1
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Chen J, Larsson L, Swarbrick A, Lundeberg J. Spatial landscapes of cancers: insights and opportunities. Nat Rev Clin Oncol 2024; 21:660-674. [PMID: 39043872 DOI: 10.1038/s41571-024-00926-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2024] [Indexed: 07/25/2024]
Abstract
Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.
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Affiliation(s)
- Julia Chen
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
- Department of Medical Oncology, St George Hospital, Sydney, New South Wales, Australia
| | - Ludvig Larsson
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
| | - Alexander Swarbrick
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
| | - Joakim Lundeberg
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden.
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2
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Chen K, Yu Q, Sha Q, Wang J, Fang J, Li X, Shen X, Fu B, Guo C. Single-cell transcriptomic analysis of immune cell dynamics in the healthy human endometrium. Biochem Biophys Rep 2024; 39:101802. [PMID: 39161579 PMCID: PMC11332207 DOI: 10.1016/j.bbrep.2024.101802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 06/25/2024] [Accepted: 07/23/2024] [Indexed: 08/21/2024] Open
Abstract
The microenvironment of the endometrial immune system is crucial to the success of placental implantation and healthy pregnancy. However, the functionalities of immune cells across various stages of the reproductive cycle have yet to be fully comprehended. To address this, we conducted advanced bioinformatic analysis on 230,049 high-quality single-cell transcriptomes from healthy endometrial samples obtained during the proliferative, secretory, early pregnancy, and late pregnancy stages. Our investigation has unveiled that proliferative natural killer (NK) cells, a potential source of endometrial NK cells, exhibit the most robust proliferative and differentiation potential during non-pregnant stages. We have also identified similar differentiation trajectories of NK cells originating from proliferative NK cells across four stages. Notably, during early pregnancy, NK cells demonstrate the highest oxidative phosphorylation metabolism activity, and, in conjunction with macrophages and T cells, exhibit the strongest type II interferon response. With spatial transcriptome data, we have discerned that the most robust immune-non-immune interactions are associated with the promotion and inhibition of cell proliferation, differentiation and migration during four stages. Furthermore, we have compiled lists of stage-specific risk genes implicated in reproductive diseases, which hold promise as potential disease biomarkers. Our study provides insights into the dynamics of the endometrial immune microenvironment during different reproductive cycle stages, thus serving as a reference for detecting pathological changes during pregnancy.
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Affiliation(s)
- Kaixing Chen
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230021, China
- CAS Center for Excellence in Molecular Cell Sciences, The CAS Key Laboratory of Innate Immunity and Chronic Disease, University of Science and Technology of China, 230027, Hefei, Anhui, China
| | - Qiaoni Yu
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230021, China
| | - Qing Sha
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230021, China
| | - Junyu Wang
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230021, China
| | - Jingwen Fang
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230021, China
- HanGene Biotech, Xiaoshan Innovation Polis, Hangzhou, Zhejiang, 311200, China
| | - Xin Li
- Department of Rheumatology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, 121001, China
| | - Xiaokun Shen
- Department of Immunology, School of Basic Medical Science, Jinzhou Medical University, Jinzhou, 121001, China
| | - Binqing Fu
- CAS Center for Excellence in Molecular Cell Sciences, The CAS Key Laboratory of Innate Immunity and Chronic Disease, University of Science and Technology of China, 230027, Hefei, Anhui, China
| | - Chuang Guo
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230021, China
- CAS Center for Excellence in Molecular Cell Sciences, The CAS Key Laboratory of Innate Immunity and Chronic Disease, University of Science and Technology of China, 230027, Hefei, Anhui, China
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3
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Youssef KK, Nieto MA. Epithelial-mesenchymal transition in tissue repair and degeneration. Nat Rev Mol Cell Biol 2024; 25:720-739. [PMID: 38684869 DOI: 10.1038/s41580-024-00733-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/26/2024] [Indexed: 05/02/2024]
Abstract
Epithelial-mesenchymal transitions (EMTs) are the epitome of cell plasticity in embryonic development and cancer; during EMT, epithelial cells undergo dramatic phenotypic changes and become able to migrate to form different tissues or give rise to metastases, respectively. The importance of EMTs in other contexts, such as tissue repair and fibrosis in the adult, has become increasingly recognized and studied. In this Review, we discuss the function of EMT in the adult after tissue damage and compare features of embryonic and adult EMT. Whereas sustained EMT leads to adult tissue degeneration, fibrosis and organ failure, its transient activation, which confers phenotypic and functional plasticity on somatic cells, promotes tissue repair after damage. Understanding the mechanisms and temporal regulation of different EMTs provides insight into how some tissues heal and has the potential to open new therapeutic avenues to promote repair or regeneration of tissue damage that is currently irreversible. We also discuss therapeutic strategies that modulate EMT that hold clinical promise in ameliorating fibrosis, and how precise EMT activation could be harnessed to enhance tissue repair.
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Affiliation(s)
| | - M Angela Nieto
- Instituto de Neurociencias (CSIC-UMH), Sant Joan d'Alacant, Spain.
- CIBERER, Centro de Investigación Biomédica en Red de Enfermedades Raras, ISCIII, Madrid, Spain.
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4
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Gulati GS, D'Silva JP, Liu Y, Wang L, Newman AM. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol 2024:10.1038/s41580-024-00768-2. [PMID: 39169166 DOI: 10.1038/s41580-024-00768-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/16/2024] [Indexed: 08/23/2024]
Abstract
Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.
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Affiliation(s)
- Gunsagar S Gulati
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Yunhe Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Aaron M Newman
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
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5
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Lapuente-Santana Ó, Sturm G, Kant J, Ausserhofer M, Zackl C, Zopoglou M, McGranahan N, Rieder D, Trajanoski Z, da Cunha Carvalho de Miranda NF, Eduati F, Finotello F. Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion. iScience 2024; 27:110529. [PMID: 39161957 PMCID: PMC11331718 DOI: 10.1016/j.isci.2024.110529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 06/03/2024] [Accepted: 07/13/2024] [Indexed: 08/21/2024] Open
Abstract
The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomics. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability explaining the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME and to predict its unfolding and fate during immunotherapy with immune checkpoint blockers.
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Affiliation(s)
- Óscar Lapuente-Santana
- Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain
| | - Gregor Sturm
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Boehringer Ingelheim International Pharma GmbH & Co KG, 55216 Ingelheim am Rhein, Germany
| | - Joan Kant
- Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
| | - Markus Ausserhofer
- Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, 6020 Innsbruck, Austria
| | - Constantin Zackl
- Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, 6020 Innsbruck, Austria
| | - Maria Zopoglou
- Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, 6020 Innsbruck, Austria
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK
- Cancer Genome Evolution Research Group, University College London Cancer Institute, London WC1E 6DD, UK
| | - Dietmar Rieder
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Zlatko Trajanoski
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | | | - Federica Eduati
- Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands
| | - Francesca Finotello
- Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, 6020 Innsbruck, Austria
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6
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Wang Y, Wang Y, Wang X, Sun W, Yang F, Yao X, Pan T, Li B, Chu J. Label-free active single-cell encapsulation enabled by microvalve-based on-demand droplet generation and real-time image processing. Talanta 2024; 276:126299. [PMID: 38788384 DOI: 10.1016/j.talanta.2024.126299] [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: 02/01/2024] [Revised: 05/01/2024] [Accepted: 05/20/2024] [Indexed: 05/26/2024]
Abstract
Droplet microfluidics-based single-cell encapsulation is a critical technology that enables large-scale parallel single-cell analysis by capturing and processing thousands of individual cells. As the efficiency of passive single-cell encapsulation is limited by Poisson distribution, active single-cell encapsulation has been developed to theoretically ensure that each droplet contains one cell. However, existing active single-cell encapsulation technologies still face issues related to fluorescence labeling and low throughput. Here, we present an active single-cell encapsulation technique by using microvalve-based drop-on-demand technology and real-time image processing to encapsulate single cells with high throughput in a label-free manner. Our experiments demonstrated that the single-cell encapsulation system can encapsulate individual polystyrene beads with 96.3 % efficiency and HeLa cells with 94.9 % efficiency. The flow speed of cells in this system can reach 150 mm/s, resulting in a corresponding theoretical encapsulation throughput of 150 Hz. This technology has significant potential in various biomedical applications, including single-cell omics, secretion detection, and drug screening.
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Affiliation(s)
- Yiming Wang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China; Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Yousu Wang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China; Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Xiaojie Wang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China; Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Wei Sun
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China; Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Fengrui Yang
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Hefei, 230026, China
| | - Xuebiao Yao
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, University of Science and Technology of China, Hefei, 230026, China
| | - Tingrui Pan
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, 215123, China
| | - Baoqing Li
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China; Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China.
| | - Jiaru Chu
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China; Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
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7
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Chokshi CR, Shaikh MV, Brakel B, Rossotti MA, Tieu D, Maich W, Anand A, Chafe SC, Zhai K, Suk Y, Kieliszek AM, Miletic P, Mikolajewicz N, Chen D, McNicol JD, Chan K, Tong AHY, Kuhlmann L, Liu L, Alizada Z, Mobilio D, Tatari N, Savage N, Aghaei N, Grewal S, Puri A, Subapanditha M, McKenna D, Ignatchenko V, Salamoun JM, Kwiecien JM, Wipf P, Sharlow ER, Provias JP, Lu JQ, Lazo JS, Kislinger T, Lu Y, Brown KR, Venugopal C, Henry KA, Moffat J, Singh SK. Targeting axonal guidance dependencies in glioblastoma with ROBO1 CAR T cells. Nat Med 2024:10.1038/s41591-024-03138-9. [PMID: 39095594 DOI: 10.1038/s41591-024-03138-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 06/18/2024] [Indexed: 08/04/2024]
Abstract
Resistance to genotoxic therapies and tumor recurrence are hallmarks of glioblastoma (GBM), an aggressive brain tumor. In this study, we investigated functional drivers of post-treatment recurrent GBM through integrative genomic analyses, genome-wide genetic perturbation screens in patient-derived GBM models and independent lines of validation. Specific genetic dependencies were found consistent across recurrent tumor models, accompanied by increased mutational burden and differential transcript and protein expression compared to its primary GBM predecessor. Our observations suggest a multi-layered genetic response to drive tumor recurrence and implicate PTP4A2 (protein tyrosine phosphatase 4A2) as a modulator of self-renewal, proliferation and tumorigenicity in recurrent GBM. Genetic perturbation or small-molecule inhibition of PTP4A2 acts through a dephosphorylation axis with roundabout guidance receptor 1 (ROBO1) and its downstream molecular players, exploiting a functional dependency on ROBO signaling. Because a pan-PTP4A inhibitor was limited by poor penetrance across the blood-brain barrier in vivo, we engineered a second-generation chimeric antigen receptor (CAR) T cell therapy against ROBO1, a cell surface receptor enriched across recurrent GBM specimens. A single dose of ROBO1-targeted CAR T cells doubled median survival in cell-line-derived xenograft (CDX) models of recurrent GBM. Moreover, in CDX models of adult lung-to-brain metastases and pediatric relapsed medulloblastoma, ROBO1 CAR T cells eradicated tumors in 50-100% of mice. Our study identifies a promising multi-targetable PTP4A-ROBO1 signaling axis that drives tumorigenicity in recurrent GBM, with potential in other malignant brain tumors.
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Affiliation(s)
- Chirayu R Chokshi
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
| | - Muhammad Vaseem Shaikh
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
- Department of Surgery, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Benjamin Brakel
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
| | - Martin A Rossotti
- Human Health Therapeutics Research Centre, Life Sciences Division, National Research Council Canada, Ottawa, ON, Canada
| | - David Tieu
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - William Maich
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
| | - Alisha Anand
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
| | - Shawn C Chafe
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
- Department of Surgery, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Kui Zhai
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
- Department of Surgery, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Yujin Suk
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
| | - Agata M Kieliszek
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
| | - Petar Miletic
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
- Department of Surgery, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Nicholas Mikolajewicz
- Program for Genetics and Genome Biology, The Hospital for Sick Children, Peter Gilgan Centre for Research & Learning, Toronto, ON, Canada
| | - David Chen
- Program for Genetics and Genome Biology, The Hospital for Sick Children, Peter Gilgan Centre for Research & Learning, Toronto, ON, Canada
| | - Jamie D McNicol
- McMaster Immunology Research Centre, McMaster University, Hamilton, ON, Canada
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Katherine Chan
- Program for Genetics and Genome Biology, The Hospital for Sick Children, Peter Gilgan Centre for Research & Learning, Toronto, ON, Canada
| | - Amy H Y Tong
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Laura Kuhlmann
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Lina Liu
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
| | - Zahra Alizada
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
- Department of Surgery, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Daniel Mobilio
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
| | - Nazanin Tatari
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
| | - Neil Savage
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
| | - Nikoo Aghaei
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
| | - Shan Grewal
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
| | - Anish Puri
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
| | | | - Dillon McKenna
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
- Department of Surgery, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | | | - Joseph M Salamoun
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jacek M Kwiecien
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Peter Wipf
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Elizabeth R Sharlow
- Department of Pharmacology, Fiske Drug Discovery Laboratory, University of Virginia, Charlottesville, VA, USA
| | - John P Provias
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Jian-Qiang Lu
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - John S Lazo
- Department of Pharmacology, Fiske Drug Discovery Laboratory, University of Virginia, Charlottesville, VA, USA
| | - Thomas Kislinger
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Yu Lu
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
| | - Kevin R Brown
- Program for Genetics and Genome Biology, The Hospital for Sick Children, Peter Gilgan Centre for Research & Learning, Toronto, ON, Canada
| | - Chitra Venugopal
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada
- Department of Surgery, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Kevin A Henry
- Human Health Therapeutics Research Centre, Life Sciences Division, National Research Council Canada, Ottawa, ON, Canada
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Jason Moffat
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Program for Genetics and Genome Biology, The Hospital for Sick Children, Peter Gilgan Centre for Research & Learning, Toronto, ON, Canada.
- Institute for Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
| | - Sheila K Singh
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada.
- Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada.
- Department of Surgery, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.
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8
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Guo DZ, Zhang X, Zhang SQ, Zhang SY, Zhang XY, Yan JY, Dong SY, Zhu K, Yang XR, Fan J, Zhou J, Huang A. Single-cell tumor heterogeneity landscape of hepatocellular carcinoma: unraveling the pro-metastatic subtype and its interaction loop with fibroblasts. Mol Cancer 2024; 23:157. [PMID: 39095854 PMCID: PMC11295380 DOI: 10.1186/s12943-024-02062-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 07/05/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND Tumor heterogeneity presents a formidable challenge in understanding the mechanisms driving tumor progression and metastasis. The heterogeneity of hepatocellular carcinoma (HCC) in cellular level is not clear. METHODS Integration analysis of single-cell RNA sequencing data and spatial transcriptomics data was performed. Multiple methods were applied to investigate the subtype of HCC tumor cells. The functional characteristics, translation factors, clinical implications and microenvironment associations of different subtypes of tumor cells were analyzed. The interaction of subtype and fibroblasts were analyzed. RESULTS We established a heterogeneity landscape of HCC malignant cells by integrated 52 single-cell RNA sequencing data and 5 spatial transcriptomics data. We identified three subtypes in tumor cells, including ARG1+ metabolism subtype (Metab-subtype), TOP2A+ proliferation phenotype (Prol-phenotype), and S100A6+ pro-metastatic subtype (EMT-subtype). Enrichment analysis found that the three subtypes harbored different features, that is metabolism, proliferating, and epithelial-mesenchymal transition. Trajectory analysis revealed that both Metab-subtype and EMT-subtype originated from the Prol-phenotype. Translation factor analysis found that EMT-subtype showed exclusive activation of SMAD3 and TGF-β signaling pathway. HCC dominated by EMT-subtype cells harbored an unfavorable prognosis and a deserted microenvironment. We uncovered a positive loop between tumor cells and fibroblasts mediated by SPP1-CD44 and CCN2/TGF-β-TGFBR1 interaction pairs. Inhibiting CCN2 disrupted the loop, mitigated the transformation to EMT-subtype, and suppressed metastasis. CONCLUSION By establishing a heterogeneity landscape of malignant cells, we identified a three-subtype classification in HCC. Among them, S100A6+ tumor cells play a crucial role in metastasis. Targeting the feedback loop between tumor cells and fibroblasts is a promising anti-metastatic strategy.
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Affiliation(s)
- De-Zhen Guo
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, 136 Yi Xue Yuan Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Xin Zhang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, 136 Yi Xue Yuan Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Sen-Quan Zhang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, 136 Yi Xue Yuan Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Shi-Yu Zhang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, 136 Yi Xue Yuan Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Xiang-Yu Zhang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, 136 Yi Xue Yuan Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jia-Yan Yan
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, 136 Yi Xue Yuan Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - San-Yuan Dong
- Department of Radiology, Zhongshan Hospital, Shanghai Institute of Medical Imaging, Fudan University, Shanghai, 200032, China
| | - Kai Zhu
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, 136 Yi Xue Yuan Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Xin-Rong Yang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, 136 Yi Xue Yuan Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jia Fan
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, 136 Yi Xue Yuan Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Institute of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Jian Zhou
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, 136 Yi Xue Yuan Road, Shanghai, 200032, China.
- Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Institute of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
- State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200032, China.
| | - Ao Huang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, 136 Yi Xue Yuan Road, Shanghai, 200032, China.
- Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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9
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Zhang Z, Sun X, Liu Y, Zhang Y, Yang Z, Dong J, Wang N, Ying J, Zhou M, Yang L. Spatial Transcriptome-Wide Profiling of Small Cell Lung Cancer Reveals Intra-Tumoral Molecular and Subtype Heterogeneity. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2402716. [PMID: 38896789 PMCID: PMC11336901 DOI: 10.1002/advs.202402716] [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: 03/14/2024] [Revised: 05/21/2024] [Indexed: 06/21/2024]
Abstract
Small cell lung cancer (SCLC) is a highly aggressive malignancy characterized by rapid growth and early metastasis and is susceptible to treatment resistance and recurrence. Understanding the intra-tumoral spatial heterogeneity in SCLC is crucial for improving patient outcomes and clinically relevant subtyping. In this study, a spatial whole transcriptome-wide analysis of 25 SCLC patients at sub-histological resolution using GeoMx Digital Spatial Profiling technology is performed. This analysis deciphered intra-tumoral multi-regional heterogeneity, characterized by distinct molecular profiles, biological functions, immune features, and molecular subtypes within spatially localized histological regions. Connections between different transcript-defined intra-tumoral phenotypes and their impact on patient survival and therapeutic response are also established. Finally, a gene signature, termed ITHtyper, based on the prevalence of intra-tumoral heterogeneity levels, which enables patient risk stratification from bulk RNA-seq profiles is identified. The prognostic value of ITHtyper is rigorously validated in independent multicenter patient cohorts. This study introduces a preliminary tumor-centric, regionally targeted spatial transcriptome resource that sheds light on previously unexplored intra-tumoral spatial heterogeneity in SCLC. These findings hold promise to improve tumor reclassification and facilitate the development of personalized treatments for SCLC patients.
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Affiliation(s)
- Zicheng Zhang
- School of Biomedical EngineeringNational Clinical Research Center for Ocular DiseasesEye HospitalWenzhou Medical UniversityWenzhou325027P. R. China
- Department of PathologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
| | - Xujie Sun
- Department of PathologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
| | - Yutao Liu
- Department of Medical OncologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
| | - Yibo Zhang
- School of Biomedical EngineeringNational Clinical Research Center for Ocular DiseasesEye HospitalWenzhou Medical UniversityWenzhou325027P. R. China
| | - Zijian Yang
- School of Biomedical EngineeringNational Clinical Research Center for Ocular DiseasesEye HospitalWenzhou Medical UniversityWenzhou325027P. R. China
| | - Jiyan Dong
- Department of PathologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
| | - Nan Wang
- Cosmos Wisdom Biotech Co. LtdBuilding 10thNo. 617 Jiner RoadHangzhou311215P. R. China
| | - Jianming Ying
- Department of PathologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
| | - Meng Zhou
- School of Biomedical EngineeringNational Clinical Research Center for Ocular DiseasesEye HospitalWenzhou Medical UniversityWenzhou325027P. R. China
| | - Lin Yang
- Department of PathologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
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10
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Huang XB, Huang Q, Jiang MC, Zhong Q, Zheng HL, Wang JB, Huang ZN, Wang HG, Liu ZY, Li YF, Xu KX, Lin M, Li P, Huang ZH, Xie JW, Lin JX, Lu J, Que JW, Zheng CH, Chen QY, Huang CM. KLHL21 suppresses gastric tumourigenesis via maintaining STAT3 signalling equilibrium in stomach homoeostasis. Gut 2024:gutjnl-2023-331111. [PMID: 38969490 DOI: 10.1136/gutjnl-2023-331111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 06/13/2024] [Indexed: 07/07/2024]
Abstract
OBJECTIVE Precancerous metaplasia transition to dysplasia poses a risk for subsequent intestinal-type gastric adenocarcinoma. However, the molecular basis underlying the transformation from metaplastic to cancerous cells remains poorly understood. DESIGN An integrated analysis of genes associated with metaplasia, dysplasia was conducted, verified and characterised in the gastric tissues of patients by single-cell RNA sequencing and immunostaining. Multiple mouse models, including homozygous conditional knockout Klhl21-floxed mice, were generated to investigate the role of Klhl21 deletion in stemness, DNA damage and tumour formation. Mass-spectrometry-based proteomics and ribosome sequencing were used to elucidate the underlying molecular mechanisms. RESULTS Kelch-like protein 21 (KLHL21) expression progressively decreased in metaplasia, dysplasia and cancer. Genetic deletion of Klhl21 enhances the rapid proliferation of Mist1+ cells and their descendant cells. Klhl21 loss during metaplasia facilitates the recruitment of damaged cells into the cell cycle via STAT3 signalling. Increased STAT3 activity was confirmed in cancer cells lacking KLHL21, boosting self-renewal and tumourigenicity. Mechanistically, the loss of KLHL21 promotes PIK3CB mRNA translation by stabilising the PABPC1-eIF4G complex, subsequently causing STAT3 activation. Pharmacological STAT3 inhibition by TTI-101 elicited anticancer effects, effectively impeding the transition from metaplasia to dysplasia. In patients with gastric cancer, low levels of KLHL21 had a shorter survival rate and a worse response to adjuvant chemotherapy. CONCLUSIONS Our findings highlighted that KLHL21 loss triggers STAT3 reactivation through PABPC1-mediated PIK3CB translational activation, and targeting STAT3 can reverse preneoplastic metaplasia in KLHL21-deficient stomachs.
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Affiliation(s)
- Xiao-Bo Huang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Qiang Huang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Mei-Chen Jiang
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Qing Zhong
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Hua-Long Zheng
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Jia-Bin Wang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Ze-Ning Huang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Hua-Gen Wang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Zhi-Yu Liu
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Yi-Fan Li
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Kai-Xiang Xu
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Mi Lin
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Ping Li
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Zhi-Hong Huang
- Public Technology Service Center, Fujian Medical University, Fuzhou, China
| | - Jian-Wei Xie
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Jian-Xian Lin
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Jun Lu
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Jian-Wen Que
- Columbia Center for Human Development, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York, USA
- Division of Digestive and Liver Diseases, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York, USA
- Herbert Irving Comprehensive Cancer Center, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Chao-Hui Zheng
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Qi-Yue Chen
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
| | - Chang-Ming Huang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fujian, China
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11
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McPherson A, Vázquez-García I, Myers MA, Zatzman M, Al-Rawi D, Weiner A, Freeman S, Mohibullah N, Satas G, Williams MJ, Ceglia N, Zhang AW, Li J, Lim JLP, Wu M, Choi S, Havasov E, Grewal D, Shi H, Kim M, Schwarz R, Kaufmann T, Dinh KN, Uhlitz F, Tran J, Wu Y, Patel R, Ramakrishnan S, Kim D, Clarke J, Green H, Ali E, DiBona M, Varice N, Kundra R, Broach V, Gardner GJ, Roche KL, Sonoda Y, Zivanovic O, Kim SH, Grisham RN, Liu YL, Viale A, Rusk N, Lakhman Y, Ellenson LH, Tavaré S, Aparicio S, Chi DS, Aghajanian C, Abu-Rustum NR, Friedman CF, Zamarin D, Weigelt B, Bakhoum SF, Shah SP. Ongoing genome doubling promotes evolvability and immune dysregulation in ovarian cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.11.602772. [PMID: 39071261 PMCID: PMC11275742 DOI: 10.1101/2024.07.11.602772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Whole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method, doubleTime , we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and cGAS-STING pathway activation result from ongoing chromosomal instability and are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily 'active' mutational process that promotes evolvability and dysregulated immunity in late stage ovarian cancer.
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12
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Lassen FH, Venkatesh SS, Baya N, Hill B, Zhou W, Bloemendal A, Neale BM, Kessler BM, Whiffin N, Lindgren CM, Palmer DS. Exome-wide evidence of compound heterozygous effects across common phenotypes in the UK Biobank. CELL GENOMICS 2024; 4:100602. [PMID: 38944039 PMCID: PMC11293579 DOI: 10.1016/j.xgen.2024.100602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 03/11/2024] [Accepted: 06/07/2024] [Indexed: 07/01/2024]
Abstract
The phenotypic impact of compound heterozygous (CH) variation has not been investigated at the population scale. We phased rare variants (MAF ∼0.001%) in the UK Biobank (UKBB) exome-sequencing data to characterize recessive effects in 175,587 individuals across 311 common diseases. A total of 6.5% of individuals carry putatively damaging CH variants, 90% of which are only identifiable upon phasing rare variants (MAF < 0.38%). We identify six recessive gene-trait associations (p < 1.68 × 10-7) after accounting for relatedness, polygenicity, nearby common variants, and rare variant burden. Of these, just one is discovered when considering homozygosity alone. Using longitudinal health records, we additionally identify and replicate a novel association between bi-allelic variation in ATP2C2 and an earlier age at onset of chronic obstructive pulmonary disease (COPD) (p < 3.58 × 10-8). Genetic phase contributes to disease risk for gene-trait pairs: ATP2C2-COPD (p = 0.000238), FLG-asthma (p = 0.00205), and USH2A-visual impairment (p = 0.0084). We demonstrate the power of phasing large-scale genetic cohorts to discover phenome-wide consequences of compound heterozygosity.
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Affiliation(s)
- Frederik H Lassen
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Samvida S Venkatesh
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Nikolas Baya
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Barney Hill
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Wei Zhou
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Alex Bloemendal
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Novo Nordisk Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin M Neale
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Benedikt M Kessler
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nicola Whiffin
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Cecilia M Lindgren
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK.
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK.
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13
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Um JH, Zheng Y, Mao Q, Nam C, Zhao H, Koh YW, Shin SJ, Park YM, Lin DC. Genomic and single-cell characterization of patient-derived tumor organoid models of head and neck squamous cell carcinoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.28.601068. [PMID: 39005427 PMCID: PMC11244938 DOI: 10.1101/2024.06.28.601068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Head and Neck Squamous Cell Carcinoma (HNSCC) remains a significant health burden due to tumor heterogeneity and treatment resistance, emphasizing the need for improved biological understanding and tailored therapies. This study enrolled 31 HNSCC patients for the establishment of patient-derived tumor organoids (PDOs), which faithfully maintained genomic features and histopathological traits of primary tumors. Long-term culture preserved key characteristics, affirming PDOs as robust representative models. PDOs demonstrated predictive capability for cisplatin treatment responses, correlating ex vivo drug sensitivity with patient outcomes. Bulk and single-cell RNA sequencing unveiled molecular subtypes and intratumor heterogeneity (ITH) in PDOs, paralleling patient tumors. Notably, a hybrid epithelial-mesenchymal transition (hEMT)-like ITH program is associated with cisplatin resistance and poor patient survival. Functional analyses identified amphiregulin (AREG) as a potential regulator of the hybrid epithelial/mesenchymal state. Moreover, AREG contributes to cisplatin resistance via EGFR pathway activation, corroborated by clinical samples. In summary, HNSCC PDOs serve as reliable and versatile models, offer predictive insights into ITH programs and treatment responses, and uncover potential therapeutic targets for personalized medicine.
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Affiliation(s)
- Jung Hyun Um
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Korea
| | - Yueyuan Zheng
- Clinical Big Data Research Center, Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| | - Qiong Mao
- Clinical Big Data Research Center, Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
- Guangzhou Municipal and Guangdong Provincial Key Laboratory of Protein Modification and Degradation, Guangzhou Medical University, Guangzhou, 510120, P.R. China
| | - Chehyun Nam
- Center for Craniofacial Molecular Biology, Herman Ostrow School of Dentistry, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, USA
| | - Hua Zhao
- Center for Craniofacial Molecular Biology, Herman Ostrow School of Dentistry, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, USA
| | - Yoon Woo Koh
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Korea
| | - Su-Jin Shin
- Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Young Min Park
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Korea
| | - De-Chen Lin
- Center for Craniofacial Molecular Biology, Herman Ostrow School of Dentistry, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, USA
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14
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Lei KC, Srinivas N, Chandra M, Kervarrec T, Coyaud E, Spassova I, Peiffer L, Houben R, Shuda M, Hoffmann D, Schrama D, Becker JC. Merkel cell polyomavirus pan-T antigen knockdown reduces cancer cell stemness and promotes neural differentiation independent of RB1. J Med Virol 2024; 96:e29789. [PMID: 38988206 DOI: 10.1002/jmv.29789] [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: 01/29/2024] [Revised: 06/10/2024] [Accepted: 07/02/2024] [Indexed: 07/12/2024]
Abstract
Merkel cell carcinoma (MCC) is a highly aggressive skin cancer associated with integration of Merkel cell polyomavirus (MCPyV). MCPyV-encoded T-antigens (TAs) are pivotal for sustaining MCC's oncogenic phenotype, i.e., repression of TAs results in reactivation of the RB pathway and subsequent cell cycle arrest. However, the MCC cell line LoKe, characterized by a homozygous loss of the RB1 gene, exhibits uninterrupted cell cycle progression after shRNA-mediated TA repression. This unique feature allows an in-depth analysis of the effects of TAs beyond inhibition of the RB pathway, revealing the decrease in expression of stem cell-related genes upon panTA-knockdown. Analysis of gene regulatory networks identified members of the E2F family (E2F1, E2F8, TFDP1) as key transcriptional regulators that maintain stem cell properties in TA-expressing MCC cells. Furthermore, minichromosome maintenance (MCM) genes, which encodes DNA-binding licensing proteins essential for stem cell maintenance, were suppressed upon panTA-knockdown. The decline in stemness occurred simultaneously with neural differentiation, marked by the increased expression of neurogenesis-related genes such as neurexins, BTG2, and MYT1L. This upregulation can be attributed to heightened activity of PBX1 and BPTF, crucial regulators of neurogenesis pathways. The observations in LoKe were confirmed in an additional MCPyV-positive MCC cell line in which RB1 was silenced before panTA-knockdown. Moreover, spatially resolved transcriptomics demonstrated reduced TA expression in situ in a part of a MCC tumor characterized by neural differentiation. In summary, TAs are critical for maintaining stemness of MCC cells and suppressing neural differentiation, irrespective of their impact on the RB-signaling pathway.
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Affiliation(s)
- Kuan Cheok Lei
- German Cancer Research Centre (DKFZ), Heidelberg, Germany
- Translational Skin Cancer Research, German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Nalini Srinivas
- German Cancer Research Centre (DKFZ), Heidelberg, Germany
- Translational Skin Cancer Research, German Cancer Consortium (DKTK), Heidelberg, Germany
- Department of Dermatology, University Hospital Essen, Essen, Germany
| | - Mitalee Chandra
- German Cancer Research Centre (DKFZ), Heidelberg, Germany
- Translational Skin Cancer Research, German Cancer Consortium (DKTK), Heidelberg, Germany
- Department of Dermatology, University Hospital Essen, Essen, Germany
| | - Thibault Kervarrec
- Department of Pathology, Université de Tours, Centre Hospitalier Universitaire de Tours, Tours, France
| | - Etienne Coyaud
- Department of Biology, University Lille, INSERM, Protéomique Réponse Inflammatoire Spectrométrie de Masse (PRISM), Lille, France
| | - Ivelina Spassova
- German Cancer Research Centre (DKFZ), Heidelberg, Germany
- Translational Skin Cancer Research, German Cancer Consortium (DKTK), Heidelberg, Germany
- Department of Dermatology, University Hospital Essen, Essen, Germany
| | - Lukas Peiffer
- German Cancer Research Centre (DKFZ), Heidelberg, Germany
- Translational Skin Cancer Research, German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Roland Houben
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, Würzburg, Germany
| | - Masahiro Shuda
- Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Daniel Hoffmann
- Bioinformatics and Computational Biophysics, Faculty of Biology, University of Duisburg-Essen, Essen, Germany
| | - David Schrama
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, Würzburg, Germany
| | - Jürgen C Becker
- German Cancer Research Centre (DKFZ), Heidelberg, Germany
- Translational Skin Cancer Research, German Cancer Consortium (DKTK), Heidelberg, Germany
- Department of Dermatology, University Hospital Essen, Essen, Germany
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15
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Hu M, Chikina M. Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods. Genome Biol 2024; 25:169. [PMID: 38956606 PMCID: PMC11218230 DOI: 10.1186/s13059-024-03292-w] [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: 01/20/2023] [Accepted: 05/29/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions. RESULTS In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers. CONCLUSIONS Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516 , enabling further developments in deconvolution methods.
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Affiliation(s)
- Mengying Hu
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, USA
- Joint Carnegie Mellon - University of Pittsburgh Computational Biology PhD Program, University of Pittsburgh, Pittsburgh, USA
| | - Maria Chikina
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, USA.
- Joint Carnegie Mellon - University of Pittsburgh Computational Biology PhD Program, University of Pittsburgh, Pittsburgh, USA.
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16
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Fu YC, Das A, Wang D, Braun R, Yi R. scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis. Genome Biol 2024; 25:164. [PMID: 38915088 PMCID: PMC11197379 DOI: 10.1186/s13059-024-03299-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 06/04/2024] [Indexed: 06/26/2024] Open
Abstract
Spatial transcriptomics has transformed our ability to study tissue complexity. However, it remains challenging to accurately dissect tissue organization at single-cell resolution. Here we introduce scHolography, a machine learning-based method designed to reconstruct single-cell spatial neighborhoods and facilitate 3D tissue visualization using spatial and single-cell RNA sequencing data. scHolography employs a high-dimensional transcriptome-to-space projection that infers spatial relationships among cells, defining spatial neighborhoods and enhancing analyses of cell-cell communication. When applied to both human and mouse datasets, scHolography enables quantitative assessments of spatial cell neighborhoods, cell-cell interactions, and tumor-immune microenvironment. Together, scHolography offers a robust computational framework for elucidating 3D tissue organization and analyzing spatial dynamics at the cellular level.
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Affiliation(s)
- Yuheng C Fu
- Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Arpan Das
- Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Dongmei Wang
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Rosemary Braun
- Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA.
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, 60208, USA.
- Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA.
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL, 60208, USA.
| | - Rui Yi
- Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
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17
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Li X, Zhang C, Yue W, Jiang Y. Modulatory effects of cancer stem cell-derived extracellular vesicles on the tumor immune microenvironment. Front Immunol 2024; 15:1362120. [PMID: 38962016 PMCID: PMC11219812 DOI: 10.3389/fimmu.2024.1362120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 06/03/2024] [Indexed: 07/05/2024] Open
Abstract
Cancer stem cells (CSCs), accounting for only a minor cell proportion (< 1%) within tumors, have profound implications in tumor initiation, metastasis, recurrence, and treatment resistance due to their inherent ability of self-renewal, multi-lineage differentiation, and tumor-initiating potential. In recent years, accumulating studies indicate that CSCs and tumor immune microenvironment act reciprocally in driving tumor progression and diminishing the efficacy of cancer therapies. Extracellular vesicles (EVs), pivotal mediators of intercellular communications, build indispensable biological connections between CSCs and immune cells. By transferring bioactive molecules, including proteins, nucleic acids, and lipids, EVs can exert mutual influence on both CSCs and immune cells. This interaction plays a significant role in reshaping the tumor immune microenvironment, creating conditions favorable for the sustenance and propagation of CSCs. Deciphering the intricate interplay between CSCs and immune cells would provide valuable insights into the mechanisms of CSCs being more susceptible to immune escape. This review will highlight the EV-mediated communications between CSCs and each immune cell lineage in the tumor microenvironment and explore potential therapeutic opportunities.
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Affiliation(s)
- Xinyu Li
- Department of Animal Science, College of Animal Science, Hebei North University, Zhangjiakou, Hebei, China
- Department of Gynecology and Obstetrics, Key Laboratory for Major Obstetric Diseases of Guangdong Province, Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Cuilian Zhang
- Reproductive Medicine Center, Henan Provincial People’s Hospital, Zhengzhou University, Zhengzhou, China
| | - Wei Yue
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
- National Clinical Research Center for Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
- Key Laboratory of Assisted Reproduction, Peking University, Ministry of Education, Beijing, China
| | - Yuening Jiang
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
- National Clinical Research Center for Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
- Key Laboratory of Assisted Reproduction, Peking University, Ministry of Education, Beijing, China
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18
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Zhang S, Xiao X, Yi Y, Wang X, Zhu L, Shen Y, Lin D, Wu C. Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets. Signal Transduct Target Ther 2024; 9:149. [PMID: 38890350 PMCID: PMC11189549 DOI: 10.1038/s41392-024-01848-7] [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: 01/01/2024] [Revised: 04/23/2024] [Accepted: 04/27/2024] [Indexed: 06/20/2024] Open
Abstract
Tumorigenesis is a multistep process, with oncogenic mutations in a normal cell conferring clonal advantage as the initial event. However, despite pervasive somatic mutations and clonal expansion in normal tissues, their transformation into cancer remains a rare event, indicating the presence of additional driver events for progression to an irreversible, highly heterogeneous, and invasive lesion. Recently, researchers are emphasizing the mechanisms of environmental tumor risk factors and epigenetic alterations that are profoundly influencing early clonal expansion and malignant evolution, independently of inducing mutations. Additionally, clonal evolution in tumorigenesis reflects a multifaceted interplay between cell-intrinsic identities and various cell-extrinsic factors that exert selective pressures to either restrain uncontrolled proliferation or allow specific clones to progress into tumors. However, the mechanisms by which driver events induce both intrinsic cellular competency and remodel environmental stress to facilitate malignant transformation are not fully understood. In this review, we summarize the genetic, epigenetic, and external driver events, and their effects on the co-evolution of the transformed cells and their ecosystem during tumor initiation and early malignant evolution. A deeper understanding of the earliest molecular events holds promise for translational applications, predicting individuals at high-risk of tumor and developing strategies to intercept malignant transformation.
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Affiliation(s)
- Shaosen Zhang
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Xinyi Xiao
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Yonglin Yi
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Xinyu Wang
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Lingxuan Zhu
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
- Changping Laboratory, 100021, Beijing, China
| | - Yanrong Shen
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China
| | - Dongxin Lin
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China.
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China.
- Changping Laboratory, 100021, Beijing, China.
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, 211166, China.
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, 510060, China.
| | - Chen Wu
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China.
- Key Laboratory of Cancer Genomic Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021, Beijing, China.
- Changping Laboratory, 100021, Beijing, China.
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, 211166, China.
- CAMS Oxford Institute, Chinese Academy of Medical Sciences, 100006, Beijing, China.
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19
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Desai P, Takahashi N, Kumar R, Nichols S, Malin J, Hunt A, Schultz C, Cao Y, Tillo D, Nousome D, Chauhan L, Sciuto L, Jordan K, Rajapakse V, Tandon M, Lissa D, Zhang Y, Kumar S, Pongor L, Singh A, Schroder B, Sharma AK, Chang T, Vilimas R, Pinkiert D, Graham C, Butcher D, Warner A, Sebastian R, Mahon M, Baker K, Cheng J, Berger A, Lake R, Abel M, Krishnamurthy M, Chrisafis G, Fitzgerald P, Nirula M, Goyal S, Atkinson D, Bateman NW, Abulez T, Nair G, Apolo A, Guha U, Karim B, El Meskini R, Ohler ZW, Jolly MK, Schaffer A, Ruppin E, Kleiner D, Miettinen M, Brown GT, Hewitt S, Conrads T, Thomas A. Microenvironment shapes small-cell lung cancer neuroendocrine states and presents therapeutic opportunities. Cell Rep Med 2024; 5:101610. [PMID: 38897168 PMCID: PMC11228806 DOI: 10.1016/j.xcrm.2024.101610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 08/04/2023] [Accepted: 05/17/2024] [Indexed: 06/21/2024]
Abstract
Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intratumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the cell-extrinsic drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the TME exhibit substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAFs) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLC's adaptable nature, opening possibilities for reprogramming the TME-tumor communications that shape SCLC tumor states.
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Affiliation(s)
- Parth Desai
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Medical Oncology, Fox Chase Cancer Center, Temple University Hospital and Lewis Katz School of Medicine, Philadelphia, PA, USA
| | - Nobuyuki Takahashi
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Medical Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Rajesh Kumar
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Samantha Nichols
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Justin Malin
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Allison Hunt
- Women's Health Integrated Research Center, Inova Health System, Falls Church, VA, USA
| | - Christopher Schultz
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yingying Cao
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Desiree Tillo
- CCR Collaborative Bioinformatics, Resource, Office of Science and Technology Resources, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Darryl Nousome
- CCR Collaborative Bioinformatics, Resource, Office of Science and Technology Resources, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lakshya Chauhan
- Center for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Linda Sciuto
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kimberly Jordan
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Vinodh Rajapakse
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mayank Tandon
- CCR Collaborative Bioinformatics, Resource, Office of Science and Technology Resources, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Delphine Lissa
- Laboratory of Human Carcinogenesis, Center for Cancer Research National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yang Zhang
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Suresh Kumar
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lorinc Pongor
- HCEMM Cancer Genomics and Epigenetics Research Group, Szeged, Hungary
| | - Abhay Singh
- Center for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Brett Schroder
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ajit Kumar Sharma
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Tiangen Chang
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rasa Vilimas
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Danielle Pinkiert
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Chante Graham
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Donna Butcher
- Molecular Histopathology Laboratory, Laboratory Animal Sciences Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Andrew Warner
- Molecular Histopathology Laboratory, Laboratory Animal Sciences Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Robin Sebastian
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mimi Mahon
- Pain and Palliative care services, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Karen Baker
- Pain and Palliative care services, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Jennifer Cheng
- Pain and Palliative care services, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Ann Berger
- Pain and Palliative care services, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Ross Lake
- Laboratory of Genitourinary cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Melissa Abel
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Manan Krishnamurthy
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - George Chrisafis
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter Fitzgerald
- CCR Collaborative Bioinformatics, Resource, Office of Science and Technology Resources, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Micheal Nirula
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shubhank Goyal
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Devon Atkinson
- Center for Advanced Preclinical Research, Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Nicholas W Bateman
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD, USA
| | - Tamara Abulez
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD, USA
| | - Govind Nair
- National Institute of Neurological Disorders and Stroke, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Andrea Apolo
- Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Udayan Guha
- Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Baktiar Karim
- Molecular Histopathology Laboratory, Laboratory Animal Sciences Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Rajaa El Meskini
- Center for Advanced Preclinical Research, Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Zoe Weaver Ohler
- Center for Advanced Preclinical Research, Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Mohit Kumar Jolly
- Center for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Alejandro Schaffer
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - David Kleiner
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Markku Miettinen
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - G Tom Brown
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen Hewitt
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Thomas Conrads
- Women's Health Integrated Research Center, Inova Health System, Falls Church, VA, USA
| | - Anish Thomas
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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20
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Mazzoccoli L, Liu B. Dendritic Cells in Shaping Anti-Tumor T Cell Response. Cancers (Basel) 2024; 16:2211. [PMID: 38927916 PMCID: PMC11201542 DOI: 10.3390/cancers16122211] [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: 05/07/2024] [Revised: 06/07/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
Among professional antigen-presenting cells, dendritic cells (DCs) orchestrate innate and adaptive immunity and play a pivotal role in anti-tumor immunity. DCs are a heterogeneous population with varying functions in the tumor microenvironment (TME). Tumor-associated DCs differentiate developmentally and functionally into three main subsets: conventional DCs (cDCs), plasmacytoid DCs (pDCs), and monocyte-derived DCs (MoDCs). There are two major subsets of cDCs in TME, cDC1 and cDC2. cDC1 is critical for cross-presenting tumor antigens to activate cytotoxic CD8+ T cells and is also required for priming earlier CD4+ T cells in certain solid tumors. cDC2 is vital for priming anti-tumor CD4+ T cells in multiple tumor models. pDC is a unique subset of DCs and produces type I IFN through TLR7 and TLR9. Studies have shown that pDCs are related to immunosuppression in the TME through the secretion of immunosuppressive cytokines and by promoting regulatory T cells. MoDCs differentiate separately from monocytes in response to inflammatory cues and infection. Also, MoDCs can cross-prime CD8+ T cells. In this review, we summarize the subsets and functions of DCs. We also discuss the role of different DC subsets in shaping T cell immunity in TME and targeting DCs for potential immunotherapeutic benefits against cancer.
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Affiliation(s)
- Luciano Mazzoccoli
- Division of Hematology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA;
- The Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA
| | - Bei Liu
- Division of Hematology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA;
- The Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA
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21
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Winter PS, Ramseier ML, Navia AW, Saksena S, Strouf H, Senhaji N, DenAdel A, Mirza M, An HH, Bilal L, Dennis P, Leahy CS, Shigemori K, Galves-Reyes J, Zhang Y, Powers F, Mulugeta N, Gupta AJ, Calistri N, Van Scoyk A, Jones K, Liu H, Stevenson KE, Ren S, Luskin MR, Couturier CP, Amini AP, Raghavan S, Kimmerling RJ, Stevens MM, Crawford L, Weinstock DM, Manalis SR, Shalek AK, Murakami MA. Mutation and cell state compatibility is required and targetable in Ph+ acute lymphoblastic leukemia minimal residual disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597767. [PMID: 38915726 PMCID: PMC11195125 DOI: 10.1101/2024.06.06.597767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Efforts to cure BCR::ABL1 B cell acute lymphoblastic leukemia (Ph+ ALL) solely through inhibition of ABL1 kinase activity have thus far been insufficient despite the availability of tyrosine kinase inhibitors (TKIs) with broad activity against resistance mutants. The mechanisms that drive persistence within minimal residual disease (MRD) remain poorly understood and therefore untargeted. Utilizing 13 patient-derived xenograft (PDX) models and clinical trial specimens of Ph+ ALL, we examined how genetic and transcriptional features co-evolve to drive progression during prolonged TKI response. Our work reveals a landscape of cooperative mutational and transcriptional escape mechanisms that differ from those causing resistance to first generation TKIs. By analyzing MRD during remission, we show that the same resistance mutation can either increase or decrease cellular fitness depending on transcriptional state. We further demonstrate that directly targeting transcriptional state-associated vulnerabilities at MRD can overcome BCR::ABL1 independence, suggesting a new paradigm for rationally eradicating MRD prior to relapse. Finally, we illustrate how cell mass measurements of leukemia cells can be used to rapidly monitor dominant transcriptional features of Ph+ ALL to help rationally guide therapeutic selection from low-input samples.
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Affiliation(s)
- Peter S. Winter
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Michelle L. Ramseier
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Andrew W. Navia
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Sachit Saksena
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Computational and Systems Biology Program, MIT, Cambridge, MA, USA
| | - Haley Strouf
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Nezha Senhaji
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Alan DenAdel
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
- Department of Biostatistics, Brown University, Providence, RI, USA
| | - Mahnoor Mirza
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Hyun Hwan An
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Laura Bilal
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Peter Dennis
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Catharine S. Leahy
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kay Shigemori
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jennyfer Galves-Reyes
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Ye Zhang
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Foster Powers
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nolawit Mulugeta
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | | | - Nicholas Calistri
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Alex Van Scoyk
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kristen Jones
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Huiyun Liu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Siyang Ren
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA USA
| | - Marlise R. Luskin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Charles P. Couturier
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | | | - Srivatsan Raghavan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Mark M. Stevens
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Lorin Crawford
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
- Department of Biostatistics, Brown University, Providence, RI, USA
- Microsoft Research, Cambridge, MA, USA
| | - David M. Weinstock
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Current Address: Merck and Co., Rahway, NJ, USA
| | - Scott R. Manalis
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Alex K. Shalek
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Mark A. Murakami
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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22
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Patel AS, Yanai I. A developmental constraint model of cancer cell states and tumor heterogeneity. Cell 2024; 187:2907-2918. [PMID: 38848676 PMCID: PMC11256907 DOI: 10.1016/j.cell.2024.04.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/29/2023] [Accepted: 04/24/2024] [Indexed: 06/09/2024]
Abstract
Cancer is a disease that stems from a fundamental liability inherent to multicellular life forms in which an individual cell is capable of reneging on the interests of the collective organism. Although cancer is commonly described as an evolutionary process, a less appreciated aspect of tumorigenesis may be the constraints imposed by the organism's developmental programs. Recent work from single-cell transcriptomic analyses across a range of cancer types has revealed the recurrence, plasticity, and co-option of distinct cellular states among cancer cell populations. Here, we note that across diverse cancer types, the observed cell states are proximate within the developmental hierarchy of the cell of origin. We thus posit a model by which cancer cell states are directly constrained by the organism's "developmental map." According to this model, a population of cancer cells traverses the developmental map, thereby generating a heterogeneous set of states whose interactions underpin emergent tumor behavior.
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Affiliation(s)
- Ayushi S Patel
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA; Department of Biochemistry & Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, USA; Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Itai Yanai
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA; Department of Biochemistry & Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, USA; Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA.
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23
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Church J. Being a Colorectal Geneticist. Dis Colon Rectum 2024; 67:743-747. [PMID: 38441139 DOI: 10.1097/dcr.0000000000003306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Affiliation(s)
- James Church
- Global Center for Integrated Colorectal Surgery and IBD Interventional Endoscopy, Columbia University Medical Center, New York, New York
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24
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Xiao S, Ma S, Sun B, Pu W, Duan S, Han J, Hong Y, Zhang J, Peng Y, He C, Yi P, Caligiuri MA, Yu J. The tumor-intrinsic role of the m 6A reader YTHDF2 in regulating immune evasion. Sci Immunol 2024; 9:eadl2171. [PMID: 38820140 DOI: 10.1126/sciimmunol.adl2171] [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: 10/05/2023] [Accepted: 05/09/2024] [Indexed: 06/02/2024]
Abstract
Tumors evade attacks from the immune system through various mechanisms. Here, we identify a component of tumor immune evasion mediated by YTH domain-containing family protein 2 (YTHDF2), a reader protein that usually destabilizes m6A-modified mRNA. Loss of tumoral YTHDF2 inhibits tumor growth and prolongs survival in immunocompetent tumor models. Mechanistically, tumoral YTHDF2 deficiency promotes the recruitment of macrophages via CX3CL1 and enhances mitochondrial respiration of CD8+ T cells by impairing tumor glycolysis metabolism. Tumoral YTHDF2 deficiency promotes inflammatory macrophage polarization and antigen presentation in the presence of IFN-γ. In addition, IFN-γ induces autophagic degradation of tumoral YTHDF2, thereby sensitizing tumor cells to CD8+ T cell-mediated cytotoxicity. Last, we identified a small molecule compound that preferentially induces YTHDF2 degradation, which shows a potent antitumor effect alone but a better effect when combined with anti-PD-L1 or anti-PD-1 antibodies. Collectively, YTHDF2 appears to be a tumor-intrinsic regulator that orchestrates immune evasion, representing a promising target for enhancing cancer immunotherapy.
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Affiliation(s)
- Sai Xiao
- Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Los Angeles, CA 91010, USA
- Hematologic Malignancies Research Institute, City of Hope National Medical Center, Los Angeles, CA 91010, USA
| | - Shoubao Ma
- Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Los Angeles, CA 91010, USA
- Hematologic Malignancies Research Institute, City of Hope National Medical Center, Los Angeles, CA 91010, USA
- Comprehensive Cancer Center, City of Hope, Los Angeles, CA 91010, USA
| | - Baofa Sun
- State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for Cell Responses, College of Life Sciences, Nankai University, Tianjin 300071, China
| | - Wenchen Pu
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Chengdu, China
| | - Songqi Duan
- College of Food Science, Sichuan Agricultural University, Ya'an, Sichuan 625014, China
| | - Jingjing Han
- Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Los Angeles, CA 91010, USA
- Hematologic Malignancies Research Institute, City of Hope National Medical Center, Los Angeles, CA 91010, USA
| | - Yaqun Hong
- Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Los Angeles, CA 91010, USA
- Hematologic Malignancies Research Institute, City of Hope National Medical Center, Los Angeles, CA 91010, USA
| | - Jianying Zhang
- Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Los Angeles, CA 91010, USA
| | - Yong Peng
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Chengdu, China
| | - Chuan He
- Department of Chemistry, Department of Biochemistry and Molecular Biology, and Institute for Biophysical Dynamics, University of Chicago, Chicago, IL 60637, USA
| | - Ping Yi
- Third Affiliated Hospital of Chongqing Medical University, Chongqing 401120, China
| | - Michael A Caligiuri
- Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Los Angeles, CA 91010, USA
- Hematologic Malignancies Research Institute, City of Hope National Medical Center, Los Angeles, CA 91010, USA
- Comprehensive Cancer Center, City of Hope, Los Angeles, CA 91010, USA
| | - Jianhua Yu
- Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Los Angeles, CA 91010, USA
- Hematologic Malignancies Research Institute, City of Hope National Medical Center, Los Angeles, CA 91010, USA
- Comprehensive Cancer Center, City of Hope, Los Angeles, CA 91010, USA
- Department of Immuno-Oncology, Beckman Research Institute, City of Hope, Los Angeles, CA 91010, USA
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25
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Zhang C, Qin Y, Wu Y, Xu H, Shu Y. Long non-coding RNA MALAT1 in hematological malignancies and its clinical applications. Chin Med J (Engl) 2024; 137:1151-1159. [PMID: 38557962 PMCID: PMC11101235 DOI: 10.1097/cm9.0000000000003090] [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: 11/17/2023] [Indexed: 04/04/2024] Open
Abstract
ABSTRACT Metastasis-associated lung adenocarcinoma transcript 1 ( MALAT1 ) is a well-established oncogenic long non-coding RNA, the higher expression of which is strongly correlated with cancer events such as tumorigenesis, progression, metastasis, drug resistance, and treatment outcome in solid cancers. Recently, a series of studies has highlighted its potential role in hematological malignancies in terms of these events. Similar to solid cancers, MALAT1 can regulate various target genes via sponging and epigenetic mechanisms, but the miRNAs sponged by MALAT1 differ from those identified in solid cancers. In this review, we systematically describe the role and underlying mechanisms of MALAT1 in multiple types of hematological malignancies, including regulation of cell proliferation, metastasis, stress response, and glycolysis. Clinically, MALAT1 expression is related to poor treatment outcome and drug resistance, therefore exhibiting potential prognostic value in multiple myeloma, lymphoma, and leukemia. Finally, we discuss the evaluation of MALAT1 as a novel therapeutic target against cancer in preclinical studies.
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Affiliation(s)
- Chunlan Zhang
- Department of Hematology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yun Qin
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yu Wu
- Department of Hematology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Heng Xu
- Department of Laboratory Medicine/Research Center of Clinical Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Biotherapy, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Institute of General Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yang Shu
- Department of Biotherapy, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of General Surgery, Gastric Cancer Center and Laboratory of Gastric Cancer, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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26
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Kang J, Lee JH, Cha H, An J, Kwon J, Lee S, Kim S, Baykan MY, Kim SY, An D, Kwon AY, An HJ, Lee SH, Choi JK, Park JE. Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types. Nat Commun 2024; 15:4067. [PMID: 38744958 PMCID: PMC11094150 DOI: 10.1038/s41467-024-48310-4] [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: 05/26/2023] [Accepted: 04/26/2024] [Indexed: 05/16/2024] Open
Abstract
The complexity of the tumor microenvironment poses significant challenges in cancer therapy. Here, to comprehensively investigate the tumor-normal ecosystems, we perform an integrative analysis of 4.9 million single-cell transcriptomes from 1070 tumor and 493 normal samples in combination with pan-cancer 137 spatial transcriptomics, 8887 TCGA, and 1261 checkpoint inhibitor-treated bulk tumors. We define a myriad of cell states constituting the tumor-normal ecosystems and also identify hallmark gene signatures across different cell types and organs. Our atlas characterizes distinctions between inflammatory fibroblasts marked by AKR1C1 or WNT5A in terms of cellular interactions and spatial co-localization patterns. Co-occurrence analysis reveals interferon-enriched community states including tertiary lymphoid structure (TLS) components, which exhibit differential rewiring between tumor, adjacent normal, and healthy normal tissues. The favorable response of interferon-enriched community states to immunotherapy is validated using immunotherapy-treated cancers (n = 1261) including our lung cancer cohort (n = 497). Deconvolution of spatial transcriptomes discriminates TLS-enriched from non-enriched cell types among immunotherapy-favorable components. Our systematic dissection of tumor-normal ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity.
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Affiliation(s)
- Junho Kang
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jun Hyeong Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Hongui Cha
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jinhyeon An
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Joonha Kwon
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- Division of Cancer Data Science, National Cancer Center, Bioinformatics Branch, Goyang, Republic of Korea
| | - Seongwoo Lee
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Seongryong Kim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Mert Yakup Baykan
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - So Yeon Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Dohyeon An
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Ah-Young Kwon
- Department of Pathology, CHA Bundang Medical Center, CHA University, Seongnam-si, Republic of Korea
| | - Hee Jung An
- Department of Pathology, CHA Bundang Medical Center, CHA University, Seongnam-si, Republic of Korea
| | - Se-Hoon Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
- Department of Health Sciences and Technology, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Jung Kyoon Choi
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
- Penta Medix Co., Ltd., Seongnam-si, Gyeonggi-do, Republic of Korea.
| | - Jong-Eun Park
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
- Biomedical Research Center, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
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27
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Schmidt M, Avagyan S, Reiche K, Binder H, Loeffler-Wirth H. A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections. Curr Issues Mol Biol 2024; 46:4701-4720. [PMID: 38785552 PMCID: PMC11119626 DOI: 10.3390/cimb46050284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.
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Affiliation(s)
- Maria Schmidt
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
| | - Susanna Avagyan
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia
| | - Kristin Reiche
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Perlickstrasse 1, 04103 Leipzig, Germany
- Institute for Clinical Immunology, University Hospital of Leipzig, 04103 Leipzig, Germany
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
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28
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Greenwald AC, Darnell NG, Hoefflin R, Simkin D, Mount CW, Gonzalez Castro LN, Harnik Y, Dumont S, Hirsch D, Nomura M, Talpir T, Kedmi M, Goliand I, Medici G, Laffy J, Li B, Mangena V, Keren-Shaul H, Weller M, Addadi Y, Neidert MC, Suvà ML, Tirosh I. Integrative spatial analysis reveals a multi-layered organization of glioblastoma. Cell 2024; 187:2485-2501.e26. [PMID: 38653236 PMCID: PMC11088502 DOI: 10.1016/j.cell.2024.03.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 01/11/2024] [Accepted: 03/21/2024] [Indexed: 04/25/2024]
Abstract
Glioma contains malignant cells in diverse states. Here, we combine spatial transcriptomics, spatial proteomics, and computational approaches to define glioma cellular states and uncover their organization. We find three prominent modes of organization. First, gliomas are composed of small local environments, each typically enriched with one major cellular state. Second, specific pairs of states preferentially reside in proximity across multiple scales. This pairing of states is consistent across tumors. Third, these pairwise interactions collectively define a global architecture composed of five layers. Hypoxia appears to drive the layers, as it is associated with a long-range organization that includes all cancer cell states. Accordingly, tumor regions distant from any hypoxic/necrotic foci and tumors that lack hypoxia such as low-grade IDH-mutant glioma are less organized. In summary, we provide a conceptual framework for the organization of cellular states in glioma, highlighting hypoxia as a long-range tissue organizer.
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Affiliation(s)
- Alissa C Greenwald
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Noam Galili Darnell
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Rouven Hoefflin
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Department of Medicine I, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Dor Simkin
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Christopher W Mount
- Department of Pathology, Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - L Nicolas Gonzalez Castro
- Department of Pathology, Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Yotam Harnik
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Sydney Dumont
- Department of Pathology, Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dana Hirsch
- Immunohistochemistry Unit, Department of Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel
| | - Masashi Nomura
- Department of Pathology, Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Tom Talpir
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Merav Kedmi
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Inna Goliand
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Gioele Medici
- Clinical Neuroscience Center, Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Julie Laffy
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Baoguo Li
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Vamsi Mangena
- Department of Pathology, Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hadas Keren-Shaul
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Michael Weller
- Clinical Neuroscience Center, Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Yoseph Addadi
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Marian C Neidert
- Clinical Neuroscience Center, Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neurosurgery, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Mario L Suvà
- Department of Pathology, Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Itay Tirosh
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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29
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Zeng L, Zhang L, Yin C, Chen X, Chen X, Sun L, Sun J. Characterization of zinc finger protein 536, a neuroendocrine regulator, using pan-cancer analysis. Eur J Med Res 2024; 29:273. [PMID: 38720348 PMCID: PMC11077744 DOI: 10.1186/s40001-024-01792-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/12/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Previous studies suggested that zinc finger protein 536 (ZNF536) was abundant in the central brain and regulated neuronal differentiation. However, the role of ZNF536 in cancer has remained unclear. METHODS ZNF536 mutation, copy number alteration, DNA methylation, and RNA expression were explored using public portals. Data from The Cancer Genome Atlas (TCGA) were utilized to analyze pathways and tumor microenvironment (TME), with a focus on prognosis in both TCGA and immunotherapy pan-cancer cohorts. Methylated ZNF536 from small cell lung cancer (SCLC) cell lines were utilized to train with probes for conducting enrichment analysis. Single-cell RNA profile demonstrated the sublocalization and co-expression of ZNF536, and validated its targets by qPCR. RESULTS Genetic alterations in ZNF536 were found to be high-frequency and a single sample could harbor different variations. ZNF536 at chromosome 19q12 exerted a bypass effect on CCNE1, supported by CRISPR data. For lung cancer, ZNF536 mutation was associated with longer survival in primary lung adenocarcinoma (LUAD), but its prognosis was poor in metastatic LUAD and SCLC. Importantly, ZNF536 mutation and amplification had opposite prognoses in Stand Up To Cancer-Mark Foundation (SU2C-MARK) LUAD cohort. ZNF536 mutation altered the patterns of genomic alterations in tumors, and had distinct impacts on the signaling pathways and TME compared to ZNF536 amplification. Additionally, ZNF536 expression was predominantly in endocrine tumors and brain tissues. High-dimensional analysis supported this finding and further revealed regulators of ZNF536. Considering that the methylation of ZNF536 was involved in the synaptic pathway associated with neuroendocrine neoplasms, demonstrating both diagnostic and prognostic value. Moreover, we experimentally verified ZNF536 upregulated neuroendocrine markers. CONCLUSIONS Our results showed that ZNF536 alterations in cancer, including variations in copy number, mutation, and methylation. We proved the involvement of ZNF536 in neuroendocrine regulation, and identified highly altered ZNF536 as a potential biomarker for immunotherapy.
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Affiliation(s)
- Longjin Zeng
- Department of Basic Medicine, Army Medical University, Chongqing, 400038, People's Republic of China
| | - Longyao Zhang
- Cancer Institute, Xinqiao Hospital, Army Medical University, Chongqing, 400037, People's Republic of China
| | - Chenrui Yin
- Cancer Institute, Xinqiao Hospital, Army Medical University, Chongqing, 400037, People's Republic of China
| | - Xu Chen
- Department of Medical Affairs, Xinqiao Hospital, Army Medical University, Chongqing, 400037, People's Republic of China
| | - Xiewan Chen
- Department of Basic Medicine, Army Medical University, Chongqing, 400038, People's Republic of China
| | - Lingyou Sun
- Cancer Institute, Xinqiao Hospital, Army Medical University, Chongqing, 400037, People's Republic of China
| | - Jianguo Sun
- Cancer Institute, Xinqiao Hospital, Army Medical University, Chongqing, 400037, People's Republic of China.
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30
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Kotliar D, Curtis M, Agnew R, Weinand K, Nathan A, Baglaenko Y, Zhao Y, Sabeti PC, Rao DA, Raychaudhuri S. Reproducible single cell annotation of programs underlying T-cell subsets, activation states, and functions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.03.592310. [PMID: 38746317 PMCID: PMC11092745 DOI: 10.1101/2024.05.03.592310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
T-cells recognize antigens and induce specialized gene expression programs (GEPs) enabling functions including proliferation, cytotoxicity, and cytokine production. Traditionally, different classes of helper T-cells express mutually exclusive responses - for example, Th1, Th2, and Th17 programs. However, new single-cell RNA sequencing (scRNA-Seq) experiments have revealed a continuum of T-cell states without discrete clusters corresponding to these subsets, implying the need for new analytical frameworks. Here, we advance the characterization of T-cells with T-CellAnnoTator (TCAT), a pipeline that simultaneously quantifies pre-defined GEPs capturing activation states and cellular subsets. From 1,700,000 T-cells from 700 individuals across 38 tissues and five diverse disease contexts, we discover 46 reproducible GEPs reflecting the known core functions of T-cells including proliferation, cytotoxicity, exhaustion, and T helper effector states. We experimentally characterize several novel activation programs and apply TCAT to describe T-cell activation and exhaustion in Covid-19 and cancer, providing insight into T-cell function in these diseases.
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Affiliation(s)
- Dylan Kotliar
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA
| | - Michelle Curtis
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ryan Agnew
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kathryn Weinand
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Yuriy Baglaenko
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Center for Autoimmune Genetics and Etiology and Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH 45219, USA
| | - Yu Zhao
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Pardis C. Sabeti
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Organismic and Evolutionary Biology, FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138, USA
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Deepak A. Rao
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
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31
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Hu R, Yang W, Li J, Jiang L, Li M, Zhang M, Kang Y, Cheng X, Zhu S, Zhao L, He W, Guo M, Ding S, Wu H, Cheng W. Multiple RNA Rapid In Situ Imaging Based on Cas9 Code Key System. SMALL METHODS 2024:e2400195. [PMID: 38699929 DOI: 10.1002/smtd.202400195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/29/2024] [Indexed: 05/05/2024]
Abstract
Existing RNA in situ imaging strategies mostly utilize parallel repetitive nucleic acid self-assembly to achieve multiple analysis, with limitations of complicated systems and cumbersome steps. Here, a Cas9 code key system with key probe (KP) encoder and CRISPR/Cas9 signal exporter is developed. This system triggers T-protospacer adjacent motif (T-PAM structural transitions of multiple KP encoders to form coding products with uniform single-guide RNA (sgRNA) target sequences as tandem nodes. Only single sgRNA/Cas9 complex is required to cleave multiple coding products, enabling efficient "many-to-one" tandem signaling, and non-collateral cleavage activity-dependent automatic signaling output through active introduction of mismatched bases. Compared with conventional parallel multiple signaling analysis model, the proposed system greatly simplifies reaction process and enhances detection efficiency. Further, a rapid multiple RNA in situ imaging system is developed by combining the Cas9 code key system with a T-strand displacement amplification (T-SDA) signal amplifier. The constructed system is applied to tumor cells and clinicopathology slices, generating clear multi-mRNA imaging profiles in less than an hour with just one step. Therefore, this work provides reliable technical support for clinical tumor typing and molecular mechanism investigation.
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Affiliation(s)
- Ruiwei Hu
- The Center for Clinical Molecular Medical Detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400042, China
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Wei Yang
- Laboratory Medicine Center, Department of Clinical Laboratory, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310014, China
| | - Jia Li
- The Center for Clinical Molecular Medical Detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400042, China
| | - Lanxin Jiang
- The Center for Clinical Molecular Medical Detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400042, China
| | - Menghan Li
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Mengxuan Zhang
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Yuexi Kang
- The Center for Clinical Molecular Medical Detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400042, China
| | - Xiaoxue Cheng
- The Center for Clinical Molecular Medical Detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400042, China
| | - Shasha Zhu
- The Center for Clinical Molecular Medical Detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400042, China
| | - Lina Zhao
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Wen He
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Minghui Guo
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Shijia Ding
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Haiping Wu
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Wei Cheng
- The Center for Clinical Molecular Medical Detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400042, China
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China
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32
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Jubran J, Slutsky R, Rozenblum N, Rokach L, Ben-David U, Yeger-Lotem E. Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes. Genome Biol 2024; 25:95. [PMID: 38622679 PMCID: PMC11020441 DOI: 10.1186/s13059-024-03225-7] [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: 07/05/2023] [Accepted: 03/26/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND Aneuploidy, an abnormal number of chromosomes within a cell, is a hallmark of cancer. Patterns of aneuploidy differ across cancers, yet are similar in cancers affecting closely related tissues. The selection pressures underlying aneuploidy patterns are not fully understood, hindering our understanding of cancer development and progression. RESULTS Here, we apply interpretable machine learning methods to study tissue-selective aneuploidy patterns. We define 20 types of features corresponding to genomic attributes of chromosome-arms, normal tissues, primary tumors, and cancer cell lines (CCLs), and use them to model gains and losses of chromosome arms in 24 cancer types. To reveal the factors that shape the tissue-specific cancer aneuploidy landscapes, we interpret the machine learning models by estimating the relative contribution of each feature to the models. While confirming known drivers of positive selection, our quantitative analysis highlights the importance of negative selection for shaping aneuploidy landscapes. This is exemplified by tumor suppressor gene density being a better predictor of gain patterns than oncogene density, and vice versa for loss patterns. We also identify the importance of tissue-selective features and demonstrate them experimentally, revealing KLF5 as an important driver for chr13q gain in colon cancer. Further supporting an important role for negative selection in shaping the aneuploidy landscapes, we find compensation by paralogs to be among the top predictors of chromosome arm loss prevalence and demonstrate this relationship for one paralog interaction. Similar factors shape aneuploidy patterns in human CCLs, demonstrating their relevance for aneuploidy research. CONCLUSIONS Our quantitative, interpretable machine learning models improve the understanding of the genomic properties that shape cancer aneuploidy landscapes.
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Affiliation(s)
- Juman Jubran
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, 84105, Beer Sheva, Israel
| | - Rachel Slutsky
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Nir Rozenblum
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Lior Rokach
- Department of Software & Information Systems Engineering, Ben-Gurion University of the Negev, 84105, Beer Sheva, Israel
| | - Uri Ben-David
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, 84105, Beer Sheva, Israel.
- The National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, 84105, Beer Sheva, Israel.
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33
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Luo R, Liu J, Wen J, Zhou X. Single-cell Landscape of Malignant Transition: Unraveling Cancer Cell-of-Origin and Heterogeneous Tissue Microenvironment. RESEARCH SQUARE 2024:rs.3.rs-4085185. [PMID: 38645221 PMCID: PMC11030487 DOI: 10.21203/rs.3.rs-4085185/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Understanding disease progression and sophisticated tumor ecosystems is imperative for investigating tumorigenesis mechanisms and developing novel prevention strategies. Here, we dissected heterogeneous microenvironments during malignant transitions by leveraging data from 1396 samples spanning 13 major tissues. Within transitional stem-like subpopulations highly enriched in precancers and cancers, we identified 30 recurring cellular states strongly linked to malignancy, including hypoxia and epithelial senescence, revealing a high degree of plasticity in epithelial stem cells. By characterizing dynamics in stem-cell crosstalk with the microenvironment along the pseudotime axis, we found differential roles of ANXA1 at different stages of tumor development. In precancerous stages, reduced ANXA1 levels promoted monocyte differentiation toward M1 macrophages and inflammatory responses, whereas during malignant progression, upregulated ANXA1 fostered M2 macrophage polarization and cancer-associated fibroblast transformation by increasing TGF-β production. Our spatiotemporal analysis further provided insights into mechanisms responsible for immunosuppression and a potential target to control evolution of precancer and mitigate the risk for cancer development.
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Affiliation(s)
| | - Jiajia Liu
- The University of Texas Health Science Center at Houston
| | - Jianguo Wen
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston
| | - Xiaobo Zhou
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston
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34
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Gondal MN, Cieslik M, Chinnaiyan AM. Integrated cancer cell-specific single-cell RNA-seq datasets of immune checkpoint blockade-treated patients. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.17.576110. [PMID: 38328153 PMCID: PMC10849474 DOI: 10.1101/2024.01.17.576110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Immune checkpoint blockade (ICB) therapies have emerged as a promising avenue for the treatment of various cancers. Despite their success, the efficacy of these treatments is variable across patients and cancer types. Numerous single-cell RNA-sequencing (scRNA-seq) studies have been conducted to unravel cell-specific responses to ICB treatment. However, these studies are limited in their sample sizes and require advanced coding skills for exploration. Here, we have compiled eight scRNA-seq datasets from nine cancer types, encompassing 174 patients, and 90,270 cancer cells. This compilation forms a unique resource tailored for investigating how cancer cells respond to ICB treatment across cancer types. We meticulously curated, quality-checked, pre-processed, and analyzed the data, ensuring easy access for researchers. Moreover, we designed a user-friendly interface for seamless exploration. By sharing the code and data for creating these interfaces, we aim to assist fellow researchers. These resources offer valuable support to those interested in leveraging and exploring single-cell datasets across diverse cancer types, facilitating a comprehensive understanding of ICB responses.
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Affiliation(s)
- Mahnoor N. Gondal
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
| | - Marcin Cieslik
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Pathology, University of Michigan, Ann Arbor, MI USA
- University of Michigan Rogel Cancer Center, Ann Arbor, MI USA
| | - Arul M. Chinnaiyan
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Urology, University of Michigan, Ann Arbor, MI USA
- Howard Hughes Medical Institute, Ann Arbor, MI USA
- University of Michigan Rogel Cancer Center, Ann Arbor, MI USA
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35
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Manoutcharian K, Gevorkian G. Are we getting closer to a successful neoantigen cancer vaccine? Mol Aspects Med 2024; 96:101254. [PMID: 38354548 DOI: 10.1016/j.mam.2024.101254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/16/2024]
Abstract
Although significant advances in immunotherapy have revolutionized the treatment of many cancer types over the past decade, the field of vaccine therapy, an important component of cancer immunotherapy, despite decades-long intense efforts, is still transmitting signals of promises and awaiting strong data on efficacy to proceed with regulatory approval. The field of cancer vaccines faces standard challenges, such as tumor-induced immunosuppression, immune response in inhibitory tumor microenvironment (TME), intratumor heterogeneity (ITH), permanently evolving cancer mutational landscape leading to neoantigens, and less known obstacles: neoantigen gain/loss upon immunotherapy, the timing and speed of appearance of neoantigens and responding T cell clonotypes and possible involvement of immune interference/heterologous immunity, in the complex interplay between evolving tumor epitopes and the immune system. In this review, we discuss some key issues related to challenges hampering the development of cancer vaccines, along with the current approaches focusing on neoantigens. We summarize currently well-known ideas/rationales, thus revealing the need for alternative vaccine approaches. Such a discussion should stimulate vaccine researchers to apply out-of-box, unconventional thinking in search of new avenues to deal with critical, often yet unaddressed challenges on the road to a new generation of therapeutics and vaccines.
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Affiliation(s)
- Karen Manoutcharian
- Instituto de Investigaciones Biomedicas, Universidad Nacional Autonoma de Mexico (UNAM), CDMX, Apartado Postal 70228, Cuidad Universitaria, Mexico DF, CP, 04510, Mexico.
| | - Goar Gevorkian
- Instituto de Investigaciones Biomedicas, Universidad Nacional Autonoma de Mexico (UNAM), CDMX, Apartado Postal 70228, Cuidad Universitaria, Mexico DF, CP, 04510, Mexico.
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36
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Buckenmeyer MJ, Brooks EA, Taylor MS, Yang L, Holewinski RJ, Meyer TJ, Galloux M, Garmendia-Cedillos M, Pohida TJ, Andresson T, Croix B, Wolf MT. Engineering Tumor Stroma Morphogenesis Using Dynamic Cell-Matrix Spheroid Assembly. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585805. [PMID: 38903106 PMCID: PMC11188064 DOI: 10.1101/2024.03.19.585805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
The tumor microenvironment consists of resident tumor cells organized within a compositionally diverse, three-dimensional (3D) extracellular matrix (ECM) network that cannot be replicated in vitro using bottom-up synthesis. We report a new self-assembly system to engineer ECM-rich 3D MatriSpheres wherein tumor cells actively organize and concentrate microgram quantities of decellularized ECM dispersions which modulate cell phenotype. 3D colorectal cancer (CRC) MatriSpheres were created using decellularized small intestine submucosa (SIS) as an orthotopic ECM source that had greater proteomic homology to CRC tumor ECM than traditional ECM formulations such as Matrigel. SIS ECM was rapidly concentrated from its environment and assembled into ECM-rich 3D stroma-like regions by mouse and human CRC cell lines within 4-5 days via a mechanism that was rheologically distinct from bulk hydrogel formation. Both ECM organization and transcriptional regulation by 3D ECM cues affected programs of malignancy, lipid metabolism, and immunoregulation that corresponded with an in vivo MC38 tumor cell subpopulation identified via single cell RNA sequencing. This 3D modeling approach stimulates tumor specific tissue morphogenesis that incorporates the complexities of both cancer cell and ECM compartments in a scalable, spontaneous assembly process that may further facilitate precision medicine.
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Affiliation(s)
- Michael J. Buckenmeyer
- Cancer Biomaterials Engineering Laboratory, Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Elizabeth A. Brooks
- Cancer Biomaterials Engineering Laboratory, Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Madison S. Taylor
- Cancer Biomaterials Engineering Laboratory, Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Liping Yang
- Tumor Angiogenesis Unit, Mouse Cancer Genetics Program, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Ronald J. Holewinski
- Protein Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21701, USA
| | - Thomas J. Meyer
- CCR Collaborative Bioinformatics Resource, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Mélissa Galloux
- Independent Bioinformatician, Marseille, Provence-Alpes-Côte d’Azur, France
| | - Marcial Garmendia-Cedillos
- Instrumentation Development and Engineering Application Solutions, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Thomas J. Pohida
- Instrumentation Development and Engineering Application Solutions, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Thorkell Andresson
- Protein Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21701, USA
| | - Brad Croix
- Tumor Angiogenesis Unit, Mouse Cancer Genetics Program, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Matthew T. Wolf
- Cancer Biomaterials Engineering Laboratory, Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
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37
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Kang M, Armenteros JJA, Gulati GS, Gleyzer R, Avagyan S, Brown EL, Zhang W, Usmani A, Earland N, Wu Z, Zou J, Fields RC, Chen DY, Chaudhuri AA, Newman AM. Mapping single-cell developmental potential in health and disease with interpretable deep learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585637. [PMID: 38562882 PMCID: PMC10983880 DOI: 10.1101/2024.03.19.585637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cell fate in developmental systems. However, identifying the molecular hallmarks of potency - the capacity of a cell to differentiate into other cell types - has remained challenging. Here, we introduce CytoTRACE 2, an interpretable deep learning framework for characterizing potency and differentiation states on an absolute scale from scRNA-seq data. Across 31 human and mouse scRNA-seq datasets encompassing 28 tissue types, CytoTRACE 2 outperformed existing methods for recovering experimentally determined potency levels and differentiation states covering the entire range of cellular ontogeny. Moreover, it reconstructed the temporal hierarchy of mouse embryogenesis across 62 timepoints; identified pan-tissue expression programs that discriminate major potency levels; and facilitated discovery of cellular phenotypes in cancer linked to survival and immunotherapy resistance. Our results illuminate a fundamental feature of cell biology and provide a broadly applicable platform for delineating single-cell differentiation landscapes in health and disease.
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38
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Kim KH, Migliozzi S, Koo H, Hong JH, Park SM, Kim S, Kwon HJ, Ha S, Garofano L, Oh YT, D'Angelo F, Kim CI, Kim S, Lee JY, Kim J, Hong J, Jang EH, Mathon B, Di Stefano AL, Bielle F, Laurenge A, Nesvizhskii AI, Hur EM, Yin J, Shi B, Kim Y, Moon KS, Kwon JT, Lee SH, Lee SH, Gwak HS, Lasorella A, Yoo H, Sanson M, Sa JK, Park CK, Nam DH, Iavarone A, Park JB. Integrated proteogenomic characterization of glioblastoma evolution. Cancer Cell 2024; 42:358-377.e8. [PMID: 38215747 PMCID: PMC10939876 DOI: 10.1016/j.ccell.2023.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 07/11/2023] [Accepted: 12/14/2023] [Indexed: 01/14/2024]
Abstract
The evolutionary trajectory of glioblastoma (GBM) is a multifaceted biological process that extends beyond genetic alterations alone. Here, we perform an integrative proteogenomic analysis of 123 longitudinal glioblastoma pairs and identify a highly proliferative cellular state at diagnosis and replacement by activation of neuronal transition and synaptogenic pathways in recurrent tumors. Proteomic and phosphoproteomic analyses reveal that the molecular transition to neuronal state at recurrence is marked by post-translational activation of the wingless-related integration site (WNT)/ planar cell polarity (PCP) signaling pathway and BRAF protein kinase. Consistently, multi-omic analysis of patient-derived xenograft (PDX) models mirror similar patterns of evolutionary trajectory. Inhibition of B-raf proto-oncogene (BRAF) kinase impairs both neuronal transition and migration capability of recurrent tumor cells, phenotypic hallmarks of post-therapy progression. Combinatorial treatment of temozolomide (TMZ) with BRAF inhibitor, vemurafenib, significantly extends the survival of PDX models. This study provides comprehensive insights into the biological mechanisms of glioblastoma evolution and treatment resistance, highlighting promising therapeutic strategies for clinical intervention.
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Affiliation(s)
- Kyung-Hee Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea; Proteomics Core Facility, Research Core Center, Research Institute, National Cancer Center, Goyang, Korea
| | - Simona Migliozzi
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Harim Koo
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea; Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea; Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Korea
| | - Jun-Hee Hong
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Seung Min Park
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Sooheon Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Hyung Joon Kwon
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Seokjun Ha
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Luciano Garofano
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Young Taek Oh
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Fulvio D'Angelo
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Chan Il Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Seongsoo Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Ji Yoon Lee
- Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea; Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Korea
| | - Jiwon Kim
- Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea; Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Korea
| | - Jisoo Hong
- Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea; Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Korea
| | - Eun-Hae Jang
- Laboratory of Neuroscience, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul, Korea
| | - Bertrand Mathon
- Service de Neurochirurgie, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Paris, France
| | - Anna-Luisa Di Stefano
- Institut de Neurologie, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Paris, France; Sorbonne Université, Inserm, CNRS, UMR S 1127, Paris Brain Institute (ICM), Equipe labellisée LNCC, Paris, France; Onconeurotek, AP-HP, Hôpital Pitié-Salpêtrière, F-75013 Paris, France; Department of Neurology, Foch Hospital, Suresnes, France
| | - Franck Bielle
- Institut de Neurologie, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Paris, France; Sorbonne Université, Inserm, CNRS, UMR S 1127, Paris Brain Institute (ICM), Equipe labellisée LNCC, Paris, France; Onconeurotek, AP-HP, Hôpital Pitié-Salpêtrière, F-75013 Paris, France
| | - Alice Laurenge
- Institut de Neurologie, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Paris, France; Sorbonne Université, Inserm, CNRS, UMR S 1127, Paris Brain Institute (ICM), Equipe labellisée LNCC, Paris, France; Onconeurotek, AP-HP, Hôpital Pitié-Salpêtrière, F-75013 Paris, France
| | | | - Eun-Mi Hur
- Laboratory of Neuroscience, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul, Korea; BK21 Four Future Veterinary Medicine Leading Education & Research Center, College of Veterinary Medicine, Seoul National University, Seoul, Korea
| | - Jinlong Yin
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea; Henan-Macquarie University Joint Centre for Biomedical Innovation, School of Life Sciences, Henan University, Kaifeng, Henan, China
| | - Bingyang Shi
- Henan-Macquarie University Joint Centre for Biomedical Innovation, School of Life Sciences, Henan University, Kaifeng, Henan, China
| | - Youngwook Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Kyung-Sub Moon
- Department of Neurosurgery, Chonnam National University Hwasun Hospital and Medical School, Hwasun, Korea
| | - Jeong Taik Kwon
- Department of Neurosurgery, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea
| | - Shin Heon Lee
- Department of Neurosurgery, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea
| | - Seung Hoon Lee
- Department of Neurosurgery, Eulji University School of Medicine, Daejeon, Korea
| | - Ho Shin Gwak
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Anna Lasorella
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA; Department of Biochemistry, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Heon Yoo
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Marc Sanson
- Institut de Neurologie, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Paris, France; Sorbonne Université, Inserm, CNRS, UMR S 1127, Paris Brain Institute (ICM), Equipe labellisée LNCC, Paris, France; Onconeurotek, AP-HP, Hôpital Pitié-Salpêtrière, F-75013 Paris, France.
| | - Jason K Sa
- Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea; Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Korea.
| | - Chul-Kee Park
- Deparment of Neurosurgery, Seoul National University College of Medicine, Seoul, Korea.
| | - Do-Hyun Nam
- Department of Neurosurgery and Samsung Advanced Institute for Health Sciences and Technology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
| | - Antonio Iavarone
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA; Department of Neurological Surgery and Department of Biochemistry, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Jong Bae Park
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea; Department of Clinical Research, Research Institute and Hospital, National Cancer Center, Goyang, Korea.
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Lv H, Mu Y, Zhang C, Zhao M, Jiang P, Xiao S, Sun H, Wu N, Sun D, Jin Y. Comparative analysis of single-cell transcriptome reveals heterogeneity and commonality in the immune microenvironment of colorectal cancer and inflammatory bowel disease. Front Immunol 2024; 15:1356075. [PMID: 38529274 PMCID: PMC10961339 DOI: 10.3389/fimmu.2024.1356075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 02/26/2024] [Indexed: 03/27/2024] Open
Abstract
Background During aging, chronic inflammation can promote tumor development and metastasis. Patients with chronic inflammatory bowel diseases (IBD) are at an increased risk of developing colorectal cancer (CRC). However, the molecular mechanism underlying is still unclear. Methods We conducted a large-scale single-cell sequencing analysis comprising 432,314 single cells from 92 CRC and 24 IBD patients. The analysis focused on the heterogeneity and commonality of CRC and IBD with respect to immune cell landscape, cellular communication, aging and inflammatory response, and Meta programs. Results The CRC and IBD had significantly different propensities in terms of cell proportions, differential genes and their functions, and cellular communication. The progression of CRC was mainly associated with epithelial cells, fibroblasts, and monocyte-macrophages, which displayed pronounced metabolic functions. In particular, monocyte-macrophages were enriched for the aging and inflammation-associated NF-κB pathway. And IBD was enriched in immune-related functions with B cells and T cells. Cellular communication analysis in CRC samples displayed an increase in MIF signaling from epithelial cells to T cells, and an increase in the efferent signal of senescence-associated SPP1 signaling from monocyte-macrophages. Notably, we also found some commonalities between CRC and IBD. The efferent and afferent signals showed that the pro-inflammatory cytokine played an important role. And the activity of aging and inflammatory response with AUCell analysis also showed a high degree of commonality. Furthermore, using the Meta programs (MPs) with the NMF algorithm, we found that the CRC non-malignant cells shared a substantial proportion of the MP genes with CRC malignant cells (68% overlap) and IBD epithelial cells (52% overlap), respectively. And it was extensively involved in functions of cell cycle and immune response, revealing its dual properties of inflammation and cancer. In addition, CRC malignant and non-malignant cells were enriched for the senescence-related cell cycle G2M phase transition and the p53 signaling pathway. Conclusion Our study highlights the characteristics of aging, inflammation and tumor in CRC and IBD at the single-cell level, and the dual property of inflammation-cancer in CRC non-malignant cells may provide a more up-to-date understanding of disease transformation.
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Affiliation(s)
- Hongchao Lv
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, Heilongjiang, China
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, Heilongjiang, China
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yu Mu
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, Heilongjiang, China
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, Heilongjiang, China
| | - Chen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Meiqi Zhao
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, Heilongjiang, China
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, Heilongjiang, China
| | - Ping Jiang
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, Heilongjiang, China
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, Heilongjiang, China
| | - Shan Xiao
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, Heilongjiang, China
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, Heilongjiang, China
| | - Haiming Sun
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, Heilongjiang, China
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, Heilongjiang, China
| | - Nan Wu
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, Heilongjiang, China
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, Heilongjiang, China
| | - Donglin Sun
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, Heilongjiang, China
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, Heilongjiang, China
| | - Yan Jin
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, Heilongjiang, China
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, Heilongjiang, China
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
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Han G, Sinjab A, Rahal Z, Lynch AM, Treekitkarnmongkol W, Liu Y, Serrano AG, Feng J, Liang K, Khan K, Lu W, Hernandez SD, Liu Y, Cao X, Dai E, Pei G, Hu J, Abaya C, Gomez-Bolanos LI, Peng F, Chen M, Parra ER, Cascone T, Sepesi B, Moghaddam SJ, Scheet P, Negrao MV, Heymach JV, Li M, Dubinett SM, Stevenson CS, Spira AE, Fujimoto J, Solis LM, Wistuba II, Chen J, Wang L, Kadara H. An atlas of epithelial cell states and plasticity in lung adenocarcinoma. Nature 2024; 627:656-663. [PMID: 38418883 PMCID: PMC10954546 DOI: 10.1038/s41586-024-07113-9] [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: 05/10/2022] [Accepted: 01/24/2024] [Indexed: 03/02/2024]
Abstract
Understanding the cellular processes that underlie early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies1. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8+ cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation. This study provides new insights into epithelial cell states at the root of LUAD development, and such states could harbour potential targets for prevention or intervention.
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Affiliation(s)
- Guangchun Han
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ansam Sinjab
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zahraa Rahal
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anne M Lynch
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Graduate Program in Developmental Biology, Baylor College of Medicine, Houston, TX, USA
| | - Warapen Treekitkarnmongkol
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yuejiang Liu
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Alejandra G Serrano
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jiping Feng
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ke Liang
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Khaja Khan
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wei Lu
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sharia D Hernandez
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yunhe Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xuanye Cao
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Enyu Dai
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Guangsheng Pei
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jian Hu
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Camille Abaya
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lorena I Gomez-Bolanos
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Fuduan Peng
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Minyue Chen
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Edwin R Parra
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tina Cascone
- Department of Thoracic, Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Boris Sepesi
- Department of Cardiovascular and Thoracic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Seyed Javad Moghaddam
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Paul Scheet
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Marcelo V Negrao
- Department of Thoracic, Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John V Heymach
- Department of Thoracic, Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven M Dubinett
- Department of Medicine, The University of California Los Angeles, Los Angeles, CA, USA
| | | | - Avrum E Spira
- Lung Cancer Initiative at Johnson & Johnson, Boston, MA, USA
- Section of Computational Biomedicine, School of Medicine, Boston University, Boston, MA, USA
| | - Junya Fujimoto
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Luisa M Solis
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jichao Chen
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
| | - Humam Kadara
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
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Tape CJ. Plastic persisters: revival stem cells in colorectal cancer. Trends Cancer 2024; 10:185-195. [PMID: 38071119 DOI: 10.1016/j.trecan.2023.11.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 03/16/2024]
Abstract
Colorectal cancer (CRC) is traditionally considered to be a genetically driven disease. However, nongenetic plasticity has recently emerged as a major driver of tumour initiation, metastasis, and therapy response in CRC. Central to these processes is a recently discovered cell type, the revival colonic stem cell (revCSC). In contrast to traditional proliferative CSCs (proCSCs), revCSCs prioritise survival over propagation. revCSCs play an essential role in primary tumour formation, metastatic dissemination, and nongenetic chemoresistance. Current evidence suggests that CRC tumours leverage intestinal stem cell plasticity to both proliferate (via proCSCs) when unchallenged and survive (via revCSCs) in response to cell-extrinsic pressures. Although revCSCs likely represent a major source of therapeutic failure in CRC, our increasing knowledge of this important stem cell fate provides novel opportunities for therapeutic intervention.
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Affiliation(s)
- 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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Chen H, Fang X, Shao J, Zhang Q, Xu L, Chen J, Mei Y, Jiang M, Wang Y, Li Z, Chen Z, Chen Y, Yu C, Ma L, Zhang P, Zhang T, Liao Y, Lv Y, Wang X, Yang L, Fu Y, Chen D, Jiang L, Yan F, Lu W, Chen G, Shen H, Wang J, Wang C, Liang T, Han X, Wang Y, Guo G. Pan-Cancer Single-Nucleus Total RNA Sequencing Using snHH-Seq. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2304755. [PMID: 38010945 PMCID: PMC10837386 DOI: 10.1002/advs.202304755] [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: 07/13/2023] [Revised: 10/11/2023] [Indexed: 11/29/2023]
Abstract
Tumor heterogeneity and its drivers impair tumor progression and cancer therapy. Single-cell RNA sequencing is used to investigate the heterogeneity of tumor ecosystems. However, most methods of scRNA-seq amplify the termini of polyadenylated transcripts, making it challenging to perform total RNA analysis and somatic mutation analysis.Therefore, a high-throughput and high-sensitivity method called snHH-seq is developed, which combines random primers and a preindex strategy in the droplet microfluidic platform. This innovative method allows for the detection of total RNA in single nuclei from clinically frozen samples. A robust pipeline to facilitate the analysis of full-length RNA-seq data is also established. snHH-seq is applied to more than 730 000 single nuclei from 32 patients with various tumor types. The pan-cancer study enables it to comprehensively profile data on the tumor transcriptome, including expression levels, mutations, splicing patterns, clone dynamics, etc. New malignant cell subclusters and exploring their specific function across cancers are identified. Furthermore, the malignant status of epithelial cells is investigated among different cancer types with respect to mutation and splicing patterns. The ability to detect full-length RNA at the single-nucleus level provides a powerful tool for studying complex biological systems and has broad implications for understanding tumor pathology.
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Affiliation(s)
- Haide Chen
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
- M20 Genomics, Hangzhou, 311121, China
| | - Xiunan Fang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, 999077, China
| | - Jikai Shao
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
| | - Qi Zhang
- Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
- Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou, 310006, China
- The Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, 310006, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Liwei Xu
- Department of Thoracic Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
| | | | - Yuqing Mei
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Mengmeng Jiang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
| | - Yuting Wang
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Zhouyang Li
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Zihang Chen
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Key Laboratory of Precise Treatment and Clinical Translational Research of Neurological Diseases, Hangzhou, 310009, China
| | - Yang Chen
- Zhejiang Key Laboratory of Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou, 310022, China
- The Second Clinical Medical College of Zhejiang Chinese Medical University Hangzhou, Hangzhou, 310053, China
| | - Chengxuan Yu
- Department of Colorectal Surgery and Oncology, Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, Zhejiang Provincial Clinical Research Center for Cancer, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Lifeng Ma
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Peijing Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, 310058, China
| | | | - Yuan Liao
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- M20 Genomics, Hangzhou, 311121, China
| | | | - Xueyi Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Lei Yang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Yuting Fu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Daobao Chen
- Department of Breast Surgery, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
| | - Liming Jiang
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
| | - Feng Yan
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Key Laboratory of Precise Treatment and Clinical Translational Research of Neurological Diseases, Hangzhou, 310009, China
| | - Wei Lu
- Department of Colorectal Surgery and Oncology, Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, Zhejiang Provincial Clinical Research Center for Cancer, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Gao Chen
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Key Laboratory of Precise Treatment and Clinical Translational Research of Neurological Diseases, Hangzhou, 310009, China
| | - Huahao Shen
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- State Key Laboratory of Respiratory Disease, Guangzhou, 510120, China
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
| | - Changchun Wang
- Department of Thoracic Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
- Zhejiang Key Laboratory of Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou, 310022, China
| | - Tingbo Liang
- Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
- Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou, 310006, China
- The Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, 310006, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Xiaoping Han
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Yongcheng Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, 310058, China
- Institute of Hematology, Zhejiang University, Hangzhou, 310006, China
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Li H, Huang H, Tan H, Jia Q, Song W, Zhang Q, Zhou B, Bai J. Key processes in tumor metastasis and therapeutic strategies with nanocarriers: a review. Mol Biol Rep 2024; 51:197. [PMID: 38270746 DOI: 10.1007/s11033-023-08910-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 11/14/2023] [Indexed: 01/26/2024]
Abstract
Cancer metastasis is the leading cause of cancer-related death. Metastasis occurs at all stages of tumor development, with unexplored changes occurring at the primary site and distant colonization sites. The growing understanding of the metastatic process of tumor cells has contributed to the emergence of better treatment options and strategies. This review summarizes a range of features related to tumor cell metastasis and nanobased drug delivery systems for inhibiting tumor metastasis. The mechanisms of tumor metastasis in the ideal order of metastatic progression were summarized. We focus on the prominent role of nanocarriers in the treatment of tumor metastasis, summarizing the latest applications of nanocarriers in combination with drugs to target important components and processes of tumor metastasis and providing ideas for more effective nanodrug delivery systems.
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Affiliation(s)
- Hongjie Li
- School of Clinical Medicine, Weifang Medical University, 261053, Weifang, China
| | - Haiqin Huang
- School of Bioscience and Technology, Weifang Medical University, 261053, Weifang, China
| | - Haining Tan
- National Glycoengineering Research Center, Shandong University, 250012, Jinan, China
| | - Qitao Jia
- School of Bioscience and Technology, Weifang Medical University, 261053, Weifang, China
| | - Weina Song
- Department of Pediatric Respiratory and Critical Care, Qilu Hospital of Shandong University Dezhou Hospital, 253000, Dezhou, China
| | - Qingdong Zhang
- School of Bioscience and Technology, Weifang Medical University, 261053, Weifang, China.
| | - Baolong Zhou
- School of Pharmacy, Weifang Medical University, 261053, Weifang, China.
| | - Jingkun Bai
- School of Bioscience and Technology, Weifang Medical University, 261053, Weifang, China.
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Qu L, Ding S, Long Q, Zheng S, Chen ZS, Yi W. Editorial: DNA methylation, tumor microenvironment and their effects in immunotherapy and drug resistance in thoracic tumors. Front Immunol 2024; 15:1357278. [PMID: 38288309 PMCID: PMC10822968 DOI: 10.3389/fimmu.2024.1357278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 01/04/2024] [Indexed: 01/31/2024] Open
Affiliation(s)
- Limeng Qu
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Shirong Ding
- Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Qian Long
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center For Breast Disease In Hunan Province, Changsha, China
| | - Shaoquan Zheng
- Department of Breast Surgery, Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhe-Sheng Chen
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St John’s University, Queens, NY, United States
| | - Wenjun Yi
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center For Breast Disease In Hunan Province, Changsha, China
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Ciriello G, Magnani L, Aitken SJ, Akkari L, Behjati S, Hanahan D, Landau DA, Lopez-Bigas N, Lupiáñez DG, Marine JC, Martin-Villalba A, Natoli G, Obenauf AC, Oricchio E, Scaffidi P, Sottoriva A, Swarbrick A, Tonon G, Vanharanta S, Zuber J. Cancer Evolution: A Multifaceted Affair. Cancer Discov 2024; 14:36-48. [PMID: 38047596 PMCID: PMC10784746 DOI: 10.1158/2159-8290.cd-23-0530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/29/2023] [Accepted: 10/23/2023] [Indexed: 12/05/2023]
Abstract
Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery. SIGNIFICANCE Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.
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Affiliation(s)
- Giovanni Ciriello
- Swiss Cancer Center Leman, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Luca Magnani
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, United Kingdom
- Breast Epigenetic Plasticity and Evolution Laboratory, Division of Breast Cancer Research, The Institute of Cancer Research, London, United Kingdom
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Sarah J. Aitken
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, United Kingdom
- Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Leila Akkari
- Division of Tumor Biology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Sam Behjati
- Wellcome Sanger Institute, Hinxton, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Paediatrics, University of Cambridge, Cambridge, United Kingdom
| | - Douglas Hanahan
- Swiss Cancer Center Leman, Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne, Switzerland
| | - Dan A. Landau
- New York Genome Center, New York, New York
- Division of Hematology and Medical Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, New York
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York
| | - Nuria Lopez-Bigas
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red en Cáncer (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Darío G. Lupiáñez
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Jean-Christophe Marine
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory for Molecular Cancer Biology, Department of Oncology, KULeuven, Leuven, Belgium
| | - Ana Martin-Villalba
- Department of Molecular Neurobiology, German Cancer Research Center (DFKZ), Heidelberg, Germany
| | - Gioacchino Natoli
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
| | - Anna C. Obenauf
- Research Institute of Molecular Pathology, Vienna Biocenter, Vienna, Austria
| | - Elisa Oricchio
- Swiss Cancer Center Leman, Lausanne, Switzerland
- Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - Paola Scaffidi
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
- Cancer Epigenetic Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Andrea Sottoriva
- Computational Biology Research Centre, Human Technopole, Milan, Italy
| | - Alexander Swarbrick
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Darlinghurst, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
| | - Giovanni Tonon
- Vita-Salute San Raffaele University, Milan, Italy
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Sakari Vanharanta
- Translational Cancer Medicine Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Johannes Zuber
- Research Institute of Molecular Pathology, Vienna Biocenter, Vienna, Austria
- Medical University of Vienna, Vienna BioCenter (VBC), Vienna, Austria
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Tsagiopoulou M, Gut IG. Machine learning and multi-omics data in chronic lymphocytic leukemia: the future of precision medicine? Front Genet 2024; 14:1304661. [PMID: 38283149 PMCID: PMC10811210 DOI: 10.3389/fgene.2023.1304661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/27/2023] [Indexed: 01/30/2024] Open
Abstract
Chronic lymphocytic leukemia is a complex and heterogeneous hematological malignancy. The advance of high-throughput multi-omics technologies has significantly influenced chronic lymphocytic leukemia research and paved the way for precision medicine approaches. In this review, we explore the role of machine learning in the analysis of multi-omics data in this hematological malignancy. We discuss recent literature on different machine learning models applied to single omic studies in chronic lymphocytic leukemia, with a special focus on the potential contributions to precision medicine. Finally, we highlight the recently published machine learning applications in multi-omics data in this area of research as well as their potential and limitations.
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Affiliation(s)
| | - Ivo G. Gut
- Centro Nacional de Analisis Genomico (CNAG), Barcelona, Spain
- Universitat de Barcelona (UB), Barcelona, Spain
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Deng Y, Chen P, Xiao J, Li M, Shen J, Qin S, Jia T, Li C, Chang A, Zhang W, Liu H, Xue R, Zhang N, Wang X, Huang L, Chen D. SCAR: Single-cell and Spatially-resolved Cancer Resources. Nucleic Acids Res 2024; 52:D1407-D1417. [PMID: 37739405 PMCID: PMC10767865 DOI: 10.1093/nar/gkad753] [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: 06/08/2023] [Revised: 08/16/2023] [Accepted: 09/05/2023] [Indexed: 09/24/2023] Open
Abstract
Advances in sequencing and imaging technologies offer a unique opportunity to unravel cell heterogeneity and develop new immunotherapy strategies for cancer research. There is an urgent need for a resource that effectively integrates a vast amount of transcriptomic profiling data to comprehensively explore cancer tissue heterogeneity and the tumor microenvironment. In this context, we developed the Single-cell and Spatially-resolved Cancer Resources (SCAR) database, a combined tumor spatial and single-cell transcriptomic platform, which is freely accessible at http://8.142.154.29/SCAR2023 or http://scaratlas.com. SCAR contains spatial transcriptomic data from 21 tumor tissues and single-cell transcriptomic data from 11 301 352 cells encompassing 395 cancer subtypes and covering a wide variety of tissues, organoids, and cell lines. This resource offers diverse functional modules to address key cancer research questions at multiple levels, including the screening of tumor cell types, metabolic features, cell communication and gene expression patterns within the tumor microenvironment. Moreover, SCAR enables the analysis of biomarker expression patterns and cell developmental trajectories. SCAR also provides a comprehensive analysis of multi-dimensional datasets based on 34 state-of-the-art omics techniques, serving as an essential tool for in-depth mining and understanding of cell heterogeneity and spatial location. The implications of this resource extend to both cancer biology research and cancer immunotherapy development.
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Affiliation(s)
- Yushan Deng
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Peixin Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Cam-Su Genomic Resource Center, Medical College of Soochow University, Suzhou 215123, China
| | - Jiedan Xiao
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Mengrou Li
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Jiayi Shen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Peninsula Cancer Research Center, School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China
| | - Siying Qin
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Tengfei Jia
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Changxiao Li
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Ashley Chang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Wensheng Zhang
- Cam-Su Genomic Resource Center, Medical College of Soochow University, Suzhou 215123, China
- Peninsula Cancer Research Center, School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China
| | - Hebin Liu
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Ruidong Xue
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University Health Science Center & Translational Cancer Research Center, Peking University First Hospital, Beijing 100191, China
| | - Ning Zhang
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University Health Science Center & Translational Cancer Research Center, Peking University First Hospital, Beijing 100191, China
| | - Xiangdong Wang
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai 200000, China
| | - Li Huang
- The Future Laboratory, Tsinghua University, Beijing 100084, China
| | - Dongsheng Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
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Pozniak J, Pedri D, Landeloos E, Van Herck Y, Antoranz A, Vanwynsberghe L, Nowosad A, Roda N, Makhzami S, Bervoets G, Maciel LF, Pulido-Vicuña CA, Pollaris L, Seurinck R, Zhao F, Flem-Karlsen K, Damsky W, Chen L, Karagianni D, Cinque S, Kint S, Vandereyken K, Rombaut B, Voet T, Vernaillen F, Annaert W, Lambrechts D, Boecxstaens V, Saeys Y, van den Oord J, Bosisio F, Karras P, Shain AH, Bosenberg M, Leucci E, Paschen A, Rambow F, Bechter O, Marine JC. A TCF4-dependent gene regulatory network confers resistance to immunotherapy in melanoma. Cell 2024; 187:166-183.e25. [PMID: 38181739 DOI: 10.1016/j.cell.2023.11.037] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 08/23/2023] [Accepted: 11/29/2023] [Indexed: 01/07/2024]
Abstract
To better understand intrinsic resistance to immune checkpoint blockade (ICB), we established a comprehensive view of the cellular architecture of the treatment-naive melanoma ecosystem and studied its evolution under ICB. Using single-cell, spatial multi-omics, we showed that the tumor microenvironment promotes the emergence of a complex melanoma transcriptomic landscape. Melanoma cells harboring a mesenchymal-like (MES) state, a population known to confer resistance to targeted therapy, were significantly enriched in early on-treatment biopsies from non-responders to ICB. TCF4 serves as the hub of this landscape by being a master regulator of the MES signature and a suppressor of the melanocytic and antigen presentation transcriptional programs. Targeting TCF4 genetically or pharmacologically, using a bromodomain inhibitor, increased immunogenicity and sensitivity of MES cells to ICB and targeted therapy. We thereby uncovered a TCF4-dependent regulatory network that orchestrates multiple transcriptional programs and contributes to resistance to both targeted therapy and ICB in melanoma.
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Affiliation(s)
- Joanna Pozniak
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium.
| | - Dennis Pedri
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium; Laboratory for Membrane Trafficking, Center for Brain and Disease Research, VIB, Leuven, Belgium
| | - Ewout Landeloos
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium; Department of General Medical Oncology, UZ Leuven, Leuven, Belgium
| | | | - Asier Antoranz
- Laboratory of Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven and UZ Leuven, Leuven, Belgium
| | - Lukas Vanwynsberghe
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Ada Nowosad
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Niccolò Roda
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Samira Makhzami
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Greet Bervoets
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Lucas Ferreira Maciel
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Carlos Ariel Pulido-Vicuña
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - Lotte Pollaris
- Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Ruth Seurinck
- Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Fang Zhao
- Laboratory of Molecular Tumor Immunology, Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), Partner Site Essen, Essen, Germany
| | - Karine Flem-Karlsen
- Department of Dermatology, Yale University, 15 York Street, New Haven, CT 05610, USA
| | - William Damsky
- Departments of Dermatology and Pathology, Yale University, 15 York Street, New Haven, CT 05610, USA
| | - Limin Chen
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Despoina Karagianni
- Immune Regulation and Tumor Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, UCL Cancer Institute, London WC1E 6DD, UK
| | - Sonia Cinque
- Laboratory for RNA Cancer Biology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Sam Kint
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Belgium
| | - Katy Vandereyken
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Belgium
| | - Benjamin Rombaut
- Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Thierry Voet
- Laboratory of Reproductive Genomics, Department of Human Genetics, KU Leuven, Leuven, Belgium; KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Belgium
| | | | - Wim Annaert
- Laboratory for Membrane Trafficking, Center for Brain and Disease Research, VIB, Leuven, Belgium
| | - Diether Lambrechts
- Laboratory of Translational Genetics, Center for Cancer Biology, VIB, Leuven, Belgium; Center for Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Yvan Saeys
- Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Joost van den Oord
- Laboratory of Translational Cell and Tissue Research, Department of Pathology, UZ Leuven, Leuven, Belgium
| | - Francesca Bosisio
- Laboratory of Translational Cell and Tissue Research, Department of Pathology, UZ Leuven, Leuven, Belgium
| | - Panagiotis Karras
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium
| | - A Hunter Shain
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Marcus Bosenberg
- Departments of Dermatology, Pathology and Immunobiology, Yale University, New Haven, CT 05610, USA
| | - Eleonora Leucci
- Laboratory for RNA Cancer Biology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Annette Paschen
- Laboratory of Molecular Tumor Immunology, Department of Dermatology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), Partner Site Essen, Essen, Germany
| | - Florian Rambow
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium; Department of Applied Computational Cancer Research, Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany; University Duisburg-Essen, Essen, Germany.
| | - Oliver Bechter
- Department of General Medical Oncology, UZ Leuven, Leuven, Belgium.
| | - Jean-Christophe Marine
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium; Department of Oncology, KU Leuven, Leuven, Belgium.
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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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50
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Torborg SR, Grbovic-Huezo O, Singhal A, Holm M, Wu K, Han X, Ho YJ, Haglund C, Mitchell MJ, Lowe SW, Dow LE, Pitter KL, Sanchez-Rivera FJ, Levchenko A, Tammela T. Solid tumor growth depends on an intricate equilibrium of malignant cell states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.30.573100. [PMID: 38234855 PMCID: PMC10793475 DOI: 10.1101/2023.12.30.573100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Control of cell identity and number is central to tissue function, yet principles governing organization of malignant cells in tumor tissues remain poorly understood. Using mathematical modeling and candidate-based analysis, we discover primary and metastatic pancreatic ductal adenocarcinoma (PDAC) organize in a stereotypic pattern whereby PDAC cells responding to WNT signals (WNT-R) neighbor WNT-secreting cancer cells (WNT-S). Leveraging lineage-tracing, we reveal the WNT-R state is transient and gives rise to the WNT-S state that is highly stable and committed to organizing malignant tissue. We further show that a subset of WNT-S cells expressing the Notch ligand DLL1 form a functional niche for WNT-R cells. Genetic inactivation of WNT secretion or Notch pathway components, or cytoablation of the WNT-S state disrupts PDAC tissue organization, suppressing tumor growth and metastasis. This work indicates PDAC growth depends on an intricately controlled equilibrium of functionally distinct cancer cell states, uncovering a fundamental principle governing solid tumor growth and revealing new opportunities for therapeutic intervention.
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