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Zhou S, Lin N, Yu L, Su X, Liu Z, Yu X, Gao H, Lin S, Zeng Y. Single-cell multi-omics in the study of digestive system cancers. Comput Struct Biotechnol J 2024; 23:431-445. [PMID: 38223343 PMCID: PMC10787224 DOI: 10.1016/j.csbj.2023.12.007] [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: 08/04/2023] [Revised: 12/07/2023] [Accepted: 12/07/2023] [Indexed: 01/16/2024] Open
Abstract
Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.
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Affiliation(s)
- Shuang Zhou
- The Second Clinical Medical School of Fujian Medical University, Quanzhou, Fujian Province, China
- The Clinical Center of Molecular Diagnosis and Therapy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Nanfei Lin
- The Clinical Center of Molecular Diagnosis and Therapy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Liying Yu
- The Clinical Center of Molecular Diagnosis and Therapy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Xiaoshan Su
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Respirology Medicine Centre of Fujian Province, Quanzhou, China
| | - Zhenlong Liu
- Lady Davis Institute for Medical Research, Jewish General Hospital, & Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, QC, Canada
| | - Xiaowan Yu
- Clinical Laboratory, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Hongzhi Gao
- The Clinical Center of Molecular Diagnosis and Therapy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Shu Lin
- Centre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
- Diabetes and Metabolism Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, Sydney, NSW 2010, Australia
| | - Yiming Zeng
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Respirology Medicine Centre of Fujian Province, Quanzhou, China
- Fujian Provincial Key Laboratory of Lung Stem Cells, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
- Jinan Microecological Biomedicine Shandong Laboratory, Jinan, Shandong Province, China
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Klemm JD, Singer DS, Mesirov JP. Transforming Cancer Research through Informatics. Cancer Discov 2024; 14:1779-1782. [PMID: 39363746 PMCID: PMC11463720 DOI: 10.1158/2159-8290.cd-24-0604] [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: 07/08/2024] [Revised: 08/06/2024] [Accepted: 08/07/2024] [Indexed: 10/05/2024]
Abstract
For more than three decades, concurrent advances in laboratory technologies and computer science have driven the rise of cancer informatics. Today, software tools for cancer research are indispensable to the entire cancer research enterprise.
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Affiliation(s)
- Juli D. Klemm
- Center for Strategic Scientific Initiatives, National Cancer Institute, NIH, Bethesda, Maryland.
| | - Dinah S. Singer
- Center for Strategic Scientific Initiatives, National Cancer Institute, NIH, Bethesda, Maryland.
| | - Jill P. Mesirov
- Department of Medicine, University of California San Diego, La Jolla, California.
- Moores Cancer Center, University of California San Diego, La Jolla, California.
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3
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Nagasawa S, Zenkoh J, Suzuki Y, Suzuki A. Spatial omics technologies for understanding molecular status associated with cancer progression. Cancer Sci 2024; 115:3208-3217. [PMID: 39042942 PMCID: PMC11447966 DOI: 10.1111/cas.16283] [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: 05/30/2024] [Accepted: 07/02/2024] [Indexed: 07/25/2024] Open
Abstract
Cancer cells are generally exposed to numerous extrinsic stimulations in the tumor microenvironment. In this environment, cancer cells change their expression profiles to fight against circumstantial stresses, allowing their progression in the challenging tissue space. Technological advancements of spatial omics have had substantial influence on cancer genomics. This technical progress, especially that occurring in the spatial transcriptome, has been drastic and rapid. Here, we describe the latest spatial analytical technologies that have allowed omics feature characterization to retain their spatial and histopathological information in cancer tissues. Several spatial omics platforms have been launched, and the latest platforms finally attained single-cell level or even higher subcellular level resolution. We discuss several key papers elucidating the initial utility of the spatial analysis. In fact, spatial transcriptome analyses reveal comprehensive omics characteristics not only in cancer cells but also their surrounding cells, such as tumor infiltrating immune cells and cancer-associated fibroblasts. We also introduce several spatial omics platforms. We describe our own attempts to investigate molecular events associated with cancer progression. Furthermore, we discuss the next challenges in analyzing the multiomics status of cells, including their morphology and location. These novel technologies, in conjunction with spatial transcriptome analysis and, more importantly, with histopathology, will elucidate even novel key aspects of the intratumor heterogeneity of cancers. Such enhanced knowledge is expected to open a new path for overcoming therapeutic resistance and eventually to precisely stratify patients.
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Affiliation(s)
- Satoi Nagasawa
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier SciencesThe University of TokyoChibaJapan
| | - Junko Zenkoh
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier SciencesThe University of TokyoChibaJapan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier SciencesThe University of TokyoChibaJapan
| | - Ayako Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier SciencesThe University of TokyoChibaJapan
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Tirosh I, Suva ML. Cancer cell states: Lessons from ten years of single-cell RNA-sequencing of human tumors. Cancer Cell 2024; 42:1497-1506. [PMID: 39214095 DOI: 10.1016/j.ccell.2024.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 07/22/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024]
Abstract
Human tumors are intricate ecosystems composed of diverse genetic clones and malignant cell states that evolve in a complex tumor micro-environment. Single-cell RNA-sequencing (scRNA-seq) provides a compelling strategy to dissect this intricate biology and has enabled a revolution in our ability to understand tumor biology over the last ten years. Here we reflect on this first decade of scRNA-seq in human tumors and highlight some of the powerful insights gleaned from these studies. We first focus on computational approaches for robustly defining cancer cell states and their diversity and highlight some of the most common patterns of gene expression intra-tumor heterogeneity (eITH) observed across cancer types. We then discuss ambiguities in the field in defining and naming such eITH programs. Finally, we highlight critical developments that will facilitate future research and the broader implementation of these technologies in clinical settings.
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Affiliation(s)
- Itay Tirosh
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 761001, Israel.
| | - Mario L Suva
- Department of Pathology and Krantz Family Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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5
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Bell ATF, Mitchell JT, Kiemen AL, Lyman M, Fujikura K, Lee JW, Coyne E, Shin SM, Nagaraj S, Deshpande A, Wu PH, Sidiropoulos DN, Erbe R, Stern J, Chan R, Williams S, Chell JM, Ciotti L, Zimmerman JW, Wirtz D, Ho WJ, Zaidi N, Thompson E, Jaffee EM, Wood LD, Fertig EJ, Kagohara LT. PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration. Cell Syst 2024; 15:753-769.e5. [PMID: 39116880 PMCID: PMC11409191 DOI: 10.1016/j.cels.2024.07.001] [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: 07/07/2023] [Revised: 02/06/2024] [Accepted: 07/08/2024] [Indexed: 08/10/2024]
Abstract
This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.
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Affiliation(s)
- Alexander T F Bell
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jacob T Mitchell
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Ashley L Kiemen
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA; Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Melissa Lyman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kohei Fujikura
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jae W Lee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Erin Coyne
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sarah M Shin
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sushma Nagaraj
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Atul Deshpande
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Pei-Hsun Wu
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Dimitrios N Sidiropoulos
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Rossin Erbe
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | | | | | | | - Lauren Ciotti
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jacquelyn W Zimmerman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA
| | - Denis Wirtz
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA; Department of Materials Science and Engineering, The Johns Hopkins University, Baltimore, MD, USA; Johns Hopkins Physical Sciences - Oncology Center, The Johns Hopkins University, Baltimore, MD, USA
| | - Won Jin Ho
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA
| | - Neeha Zaidi
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA
| | - Elizabeth Thompson
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA
| | - Elizabeth M Jaffee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA
| | - Laura D Wood
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA
| | - Elana J Fertig
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA.
| | - Luciane T Kagohara
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The Skip Viragh Center for Clinical and Translational Research, Baltimore, MD, USA.
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Chai X, Zhang Y, Zhang W, Feng K, Jiang Y, Zhu A, Chen X, Di L, Wang R. Tumor Metabolism: A New Field for the Treatment of Glioma. Bioconjug Chem 2024; 35:1116-1141. [PMID: 39013195 DOI: 10.1021/acs.bioconjchem.4c00287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
The clinical treatment of glioma remains relatively immature. Commonly used clinical treatments for gliomas are surgery combined with chemotherapy and radiotherapy, but there is a problem of drug resistance. In addition, immunotherapy and targeted therapies also suffer from the problem of immune evasion. The advent of metabolic therapy holds immense potential for advancing more efficacious and tolerable therapies against this aggressive disease. Metabolic therapy alters the metabolic processes of tumor cells at the molecular level to inhibit tumor growth and spread, and lead to better outcomes for patients with glioma that are insensitive to conventional treatments. Moreover, compared with conventional therapy, it has less impact on normal cells, less toxicity and side effects, and higher safety. The objective of this review is to examine the changes in metabolic characteristics throughout the development of glioma, enumerate the current methodologies employed for studying tumor metabolism, and highlight the metabolic reprogramming pathways of glioma along with their potential molecular mechanisms. Importantly, it seeks to elucidate potential metabolic targets for glioblastoma (GBM) therapy and summarize effective combination treatment strategies based on various studies.
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Affiliation(s)
- Xiaoqian Chai
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
- Jiangsu Provincial TCM Engineering Technology Research Center of High Efficient Drug Delivery System, Nanjing 210023, China
| | - Yingjie Zhang
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
- Jiangsu Provincial TCM Engineering Technology Research Center of High Efficient Drug Delivery System, Nanjing 210023, China
| | - Wen Zhang
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
- Jiangsu Provincial TCM Engineering Technology Research Center of High Efficient Drug Delivery System, Nanjing 210023, China
| | - Kuanhan Feng
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
- Jiangsu Provincial TCM Engineering Technology Research Center of High Efficient Drug Delivery System, Nanjing 210023, China
| | - Yingyu Jiang
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
- Jiangsu Provincial TCM Engineering Technology Research Center of High Efficient Drug Delivery System, Nanjing 210023, China
| | - Anran Zhu
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
- Jiangsu Provincial TCM Engineering Technology Research Center of High Efficient Drug Delivery System, Nanjing 210023, China
| | - Xiaojin Chen
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
- Jiangsu Provincial TCM Engineering Technology Research Center of High Efficient Drug Delivery System, Nanjing 210023, China
| | - Liuqing Di
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
- Jiangsu Provincial TCM Engineering Technology Research Center of High Efficient Drug Delivery System, Nanjing 210023, China
| | - Ruoning Wang
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
- Jiangsu Provincial TCM Engineering Technology Research Center of High Efficient Drug Delivery System, Nanjing 210023, China
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Liu B, Hu S, Wang X. Applications of single-cell technologies in drug discovery for tumor treatment. iScience 2024; 27:110486. [PMID: 39171294 PMCID: PMC11338156 DOI: 10.1016/j.isci.2024.110486] [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] [Indexed: 08/23/2024] Open
Abstract
Single-cell technologies have been known as advanced and powerful tools to study tumor biological systems at the single-cell resolution and are playing increasingly critical roles in multiple stages of drug discovery and development. Specifically, single-cell technologies can promote the discovery of drug targets, help high-throughput screening at single-cell level, and contribute to pharmacokinetic studies of anti-tumor drugs. Emerging single-cell analysis technologies have been developed to further integrating multidimensional single-cell molecular features, expanding the scale of single-cell data, profiling phenotypic impact of genes in single cell, and providing full-length coverage single-cell sequencing. In this review, we systematically summarized the applications of single-cell technologies in various sections of drug discovery for tumor treatment, including target identification, high-throughput drug screening, and pharmacokinetic evaluation and highlighted emerging single-cell technologies in providing in-depth understanding of tumor biology. Single-cell-technology-based drug discovery is expected to further optimize therapeutic strategies and improve clinical outcomes of tumor patients.
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Affiliation(s)
- Bingyu Liu
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
| | - Shunfeng Hu
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Xin Wang
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
- Taishan Scholars Program of Shandong Province, Jinan, Shandong 250021, China
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Tuersun A, Huo J, Lv Z, Zhang Y, Chen F, Zhao J, Feng W, Xu Z, Mao Z, Xue P, Lu A. Establishment of a chemokine-based prognostic model and identification of CXCL10+ M1 macrophages as predictors of neoadjuvant therapy efficacy in colorectal cancer. Front Immunol 2024; 15:1400722. [PMID: 39170612 PMCID: PMC11335547 DOI: 10.3389/fimmu.2024.1400722] [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: 03/14/2024] [Accepted: 07/22/2024] [Indexed: 08/23/2024] Open
Abstract
Background Although neoadjuvant therapy has brought numerous benefits to patients, not all patients can benefit from it. Chemokines play a crucial role in the tumor microenvironment and are closely associated with the prognosis and treatment of colorectal cancer. Therefore, constructing a prognostic model based on chemokines will help risk stratification and providing a reference for the personalized treatment. Methods Employing LASSO-Cox predictive modeling, a chemokine-based prognostic model was formulated, harnessing the data from TCGA and GEO databases. Then, our exploration focused on the correlation between the chemokine signature and elements such as the immune landscape, somatic mutations, copy number variations, and drug sensitivity. CXCL10+M1 macrophages identified via scRNA-seq. Monocle2 showed cell pseudotime trajectories, CellChat characterized intercellular communication. CytoTRACE analyzed neoadjuvant therapy stemness, SCENIC detected cell type-specific regulation. Lastly, validation was performed through multiplex immunofluorescence experiments. Results A model based on 15 chemokines was constructed and validated. High-risk scores correlated with poorer prognosis and advanced TNM and clinical stages. Individuals presenting elevated risk scores demonstrated an increased propensity towards the development of chemotherapy resistance. Subsequent scRNA-seq data analysis indicated that patients with higher presence of CXCL10+ M1 macrophages in tumor tissues are more likely to benefit from neoadjuvant therapy. Conclusion We developed a chemokine-based prognostic model by integrating both single-cell and bulk RNA-seq data. Furthermore, we revealed epithelial cell heterogeneity in neoadjuvant outcomes and identified CXCL10+ M1 macrophages as potential therapy response predictors. These findings could significantly contribute to risk stratification and serve as a key guide for the advancement of personalized therapeutic approaches.
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Affiliation(s)
- Abudumaimaitijiang Tuersun
- Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Department of General Surgery, Second People’s Hospital, Kashi, Xinjiang Uygur Autonomous Region, China
| | - Jianting Huo
- Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Zeping Lv
- Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yuchen Zhang
- Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Fangqian Chen
- Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jingkun Zhao
- Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wenqing Feng
- Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Zhuoqing Xu
- Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Zhihai Mao
- Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Pei Xue
- Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Aiguo Lu
- Department of General Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
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Ren A, Chen F, Ren C, Yang M, Wang C, Feng X, Zhang F. Rapid Screening of Biomarkers in KYSE-150 Cells Exposed to Polycyclic Aromatic Hydrocarbons via Inkjet Printing Single-Cell Mass Spectrometry. Anal Chem 2024; 96:12817-12826. [PMID: 39052489 DOI: 10.1021/acs.analchem.4c02332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
Single-cell analysis by mass spectrometry (MS) is emerging as a powerful tool that not only contributes to cellular heterogeneity but also offers an unprecedented opportunity to predict pathology onset and facilitates novel biomarker discovery. However, the development of single-cell MS analysis techniques with a focus on sample extraction, separation, and ionization methods for volume-limited samples and complexity of cellular samples are still a big challenge. In this study, we present a high-throughput approach to inkjet drop on demand printing single-cell MS for rapid screening of biomarkers of polycyclic aromatic hydrocarbon (PAH) exposure at the KYSE-150 cell, aiming to elucidate the pathogenesis of PAH-induced esophageal cancer. With an analytical bulk KYSE-150 cell throughput of up to 51 cells per minute, the method provides a new opportunity for simultaneous single-cell analysis of multiple biomarkers. We screened 930 characteristic ions from 3,683 detected peak signals and identified 91 distinctive molecules that exhibited significant differences under various concentrations of PAH exposure. These molecules have potential as clinical diagnostic biomarkers. Additionally, the current study identifies specific biomarkers that behave completely opposite in single-cell and multicell lipidomics as the concentration of PAH changes. These biomarkers potentially subdivide KYSE-150 cells into PAH-sensitive and PAH-insensitive types, providing a basis for revealing PAH toxicity and disease pathogenesis from the heterogeneity of cellular metabolism.
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Affiliation(s)
- Ai Ren
- Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
- Key Laboratory of Food Quality and Safety for State Market Regulation, School of Pharmacy, China Medical University, Shenyang 110122, China
| | - Fengming Chen
- Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
- Key Laboratory of Food Quality and Safety for State Market Regulation, School of Pharmacy, China Medical University, Shenyang 110122, China
| | - Chenjie Ren
- Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
- Key Laboratory of Food Quality and Safety for State Market Regulation, School of Pharmacy, China Medical University, Shenyang 110122, China
| | - Minli Yang
- Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
- Key Laboratory of Food Quality and Safety for State Market Regulation, School of Pharmacy, China Medical University, Shenyang 110122, China
| | - Chang Wang
- Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
- Key Laboratory of Food Quality and Safety for State Market Regulation, School of Pharmacy, China Medical University, Shenyang 110122, China
| | - Xuesong Feng
- School of Pharmacy, China Medical University, Shenyang 110122, China
| | - Feng Zhang
- Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
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10
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Lu J, Rui J, Xu XY, Shen JK. Exploring the Role of Neutrophil-Related Genes in Osteosarcoma via an Integrative Analysis of Single-Cell and Bulk Transcriptome. Biomedicines 2024; 12:1513. [PMID: 39062086 PMCID: PMC11274533 DOI: 10.3390/biomedicines12071513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 06/29/2024] [Accepted: 07/05/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND The involvement of neutrophil-related genes (NRGs) in patients with osteosarcoma (OS) has not been adequately explored. In this study, we aimed to examine the association between NRGs and the prognosis as well as the tumor microenvironment of OS. METHODS The OS data were obtained from the TARGET-OS and GEO database. Initially, we extracted NRGs by intersecting 538 NRGs from single-cell RNA sequencing (scRNA-seq) data between aneuploid and diploid groups, as well as 161 up-regulated differentially expressed genes (DEGs) from the TARGET-OS datasets. Subsequently, we conducted Least Absolute Shrinkage and Selection Operator (Lasso) analyses to identify the hub genes for constructing the NRG-score and NRG-signature. To assess the prognostic value of the NRG signatures in OS, we performed Kaplan-Meier analysis and generated time-dependent receiver operating characteristic (ROC) curves. Gene enrichment analysis (GSEA) and gene set variation analysis (GSVA) were utilized to ascertain the presence of tumor immune microenvironments (TIMEs) and immunomodulators (IMs). Additionally, the KEGG neutrophil signaling pathway was evaluated using ssGSEA. Subsequently, PCR and IHC were conducted to validate the expression of hub genes and transcription factors (TFs) in K7M2-induced OS mice. RESULTS FCER1G and C3AR1 have been identified as prognostic biomarkers for overall survival. The findings indicate a significantly improved prognosis for OS patients. The effectiveness and precision of the NRG signature in prognosticating OS patients were validated through survival ROC curves and an external validation dataset. The results clearly demonstrate that patients with elevated NRG scores exhibit decreased levels of immunomodulators, stromal score, immune score, ESTIMATE score, and infiltrating immune cell populations. Furthermore, our findings substantiate the potential role of SPI1 as a transcription factor in the regulation of the two central genes involved in osteosarcoma development. Moreover, our analysis unveiled a significant correlation and activation of the KEGG neutrophil signaling pathway with FCER1G and C3AR1. Notably, PCR and IHC demonstrated a significantly higher expression of C3AR1, FCER1G, and SPI1 in Balb/c mice induced with K7M2. CONCLUSIONS Our research emphasizes the significant contribution of neutrophils within the TIME of osteosarcoma. The newly developed NRG signature could serve as a good instrument for evaluating the prognosis and therapeutic approach for OS.
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Affiliation(s)
- Jing Lu
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215025, China;
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200235, China; (J.R.); (X.-Y.X.)
| | - Jiang Rui
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200235, China; (J.R.); (X.-Y.X.)
| | - Xiao-Yu Xu
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200235, China; (J.R.); (X.-Y.X.)
| | - Jun-Kang Shen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215025, China;
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11
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Sin DD. What Single Cell RNA Sequencing Has Taught Us about Chronic Obstructive Pulmonary Disease. Tuberc Respir Dis (Seoul) 2024; 87:252-260. [PMID: 38369875 PMCID: PMC11222093 DOI: 10.4046/trd.2024.0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 02/17/2024] [Indexed: 02/20/2024] Open
Abstract
Chronic obstructive pulmonary disease (COPD) affects close to 400 million people worldwide and is the 3rd leading cause of mortality. It is a heterogeneous disorder with multiple endophenotypes, each driven by specific molecular networks and processes. Therapeutic discovery in COPD has lagged behind other disease areas owing to a lack of understanding of its pathobiology and scarcity of biomarkers to guide therapies. Single cell RNA sequencing (scRNA-seq) is a powerful new tool to identify important cellular and molecular networks that play a crucial role in disease pathogenesis. This paper provides an overview of the scRNA-seq technology and its application in COPD and the lessons learned to date from scRNA-seq experiments in COPD.
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Affiliation(s)
- Don D. Sin
- Centre for Heart Lung Innovation, St. Paul’s Hospital and Division of Respiratory Medicine, University of British Columbia, Vancouver, BC, Canada
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12
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Raja KKB, Yeung K, Li Y, Chen R, Mardon G. A single cell RNA sequence atlas of the early Drosophila larval eye. BMC Genomics 2024; 25:616. [PMID: 38890587 PMCID: PMC11186242 DOI: 10.1186/s12864-024-10423-x] [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: 04/03/2024] [Accepted: 05/16/2024] [Indexed: 06/20/2024] Open
Abstract
The Drosophila eye has been an important model to understand principles of differentiation, proliferation, apoptosis and tissue morphogenesis. However, a single cell RNA sequence resource that captures gene expression dynamics from the initiation of differentiation to the specification of different cell types in the larval eye disc is lacking. Here, we report transcriptomic data from 13,000 cells that cover six developmental stages of the larval eye. Our data show cell clusters that correspond to all major cell types present in the eye disc ranging from the initiation of the morphogenetic furrow to the differentiation of each photoreceptor cell type as well as early cone cells. We identify dozens of cell type-specific genes whose function in different aspects of eye development have not been reported. These single cell data will greatly aid research groups studying different aspects of early eye development and will facilitate a deeper understanding of the larval eye as a model system.
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Affiliation(s)
- Komal Kumar Bollepogu Raja
- Department of Pathology and Immunology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Kelvin Yeung
- Department of Pathology and Immunology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Yumei Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Rui Chen
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Graeme Mardon
- Department of Pathology and Immunology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
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13
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Murciano-Goroff YR, Uppal M, Chen M, Harada G, Schram AM. Basket Trials: Past, Present, and Future. ANNUAL REVIEW OF CANCER BIOLOGY 2024; 8:59-80. [PMID: 38938274 PMCID: PMC11210107 DOI: 10.1146/annurev-cancerbio-061421-012927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
Large-scale tumor molecular profiling has revealed that diverse cancer histologies are driven by common pathways with unifying biomarkers that can be exploited therapeutically. Disease-agnostic basket trials have been increasingly utilized to test biomarker-driven therapies across cancer types. These trials have led to drug approvals and improved the lives of patients while simultaneously advancing our understanding of cancer biology. This review focuses on the practicalities of implementing basket trials, with an emphasis on molecularly targeted trials. We examine the biologic subtleties of genomic biomarker and patient selection, discuss previous successes in drug development facilitated by basket trials, describe certain novel targets and drugs, and emphasize practical considerations for participant recruitment and study design. This review also highlights strategies for aiding patient access to basket trials. As basket trials become more common, steps to ensure equitable implementation of these studies will be critical for molecularly targeted drug development.
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Affiliation(s)
| | - Manik Uppal
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
| | - Monica Chen
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Guilherme Harada
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alison M Schram
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
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14
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Sun Y, Liu Y, Sun D, Liu K, Li Y, Liu Y, Zhang S. A facile single-cell patterning strategy based on harbor-like microwell microfluidics. Biomed Mater 2024; 19:045018. [PMID: 38772387 DOI: 10.1088/1748-605x/ad4e83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 05/21/2024] [Indexed: 05/23/2024]
Abstract
Single-cell analysis is an effective method for conducting comprehensive heterogeneity studies ranging from cell phenotype to gene expression. The ability to arrange different cells in a predetermined pattern at single-cell resolution has a wide range of applications in cell-based analysis and plays an important role in facilitating interdisciplinary research by researchers in various fields. Most existing microfluidic microwell chips is a simple and straightforward method, which typically use small-sized microwells to accommodate single cells. However, this method imposes certain limitations on cells of various sizes, and the single-cell capture efficiency is relatively low without the assistance of external forces. Moreover, the microwells limit the spatiotemporal resolution of reagent replacement, as well as cell-to-cell communication. In this study, we propose a new strategy to prepare a single-cell array on a planar microchannel based on microfluidic flip microwells chip platform with large apertures (50 μm), shallow channels (50 μm), and deep microwells (50 μm). The combination of three configuration characteristics contributes to multi-cell trapping and a single-cell array within microwells, while the subsequent chip flipping accomplishes the transfer of the single-cell array to the opposite planar microchannel for cells adherence and growth. Further assisted by protein coating of bovine serum albumin and fibronectin on different layers, the single-cell capture efficiency in microwells is achieved at 92.1% ± 1%, while ultimately 85% ± 3.4% on planar microchannel. To verify the microfluidic flip microwells chip platform, the real-time and heterogeneous study of calcium release and apoptosis behaviours of single cells is carried out. To our knowledge, this is the first time that high-efficiency single-cell acquisition has been accomplished using a circular-well chip design that combines shallow channel, large aperture and deep microwell together. The chip is effective in avoiding the shearing force of high flow rates on cells, and the large apertures better allows cells to sedimentation. Therefore, this strategy owns the advantages of easy preparation and user-friendliness, which is especially valuable for researchers from different fields.
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Affiliation(s)
- Yingnan Sun
- Shandong Province Key Laboratory of Detection Technology for Tumor Makers, School of Medicine, Linyi University, Linyi 276005, People's Republic of China
| | - Yongshu Liu
- Shandong Province Key Laboratory of Detection Technology for Tumor Makers, School of Medicine, Linyi University, Linyi 276005, People's Republic of China
| | - Dezhi Sun
- Xinjiang Key Laboratory of Signal Detection and Processing, School of Computer Science and Technology, Xinjiang University, Urumqi 830046, People's Republic of China
| | - Kexin Liu
- Shandong Province Key Laboratory of Detection Technology for Tumor Makers, School of Medicine, Linyi University, Linyi 276005, People's Republic of China
| | - Yuyan Li
- Shandong Province Key Laboratory of Detection Technology for Tumor Makers, School of Medicine, Linyi University, Linyi 276005, People's Republic of China
| | - Yumin Liu
- Shandong Province Key Laboratory of Detection Technology for Tumor Makers, School of Medicine, Linyi University, Linyi 276005, People's Republic of China
| | - Shusheng Zhang
- Shandong Province Key Laboratory of Detection Technology for Tumor Makers, School of Medicine, Linyi University, Linyi 276005, People's Republic of China
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15
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Mori Y, Okimoto Y, Sakai H, Kanda Y, Ohata H, Shiokawa D, Suzuki M, Yoshida H, Ueda H, Sekizuka T, Tamura R, Yamawaki K, Ishiguro T, Mateos RN, Shiraishi Y, Yatabe Y, Hamada A, Yoshihara K, Enomoto T, Okamoto K. Targeting PDGF signaling of cancer-associated fibroblasts blocks feedback activation of HIF-1α and tumor progression of clear cell ovarian cancer. Cell Rep Med 2024; 5:101532. [PMID: 38670097 PMCID: PMC11149410 DOI: 10.1016/j.xcrm.2024.101532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 01/04/2024] [Accepted: 04/04/2024] [Indexed: 04/28/2024]
Abstract
Ovarian clear cell carcinoma (OCCC) is a gynecological cancer with a dismal prognosis; however, the mechanism underlying OCCC chemoresistance is not well understood. To explore the intracellular networks associated with the chemoresistance, we analyze surgical specimens by performing integrative analyses that combine single-cell analyses and spatial transcriptomics. We find that a chemoresistant OCCC subpopulation with elevated HIF activity localizes mainly in areas populated by cancer-associated fibroblasts (CAFs) with a myofibroblastic phenotype, which is corroborated by quantitative immunostaining. CAF-enhanced chemoresistance and HIF-1α induction are recapitulated in co-culture assays, which show that cancer-derived platelet-derived growth factor (PDGF) contributes to the chemoresistance and HIF-1α induction via PDGF receptor signaling in CAFs. Ripretinib is identified as an effective receptor tyrosine kinase inhibitor against CAF survival. In the co-culture system and xenograft tumors, ripretinib prevents CAF survival and suppresses OCCC proliferation in the presence of carboplatin, indicating that combination of conventional chemotherapy and CAF-targeted agents is effective against OCCC.
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MESH Headings
- Female
- Humans
- Cancer-Associated Fibroblasts/metabolism
- Cancer-Associated Fibroblasts/pathology
- Cancer-Associated Fibroblasts/drug effects
- Hypoxia-Inducible Factor 1, alpha Subunit/metabolism
- Hypoxia-Inducible Factor 1, alpha Subunit/genetics
- Ovarian Neoplasms/pathology
- Ovarian Neoplasms/metabolism
- Ovarian Neoplasms/drug therapy
- Ovarian Neoplasms/genetics
- Platelet-Derived Growth Factor/metabolism
- Signal Transduction/drug effects
- Animals
- Mice
- Cell Line, Tumor
- Drug Resistance, Neoplasm/drug effects
- Drug Resistance, Neoplasm/genetics
- Disease Progression
- Coculture Techniques
- Cell Proliferation/drug effects
- Mice, Nude
- Adenocarcinoma, Clear Cell/metabolism
- Adenocarcinoma, Clear Cell/pathology
- Adenocarcinoma, Clear Cell/drug therapy
- Adenocarcinoma, Clear Cell/genetics
- Feedback, Physiological/drug effects
- Xenograft Model Antitumor Assays
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Affiliation(s)
- Yutaro Mori
- Advanced Comprehensive Research Organization, Teikyo University, Tokyo 173-0003, Japan; Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Yoshie Okimoto
- Advanced Comprehensive Research Organization, Teikyo University, Tokyo 173-0003, Japan
| | - Hiroaki Sakai
- Advanced Comprehensive Research Organization, Teikyo University, Tokyo 173-0003, Japan
| | - Yusuke Kanda
- Advanced Comprehensive Research Organization, Teikyo University, Tokyo 173-0003, Japan
| | - Hirokazu Ohata
- Advanced Comprehensive Research Organization, Teikyo University, Tokyo 173-0003, Japan
| | - Daisuke Shiokawa
- Ehime University Hospital Translational Research Center, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Mikiko Suzuki
- Division of Molecular Pharmacology, National Cancer Center Research Institute, Tokyo 104-0045, Japan
| | - Hiroshi Yoshida
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo 104-0045, Japan
| | - Haruka Ueda
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Tomoyuki Sekizuka
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Ryo Tamura
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Kaoru Yamawaki
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Tatsuya Ishiguro
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Raul Nicolas Mateos
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo 104-0045, Japan
| | - Yuichi Shiraishi
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo 104-0045, Japan
| | - Yasushi Yatabe
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo 104-0045, Japan
| | - Akinobu Hamada
- Division of Molecular Pharmacology, National Cancer Center Research Institute, Tokyo 104-0045, Japan
| | - Kosuke Yoshihara
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Takayuki Enomoto
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Koji Okamoto
- Advanced Comprehensive Research Organization, Teikyo University, Tokyo 173-0003, Japan.
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16
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Beigi YZ, Lanjanian H, Fayazi R, Salimi M, Hoseyni BHM, Noroozizadeh MH, Masoudi-Nejad A. Heterogeneity and molecular landscape of melanoma: implications for targeted therapy. MOLECULAR BIOMEDICINE 2024; 5:17. [PMID: 38724687 PMCID: PMC11082128 DOI: 10.1186/s43556-024-00182-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 04/08/2024] [Indexed: 05/12/2024] Open
Abstract
Uveal cancer (UM) offers a complex molecular landscape characterized by substantial heterogeneity, both on the genetic and epigenetic levels. This heterogeneity plays a critical position in shaping the behavior and response to therapy for this uncommon ocular malignancy. Targeted treatments with gene-specific therapeutic molecules may prove useful in overcoming radiation resistance, however, the diverse molecular makeups of UM call for a patient-specific approach in therapy procedures. We need to understand the intricate molecular landscape of UM to develop targeted treatments customized to each patient's specific genetic mutations. One of the promising approaches is using liquid biopsies, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), for detecting and monitoring the disease at the early stages. These non-invasive methods can help us identify the most effective treatment strategies for each patient. Single-cellular is a brand-new analysis platform that gives treasured insights into diagnosis, prognosis, and remedy. The incorporation of this data with known clinical and genomics information will give a better understanding of the complicated molecular mechanisms that UM diseases exploit. In this review, we focused on the heterogeneity and molecular panorama of UM, and to achieve this goal, the authors conducted an exhaustive literature evaluation spanning 1998 to 2023, using keywords like "uveal melanoma, "heterogeneity". "Targeted therapies"," "CTCs," and "single-cellular analysis".
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Affiliation(s)
- Yasaman Zohrab Beigi
- Laboratory of System Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Hossein Lanjanian
- Software Engineering Department, Engineering Faculty, Istanbul Topkapi University, Istanbul, Turkey
| | - Reyhane Fayazi
- Laboratory of System Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Mahdieh Salimi
- Department of Medical Genetics, Institute of Medical Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
| | - Behnaz Haji Molla Hoseyni
- Laboratory of System Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | | | - Ali Masoudi-Nejad
- Laboratory of System Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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17
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Wang K, Zerdes I, Johansson HJ, Sarhan D, Sun Y, Kanellis DC, Sifakis EG, Mezheyeuski A, Liu X, Loman N, Hedenfalk I, Bergh J, Bartek J, Hatschek T, Lehtiö J, Matikas A, Foukakis T. Longitudinal molecular profiling elucidates immunometabolism dynamics in breast cancer. Nat Commun 2024; 15:3837. [PMID: 38714665 PMCID: PMC11076527 DOI: 10.1038/s41467-024-47932-y] [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: 05/08/2023] [Accepted: 04/12/2024] [Indexed: 05/10/2024] Open
Abstract
Although metabolic reprogramming within tumor cells and tumor microenvironment (TME) is well described in breast cancer, little is known about how the interplay of immune state and cancer metabolism evolves during treatment. Here, we characterize the immunometabolic profiles of tumor tissue samples longitudinally collected from individuals with breast cancer before, during and after neoadjuvant chemotherapy (NAC) using proteomics, genomics and histopathology. We show that the pre-, on-treatment and dynamic changes of the immune state, tumor metabolic proteins and tumor cell gene expression profiling-based metabolic phenotype are associated with treatment response. Single-cell/nucleus RNA sequencing revealed distinct tumor and immune cell states in metabolism between cold and hot tumors. Potential drivers of NAC based on above analyses were validated in vitro. In summary, the study shows that the interaction of tumor-intrinsic metabolic states and TME is associated with treatment outcome, supporting the concept of targeting tumor metabolism for immunoregulation.
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Affiliation(s)
- Kang Wang
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Ioannis Zerdes
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Theme Cancer, Karolinska University Hospital and Karolinska Comprehensive Cancer Center, Stockholm, Sweden
| | - Henrik J Johansson
- Department of Oncology-Pathology, Karolinska Institutet, and Science for Life Laboratory, Stockholm, Sweden
| | - Dhifaf Sarhan
- Department of Laboratory Medicine, Division of Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Yizhe Sun
- Department of Laboratory Medicine, Division of Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Dimitris C Kanellis
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | | | - Artur Mezheyeuski
- Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, Uppsala, Sweden
- Molecular Oncology Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Xingrong Liu
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Niklas Loman
- Department of Hematology, Oncology and Radiation Physics, Lund University Hospital, Lund, Sweden
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Ingrid Hedenfalk
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Jonas Bergh
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Breast Center, Theme Cancer, Karolinska University Hospital and Karolinska Comprehensive Cancer Center, Stockholm, Sweden
| | - Jiri Bartek
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
- Danish Cancer Institute, DK-2100, Copenhagen, Denmark
| | - Thomas Hatschek
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Breast Center, Theme Cancer, Karolinska University Hospital and Karolinska Comprehensive Cancer Center, Stockholm, Sweden
| | - Janne Lehtiö
- Department of Oncology-Pathology, Karolinska Institutet, and Science for Life Laboratory, Stockholm, Sweden
- Division of Pathology, Karolinska University Hospital and Karolinska Comprehensive Cancer Center, Stockholm, Sweden
| | - Alexios Matikas
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Breast Center, Theme Cancer, Karolinska University Hospital and Karolinska Comprehensive Cancer Center, Stockholm, Sweden
| | - Theodoros Foukakis
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
- Breast Center, Theme Cancer, Karolinska University Hospital and Karolinska Comprehensive Cancer Center, Stockholm, Sweden.
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18
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Chen Y, Gao R, Jing D, Shi L, Kuang F, Jing R. Classification and prediction of chemoradiotherapy response and survival from esophageal carcinoma histopathology images. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 312:124030. [PMID: 38368818 DOI: 10.1016/j.saa.2024.124030] [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: 08/12/2023] [Revised: 01/27/2024] [Accepted: 02/08/2024] [Indexed: 02/20/2024]
Abstract
Whole slide imaging (WSI) of Hematoxylin and Eosin-stained biopsy specimens has been used to predict chemoradiotherapy (CRT) response and overall survival (OS) of esophageal squamous cell carcinoma (ESCC) patients. This retrospective study collected 279 specimens in 89 non-surgical ESCC patients through endoscopic biopsy between January 2010 and January 2019. These patients were divided into a CRT response group (CR + PR group) and a CRT non-response group (SD + PD group). The WSIs have segmented approximately 1,206,000 non-overlapping patches. Two experienced pathologists manually delineated the eight types of tissues on 32 WSIs, including esophagus tumor cell (TUM), cancer-associated stroma (CAS), normal epithelium layer (NEL), smooth muscle (MUS), lymphocytes (LYM), Red cells (RED), debris (DEB), uneven areas (UNE). The chemoradiotherapy response prediction models were built using maximum relevance-minimum redundancy (MRMR) feature selection and least absolute shrinkage and selection operator (LASSO) regression. However, pathological features with p < 0.1 were selected and integrated to be further screened using a LASSO Cox regression model to build a multivariate Cox proportional hazards model for predicting the OS. The testing accuracy of the tissue classification model was 91.3 %. The pathological model created using two CAS in-depth features and eight TUM in-depth features performed best for the prediction of treatment response and achieved an AUC of 0.744. For the prediction of OS, the testing AUC of this model at one year and three years were 0.675 and 0.870, respectively. The TUM model showed the highest AUC at one year (0.712). With its high accuracy rate, the deep learning model has the potential to transform from bench to bedside in clinical practice, improve patient's quality of life, and prolong the OS rate.
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Affiliation(s)
- Yu Chen
- Department of Oncology, Xiangya Hospital National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Ruihuan Gao
- Department of Oncology, Xiangya Hospital National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Di Jing
- Department of Oncology, Xiangya Hospital National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Liting Shi
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian 271016, China
| | - Feng Kuang
- Department of Cardiovascular Surgery, The First Affiliated Hospital of Xiamen University, Teaching Hospital of Fujian Medical University, Xiamen, China
| | - Ran Jing
- Department of Cardiovascular Medicine, Xiangya Hospital National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, 410008 Changsha, China.
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19
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Muyas F, Sauer CM, Valle-Inclán JE, Li R, Rahbari R, Mitchell TJ, Hormoz S, Cortés-Ciriano I. De novo detection of somatic mutations in high-throughput single-cell profiling data sets. Nat Biotechnol 2024; 42:758-767. [PMID: 37414936 PMCID: PMC11098751 DOI: 10.1038/s41587-023-01863-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 06/07/2023] [Indexed: 07/08/2023]
Abstract
Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6 million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2-0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.
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Affiliation(s)
- Francesc Muyas
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Carolin M Sauer
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Jose Espejo Valle-Inclán
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Ruoyan Li
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Raheleh Rahbari
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Thomas J Mitchell
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Department of Surgery, University of Cambridge, Cambridge, UK
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Isidro Cortés-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.
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20
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Zhan F, Guo Y, He L. A novel defined programmed cell death related gene signature for predicting the prognosis of serous ovarian cancer. J Ovarian Res 2024; 17:92. [PMID: 38685095 PMCID: PMC11057167 DOI: 10.1186/s13048-024-01419-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/19/2024] [Indexed: 05/02/2024] Open
Abstract
PURPOSE This study aims to explore the contribution of differentially expressed programmed cell death genes (DEPCDGs) to the heterogeneity of serous ovarian cancer (SOC) through single-cell RNA sequencing (scRNA-seq) and assess their potential as predictors for clinical prognosis. METHODS SOC scRNA-seq data were extracted from the Gene Expression Omnibus database, and the principal component analysis was used for cell clustering. Bulk RNA-seq data were employed to analyze SOC-associated immune cell subsets key genes. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were utilized to calculate immune cell scores. Prognostic models and nomograms were developed through univariate and multivariate Cox analyses. RESULTS Our analysis revealed that 48 DEPCDGs are significantly correlated with apoptotic signaling and oxidative stress pathways and identified seven key DEPCDGs (CASP3, GADD45B, GNA15, GZMB, IL1B, ISG20, and RHOB) through survival analysis. Furthermore, eight distinct cell subtypes were characterized using scRNA-seq. It was found that G protein subunit alpha 15 (GNA15) exhibited low expression across these subtypes and a strong association with immune cells. Based on the DEGs identified by the GNA15 high- and low-expression groups, a prognostic model comprising eight genes with significant prognostic value was constructed, effectively predicting patient overall survival. Additionally, a nomogram incorporating the RS signature, age, grade, and stage was developed and validated using two large SOC datasets. CONCLUSION GNA15 emerged as an independent and excellent prognostic marker for SOC patients. This study provides valuable insights into the prognostic potential of DEPCDGs in SOC, presenting new avenues for personalized treatment strategies.
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Affiliation(s)
- Feng Zhan
- College of Engineering, Fujian Jiangxia University, Fuzhou, Fujian, 350108, China
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, 030024, China
| | - Yina Guo
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, 030024, China
| | - Lidan He
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350004, China.
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21
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Tian J, Bai X, Quek C. Single-Cell Informatics for Tumor Microenvironment and Immunotherapy. Int J Mol Sci 2024; 25:4485. [PMID: 38674070 PMCID: PMC11050520 DOI: 10.3390/ijms25084485] [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: 03/08/2024] [Revised: 04/12/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.
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Affiliation(s)
| | | | - Camelia Quek
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; (J.T.); (X.B.)
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22
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Deng Y, Xia L, Zhang J, Deng S, Wang M, Wei S, Li K, Lai H, Yang Y, Bai Y, Liu Y, Luo L, Yang Z, Chen Y, Kang R, Gan F, Pu Q, Mei J, Ma L, Lin F, Guo C, Liao H, Zhu Y, Liu Z, Liu C, Hu Y, Yuan Y, Zha Z, Yuan G, Zhang G, Chen L, Cheng Q, Shen S, Liu L. Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages. Cell Rep Med 2024; 5:101489. [PMID: 38554705 PMCID: PMC11031428 DOI: 10.1016/j.xcrm.2024.101489] [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: 09/30/2022] [Revised: 01/26/2024] [Accepted: 03/06/2024] [Indexed: 04/02/2024]
Abstract
Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8+ T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8+ T cells, driven by specific stromal cells such as CTHCR1+ fibroblasts. This study also highlights EGFR(L858R) neoantigens in GGO samples, suggesting potential CD8+ T cell activation. Our findings offer valuable insights into lung adenocarcinoma heterogeneity, suggesting avenues for targeted therapies in early-stage disease.
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Affiliation(s)
- Yulan Deng
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Liang Xia
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Jian Zhang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Senyi Deng
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Mengyao Wang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China; Faculty of Dentistry, The University of Hong Kong, Prince Philip Dental Hospital, Sai Ying Pun, Hong Kong, China
| | - Shiyou Wei
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Kaixiu Li
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China; Faculty of Dentistry, The University of Hong Kong, Prince Philip Dental Hospital, Sai Ying Pun, Hong Kong, China
| | - Hongjin Lai
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yunhao Yang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yuquan Bai
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yongcheng Liu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Lanzhi Luo
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Zhenyu Yang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yaohui Chen
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Ran Kang
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Fanyi Gan
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Qiang Pu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Jiandong Mei
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Lin Ma
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Feng Lin
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Chenglin Guo
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Hu Liao
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yunke Zhu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Zheng Liu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Chengwu Liu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yang Hu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Yong Yuan
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Zhengyu Zha
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Gang Yuan
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China
| | - Gao Zhang
- Faculty of Dentistry, The University of Hong Kong, Prince Philip Dental Hospital, Sai Ying Pun, Hong Kong, China
| | - Luonan Chen
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China; Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
| | - Qing Cheng
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Shensi Shen
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China.
| | - Lunxu Liu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, China; Western China Collaborative Innovation Center for Early Diagnosis and Multidisciplinary Therapy of Lung Cancer, Sichuan University, Chengdu 610041, China.
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23
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Zhang L, Deeb G, Deeb KK, Vale C, Peker Barclift D, Papadantonakis N. Measurable (Minimal) Residual Disease in Myelodysplastic Neoplasms (MDS): Current State and Perspectives. Cancers (Basel) 2024; 16:1503. [PMID: 38672585 PMCID: PMC11048433 DOI: 10.3390/cancers16081503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Myelodysplastic Neoplasms (MDS) have been traditionally studied through the assessment of blood counts, cytogenetics, and morphology. In recent years, the introduction of molecular assays has improved our ability to diagnose MDS. The role of Measurable (minimal) Residual Disease (MRD) in MDS is evolving, and molecular and flow cytometry techniques have been used in several studies. In this review, we will highlight the evolving concept of MRD in MDS, outline the various techniques utilized, and provide an overview of the studies reporting MRD and the correlation with outcomes.
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Affiliation(s)
- Linsheng Zhang
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - George Deeb
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Kristin K. Deeb
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Colin Vale
- Department of Hematology and Medical Oncology, Winship Cancer Institute of Emory University, Atlanta, GA 30322, USA
| | - Deniz Peker Barclift
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Nikolaos Papadantonakis
- Department of Hematology and Medical Oncology, Winship Cancer Institute of Emory University, Atlanta, GA 30322, USA
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24
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Yuzhalin AE. Redefining cancer research for therapeutic breakthroughs. Br J Cancer 2024; 130:1078-1082. [PMID: 38424166 PMCID: PMC10991368 DOI: 10.1038/s41416-024-02634-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 02/13/2024] [Accepted: 02/20/2024] [Indexed: 03/02/2024] Open
Abstract
Cancer research has played a pivotal role in improving patient outcomes. However, despite the significant investment in fundamental cancer research over the past few decades, the translation of funding into substantial advancements in cancer treatment has been limited. This perspective article employs a detailed analysis to outline strategies for promoting innovation and facilitating discoveries within the field of cancer research.
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Affiliation(s)
- Arseniy E Yuzhalin
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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25
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Liang S, Dou J, Iqbal R, Chen K. Label-aware distance mitigates temporal and spatial variability for clustering and visualization of single-cell gene expression data. Commun Biol 2024; 7:326. [PMID: 38486077 PMCID: PMC10940680 DOI: 10.1038/s42003-024-05988-y] [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: 07/03/2023] [Accepted: 02/28/2024] [Indexed: 03/18/2024] Open
Abstract
Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Label-Aware Distance (LAD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate LAD on simulated data as well as apply it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). LAD provides better cell embedding than state-of-the-art batch correction methods on longitudinal datasets. It can be used in distance-based clustering and visualization methods to combine the power of multiple samples to help make biological findings.
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Affiliation(s)
- Shaoheng Liang
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA.
- Department of Computer Science, Rice University, Houston, TX, USA.
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Jinzhuang Dou
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ramiz Iqbal
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA.
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26
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Yang S, Wang M, Hua Y, Li J, Zheng H, Cui M, Huang N, Liu Q, Liao Q. Advanced insights on tumor-associated macrophages revealed by single-cell RNA sequencing: The intratumor heterogeneity, functional phenotypes, and cellular interactions. Cancer Lett 2024; 584:216610. [PMID: 38244910 DOI: 10.1016/j.canlet.2024.216610] [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: 11/23/2022] [Revised: 11/28/2023] [Accepted: 12/18/2023] [Indexed: 01/22/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) is an emerging technology used for cellular transcriptome analysis. The application of scRNA-seq has led to profoundly advanced oncology research, continuously optimizing novel therapeutic strategies. Intratumor heterogeneity extensively consists of all tumor components, contributing to different tumor behaviors and treatment responses. Tumor-associated macrophages (TAMs), the core immune cells linking innate and adaptive immunity, play significant roles in tumor progression and resistance to therapies. Moreover, dynamic changes occur in TAM phenotypes and functions subject to the regulation of the tumor microenvironment. The heterogeneity of TAMs corresponding to the state of the tumor microenvironment has been comprehensively recognized using scRNA-seq. Herein, we reviewed recent research and summarized variations in TAM phenotypes and functions from a developmental perspective to better understand the significance of TAMs in the tumor microenvironment.
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Affiliation(s)
- Sen Yang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China
| | - Mengyi Wang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China
| | - Yuze Hua
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China
| | - Jiayi Li
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China
| | - Huaijin Zheng
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China
| | - Ming Cui
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China
| | - Nan Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China
| | - Qiaofei Liu
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China.
| | - Quan Liao
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science, and Peking Union Medical College, Beijing, 100730, China.
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27
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Serebrovskaya EO, Bryushkova EA, Lukyanov DK, Mushenkova NV, Chudakov DM, Turchaninova MA. Toolkit for mapping the clonal landscape of tumor-infiltrating B cells. Semin Immunol 2024; 72:101864. [PMID: 38301345 DOI: 10.1016/j.smim.2024.101864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 01/08/2024] [Indexed: 02/03/2024]
Abstract
Our current understanding of whether B cell involvement in the tumor microenvironment benefits the patient or the tumor - in distinct cancers, subcohorts and individual patients - is quite limited. Both statements are probably true in most cases: certain clonal B cell populations contribute to the antitumor response, while others steer the immune response away from the desired mechanics. To step up to a new level of understanding and managing B cell behaviors in the tumor microenvironment, we need to rationally discern these roles, which are cumulatively defined by B cell clonal functional programs, specificities of their B cell receptors, specificities and isotypes of the antibodies they produce, and their spatial interactions within the tumor environment. Comprehensive analysis of these characteristics of clonal B cell populations is now becoming feasible with the development of a whole arsenal of advanced technical approaches, which include (1) methods of single-cell and spatial transcriptomics, genomics, and proteomics; (2) methods of massive identification of B cell specificities; (3) methods of deep error-free profiling of B cell receptor repertoires. Here we overview existing techniques, summarize their current application for B cells studies and propose promising future directions in advancing B cells exploration.
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Affiliation(s)
- E O Serebrovskaya
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow, Russia; Current position: Miltenyi Biotec B.V. & Co. KG, Bergisch Gladbach, Germany
| | - E A Bryushkova
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow, Russia; Department of Molecular Biology, Lomonosov Moscow State University, Moscow, Russia
| | - D K Lukyanov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow, Russia; Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - N V Mushenkova
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia; Unicorn Capital Partners, 119049, Moscow, Russia
| | - D M Chudakov
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow, Russia; Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia; Central European Institute of Technology, Masaryk University, Brno, Czech Republic.
| | - M A Turchaninova
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow, Russia
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Li R, Su P, Shi Y, Shi H, Ding S, Su X, Chen P, Wu D. Gene doping detection in the era of genomics. Drug Test Anal 2024. [PMID: 38403949 DOI: 10.1002/dta.3664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 02/27/2024]
Abstract
Recent progress in gene editing has enabled development of gene therapies for many genetic diseases, but also made gene doping an emerging risk in sports and competitions. By delivery of exogenous transgenes into human body, gene doping not only challenges competition fairness but also places health risk on athletes. World Anti-Doping Agency (WADA) has clearly inhibited the use of gene and cell doping in sports, and many techniques have been developed for gene doping detection. In this review, we will summarize the main tools for gene doping detection at present, highlight the main challenges for current tools, and elaborate future utilizations of high-throughput sequencing for unbiased, sensitive, economic and large-scale gene doping detections. Quantitative real-time PCR assays are the widely used detection methods at present, which are useful for detection of known targets but are vulnerable to codon optimization at exon-exon junction sites of the transgenes. High-throughput sequencing has become a powerful tool for various applications in life and health research, and the era of genomics has made it possible for sensitive and large-scale gene doping detections. Non-biased genomic profiling could efficiently detect new doping targets, and low-input genomics amplification and long-read third-generation sequencing also have application potentials for more efficient and straightforward gene doping detection. By closely monitoring scientific advancements in gene editing and sport genetics, high-throughput sequencing could play a more and more important role in gene detection and hopefully contribute to doping-free sports in the future.
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Affiliation(s)
- Ruihong Li
- eHealth Program of Shanghai Anti-doping Laboratory, Shanghai University of Sport, Shanghai, China
- Shanghai Center of Agri-Products Quality and Safety, Shanghai, China
| | - Peipei Su
- Innovative Program of Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi Shi
- eHealth Program of Shanghai Anti-doping Laboratory, Shanghai University of Sport, Shanghai, China
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, Shanghai, China
| | - Hui Shi
- eHealth Program of Shanghai Anti-doping Laboratory, Shanghai University of Sport, Shanghai, China
- Department of Rheumatology and Immunology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shengqian Ding
- eHealth Program of Shanghai Anti-doping Laboratory, Shanghai University of Sport, Shanghai, China
| | - Xianbin Su
- eHealth Program of Shanghai Anti-doping Laboratory, Shanghai University of Sport, Shanghai, China
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Peijie Chen
- School of Exercise and Health, Shanghai University of Sport, Shanghai, China
| | - Die Wu
- eHealth Program of Shanghai Anti-doping Laboratory, Shanghai University of Sport, Shanghai, China
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29
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Zhu LH, Yang J, Zhang YF, Yan L, Lin WR, Liu WQ. Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data. World J Clin Oncol 2024; 15:329-355. [PMID: 38455135 PMCID: PMC10915942 DOI: 10.5306/wjco.v15.i2.329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 12/24/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
BACKGROUND Pyroptosis impacts the development of malignant tumors, yet its role in colorectal cancer (CRC) prognosis remains uncertain. AIM To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration. METHODS Gene expression data were obtained from The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus (GEO). Pyroptosis-related gene expression in cell clusters was analyzed, and enrichment analysis was conducted. A pyroptosis-related risk model was developed using the LASSO regression algorithm, with prediction accuracy assessed through K-M and receiver operating characteristic analyses. A nomogram predicting survival was created, and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations. Finally, the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database. RESULTS An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B, SDHB, BST2, UBE2D2, GJA1, AIM2, PDCD6IP, and SEZ6L2 (P < 0.05). Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis (P < 0.05). Patients with higher risk scores demonstrated increased death risk and reduced overall survival (P < 0.05). Significant differences in immune infiltration were observed between low- and high-risk groups, correlating with pyroptosis-related gene expression. CONCLUSION We developed a pyroptosis-related prognostic model for CRC, affirming its correlation with immune infiltration. This model may prove useful for CRC prognostic evaluation.
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Affiliation(s)
- Li-Hua Zhu
- Department of Surgical Oncology, The First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan Province, China
| | - Jun Yang
- Department of Surgical Oncology, The First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan Province, China
| | - Yun-Fei Zhang
- Department of Surgical Oncology, The First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan Province, China
| | - Li Yan
- Department of Internal Medicine-Oncology, The First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan Province, China
| | - Wan-Rong Lin
- Department of Oncology, The First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan Province, China
| | - Wei-Qing Liu
- Department of Internal Medicine-Oncology, The First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan Province, China
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Revsine M, Wang L, Forgues M, Behrens S, Craig AJ, Liu M, Tran B, Kelly M, Budhu A, Monge C, Xie C, Hernandez JM, Greten TF, Wang XW, Ma L. Lineage and ecology define liver tumor evolution in response to treatment. Cell Rep Med 2024; 5:101394. [PMID: 38280378 PMCID: PMC10897542 DOI: 10.1016/j.xcrm.2024.101394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 11/09/2023] [Accepted: 01/03/2024] [Indexed: 01/29/2024]
Abstract
A tumor ecosystem constantly evolves over time in the face of immune predation or therapeutic intervention, resulting in treatment failure and tumor progression. Here, we present a single-cell transcriptome-based strategy to determine the evolution of longitudinal tumor biopsies from liver cancer patients by measuring cellular lineage and ecology. We construct a lineage and ecological score as joint dynamics of tumor cells and their microenvironments. Tumors may be classified into four main states in the lineage-ecological space, which are associated with clinical outcomes. Analysis of longitudinal samples reveals the evolutionary trajectory of tumors in response to treatment. We validate the lineage-ecology-based scoring system in predicting clinical outcomes using bulk transcriptomic data of additional cohorts of 716 liver cancer patients. Our study provides a framework for monitoring tumor evolution in response to therapeutic intervention.
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Affiliation(s)
- Mahler Revsine
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Limin Wang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Marshonna Forgues
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Shay Behrens
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Amanda J Craig
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Meng Liu
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Bao Tran
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 20701, USA
| | - Michael Kelly
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 20701, USA
| | - Anuradha Budhu
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Cecilia Monge
- Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Changqing Xie
- Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Jonathan M Hernandez
- Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Tim F Greten
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Xin Wei Wang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.
| | - Lichun Ma
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.
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Ve K, R R, Cac P, A K, E T, Cc S, Ab O. Single Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting (SNACS): A tool for demultiplexing single-cell DNA sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.07.579345. [PMID: 38370638 PMCID: PMC10871358 DOI: 10.1101/2024.02.07.579345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Motivation Recently, single-cell DNA sequencing (scDNA-seq) and multi-modal profiling with the addition of cell-surface antibodies (scDAb-seq) have provided key insights into cancer heterogeneity. Scaling these technologies across large patient cohorts, however, is cost and time prohibitive. Multiplexing, in which cells from unique patients are pooled into a single experiment, offers a possible solution. While multiplexing methods exist for scRNAseq, accurate demultiplexing in scDNAseq remains an unmet need. Results Here, we introduce SNACS: Single-Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting. SNACS relies on a combination of patient-level cell-surface identifiers and natural variation in genetic polymorphisms to demultiplex scDNAseq data. We demonstrated the performance of SNACS on a dataset consisting of multi-sample experiments from patients with leukemia where we knew truth from single-sample experiments from the same patients. Using SNACS, accuracy ranged from 0.948 - 0.991 vs 0.552 - 0.934 using demultiplexing methods from the single-cell literature.
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Affiliation(s)
- Kennedy Ve
- Division of Hematology and Oncology, Department of Medicine, University of California San Francisco, San Francisco, CA, USA, 94143
| | - Roy R
- Hellen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA, 94143
| | - Peretz Cac
- Hellen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA, 94143
- Division of Hematology and Oncology, Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA, 94143
| | - Koh A
- Division of Hematology and Oncology, Department of Medicine, University of California San Francisco, San Francisco, CA, USA, 94143
| | - Tran E
- Division of Hematology and Oncology, Department of Medicine, University of California San Francisco, San Francisco, CA, USA, 94143
| | - Smith Cc
- Division of Hematology and Oncology, Department of Medicine, University of California San Francisco, San Francisco, CA, USA, 94143
- Hellen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA, 94143
| | - Olshen Ab
- Hellen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA, 94143
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA, 94143
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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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Moeller ME, Mon Père NV, Werner B, Huang W. Measures of genetic diversification in somatic tissues at bulk and single-cell resolution. eLife 2024; 12:RP89780. [PMID: 38265286 PMCID: PMC10945735 DOI: 10.7554/elife.89780] [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: 01/25/2024] Open
Abstract
Intra-tissue genetic heterogeneity is universal to both healthy and cancerous tissues. It emerges from the stochastic accumulation of somatic mutations throughout development and homeostasis. By combining population genetics theory and genomic information, genetic heterogeneity can be exploited to infer tissue organization and dynamics in vivo. However, many basic quantities, for example the dynamics of tissue-specific stem cells remain difficult to quantify precisely. Here, we show that single-cell and bulk sequencing data inform on different aspects of the underlying stochastic processes. Bulk-derived variant allele frequency spectra (VAF) show transitions from growing to constant stem cell populations with age in samples of healthy esophagus epithelium. Single-cell mutational burden distributions allow a sample size independent measure of mutation and proliferation rates. Mutation rates in adult hematopietic stem cells are higher compared to inferences during development, suggesting additional proliferation-independent effects. Furthermore, single-cell derived VAF spectra contain information on the number of tissue-specific stem cells. In hematopiesis, we find approximately 2 × 105 HSCs, if all stem cells divide symmetrically. However, the single-cell mutational burden distribution is over-dispersed compared to a model of Poisson distributed random mutations. A time-associated model of mutation accumulation with a constant rate alone cannot generate such a pattern. At least one additional source of stochasticity would be needed. Possible candidates for these processes may be occasional bursts of stem cell divisions, potentially in response to injury, or non-constant mutation rates either through environmental exposures or cell-intrinsic variation.
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Affiliation(s)
- Marius E Moeller
- Department of Mathematics, Queen Mary University of LondonLondonUnited Kingdom
| | - Nathaniel V Mon Père
- Evolutionary Dynamics Group, Centre for Cancer Genomics and Computational Biology, Barts Cancer Centre, Queen Mary University of LondonLondonUnited Kingdom
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de BruxellesIxellesBelgium
| | - Benjamin Werner
- Evolutionary Dynamics Group, Centre for Cancer Genomics and Computational Biology, Barts Cancer Centre, Queen Mary University of LondonLondonUnited Kingdom
| | - Weini Huang
- Department of Mathematics, Queen Mary University of LondonLondonUnited Kingdom
- Group of Theoretical Biology, The State Key Laboratory of Biocontrol, School of Life Science, Sun Yat-sen UniversityGuangzhouChina
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Rong Y, Liu SH, Tang MZ, Wu ZH, Ma GR, Li XF, Cai H. Analysis of the potential biological value of pyruvate dehydrogenase E1 subunit β in human cancer. World J Gastrointest Oncol 2024; 16:144-181. [PMID: 38292838 PMCID: PMC10824119 DOI: 10.4251/wjgo.v16.i1.144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 10/28/2023] [Accepted: 12/01/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND The pyruvate dehydrogenase E1 subunit β (PDHB) gene which regulates energy metabolism is located in mitochondria. However, few studies have elucidated the role and mechanism of PDHB in different cancers. AIM To comprehensive pan-cancer analysis of PDHB was performed based on bioinformatics approaches to explore its tumor diagnostic and prognostic value and tumor immune relevance in cancer. In vitro experiments were performed to examine the biological regulation of PDHB in liver cancer. METHODS Pan-cancer data related to PDHB were obtained from the Cancer Genome Atlas (TCGA) database. Analysis of the gene expression profiles of PDHB was based on TCGA and Genotype Tissue Expression Dataset databases. Cox regression analysis and Kaplan-Meier methods were used to assess the correlation between PDHB expression and survival prognosis in cancer patients. The correlation between PDHB and receiver operating characteristic diagnostic curve, clinicopathological staging, somatic mutation, tumor mutation burden (TMB), microsatellite instability (MSI), DNA methylation, and drug susceptibility in pan-cancer was also analyzed. Various algorithms were used to analyze the correlation between PDHB and immune cell infiltration and tumor chemotaxis environment, as well as the co-expression analysis of PDHB and immune checkpoint (ICP) genes. The expression and functional phenotype of PDHB in single tumor cells were studied by single-cell sequencing, and the functional enrichment analysis of PDHB-related genes was performed. The study also validated the level of mRNA or protein expression of PDHB in several cancers. Finally, in vitro experiments verified the regulatory effect of PDHB on the proliferation, migration, and invasion of liver cancer. RESULTS PDHB was significantly and differently expressed in most cancers. PDHB was significantly associated with prognosis in patients with a wide range of cancers, including kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, breast invasive carcinoma, and brain lower grade glioma. In some cancers, PDHB expression was clearly associated with gene mutations, clinicopathological stages, and expression of TMB, MSI, and ICP genes. The expression of PDHB was closely related to the infiltration of multiple immune cells in the immune microenvironment and the regulation of tumor chemotaxis environment. In addition, single-cell sequencing results showed that PDHB correlated with different biological phenotypes of multiple cancer single cells. This study further demonstrated that down-regulation of PDHB expression inhibited the proliferation, migration, and invasion functions of hepatoma cells. CONCLUSION As a member of pan-cancer, PDHB may be a novel cancer marker with potential value in diagnosing cancer, predicting prognosis, and in targeted therapy.
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Affiliation(s)
- Yao Rong
- First Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou 730000, Gansu Province, China
- General Surgery Clinical Medical Center, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
| | - Song-Hua Liu
- First Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou 730000, Gansu Province, China
- General Surgery Clinical Medical Center, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
| | - Ming-Zheng Tang
- First Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou 730000, Gansu Province, China
- General Surgery Clinical Medical Center, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
| | - Zhi-Hang Wu
- First Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou 730000, Gansu Province, China
| | - Guo-Rong Ma
- First Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou 730000, Gansu Province, China
| | - Xiao-Feng Li
- First Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou 730000, Gansu Province, China
| | - Hui Cai
- General Surgery Clinical Medical Center, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China
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Lin S, Li D, Yang Y, Yu M, Zhao R, Li J, Peng L. Single-cell RNA-Seq Elucidates the Crosstalk Between Cancer Stem Cells and the Tumor Microenvironment in Hepatocellular Carcinoma. J Cancer 2024; 15:1093-1109. [PMID: 38230205 PMCID: PMC10788724 DOI: 10.7150/jca.92185] [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: 11/13/2023] [Accepted: 12/16/2023] [Indexed: 01/18/2024] Open
Abstract
Background: The challenge of systemic treatment for hepatocellular carcinoma (HCC) stems from the development of drug resistance, primarily driven by the interplay between cancer stem cells (CSCs) and the tumor microenvironment (TME). However, there is a notable dearth of comprehensive research investigating the crosstalk between CSCs and stromal cells or immune cells within the TME of HCC. Methods: We procured single-cell RNA sequencing (scRNA-Seq) data from 16 patients diagnosed with HCC. Employing meticulous data quality control and cell annotation procedures, we delineated distinct CSCs subtypes and performed multi-omics analyses encompassing metabolic activity, cell communication, and cell trajectory. These analyses shed light on the potential molecular mechanisms governing the interaction between CSCs and the TME, while also identifying CSCs' developmental genes. By combining these developmental genes, we employed machine learning algorithms and RT-qPCR to construct and validate a prognostic risk model for HCC. Results: We successfully identified CSCs subtypes residing within malignant cells. Through meticulous enrichment analysis and assessment of metabolic activity, we discovered anomalous metabolic patterns within the CSCs microenvironment, including hypoxia and glucose deprivation. Moreover, CSCs exhibited aberrant activity in signaling pathways associated with lipid metabolism. Furthermore, our investigations into cell communication unveiled that CSCs possess the capacity to modulate stromal cells and immune cells through the secretion of MIF or MDK, consequently exerting regulatory control over the TME. Finally, through cell trajectory analysis, we found developmental genes of CSCs. Leveraging these genes, we successfully developed and validated a prognostic risk model (APCS, ADH4, FTH1, and HSPB1) with machine learning and RT-qPCR. Conclusions: By means of single-cell multi-omics analysis, this study offers valuable insights into the potential molecular mechanisms governing the interaction between CSCs and the TME, elucidating the pivotal role CSCs play within the TME. Additionally, we have successfully established a comprehensive clinical prognostic model through bulk RNA-Seq data.
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Affiliation(s)
- Sen Lin
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Danfei Li
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yan Yang
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Mengjiao Yu
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ruiqi Zhao
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jinghao Li
- Department of Traditional Chinese Medicine, The Sixth Affiliated Hospital, South China University of Technology, Foshan, China
| | - Lisheng Peng
- Department of Hepatology, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
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Rossi N, Gigante N, Vitacolonna N, Piazza C. Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:106-119. [PMID: 38015671 DOI: 10.1109/tcbb.2023.3337258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities. In this work we describe a cancer progression model based on Single Cell DNA Sequencing data. When constructing the model, we focus on tailoring the formalism on the specificity of the data. We operate by defining a minimal set of assumptions needed to reconstruct a flexible DAG structured model, capable of identifying progression beyond the limitation of the infinite site assumption. Our proposal is conservative in the sense that we aim to neither discard nor infer knowledge which is not represented in the data. We provide simulations and analytical results to show the features of our model, test it on real data, show how it can be integrated with other approaches to cope with input noise. Moreover, our framework can be exploited to produce simulated data that follows our theoretical assumptions. Finally, we provide an open source R implementation of our approach, called CIMICE, that is publicly available on BioConductor.
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Zhang D, Qiao L. Microfluidics Coupled Mass Spectrometry for Single Cell Multi-Omics. SMALL METHODS 2024; 8:e2301179. [PMID: 37840412 DOI: 10.1002/smtd.202301179] [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: 09/01/2023] [Revised: 10/02/2023] [Indexed: 10/17/2023]
Abstract
Population-level analysis masks significant heterogeneity between individual cells, making it difficult to accurately reflect the true intricacies of life activities. Microfluidics is a technique that can manipulate individual cells effectively and is commonly coupled with a variety of analytical methods for single-cell analysis. Single-cell omics provides abundant molecular information at the single-cell level, fundamentally revealing differences in cell types and biological states among cell individuals, leading to a deeper understanding of cellular phenotypes and life activities. Herein, this work summarizes the microfluidic chips designed for single-cell isolation, manipulation, trapping, screening, and sorting, including droplet microfluidic chips, microwell arrays, hydrodynamic microfluidic chips, and microchips with microvalves. This work further reviews the studies on single-cell proteomics, metabolomics, lipidomics, and multi-omics based on microfluidics and mass spectrometry. Finally, the challenges and future application of single-cell multi-omics are discussed.
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Affiliation(s)
- Dongxue Zhang
- Department of Chemistry, Institutes of Biomedical Sciences, and Minhang Hospital, Fudan University, Shanghai, 20000, China
| | - Liang Qiao
- Department of Chemistry, Institutes of Biomedical Sciences, and Minhang Hospital, Fudan University, Shanghai, 20000, China
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Li X, You J, Hong L, Liu W, Guo P, Hao X. Neoantigen cancer vaccines: a new star on the horizon. Cancer Biol Med 2023; 21:j.issn.2095-3941.2023.0395. [PMID: 38164734 PMCID: PMC11033713 DOI: 10.20892/j.issn.2095-3941.2023.0395] [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: 10/27/2023] [Accepted: 11/22/2023] [Indexed: 01/03/2024] Open
Abstract
Immunotherapy represents a promising strategy for cancer treatment that utilizes immune cells or drugs to activate the patient's own immune system and eliminate cancer cells. One of the most exciting advances within this field is the targeting of neoantigens, which are peptides derived from non-synonymous somatic mutations that are found exclusively within cancer cells and absent in normal cells. Although neoantigen-based therapeutic vaccines have not received approval for standard cancer treatment, early clinical trials have yielded encouraging outcomes as standalone monotherapy or when combined with checkpoint inhibitors. Progress made in high-throughput sequencing and bioinformatics have greatly facilitated the precise and efficient identification of neoantigens. Consequently, personalized neoantigen-based vaccines tailored to each patient have been developed that are capable of eliciting a robust and long-lasting immune response which effectively eliminates tumors and prevents recurrences. This review provides a concise overview consolidating the latest clinical advances in neoantigen-based therapeutic vaccines, and also discusses challenges and future perspectives for this innovative approach, particularly emphasizing the potential of neoantigen-based therapeutic vaccines to enhance clinical efficacy against advanced solid tumors.
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Affiliation(s)
- Xiaoling Li
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Jian You
- Department of Thoracic Oncology, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- Department of Thoracic Oncology Surgery, Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, China
| | - Liping Hong
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Weijiang Liu
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Peng Guo
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Xishan Hao
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
- Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, China
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Edrisi M, Huang X, Ogilvie HA, Nakhleh L. Accurate integration of single-cell DNA and RNA for analyzing intratumor heterogeneity using MaCroDNA. Nat Commun 2023; 14:8262. [PMID: 38092737 PMCID: PMC10719311 DOI: 10.1038/s41467-023-44014-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 11/27/2023] [Indexed: 12/17/2023] Open
Abstract
Cancers develop and progress as mutations accumulate, and with the advent of single-cell DNA and RNA sequencing, researchers can observe these mutations and their transcriptomic effects and predict proteomic changes with remarkable temporal and spatial precision. However, to connect genomic mutations with their transcriptomic and proteomic consequences, cells with either only DNA data or only RNA data must be mapped to a common domain. For this purpose, we present MaCroDNA, a method that uses maximum weighted bipartite matching of per-gene read counts from single-cell DNA and RNA-seq data. Using ground truth information from colorectal cancer data, we demonstrate the advantage of MaCroDNA over existing methods in accuracy and speed. Exemplifying the utility of single-cell data integration in cancer research, we suggest, based on results derived using MaCroDNA, that genomic mutations of large effect size increasingly contribute to differential expression between cells as Barrett's esophagus progresses to esophageal cancer, reaffirming the findings of the previous studies.
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Affiliation(s)
| | - Xiru Huang
- Department of Computer Science, Rice University, Houston, Texas, USA
| | - Huw A Ogilvie
- Department of Computer Science, Rice University, Houston, Texas, USA.
| | - Luay Nakhleh
- Department of Computer Science, Rice University, Houston, Texas, USA.
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Han Y, Molloy EK. Quartets enable statistically consistent estimation of cell lineage trees under an unbiased error and missingness model. Algorithms Mol Biol 2023; 18:19. [PMID: 38041123 PMCID: PMC10691101 DOI: 10.1186/s13015-023-00248-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 11/19/2023] [Indexed: 12/03/2023] Open
Abstract
Cancer progression and treatment can be informed by reconstructing its evolutionary history from tumor cells. Although many methods exist to estimate evolutionary trees (called phylogenies) from molecular sequences, traditional approaches assume the input data are error-free and the output tree is fully resolved. These assumptions are challenged in tumor phylogenetics because single-cell sequencing produces sparse, error-ridden data and because tumors evolve clonally. Here, we study the theoretical utility of methods based on quartets (four-leaf, unrooted phylogenetic trees) in light of these barriers. We consider a popular tumor phylogenetics model, in which mutations arise on a (highly unresolved) tree and then (unbiased) errors and missing values are introduced. Quartets are then implied by mutations present in two cells and absent from two cells. Our main result is that the most probable quartet identifies the unrooted model tree on four cells. This motivates seeking a tree such that the number of quartets shared between it and the input mutations is maximized. We prove an optimal solution to this problem is a consistent estimator of the unrooted cell lineage tree; this guarantee includes the case where the model tree is highly unresolved, with error defined as the number of false negative branches. Lastly, we outline how quartet-based methods might be employed when there are copy number aberrations and other challenges specific to tumor phylogenetics.
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Affiliation(s)
- Yunheng Han
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Erin K Molloy
- Department of Computer Science, University of Maryland, College Park, MD, USA.
- University of Maryland Institute for Advanced Computer Studies, College Park, MD, USA.
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Satpathy S, Thomas BE, Pilcher WJ, Bakhtiari M, Ponder LA, Pacholczyk R, Prahalad S, Bhasin SS, Munn DH, Bhasin MK. The Simple prEservatioN of Single cElls method for cryopreservation enables the generation of single-cell immune profiles from whole blood. Front Immunol 2023; 14:1271800. [PMID: 38090590 PMCID: PMC10713715 DOI: 10.3389/fimmu.2023.1271800] [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: 08/02/2023] [Accepted: 11/06/2023] [Indexed: 12/18/2023] Open
Abstract
Introduction Current multistep methods utilized for preparing and cryopreserving single-cell suspensions from blood samples for single-cell RNA sequencing (scRNA-seq) are time-consuming, requiring trained personnel and special equipment, so limiting their clinical adoption. We developed a method, Simple prEservatioN of Single cElls (SENSE), for single-step cryopreservation of whole blood (WB) along with granulocyte depletion during single-cell assay, to generate high quality single-cell profiles (SCP). Methods WB was cryopreserved using the SENSE method and peripheral blood mononuclear cells (PBMCs) were isolated and cryopreserved using the traditional density-gradient method (PBMC method) from the same blood sample (n=6). The SCPs obtained from both methods were processed using a similar pipeline and quality control parameters. Further, entropy calculation, differential gene expression, and cellular communication analysis were performed to compare cell types and subtypes from both methods. Results Highly viable (86.3 ± 1.51%) single-cell suspensions (22,353 cells) were obtained from the six WB samples cryopreserved using the SENSE method. In-depth characterization of the scRNA-seq datasets from the samples processed with the SENSE method yielded high-quality profiles of lymphoid and myeloid cell types which were in concordance with the profiles obtained with classical multistep PBMC method processed samples. Additionally, the SENSE method cryopreserved samples exhibited significantly higher T-cell enrichment, enabling deeper characterization of T-cell subtypes. Overall, the SENSE and PBMC methods processed samples exhibited transcriptional, and cellular communication network level similarities across cell types with no batch effect except in myeloid lineage cells. Discussion Comparative analysis of scRNA-seq datasets obtained with the two cryopreservation methods i.e., SENSE and PBMC methods, yielded similar cellular and molecular profiles, confirming the suitability of the former method's incorporation in clinics/labs for cryopreserving and obtaining high-quality single-cells for conducting critical translational research.
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Affiliation(s)
- Sarthak Satpathy
- Aflac Cancer and Blood Disorders Center, Children Healthcare of Atlanta, Atlanta, GA, United States
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| | - Beena E. Thomas
- Aflac Cancer and Blood Disorders Center, Children Healthcare of Atlanta, Atlanta, GA, United States
- Department of Pediatrics, Emory University, Atlanta, GA, United States
| | - William J. Pilcher
- Aflac Cancer and Blood Disorders Center, Children Healthcare of Atlanta, Atlanta, GA, United States
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| | - Mojtaba Bakhtiari
- Aflac Cancer and Blood Disorders Center, Children Healthcare of Atlanta, Atlanta, GA, United States
- Department of Pediatrics, Emory University, Atlanta, GA, United States
| | - Lori A. Ponder
- Division of Rheumatology, Children’s Healthcare of Atlanta, Atlanta, GA, United States
| | - Rafal Pacholczyk
- Georgia Cancer Center, Augusta University, Augusta, GA, United States
- Department of Biochemistry and Molecular Biology, Augusta University, Augusta, GA, United States
| | - Sampath Prahalad
- Department of Pediatrics, Emory University, Atlanta, GA, United States
- Division of Rheumatology, Children’s Healthcare of Atlanta, Atlanta, GA, United States
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, United States
| | - Swati S. Bhasin
- Aflac Cancer and Blood Disorders Center, Children Healthcare of Atlanta, Atlanta, GA, United States
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
- Department of Pediatrics, Emory University, Atlanta, GA, United States
| | - David H. Munn
- Georgia Cancer Center, Augusta University, Augusta, GA, United States
- Department of Pediatrics, Augusta University, Augusta, GA, United States
| | - Manoj K. Bhasin
- Aflac Cancer and Blood Disorders Center, Children Healthcare of Atlanta, Atlanta, GA, United States
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
- Department of Pediatrics, Emory University, Atlanta, GA, United States
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Liu ZT, Shen JT, Lei YJ, Huang YC, Zhao GQ, Zheng CH, Wang X, Wang YT, Chen L, Li ZX, Li SZ, Liao J, Yu TD. Molecular subtyping based on immune cell marker genes predicts prognosis and therapeutic response in patients with lung adenocarcinoma. BMC Cancer 2023; 23:1141. [PMID: 38001428 PMCID: PMC10668343 DOI: 10.1186/s12885-023-11579-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 10/27/2023] [Indexed: 11/26/2023] Open
Abstract
OBJECTIVE Lung adenocarcinoma (LA) is one of the most common malignancies and is responsible for the greatest number of tumor-related deaths. Our research aimed to explore the molecular subtype signatures of LA to clarify the correlation among the immune microenvironment, clinical outcomes, and therapeutic response. METHODS The LA immune cell marker genes (LICMGs) identified by single-cell RNA sequencing (scRNA-seq) analysis were used to discriminate the molecular subtypes and homologous immune and metabolic traits of GSE72094 LA cases. In addition, the model-building genes were identified from 1441 LICMGs by Cox-regression analysis, and a LA immune difference score (LIDscore) was developed to quantify individual differences in each patient, thereby predicting prognosis and susceptibility to immunotherapy and chemotherapy of LA patients. RESULTS Patients of the GSE72094 cohort were divided into two distinct molecular subtypes based on LICMGs: immune activating subtype (Cluster-C1) and metabolically activating subtype (cluster-C2). The two molecular subtypes have distinct characteristics regarding prognosis, clinicopathology, genomics, immune microenvironment, and response to immunotherapy. Among the LICMGs, LGR4, GOLM1, CYP24A1, SFTPB, COL1A1, HLA-DQA1, MS4A7, PPARG, and IL7R were enrolled to construct a LIDscore model. Low-LIDscore patients had a higher survival rate due to abundant immune cell infiltration, activated immunity, and lower genetic variation, but probably the higher levels of Treg cells in the immune microenvironment lead to immune cell dysfunction and promote tumor immune escape, thus decreasing the responsiveness to immunotherapy compared with that of the high-LIDscore patients. Overall, high-LIDscore patients had a higher responsiveness to immunotherapy and a higher sensitivity to chemotherapy than the low-LIDscore group. CONCLUSIONS Molecular subtypes based on LICMGs provided a promising strategy for predicting patient prognosis, biological characteristics, and immune microenvironment features. In addition, they helped identify the patients most likely to benefit from immunotherapy and chemotherapy.
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Affiliation(s)
- Zi-Tao Liu
- Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jun-Ting Shen
- Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yu-Jie Lei
- Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yun-Chao Huang
- Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Guang-Qiang Zhao
- Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Cheng-Hong Zheng
- Department of Ultrasound, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China
| | - Xi Wang
- Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yu-Tian Wang
- Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Long Chen
- Department of PET/CT Center, Cancer Center of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China
| | - Zi-Xuan Li
- Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shou-Zhuo Li
- Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jun Liao
- Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Ting-Dong Yu
- Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, China.
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Al-Bzour NN, Al-Bzour AN, Ababneh OE, Al-Jezawi MM, Saeed A, Saeed A. Cancer-Associated Fibroblasts in Gastrointestinal Cancers: Unveiling Their Dynamic Roles in the Tumor Microenvironment. Int J Mol Sci 2023; 24:16505. [PMID: 38003695 PMCID: PMC10671196 DOI: 10.3390/ijms242216505] [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: 10/03/2023] [Revised: 11/15/2023] [Accepted: 11/17/2023] [Indexed: 11/26/2023] Open
Abstract
Gastrointestinal cancers are highly aggressive malignancies with significant mortality rates. Recent research emphasizes the critical role of the tumor microenvironment (TME) in these cancers, which includes cancer-associated fibroblasts (CAFs), a key component of the TME that have diverse origins, including fibroblasts, mesenchymal stem cells, and endothelial cells. Several markers, such as α-SMA and FAP, have been identified to label CAFs, and some specific markers may serve as potential therapeutic targets. In this review article, we summarize the literature on the multifaceted role of CAFs in tumor progression, including their effects on angiogenesis, immune suppression, invasion, and metastasis. In addition, we highlight the use of single-cell transcriptomics to understand CAF heterogeneity and their interactions within the TME. Moreover, we discuss the dynamic interplay between CAFs and the immune system, which contributes to immunosuppression in the TME, and the potential for CAF-targeted therapies and combination approaches with immunotherapy to improve cancer treatment outcomes.
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Affiliation(s)
- Noor N. Al-Bzour
- Department of Medicine, Division of Hematology & Oncology, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA 15232, USA; (N.N.A.-B.); (A.N.A.-B.)
| | - Ayah N. Al-Bzour
- Department of Medicine, Division of Hematology & Oncology, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA 15232, USA; (N.N.A.-B.); (A.N.A.-B.)
| | - Obada E. Ababneh
- Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan; (O.E.A.); (M.M.A.-J.)
| | - Moayad M. Al-Jezawi
- Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan; (O.E.A.); (M.M.A.-J.)
| | - Azhar Saeed
- Department of Pathology and Laboratory Medicine, University of Vermont Medical Center, Burlington, VT 05401, USA;
| | - Anwaar Saeed
- Department of Medicine, Division of Hematology & Oncology, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA 15232, USA; (N.N.A.-B.); (A.N.A.-B.)
- UPMC Hillman Cancer Center, Pittsburgh, PA 15232, USA
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Patruno L, Milite S, Bergamin R, Calonaci N, D’Onofrio A, Anselmi F, Antoniotti M, Graudenzi A, Caravagna G. A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing. PLoS Comput Biol 2023; 19:e1011557. [PMID: 37917660 PMCID: PMC10645363 DOI: 10.1371/journal.pcbi.1011557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/14/2023] [Accepted: 09/30/2023] [Indexed: 11/04/2023] Open
Abstract
Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to consistently analyze these states within an evolutionary context on genetic clones. Here we present CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles onto the latent space of copy number clones. CONGAS+ clusters cells into tumour subclones with similar ploidy, rendering straightforward to compare their expression and chromatin profiles. The framework, implemented on GPU and tested on real and simulated data, scales to analyse seamlessly thousands of cells, demonstrating better performance than single-molecule models, and supporting new multi-omics assays. In prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ successfully identifies complex subclonal architectures while providing a coherent mapping between ATAC and RNA, facilitating the study of genotype-phenotype maps and their connection to genomic instability.
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Affiliation(s)
- Lucrezia Patruno
- Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, Italy
- Department of Mathematics and Geosciences, Università degli Studi di Trieste, Trieste, Italy
| | - Salvatore Milite
- Department of Mathematics and Geosciences, Università degli Studi di Trieste, Trieste, Italy
- Centre for Computational Biology, Human Technopole, Milan, Italy
| | - Riccardo Bergamin
- Department of Mathematics and Geosciences, Università degli Studi di Trieste, Trieste, Italy
| | - Nicola Calonaci
- Department of Mathematics and Geosciences, Università degli Studi di Trieste, Trieste, Italy
| | - Alberto D’Onofrio
- Department of Mathematics and Geosciences, Università degli Studi di Trieste, Trieste, Italy
| | - Fabio Anselmi
- Department of Mathematics and Geosciences, Università degli Studi di Trieste, Trieste, Italy
| | - Marco Antoniotti
- Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, Italy
- B4—Bicocca Bioinformatics Biostatistics and Bioimaging Centre, Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Alex Graudenzi
- Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, Italy
- B4—Bicocca Bioinformatics Biostatistics and Bioimaging Centre, Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Giulio Caravagna
- Department of Mathematics and Geosciences, Università degli Studi di Trieste, Trieste, Italy
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You G, Zheng Z, Huang Y, Liu G, Luo W, Huang J, Zhuo L, Tang B, Liu S, Lin C. scRNA-seq and proteomics reveal the distinction of M2-like macrophages between primary and recurrent malignant glioma and its critical role in the recurrence. CNS Neurosci Ther 2023; 29:3391-3405. [PMID: 37194413 PMCID: PMC10580349 DOI: 10.1111/cns.14269] [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: 01/09/2023] [Revised: 05/02/2023] [Accepted: 05/03/2023] [Indexed: 05/18/2023] Open
Abstract
AIMS Tumor-associated macrophages (TAMs) in the immune microenvironment play an important role in the increased drug resistance and recurrence of malignant glioma, but the mechanism remains incompletely inventoried. The focus of this study was to investigate the distinctions of M2-like TAMs in the immune microenvironment between primary and recurrent malignant glioma and its influence in the recurrence. METHODS We employed single-cell RNA sequencing to construct a single-cell atlas for a total of 23,010 individual cells from 6 patients with primary or recurrent malignant glioma and identified 5 cell types, including TAMs and malignant cells. Immunohistochemical techniques and proteomics analysis were performed to investigate the role of intercellular interaction between malignant cells and TAMs in the recurrence of malignant glioma. RESULTS Six subgroups of TAMs were annotated and M2-like TAMs were found to increase in recurrent malignant glioma significantly. A pseudotime trajectory and a dynamic gene expression profiling during the recurrence of malignant glioma were reconstructed. Up-regulation of several cancer pathways and intercellular interaction-related genes are associated with the recurrence of malignant glioma. Moreover, the M2-like TAMs can activate the PI3K/Akt/HIF-1α/CA9 pathway in the malignant glioma cells via SPP1-CD44-mediated intercellular interaction. Interestingly, high expression of CA9 can trigger the immunosuppressive response in the malignant glioma, thus promoting the degree of malignancy and drug resistance. CONCLUSION Our study uncovers the distinction of M2-like TAMs between primary and recurrent glioma, which offers unparalleled insights into the immune microenvironment of primary and recurrent malignant glioma.
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Affiliation(s)
- Guiting You
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Zhenyu Zheng
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
- Fujian Medical University, Fuzhou, China
| | - Yulong Huang
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
- Fujian Medical University, Fuzhou, China
| | - Guifen Liu
- Department of Gynaecology, Fujian Provincial Maternity and Children's Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Wei Luo
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
- Fujian Medical University, Fuzhou, China
| | - Jianhuang Huang
- Department of Neurosurgery, Affiliated Hospital of Putian University, Putian, China
| | - Longjin Zhuo
- Pingtan Comprehensive Experimental Area Hospital, Fuzhou, China
| | - Binghua Tang
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
- Fujian Medical University, Fuzhou, China
| | - Shunyi Liu
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
- Fujian Medical University, Fuzhou, China
| | - Caihou Lin
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, China
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Zhao S, Chen DP, Fu T, Yang JC, Ma D, Zhu XZ, Wang XX, Jiao YP, Jin X, Xiao Y, Xiao WX, Zhang HY, Lv H, Madabhushi A, Yang WT, Jiang YZ, Xu J, Shao ZM. Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer. Nat Commun 2023; 14:6796. [PMID: 37880211 PMCID: PMC10600153 DOI: 10.1038/s41467-023-42504-y] [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: 12/15/2022] [Accepted: 10/12/2023] [Indexed: 10/27/2023] Open
Abstract
Digital pathology allows computerized analysis of tumor ecosystem using whole slide images (WSIs). Here, we present single-cell morphological and topological profiling (sc-MTOP) to characterize tumor ecosystem by extracting the features of nuclear morphology and intercellular spatial relationship for individual cells. We construct a single-cell atlas comprising 410 million cells from 637 breast cancer WSIs and dissect the phenotypic diversity within tumor, inflammatory and stroma cells respectively. Spatially-resolved analysis identifies recurrent micro-ecological modules representing locoregional multicellular structures and reveals four breast cancer ecotypes correlating with distinct molecular features and patient prognosis. Further analysis with multiomics data uncovers clinically relevant ecosystem features. High abundance of locally-aggregated inflammatory cells indicates immune-activated tumor microenvironment and favorable immunotherapy response in triple-negative breast cancers. Morphological intratumor heterogeneity of tumor nuclei correlates with cell cycle pathway activation and CDK inhibitors responsiveness in hormone receptor-positive cases. sc-MTOP enables using WSIs to characterize tumor ecosystems at the single-cell level.
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Affiliation(s)
- Shen Zhao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - De-Pin Chen
- Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
| | - Tong Fu
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jing-Cheng Yang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | - Ding Ma
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Xiu-Zhi Zhu
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Xiang-Xue Wang
- Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
| | - Yi-Ping Jiao
- Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
| | - Xi Jin
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yi Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Wen-Xuan Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Hu-Yunlong Zhang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Hong Lv
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Anant Madabhushi
- Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Atlanta Veterans Affairs Medical Center, Atlanta, GA, USA
| | - Wen-Tao Yang
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
| | - Jun Xu
- Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China.
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
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Chen D, Zhong N, Guo Z, Ji Q, Dong Z, Zheng J, Ma Y, Zhang J, He Y, Song T. MCM10, a potential diagnostic, immunological, and prognostic biomarker in pan-cancer. Sci Rep 2023; 13:17701. [PMID: 37848534 PMCID: PMC10582070 DOI: 10.1038/s41598-023-44946-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 10/13/2023] [Indexed: 10/19/2023] Open
Abstract
Microchromosome maintenance (MCM) proteins are a number of nuclear proteins with significant roles in the development of cancer by influencing the process of cellular DNA replication. Of the MCM protein family, MCM10 is a crucial member that maintains the stability and extension of DNA replication forks during DNA replication and is significantly overexpressed in a variety of cancer tissues, regulating the biological behaviour of cancer cells. But little is understood about MCM10's functional role and regulatory mechanisms in a range of malignancies. We investigate the impact of MCM10 in human cancers by analyzing data from databases like the Gene Expression Profiling Interaction Analysis (GEPIA2), Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA), among others. Possible relationships between MCM10 and clinical staging, diagnosis, prognosis, Mutation burden (TMB), microsatellite instability (MSI), immunological checkpoints, DNA methylation, and tumor stemness were identified. The findings demonstrated that MCM10 expression was elevated in the majority of cancer types and was connected to tumor dryness, immunocytic infiltration, immunological checkpoints, TMB and MSI. Functional enrichment analysis in multiple tumors also identified possible pathways of MCM10 involvement in tumorigenesis. We also discovered promising MCM10-targeting chemotherapeutic drugs. In conclusion, MCM10 may be a desirable pan-cancer biomarker and offer fresh perspectives on cancer therapy.
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Affiliation(s)
- Dengwang Chen
- Department of Immunology, Zunyi Medical University, Zunyi, China
| | - Na Zhong
- Department of Immunology, Zunyi Medical University, Zunyi, China
| | - Zhanwen Guo
- School of Medical Information Engineering, Zunyi Medical University, Zunyi, China
| | - Qinglu Ji
- School of Pharmacy, Zunyi Medical University, Zunyi, China
| | - Zixuan Dong
- Department of Immunology, Zunyi Medical University, Zunyi, China
| | - Jishan Zheng
- Department of Immunology, Zunyi Medical University, Zunyi, China
| | - Yunyan Ma
- Department of Immunology, Zunyi Medical University, Zunyi, China
| | - Jidong Zhang
- Department of Immunology, Zunyi Medical University, Zunyi, China.
- Collaborative Innovation Center of Tissue Damage Repair and Regeneration Medicine, Zunyi Medical University, Zunyi, China.
- Special Key Laboratory of Gene Detection and Therapy of Guizhou Province, Zunyi Medical University, Zunyi, China.
| | - Yuqi He
- School of Pharmacy, Zunyi Medical University, Zunyi, China.
| | - Tao Song
- Department of Immunology, Zunyi Medical University, Zunyi, China.
- Collaborative Innovation Center of Tissue Damage Repair and Regeneration Medicine, Zunyi Medical University, Zunyi, China.
- Special Key Laboratory of Gene Detection and Therapy of Guizhou Province, Zunyi Medical University, Zunyi, China.
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Adeuyan O, Gordon ER, Kenchappa D, Bracero Y, Singh A, Espinoza G, Geskin LJ, Saenger YM. An update on methods for detection of prognostic and predictive biomarkers in melanoma. Front Cell Dev Biol 2023; 11:1290696. [PMID: 37900283 PMCID: PMC10611507 DOI: 10.3389/fcell.2023.1290696] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 10/04/2023] [Indexed: 10/31/2023] Open
Abstract
The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.
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Affiliation(s)
- Oluwaseyi Adeuyan
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Emily R. Gordon
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Divya Kenchappa
- Albert Einstein College of Medicine, Bronx, NY, United States
| | - Yadriel Bracero
- Albert Einstein College of Medicine, Bronx, NY, United States
| | - Ajay Singh
- Albert Einstein College of Medicine, Bronx, NY, United States
| | | | - Larisa J. Geskin
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, United States
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Luo Y, Liang H. Single-cell dissection of tumor microenvironmental response and resistance to cancer therapy. Trends Genet 2023; 39:758-772. [PMID: 37658004 PMCID: PMC10529478 DOI: 10.1016/j.tig.2023.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/13/2023] [Accepted: 07/17/2023] [Indexed: 09/03/2023]
Abstract
Cancer treatment strategies have evolved significantly over the years, with chemotherapy, targeted therapy, and immunotherapy as major pillars. Each modality leads to unique treatment outcomes by interacting with the tumor microenvironment (TME), which imposes a fundamental selective pressure on cancer progression. The advent of single-cell profiling technologies has revolutionized our understanding of the intricate and heterogeneous nature of the TME at an unprecedented resolution. This review delves into the commonalities and differential manifestations of how cancer therapies reshape the microenvironment in diverse cancer types. We highlight how groundbreaking immune checkpoint blockade (ICB) strategies alone or in combination with tumor-targeting treatments are endowed with comprehensive mechanistic insights when decoded at the single-cell level, aiming to drive forward future research directions on personalized treatments.
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Affiliation(s)
- Yikai Luo
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Han Liang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA.
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Han X, Xu X, Yang C, Liu G. Microfluidic design in single-cell sequencing and application to cancer precision medicine. CELL REPORTS METHODS 2023; 3:100591. [PMID: 37725985 PMCID: PMC10545941 DOI: 10.1016/j.crmeth.2023.100591] [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: 03/31/2023] [Revised: 07/01/2023] [Accepted: 08/24/2023] [Indexed: 09/21/2023]
Abstract
Single-cell sequencing (SCS) is a crucial tool to reveal the genetic and functional heterogeneity of tumors, providing unique insights into the clonal evolution, microenvironment, drug resistance, and metastatic progression of cancers. Microfluidics is a critical component of many SCS technologies and workflows, conferring advantages in throughput, economy, and automation. Here, we review the current landscape of microfluidic architectures and sequencing techniques for single-cell omics analysis and highlight how these have enabled recent applications in oncology research. We also discuss the challenges and the promise of microfluidics-based single-cell analysis in the future of precision oncology.
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Affiliation(s)
- Xin Han
- CUHK(SZ)-Boyalife Joint Laboratory of Regenerative Medicine Engineering, Biomedical Engineering Programme, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China; Ciechanover Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Xing Xu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, Key Laboratory for Chemical Biology of Fujian Province, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P.R. China; Institute of Molecular Medicine, State Key Laboratory of Oncogenes and Related 12 Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
| | - Chaoyang Yang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, Key Laboratory for Chemical Biology of Fujian Province, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P.R. China; Institute of Molecular Medicine, State Key Laboratory of Oncogenes and Related 12 Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, P.R. China.
| | - Guozhen Liu
- CUHK(SZ)-Boyalife Joint Laboratory of Regenerative Medicine Engineering, Biomedical Engineering Programme, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China; Ciechanover Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.
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