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Whiting FJH, Househam J, Baker AM, Sottoriva A, Graham TA. Phenotypic noise and plasticity in cancer evolution. Trends Cell Biol 2024; 34:451-464. [PMID: 37968225 DOI: 10.1016/j.tcb.2023.10.002] [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: 07/13/2023] [Revised: 09/29/2023] [Accepted: 10/04/2023] [Indexed: 11/17/2023]
Abstract
Non-genetic alterations can produce changes in a cell's phenotype. In cancer, these phenomena can influence a cell's fitness by conferring access to heritable, beneficial phenotypes. Herein, we argue that current discussions of 'phenotypic plasticity' in cancer evolution ignore a salient feature of the original definition: namely, that it occurs in response to an environmental change. We suggest 'phenotypic noise' be used to distinguish non-genetic changes in phenotype that occur independently from the environment. We discuss the conceptual and methodological techniques used to identify these phenomena during cancer evolution. We propose that the distinction will guide efforts to define mechanisms of phenotype change, accelerate translational work to manipulate phenotypes through treatment, and, ultimately, improve patient outcomes.
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Affiliation(s)
| | - Jacob Househam
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Ann-Marie Baker
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK; Computational Biology Research Centre, Human Technopole, Milan, Italy
| | - Trevor A Graham
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
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2
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Augustin RC, Cai WL, Luke JJ, Bao R. Facts and Hopes in Using Omics to Advance Combined Immunotherapy Strategies. Clin Cancer Res 2024; 30:1724-1732. [PMID: 38236069 PMCID: PMC11062841 DOI: 10.1158/1078-0432.ccr-22-2241] [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: 07/22/2023] [Revised: 09/28/2023] [Accepted: 12/22/2023] [Indexed: 01/19/2024]
Abstract
The field of oncology has been transformed by immune checkpoint inhibitors (ICI) and other immune-based agents; however, many patients do not receive a durable benefit. While biomarker assessments from pivotal ICI trials have uncovered certain mechanisms of resistance, results thus far have only scraped the surface. Mechanisms of resistance are as complex as the tumor microenvironment (TME) itself, and the development of effective therapeutic strategies will only be possible by building accurate models of the tumor-immune interface. With advancement of multi-omic technologies, high-resolution characterization of the TME is now possible. In addition to sequencing of bulk tumor, single-cell transcriptomic, proteomic, and epigenomic data as well as T-cell receptor profiling can now be simultaneously measured and compared between responders and nonresponders to ICI. Spatial sequencing and imaging platforms have further expanded the dimensionality of existing technologies. Rapid advancements in computation and data sharing strategies enable development of biologically interpretable machine learning models to integrate data from high-resolution, multi-omic platforms. These models catalyze the identification of resistance mechanisms and predictors of benefit in ICI-treated patients, providing scientific foundation for novel clinical trials. Moving forward, we propose a framework by which in silico screening, functional validation, and clinical trial biomarker assessment can be used for the advancement of combined immunotherapy strategies.
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Affiliation(s)
- Ryan C. Augustin
- UPMC Hillman Cancer Center, Pittsburgh, PA
- University of Pittsburgh, Department of Medicine, Pittsburgh, PA
- Mayo Clinic, Department of Medical Oncology, Rochester, MN
| | - Wesley L. Cai
- University of Pittsburgh, Department of Medicine, Pittsburgh, PA
| | - Jason J. Luke
- UPMC Hillman Cancer Center, Pittsburgh, PA
- University of Pittsburgh, Department of Medicine, Pittsburgh, PA
| | - Riyue Bao
- UPMC Hillman Cancer Center, Pittsburgh, PA
- University of Pittsburgh, Department of Medicine, Pittsburgh, PA
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3
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Rossi M, Radisky DC. Multiplex Digital Spatial Profiling in Breast Cancer Research: State-of-the-Art Technologies and Applications across the Translational Science Spectrum. Cancers (Basel) 2024; 16:1615. [PMID: 38730568 PMCID: PMC11083340 DOI: 10.3390/cancers16091615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/17/2024] [Accepted: 04/21/2024] [Indexed: 05/13/2024] Open
Abstract
While RNA sequencing and multi-omic approaches have significantly advanced cancer diagnosis and treatment, their limitation in preserving critical spatial information has been a notable drawback. This spatial context is essential for understanding cellular interactions and tissue dynamics. Multiplex digital spatial profiling (MDSP) technologies overcome this limitation by enabling the simultaneous analysis of transcriptome and proteome data within the intact spatial architecture of tissues. In breast cancer research, MDSP has emerged as a promising tool, revealing complex biological questions related to disease evolution, identifying biomarkers, and discovering drug targets. This review highlights the potential of MDSP to revolutionize clinical applications, ranging from risk assessment and diagnostics to prognostics, patient monitoring, and the customization of treatment strategies, including clinical trial guidance. We discuss the major MDSP techniques, their applications in breast cancer research, and their integration in clinical practice, addressing both their potential and current limitations. Emphasizing the strategic use of MDSP in risk stratification for women with benign breast disease, we also highlight its transformative potential in reshaping the landscape of breast cancer research and treatment.
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Affiliation(s)
| | - Derek C. Radisky
- Department of Cancer Biology, Mayo Clinic, Jacksonville, FL 32224, USA;
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4
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Ahn S, Lee HS. Applicability of Spatial Technology in Cancer Research. Cancer Res Treat 2024; 56:343-356. [PMID: 38291743 PMCID: PMC11016655 DOI: 10.4143/crt.2023.1302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 01/29/2024] [Indexed: 02/01/2024] Open
Abstract
This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.
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Affiliation(s)
- Sangjeong Ahn
- Department of Pathology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- Artificial Intelligence Center, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- Department of Medical Informatics, Korea University College of Medicine, Seoul, Korea
| | - Hye Seung Lee
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
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5
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Yan K, Liu Q, Huang R, Jiang Y, Bian Z, Li S, Li L, Shen F, Tsuneyama K, Zhang Q, Lian Z, Guan H, Xu B. Spatial transcriptomics reveals prognosis-associated cellular heterogeneity in the papillary thyroid carcinoma microenvironment. Clin Transl Med 2024; 14:e1594. [PMID: 38426403 PMCID: PMC10905537 DOI: 10.1002/ctm2.1594] [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: 08/02/2023] [Revised: 01/28/2024] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Papillary thyroid carcinoma (PTC) is the most common malignant endocrine tumour, and its incidence and prevalence are increasing considerably. Cellular heterogeneity in the tumour microenvironment is important for PTC prognosis. Spatial transcriptomics is a powerful technique for cellular heterogeneity study. METHODS In conjunction with a clinical pathologist identification method, spatial transcriptomics was employed to characterise the spatial location and RNA profiles of PTC-associated cells within the tissue sections. The spatial RNA-clinical signature genes for each cell type were extracted and applied to outlining the distribution regions of specific cells on the entire section. The cellular heterogeneity of each cell type was further revealed by ContourPlot analysis, monocle analysis, trajectory analysis, ligand-receptor analysis and Gene Ontology enrichment analysis. RESULTS The spatial distribution region of tumour cells, typical and atypical follicular cells (FCs and AFCs) and immune cells were accurately and comprehensively identified in all five PTC tissue sections. AFCs were identified as a transitional state between FCs and tumour cells, exhibiting a higher resemblance to the latter. Three tumour foci were shared among all patients out of the 13 observed. Notably, tumour foci No. 2 displayed elevated expression levels of genes associated with lower relapse-free survival in PTC patients. We discovered key ligand-receptor interactions, including LAMB3-ITGA2, FN1-ITGA3 and FN1-SDC4, involved in the transition of PTC cells from FCs to AFCs and eventually to tumour cells. High expression of these patterns correlated with reduced relapse-free survival. In the tumour immune microenvironment, reduced interaction between myeloid-derived TGFB1 and TGFBR1 in tumour focus No. 2 contributed to tumourigenesis and increased heterogeneity. The spatial RNA-clinical analysis method developed here revealed prognosis-associated cellular heterogeneity in the PTC microenvironment. CONCLUSIONS The occurrence of tumour foci No. 2 and three enhanced ligand-receptor interactions in the AFC area/tumour foci reduced the relapse-free survival of PTC patients, potentially leading to improved prognostic strategies and targeted therapies for PTC patients.
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Affiliation(s)
- Kai Yan
- Guangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina
| | - Qing‐Zhi Liu
- Chronic Disease LaboratoryInstitutes for Life SciencesSouth China University of TechnologyGuangzhouChina
| | - Rong‐Rong Huang
- Guangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina
| | - Yi‐Hua Jiang
- Guangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and ApplicationGuangzhouChina
| | - Zhen‐Hua Bian
- School of Biomedical Sciences and EngineeringSouth China University of TechnologyGuangzhou International CampusGuangzhouChina
| | - Si‐Jin Li
- Department of Thyroid SurgeryGuangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Liang Li
- Medical Research InstituteGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Fei Shen
- Department of Thyroid SurgeryGuangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Koichi Tsuneyama
- Department of Pathology and Laboratory MedicineInstitute of Biomedical SciencesTokushima University Graduate SchoolTokushimaJapan
| | - Qing‐Ling Zhang
- Department of PathologyGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Zhe‐Xiong Lian
- Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Haixia Guan
- Department of EndocrinologyGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Bo Xu
- Department of Thyroid SurgeryGuangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
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6
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Varrone M, Tavernari D, Santamaria-Martínez A, Walsh LA, Ciriello G. CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity. Nat Genet 2024; 56:74-84. [PMID: 38066188 DOI: 10.1038/s41588-023-01588-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 10/23/2023] [Indexed: 12/20/2023]
Abstract
Tissues are organized in cellular niches, the composition and interactions of which can be investigated using spatial omics technologies. However, systematic analyses of tissue composition are challenged by the scale and diversity of the data. Here we present CellCharter, an algorithmic framework to identify, characterize, and compare cellular niches in spatially resolved datasets. CellCharter outperformed existing approaches and effectively identified cellular niches across datasets generated using different technologies, and comprising hundreds of samples and millions of cells. In multiple human lung cancer cohorts, CellCharter uncovered a cellular niche composed of tumor-associated neutrophil and cancer cells expressing markers of hypoxia and cell migration. This cancer cell state was spatially segregated from more proliferative tumor cell clusters and was associated with tumor-associated neutrophil infiltration and poor prognosis in independent patient cohorts. Overall, CellCharter enables systematic analyses across data types and technologies to decode the link between spatial tissue architectures and cell plasticity.
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Affiliation(s)
- Marco Varrone
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Léman, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Daniele Tavernari
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Léman, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Swiss Institute for Experimental Cancer Research, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Albert Santamaria-Martínez
- Swiss Cancer Center Léman, Lausanne, Switzerland
- Swiss Institute for Experimental Cancer Research, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Logan A Walsh
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Giovanni Ciriello
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Cancer Center Léman, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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7
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Nguyen JH. Draining Lymph Nodes in Human Kidney Pancreas Transplant: Potential Implications in Alloimmunity and Tolerance. Transplant Proc 2023; 55:2183-2185. [PMID: 37748965 DOI: 10.1016/j.transproceed.2023.08.010] [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: 04/21/2023] [Revised: 07/07/2023] [Accepted: 08/01/2023] [Indexed: 09/27/2023]
Abstract
BACKGROUND Draining lymph nodes (DLNs) are implicated in alloimmunity and tolerance regulation. The role of DLNs in human organ transplant is unknown due to the lack of relevant clinical tissues. METHODS During a combined kidney and pancreas transplant, the distal vena cava and both right and left common and external iliac vessels were mobilized and exposed. Draining lymph nodes along these vessels were removed sequentially and archived before the pancreas transplant. Similarly, the DLNs along the left iliac vessels were removed and archived before implantation of the kidney allograft. RESULTS Among 13 patients undergoing kidney and pancreas transplants, 6 had pre- and postreperfusion DLNs clinically archived. The remaining 7 either had only prereperfusion DLNs archived or none. Clinical archiving of DLNs did not alter operative times or parameters. CONCLUSION This study describes an approach for clinical archiving of DLNs obtained within the operative field during combined kidney-pancreas transplant in humans. The availability of relevant DLNs is essential for investigating fundamental initial primary immune responses potentially important in allograft alloimmunity and tolerance.
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Affiliation(s)
- Justin H Nguyen
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, Florida.
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8
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Jung N, Kim TK. Spatial transcriptomics in neuroscience. Exp Mol Med 2023; 55:2105-2115. [PMID: 37779145 PMCID: PMC10618223 DOI: 10.1038/s12276-023-01093-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: 02/28/2023] [Revised: 07/07/2023] [Accepted: 07/09/2023] [Indexed: 10/03/2023] Open
Abstract
The brain is one of the most complex living tissue types and is composed of an exceptional diversity of cell types displaying unique functional connectivity. Single-cell RNA sequencing (scRNA-seq) can be used to efficiently map the molecular identities of the various cell types in the brain by providing the transcriptomic profiles of individual cells isolated from the tissue. However, the lack of spatial context in scRNA-seq prevents a comprehensive understanding of how different configurations of cell types give rise to specific functions in individual brain regions and how each distinct cell is connected to form a functional unit. To understand how the various cell types contribute to specific brain functions, it is crucial to correlate the identities of individual cells obtained through scRNA-seq with their spatial information in intact tissue. Spatial transcriptomics (ST) can resolve the complex spatial organization of cell types in the brain and their connectivity. Various ST tools developed during the past decade based on imaging and sequencing technology have permitted the creation of functional atlases of the brain and have pulled the properties of neural circuits into ever-sharper focus. In this review, we present a summary of several ST tools and their applications in neuroscience and discuss the unprecedented insights these tools have made possible.
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Affiliation(s)
- Namyoung Jung
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, 37673, Republic of Korea
| | - Tae-Kyung Kim
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, 37673, Republic of Korea.
- Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, 03722, Republic of Korea.
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Wang Q, Zhi Y, Zi M, Mo Y, Wang Y, Liao Q, Zhang S, Gong Z, Wang F, Zeng Z, Guo C, Xiong W. Spatially Resolved Transcriptomics Technology Facilitates Cancer Research. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302558. [PMID: 37632718 PMCID: PMC10602551 DOI: 10.1002/advs.202302558] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/16/2023] [Indexed: 08/28/2023]
Abstract
Single cell RNA sequencing (scRNA-seq) provides a great convenience for studying tumor occurrence and development for its ability to study gene expression at the individual cell level. However, patient-derived tumor tissues are composed of multiple types of cells including tumor cells and adjacent non-malignant cells such as stromal cells and immune cells. The spatial locations of various cells in situ tissues plays a pivotal role in the occurrence and development of tumors, which cannot be elucidated by scRNA-seq alone. Spatially resolved transcriptomics (SRT) technology emerges timely to explore the unrecognized relationship between the spatial background of a particular cell and its functions, and is increasingly used in cancer research. This review provides a systematic overview of the SRT technologies that are developed, in particular the more widely used cutting-edge SRT technologies based on next-generation sequencing (NGS). In addition, the main achievements by SRT technologies in precisely unveiling the underappreciated spatial locations on gene expression and cell function with unprecedented high-resolution in cancer research are emphasized, with the aim of developing more effective clinical therapeutics oriented to a deeper understanding of the interaction between tumor cells and surrounding non-malignant cells.
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Affiliation(s)
- Qian Wang
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Yuan Zhi
- Department of Oral and Maxillofacial SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaHunan410012P. R. China
| | - Moxin Zi
- Department of Oral and Maxillofacial SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaHunan410012P. R. China
| | - Yongzhen Mo
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
- Department of Otolaryngology Head and Neck SurgeryXiangya HospitalCentral South UniversityChangshaHunan410008P. R. China
| | - Yumin Wang
- Department of Otolaryngology Head and Neck SurgeryXiangya HospitalCentral South UniversityChangshaHunan410008P. R. China
| | - Qianjin Liao
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
| | - Shanshan Zhang
- Department of Otolaryngology Head and Neck SurgeryXiangya HospitalCentral South UniversityChangshaHunan410008P. R. China
| | - Zhaojian Gong
- Department of Oral and Maxillofacial SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaHunan410012P. R. China
| | - Fuyan Wang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Zhaoyang Zeng
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Can Guo
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Wei Xiong
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
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10
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Radosevic-Robin N, Kossai M, Penault-Llorca F. New-generation technologies for spatial tissue analysis, indispensable tools for deciphering intratumor heterogeneity in the development of antibody-drug conjugates and radio-immunoconjugates for cancer treatment. TRANSLATIONAL BREAST CANCER RESEARCH : A JOURNAL FOCUSING ON TRANSLATIONAL RESEARCH IN BREAST CANCER 2023; 4:28. [PMID: 38751472 PMCID: PMC11093076 DOI: 10.21037/tbcr-23-38] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/12/2023] [Indexed: 05/18/2024]
Abstract
Technologies allowing in situ tissue molecular analysis of the "high-plex" type (>20 molecules per tissue section) are the 21st century inventions that are revolutionizing our knowledge of the biology of malignant tumors and many benign alterations. These technologies are based on specific probe labeling systems for the detection of tissue components [proteins, messenger RNA (mRNA)], as well as on detailed image analysis, combined with computational tools. We are synthetically presenting technologies based on image analysis, such as multiplex immunofluorescence (mIF), imaging mass cytometry (IMC), and multiplexed ion beam imaging (MIBI), as well as the ones not based on image analysis, such as multiplex in situ hybridizations (ISHs) using various principles. All of them are supported by powerful software which enable both tissue segmentation and data analysis. In the context of cancer treatment personalization, these technologies can reveal areas of tumor tissue and/or cellular subpopulations that are responsible for good or bad responses to anticancer drugs. Thus, they represent an unprecedented aid in the exploration of intratumor heterogeneity (ITH), which has already been shown to be one of the main reasons for the therapeutic failure of targeted anticancer treatments. The arrival of antibody-drug conjugates (ADCs) and radio-immunoconjugates (RICs) in the therapeutic arsenal in oncology imposes a deep exploration of molecular ITH, where technologies of spatial tissue analysis reveal an emerging category of biomarkers-spatial biomarkers.
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Affiliation(s)
- Nina Radosevic-Robin
- Platform for Advanced or/and Novel Tissue Analyses (TANYA), Department of Pathology, The Jean Perrin Comprehensive Cancer Center, Clermont-Ferrand, France
- University Clermont Auvergne, INSERM U1240 [Molecular Imaging & Theragnostic Strategies (IMOST)], Clermont-Ferrand, France
| | - Myriam Kossai
- Platform for Advanced or/and Novel Tissue Analyses (TANYA), Department of Pathology, The Jean Perrin Comprehensive Cancer Center, Clermont-Ferrand, France
- University Clermont Auvergne, INSERM U1240 [Molecular Imaging & Theragnostic Strategies (IMOST)], Clermont-Ferrand, France
| | - Frederique Penault-Llorca
- Platform for Advanced or/and Novel Tissue Analyses (TANYA), Department of Pathology, The Jean Perrin Comprehensive Cancer Center, Clermont-Ferrand, France
- University Clermont Auvergne, INSERM U1240 [Molecular Imaging & Theragnostic Strategies (IMOST)], Clermont-Ferrand, France
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11
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Thakur S, Haider S, Natrajan R. Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer. J Pathol 2023; 260:621-636. [PMID: 37587096 DOI: 10.1002/path.6158] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/11/2023] [Accepted: 06/14/2023] [Indexed: 08/18/2023]
Abstract
Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre-clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Shefali Thakur
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Syed Haider
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Rachael Natrajan
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
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12
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Ma J, Guo J, Fan Z, Zhao W, Zhou X. CVAM: CNA Profile Inference of the Spatial Transcriptome Based on the VGAE and HMM. Biomolecules 2023; 13:767. [PMID: 37238637 PMCID: PMC10216626 DOI: 10.3390/biom13050767] [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: 03/18/2023] [Revised: 04/20/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Tumors are often polyclonal due to copy number alteration (CNA) events. Through the CNA profile, we can understand the tumor heterogeneity and consistency. CNA information is usually obtained through DNA sequencing. However, many existing studies have shown a positive correlation between the gene expression and gene copy number identified from DNA sequencing. With the development of spatial transcriptome technologies, it is urgent to develop new tools to identify genomic variation from the spatial transcriptome. Therefore, in this study, we developed CVAM, a tool to infer the CNA profile from spatial transcriptome data. Compared with existing tools, CVAM integrates the spatial information with the spot's gene expression information together and the spatial information is indirectly introduced into the CNA inference. By applying CVAM to simulated and real spatial transcriptome data, we found that CVAM performed better in identifying CNA events. In addition, we analyzed the potential co-occurrence and mutual exclusion between CNA events in tumor clusters, which is helpful to analyze the potential interaction between genes in mutation. Last but not least, Ripley's K-function is also applied to CNA multi-distance spatial pattern analysis so that we can figure out the differences of different gene CNA events in spatial distribution, which is helpful for tumor analysis and implementing more effective treatment measures based on spatial characteristics of genes.
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Affiliation(s)
- Jian Ma
- College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
| | - Jingjing Guo
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu 610041, China
| | - Zhiwei Fan
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610040, China
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Weiling Zhao
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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13
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Suoangbaji T, Zhang VX, Ng IOL, Ho DWH. Single-Cell Analysis of Primary Liver Cancer in Mouse Models. Cells 2023; 12:cells12030477. [PMID: 36766817 PMCID: PMC9914042 DOI: 10.3390/cells12030477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 01/17/2023] [Accepted: 01/29/2023] [Indexed: 02/05/2023] Open
Abstract
Primary liver cancer (PLC), consisting mainly of hepatocellular carcinoma and intrahepatic cholangiocarcinoma, is one of the major causes of cancer-related mortality worldwide. The curative therapy for PLC is surgical resection and liver transplantation, but most PLCs are inoperable at diagnosis. Even after surgery, there is a high rate of tumor recurrence. There is an unmet clinical need to discover more effective treatment options for advanced PLCs. Pre-clinical mouse models in PLC research have played a critical role in identifying key oncogenic drivers and signaling pathways in hepatocarcinogenesis. Furthermore, recent advances in single-cell RNA sequencing (scRNA-seq) have provided an unprecedented degree of resolution in such characterization. In this review, we will summarize the recent studies that utilized pre-clinical mouse models with the combination of scRNA-seq to provide an understanding of different aspects of PLC. We will focus particularly on the potentially actionable targets regarding the cellular and molecular components. We anticipate that the findings in mouse models could complement those in patients. With more defined etiological background, mouse models may provide valuable insights.
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Affiliation(s)
| | | | - Irene Oi-Lin Ng
- Correspondence: (I.O.-L.N.); (D.W.-H.H.); Fax: +852-28872-5197 (I.O.-L.N.); +852-2819-5375 (D.W.-H.H.)
| | - Daniel Wai-Hung Ho
- Correspondence: (I.O.-L.N.); (D.W.-H.H.); Fax: +852-28872-5197 (I.O.-L.N.); +852-2819-5375 (D.W.-H.H.)
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14
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Bassiouni R, Idowu MO, Gibbs LD, Robila V, Grizzard PJ, Webb MG, Song J, Noriega A, Craig DW, Carpten JD. Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer. Cancer Res 2023; 83:34-48. [PMID: 36283023 PMCID: PMC9812886 DOI: 10.1158/0008-5472.can-22-2682] [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: 08/23/2022] [Revised: 10/18/2022] [Accepted: 10/20/2022] [Indexed: 02/03/2023]
Abstract
Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities. SIGNIFICANCE Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.
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Affiliation(s)
- Rania Bassiouni
- Department of Translational Genomics, Keck School of Medicine, University of Southern California; Los Angeles, CA
| | - Michael O. Idowu
- Department of Pathology, Virginia Commonwealth University; Richmond, VA
| | - Lee D. Gibbs
- Department of Translational Genomics, Keck School of Medicine, University of Southern California; Los Angeles, CA
| | - Valentina Robila
- Department of Pathology, Virginia Commonwealth University; Richmond, VA
| | | | - Michelle G. Webb
- Department of Translational Genomics, Keck School of Medicine, University of Southern California; Los Angeles, CA
| | - Jiarong Song
- Department of Translational Genomics, Keck School of Medicine, University of Southern California; Los Angeles, CA
| | - Ashley Noriega
- Department of Translational Genomics, Keck School of Medicine, University of Southern California; Los Angeles, CA
| | - David W. Craig
- Department of Translational Genomics, Keck School of Medicine, University of Southern California; Los Angeles, CA
- Translational and Clinical Sciences Program, Norris Comprehensive Cancer Center, University of Southern California; Los Angeles, CA
| | - John D. Carpten
- Department of Translational Genomics, Keck School of Medicine, University of Southern California; Los Angeles, CA
- Translational and Clinical Sciences Program, Norris Comprehensive Cancer Center, University of Southern California; Los Angeles, CA
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15
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A systems biology approach to better understand human tick-borne diseases. Trends Parasitol 2023; 39:53-69. [PMID: 36400674 DOI: 10.1016/j.pt.2022.10.006] [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: 08/11/2022] [Revised: 10/28/2022] [Accepted: 10/28/2022] [Indexed: 11/17/2022]
Abstract
Tick-borne diseases (TBDs) are a growing global health concern. Despite extensive studies, ill-defined tick-associated pathologies remain with unknown aetiologies. Human immunological responses after tick bite, and inter-individual variations of immune-response phenotypes, are not well characterised. Current reductive experimental methodologies limit our understanding of more complex tick-associated illness, which results from the interactions between the host, tick, and microbes. An unbiased, systems-level integration of clinical metadata and biological host data - obtained via transcriptomics, proteomics, and metabolomics - offers to drive the data-informed generation of testable hypotheses in TBDs. Advanced computational tools have rendered meaningful analysis of such large data sets feasible. This review highlights the advantages of integrative system biology approaches as essential for understanding the complex pathobiology of TBDs.
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16
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Kim Y, Danaher P, Cimino PJ, Hurth K, Warren S, Glod J, Beechem JM, Zada G, McEachron TA. Highly Multiplexed Spatially Resolved Proteomic and Transcriptional Profiling of the Glioblastoma Microenvironment Using Archived Formalin-Fixed Paraffin-Embedded Specimens. Mod Pathol 2023; 36:100034. [PMID: 36788070 PMCID: PMC9937641 DOI: 10.1016/j.modpat.2022.100034] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 09/16/2022] [Accepted: 09/22/2022] [Indexed: 01/19/2023]
Abstract
Glioblastoma is a heterogeneous tumor for which effective treatment options are limited and often insufficient. Few studies have examined the intratumoral transcriptional and proteomic heterogeneity of the glioblastoma microenvironment to characterize the spatial distribution of potential molecular and cellular therapeutic immunooncology targets. We applied an integrated multimodal approach comprised of NanoString GeoMx Digital Spatial Profiling, single-cell RNA-seq (scRNA-seq), and expert neuropathologic assessment to characterize archival formalin-fixed paraffin-embedded glioblastoma specimens. Clustering analysis and spatial cluster maps highlighted the intratumoral heterogeneity of each specimen. Mixed cell deconvolution analysis revealed that neoplastic and vascular cells were the prominent cell types throughout each specimen, with macrophages, oligodendrocyte precursors, neurons, astrocytes, and oligodendrocytes present in lower abundance and illustrated the regional distribution of the respective cellular enrichment scores. The spatial resolution of the actionable immunotherapeutic landscape showed that robust B7H3 gene and protein expression was broadly distributed throughout each specimen and identified STING and VISTA as potential targets. Lastly, we uncovered remarkable variability in VEGFA expression and discovered unanticipated associations between VEGFA, endothelial cell markers, hypoxia, and the expression of immunoregulatory genes, indicative of regionally distinct immunosuppressive microdomains. This work provides an early demonstration of the ability of an integrated panel-based spatial biology approach to characterize and quantify the intrinsic molecular heterogeneity of the glioblastoma microenvironment.
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Affiliation(s)
- Youngmi Kim
- NanoString Technologies, Seattle, Washington
| | | | - Patrick J Cimino
- Department of Laboratory Medicine and Pathology, Division of Neuropathology, University of Washington, Seattle, Washington; Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland
| | - Kyle Hurth
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, California
| | | | - John Glod
- Pediatric Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | | | - Gabriel Zada
- Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Troy A McEachron
- Pediatric Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.
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17
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Yang C, Zhang S, Cheng Z, Liu Z, Zhang L, Jiang K, Geng H, Qian R, Wang J, Huang X, Chen M, Li Z, Qin W, Xia Q, Kang X, Wang C, Hang H. Multi-region sequencing with spatial information enables accurate heterogeneity estimation and risk stratification in liver cancer. Genome Med 2022; 14:142. [PMID: 36527145 PMCID: PMC9758830 DOI: 10.1186/s13073-022-01143-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 11/22/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Numerous studies have used multi-region sampling approaches to characterize intra-tumor heterogeneity (ITH) in hepatocellular carcinoma (HCC). However, conventional multi-region sampling strategies do not preserve the spatial details of samples, and thus, the potential influences of spatial distribution on patient-wise ITH (represents the overall heterogeneity level of the tumor in a given patient) have long been overlooked. Furthermore, gene-wise transcriptional ITH (represents the expression pattern of genes across different intra-tumor regions) in HCC is also under-explored, highlighting the need for a comprehensive investigation. METHODS To address the problem of spatial information loss, we propose a simple and easy-to-implement strategy called spatial localization sampling (SLS). We performed multi-region sampling and sequencing on 14 patients with HCC, collecting a total of 75 tumor samples with spatial information and molecular data. Normalized diversity score and integrated heterogeneity score (IHS) were then developed to measure patient-wise and gene-wise ITH, respectively. RESULTS A significant correlation between spatial and molecular heterogeneity was uncovered, implying that spatial distribution of sampling sites did influence ITH estimation in HCC. We demonstrated that the normalized diversity score had the ability to overcome sampling location bias and provide a more accurate estimation of patient-wise ITH. According to this metric, HCC tumors could be divided into two classes (low-ITH and high-ITH tumors) with significant differences in multiple biological properties. Through IHS analysis, we revealed a highly heterogenous immune microenvironment in HCC and identified some low-ITH checkpoint genes with immunotherapeutic potential. We also constructed a low-heterogeneity risk stratification (LHRS) signature based on the IHS results which could accurately predict the survival outcome of patients with HCC on a single tumor biopsy sample. CONCLUSIONS This study provides new insights into the complex phenotypes of HCC and may serve as a guide for future studies in this field.
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Affiliation(s)
- Chen Yang
- grid.16821.3c0000 0004 0368 8293Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China ,grid.16821.3c0000 0004 0368 8293State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Senquan Zhang
- grid.16821.3c0000 0004 0368 8293Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhuoan Cheng
- grid.16821.3c0000 0004 0368 8293Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China ,grid.16821.3c0000 0004 0368 8293State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhicheng Liu
- grid.412793.a0000 0004 1799 5032Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Linmeng Zhang
- grid.16821.3c0000 0004 0368 8293State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kai Jiang
- grid.16821.3c0000 0004 0368 8293Renji Biobank, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haigang Geng
- grid.16821.3c0000 0004 0368 8293Department of Gastrointestinal Surgery, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Ruolan Qian
- grid.16821.3c0000 0004 0368 8293State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Wang
- grid.16821.3c0000 0004 0368 8293State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaowen Huang
- grid.16821.3c0000 0004 0368 8293Key Laboratory of Gastroenterology and Hepatology, Division of Gastroenterology and Hepatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mo Chen
- grid.16821.3c0000 0004 0368 8293Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhe Li
- grid.16821.3c0000 0004 0368 8293Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenxin Qin
- grid.16821.3c0000 0004 0368 8293State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiang Xia
- grid.16821.3c0000 0004 0368 8293Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China ,grid.16821.3c0000 0004 0368 8293State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaonan Kang
- grid.16821.3c0000 0004 0368 8293Renji Biobank, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Cun Wang
- grid.16821.3c0000 0004 0368 8293Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China ,grid.16821.3c0000 0004 0368 8293State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hualian Hang
- grid.16821.3c0000 0004 0368 8293Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China ,grid.16821.3c0000 0004 0368 8293State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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18
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Ke M, Elshenawy B, Sheldon H, Arora A, Buffa FM. Single cell RNA-sequencing: A powerful yet still challenging technology to study cellular heterogeneity. Bioessays 2022; 44:e2200084. [PMID: 36068142 DOI: 10.1002/bies.202200084] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/18/2022] [Accepted: 08/19/2022] [Indexed: 11/11/2022]
Abstract
Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic 'average' cannot outright be used as representative of the 'average cell'. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single-cell RNA sequencing (scRNA-Seq) enables the comparison of the transcriptomes of individual cells. This provides high-resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context-specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA-Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA-Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research.
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Affiliation(s)
- May Ke
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Badran Elshenawy
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Helen Sheldon
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Anjali Arora
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Francesca M Buffa
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK.,Department of Computing Sciences, Bocconi University, Milano, Italy.,Institute for Data Science and Analytics, Bocconi University, Milano, Italy
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19
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Chang Y, He F, Wang J, Chen S, Li J, Liu J, Yu Y, Su L, Ma A, Allen C, Lin Y, Sun S, Liu B, Javier Otero J, Chung D, Fu H, Li Z, Xu D, Ma Q. Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning. Comput Struct Biotechnol J 2022; 20:4600-4617. [PMID: 36090815 PMCID: PMC9440291 DOI: 10.1016/j.csbj.2022.08.029] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 11/29/2022] Open
Abstract
Spatially resolved transcriptomics provides a new way to define spatial contexts and understand the pathogenesis of complex human diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs such as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from spatial transcriptomics. The embedding is then visualized by mapping into color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of 10x Genomics Visium spatial transcriptomics datasets on the human and mouse cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in-house AD samples, RESEPT can localize cortex layers and cell types based on pre-defined region- or cell-type-enriched genes and furthermore provide critical insights into the identification of amyloid-beta plaques in Alzheimer's disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor-enriched, non-tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.
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Affiliation(s)
- Yuzhou Chang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
- The Pelotonia Institute for Immuno-oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA
| | - Fei He
- School of Information Science and Technology, Northeast Normal University, Changchun, Jilin 130117, China
| | - Juexin Wang
- Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Shuo Chen
- Department of Neuroscience, The Ohio State University, Columbus, OH 43210, USA
| | - Jingyi Li
- School of Information Science and Technology, Northeast Normal University, Changchun, Jilin 130117, China
| | - Jixin Liu
- School of Mathematics, Shandong University, Jinan 250100, China
| | - Yang Yu
- School of Information Science and Technology, Northeast Normal University, Changchun, Jilin 130117, China
| | - Li Su
- Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Anjun Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
- The Pelotonia Institute for Immuno-oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA
| | - Carter Allen
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
| | - Yu Lin
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
| | - Shaoli Sun
- Department of Pathology, The Ohio State University, Columbus, OH 43210, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan 250100, China
| | - José Javier Otero
- Departments of Neuroscience, Pathology, Neuropathology, The Ohio State University, Columbus, OH 43210, USA
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
- The Pelotonia Institute for Immuno-oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA
| | - Hongjun Fu
- Department of Neuroscience, The Ohio State University, Columbus, OH 43210, USA
| | - Zihai Li
- The Pelotonia Institute for Immuno-oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Qin Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
- The Pelotonia Institute for Immuno-oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA
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20
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Garg T, Weiss CR, Sheth RA. Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology. Cancers (Basel) 2022; 14:3628. [PMID: 35892890 PMCID: PMC9332307 DOI: 10.3390/cancers14153628] [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: 07/08/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 12/07/2022] Open
Abstract
In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient's prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies.
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Affiliation(s)
- Tushar Garg
- Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (T.G.); (C.R.W.)
| | - Clifford R. Weiss
- Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (T.G.); (C.R.W.)
| | - Rahul A. Sheth
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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21
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Dohmen J, Baranovskii A, Ronen J, Uyar B, Franke V, Akalin A. Identifying tumor cells at the single-cell level using machine learning. Genome Biol 2022; 23:123. [PMID: 35637521 PMCID: PMC9150321 DOI: 10.1186/s13059-022-02683-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 05/06/2022] [Indexed: 12/15/2022] Open
Abstract
Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation—the assignment of cell type or cell state to each sequenced cell—is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments. Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts.
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Affiliation(s)
- Jan Dohmen
- Bioinformatics and Omics Data Science Platform, Berlin Institute For Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str.28, 10115, Berlin, Germany
| | - Artem Baranovskii
- Non-coding RNAs and Mechanisms of Cytoplasmic Gene Regulation Lab, Berlin Institute for Medical Systems Biology, Hannoversche Str. 28, 10115, Berlin, Germany.,Free University Berlin, Kaiserswerther Str. 16-18, 14195, Berlin, Germany
| | - Jonathan Ronen
- Bioinformatics and Omics Data Science Platform, Berlin Institute For Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str.28, 10115, Berlin, Germany
| | - Bora Uyar
- Bioinformatics and Omics Data Science Platform, Berlin Institute For Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str.28, 10115, Berlin, Germany
| | - Vedran Franke
- Bioinformatics and Omics Data Science Platform, Berlin Institute For Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str.28, 10115, Berlin, Germany.
| | - Altuna Akalin
- Bioinformatics and Omics Data Science Platform, Berlin Institute For Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str.28, 10115, Berlin, Germany.
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22
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Bridges K, Miller-Jensen K. Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment. Front Immunol 2022; 13:885267. [PMID: 35572582 PMCID: PMC9096838 DOI: 10.3389/fimmu.2022.885267] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 03/25/2022] [Indexed: 01/25/2023] Open
Abstract
Recent advances in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq), have permitted high throughput transcriptional profiling of a wide variety of biological systems. As scRNA-seq supports inference of cell-cell communication, this technology has and continues to anchor groundbreaking studies into the efficacy and mechanism of novel immunotherapies for cancer treatment. In this review, we will highlight methods developed to infer inter- and intracellular signaling from scRNA-seq and discuss how they have contributed to studies of immunotherapeutic intervention in the tumor microenvironment (TME). However, a central challenge remains in validating the hypothesized cell-cell interactions. Therefore, this review will also cover strategies for integration of these scRNA-seq-derived interaction networks with existing experimental and computational approaches. Integration of these networks with imaging, protein secretion measurements, and network analysis and mathematical modeling tools addresses challenges that remain with scRNA-seq to enhance studies of immunosuppressive and immunotherapy-altered signaling in the TME.
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Affiliation(s)
- Kate Bridges
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States
| | - Kathryn Miller-Jensen
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, United States
- Systems Biology Institute, Yale University, New Haven, CT, United States
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23
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Zeng Z, Li Y, Li Y, Luo Y. Statistical and machine learning methods for spatially resolved transcriptomics data analysis. Genome Biol 2022; 23:83. [PMID: 35337374 PMCID: PMC8951701 DOI: 10.1186/s13059-022-02653-7] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/15/2022] [Indexed: 01/28/2023] Open
Abstract
The recent advancement in spatial transcriptomics technology has enabled multiplexed profiling of cellular transcriptomes and spatial locations. As the capacity and efficiency of the experimental technologies continue to improve, there is an emerging need for the development of analytical approaches. Furthermore, with the continuous evolution of sequencing protocols, the underlying assumptions of current analytical methods need to be re-evaluated and adjusted to harness the increasing data complexity. To motivate and aid future model development, we herein review the recent development of statistical and machine learning methods in spatial transcriptomics, summarize useful resources, and highlight the challenges and opportunities ahead.
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Affiliation(s)
- Zexian Zeng
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100084, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100084, China
- Department of Data Sciences, Dana Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - Yawei Li
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Yiming Li
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Yuan Luo
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Northwestern University Clinical and Translational Sciences Institute, Chicago, IL, 60611, USA.
- Institute for Augmented Intelligence in Medicine, Northwestern University, Chicago, IL, 60611, USA.
- Center for Health Information Partnerships, Northwestern University, Chicago, IL, 60611, USA.
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24
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Kumar V, Ramnarayanan K, Sundar R, Padmanabhan N, Srivastava S, Koiwa M, Yasuda T, Koh V, Huang KK, Tay ST, Ho SWT, Tan ALK, Ishimoto T, Kim G, Shabbir A, Chen Q, Zhang B, Xu S, Lam KP, Lum HYJ, Teh M, Yong WP, So JBY, Tan P. Single-Cell Atlas of Lineage States, Tumor Microenvironment, and Subtype-Specific Expression Programs in Gastric Cancer. Cancer Discov 2022; 12:670-691. [PMID: 34642171 PMCID: PMC9394383 DOI: 10.1158/2159-8290.cd-21-0683] [Citation(s) in RCA: 168] [Impact Index Per Article: 84.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 09/27/2021] [Accepted: 10/07/2021] [Indexed: 01/07/2023]
Abstract
Gastric cancer heterogeneity represents a barrier to disease management. We generated a comprehensive single-cell atlas of gastric cancer (>200,000 cells) comprising 48 samples from 31 patients across clinical stages and histologic subtypes. We identified 34 distinct cell-lineage states including novel rare cell populations. Many lineage states exhibited distinct cancer-associated expression profiles, individually contributing to a combined tumor-wide molecular collage. We observed increased plasma cell proportions in diffuse-type tumors associated with epithelial-resident KLF2 and stage-wise accrual of cancer-associated fibroblast subpopulations marked by high INHBA and FAP coexpression. Single-cell comparisons between patient-derived organoids (PDO) and primary tumors highlighted inter- and intralineage similarities and differences, demarcating molecular boundaries of PDOs as experimental models. We complemented these findings by spatial transcriptomics, orthogonal validation in independent bulk RNA-sequencing cohorts, and functional demonstration using in vitro and in vivo models. Our results provide a high-resolution molecular resource of intra- and interpatient lineage states across distinct gastric cancer subtypes. SIGNIFICANCE We profiled gastric malignancies at single-cell resolution and identified increased plasma cell proportions as a novel feature of diffuse-type tumors. We also uncovered distinct cancer-associated fibroblast subtypes with INHBA-FAP-high cell populations as predictors of poor clinical prognosis. Our findings highlight potential origins of deregulated cell states in the gastric tumor ecosystem. This article is highlighted in the In This Issue feature, p. 587.
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Affiliation(s)
- Vikrant Kumar
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore
| | | | - Raghav Sundar
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore.,Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,The N.1 Institute for Health, National University of Singapore, Singapore.,Singapore Gastric Cancer Consortium, Singapore
| | - Nisha Padmanabhan
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore
| | | | - Mayu Koiwa
- Gastrointestinal Cancer Biology, International Research Center for Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Tadahito Yasuda
- Gastrointestinal Cancer Biology, International Research Center for Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Vivien Koh
- Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore.,Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Kie Kyon Huang
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore
| | - Su Ting Tay
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore
| | - Shamaine Wei Ting Ho
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore.,Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Angie Lay Keng Tan
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore
| | - Takatsugu Ishimoto
- Gastrointestinal Cancer Biology, International Research Center for Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Guowei Kim
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Department of Surgery, University Surgical Cluster, National University Health System, Singapore
| | - Asim Shabbir
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Department of Surgery, University Surgical Cluster, National University Health System, Singapore
| | - Qingfeng Chen
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore.,Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, Singapore
| | - Biyan Zhang
- Singapore Immunology Network (SIgN), A*STAR, Singapore
| | - Shengli Xu
- Singapore Immunology Network (SIgN), A*STAR, Singapore.,Department of Physiology, National University of Singapore, Singapore
| | - Kong-Peng Lam
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, Singapore.,Singapore Immunology Network (SIgN), A*STAR, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore
| | | | - Ming Teh
- Department of Pathology, National University Health System, Singapore
| | - Wei Peng Yong
- Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore.,Singapore Gastric Cancer Consortium, Singapore.,Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Jimmy Bok Yan So
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Singapore Gastric Cancer Consortium, Singapore.,Department of Surgery, University Surgical Cluster, National University Health System, Singapore.,Division of Surgical Oncology, National University Cancer Institute, Singapore
| | - Patrick Tan
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore.,Singapore Gastric Cancer Consortium, Singapore.,Cancer Science Institute of Singapore, National University of Singapore, Singapore.,Department of Physiology, National University of Singapore, Singapore.,Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore.,SingHealth/Duke-NUS Institute of Precision Medicine, National Heart Centre Singapore, Singapore.,Corresponding Author: Patrick Tan, Cancer and Stem Cell Biology Program, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore. Phone: 65-6516-1783; Fax: 65-6221-2402; E-mail:
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25
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Hadadi E, Deschoemaeker S, Vicente Venegas G, Laoui D. Heterogeneity and function of macrophages in the breast during homeostasis and cancer. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2022; 367:149-182. [PMID: 35461657 DOI: 10.1016/bs.ircmb.2022.01.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Macrophages are diverse immune cells populating all tissues and adopting a unique tissue-specific identity. Breast macrophages play an essential role in the development and function of the mammary gland over one's lifetime. In the recent years, with the development of fate-mapping, imaging and scRNA-seq technologies we grew a better understanding of the origin, heterogeneity and function of mammary macrophages in homeostasis but also during breast cancer development. Here, we aim to provide a comprehensive review of the latest improvements in studying the macrophage heterogeneity in healthy mammary tissues and breast cancer.
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Affiliation(s)
- Eva Hadadi
- Myeloid Cell Immunology Lab, VIB Center for Inflammation Research, Brussels, Belgium; Lab of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Sofie Deschoemaeker
- Myeloid Cell Immunology Lab, VIB Center for Inflammation Research, Brussels, Belgium; Lab of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Gerard Vicente Venegas
- Myeloid Cell Immunology Lab, VIB Center for Inflammation Research, Brussels, Belgium; Lab of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Damya Laoui
- Myeloid Cell Immunology Lab, VIB Center for Inflammation Research, Brussels, Belgium; Lab of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium.
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26
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Chen Y, Qian W, Lin L, Cai L, Yin K, Jiang S, Song J, Han RPS, Yang C. Mapping Gene Expression in the Spatial Dimension. SMALL METHODS 2021; 5:e2100722. [PMID: 34927963 DOI: 10.1002/smtd.202100722] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/25/2021] [Indexed: 06/14/2023]
Abstract
The main function and biological processes of tissues are determined by the combination of gene expression and spatial organization of their cells. RNA sequencing technologies have primarily interrogated gene expression without preserving the native spatial context of cells. However, the emergence of various spatially-resolved transcriptome analysis methods now makes it possible to map the gene expression to specific coordinates within tissues, enabling transcriptional heterogeneity between different regions, and for the localization of specific transcripts and novel spatial markers to be revealed. Hence, spatially-resolved transcriptome analysis technologies have broad utility in research into human disease and developmental biology. Here, recent advances in spatially-resolved transcriptome analysis methods are summarized, including experimental technologies and computational methods. Strengths, challenges, and potential applications of those methods are highlighted, and perspectives in this field are provided.
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Affiliation(s)
- Yingwen Chen
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Weizhou Qian
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Li Lin
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Linfeng Cai
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Kun Yin
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Shaowei Jiang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Jia Song
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Ray P S Han
- Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, 33004, China
| | - Chaoyong Yang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
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27
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Quek C, Bai X, Long GV, Scolyer RA, Wilmott JS. High-Dimensional Single-Cell Transcriptomics in Melanoma and Cancer Immunotherapy. Genes (Basel) 2021; 12:1629. [PMID: 34681023 PMCID: PMC8535767 DOI: 10.3390/genes12101629] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/08/2021] [Accepted: 10/11/2021] [Indexed: 12/19/2022] Open
Abstract
Recent advances in single-cell transcriptomics have greatly improved knowledge of complex transcriptional programs, rapidly expanding our knowledge of cellular phenotypes and functions within the tumour microenvironment and immune system. Several new single-cell technologies have been developed over recent years that have enabled expanded understanding of the mechanistic cells and biological pathways targeted by immunotherapies such as immune checkpoint inhibitors, which are now routinely used in patient management with high-risk early-stage or advanced melanoma. These technologies have method-specific strengths, weaknesses and capabilities which need to be considered when utilising them to answer translational research questions. Here, we provide guidance for the implementation of single-cell transcriptomic analysis platforms by reviewing the currently available experimental and analysis workflows. We then highlight the use of these technologies to dissect the tumour microenvironment in the context of cancer patients treated with immunotherapy. The strategic use of single-cell analytics in clinical settings are discussed and potential future opportunities are explored with a focus on their use to rationalise the design of novel immunotherapeutic drug therapies that will ultimately lead to improved cancer patient outcomes.
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Affiliation(s)
- Camelia Quek
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2006, Australia; (X.B.); (G.V.L.); (R.A.S.); (J.S.W.)
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
| | - Xinyu Bai
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2006, Australia; (X.B.); (G.V.L.); (R.A.S.); (J.S.W.)
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
| | - Georgina V. Long
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2006, Australia; (X.B.); (G.V.L.); (R.A.S.); (J.S.W.)
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
- Royal North Shore and Mater Hospitals, Sydney, NSW 2065, Australia
| | - Richard A. Scolyer
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2006, Australia; (X.B.); (G.V.L.); (R.A.S.); (J.S.W.)
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
- Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, NSW 2050, Australia
| | - James S. Wilmott
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2006, Australia; (X.B.); (G.V.L.); (R.A.S.); (J.S.W.)
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
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28
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Liotta LA, Pappalardo PA, Carpino A, Haymond A, Howard M, Espina V, Wulfkuhle J, Petricoin E. Laser Capture Proteomics: spatial tissue molecular profiling from the bench to personalized medicine. Expert Rev Proteomics 2021; 18:845-861. [PMID: 34607525 PMCID: PMC10720974 DOI: 10.1080/14789450.2021.1984886] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/21/2021] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Laser Capture Microdissection (LCM) uses a laser to isolate, or capture, specific cells of interest in a complex heterogeneous tissue section, under direct microscopic visualization. Recently, there has been a surge of publications using LCM for tissue spatial molecular profiling relevant to a wide range of research topics. AREAS COVERED We summarize the many advances in tissue Laser Capture Proteomics (LCP) using mass spectrometry for discovery, and protein arrays for signal pathway network mapping. This review emphasizes: a) transition of LCM phosphoproteomics from the lab to the clinic for individualized cancer therapy, and b) the emerging frontier of LCM single cell molecular analysis combining proteomics with genomic, and transcriptomic analysis. The search strategy was based on the combination of MeSH terms with expert refinement. EXPERT OPINION LCM is complemented by a rich set of instruments, methodology protocols, and analytical A.I. (artificial intelligence) software for basic and translational research. Resolution is advancing to the tissue single cell level. A vision for the future evolution of LCM is presented. Emerging LCM technology is combining digital and AI guided remote imaging with automation, and telepathology, to a achieve multi-omic profiling that was not previously possible.
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Affiliation(s)
- Lance A. Liotta
- Center For Applied Proteomics and Molecular Medicine (CAPMM) School of Systems Biology, College of Sciences, George Mason University, Manassas, VA 20110, USA
| | - Philip A. Pappalardo
- Center For Applied Proteomics and Molecular Medicine (CAPMM) School of Systems Biology, College of Sciences, George Mason University, Manassas, VA 20110, USA
| | - Alan Carpino
- Fluidigm Corporation, South San Francisco, CA, USA
| | - Amanda Haymond
- Center For Applied Proteomics and Molecular Medicine (CAPMM) School of Systems Biology, College of Sciences, George Mason University, Manassas, VA 20110, USA
| | - Marissa Howard
- Center For Applied Proteomics and Molecular Medicine (CAPMM) School of Systems Biology, College of Sciences, George Mason University, Manassas, VA 20110, USA
| | - Virginia Espina
- Center For Applied Proteomics and Molecular Medicine (CAPMM) School of Systems Biology, College of Sciences, George Mason University, Manassas, VA 20110, USA
| | - Julie Wulfkuhle
- Center For Applied Proteomics and Molecular Medicine (CAPMM) School of Systems Biology, College of Sciences, George Mason University, Manassas, VA 20110, USA
| | - Emanuel Petricoin
- Center For Applied Proteomics and Molecular Medicine (CAPMM) School of Systems Biology, College of Sciences, George Mason University, Manassas, VA 20110, USA
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