction is important in the study of cancer evolutionary history, typically they are computationally expensive in terms of running time and memory consumption due to the huge search space of the tree structure. On the other hand, subclonality characterization of single cells can be converted into a cell clustering problem, the dimension of which is much smaller, and the turnaround time is much shorter. Despite the existence of a few state-of-the-art cell clustering computational tools for scDNAseq, there lacks a comprehensive and objective comparison under different settings. RESULTS In this paper, we evaluated six state-of-the-art cell clustering tools-SCG, BnpC, SCClone, RobustClone, SCITE and SBMClone-on simulated data sets given a variety of parameter settings and a real data set. We designed a simulator specifically for cell clustering, and compared these methods' performances in terms of their clustering accuracy, specificity and sensitivity and running time. For SBMClone, we specifically designed an ultra-low coverage large data set to evaluate its performance in the face of an extremely high missing rate. CONCLUSION From the benchmark study, we conclude that BnpC and SCG's clustering accuracy are the highest and comparable to each other. However, BnpC is more advantageous in terms of running time when cell number is high (> 1500). It also has a higher clustering accuracy than SCG when cluster number is high (> 16). SCClone's accuracy in estimating the number of clusters is the highest. RobustClone and SCITE's clustering accuracy are the lowest for all experiments. SCITE tends to over-estimate the cluster number and has a low specificity, whereas RobustClone tends to under-estimate the cluster number and has a much lower sensitivity than other methods. SBMClone produced reasonably good clustering (V-measure > 0.9) when coverage is > = 0.03 and thus is highly recommended for ultra-low coverage large scDNAseq data sets.
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Affiliation(s)
- Rituparna Khan
- Department of Computer Science, Florida State University, Tallahassee, Florida, United States of America
| | - Xian Mallory
- Department of Computer Science, Florida State University, Tallahassee, Florida, United States of America
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Shin J, Kim JY, Oh JM, Lee JE, Kim SW, Nam SJ, Park W, Park YH, Ahn JS, Im YH. Comprehensive Clinical Characterization of Decade-Long Survivors of Metastatic Breast Cancer. Cancers (Basel) 2023; 15:4720. [PMID: 37835414 PMCID: PMC10571750 DOI: 10.3390/cancers15194720] [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: 09/03/2023] [Revised: 09/22/2023] [Accepted: 09/24/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Elucidating the clinical features of metastatic breast cancer (MBC) patients with an exceptionally favorable prognosis may offer insights to improve the survival of more typical patients. METHODS We collected comprehensive real-world data on clinicopathologic characteristics, treatments, and outcomes of 110 consecutive MBC patients who survived for over ten years from the clinical data warehouse of Samsung Medical Center. RESULTS The cohort included 54 hormone receptor (HR)-positive/HER2-negative (HR+/HER2-), 21 HR+/HER2+, 16 HR-/HER2+, and 14 triple-negative breast cancer (TNBC) patients. The median age at MBC diagnosis was 48.5 years. Approximately 70% of patients initially had a single-organ metastasis. The most common site of metastasis was the lung (46.4%), followed by distant lymph nodes (37.3%). During a median follow-up of 14.6 years, the median duration of systemic therapy was 11, 8.4, 7.3, and 0.8 years in the HR+/HER2-, HR+/HER2+, HR-/HER2+, and TNBC subgroups, respectively. Seven HER2+ and ten TNBC patients received systemic treatment for less than two years and remained treatment-free for most of the follow-up period, suggesting a potential chance of cure. The TNBC subtype (p < 0.001) and local treatment with curative intent within 1 year of MBC diagnosis (p = 0.002) were significantly associated with long-term treatment-free survival. The survival of HER2+ MBC and TNBC patients, but not that of HR+/HER2- patients, plateaued approximately 13 years after MBC diagnosis. CONCLUSIONS A small subset of patients with HER2+ MBC and metastatic TNBC may be curable with multimodality therapy. Prospective studies integrating clinical and genomic data may identify unique clinicogenomic features of MBC patients who can achieve durable disease control without prolonged chemotherapy.
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Affiliation(s)
- Junghoon Shin
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Seoul 06351, Republic of Korea; (J.S.)
| | - Ji-Yeon Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Seoul 06351, Republic of Korea; (J.S.)
- Biomedical Research Institute, Samsung Medical Center, Seoul 06351, Republic of Korea
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jung Min Oh
- Biomedical Research Institute, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Jeong Eon Lee
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Seok Won Kim
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Seok Jin Nam
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Won Park
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Radiation Oncology, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Yeon Hee Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Seoul 06351, Republic of Korea; (J.S.)
- Biomedical Research Institute, Samsung Medical Center, Seoul 06351, Republic of Korea
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jin Seok Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Seoul 06351, Republic of Korea; (J.S.)
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Young-Hyuck Im
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Seoul 06351, Republic of Korea; (J.S.)
- Biomedical Research Institute, Samsung Medical Center, Seoul 06351, Republic of Korea
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
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Ashouri A, Zhang C, Gaiti F. Decoding Cancer Evolution: Integrating Genetic and Non-Genetic Insights. Genes (Basel) 2023; 14:1856. [PMID: 37895205 PMCID: PMC10606072 DOI: 10.3390/genes14101856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/19/2023] [Accepted: 09/22/2023] [Indexed: 10/29/2023] Open
Abstract
The development of cancer begins with cells transitioning from their multicellular nature to a state akin to unicellular organisms. This shift leads to a breakdown in the crucial regulators inherent to multicellularity, resulting in the emergence of diverse cancer cell subpopulations that have enhanced adaptability. The presence of different cell subpopulations within a tumour, known as intratumoural heterogeneity (ITH), poses challenges for cancer treatment. In this review, we delve into the dynamics of the shift from multicellularity to unicellularity during cancer onset and progression. We highlight the role of genetic and non-genetic factors, as well as tumour microenvironment, in promoting ITH and cancer evolution. Additionally, we shed light on the latest advancements in omics technologies that allow for in-depth analysis of tumours at the single-cell level and their spatial organization within the tissue. Obtaining such detailed information is crucial for deepening our understanding of the diverse evolutionary paths of cancer, allowing for the development of effective therapies targeting the key drivers of cancer evolution.
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Affiliation(s)
- Arghavan Ashouri
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
| | - Chufan Zhang
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Federico Gaiti
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
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45
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Andrade JR, Gallagher AD, Maharaj J, McClelland SE. Disentangling the roles of aneuploidy, chromosomal instability and tumour heterogeneity in developing resistance to cancer therapies. Chromosome Res 2023; 31:28. [PMID: 37721639 PMCID: PMC10506951 DOI: 10.1007/s10577-023-09737-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/26/2023] [Accepted: 08/29/2023] [Indexed: 09/19/2023]
Abstract
Aneuploidy is defined as the cellular state of having a number of chromosomes that deviates from a multiple of the normal haploid chromosome number of a given organism. Aneuploidy can be present in a static state: Down syndrome individuals stably maintain an extra copy of chromosome 21 in their cells. In cancer cells, however, aneuploidy is usually present in combination with chromosomal instability (CIN) which leads to a continual generation of new chromosomal alterations and the development of intratumour heterogeneity (ITH). The prevalence of cells with specific chromosomal alterations is further shaped by evolutionary selection, for example, during the administration of cancer therapies. Aneuploidy, CIN and ITH have each been individually associated with poor prognosis in cancer, and a wealth of evidence suggests they contribute, either alone or in combination, to cancer therapy resistance by providing a reservoir of potential resistant states, or the ability to rapidly evolve resistance. A full understanding of the contribution and interplay between aneuploidy, CIN and ITH is required to tackle therapy resistance in cancer patients. However, these characteristics often co-occur and are intrinsically linked, presenting a major challenge to defining their individual contributions. Moreover, their accurate measurement in both experimental and clinical settings is a technical hurdle. Here, we attempt to deconstruct the contribution of the individual and combined roles of aneuploidy, CIN and ITH to therapy resistance in cancer, and outline emerging approaches to measure and disentangle their roles as a step towards integrating these principles into cancer therapeutic strategy.
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Affiliation(s)
- Joana Reis Andrade
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M6BQ, England
| | - Annie Dinky Gallagher
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M6BQ, England
| | - Jovanna Maharaj
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M6BQ, England
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Borgsmüller N, Valecha M, Kuipers J, Beerenwinkel N, Posada D. Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues. CELL GENOMICS 2023; 3:100380. [PMID: 37719146 PMCID: PMC10504633 DOI: 10.1016/j.xgen.2023.100380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 05/03/2023] [Accepted: 07/18/2023] [Indexed: 09/19/2023]
Abstract
Cell lineages accumulate somatic mutations during organismal development, potentially leading to pathological states. The rate of somatic evolution within a cell population can vary due to multiple factors, including selection, a change in the mutation rate, or differences in the microenvironment. Here, we developed a statistical test called the Poisson Tree (PT) test to detect varying evolutionary rates among cell lineages, leveraging the phylogenetic signal of single-cell DNA sequencing (scDNA-seq) data. We applied the PT test to 24 healthy and cancer samples, rejecting a constant evolutionary rate in 11 out of 15 cancer and five out of nine healthy scDNA-seq datasets. In six cancer datasets, we identified subclonal mutations in known driver genes that could explain the rate accelerations of particular cancer lineages. Our findings demonstrate the efficacy of scDNA-seq for studying somatic evolution and suggest that cell lineages often evolve at different rates within cancer and healthy tissues.
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Affiliation(s)
- Nico Borgsmüller
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - Monica Valecha
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
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Kim J, Kim S, Yeom H, Song SW, Shin K, Bae S, Ryu HS, Kim JY, Choi A, Lee S, Ryu T, Choi Y, Kim H, Kim O, Jung Y, Kim N, Han W, Lee HB, Lee AC, Kwon S. Barcoded multiple displacement amplification for high coverage sequencing in spatial genomics. Nat Commun 2023; 14:5261. [PMID: 37644058 PMCID: PMC10465490 DOI: 10.1038/s41467-023-41019-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023] Open
Abstract
Determining mutational landscapes in a spatial context is essential for understanding genetically heterogeneous cell microniches. Current approaches, such as Multiple Displacement Amplification (MDA), offer high genome coverage but limited multiplexing, which hinders large-scale spatial genomic studies. Here, we introduce barcoded MDA (bMDA), a technique that achieves high-coverage genomic analysis of low-input DNA while enhancing the multiplexing capabilities. By incorporating cell barcodes during MDA, bMDA streamlines library preparation in one pot, thereby overcoming a key bottleneck in spatial genomics. We apply bMDA to the integrative spatial analysis of triple-negative breast cancer tissues by examining copy number alterations, single nucleotide variations, structural variations, and kataegis signatures for each spatial microniche. This enables the assessment of subclonal evolutionary relationships within a spatial context. Therefore, bMDA has emerged as a scalable technology with the potential to advance the field of spatial genomics significantly.
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Affiliation(s)
- Jinhyun Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sungsik Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Huiran Yeom
- Division of Data Science, College of Information and Communication Technology, The University of Suwon, Hwaseong, 18323, Republic of Korea
| | - Seo Woo Song
- Basic Science and Engineering Initiative, Children's Heart Center, Stanford University, Stanford, CA, USA
| | - Kyoungseob Shin
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sangwook Bae
- Renal Division and Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Han Suk Ryu
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Department of Pathology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Ji Young Kim
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Ahyoun Choi
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech, Co. Ltd., Seoul, 08826, Republic of Korea
| | - Taehoon Ryu
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Yeongjae Choi
- School of Materials Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea
| | - Hamin Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Okju Kim
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Yushin Jung
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Namphil Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Wonshik Han
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea
- Department of Surgery, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Han-Byoel Lee
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Department of Surgery, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
| | - Amos C Lee
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Meteor Biotech, Co. Ltd., Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
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48
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Zhu Z, Jiang L, Ding X. Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels. Cancers (Basel) 2023; 15:4164. [PMID: 37627192 PMCID: PMC10452610 DOI: 10.3390/cancers15164164] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/23/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.
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Affiliation(s)
- Zijian Zhu
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai 200030, China;
| | - Lai Jiang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200025, China;
| | - Xianting Ding
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai 200030, China;
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200025, China;
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Shegekar T, Vodithala S, Juganavar A. The Emerging Role of Liquid Biopsies in Revolutionising Cancer Diagnosis and Therapy. Cureus 2023; 15:e43650. [PMID: 37719630 PMCID: PMC10505053 DOI: 10.7759/cureus.43650] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 08/17/2023] [Indexed: 09/19/2023] Open
Abstract
A potential non-invasive technique for identifying and tracking cancer is a liquid biopsy. This review article provides a comprehensive overview of the principles, applications, and challenges associated with liquid biopsies. The circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), exosomes, and microRNAs are just a few of the biomarkers we cover in this article that are discovered in liquid biopsies. The clinical application of liquid biopsies in many stages of cancer management, including early cancer identification, therapy selection and response monitoring, and minimum residual illness, is also investigated. The technical advancements in liquid biopsy techniques, including digital polymerase chain reaction (dPCR) and next-generation sequencing (NGS), have improved the sensitivity and specificity of biomarker identification. Liquid biopsies require assistance with cost-effectiveness, sensitivity, and standardisation despite the potential benefits. We talk about these restrictions and potential solutions. In conclusion, liquid biopsies revolutionise personalised therapies and cancer diagnostics by providing a real-time, non-invasive tool for characterising and monitoring tumours. It will be possible to expand the use of liquid biopsies in clinical practises by having a better understanding of their current state and predicted future developments.
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Affiliation(s)
- Tejas Shegekar
- Department of Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Sahitya Vodithala
- Department of Pathology and Laboratory Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Anup Juganavar
- Department of Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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50
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Thakur S, Haider S, Natrajan R. Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer. J Pathol 2023; 260:621-636. [PMID: 37587096 DOI: 10.1002/path.6158] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/11/2023] [Accepted: 06/14/2023] [Indexed: 08/18/2023]
Abstract
Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre-clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Shefali Thakur
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Syed Haider
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Rachael Natrajan
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
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