51
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Solta A, Ernhofer B, Boettiger K, Megyesfalvi Z, Heeke S, Hoda MA, Lang C, Aigner C, Hirsch FR, Schelch K, Döme B. Small cells - big issues: biological implications and preclinical advancements in small cell lung cancer. Mol Cancer 2024; 23:41. [PMID: 38395864 PMCID: PMC10893629 DOI: 10.1186/s12943-024-01953-9] [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: 09/11/2023] [Accepted: 01/31/2024] [Indexed: 02/25/2024] Open
Abstract
Current treatment guidelines refer to small cell lung cancer (SCLC), one of the deadliest human malignancies, as a homogeneous disease. Accordingly, SCLC therapy comprises chemoradiation with or without immunotherapy. Meanwhile, recent studies have made significant advances in subclassifying SCLC based on the elevated expression of the transcription factors ASCL1, NEUROD1, and POU2F3, as well as on certain inflammatory characteristics. The role of the transcription regulator YAP1 in defining a unique SCLC subset remains to be established. Although preclinical analyses have described numerous subtype-specific characteristics and vulnerabilities, the so far non-existing clinical subtype distinction may be a contributor to negative clinical trial outcomes. This comprehensive review aims to provide a framework for the development of novel personalized therapeutic approaches by compiling the most recent discoveries achieved by preclinical SCLC research. We highlight the challenges faced due to limited access to patient material as well as the advances accomplished by implementing state-of-the-art models and methodologies.
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Affiliation(s)
- Anna Solta
- Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Büsra Ernhofer
- Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Kristiina Boettiger
- Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Zsolt Megyesfalvi
- Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Department of Thoracic Surgery, Semmelweis University and National Institute of Oncology, Budapest, Hungary
- National Koranyi Institute of Pulmonology, Budapest, Hungary
| | - Simon Heeke
- Department of Thoracic Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mir Alireza Hoda
- Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Christian Lang
- Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Division of Pulmonology, Department of Medicine II, Medical University of Vienna, Vienna, Austria
| | - Clemens Aigner
- Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Fred R Hirsch
- Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Center for Thoracic Oncology, Mount Sinai Health System, Tisch Cancer Institute, New York, NY, USA.
| | - Karin Schelch
- Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Center for Cancer Research, Medical University of Vienna, Vienna, Austria
| | - Balazs Döme
- Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
- Department of Thoracic Surgery, Semmelweis University and National Institute of Oncology, Budapest, Hungary.
- National Koranyi Institute of Pulmonology, Budapest, Hungary.
- Department of Translational Medicine, Lund University, Lund, Sweden.
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52
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Mold JE, Weissman MH, Ratz M, Hagemann-Jensen M, Hård J, Eriksson CJ, Toosi H, Berghenstråhle J, Ziegenhain C, von Berlin L, Martin M, Blom K, Lagergren J, Lundeberg J, Sandberg R, Michaëlsson J, Frisén J. Clonally heritable gene expression imparts a layer of diversity within cell types. Cell Syst 2024; 15:149-165.e10. [PMID: 38340731 DOI: 10.1016/j.cels.2024.01.004] [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: 08/24/2022] [Revised: 05/25/2023] [Accepted: 01/18/2024] [Indexed: 02/12/2024]
Abstract
Cell types can be classified according to shared patterns of transcription. Non-genetic variability among individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. Using high-coverage single-cell RNA profiling, we asked whether long-term, heritable differences in gene expression can impart diversity within cells of the same type. Studying clonal human lymphocytes and mouse brain cells, we uncovered a vast diversity of heritable gene expression patterns among different clones of cells of the same type in vivo. We combined chromatin accessibility and RNA profiling on different lymphocyte clones to reveal thousands of regulatory regions exhibiting interclonal variation, which could be directly linked to interclonal variation in gene expression. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging, and disease. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Jeff E Mold
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Martin H Weissman
- Mathematics Department, University of California, Santa Cruz, CA, USA.
| | - Michael Ratz
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden; SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | | | - Joanna Hård
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Carl-Johan Eriksson
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Hosein Toosi
- SciLifeLab, Computational Science and Technology Department, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Joseph Berghenstråhle
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Christoph Ziegenhain
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Leonie von Berlin
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Marcel Martin
- Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, SciLifeLab, Stockholm University, Stockholm, Sweden
| | - Kim Blom
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institute, Stockholm, Sweden
| | - Jens Lagergren
- SciLifeLab, Computational Science and Technology Department, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Joakim Lundeberg
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Rickard Sandberg
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Jakob Michaëlsson
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institute, Stockholm, Sweden.
| | - Jonas Frisén
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
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53
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Qin X, Tape CJ. Functional analysis of cell plasticity using single-cell technologies. Trends Cell Biol 2024:S0962-8924(24)00006-0. [PMID: 38355348 DOI: 10.1016/j.tcb.2024.01.006] [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: 11/02/2023] [Revised: 01/09/2024] [Accepted: 01/15/2024] [Indexed: 02/16/2024]
Abstract
Metazoan organisms are heterocellular systems composed of hundreds of different cell types, which arise from an isogenic genome through differentiation. Cellular 'plasticity' further enables cells to alter their fate in response to exogenous cues and is involved in a variety of processes, such as wound healing, infection, and cancer. Recent advances in cellular model systems, high-dimensional single-cell technologies, and lineage tracing have sparked a renaissance in plasticity research. Here, we discuss the definition of cell plasticity, evaluate state-of-the-art model systems and techniques to study cell-fate dynamics, and explore the application of single-cell technologies to obtain functional insights into cell plasticity in healthy and diseased tissues. The integration of advanced biomimetic model systems, single-cell technologies, and high-throughput perturbation studies is enabling a new era of research into non-genetic plasticity in metazoan systems.
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Affiliation(s)
- Xiao Qin
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Oxford, OX3 9DS, UK.
| | - Christopher J Tape
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, 72 Huntley Street, London, WC1E 6DD, UK.
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54
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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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55
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Zhang C, Liang S, Zhang H, Wang R, Qiao H. Epigenetic regulation of mRNA mediates the phenotypic plasticity of cancer cells during metastasis and therapeutic resistance (Review). Oncol Rep 2024; 51:28. [PMID: 38131215 PMCID: PMC10777459 DOI: 10.3892/or.2023.8687] [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] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Plasticity, the ability of cancer cells to transition between differentiation states without genomic alterations, has been recognized as a major source of intratumoral heterogeneity. It has a crucial role in cancer metastasis and treatment resistance. Thus, targeting plasticity holds tremendous promise. However, the molecular mechanisms of plasticity in cancer cells remain poorly understood. Several studies found that mRNA, which acts as a bridge linking the genetic information of DNA and protein, has an important role in translating genotypes into phenotypes. The present review provided an overview of the regulation of cancer cell plasticity occurring via changes in the transcription and editing of mRNAs. The role of the transcriptional regulation of mRNA in cancer cell plasticity was discussed, including DNA‑binding transcriptional factors, DNA methylation, histone modifications and enhancers. Furthermore, the role of mRNA editing in cancer cell plasticity was debated, including mRNA splicing and mRNA modification. In addition, the role of non‑coding (nc)RNAs in cancer plasticity was expounded, including microRNAs, long intergenic ncRNAs and circular RNAs. Finally, different strategies for targeting cancer cell plasticity to overcome metastasis and therapeutic resistance in cancer were discussed.
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Affiliation(s)
- Chunzhi Zhang
- Department of Radiation Oncology, Tianjin Hospital, Tianjin University, Tianjin 300211, P.R. China
| | - Siyuan Liang
- Functional Materials Laboratory, Institute of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300211, P.R. China
| | - Hanning Zhang
- Clinical Medical College of Tianjin Medical University, Tianjin 300270, P.R. China
| | - Ruoxi Wang
- Sophomore, Farragut School #3 of Yangtai Road, Tianjin 300042, P.R. China
| | - Huanhuan Qiao
- Functional Materials Laboratory, Institute of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300211, P.R. China
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56
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Celià-Terrassa T, Kang Y. How important is EMT for cancer metastasis? PLoS Biol 2024; 22:e3002487. [PMID: 38324529 PMCID: PMC10849258 DOI: 10.1371/journal.pbio.3002487] [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] [Indexed: 02/09/2024] Open
Abstract
Epithelial-to-mesenchymal transition (EMT), a biological phenomenon of cellular plasticity initially reported in embryonic development, has been increasingly recognized for its importance in cancer progression and metastasis. Despite tremendous progress being made in the past 2 decades in our understanding of the molecular mechanism and functional importance of EMT in cancer, there are several mysteries around EMT that remain unresolved. In this Unsolved Mystery, we focus on the variety of EMT types in metastasis, cooperative and collective EMT behaviors, spatiotemporal characterization of EMT, and strategies of therapeutically targeting EMT. We also highlight new technical advances that will facilitate the efforts to elucidate the unsolved mysteries of EMT in metastasis.
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Affiliation(s)
- Toni Celià-Terrassa
- Cancer Research Program, Hospital del Mar Research Institute, Barcelona, Spain
| | - Yibin Kang
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States of America
- Ludwig Institute for Cancer Research Princeton Branch, Princeton, New Jersey, United States of America
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57
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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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58
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Wang S, Mao X, Wang F, Zuo X, Fan C. Data Storage Using DNA. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307499. [PMID: 37800877 DOI: 10.1002/adma.202307499] [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: 07/27/2023] [Revised: 10/01/2023] [Indexed: 10/07/2023]
Abstract
The exponential growth of global data has outpaced the storage capacities of current technologies, necessitating innovative storage strategies. DNA, as a natural medium for preserving genetic information, has emerged as a highly promising candidate for next-generation storage medium. Storing data in DNA offers several advantages, including ultrahigh physical density and exceptional durability. Facilitated by significant advancements in various technologies, such as DNA synthesis, DNA sequencing, and DNA nanotechnology, remarkable progress has been made in the field of DNA data storage over the past decade. However, several challenges still need to be addressed to realize practical applications of DNA data storage. In this review, the processes and strategies of in vitro DNA data storage are first introduced, highlighting recent advancements. Next, a brief overview of in vivo DNA data storage is provided, with a focus on the various writing strategies developed to date. At last, the challenges encountered in each step of DNA data storage are summarized and promising techniques are discussed that hold great promise in overcoming these obstacles.
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Affiliation(s)
- Shaopeng Wang
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Xiuhai Mao
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Fei Wang
- School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhangjiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xiaolei Zuo
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
- School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhangjiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Chunhai Fan
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
- School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhangjiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China
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59
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Stanger BZ, Wahl GM. Cancer as a Disease of Development Gone Awry. ANNUAL REVIEW OF PATHOLOGY 2024; 19:397-421. [PMID: 37832945 DOI: 10.1146/annurev-pathmechdis-031621-025610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/15/2023]
Abstract
In the 160 years since Rudolf Virchow first postulated that neoplasia arises by the same law that regulates embryonic development, scientists have come to recognize the striking overlap between the molecular and cellular programs used by cancers and embryos. Advances in cancer biology and molecular techniques have further highlighted the similarities between carcinogenesis and embryogenesis, where cellular growth, differentiation, motility, and intercellular cross talk are mediated by common drivers and regulatory networks. This review highlights the many connections linking cancer biology and developmental biology to provide a deeper understanding of how a tissue's developmental history may both enable and constrain cancer cell evolution.
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Affiliation(s)
- Ben Z Stanger
- Division of Gastroenterology, Department of Medicine, Abramson Family Cancer Research Institute, and Department of Cell and Developmental Biology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Geoffrey M Wahl
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, California, USA;
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60
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Peyvandi S, Bulliard M, Yilmaz A, Kauzlaric A, Marcone R, Haerri L, Coquoz O, Huang YT, Duffey N, Gafner L, Lorusso G, Fournier N, Lan Q, Rüegg C. Tumor-educated Gr1+CD11b+ cells drive breast cancer metastasis via OSM/IL-6/JAK-induced cancer cell plasticity. J Clin Invest 2024; 134:e166847. [PMID: 38236642 PMCID: PMC10940099 DOI: 10.1172/jci166847] [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: 11/07/2022] [Accepted: 01/17/2024] [Indexed: 03/16/2024] Open
Abstract
Cancer cell plasticity contributes to therapy resistance and metastasis, which represent the main causes of cancer-related death, including in breast cancer. The tumor microenvironment drives cancer cell plasticity and metastasis, and unraveling the underlying cues may provide novel strategies for managing metastatic disease. Using breast cancer experimental models and transcriptomic analyses, we show that stem cell antigen-1 positive (SCA1+) murine breast cancer cells enriched during tumor progression and metastasis had higher in vitro cancer stem cell-like properties, enhanced in vivo metastatic ability, and generated tumors rich in Gr1hiLy6G+CD11b+ cells. In turn, tumor-educated Gr1+CD11b+ (Tu-Gr1+CD11b+) cells rapidly and transiently converted low metastatic SCA1- cells into highly metastatic SCA1+ cells via secreted oncostatin M (OSM) and IL-6. JAK inhibition prevented OSM/IL-6-induced SCA1+ population enrichment, while OSM/IL-6 depletion suppressed Tu-Gr1+CD11b+-induced SCA1+ population enrichment in vitro and metastasis in vivo. Moreover, chemotherapy-selected highly metastatic 4T1 cells maintained high SCA1+ positivity through autocrine IL-6 production, and in vitro JAK inhibition blunted SCA1 positivity and metastatic capacity. Importantly, Tu-Gr1+CD11b+ cells invoked a gene signature in tumor cells predicting shorter overall survival (OS), relapse-free survival (RFS), and lung metastasis in breast cancer patients. Collectively, our data identified OSM/IL-6/JAK as a clinically relevant paracrine/autocrine axis instigating breast cancer cell plasticity and triggering metastasis.
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Affiliation(s)
- Sanam Peyvandi
- Pathology Unit, Department of Oncology, Microbiology and Immunology (OMI), Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Manon Bulliard
- Pathology Unit, Department of Oncology, Microbiology and Immunology (OMI), Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Alev Yilmaz
- Pathology Unit, Department of Oncology, Microbiology and Immunology (OMI), Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Annamaria Kauzlaric
- Translational Data Science Group, Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Rachel Marcone
- Translational Data Science Group, Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Lisa Haerri
- Pathology Unit, Department of Oncology, Microbiology and Immunology (OMI), Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Oriana Coquoz
- Pathology Unit, Department of Oncology, Microbiology and Immunology (OMI), Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Yu-Ting Huang
- Pathology Unit, Department of Oncology, Microbiology and Immunology (OMI), Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Nathalie Duffey
- Pathology Unit, Department of Oncology, Microbiology and Immunology (OMI), Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Laetitia Gafner
- Pathology Unit, Department of Oncology, Microbiology and Immunology (OMI), Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Girieca Lorusso
- Pathology Unit, Department of Oncology, Microbiology and Immunology (OMI), Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Nadine Fournier
- Translational Data Science Group, Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Qiang Lan
- Pathology Unit, Department of Oncology, Microbiology and Immunology (OMI), Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
- Cell and Tissue Dynamics Research Program, Institute of Biotechnology, Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Curzio Rüegg
- Pathology Unit, Department of Oncology, Microbiology and Immunology (OMI), Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
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61
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Mehta A, Stanger BZ. Lineage Plasticity: The New Cancer Hallmark on the Block. Cancer Res 2024; 84:184-191. [PMID: 37963209 PMCID: PMC10841583 DOI: 10.1158/0008-5472.can-23-1067] [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: 04/19/2023] [Revised: 09/12/2023] [Accepted: 11/07/2023] [Indexed: 11/16/2023]
Abstract
Plasticity refers to the ability of cells to adopt a spectrum of states or phenotypes. In cancer, it is a critical contributor to tumor initiation, progression, invasiveness, and therapy resistance, and it has recently been recognized as an emerging cancer hallmark. Plasticity can occur as a result of cell-intrinsic factors (e.g., genetic, transcriptional, or epigenetic fluctuations), or through cell-extrinsic cues (e.g., signaling from components of the tumor microenvironment or selective pressure from therapy). Over the past decade, technological advances, analysis of patient samples, and studies in mouse model systems have led to a deeper understanding of how such plastic states come about. In this review, we discuss: (i) the definition of plasticity; (ii) methods to measure and quantify plasticity; (iii) the clinical relevance of plasticity; and (iv) therapeutic hypotheses to modulate plasticity in the clinic.
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Affiliation(s)
- Arnav Mehta
- Department of Medicine, Massachusetts General Hospital, Boston, MA
- Massachusetts General Hospital Cancer Center, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Ben Z. Stanger
- Abramson Family Cancer Research Institute, Perelman School of Medicine at University of Pennsylvania, Philadelphia, PA
- Department of Medicine, Perelman School of Medicine at University of Pennsylvania, Philadelphia, PA
- Department of Cell and Developmental Biology, Perelman School of Medicine at University of Pennsylvania, Philadelphia, PA
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62
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Wang L, Dong W, Yin Z, Sheng J, Ezeana CF, Yang L, Yu X, Wong SSY, Wan Z, Danforth RL, Han K, Gao D, Wong STC. Charting Single Cell Lineage Dynamics and Mutation Networks via Homing CRISPR. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.05.574236. [PMID: 38260351 PMCID: PMC10802354 DOI: 10.1101/2024.01.05.574236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Single cell lineage tracing, essential for unraveling cellular dynamics in disease evolution is critical for developing targeted therapies. CRISPR-Cas9, known for inducing permanent and cumulative mutations, is a cornerstone in lineage tracing. The novel homing guide RNA (hgRNA) technology enhances this by enabling dynamic retargeting and facilitating ongoing genetic modifications. Charting these mutations, especially through successive hgRNA edits, poses a significant challenge. Our solution, LINEMAP, is a computational framework designed to trace and map these mutations with precision. LINEMAP meticulously discerns mutation alleles at single-cell resolution and maps their complex interrelationships through a mutation evolution network. By utilizing a Markov Process model, we can predict mutation transition probabilities, revealing potential mutational routes and pathways. Our reconstruction algorithm, anchored in the Markov model's attributes, reconstructs cellular lineage pathways, shedding light on the cell's evolutionary journey to the minutiae of single-cell division. Our findings reveal an intricate network of mutation evolution paired with a predictive Markov model, advancing our capability to reconstruct single-cell lineage via hgRNA. This has substantial implications for advancing our understanding of biological mechanisms and propelling medical research forward.
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Affiliation(s)
- Lin Wang
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Wenjuan Dong
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Zheng Yin
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
- Biostatistics and Bioinformatics Shared Resource, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Jianting Sheng
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Chika F. Ezeana
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Li Yang
- T.T. and W. F. Chao Center for BRAIN, Houston Methodist Research Institute, Houston, Texas 77030
| | - Xiaohui Yu
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | | | - Zhihao Wan
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Rebecca L. Danforth
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Kun Han
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Dingcheng Gao
- Department of Cell & Development Biology, Weill Cornell Medical College, New York, NY 10065
| | - Stephen T. C. Wong
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
- Departments of Radiology, Pathology and Genomic Medicine, Houston Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030
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63
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Li Z, Yang W, Wu P, Shan Y, Zhang X, Chen F, Yang J, Yang JR. Reconstructing cell lineage trees with genomic barcoding: approaches and applications. J Genet Genomics 2024; 51:35-47. [PMID: 37269980 DOI: 10.1016/j.jgg.2023.05.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/19/2023] [Accepted: 05/20/2023] [Indexed: 06/05/2023]
Abstract
In multicellular organisms, developmental history of cell divisions and functional annotation of terminal cells can be organized into a cell lineage tree (CLT). The reconstruction of the CLT has long been a major goal in developmental biology and other related fields. Recent technological advancements, especially those in editable genomic barcodes and single-cell high-throughput sequencing, have sparked a new wave of experimental methods for reconstructing CLTs. Here we review the existing experimental approaches to the reconstruction of CLT, which are broadly categorized as either image-based or DNA barcode-based methods. In addition, we present a summary of the related literature based on the biological insight provided by the obtained CLTs. Moreover, we discuss the challenges that will arise as more and better CLT data become available in the near future. Genomic barcoding-based CLT reconstructions and analyses, due to their wide applicability and high scalability, offer the potential for novel biological discoveries, especially those related to general and systemic properties of the developmental process.
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Affiliation(s)
- Zizhang Li
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Wenjing Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Peng Wu
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yuyan Shan
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xiaoyu Zhang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Feng Chen
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Junnan Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Jian-Rong Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, Guangdong 510080, China.
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64
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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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65
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Handly-Santana A, Oren Y. Characterizing Rare Dormant and Cycling Lineages Using the Watermelon System. Methods Mol Biol 2024; 2811:165-175. [PMID: 39037657 DOI: 10.1007/978-1-0716-3882-8_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
Abstract
Barcode-based lineage tracing approaches enable molecular characterization of clonal cell families. Barcodes that are expressed as mRNA can be used to deconvolve lineage identity from single-cell RNA sequencing transcriptional data. Here we describe the Watermelon system, which facilitates the simultaneous tracing of lineage, transcriptional, and proliferative state at a single cell level.
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Affiliation(s)
| | - Yaara Oren
- Department of Human Molecular Genetics & Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
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66
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Kim IS. DNA Barcoding Technology for Lineage Recording and Tracing to Resolve Cell Fate Determination. Cells 2023; 13:27. [PMID: 38201231 PMCID: PMC10778210 DOI: 10.3390/cells13010027] [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: 11/18/2023] [Revised: 12/18/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
In various biological contexts, cells receive signals and stimuli that prompt them to change their current state, leading to transitions into a future state. This change underlies the processes of development, tissue maintenance, immune response, and the pathogenesis of various diseases. Following the path of cells from their initial identity to their current state reveals how cells adapt to their surroundings and undergo transformations to attain adjusted cellular states. DNA-based molecular barcoding technology enables the documentation of a phylogenetic tree and the deterministic events of cell lineages, providing the mechanisms and timing of cell lineage commitment that can either promote homeostasis or lead to cellular dysregulation. This review comprehensively presents recently emerging molecular recording technologies that utilize CRISPR/Cas systems, base editing, recombination, and innate variable sequences in the genome. Detailing their underlying principles, applications, and constraints paves the way for the lineage tracing of every cell within complex biological systems, encompassing the hidden steps and intermediate states of organism development and disease progression.
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Affiliation(s)
- Ik Soo Kim
- Department of Microbiology, Gachon University College of Medicine, Incheon 21999, Republic of Korea
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67
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Sashittal P, Schmidt H, Chan M, Raphael BJ. Startle: A star homoplasy approach for CRISPR-Cas9 lineage tracing. Cell Syst 2023; 14:1113-1121.e9. [PMID: 38128483 PMCID: PMC11257033 DOI: 10.1016/j.cels.2023.11.005] [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: 04/01/2023] [Revised: 10/31/2023] [Accepted: 11/17/2023] [Indexed: 12/23/2023]
Abstract
CRISPR-Cas9-based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or cellular lineage, in tissues and whole organisms. Although standard phylogenetic approaches may be applied to reconstruct cellular lineage trees from this data, the unique features of the CRISPR-Cas9 editing process motivate the development of specialized models that describe the evolution of CRISPR-Cas9-induced mutations. Here, we introduce the "star homoplasy" evolutionary model that constrains a phylogenetic character to mutate at most once along a lineage, capturing the "non-modifiability" property of CRISPR-Cas9 mutations. We derive a combinatorial characterization of star homoplasy phylogenies and use this characterization to develop an algorithm, "Startle", that computes a maximum parsimony star homoplasy phylogeny. We demonstrate that Startle infers more accurate phylogenies on simulated lineage tracing data compared with existing methods and finds parsimonious phylogenies with fewer metastatic migrations on lineage tracing data from mouse metastatic lung adenocarcinoma.
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Affiliation(s)
- Palash Sashittal
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Henri Schmidt
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Michelle Chan
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
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68
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Prillo S, Ravoor A, Yosef N, Song YS. ConvexML: Scalable and accurate inference of single-cell chronograms from CRISPR/Cas9 lineage tracing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.03.569785. [PMID: 38076815 PMCID: PMC10705529 DOI: 10.1101/2023.12.03.569785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
CRISPR/Cas9 gene editing technology has enabled lineage tracing for thousands of cells in vivo. However, most of the analysis of CRISPR/Cas9 lineage tracing data has so far been limited to the reconstruction of single-cell tree topologies, which depict lineage relationships between cells, but not the amount of time that has passed between ancestral cell states and the present. Time-resolved trees, known as chronograms, would allow one to study the evolutionary dynamics of cell populations at an unprecedented level of resolution. Indeed, time-resolved trees would reveal the timing of events on the tree, the relative fitness of subclones, and the dynamics underlying phenotypic changes in the cell population - among other important applications. In this work, we introduce the first scalable and accurate method to refine any given single-cell tree topology into a single-cell chronogram by estimating its branch lengths. To do this, we leverage a statistical model of CRISPR/Cas9 cutting with missing data, paired with a conservative version of maximum parsimony that reconstructs only the ancestral states that we are confident about. As part of our method, we propose a novel approach to represent and handle missing data - specifically, double-resection events - which greatly simplifies and speeds up branch length estimation without compromising quality. All this leads to a convex maximum likelihood estimation (MLE) problem that can be readily solved in seconds with off-the-shelf convex optimization solvers. To stabilize estimates in low-information regimes, we propose a simple penalized version of MLE using a minimum branch length and pseudocounts. We benchmark our method using simulations and show that it performs well on several tasks, outperforming more naive baselines. Our method, which we name 'ConvexML', is available through the cassiopeia open source Python package.
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Affiliation(s)
| | - Akshay Ravoor
- Computer Science Division, University of California, Berkeley
| | - Nir Yosef
- Computer Science Division, University of California, Berkeley
- Department of Systems Immunology, Weizmann Institute of Science
| | - Yun S. Song
- Computer Science Division, University of California, Berkeley
- Department of Statistics, University of California, Berkeley
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69
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Hyug Choi J, Sook Jun M, Yong Jeon J, Kim HS, Kyung Kim Y, Ho Jeon C, Hwan Choi S, Sun Kim D, Han MH, Won Oh J. Global lineage evolution pattern of sars-cov-2 in Africa, America, Europe, and Asia: A comparative analysis of variant clusters and their relevance across continents. J Transl Int Med 2023; 11:410-422. [PMID: 38130632 PMCID: PMC10732492 DOI: 10.2478/jtim-2023-0118] [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] [Indexed: 12/23/2023] Open
Abstract
Objective The objective of this study is to provide a comparative analysis of variant clusters and their relevance across Africa, America, Europe, and Asia, in order to understand the evolutionary patterns of the virus across different regions and to inform the development of targeted interventions and genomic surveillance eforts. Methods The study analyzed the global lineage evolution pattern of 74, 075 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from 32 countries across four continents, focusing on variant clusters and their relevance across regions. Variants were weighted according to their hierarchical level. The correlation between variants was visualized through Dimensionality reduction analysis and Pairwise Pearson's correlation. We presented a reconstructed phylogenetic tree based on correlation analysis and variant weights. Results The analysis revealed that each continent had distinct variant clusters and different evolutionary patterns. The Americas had two clustered variants before lineage divergence and a downstream confluence lineage, Europe had bifurcation into two global lineages with an early occurrence of certain cluster while Asia had a downstream confluence of two large lineages diverging by two distinct clusters. Based on the cluster patterns of shared variants of the SARS-CoV-2 virus, Africa demonstrated a relatively clear distinction among three distinct regions. Conclusions The study provides insights into the evolutionary patterns of SARS-CoV-2 and highlights the importance of international collaboration in tracking and responding to emerging variants. The study found that the global pandemic was driven by Omicron variants that evolved with significant differences between countries and regions, and with different patterns across continents.
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Affiliation(s)
- June Hyug Choi
- Department of Anatomy, BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Mee Sook Jun
- Department of Internal Medicine, Chungbuk National University, Cheongju, Yonsei University-Industry Foundation, Seoul, Republic of Korea
| | | | - Hae-Suk Kim
- Theragen Bio Co., Ltd., Seongnam-si, Republic of Korea
| | - Yu Kyung Kim
- Department of Clinical Pathology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Chang Ho Jeon
- Department of Laboratory Medicine, Catholic University of Daegu School of Medicine, Daegu, Republic of Korea
| | - Seock Hwan Choi
- Department of Urology, School of Medicine, BioMedical Research Institute, Kyungpook National University Hospital, Kyungpook National University, Daegu, Republic of Korea
| | - Dong Sun Kim
- Department of Anatomy, BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Man-Hoon Han
- Department of Pathology, School of Medicine, BioMedical Research Institute, Kyungpook National University Hospital, Kyungpook National University, Daegu, Republic of Korea
| | - Ji Won Oh
- Department of Anatomy, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
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70
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Li L, Bowling S, McGeary SE, Yu Q, Lemke B, Alcedo K, Jia Y, Liu X, Ferreira M, Klein AM, Wang SW, Camargo FD. A mouse model with high clonal barcode diversity for joint lineage, transcriptomic, and epigenomic profiling in single cells. Cell 2023; 186:5183-5199.e22. [PMID: 37852258 DOI: 10.1016/j.cell.2023.09.019] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 07/11/2023] [Accepted: 09/19/2023] [Indexed: 10/20/2023]
Abstract
Cellular lineage histories and their molecular states encode fundamental principles of tissue development and homeostasis. Current lineage-recording mouse models have insufficient barcode diversity and single-cell lineage coverage for profiling tissues composed of millions of cells. Here, we developed DARLIN, an inducible Cas9 barcoding mouse line that utilizes terminal deoxynucleotidyl transferase (TdT) and 30 CRISPR target sites. DARLIN is inducible, generates massive lineage barcodes across tissues, and enables the detection of edited barcodes in ∼70% of profiled single cells. Using DARLIN, we examined fate bias within developing hematopoietic stem cells (HSCs) and revealed unique features of HSC migration. Additionally, we established a protocol for joint transcriptomic and epigenomic single-cell measurements with DARLIN and found that cellular clonal memory is associated with genome-wide DNA methylation rather than gene expression or chromatin accessibility. DARLIN will enable the high-resolution study of lineage relationships and their molecular signatures in diverse tissues and physiological contexts.
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Affiliation(s)
- Li Li
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Sarah Bowling
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Sean E McGeary
- Department of Systems Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Qi Yu
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Bianca Lemke
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Karel Alcedo
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Yuemeng Jia
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Xugeng Liu
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Mark Ferreira
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Allon M Klein
- Department of Systems Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Shou-Wen Wang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; School of Science, Westlake University, Hangzhou, Zhejiang 310024, China.
| | - Fernando D Camargo
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
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71
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Marr KD, Gard JMC, Harryman WL, Keeswood EJ, Paxson AI, Wolgemuth C, Knudsen BS, Nagle RB, Hazlehurst L, Sorbellini M, Cress AE. Biophysical phenotype mixtures reveal advantages for tumor muscle invasion in vivo. Biophys J 2023; 122:4194-4206. [PMID: 37766428 PMCID: PMC10645557 DOI: 10.1016/j.bpj.2023.09.016] [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: 03/13/2023] [Revised: 08/23/2023] [Accepted: 09/25/2023] [Indexed: 09/29/2023] Open
Abstract
Bladder, colon, gastric, prostate, and uterine cancers originate in organs surrounded by laminin-coated smooth muscle. In human prostate cancer, tumors that are organ confined, without extracapsular extension through muscle, have an overall cancer survival rate of up to 97% compared with 32% for metastatic disease. Our previous work modeling extracapsular extension reported the blocking of tumor invasion by mutation of a laminin-binding integrin called α6β1. Expression of the α6AA mutant resulted in a biophysical switch from cell-ECM (extracellular matrix) to cell-cell adhesion with drug sensitivity properties and an inability to invade muscle. Here we used different admixtures of α6AA and α6WT cells to test the cell heterogeneity requirements for muscle invasion. Time-lapse video microscopy revealed that tumor mixtures self-assembled into invasive networks in vitro, whereas α6AA cells assembled only as cohesive clusters. Invasion of α6AA cells into and through live muscle occurred using a 1:1 mixture of α6AA and α6WT cells. Electric cell-substrate impedance sensing measurements revealed that compared with α6AA cells, invasion-competent α6WT cells were 2.5-fold faster at closing a cell-ECM or cell-cell wound, respectively. Cell-ECM rebuilding kinetics show that an increased response occurred in mixtures since the response was eightfold greater compared with populations containing only one cell type. A synthetic cell adhesion cyclic peptide called MTI-101 completely blocked electric cell-substrate impedance sensing cell-ECM wound recovery that persisted in vitro up to 20 h after the wound. Treatment of tumor-bearing animals with 10 mg/kg MTI-101 weekly resulted in a fourfold decrease of muscle invasion by tumor and a decrease of the depth of invasion into muscle comparable to the α6AA cells. Taken together, these data suggest that mixed biophysical phenotypes of tumor cells within a population can provide functional advantages for tumor invasion into and through muscle that can be potentially inhibited by a synthetic cell adhesion molecule.
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Affiliation(s)
- Kendra D Marr
- Cancer Biology Graduate Interdisciplinary Program, University of Arizona, Tucson, Arizona; Medical Scientist Training Program, College of Medicine, University of Arizona, Tucson, Arizona
| | | | | | - Elijah J Keeswood
- University of Arizona Cancer Center, Tucson, Arizona; Partnership for Native American Cancer Prevention, University of Arizona, Tucson, Arizona
| | - Allan I Paxson
- Partnership for Native American Cancer Prevention, University of Arizona, Tucson, Arizona
| | | | - Beatrice S Knudsen
- Department of Pathology, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Raymond B Nagle
- Department of Pathology, University of Arizona Cancer Center, Tucson, Arizona
| | - Lori Hazlehurst
- Associate Director of Basic Research, Co-Leader Alexander B. Osborn Hematopoietic Malignancy and Transplantation, West Virginia University, Morgantown, West Virginia
| | | | - Anne E Cress
- University of Arizona Cancer Center, Tucson, Arizona; Department of Cellular and Molecular Medicine and Department of Radiation Oncology, College of Medicine, University of Arizona, Tucson, Arizona.
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72
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Maia-Silva D, Schier AC, Skopelitis D, Kechejian V, Alpsoy A, Liverpool J, Taatjes DJ, Vakoc CR. Marker-based CRISPR screening reveals a MED12-p63 interaction that activates basal identity in pancreatic ductal adenocarcinoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.24.563848. [PMID: 37961243 PMCID: PMC10634811 DOI: 10.1101/2023.10.24.563848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The presence of basal lineage characteristics signifies hyper-aggressive human adenocarcinomas of the breast, bladder, and pancreas. However, the biochemical mechanisms that maintain this aberrant cell state are poorly understood. Here we performed marker-based genetic screens in search of factors needed to maintain basal identity in pancreatic ductal adenocarcinoma (PDAC). This approach revealed MED12 as a powerful regulator of the basal cell state in this disease. Using biochemical reconstitution and epigenomics, we show that MED12 carries out this function by bridging the transcription factor p63, a known master regulator of the basal lineage, with the Mediator complex to activate lineage-specific enhancer elements. Consistent with this finding, the growth of basal-like PDAC is hypersensitive to MED12 loss when compared to classical PDAC. Taken together, our comprehensive genetic screens have revealed a biochemical interaction that sustains basal identity in human cancer, which could serve as a target for tumor lineage-directed therapeutics.
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Affiliation(s)
| | | | | | | | - Aktan Alpsoy
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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73
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Razo-Mejia M, Mani M, Petrov D. Bayesian inference of relative fitness on high-throughput pooled competition assays. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.14.562365. [PMID: 37904971 PMCID: PMC10614806 DOI: 10.1101/2023.10.14.562365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.
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Affiliation(s)
| | - Madhav Mani
- NSF-Simons Center for Quantitative Biology, Northwestern University
- Department of Molecular Biosciences, Northwestern University
| | - Dmitri Petrov
- Department of Biology, Stanford University
- Stanford Cancer Institute, Stanford University School of Medicine
- Chan Zuckerberg Biohub
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74
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Wang Y, Zhang M, Shi J, Zhu Y, Wang X, Zhang S, Wang F. Cracking the pattern of tumor evolution based on single-cell copy number alterations. Brief Bioinform 2023; 24:bbad341. [PMID: 37791583 DOI: 10.1093/bib/bbad341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 09/02/2023] [Accepted: 09/11/2023] [Indexed: 10/05/2023] Open
Abstract
Copy number alterations (CNAs) are a key characteristic of tumor development and progression. The accumulation of various CNAs during tumor development plays a critical role in driving tumor evolution. Heterogeneous clones driven by distinct CNAs have different selective advantages, leading to differential patterns of tumor evolution that are essential for developing effective cancer therapies. Recent advances in single-cell sequencing technology have enabled genome-wide copy number profiling of tumor cell populations at single-cell resolution. This has made it possible to explore the evolutionary patterns of CNAs and accurately discover the mechanisms of intra-tumor heterogeneity. Here, we propose a two-step statistical approach that distinguishes neutral, linear, branching and punctuated evolutionary patterns for a tumor cell population based on single-cell copy number profiles. We assessed our approach using a variety of simulated and real single-cell genomic and transcriptomic datasets, demonstrating its high accuracy and robustness in predicting tumor evolutionary patterns. We applied our approach to single-cell DNA sequencing data from 20 breast cancer patients and observed that punctuated evolution is the dominant evolutionary pattern in breast cancer. Similar conclusions were drawn when applying the approach to single-cell RNA sequencing data obtained from 132 various cancer patients. Moreover, we found that differential immune cell infiltration is associated with specific evolutionary patterns. The source code of our study is available at https://github.com/FangWang-SYSU/PTEM.
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Affiliation(s)
- Ying Wang
- Guangdong Cardiovascular Institute,Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences and Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
| | - Min Zhang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yan-Sen University
| | - Jian Shi
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University
| | - Yue Zhu
- Department of Breast Surgery at Harbin Medical University Cancer Hospital and the Medical Research Institute of Guangdong Provincial People's Hospital
| | - Xin Wang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yan-Sen University
| | - Shaojun Zhang
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
| | - Fang Wang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yan-Sen University
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75
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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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76
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Prusokiene A, Prusokas A, Retkute R. Machine learning based lineage tree reconstruction improved with knowledge of higher level relationships between cells and genomic barcodes. NAR Genom Bioinform 2023; 5:lqad077. [PMID: 37608801 PMCID: PMC10440785 DOI: 10.1093/nargab/lqad077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/26/2023] [Accepted: 08/11/2023] [Indexed: 08/24/2023] Open
Abstract
Tracking cells as they divide and progress through differentiation is a fundamental step in understanding many biological processes, such as the development of organisms and progression of diseases. In this study, we investigate a machine learning approach to reconstruct lineage trees in experimental systems based on mutating synthetic genomic barcodes. We refine previously proposed methodology by embedding information of higher level relationships between cells and single-cell barcode values into a feature space. We test performance of the algorithm on shallow trees (up to 100 cells) and deep trees (up to 10 000 cells). Our proposed algorithm can improve tree reconstruction accuracy in comparison to reconstructions based on a maximum parsimony method, but this comes at a higher computational time requirement.
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Affiliation(s)
- Alisa Prusokiene
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | | | - Renata Retkute
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
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77
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Wollenzien H, Tecleab YA, Szczepaniak-Sloane R, Restaino A, Kareta MS. Single-Cell Evolutionary Analysis Reveals Drivers of Plasticity and Mediators of Chemoresistance in Small Cell Lung Cancer. Mol Cancer Res 2023; 21:892-907. [PMID: 37256926 PMCID: PMC10527088 DOI: 10.1158/1541-7786.mcr-22-0881] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 03/11/2023] [Accepted: 05/24/2023] [Indexed: 06/02/2023]
Abstract
Small cell lung cancer (SCLC) is often a heterogeneous tumor, where dynamic regulation of key transcription factors can drive multiple populations of phenotypically different cells which contribute differentially to tumor dynamics. This tumor is characterized by a very low 2-year survival rate, high rates of metastasis, and rapid acquisition of chemoresistance. The heterogeneous nature of this tumor makes it difficult to study and to treat, as it is not clear how or when this heterogeneity arises. Here we describe temporal, single-cell analysis of SCLC to investigate tumor initiation and chemoresistance in both SCLC xenografts and an autochthonous SCLC model. We identify an early population of tumor cells with high expression of AP-1 network genes that are critical for tumor growth. Furthermore, we have identified and validated the cancer testis antigens (CTA) PAGE5 and GAGE2A as mediators of chemoresistance in human SCLC. CTAs have been successfully targeted in other tumor types and may be a promising avenue for targeted therapy in SCLC. IMPLICATIONS Understanding the evolutionary dynamics of SCLC can shed light on key mechanisms such as cellular plasticity, heterogeneity, and chemoresistance.
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Affiliation(s)
- Hannah Wollenzien
- Cancer Biology and Immunotherapies Group, Sanford Research, Sioux Falls, South Dakota, USA
- Genetics & Genomics Group, Sanford Research, Sioux Falls, South Dakota, USA
- Division of Basic Biomedical Sciences, University of South Dakota, Vermillion, South Dakota, USA
| | | | - Robert Szczepaniak-Sloane
- Cancer Biology and Immunotherapies Group, Sanford Research, Sioux Falls, South Dakota, USA
- Genetics & Genomics Group, Sanford Research, Sioux Falls, South Dakota, USA
| | - Anthony Restaino
- Cancer Biology and Immunotherapies Group, Sanford Research, Sioux Falls, South Dakota, USA
- Department of Pediatrics, Sanford School of Medicine, Sioux Falls, South Dakota, USA
| | - Michael S. Kareta
- Cancer Biology and Immunotherapies Group, Sanford Research, Sioux Falls, South Dakota, USA
- Genetics & Genomics Group, Sanford Research, Sioux Falls, South Dakota, USA
- Division of Basic Biomedical Sciences, University of South Dakota, Vermillion, South Dakota, USA
- Functional Genomics & Bioinformatics Core, Sanford Research, Sioux Falls, SD, USA
- Department of Pediatrics, Sanford School of Medicine, Sioux Falls, South Dakota, USA
- Department of Biochemistry, South Dakota State University, Brookings, South Dakota, USA
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78
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Liu X, Griffiths JI, Bishara I, Liu J, Bild AH, Chang JT. Phylogenetic inference from single-cell RNA-seq data. Sci Rep 2023; 13:12854. [PMID: 37553438 PMCID: PMC10409753 DOI: 10.1038/s41598-023-39995-6] [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: 01/03/2023] [Accepted: 08/03/2023] [Indexed: 08/10/2023] Open
Abstract
Tumors are comprised of subpopulations of cancer cells that harbor distinct genetic profiles and phenotypes that evolve over time and during treatment. By reconstructing the course of cancer evolution, we can understand the acquisition of the malignant properties that drive tumor progression. Unfortunately, recovering the evolutionary relationships of individual cancer cells linked to their phenotypes remains a difficult challenge. To address this need, we have developed PhylinSic, a method that reconstructs the phylogenetic relationships among cells linked to their gene expression profiles from single cell RNA-sequencing (scRNA-Seq) data. This method calls nucleotide bases using a probabilistic smoothing approach and then estimates a phylogenetic tree using a Bayesian modeling algorithm. We showed that PhylinSic identified evolutionary relationships underpinning drug selection and metastasis and was sensitive enough to identify subclones from genetic drift. We found that breast cancer tumors resistant to chemotherapies harbored multiple genetic lineages that independently acquired high K-Ras and β-catenin, suggesting that therapeutic strategies may need to control multiple lineages to be durable. These results demonstrated that PhylinSic can reconstruct evolution and link the genotypes and phenotypes of cells across monophyletic tumors using scRNA-Seq.
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Affiliation(s)
- Xuan Liu
- Department of Integrative Biology & Pharmacology, University of Texas Health Science Center at Houston, 6431 Fannin St, MSB 4.218, Houston, TX, 77030, USA
| | - Jason I Griffiths
- Division of Molecular Pharmacology, Department of Medical Oncology & Clinical Therapeutics, City of Hope, Monrovia, CA, USA
| | - Isaac Bishara
- Division of Molecular Pharmacology, Department of Medical Oncology & Clinical Therapeutics, City of Hope, Monrovia, CA, USA
| | - Jiayi Liu
- Department of Integrative Biology & Pharmacology, University of Texas Health Science Center at Houston, 6431 Fannin St, MSB 4.218, Houston, TX, 77030, USA
| | - Andrea H Bild
- Division of Molecular Pharmacology, Department of Medical Oncology & Clinical Therapeutics, City of Hope, Monrovia, CA, USA
| | - Jeffrey T Chang
- Department of Integrative Biology & Pharmacology, University of Texas Health Science Center at Houston, 6431 Fannin St, MSB 4.218, Houston, TX, 77030, USA.
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79
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Davies A, Zoubeidi A, Beltran H, Selth LA. The Transcriptional and Epigenetic Landscape of Cancer Cell Lineage Plasticity. Cancer Discov 2023; 13:1771-1788. [PMID: 37470668 PMCID: PMC10527883 DOI: 10.1158/2159-8290.cd-23-0225] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/25/2023] [Accepted: 06/09/2023] [Indexed: 07/21/2023]
Abstract
Lineage plasticity, a process whereby cells change their phenotype to take on a different molecular and/or histologic identity, is a key driver of cancer progression and therapy resistance. Although underlying genetic changes within the tumor can enhance lineage plasticity, it is predominantly a dynamic process controlled by transcriptional and epigenetic dysregulation. This review explores the transcriptional and epigenetic regulators of lineage plasticity and their interplay with other features of malignancy, such as dysregulated metabolism, the tumor microenvironment, and immune evasion. We also discuss strategies for the detection and treatment of highly plastic tumors. SIGNIFICANCE Lineage plasticity is a hallmark of cancer and a critical facilitator of other oncogenic features such as metastasis, therapy resistance, dysregulated metabolism, and immune evasion. It is essential that the molecular mechanisms of lineage plasticity are elucidated to enable the development of strategies to effectively target this phenomenon. In this review, we describe key transcriptional and epigenetic regulators of cancer cell plasticity, in the process highlighting therapeutic approaches that may be harnessed for patient benefit.
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Affiliation(s)
- Alastair Davies
- Oncology Research Discovery, Pfizer Worldwide Research and Development, San Diego, CA, USA
| | - Amina Zoubeidi
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
- Vancouver Prostate Centre, Vancouver, British Columbia, Canada
| | - Himisha Beltran
- Department of Medical Oncology, Dana Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Luke A. Selth
- Flinders Health and Medical Research Institute and Freemasons Centre for Male Health and Wellbeing, Flinders University, Bedford Park, South Australia, 5042 Australia
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, 5005 Australia
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80
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Masud MA, Kim JY, Kim E. Modeling the effect of acquired resistance on cancer therapy outcomes. Comput Biol Med 2023; 162:107035. [PMID: 37276754 DOI: 10.1016/j.compbiomed.2023.107035] [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: 02/05/2023] [Revised: 04/17/2023] [Accepted: 05/11/2023] [Indexed: 06/07/2023]
Abstract
Adaptive therapy (AT) is an evolution-based treatment strategy that exploits cell-cell competition. Acquired resistance can change the competitive nature of cancer cells in a tumor, impacting AT outcomes. We aimed to determine if adaptive therapy can still be effective with cell's acquiring resistance. We developed an agent-based model for spatial tumor growth considering three different types of acquired resistance: random genetic mutations during cell division, drug-induced reversible (plastic) phenotypic changes, and drug-induced irreversible phenotypic changes. These three resistance mechanisms lead to different spatial distributions of resistant cells. To quantify the spatial distribution, we propose an extension of Ripley's K-function, Sampled Ripley's K-function (SRKF), which calculates the non-randomness of the resistance distribution over the tumor domain. Our model predicts that the emergent spatial distribution of resistance can determine the time to progression under both adaptive and continuous therapy (CT). Notably, a high rate of random genetic mutations leads to quicker progression under AT than CT due to the emergence of many small clumps of resistant cells. Drug-induced phenotypic changes accelerate tumor progression irrespective of the treatment strategy. Low-rate switching to a sensitive state reduces the benefits of AT compared to CT. Furthermore, we also demonstrated that drug-induced resistance necessitates aggressive treatment under CT, regardless of the presence of cancer-associated fibroblasts. However, there is an optimal dose that can most effectively delay tumor relapse under AT by suppressing resistance. In conclusion, this study demonstrates that diverse resistance mechanisms can shape the distribution of resistance and thus determine the efficacy of adaptive therapy.
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Affiliation(s)
- M A Masud
- Natural Product Informatics Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Republic of Korea.
| | - Jae-Young Kim
- Graduate School of Analytical Science and Technology (GRAST), Chungnam National University, Daejeon 34134, Republic of Korea.
| | - Eunjung Kim
- Natural Product Informatics Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Republic of Korea.
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81
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Xie L, Liu H, You Z, Wang L, Li Y, Zhang X, Ji X, He H, Yuan T, Zheng W, Wu Z, Xiong M, Wei W, Chen Y. Comprehensive spatiotemporal mapping of single-cell lineages in developing mouse brain by CRISPR-based barcoding. Nat Methods 2023; 20:1244-1255. [PMID: 37460718 DOI: 10.1038/s41592-023-01947-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 06/06/2023] [Indexed: 08/09/2023]
Abstract
A fundamental interest in developmental neuroscience lies in the ability to map the complete single-cell lineages within the brain. To this end, we developed a CRISPR editing-based lineage-specific tracing (CREST) method for clonal tracing in Cre mice. We then used two complementary strategies based on CREST to map single-cell lineages in developing mouse ventral midbrain (vMB). By applying snapshotting CREST (snapCREST), we constructed a spatiotemporal lineage landscape of developing vMB and identified six progenitor archetypes that could represent the principal clonal fates of individual vMB progenitors and three distinct clonal lineages in the floor plate that specified glutamatergic, dopaminergic or both neurons. We further created pandaCREST (progenitor and derivative associating CREST) to associate the transcriptomes of progenitor cells in vivo with their differentiation potentials. We identified multiple origins of dopaminergic neurons and demonstrated that a transcriptome-defined progenitor type comprises heterogeneous progenitors, each with distinct clonal fates and molecular signatures. Therefore, the CREST method and strategies allow comprehensive single-cell lineage analysis that could offer new insights into the molecular programs underlying neural specification.
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Affiliation(s)
- Lianshun Xie
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hengxin Liu
- University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Zhiwen You
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Luyue Wang
- University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Yiwen Li
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Xinyue Zhang
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoshan Ji
- Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - Hui He
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Tingli Yuan
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Wenping Zheng
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Ziyan Wu
- UniXell Biotechnology, Shanghai, China
| | - Man Xiong
- State Key Laboratory of Medical Neurobiology-Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Wu Wei
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
- Center for Biomedical Informatics, Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.
- Lingang Laboratory, Shanghai, China.
| | - Yuejun Chen
- Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
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82
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Reyes-Castro RA, Chen SY, Seemann J, Kundu ST, Gibbons DL, Arur S. Phosphorylated nuclear DICER1 promotes open chromatin state and lineage plasticity of AT2 tumor cells in lung adenocarcinomas. SCIENCE ADVANCES 2023; 9:eadf6210. [PMID: 37494452 PMCID: PMC10371025 DOI: 10.1126/sciadv.adf6210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 06/22/2023] [Indexed: 07/28/2023]
Abstract
KRAS/ERK pathway phosphorylates DICER1, causing its nuclear translocation, and phosphomimetic Dicer1 contributes to tumorigenesis in mice. Mechanisms through which phospho-DICER1 regulates tumor progression remain undefined. While DICER1 canonically regulates microRNAs (miRNA) and epithelial-to-mesenchymal transition (EMT), we found that phosphorylated nuclear DICER1 (phospho-nuclear DICER1) promotes late-stage tumor progression in mice with oncogenic Kras, independent of miRNAs and EMT. Instead, we observe that the murine AT2 tumor cells exhibit altered chromatin compaction, and cells from disorganized advanced tumors, but not localized tumors, express gastric genes. Collectively, this results in subpopulations of tumor cells transitioning from a restricted alveolar to a broader endodermal lineage state. In human LUADs, we observed expression of phospho-nuclear DICER1 in advanced tumors together with the expression of gastric genes. We define a multimeric chromatin-DICER1 complex composed of the Mediator complex subunit 12, CBX1, MACROH2A.1, and transcriptional regulators supporting the model that phospho-nuclear DICER1 leads to lineage reprogramming of AT2 tumor cells to mediate lung cancer progression.
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Affiliation(s)
- Raisa A. Reyes-Castro
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- School of Medicine, University of Puerto Rico, San Juan, Puerto Rico
- Genetics and Epigenetics Graduate Program, The University of Texas MD Anderson Cancer Center and UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Shin-Yu Chen
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jacob Seemann
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Samrat T. Kundu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Don L. Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Swathi Arur
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Genetics and Epigenetics Graduate Program, The University of Texas MD Anderson Cancer Center and UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
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83
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Rashidfarrokhi A, Pillai R, Hao Y, Wu WL, Karadal-Ferrena B, Dimitriadoy SG, Cross M, Yeaton AH, Huang SM, Bhutkar AJ, Herrera A, Rajalingam S, Hayashi M, Huang KL, Bartnicki E, Zavitsanou AM, Wohlhieter CA, Leboeuf SE, Chen T, Loomis C, Mezzano V, Kulicke R, Davis FP, Stransky N, Smolen GA, Rudin CM, Moreira AL, Khanna KM, Pass HI, Wong KK, Koide S, Tsirigos A, Koralov SB, Papagiannakopoulos T. Tumor-intrinsic LKB1-LIF signaling axis establishes a myeloid niche to promote immune evasion and tumor growth. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.15.549147. [PMID: 37502974 PMCID: PMC10370066 DOI: 10.1101/2023.07.15.549147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Tumor mutations can influence the surrounding microenvironment leading to suppression of anti-tumor immune responses and thereby contributing to tumor progression and failure of cancer therapies. Here we use genetically engineered lung cancer mouse models and patient samples to dissect how LKB1 mutations accelerate tumor growth by reshaping the immune microenvironment. Comprehensive immune profiling of LKB1 -mutant vs wildtype tumors revealed dramatic changes in myeloid cells, specifically enrichment of Arg1 + interstitial macrophages and SiglecF Hi neutrophils. We discovered a novel mechanism whereby autocrine LIF signaling in Lkb1 -mutant tumors drives tumorigenesis by reprogramming myeloid cells in the immune microenvironment. Inhibiting LIF signaling in Lkb1 -mutant tumors, via gene targeting or with a neutralizing antibody, resulted in a striking reduction in Arg1 + interstitial macrophages and SiglecF Hi neutrophils, expansion of antigen specific T cells, and inhibition of tumor progression. Thus, targeting LIF signaling provides a new therapeutic approach to reverse the immunosuppressive microenvironment of LKB1 -mutant tumors.
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84
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Otto D, Jordan C, Dury B, Dien C, Setty M. Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes using Mellon. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.09.548272. [PMID: 37502954 PMCID: PMC10369887 DOI: 10.1101/2023.07.09.548272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive cellular differentiation, regeneration, and disease. Here, we present Mellon, a novel computational algorithm for high-resolution estimation of cell-state densities from single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of various differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. Utilizing hematopoietic stem cell fate specification to B-cells as a case study, we present evidence implicating enhancer priming and the activation of master regulators in the emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during the inherently continuous developmental processes. Scalable and adaptable, Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide new insights into the regulatory mechanisms guiding cellular fate decisions.
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Affiliation(s)
- Dominik Otto
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
| | - Cailin Jordan
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
- Molecular and Cellular Biology Program, University of Washington, Seattle WA
| | - Brennan Dury
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
| | - Christine Dien
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
| | - Manu Setty
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
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85
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Tang YJ, Shuldiner EG, Karmakar S, Winslow MM. High-Throughput Identification, Modeling, and Analysis of Cancer Driver Genes In Vivo. Cold Spring Harb Perspect Med 2023; 13:a041382. [PMID: 37277208 PMCID: PMC10317066 DOI: 10.1101/cshperspect.a041382] [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] [Indexed: 06/07/2023]
Abstract
The vast number of genomic and molecular alterations in cancer pose a substantial challenge to uncovering the mechanisms of tumorigenesis and identifying therapeutic targets. High-throughput functional genomic methods in genetically engineered mouse models allow for rapid and systematic investigation of cancer driver genes. In this review, we discuss the basic concepts and tools for multiplexed investigation of functionally important cancer genes in vivo using autochthonous cancer models. Furthermore, we highlight emerging technical advances in the field, potential opportunities for future investigation, and outline a vision for integrating multiplexed genetic perturbations with detailed molecular analyses to advance our understanding of the genetic and molecular basis of cancer.
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Affiliation(s)
- Yuning J Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Emily G Shuldiner
- Department of Biology, Stanford University, Stanford, California 94305, USA
| | - Saswati Karmakar
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Monte M Winslow
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA
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86
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Juul NH, Yoon JK, Martinez MC, Rishi N, Kazadaeva YI, Morri M, Neff NF, Trope WL, Shrager JB, Sinha R, Desai TJ. KRAS(G12D) drives lepidic adenocarcinoma through stem-cell reprogramming. Nature 2023; 619:860-867. [PMID: 37468622 PMCID: PMC10423036 DOI: 10.1038/s41586-023-06324-w] [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: 09/18/2022] [Accepted: 06/14/2023] [Indexed: 07/21/2023]
Abstract
Many cancers originate from stem or progenitor cells hijacked by somatic mutations that drive replication, exemplified by adenomatous transformation of pulmonary alveolar epithelial type II (AT2) cells1. Here we demonstrate a different scenario: expression of KRAS(G12D) in differentiated AT1 cells reprograms them slowly and asynchronously back into AT2 stem cells that go on to generate indolent tumours. Like human lepidic adenocarcinoma, the tumour cells slowly spread along alveolar walls in a non-destructive manner and have low ERK activity. We find that AT1 and AT2 cells act as distinct cells of origin and manifest divergent responses to concomitant WNT activation and KRAS(G12D) induction, which accelerates AT2-derived but inhibits AT1-derived adenoma proliferation. Augmentation of ERK activity in KRAS(G12D)-induced AT1 cells increases transformation efficiency, proliferation and progression from lepidic to mixed tumour histology. Overall, we have identified a new cell of origin for lung adenocarcinoma, the AT1 cell, which recapitulates features of human lepidic cancer. In so doing, we also uncover a capacity for oncogenic KRAS to reprogram a differentiated and quiescent cell back into its parent stem cell en route to adenomatous transformation. Our work further reveals that irrespective of a given cancer's current molecular profile and driver oncogene, the cell of origin exerts a pervasive and perduring influence on its subsequent behaviour.
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Affiliation(s)
- Nicholas H Juul
- Division of Pulmonary, Allergy and Critical Care, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Jung-Ki Yoon
- Division of Pulmonary, Allergy and Critical Care, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Marina C Martinez
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Neha Rishi
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Yana I Kazadaeva
- Division of Pulmonary, Allergy and Critical Care, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | - Winston L Trope
- Division of Thoracic Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph B Shrager
- Division of Thoracic Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Rahul Sinha
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Tushar J Desai
- Division of Pulmonary, Allergy and Critical Care, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA.
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87
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Wang Y, Zhang X, Wang Z. Cellular barcoding: From developmental tracing to anti-tumor drug discovery. Cancer Lett 2023:216281. [PMID: 37336285 DOI: 10.1016/j.canlet.2023.216281] [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: 03/22/2023] [Revised: 05/31/2023] [Accepted: 06/10/2023] [Indexed: 06/21/2023]
Abstract
Clonal evolution has gained immense attention in explaining cancer cell status, history, and fate during cancer progression. Current single-cell or spatial transcriptome technologies have broadened our understanding of various mechanisms underlying cancer initiation, relapse, and drug resistance. However, technical challenges still hinder a better understanding of the dynamics of distinctive phenotypic states and abnormal trajectories from normal physiological transition to malignant stages. Cellular barcoding enabled lineage tracing on parallelly massive cells at single-cell resolution through different mechanisms lately, enabling new insights into exploring developmental trajectories, cancer progression, and targeted therapies. This review summarizes the latest noteworthy and robust strategies for different types of cellular barcodes. To introduce the major characteristics, advantages and limitations of these different strategies, this review will further guide in choosing or improving cellular barcoding technologies and their applications in cancer research.
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Affiliation(s)
- Yuqing Wang
- Medical Center of Hematology, The Second Affiliated Hospital, Army Medical University, Chongqing, 40037, China; State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 40037, China
| | - Xi Zhang
- Medical Center of Hematology, The Second Affiliated Hospital, Army Medical University, Chongqing, 40037, China; State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 40037, China; Jinfeng Laboratory, Chongqing, 401329, China.
| | - Zheng Wang
- Medical Center of Hematology, The Second Affiliated Hospital, Army Medical University, Chongqing, 40037, China; State Key Laboratory of Trauma, Burn and Combined Injury, Army Medical University, Chongqing, 40037, China; Bio-Med Informatics Research Center & Clinical Research Center, The Second Affiliated Hospital, Army Medical University, Chongqing, 400037, China; Jinfeng Laboratory, Chongqing, 401329, China.
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88
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Tran M, Askary A, Elowitz MB. Lineage motifs: developmental modules for control of cell type proportions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.06.543925. [PMID: 37333085 PMCID: PMC10274800 DOI: 10.1101/2023.06.06.543925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
In multicellular organisms, cell types must be produced and maintained in appropriate proportions. One way this is achieved is through committed progenitor cells that produce specific sets of descendant cell types. However, cell fate commitment is probabilistic in most contexts, making it difficult to infer progenitor states and understand how they establish overall cell type proportions. Here, we introduce Lineage Motif Analysis (LMA), a method that recursively identifies statistically overrepresented patterns of cell fates on lineage trees as potential signatures of committed progenitor states. Applying LMA to published datasets reveals spatial and temporal organization of cell fate commitment in zebrafish and rat retina and early mouse embryo development. Comparative analysis of vertebrate species suggests that lineage motifs facilitate adaptive evolutionary variation of retinal cell type proportions. LMA thus provides insight into complex developmental processes by decomposing them into simpler underlying modules.
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Affiliation(s)
- Martin Tran
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Amjad Askary
- Department of Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Michael B. Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
- Lead contact
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89
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Glasner A, Rose SA, Sharma R, Gudjonson H, Chu T, Green JA, Rampersaud S, Valdez IK, Andretta ES, Dhillon BS, Schizas M, Dikiy S, Mendoza A, Hu W, Wang ZM, Chaudhary O, Xu T, Mazutis L, Rizzuto G, Quintanal-Villalonga A, Manoj P, de Stanchina E, Rudin CM, Pe'er D, Rudensky AY. Conserved transcriptional connectivity of regulatory T cells in the tumor microenvironment informs new combination cancer therapy strategies. Nat Immunol 2023; 24:1020-1035. [PMID: 37127830 PMCID: PMC10232368 DOI: 10.1038/s41590-023-01504-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/05/2023] [Indexed: 05/03/2023]
Abstract
While regulatory T (Treg) cells are traditionally viewed as professional suppressors of antigen presenting cells and effector T cells in both autoimmunity and cancer, recent findings of distinct Treg cell functions in tissue maintenance suggest that their regulatory purview extends to a wider range of cells and is broader than previously assumed. To elucidate tumoral Treg cell 'connectivity' to diverse tumor-supporting accessory cell types, we explored immediate early changes in their single-cell transcriptomes upon punctual Treg cell depletion in experimental lung cancer and injury-induced inflammation. Before any notable T cell activation and inflammation, fibroblasts, endothelial and myeloid cells exhibited pronounced changes in their gene expression in both cancer and injury settings. Factor analysis revealed shared Treg cell-dependent gene programs, foremost, prominent upregulation of VEGF and CCR2 signaling-related genes upon Treg cell deprivation in either setting, as well as in Treg cell-poor versus Treg cell-rich human lung adenocarcinomas. Accordingly, punctual Treg cell depletion combined with short-term VEGF blockade showed markedly improved control of PD-1 blockade-resistant lung adenocarcinoma progression in mice compared to the corresponding monotherapies, highlighting a promising factor-based querying approach to elucidating new rational combination treatments of solid organ cancers.
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Affiliation(s)
- Ariella Glasner
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samuel A Rose
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Roshan Sharma
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Herman Gudjonson
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tinyi Chu
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jesse A Green
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sham Rampersaud
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Izabella K Valdez
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emma S Andretta
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Bahawar S Dhillon
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michail Schizas
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Stanislav Dikiy
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alejandra Mendoza
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Wei Hu
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Zhong-Min Wang
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ojasvi Chaudhary
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tianhao Xu
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Linas Mazutis
- Institute of Biotechnology, Life Sciences Centre, Vilnius University, Vilnius, Lithuania
| | - Gabrielle Rizzuto
- Human Oncology & Pathogenesis Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology & Laboratory Medicine, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Parvathy Manoj
- Department of Medicine, Thoracic Oncology Service, New York, NY, USA
| | - Elisa de Stanchina
- Antitumor Assessment Core Facility, New York, NY, USA
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Charles M Rudin
- Department of Medicine, Thoracic Oncology Service, New York, NY, USA
| | - Dana Pe'er
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Alexander Y Rudensky
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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90
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Zhu X, Zhao W, Zhou Z, Gu X. Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools. J Mol Evol 2023:10.1007/s00239-023-10117-0. [PMID: 37246992 DOI: 10.1007/s00239-023-10117-0] [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: 12/30/2022] [Accepted: 05/09/2023] [Indexed: 05/30/2023]
Abstract
Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.
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Affiliation(s)
- Xunuo Zhu
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Wenyi Zhao
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhan Zhou
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, China.
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China.
| | - Xun Gu
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, 50011, USA.
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91
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Grody EI, Abraham A, Shukla V, Goyal Y. Toward a systems-level probing of tumor clonality. iScience 2023; 26:106574. [PMID: 37192968 PMCID: PMC10182304 DOI: 10.1016/j.isci.2023.106574] [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] [Indexed: 05/18/2023] Open
Abstract
Cancer has been described as a genetic disease that clonally evolves in the face of selective pressures imposed by cell-intrinsic and extrinsic factors. Although classical models based on genetic data predominantly propose Darwinian mechanisms of cancer evolution, recent single-cell profiling of cancers has described unprecedented heterogeneity in tumors providing support for alternative models of branched and neutral evolution through both genetic and non-genetic mechanisms. Emerging evidence points to a complex interplay between genetic, non-genetic, and extrinsic environmental factors in shaping the evolution of tumors. In this perspective, we briefly discuss the role of cell-intrinsic and extrinsic factors that shape clonal behaviors during tumor progression, metastasis, and drug resistance. Taking examples of pre-malignant states associated with hematological malignancies and esophageal cancer, we discuss recent paradigms of tumor evolution and prospective approaches to further enhance our understanding of this spatiotemporally regulated process.
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Affiliation(s)
- Emanuelle I. Grody
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL 60208, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Ajay Abraham
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Human Immunobiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Vipul Shukla
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Human Immunobiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL 60208, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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92
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Yang X, Bai Q, Chen W, Liang J, Wang F, Gu W, Liu L, Li Q, Chen Z, Zhou A, Long J, Tian H, Wu J, Ding X, Zhou N, Li M, Yang Y, Cai J. m 6 A-Dependent Modulation via IGF2BP3/MCM5/Notch Axis Promotes Partial EMT and LUAD Metastasis. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2206744. [PMID: 37171793 PMCID: PMC10369244 DOI: 10.1002/advs.202206744] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 04/21/2023] [Indexed: 05/13/2023]
Abstract
The importance of mRNA N6-methyladenosine (m6 A) modification during tumor metastasis is controversial as it plays distinct roles in different biological contexts. Moreover, how cancer cell plasticity is shaped by m6 A modification is interesting but remains uncharacterized. Here, this work shows that m6 A reader insulin like growth factor 2 mRNA binding protein 3 (IGF2BP3) is remarkably upregulated in metastatic lung adenocarcinoma (LUAD) and indicates worse prognosis of patients. Interestingly, IGF2BP3 induces partial epithelial-mesenchymal-transition (EMT) and confers LUAD cells plasticity to metastasize through m6 A-dependent overactivation of Notch signaling. Mechanistically, IGF2BP3 recognized m6 A-modified minichromosome maintenance complex component (MCM5) mRNAs to prolong stability of them, subsequently upregulating MCM5 protein, which competitively inhibits SIRT1-mediated deacetylation of Notch1 intracellular domain (NICD1), stabilizes NICD1 protein and contributes to m6 A-dependent IGF2BP3-mediated cellular plasticity. Notably, a tight correlation of the IGF2BP3/MCM5/Notch axis is evidenced in clinical LUAD specimens. Therefore, this study elucidates a critical role of m6 A modification on LUAD cell plasticity in fostering tumor metastasis via the above axis, providing potential targets for metastatic LUAD.
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Affiliation(s)
- Xia Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Qiaorui Bai
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Weizhong Chen
- Department of Pharmacology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Jiaer Liang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Fang Wang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Weiqi Gu
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Lei Liu
- Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Quanfeng Li
- Department of Orthopedics, The Eighth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 518033, China
| | - Zishuo Chen
- Cancer Institute, Southern Medical University, Shenzhen, 510515, China
| | - Anni Zhou
- Department of Pharmacology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Jianting Long
- Department of Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Han Tian
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Jueheng Wu
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Xiaofan Ding
- Faculty of Health Sciences Building University of Macau, Macau, 999078, China
| | - Ningning Zhou
- Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Mengfeng Li
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
- Cancer Institute, Southern Medical University, Shenzhen, 510515, China
| | - Yi Yang
- Department of Pharmacology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Junchao Cai
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, China
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93
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Taylor MA, Kandyba E, Halliwill K, Delrosario R, Koroshkin M, Goodarzi H, Quigley D, Li YR, Wu D, Bollam S, Mirzoeva O, Akhurst RJ, Balmain A. Gene networks reveal stem-cell state convergence during preneoplasia and progression to malignancy in multistage skin carcinogenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.08.539863. [PMID: 37215032 PMCID: PMC10197547 DOI: 10.1101/2023.05.08.539863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Adult mammalian stem cells play critical roles in normal tissue homeostasis, as well as in tumor development, by contributing to cell heterogeneity, plasticity, and development of drug resistance. The relationship between different types of normal and cancer stem cells is highly controversial and poorly understood. Here, we carried out gene expression network analysis of normal and tumor samples from genetically heterogeneous mice to create network metagenes for visualization of stem-cell networks, rather than individual stem-cell markers, at the single-cell level during multistage carcinogenesis. We combined this approach with lineage tracing and single-cell RNASeq of stem cells and their progeny, identifying a previously unrecognized hierarchy in which Lgr6+ stem cells from tumors generate progeny that express a range of other stem-cell markers including Sox2, Pitx1, Foxa1, Klf5, and Cd44. Our data identify a convergence of multiple stem-cell and tumor-suppressor pathways in benign tumor cells expressing markers of lineage plasticity and oxidative stress. This same single-cell population expresses network metagenes corresponding to markers of cancer drug resistance in human tumors of the skin, lung and prostate. Treatment of mouse squamous carcinomas in vivo with the chemotherapeutic cis-platin resulted in elevated expression of the genes that mark this cell population. Our data have allowed us to create a simplified model of multistage carcinogenesis that identifies distinct stem-cell states at different stages of tumor progression, thereby identifying networks involved in lineage plasticity, drug resistance, and immune surveillance, providing a rich source of potential targets for cancer therapy.
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Affiliation(s)
- Mark A. Taylor
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco; San Francisco, 94158, USA
| | - Eve Kandyba
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco; San Francisco, 94158, USA
| | - Kyle Halliwill
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco; San Francisco, 94158, USA
| | - Reyno Delrosario
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco; San Francisco, 94158, USA
| | - Matvei Koroshkin
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco; San Francisco, 94158, USA
| | - Hani Goodarzi
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco; San Francisco, 94158, USA
| | - David Quigley
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco; San Francisco, 94158, USA
| | - Yun Rose Li
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco; San Francisco, 94158, USA
| | - Di Wu
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco; San Francisco, 94158, USA
| | - Saumya Bollam
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco; San Francisco, 94158, USA
| | - Olga Mirzoeva
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco; San Francisco, 94158, USA
| | - Rosemary J. Akhurst
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco; San Francisco, 94158, USA
| | - Allan Balmain
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco; San Francisco, 94158, USA
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94
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Hebert JD, Neal JW, Winslow MM. Dissecting metastasis using preclinical models and methods. Nat Rev Cancer 2023; 23:391-407. [PMID: 37138029 DOI: 10.1038/s41568-023-00568-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 05/05/2023]
Abstract
Metastasis has long been understood to lead to the overwhelming majority of cancer-related deaths. However, our understanding of the metastatic process, and thus our ability to prevent or eliminate metastases, remains frustratingly limited. This is largely due to the complexity of metastasis, which is a multistep process that likely differs across cancer types and is greatly influenced by many aspects of the in vivo microenvironment. In this Review, we discuss the key variables to consider when designing assays to study metastasis: which source of metastatic cancer cells to use and where to introduce them into mice to address different questions of metastasis biology. We also examine methods that are being used to interrogate specific steps of the metastatic cascade in mouse models, as well as emerging techniques that may shed new light on previously inscrutable aspects of metastasis. Finally, we explore approaches for developing and using anti-metastatic therapies, and how mouse models can be used to test them.
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Affiliation(s)
- Jess D Hebert
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Joel W Neal
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Monte M Winslow
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
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95
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Linghu C, An B, Shpokayte M, Celiker OT, Shmoel N, Zhang R, Zhang C, Park D, Park WM, Ramirez S, Boyden ES. Recording of cellular physiological histories along optically readable self-assembling protein chains. Nat Biotechnol 2023; 41:640-651. [PMID: 36593405 PMCID: PMC10188365 DOI: 10.1038/s41587-022-01586-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 10/21/2022] [Indexed: 01/03/2023]
Abstract
Observing cellular physiological histories is key to understanding normal and disease-related processes. Here we describe expression recording islands-a fully genetically encoded approach that enables both continual digital recording of biological information within cells and subsequent high-throughput readout in fixed cells. The information is stored in growing intracellular protein chains made of self-assembling subunits, human-designed filament-forming proteins bearing different epitope tags that each correspond to a different cellular state or function (for example, gene expression downstream of neural activity or pharmacological exposure), allowing the physiological history to be read out along the ordered subunits of protein chains with conventional optical microscopy. We use expression recording islands to record gene expression timecourse downstream of specific pharmacological and physiological stimuli in cultured neurons and in living mouse brain, with a time resolution of a fraction of a day, over periods of days to weeks.
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Affiliation(s)
- Changyang Linghu
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Biological Engineering, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Cell and Developmental Biology, Program in Single Cell Spatial Analysis, and Michigan Neuroscience Institute, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Bobae An
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Biological Engineering, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Monika Shpokayte
- Graduate Program for Neuroscience, Boston University, Boston, MA, USA
- Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Orhan T Celiker
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Biological Engineering, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Nava Shmoel
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Biological Engineering, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Ruihan Zhang
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Biological Engineering, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Chi Zhang
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Biological Engineering, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Demian Park
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Biological Engineering, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Won Min Park
- Chemical Engineering, Kansas State University, Manhattan, KS, USA
| | - Steve Ramirez
- Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Edward S Boyden
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
- Biological Engineering, MIT, Cambridge, MA, USA.
- Media Arts and Sciences, MIT, Cambridge, MA, USA.
- McGovern Institute, MIT, Cambridge, MA, USA.
- Howard Hughes Medical Institute, MIT, Cambridge, MA, USA.
- K Lisa Yang Center for Bionics, MIT, Cambridge, MA, USA.
- Center for Neurobiological Engineering, MIT, Cambridge, MA, USA.
- Koch Institute, MIT, Cambridge, MA, USA.
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96
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Adler FR, Anderson ARA, Bhushan A, Bogdan P, Bravo-Cordero JJ, Brock A, Chen Y, Cukierman E, DelGiorno KE, Denis GV, Ferrall-Fairbanks MC, Gartner ZJ, Germain RN, Gordon DM, Hunter G, Jolly MK, Karacosta LG, Mythreye K, Katira P, Kulkarni RP, Kutys ML, Lander AD, Laughney AM, Levine H, Lou E, Lowenstein PR, Masters KS, Pe'er D, Peyton SR, Platt MO, Purvis JE, Quon G, Richer JK, Riddle NC, Rodriguez A, Snyder JC, Lee Szeto G, Tomlin CJ, Yanai I, Zervantonakis IK, Dueck H. Modeling collective cell behavior in cancer: Perspectives from an interdisciplinary conversation. Cell Syst 2023; 14:252-257. [PMID: 37080161 PMCID: PMC10760508 DOI: 10.1016/j.cels.2023.03.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 12/20/2022] [Accepted: 03/08/2023] [Indexed: 04/22/2023]
Abstract
Collective cell behavior contributes to all stages of cancer progression. Understanding how collective behavior emerges through cell-cell interactions and decision-making will advance our understanding of cancer biology and provide new therapeutic approaches. Here, we summarize an interdisciplinary discussion on multicellular behavior in cancer, draw lessons from other scientific disciplines, and identify future directions.
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Affiliation(s)
- Frederick R Adler
- Department of Mathematics, University of Utah, Salt Lake City, UT 84112, USA; School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Alexander R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Abhinav Bhushan
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Paul Bogdan
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Jose Javier Bravo-Cordero
- Division of Hematology and Oncology, Department of Medicine, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Yun Chen
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Edna Cukierman
- Cancer Signaling and Microenvironment Program, Marvin and Concetta Greenberg Pancreatic Cancer Institute, Fox Chase Cancer Center, Philadelphia, PA 19111, USA; Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19140, USA
| | - Kathleen E DelGiorno
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Gerald V Denis
- Boston University-Boston Medical Center Cancer Center, Boston University School of Medicine, Boston, MA 02118, USA
| | - Meghan C Ferrall-Fairbanks
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA; University of Florida Health Cancer Center, University of Florida, Gainesville, FL 32611, USA
| | - Zev Jordan Gartner
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA; NSF Center for Cellular Construction, San Francisco, CA 94158, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Ronald N Germain
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Deborah M Gordon
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Ginger Hunter
- Department of Biology, Clarkson University, Potsdam, NY 13699, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, 560012, India
| | - Loukia Georgiou Karacosta
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Karthikeyan Mythreye
- Department of Pathology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; O'Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Parag Katira
- Mechanical Engineering Department, San Diego State University, San Diego, CA 92182, USA; Computational Sciences Research Center, San Diego State University, San Diego, CA 92182, USA
| | - Rajan P Kulkarni
- Department of Dermatology, Oregon Health and Science University, Portland, OR 97239, USA; Department Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA; Department Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA; Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA; Operative Care Division, VA Portland Health Care System, Portland, OR 97239, USA
| | - Matthew L Kutys
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Arthur D Lander
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA 92697, USA; Center for Complex Biological Systems, University of California, Irvine, Irvine, CA 92697, USA
| | - Ashley M Laughney
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA; Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA
| | - Herbert Levine
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA
| | - Emil Lou
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA
| | - Pedro R Lowenstein
- Department of Neurosurgery, Rogel Cancer Center, The University of Michigan, Ann Arbor, MI 48109, USA; Department of Cell and Developmental Biology, Rogel Cancer Center, The University of Michigan, Ann Arbor, MI 48109, USA; Department of Biomedical Engineering, The University of Michigan, Ann Arbor, MI 48109, USA
| | - Kristyn S Masters
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Shelly R Peyton
- Department of Chemical Engineering, University of Massachusetts, Amherst, MA 01003, USA
| | - Manu O Platt
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA 30322, USA; Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Emory University, Atlanta, GA 30322, USA
| | - Jeremy E Purvis
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Gerald Quon
- Department of Molecular and Cellular Biology, University of California, Davis, Davis, CA 95616, USA
| | - Jennifer K Richer
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; University of Colorado Cancer Center, Aurora, CO 80045, USA
| | - Nicole C Riddle
- Department of Biology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Analiz Rodriguez
- Department of Neurosurgery, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Joshua C Snyder
- Department of Surgery, Duke University, Durham, NC 27710, USA; Department of Cell Biology, Duke University, Durham, NC 27710, USA
| | - Gregory Lee Szeto
- Allen Institute for Immunology, Seattle, WA 98109, USA; Seagen, Bothell, WA 98021, USA
| | - Claire J Tomlin
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Itai Yanai
- Perlmutter Cancer Center, NYU School of Medicine, New York, NY 10016, USA; Institute for Computational Medicine, NYU Langone Health, New York, NY 10016, USA
| | - Ioannis K Zervantonakis
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15232, USA; Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15232, USA
| | - Hannah Dueck
- Division of Cancer Biology, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA.
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97
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Avraham R. Untangling Cellular Host-Pathogen Encounters at Infection Bottlenecks. Infect Immun 2023; 91:e0043822. [PMID: 36939328 PMCID: PMC10112260 DOI: 10.1128/iai.00438-22] [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: 03/21/2023] Open
Abstract
Bacterial pathogens can invade the tissue and establish a protected intracellular niche at the site of invasion that can spread locally (e.g., microcolonies) or to systemic sites (e.g., granulomas). Invasion of the tissue and establishment of intracellular infection are rare events that are difficult to study in the in vivo setting but have critical clinical consequences, such as long-term carriage, reinfections, and emergence of antibiotic resistance. Here, I discuss Salmonella interactions with its host macrophage during early stages of infection and their critical role in determining infection outcome. The dynamics of host-pathogen interactions entail highly heterogenous host immunity, bacterial virulence, and metabolic cross talk, requiring in vivo analysis at single-cell resolution. I discuss models and single-cell approaches that provide a global understanding of the establishment of a protected intracellular niche within the tissue and the host-pathogen landscape at infection bottlenecks during early stages of infection. Studying cellular host-pathogen interactions in vivo can improve our knowledge of the trajectory of infection between the initial inoculation with a dose of pathogens and the appearance of symptoms of disease.
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Affiliation(s)
- Roi Avraham
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
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98
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Gerstberger S, Jiang Q, Ganesh K. Metastasis. Cell 2023; 186:1564-1579. [PMID: 37059065 PMCID: PMC10511214 DOI: 10.1016/j.cell.2023.03.003] [Citation(s) in RCA: 165] [Impact Index Per Article: 165.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/16/2023] [Accepted: 03/02/2023] [Indexed: 04/16/2023]
Abstract
Most cancer-associated deaths occur due to metastasis, yet our understanding of metastasis as an evolving, heterogeneous, systemic disease and of how to effectively treat it is still emerging. Metastasis requires the acquisition of a succession of traits to disseminate, variably enter and exit dormancy, and colonize distant organs. The success of these events is driven by clonal selection, the potential of metastatic cells to dynamically transition into distinct states, and their ability to co-opt the immune environment. Here, we review the main principles of metastasis and highlight emerging opportunities to develop more effective therapies for metastatic cancer.
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Affiliation(s)
- Stefanie Gerstberger
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Qingwen Jiang
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Karuna Ganesh
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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99
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Wang D, Liu B, Zhang Z. Accelerating the understanding of cancer biology through the lens of genomics. Cell 2023; 186:1755-1771. [PMID: 37059071 DOI: 10.1016/j.cell.2023.02.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/11/2023] [Accepted: 02/08/2023] [Indexed: 04/16/2023]
Abstract
A core mission of cancer genomics is to comprehensively chart molecular underpinnings of cancer-driving events and to provide personalized therapeutic strategies. Primarily focused on cancer cells, cancer genomics studies have successfully uncovered many drivers for major cancer types. Since the emergence of cancer immune evasion as a critical cancer hallmark, the paradigm has been elevated to the holistic tumor ecosystem, with distinct cellular components and their functional states elucidated. We highlight the milestones of cancer genomics, depict the evolving path of the field, and discuss future directions in completing the understanding of the tumor ecosystem and in advancing therapeutic strategies.
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Affiliation(s)
- Dongfang Wang
- Biomedical Pioneering Innovative Center and School of Life Sciences, Peking University, Beijing 100871, China
| | - Baolin Liu
- Biomedical Pioneering Innovative Center and School of Life Sciences, Peking University, Beijing 100871, China
| | - Zemin Zhang
- Biomedical Pioneering Innovative Center and School of Life Sciences, Peking University, Beijing 100871, China; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Changping Laboratory, Beijing, China.
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100
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Qiu C, Martin BK, Welsh IC, Daza RM, Le TM, Huang X, Nichols EK, Taylor ML, Fulton O, O’Day DR, Gomes AR, Ilcisin S, Srivatsan S, Deng X, Disteche CM, Noble WS, Hamazaki N, Moens CB, Kimelman D, Cao J, Schier AF, Spielmann M, Murray SA, Trapnell C, Shendure J. A single-cell transcriptional timelapse of mouse embryonic development, from gastrula to pup. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.05.535726. [PMID: 37066300 PMCID: PMC10104014 DOI: 10.1101/2023.04.05.535726] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
The house mouse, Mus musculus, is an exceptional model system, combining genetic tractability with close homology to human biology. Gestation in mouse development lasts just under three weeks, a period during which its genome orchestrates the astonishing transformation of a single cell zygote into a free-living pup composed of >500 million cells. Towards a global framework for exploring mammalian development, we applied single cell combinatorial indexing (sci-*) to profile the transcriptional states of 12.4 million nuclei from 83 precisely staged embryos spanning late gastrulation (embryonic day 8 or E8) to birth (postnatal day 0 or P0), with 2-hr temporal resolution during somitogenesis, 6-hr resolution through to birth, and 20-min resolution during the immediate postpartum period. From these data (E8 to P0), we annotate dozens of trajectories and hundreds of cell types and perform deeper analyses of the unfolding of the posterior embryo during somitogenesis as well as the ontogenesis of the kidney, mesenchyme, retina, and early neurons. Finally, we leverage the depth and temporal resolution of these whole embryo snapshots, together with other published data, to construct and curate a rooted tree of cell type relationships that spans mouse development from zygote to pup. Throughout this tree, we systematically nominate sets of transcription factors (TFs) and other genes as candidate drivers of the in vivo differentiation of hundreds of mammalian cell types. Remarkably, the most dramatic shifts in transcriptional state are observed in a restricted set of cell types in the hours immediately following birth, and presumably underlie the massive changes in physiology that must accompany the successful transition of a placental mammal to extrauterine life.
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Affiliation(s)
- Chengxiang Qiu
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Beth K. Martin
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Riza M. Daza
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Truc-Mai Le
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Xingfan Huang
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Eva K. Nichols
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Megan L. Taylor
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Olivia Fulton
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Diana R. O’Day
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | | | - Saskia Ilcisin
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Sanjay Srivatsan
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA, USA
| | - Xinxian Deng
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Christine M. Disteche
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - William Stafford Noble
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Nobuhiko Hamazaki
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | - Cecilia B. Moens
- Division of Basic Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - David Kimelman
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Junyue Cao
- Laboratory of Single-cell genomics and Population dynamics, The Rockefeller University, New York, NY, USA
| | - Alexander F. Schier
- Biozentrum, University of Basel, Basel, Switzerland
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
| | - Malte Spielmann
- Max Planck Institute for Molecular Genetics, Berlin, Germany
- Institute of Human Genetics, University Hospitals Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Kiel, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Hamburg, Lübeck, Kiel, Lübeck, Germany
| | | | - Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
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