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Junaid M, Lee EJ, Lim SB. Single-cell and spatial omics: exploring hypothalamic heterogeneity. Neural Regen Res 2025; 20:1525-1540. [PMID: 38993130 DOI: 10.4103/nrr.nrr-d-24-00231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 06/03/2024] [Indexed: 07/13/2024] Open
Abstract
Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.
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Affiliation(s)
- Muhammad Junaid
- Department of Biochemistry & Molecular Biology, Ajou University School of Medicine, Suwon, South Korea
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
| | - Eun Jeong Lee
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
- Department of Brain Science, Ajou University School of Medicine, Suwon, South Korea
| | - Su Bin Lim
- Department of Biochemistry & Molecular Biology, Ajou University School of Medicine, Suwon, South Korea
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
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2
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Krull D, Haynes P, Kesarwani A, Tessier J, Chen BJ, Hunter K, Rodriguez D, Liang Y, Mansfield J, McClain M, Ramos C, Bonnevie E, Anguiano E. A best practices framework for spatial biology studies in drug discovery and development: enabling successful cohort studies using digital spatial profiling. J Histotechnol 2024:1-20. [PMID: 39225147 DOI: 10.1080/01478885.2024.2391683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 08/08/2024] [Indexed: 09/04/2024]
Abstract
The discovery of biomarkers, essential for successful drug development, is often hindered by the limited availability of tissue samples, typically obtained through core needle biopsies. Standard 'omics platforms can consume significant amounts of tissue, forcing scientist to trade off spatial context for high-plex assays, such as genome-wide assays. While bulk gene expression approaches and standard single-cell transcriptomics have been valuable in defining various molecular and cellular mechanisms, they do not retain spatial context. As such, they have limited power in resolving tissue heterogeneity and cell-cell interactions. Current spatial transcriptomics platforms offer limited transcriptome coverage and have low throughput, restricting the number of samples that can be analyzed daily or even weekly. While the Digital Spatial Profiling (DSP) method does not provide single-cell resolution, it presents a significant advancement by enabling scalable whole transcriptome and ultrahigh-plex protein analysis from distinct tissue compartments and structures using a single tissue slide. These capabilities overcome significant constraints in biomarker analysis in solid tissue specimens. These advancements in tissue profiling play a crucial role in deepening our understanding of disease biology and in identifying potential therapeutic targets and biomarkers. To enhance the use of spatial biology tools in drug discovery and development, the DSP Scientific Consortium has created best practices guidelines. These guidelines, built on digital spatial profiling data and expertise, offer a practical framework for designing spatial studies and using current and future spatial biology platforms. The aim is to improve tissue analysis in all research areas supporting drug discovery and development.
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Affiliation(s)
- David Krull
- Precision Medicine, GlaxoSmithKline, Collegeville, USA
| | - Premi Haynes
- Cancer Immunology & Cell Therapy Thematic Research Center, Bristol Myers Squibb, Seattle, WA, USA
| | | | - Julien Tessier
- Precision Medicine and Computational Biology, Sanofi, Cambridge, USA
| | - Benjamin J Chen
- Translational Medicine, Bristol Myers Squibb, Cambridge, MA, USA
| | - Kelly Hunter
- Molecular Pathology and Histology, ProPath-UK Ltd, Hereford, UK
| | | | - Yan Liang
- Spatial Platforms Product Development and Support, NanoString Technologies Inc, Seattle, USA
| | - Jim Mansfield
- Research Business Development, Visiopharm Corp, Broomfield, USA
| | - Maxine McClain
- Spatial Platforms Product Development and Support, NanoString Technologies Inc, Seattle, USA
| | | | - Edward Bonnevie
- Cancer Immunology & Cell Therapy Thematic Research Center, Bristol Myers Squibb, Seattle, WA, USA
| | - Esperanza Anguiano
- Spatial Platforms Product Development and Support, NanoString Technologies Inc, Seattle, USA
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3
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Holder AM, Dedeilia A, Sierra-Davidson K, Cohen S, Liu D, Parikh A, Boland GM. Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours. Nat Rev Cancer 2024; 24:498-512. [PMID: 38867074 DOI: 10.1038/s41568-024-00705-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 06/14/2024]
Abstract
Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.
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Affiliation(s)
- Ashley M Holder
- Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | | | - Sonia Cohen
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - David Liu
- Dana Farber Cancer Institute, Boston, MA, USA
| | - Aparna Parikh
- Cancer Center, Massachusetts General Hospital, Boston, MA, USA
| | - Genevieve M Boland
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
- Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
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4
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Moutafi MK, Bates KM, Aung TN, Milian RG, Xirou V, Vathiotis IA, Gavrielatou N, Angelakis A, Schalper KA, Salichos L, Rimm DL. High-throughput transcriptome profiling indicates ribosomal RNAs to be associated with resistance to immunotherapy in non-small cell lung cancer (NSCLC). J Immunother Cancer 2024; 12:e009039. [PMID: 38857914 PMCID: PMC11168162 DOI: 10.1136/jitc-2024-009039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/27/2024] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND Despite the impressive outcomes with immune checkpoint inhibitor (ICI) in non-small cell lung cancer (NSCLC), only a minority of the patients show long-term benefits from ICI. In this study, we used retrospective cohorts of ICI treated patients with NSCLC to discover and validate spatially resolved protein markers associated with resistance to programmed cell death protein-1 (PD-1) axis inhibition. METHODS Pretreatment samples from 56 patients with NSCLC treated with ICI were collected and analyzed in a tissue microarray (TMA) format in including four different tumor regions per patient using the GeoMx platform for spatially informed transcriptomics. 34 patients had assessable tissue with tumor compartment in all 4 TMA spots, 22 with leukocyte compartment and 12 with CD68 compartment. The patients' tissue that was not assessable in fourfold redundancy in each compartment was designated as the validation cohort; cytokeratin (CK) (N=22), leukocytes CD45 (N=31), macrophages, CD68 (N=43). The human whole transcriptome, represented by~18,000 individual genes assessed by oligonucleotide-tagged in situ hybridization, was sequenced on the NovaSeq platform to quantify the RNAs present in each region of interest. RESULTS 54,000 gene variables were generated per case, from them 25,740 were analyzed after removing targets with expression lower than a prespecified frequency. Cox proportional-hazards model analysis was performed for overall and progression-free survival (OS, PFS, respectively). After identifying genes significantly associated with limited survival benefit (HR>1)/progression per spot per patient, we used the intersection of them across the four TMA spots per patient. This resulted in a list of 12 genes in the tumor-cell compartment (RPL13A, GNL3, FAM83A, CYBA, ACSL4, SLC25A6, EPAS1, RPL5, APOL1, HSPD1, RPS4Y1, ADI1). RPL13A, GNL3 in tumor-cell compartment were also significantly associated with OS and PFS, respectively, in the validation cohort (CK: HR, 2.48; p=0.02 and HR, 5.33; p=0.04). In CD45 compartment, secreted frizzled-related protein 2, was associated with OS in the discovery cohort but not in the validation cohort. Similarly, in the CD68 compartment ARHGAP and PNN interacting serine and arginine rich protein were significantly associated with PFS and OS, respectively, in the majority but not all four spots per patient. CONCLUSION This work highlights RPL13A and GNL3 as potential indicative biomarkers of resistance to PD-1 axis blockade that might help to improve precision immunotherapy strategies for lung cancer.
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Affiliation(s)
- Myrto K Moutafi
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Katherine M Bates
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Thazin Nwe Aung
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
- Yale School of Medicine, New Haven, Connecticut, USA
| | - Rolando Garcia Milian
- Bioinformatics Support Program, Cushing/Whitney Medical Library, Yale School of Medicine, New Haven, Connecticut, USA
| | - Vasiliki Xirou
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
- Yale School of Medicine, New Haven, Connecticut, USA
| | - Ioannis A Vathiotis
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Niki Gavrielatou
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
- Yale School of Medicine, New Haven, Connecticut, USA
| | - Athanasios Angelakis
- Epidemiology and Data Science, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
- Department of Methodology, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Kurt A Schalper
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Leonidas Salichos
- Biomedical Data Science Center Director, Center for Cancer Research, Department of Computational Biology at New York Institute of Technology, New York Institute of Technology, Old Westbury, New York, USA
| | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA
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5
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Zhang Y, Lee RY, Tan CW, Guo X, Yim WWY, Lim JC, Wee FY, Yang WU, Kharbanda M, Lee JYJ, Ngo NT, Leow WQ, Loo LH, Lim TK, Sobota RM, Lau MC, Davis MJ, Yeong J. Spatial omics techniques and data analysis for cancer immunotherapy applications. Curr Opin Biotechnol 2024; 87:103111. [PMID: 38520821 DOI: 10.1016/j.copbio.2024.103111] [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: 07/27/2023] [Revised: 03/01/2024] [Accepted: 03/03/2024] [Indexed: 03/25/2024]
Abstract
In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.
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Affiliation(s)
- Yue Zhang
- Duke-NUS Medical School, Singapore 169856, Singapore
| | - Ren Yuan Lee
- Yong Loo Lin School of Medicine, National University of Singapore, 169856 Singapore; Singapore Thong Chai Medical Institution, Singapore 169874, Singapore
| | - Chin Wee Tan
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia
| | - Xue Guo
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Willa W-Y Yim
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Jeffrey Ct Lim
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Felicia Yt Wee
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - W U Yang
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Malvika Kharbanda
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Jia-Ying J Lee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Nye Thane Ngo
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Wei Qiang Leow
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Lit-Hsin Loo
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Tony Kh Lim
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Radoslaw M Sobota
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Mai Chan Lau
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A⁎STAR), Singapore 138648, Singapore
| | - Melissa J Davis
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia; immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia; Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Joe Yeong
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore; Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore.
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6
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Domanskyi S, Srivastava A, Kaster J, Li H, Herlyn M, Rubinstein JC, Chuang JH. Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples. CELL REPORTS METHODS 2024; 4:100759. [PMID: 38626768 PMCID: PMC11133696 DOI: 10.1016/j.crmeth.2024.100759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/23/2024] [Accepted: 03/25/2024] [Indexed: 04/18/2024]
Abstract
We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole-slide image (WSI), optimized for patient-derived xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genome input and stand-alone image analysis. We show the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of H&E imaging features reveal similar patterns arising from the two data modalities.
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Affiliation(s)
- Sergii Domanskyi
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
| | - Anuj Srivastava
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | | | - Haiyin Li
- The Wistar Institute, Philadelphia, PA 19104, USA
| | | | - Jill C Rubinstein
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Hartford HealthCare Cancer Institute at St. Vincent's Medical Center, Bridgeport, CT 06606, USA
| | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; UCONN Health Department of Genetics and Genome Sciences, Farmington, CT 06032, USA.
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7
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Ospina OE, Soupir AC, Manjarres-Betancur R, Gonzalez-Calderon G, Yu X, Fridley BL. Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models. Sci Rep 2024; 14:10967. [PMID: 38744956 PMCID: PMC11094014 DOI: 10.1038/s41598-024-61758-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/09/2024] [Indexed: 05/16/2024] Open
Abstract
Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or "niches" followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).
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Affiliation(s)
- Oscar E Ospina
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Alex C Soupir
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Xiaoqing Yu
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Brooke L Fridley
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
- Biostatistics and Epidemiology Core, Division of Health Services & Outcomes Research, Children's Mercy, Kansas City, MO, USA.
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8
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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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9
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Pang JMB, Byrne DJ, Bergin ART, Caramia F, Loi S, Gorringe KL, Fox SB. Spatial transcriptomics and the anatomical pathologist: Molecular meets morphology. Histopathology 2024; 84:577-586. [PMID: 37991396 DOI: 10.1111/his.15093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/23/2023]
Abstract
In recent years anatomical pathology has been revolutionised by the incorporation of molecular findings into routine diagnostic practice, and in some diseases the presence of specific molecular alterations are now essential for diagnosis. Spatial transcriptomics describes a group of technologies that provide up to transcriptome-wide expression profiling while preserving the spatial origin of the data, with many of these technologies able to provide these data using a single tissue section. Spatial transcriptomics allows expression profiling of highly specific areas within a tissue section potentially to subcellular resolution, and allows correlation of expression data with morphology, tissue type and location relative to other structures. While largely still research laboratory-based, several spatial transcriptomics methods have now achieved compatibility with formalin-fixed paraffin-embedded tissue (FFPE), allowing their use in diagnostic tissue samples, and with further development potentially leading to their incorporation in routine anatomical pathology practice. This mini review provides an overview of spatial transcriptomics methods, with an emphasis on platforms compatible with FFPE tissue, approaches to assess the data and potential applications in anatomical pathology practice.
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Affiliation(s)
- Jia-Min B Pang
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - David J Byrne
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Alice R T Bergin
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Franco Caramia
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Sherene Loi
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Kylie L Gorringe
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Stephen B Fox
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
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10
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Jiang Y, Shen L, Wang B. Non-electrophysiological techniques targeting transient receptor potential (TRP) gene of gastrointestinal tract. Int J Biol Macromol 2024; 262:129551. [PMID: 38367416 DOI: 10.1016/j.ijbiomac.2024.129551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/10/2024] [Accepted: 01/15/2024] [Indexed: 02/19/2024]
Abstract
Transient receptor potential (TRP) channels are cation channels related to a wide range of physical and chemical stimuli, they are expressed all along the gastrointestinal system, and a myriad of diseases are often associated with aberrant expression or mutation of the TRP gene, suggesting that TRPs are promising targets for drug therapy. Therefore, a better understanding of the information of TRPs in health and disease could facilitate the development of effective drugs for the treatment of gastrointestinal diseases like IBD. But there are very few generalizations about the experimental techniques studied in this field. In view of the promise of TRP as a therapeutic target, we discuss experimental methods that can be used for TRPs including their distribution, function and interaction with other proteins, as well as some promising emerging technologies to provide experimental methods for future studies.
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Affiliation(s)
- Yuting Jiang
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Center for Pharmaceutics Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
| | - Lan Shen
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
| | - Bing Wang
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Center for Pharmaceutics Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China.
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11
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Saeed A, Park R, Pathak H, Al-Bzour AN, Dai J, Phadnis M, Al-Rajabi R, Kasi A, Baranda J, Sun W, Williamson S, Chiu YC, Osmanbeyoglu HU, Madan R, Abushukair H, Mulvaney K, Godwin AK, Saeed A. Clinical and biomarker results from a phase II trial of combined cabozantinib and durvalumab in patients with chemotherapy-refractory colorectal cancer (CRC): CAMILLA CRC cohort. Nat Commun 2024; 15:1533. [PMID: 38378868 PMCID: PMC10879200 DOI: 10.1038/s41467-024-45960-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/31/2024] [Indexed: 02/22/2024] Open
Abstract
CAMILLA is a basket trial (NCT03539822) evaluating cabozantinib plus the ICI durvalumab in chemorefractory gastrointestinal cancer. Herein, are the phase II colorectal cohort results. 29 patients were evaluable. 100% had confirmed pMMR/MSS tumors. Primary endpoint was met with ORR of 27.6% (95% CI 12.7-47.2%). Secondary endpoints of 4-month PFS rate was 44.83% (95% CI 26.5-64.3%); and median OS was 9.1 months (95% CI 5.8-20.2). Grade≥3 TRAE occurred in 39%. In post-hoc analysis of patients with RAS wild type tumors, ORR was 50% and median PFS and OS were 6.3 and 21.5 months respectively. Exploratory spatial transcriptomic profiling of pretreatment tumors showed upregulation of VEGF and MET signaling, increased extracellular matrix activity and preexisting anti-tumor immune responses coexisting with immune suppressive features like T cell migration barriers in responders versus non-responders. Cabozantinib plus durvalumab demonstrated anti-tumor activity, manageable toxicity, and have led to the activation of the phase III STELLAR-303 trial.
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Affiliation(s)
- Anwaar Saeed
- Department of Medicine, Division of Hematology & Oncology, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.
- UPMC Hillman Cancer Center, Pittsburgh, PA, USA.
| | - Robin Park
- Division of Hematology and Medical Oncology, Moffitt Cancer Cente, Tampa, FL, USA
| | - Harsh Pathak
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, USA
| | - Ayah Nedal Al-Bzour
- Department of Medicine, Division of Hematology & Oncology, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Junqiang Dai
- Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA
| | - Milind Phadnis
- Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA
| | - Raed Al-Rajabi
- Department of Medicine, Division of Medical Oncology, University of Kansas Medical Center, Kansas City, Ks, USA
- University of Kansas Cancer Center, Kansas City, KS, USA
| | - Anup Kasi
- Department of Medicine, Division of Medical Oncology, University of Kansas Medical Center, Kansas City, Ks, USA
- University of Kansas Cancer Center, Kansas City, KS, USA
| | - Joaquina Baranda
- Department of Medicine, Division of Medical Oncology, University of Kansas Medical Center, Kansas City, Ks, USA
- University of Kansas Cancer Center, Kansas City, KS, USA
| | - Weijing Sun
- Department of Medicine, Division of Medical Oncology, University of Kansas Medical Center, Kansas City, Ks, USA
- University of Kansas Cancer Center, Kansas City, KS, USA
| | - Stephen Williamson
- Department of Medicine, Division of Medical Oncology, University of Kansas Medical Center, Kansas City, Ks, USA
- University of Kansas Cancer Center, Kansas City, KS, USA
| | | | | | - Rashna Madan
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, USA
| | - Hassan Abushukair
- Department of Medicine, Division of Hematology & Oncology, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Kelly Mulvaney
- University of Kansas Cancer Center, Kansas City, KS, USA
| | - Andrew K Godwin
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, USA
- University of Kansas Cancer Center, Kansas City, KS, USA
- Kansas Institute for Precision Medicine, University of Kansas Medical Center, Kansas City, KS, USA
| | - Azhar Saeed
- Department of Pathology and Laboratory Medicine, University of Vermont Medical Center, Burlington, VT, USA
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12
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Liu N, Bhuva DD, Mohamed A, Bokelund M, Kulasinghe A, Tan C, Davis M. standR: spatial transcriptomic analysis for GeoMx DSP data. Nucleic Acids Res 2024; 52:e2. [PMID: 37953397 PMCID: PMC10783521 DOI: 10.1093/nar/gkad1026] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/08/2023] [Accepted: 10/24/2023] [Indexed: 11/14/2023] Open
Abstract
To gain a better understanding of the complexity of gene expression in normal and diseased tissues it is important to account for the spatial context and identity of cells in situ. State-of-the-art spatial profiling technologies, such as the Nanostring GeoMx Digital Spatial Profiler (DSP), now allow quantitative spatially resolved measurement of the transcriptome in tissues. However, the bioinformatics pipelines currently used to analyse GeoMx data often fail to successfully account for the technical variability within the data and the complexity of experimental designs, thus limiting the accuracy and reliability of the subsequent analysis. Carefully designed quality control workflows, that include in-depth experiment-specific investigations into technical variation and appropriate adjustment for such variation can address this issue. Here, we present standR, an R/Bioconductor package that enables an end-to-end analysis of GeoMx DSP data. With four case studies from previously published experiments, we demonstrate how the standR workflow can enhance the statistical power of GeoMx DSP data analysis and how the application of standR enables scientists to develop in-depth insights into the biology of interest.
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Affiliation(s)
- Ning Liu
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Dharmesh D Bhuva
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Ahmed Mohamed
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Micah Bokelund
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia
| | - Arutha Kulasinghe
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia
| | - Chin Wee Tan
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia
| | - Melissa J Davis
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia
- Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
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13
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Smith KD, Prince DK, MacDonald JW, Bammler TK, Akilesh S. Challenges and Opportunities for the Clinical Translation of Spatial Transcriptomics Technologies. GLOMERULAR DISEASES 2024; 4:49-63. [PMID: 38600956 PMCID: PMC11006413 DOI: 10.1159/000538344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 03/10/2024] [Indexed: 04/12/2024]
Abstract
Background The first spatially resolved transcriptomics platforms, GeoMx (Nanostring) and Visium (10x Genomics) were launched in 2019 and were recognized as the method of the year by Nature Methods in 2020. The subsequent refinement and expansion of these and other technologies to increase -plex, work with formalin-fixed paraffin-embedded tissue, and analyze protein in addition to gene expression have only added to their significance and impact on the biomedical sciences. In this perspective, we focus on two platforms for spatial transcriptomics, GeoMx and Visium, and how these platforms have been used to provide novel insight into kidney disease. The choice of platform will depend largely on experimental questions and design. The application of these technologies to clinically sourced biopsies presents the opportunity to identify specific tissue biomarkers that help define disease etiology and more precisely target therapeutic interventions in the future. Summary In this review, we provide a description of the existing and emerging technologies that can be used to capture spatially resolved gene and protein expression data from tissue. These technologies have provided new insight into the spatial heterogeneity of diseases, how reactions to disease are distributed within a tissue, which cells are affected, and molecular pathways that predict disease and response to therapy. Key Message The upcoming years will see intense use of spatial transcriptomics technologies to better define the pathophysiology of kidney diseases and develop novel diagnostic tests to guide personalized treatments for patients.
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Affiliation(s)
- Kelly D. Smith
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | | | - James W. MacDonald
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Theo K. Bammler
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Shreeram Akilesh
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Kidney Research Institute, Seattle, WA, USA
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14
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Qurat-ul-Ain, Frei NF, Khoshiwal AM, Stougie P, Odze R, Camilleri-Broet S, Ferri L, Duits LC, Bergman J, Stachler MD. Feasibility Study Utilizing NanoString's Digital Spatial Profiling (DSP) Technology for Characterizing the Immune Microenvironment in Barrett's Esophagus Formalin-Fixed Paraffin-Embedded Tissues. Cancers (Basel) 2023; 15:5895. [PMID: 38136440 PMCID: PMC10742302 DOI: 10.3390/cancers15245895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/14/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
Characterization of the Barrett's esophagus (BE) microenvironment in patients with a known progression status, to determine how it may influence BE progression to esophageal adenocarcinoma (EAC), has been understudied, hindering both the biological understanding of the progression and the development of novel diagnostics and therapies. This study's aim was to determine if a highly multiplex interrogation of the microenvironment can be performed on endoscopic formalin-fixed, paraffin-embedded (FFPE) samples, utilizing the NanoString GeoMx digital spatial profiling (GeoMx DSP) platform and if it can begin to identify the types of immune cells and pathways that may mediate the progression of BE. We performed a spatial proteomic analysis of 49 proteins expressed in the microenvironment and epithelial cells of FFPE endoscopic biopsies from patients with non-dysplastic BE (NDBE) who later progressed to high-grade dysplasia or EAC (n = 7) or from patients who, after at least 5 years follow-up, did not (n = 8). We then performed an RNA analysis of 1812 cancer-related transcripts on three endoscopic mucosal resections containing regions of BE, dysplasia, and EAC. Profiling with GeoMx DSP showed reasonable quality metrics and detected expected differences between epithelium and stroma. Several proteins were found to have an increased expression within NDBE biopsies from progressors compared to non-progressors, suggesting further studies are warranted.
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Affiliation(s)
- Qurat-ul-Ain
- Department of Pathology, University of California, San Francisco, CA 94143, USA;
| | - Nicola F. Frei
- Amsterdam UMC Locatie AMC, 1105 AZ Amsterdam, The Netherlands
| | | | - Pim Stougie
- Amsterdam UMC Locatie AMC, 1105 AZ Amsterdam, The Netherlands
| | - Robert Odze
- Department of Pathology, School of Medicine, Tufts University, Boston, MA 02111, USA
| | - Sophie Camilleri-Broet
- Division of Thoracic and Upper Gastrointestinal Surgery, Montreal General Hospital, McGill University, Montreal, QC H3G 1A4, Canada
| | - Lorenzo Ferri
- Division of Thoracic and Upper Gastrointestinal Surgery, Montreal General Hospital, McGill University, Montreal, QC H3G 1A4, Canada
| | - Lucas C. Duits
- Amsterdam UMC Locatie AMC, 1105 AZ Amsterdam, The Netherlands
| | - Jacques Bergman
- Amsterdam UMC Locatie AMC, 1105 AZ Amsterdam, The Netherlands
| | - Matthew D. Stachler
- Department of Pathology, University of California, San Francisco, CA 94143, USA;
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15
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Huang Y, Durall RT, Luong NM, Hertzler HJ, Huang J, Gokhale PC, Leeper BA, Persky NS, Root DE, Anekal PV, Montero Llopis PD, David CN, Kutok JL, Raimondi A, Saluja K, Luo J, Zahnow CA, Adane B, Stegmaier K, Hawkins CE, Ponne C, Le Q, Shapiro GI, Lemieux ME, Eagen KP, French CA. EZH2 Cooperates with BRD4-NUT to Drive NUT Carcinoma Growth by Silencing Key Tumor Suppressor Genes. Cancer Res 2023; 83:3956-3973. [PMID: 37747726 PMCID: PMC10843040 DOI: 10.1158/0008-5472.can-23-1475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/31/2023] [Accepted: 09/21/2023] [Indexed: 09/26/2023]
Abstract
NUT carcinoma is an aggressive carcinoma driven by the BRD4-NUT fusion oncoprotein, which activates chromatin to promote expression of progrowth genes. BET bromodomain inhibitors (BETi) are a promising treatment for NUT carcinoma that can impede BRD4-NUT's ability to activate genes, but the efficacy of BETi as monotherapy is limited. Here, we demonstrated that enhancer of zeste homolog 2 (EZH2), which silences genes through establishment of repressive chromatin, is a dependency in NUT carcinoma. Inhibition of EZH2 with the clinical compound tazemetostat potently blocked growth of NUT carcinoma cells. Epigenetic and transcriptomic analysis revealed that tazemetostat reversed the EZH2-specific H3K27me3 silencing mark and restored expression of multiple tumor suppressor genes while having no effect on key oncogenic BRD4-NUT-regulated genes. Indeed, H3K27me3 and H3K27ac domains were found to be mutually exclusive in NUT carcinoma cells. CDKN2A was identified as the only gene among all tazemetostat-derepressed genes to confer resistance to tazemetostat in a CRISPR-Cas9 screen. Combined inhibition of EZH2 and BET synergized to downregulate cell proliferation genes, resulting in more pronounced growth arrest and differentiation than either inhibitor alone. In preclinical models, combined tazemetostat and BETi synergistically blocked tumor growth and prolonged survival of NUT carcinoma-xenografted mice, with complete remission without relapse in one cohort. Identification of EZH2 as a dependency in NUT carcinoma substantiates the reliance of NUT carcinoma tumor cells on epigenetic dysregulation of functionally opposite, yet highly complementary, chromatin regulatory pathways to maintain NUT carcinoma growth. SIGNIFICANCE Repression of tumor suppressor genes, including CDKN2A, by EZH2 provides a mechanistic rationale for combining EZH2 and BET inhibitors for the clinical treatment of NUT carcinoma. See related commentary by Kazansky and Kentsis, p. 3827.
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Affiliation(s)
- Yeying Huang
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - R. Taylor Durall
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Nhi M. Luong
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Hans J. Hertzler
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Julianna Huang
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Prafulla C. Gokhale
- Experimental Therapeutics Core and Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Brittaney A. Leeper
- Experimental Therapeutics Core and Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - David E. Root
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Praju V. Anekal
- MicRoN, Department of Microbiology, Harvard Medical School, Boston, MA, USA
| | | | | | | | | | - Karan Saluja
- Department of Pathology and Laboratory Medicine, University of Texas Health Science Center at Houston, TX, USA
| | - Jia Luo
- Department of Medical Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Cynthia A. Zahnow
- Department of Oncology, The Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Biniam Adane
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kimberly Stegmaier
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Division of Pediatric Hematology/Oncology, Boston Children’s Hospital, Boston, MA, USA
| | - Catherine E. Hawkins
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Christopher Ponne
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Quan Le
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Geoffrey I. Shapiro
- Department of Medical Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | | | - Kyle P. Eagen
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
- Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, USA
- Stem Cells and Regenerative Medicine Center, Baylor College of Medicine, Houston, TX, USA
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Christopher A. French
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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16
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Dunne J, Griner J, Romeo M, Macdonald J, Krieg C, Lim M, Yagnik G, Rothschild KJ, Drake RR, Mehta AS, Angel PM. Evaluation of antibody-based single cell type imaging techniques coupled to multiplexed imaging of N-glycans and collagen peptides by matrix-assisted laser desorption/ionization mass spectrometry imaging. Anal Bioanal Chem 2023; 415:7011-7024. [PMID: 37843548 PMCID: PMC10632234 DOI: 10.1007/s00216-023-04983-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 09/07/2023] [Accepted: 09/18/2023] [Indexed: 10/17/2023]
Abstract
The integration of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) with single cell spatial omics methods allows for a comprehensive investigation of single cell spatial information and matrisomal N-glycan and extracellular matrix protein imaging. Here, the performance of the antibody-directed single cell workflows coupled with MALDI-MSI are evaluated. Miralys™ photocleavable mass-tagged antibody probes (MALDI-IHC, AmberGen, Inc.), GeoMx DSP® (NanoString, Inc.), and Imaging Mass Cytometry (IMC, Standard BioTools Inc.) were used in series with MALDI-MSI of N-glycans and extracellular matrix peptides on formalin-fixed paraffin-embedded tissues. Single cell omics protocols were performed before and after MALDI-MSI. The data suggests that for each modality combination, there is an optimal order for performing both techniques on the same tissue section. An overall conclusion is that MALDI-MSI studies may be completed on the same tissue section as used for antibody-directed single cell modalities. This work increases access to combined cellular and extracellular information within the tissue microenvironment to enhance research on the pathological origins of disease.
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Affiliation(s)
- Jaclyn Dunne
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Medical University of South Carolina, 173 Ashley Avenue BSB 358, Charleston, SC, 29425, USA
| | - Jake Griner
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Medical University of South Carolina, 173 Ashley Avenue BSB 358, Charleston, SC, 29425, USA
| | - Martin Romeo
- Translational Science Laboratory, Hollings Cancer Center, Charleston, SC, 29425, USA
| | - Jade Macdonald
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Medical University of South Carolina, 173 Ashley Avenue BSB 358, Charleston, SC, 29425, USA
| | - Carsten Krieg
- Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Mark Lim
- AmberGen, Inc, 44 Manning Road, Billerica, MA, 01821, USA
| | - Gargey Yagnik
- AmberGen, Inc, 44 Manning Road, Billerica, MA, 01821, USA
| | - Kenneth J Rothschild
- AmberGen, Inc, 44 Manning Road, Billerica, MA, 01821, USA
- Department of Physics and Photonics Center, Boston University, Boston, MA, 02215, USA
| | - Richard R Drake
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Medical University of South Carolina, 173 Ashley Avenue BSB 358, Charleston, SC, 29425, USA
| | - Anand S Mehta
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Medical University of South Carolina, 173 Ashley Avenue BSB 358, Charleston, SC, 29425, USA
| | - Peggi M Angel
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Medical University of South Carolina, 173 Ashley Avenue BSB 358, Charleston, SC, 29425, USA.
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17
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Behera RN, Bisht VS, Giri K, Ambatipudi K. Realm of proteomics in breast cancer management and drug repurposing to alleviate intricacies of treatment. Proteomics Clin Appl 2023; 17:e2300016. [PMID: 37259687 DOI: 10.1002/prca.202300016] [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: 02/16/2023] [Revised: 05/10/2023] [Accepted: 05/15/2023] [Indexed: 06/02/2023]
Abstract
Breast cancer, a multi-networking heterogeneous disease, has emerged as a serious impediment to progress in clinical oncology. Although technological advancements and emerging cancer research studies have mitigated breast cancer lethality, a precision cancer-oriented solution has not been achieved. Thus, this review will persuade the acquiescence of proteomics-based diagnostic and therapeutic options in breast cancer management. Recently, the evidence of breast cancer health surveillance through imaging proteomics, single-cell proteomics, interactomics, and post-translational modification (PTM) tracking, to construct proteome maps and proteotyping for stage-specific and sample-specific cancer subtyping have outperformed conventional ways of dealing with breast cancer by increasing diagnostic efficiency, prognostic value, and predictive response. Additionally, the paradigm shift in applied proteomics for designing a chemotherapy regimen to identify novel drug targets with minor adverse effects has been elaborated. Finally, the potential of proteomics in alleviating the occurrence of chemoresistance and enhancing reprofiled drugs' effectiveness to combat therapeutic obstacles has been discussed. Owing to the enormous potential of proteomics techniques, the clinical recognition of proteomics in breast cancer management can be achievable and therapeutic intricacies can be surmountable.
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Affiliation(s)
- Rama N Behera
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
| | - Vinod S Bisht
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
| | - Kuldeep Giri
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
| | - Kiran Ambatipudi
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
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18
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Hussain MS, Majami AA, Ali H, Gupta G, Almalki WH, Alzarea SI, Kazmi I, Syed RU, Khalifa NE, Bin Break MK, Khan R, Altwaijry N, Sharma R. The complex role of MEG3: An emerging long non-coding RNA in breast cancer. Pathol Res Pract 2023; 251:154850. [PMID: 37839358 DOI: 10.1016/j.prp.2023.154850] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/24/2023] [Accepted: 10/02/2023] [Indexed: 10/17/2023]
Abstract
MEG3, a significant long non-coding RNA (lncRNA), substantially functions in diverse biological processes, particularly breast cancer (BC) development. Within the imprinting DLK-MEG3 region on human chromosomal region 14q32.3, MEG3 spans 35 kb and encompasses ten exons. It exerts regulatory effects through intricate interactions with miRNAs, proteins, and epigenetic modifications. MEG3's multifaceted function in BC is evident in gene expression modulation, osteogenic tissue differentiation, and involvement in bone-related conditions. Its role as a tumor suppressor is highlighted by its influence on miR-182 and miRNA-29 expression in BC. Additionally, MEG3 is implicated in acute myocardial infarction and endothelial cell function, emphasising cell-specific regulatory mechanisms. MEG3's impact on gene activity encompasses transcriptional and post-translational adjustments, including DNA methylation, histone modifications, and interactions with transcription factors. MEG3 dysregulation is linked to unfavourable outcomes and drug resistance. Notably, higher MEG3 expression is associated with enhanced survival in BC patients. Overcoming challenges such as unravelling context-specific interactions, understanding epigenetic control, and translating findings into clinical applications is imperative. Prospective endeavours involve elucidating underlying mechanisms, exploring epigenetic alterations, and advancing MEG3-based diagnostic and therapeutic approaches. A comprehensive investigation into broader signaling networks and rigorous clinical trials are pivotal. Rigorous validation through functional and molecular analyses will shed light on MEG3's intricate contribution to BC progression.
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Affiliation(s)
- Md Sadique Hussain
- School of Pharmaceutical Sciences, Jaipur National University, Jagatpura, 302017, Jaipur, Rajasthan, India
| | - Abdullah A Majami
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Haider Ali
- Department of Pharmacology, Kyrgyz State Medical College, Bishkek, Kyrgyzstan.
| | - Gaurav Gupta
- Centre for Global Health Research, Saveetha Medical College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, India; School of Pharmacy, Graphic Era Hill University, Dehradun 248007, India; School of Pharmacy, Suresh Gyan Vihar University, Jagatpura, 302017, Jaipur, India
| | - Waleed Hassan Almalki
- Department of Pharmacology, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Sami I Alzarea
- Department of Pharmacology, College of Pharmacy, Jouf University, Sakaka, Al-Jouf, Saudi Arabia
| | - Imran Kazmi
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Rahamat Unissa Syed
- Department of Pharmaceutics, College of Pharmacy, University of Hail, Hail 81442, Saudi Arabia; Medical and Diagnostic Research Centre, University of Hail, Hail 55473, Saudi Arabia
| | - Nasrin E Khalifa
- Department of Pharmaceutics, College of Pharmacy, University of Hail, Hail 81442, Saudi Arabia; Medical and Diagnostic Research Centre, University of Hail, Hail 55473, Saudi Arabia; Department of Pharmaceutics, Faculty of Pharmacy, University of Khartoum, 11115, Sudan
| | - Mohammed Khaled Bin Break
- Medical and Diagnostic Research Centre, University of Hail, Hail 55473, Saudi Arabia; Department of Pharmaceutical Chemistry, College of Pharmacy, University of Hail, Hail 81442, Saudi Arabia
| | - Ruqaiyah Khan
- Department of Basic Health Sciences, Deanship of Preparatory Year for the Health Colleges, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Najla Altwaijry
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint, Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Rahul Sharma
- School of Pharmaceutical Sciences, Jaipur National University, Jagatpura, 302017, Jaipur, Rajasthan, India
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19
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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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20
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Schneider F, Kaczorowski A, Jurcic C, Kirchner M, Schwab C, Schütz V, Görtz M, Zschäbitz S, Jäger D, Stenzinger A, Hohenfellner M, Duensing S, Duensing A. Digital Spatial Profiling Identifies the Tumor Periphery as a Highly Active Biological Niche in Clear Cell Renal Cell Carcinoma. Cancers (Basel) 2023; 15:5050. [PMID: 37894418 PMCID: PMC10605891 DOI: 10.3390/cancers15205050] [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: 06/21/2023] [Revised: 10/03/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is characterized by a high degree of intratumoral heterogeneity (ITH). Besides genomic ITH, there is considerable functional ITH, which encompasses spatial niches with distinct proliferative and signaling activities. The full extent of functional spatial heterogeneity in ccRCC is incompletely understood. In the present study, a total of 17 ccRCC tissue specimens from different sites (primary tumor, n = 11; local recurrence, n = 1; distant metastasis, n = 5) were analyzed using digital spatial profiling (DSP) of protein expression. A total of 128 regions of interest from the tumor periphery and tumor center were analyzed for the expression of 46 proteins, comprising three major signaling pathways as well as immune cell markers. Results were correlated to clinico-pathological variables. The differential expression of granzyme B was validated using conventional immunohistochemistry and was correlated to the cancer-specific patient survival. We found that a total of 37 proteins were differentially expressed between the tumor periphery and tumor center. Thirty-five of the proteins were upregulated in the tumor periphery compared to the center. These included proteins involved in cell proliferation, MAPK and PI3K/AKT signaling, apoptosis regulation, epithelial-to-mesenchymal transition, as well as immune cell markers. Among the most significantly upregulated proteins in the tumor periphery was granzyme B. Granzyme B upregulation in the tumor periphery correlated with a significantly reduced cancer-specific patient survival. In conclusion, this study highlights the unique cellular contexture of the tumor periphery in ccRCC. The correlation between granzyme B upregulation in the tumor periphery and patient survival suggests local selection pressure for aggressive tumor growth and disease progression. Our results underscore the potential of spatial biology for biomarker discovery in ccRCC and cancer in general.
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Affiliation(s)
- Felix Schneider
- Molecular Urooncology, Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120 Heidelberg, Germany
| | - Adam Kaczorowski
- Molecular Urooncology, Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120 Heidelberg, Germany
| | - Christina Jurcic
- Molecular Urooncology, Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120 Heidelberg, Germany
| | - Martina Kirchner
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, D-69120 Heidelberg, Germany
| | - Constantin Schwab
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, D-69120 Heidelberg, Germany
| | - Viktoria Schütz
- Department of Urology, University Hospital Heidelberg, and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 420, D-69120 Heidelberg, Germany
| | - Magdalena Görtz
- Department of Urology, University Hospital Heidelberg, and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 420, D-69120 Heidelberg, Germany
| | - Stefanie Zschäbitz
- Department of Medical Oncology, University Hospital Heidelberg, and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, D-69120 Heidelberg, Germany
| | - Dirk Jäger
- Department of Medical Oncology, University Hospital Heidelberg, and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, D-69120 Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, D-69120 Heidelberg, Germany
| | - Markus Hohenfellner
- Department of Urology, University Hospital Heidelberg, and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 420, D-69120 Heidelberg, Germany
| | - Stefan Duensing
- Molecular Urooncology, Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120 Heidelberg, Germany
- Department of Urology, University Hospital Heidelberg, and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 420, D-69120 Heidelberg, Germany
| | - Anette Duensing
- Department of Urology, University Hospital Heidelberg, and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 420, D-69120 Heidelberg, Germany
- Cancer Therapeutics Program, UPMC Hillman Cancer Center, 5117 Centre Avenue, Pittsburgh, PA 15213, USA
- Department of Pathology, University of Pittsburgh School of Medicine, 200 Lothrop Street, Pittsburgh, PA 15213, USA
- Precision Oncology of Urological Malignancies, Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120 Heidelberg, Germany
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21
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Zubair H, Azim S, Maluf DG, Mas VR, Martins PN. Contribution of Proteomics in Transplantation: Identification of Injury and Rejection Markers. Transplantation 2023; 107:2143-2154. [PMID: 36814094 DOI: 10.1097/tp.0000000000004542] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Solid organ transplantation saves thousands of lives suffering from end-stage diseases. Although early transplants experienced acute organ injury, medical breakthroughs, such as tissue typing, and use of immunosuppressive agents have considerably improved graft survival. However, the overall incidence of allograft injury and chronic rejection remains high. Often the clinical manifestations of organ injury or rejection are nonspecific and late. Current requirement for successful organ transplantation is the identification of reliable, accurate, disease-specific, noninvasive methods for the early diagnosis of graft injury or rejection. Development of noninvasive techniques is important to allow routine follow-ups without the discomfort and risks associated with a graft biopsy. Multiple biofluids have been successfully tested for the presence of potential proteomic biomarkers; these include serum, plasma, urine, and whole blood. Kidney transplant research has provided significant evidence to the potential of proteomics-based biomarkers for acute and chronic kidney rejection, delayed graft function, early detection of declining allograft health. Multiple proteins have been implicated as biomarkers; however, recent observations implicate the use of similar canonical pathways and biofunctions associated with graft injury/rejection with altered proteins as potential biomarkers. Unfortunately, the current biomarker studies lack high sensitivity and specificity, adding to the complexity of their utility in the clinical space. In this review, we first describe the high-throughput proteomics technologies and then discuss the outcomes of proteomics profiling studies in the transplantation of several organs. Existing literature provides hope that novel biomarkers will emerge from ongoing efforts and guide physicians in delivering specific therapies to prolong graft survival.
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Affiliation(s)
- Haseeb Zubair
- Surgical Sciences Division, Department of Surgery, School of Medicine, University of Maryland, Baltimore, MD
| | - Shafquat Azim
- Surgical Sciences Division, Department of Surgery, School of Medicine, University of Maryland, Baltimore, MD
| | - Daniel G Maluf
- Program in Transplantation, University of Maryland Medical System, Baltimore, MD
| | - Valeria R Mas
- Surgical Sciences Division, Department of Surgery, School of Medicine, University of Maryland, Baltimore, MD
| | - Paulo N Martins
- Division of Organ Transplantation, Department of Surgery, University of Massachusetts, UMass Memorial Hospital, University of Massachusetts, Worcester, MA
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22
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Ottaiano A, Ianniello M, Santorsola M, Ruggiero R, Sirica R, Sabbatino F, Perri F, Cascella M, Di Marzo M, Berretta M, Caraglia M, Nasti G, Savarese G. From Chaos to Opportunity: Decoding Cancer Heterogeneity for Enhanced Treatment Strategies. BIOLOGY 2023; 12:1183. [PMID: 37759584 PMCID: PMC10525472 DOI: 10.3390/biology12091183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 08/24/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023]
Abstract
Cancer manifests as a multifaceted disease, characterized by aberrant cellular proliferation, survival, migration, and invasion. Tumors exhibit variances across diverse dimensions, encompassing genetic, epigenetic, and transcriptional realms. This heterogeneity poses significant challenges in prognosis and treatment, affording tumors advantages through an increased propensity to accumulate mutations linked to immune system evasion and drug resistance. In this review, we offer insights into tumor heterogeneity as a crucial characteristic of cancer, exploring the difficulties associated with measuring and quantifying such heterogeneity from clinical and biological perspectives. By emphasizing the critical nature of understanding tumor heterogeneity, this work contributes to raising awareness about the importance of developing effective cancer therapies that target this distinct and elusive trait of cancer.
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Affiliation(s)
- Alessandro Ottaiano
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Monica Ianniello
- AMES, Centro Polidiagnostico Strumentale srl, Via Padre Carmine Fico 24, 80013 Casalnuovo Di Napoli, Italy; (M.I.); (R.R.); (R.S.); (G.S.)
| | - Mariachiara Santorsola
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Raffaella Ruggiero
- AMES, Centro Polidiagnostico Strumentale srl, Via Padre Carmine Fico 24, 80013 Casalnuovo Di Napoli, Italy; (M.I.); (R.R.); (R.S.); (G.S.)
| | - Roberto Sirica
- AMES, Centro Polidiagnostico Strumentale srl, Via Padre Carmine Fico 24, 80013 Casalnuovo Di Napoli, Italy; (M.I.); (R.R.); (R.S.); (G.S.)
| | - Francesco Sabbatino
- Oncology Unit, Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy;
| | - Francesco Perri
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Marco Cascella
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Massimiliano Di Marzo
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Massimiliano Berretta
- Department of Clinical and Experimental Medicine, University of Messina, 98122 Messina, Italy;
| | - Michele Caraglia
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, Via Luigi De Crecchio 7, 80138 Naples, Italy;
| | - Guglielmo Nasti
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Giovanni Savarese
- AMES, Centro Polidiagnostico Strumentale srl, Via Padre Carmine Fico 24, 80013 Casalnuovo Di Napoli, Italy; (M.I.); (R.R.); (R.S.); (G.S.)
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23
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Huang Y, Durall RT, Luong NM, Hertzler HJ, Huang J, Gokhale PC, Leeper BA, Persky NS, Root DE, Anekal PV, Montero Llopis PD, David CN, Kutok JL, Raimondi A, Saluja K, Luo J, Zahnow CA, Adane B, Stegmaier K, Hawkins CE, Ponne C, Le Q, Shapiro GI, Lemieux ME, Eagen KP, French CA. EZH2 synergizes with BRD4-NUT to drive NUT carcinoma growth through silencing of key tumor suppressor genes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.15.553204. [PMID: 37645799 PMCID: PMC10461970 DOI: 10.1101/2023.08.15.553204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
NUT carcinoma (NC) is an aggressive carcinoma driven by the BRD4-NUT fusion oncoprotein, which activates chromatin to promote expression of pro-growth genes. BET bromodomain inhibitors (BETi) impede BRD4-NUT's ability to activate genes and are thus a promising treatment but limited as monotherapy. The role of gene repression in NC is unknown. Here, we demonstrate that EZH2, which silences genes through establishment of repressive chromatin, is a dependency in NC. Inhibition of EZH2 with the clinical compound tazemetostat (taz) potently blocked growth of NC cells. Epigenetic and transcriptomic analysis revealed that taz reversed the EZH2-specific H3K27me3 silencing mark, and restored expression of multiple tumor suppressor genes while having no effect on key oncogenic BRD4- NUT-regulated genes. CDKN2A was identified as the only gene amongst all taz-derepressed genes to confer resistance to taz in a CRISPR-Cas9 screen. Combined EZH2 inhibition and BET inhibition synergized to downregulate cell proliferation genes resulting in more pronounced growth arrest and differentiation than either inhibitor alone. In pre-clinical models, combined taz and BETi synergistically blocked growth and prolonged survival of NC-xenografted mice, with all mice cured in one cohort. STATEMENT OF SIGNIFICANCE Identification of EZH2 as a dependency in NC substantiates the reliance of NC tumor cells on epigenetic dysregulation of functionally opposite, yet highly complementary chromatin regulatory pathways to maintain NC growth. In particular, repression of CDKN2A expression by EZH2 provides a mechanistic rationale for combining EZH2i with BETi for the clinical treatment of NC.
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Affiliation(s)
- Yeying Huang
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - R. Taylor Durall
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Nhi M. Luong
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Hans J. Hertzler
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Julianna Huang
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Prafulla C. Gokhale
- Experimental Therapeutics Core and Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Brittaney A. Leeper
- Experimental Therapeutics Core and Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - David E. Root
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Praju V. Anekal
- MicRoN, Department of Microbiology, Harvard Medical School, Boston, MA, USA
| | | | | | | | | | - Karan Saluja
- Department of Pathology and Laboratory Medicine, University of Texas Health Science Center at Houston, TX, USA
| | - Jia Luo
- Department of Medical Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Cynthia A. Zahnow
- Department of Oncology, The Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Biniam Adane
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kimberly Stegmaier
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Division of Pediatric Hematology/Oncology, Boston Children’s Hospital, Boston, MA, USA
| | - Catherine E. Hawkins
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Christopher Ponne
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Quan Le
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Geoffrey I. Shapiro
- Department of Medical Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | | | - Kyle P. Eagen
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Christopher A. French
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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24
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Domanskyi S, Srivastava A, Kaster J, Li H, Herlyn M, Rubinstein JC, Chuang JH. Nextflow Pipeline for Visium and H&E Data from Patient-Derived Xenograft Samples. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.27.550727. [PMID: 37546876 PMCID: PMC10402090 DOI: 10.1101/2023.07.27.550727] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Highlights We have developed an automated data processing pipeline to quantify mouse and human data from patient-derived xenograft samples assayed by Visium spatial transcriptomics with matched hematoxylin and eosin (H&E) stained image. We enable deconvolution of reads with Xenome, quantification of spatial gene expression from host and graft species with Space Ranger, extraction of B-allele frequencies, and splicing quantification with Velocyto. In the H&E image processing sub-workflow, we generate morphometric and deep learning-derived feature quantifications complementary to the Visium spots, enabling multi-modal H&E/expression comparisons. We have wrapped the pipeline into Nextflow DSL2 in a scalable, portable, and easy-to-use framework. Summary We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole slide image (WSI), optimized for Patient-Derived Xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genomes input and stand-alone image analysis. We showed the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of imaging features of H&E data reveal similar patterns arising from the two data modalities.
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25
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Harms PW, Frankel TL, Moutafi M, Rao A, Rimm DL, Taube JM, Thomas D, Chan MP, Pantanowitz L. Multiplex Immunohistochemistry and Immunofluorescence: A Practical Update for Pathologists. Mod Pathol 2023; 36:100197. [PMID: 37105494 DOI: 10.1016/j.modpat.2023.100197] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 03/07/2023] [Accepted: 04/19/2023] [Indexed: 04/29/2023]
Abstract
Our understanding of the biology and management of human disease has undergone a remarkable evolution in recent decades. Improved understanding of the roles of complex immune populations in the tumor microenvironment has advanced our knowledge of antitumor immunity, and immunotherapy has radically improved outcomes for many advanced cancers. Digital pathology has unlocked new possibilities for the assessment and discovery of the tumor microenvironment, such as quantitative and spatial image analysis. Despite these advances, tissue-based evaluations for diagnosis and prognosis continue to rely on traditional practices, such as hematoxylin and eosin staining, supplemented by the assessment of single biomarkers largely using chromogenic immunohistochemistry (IHC). Such approaches are poorly suited to complex quantitative analyses and the simultaneous evaluation of multiple biomarkers. Thus, multiplex staining techniques have significant potential to improve diagnostic practice and immuno-oncology research. The different approaches to achieve multiplexed IHC and immunofluorescence are described in this study. Alternatives to multiplex immunofluorescence/IHC include epitope-based tissue mass spectrometry and digital spatial profiling (DSP), which require specialized platforms not available to most clinical laboratories. Virtual multiplexing, which involves digitally coregistering singleplex IHC stains performed on serial sections, is another alternative to multiplex staining. Regardless of the approach, analysis of multiplexed stains sequentially or simultaneously will benefit from standardized protocols and digital pathology workflows. Although this is a complex and rapidly advancing field, multiplex staining is now technically feasible for most clinical laboratories and may soon be leveraged for routine diagnostic use. This review provides an update on the current state of the art for tissue multiplexing, including the capabilities and limitations of different techniques, with an emphasis on potential relevance to clinical diagnostic practice.
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Affiliation(s)
- Paul W Harms
- Department of Pathology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Dermatology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Rogel Cancer Center, Michigan Medicine/University of Michigan, Ann Arbor, Michigan.
| | - Timothy L Frankel
- Rogel Cancer Center, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Surgery, Michigan Medicine/University of Michigan, Ann Arbor, Michigan
| | - Myrto Moutafi
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Radiation Oncology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Janis M Taube
- Department of Oncology, Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, and Johns Hopkins Bloomberg-Kimmel Institute for Cancer Immunotherapy, Baltimore, Maryland; Department of Dermatology, Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, and Johns Hopkins Bloomberg-Kimmel Institute for Cancer Immunotherapy, Baltimore, Maryland; Department of Pathology, Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, and Johns Hopkins Bloomberg-Kimmel Institute for Cancer Immunotherapy, Baltimore, Maryland
| | - Dafydd Thomas
- Department of Pathology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Rogel Cancer Center, Michigan Medicine/University of Michigan, Ann Arbor, Michigan
| | - May P Chan
- Department of Pathology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Dermatology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan
| | - Liron Pantanowitz
- Department of Pathology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan
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26
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Valberg SJ, Williams ZJ, Henry ML, Finno CJ. Cerebellar axonopathy in Shivers horses identified by spatial transcriptomic and proteomic analyses. J Vet Intern Med 2023; 37:1568-1579. [PMID: 37288990 PMCID: PMC10365050 DOI: 10.1111/jvim.16784] [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: 03/31/2023] [Accepted: 05/21/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND Shivers in horses is characterized by abnormal hindlimb movement when walking backward and is proposed to be caused by a Purkinje cell (PC) axonopathy based on histopathology. OBJECTIVES Define region-specific differences in gene expression within the lateral cerebellar hemisphere and compare cerebellar protein expression between Shivers horses and controls. ANIMALS Case-control study of 5 Shivers and 4 control geldings ≥16.2 hands in height. METHODS Using spatial transcriptomics, gene expression was compared between Shivers and control horses in PC soma and lateral cerebellar hemisphere white matter, consisting primarily of axons. Tandem-mass-tag (TMT-11) proteomic analysis was performed on lateral cerebellar hemisphere homogenates. RESULTS Differences in gene expression between Shivers and control horses were evident in principal component analysis of axon-containing white matter but not PC soma. In white matter, there were 455/1846 differentially expressed genes (DEG; 350 ↓DEG, 105 ↑DEG) between Shivers and controls, with significant gene set enrichment of the Toll-Like Receptor 4 (TLR4) cascade, highlighting neuroinflammation. There were 50/936 differentially expressed proteins (DEP). The 27 ↓DEP highlighted loss of axonal proteins including intermediate filaments (5), myelin (3), cytoskeleton (2), neurite outgrowth (2), and Na/K ATPase (1). The 23 ↑DEP were involved in the extracellular matrix (7), cytoskeleton (7), redox balance (2), neurite outgrowth (1), signal transduction (1), and others. CONCLUSION AND CLINICAL IMPORTANCE Our findings support axonal degeneration as a characteristic feature of Shivers. Combined with histopathology, these findings are consistent with the known distinctive response of PC to injury where axonal changes occur without a substantial impact on PC soma.
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Affiliation(s)
- Stephanie J. Valberg
- Department of Large Animal Clinical Sciences, College of Veterinary MedicineMichigan State UniversityEast LansingMichiganUSA
| | - Zoë J. Williams
- C. Wayne McIlwraith Translational Medicine, College of Veterinary Medicine and Biomedical SciencesColorado State UniversityFort CollinsColoradoUSA
| | - Marisa L. Henry
- Department of Large Animal Clinical Sciences, College of Veterinary MedicineMichigan State UniversityEast LansingMichiganUSA
| | - Carrie J. Finno
- Department of Population Health and Reproduction, School of Veterinary MedicineUniversity of California‐DavisDavisCaliforniaUSA
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27
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Park H, Jo SH, Lee RH, Macks CP, Ku T, Park J, Lee CW, Hur JK, Sohn CH. Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206939. [PMID: 37026425 PMCID: PMC10238226 DOI: 10.1002/advs.202206939] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 03/10/2023] [Indexed: 06/04/2023]
Abstract
Spatial transcriptomics is a newly emerging field that enables high-throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to "in situ" biology, spatial transcriptomics can provide transcriptome-scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi-omics and their expanding roles in biomedical applications are emphasized.
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Affiliation(s)
- Han‐Eol Park
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
- School of Biological SciencesSeoul National UniversitySeoul08826Republic of Korea
| | - Song Hyun Jo
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Rosalind H. Lee
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Christian P. Macks
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
| | - Taeyun Ku
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Jihwan Park
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Chung Whan Lee
- Department of ChemistryGachon UniversitySeongnamGyeonggi‐do13120Republic of Korea
| | - Junho K. Hur
- Department of GeneticsCollege of MedicineHanyang UniversitySeoul04763Republic of Korea
| | - Chang Ho Sohn
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
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28
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Kumar N, Gann PH, McGregor SM, Sethi A. Quantification of subtype purity in Luminal A breast cancer predicts clinical characteristics and survival. Breast Cancer Res Treat 2023:10.1007/s10549-023-06961-9. [PMID: 37209182 DOI: 10.1007/s10549-023-06961-9] [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: 01/07/2023] [Accepted: 04/26/2023] [Indexed: 05/22/2023]
Abstract
PURPOSE PAM50 profiling assigns each breast cancer to a single intrinsic subtype based on a bulk tissue sample. However, individual cancers may show evidence of admixture with an alternate subtype that could affect prognosis and treatment response. We developed a method to model subtype admixture using whole transcriptome data and associated it with tumor, molecular, and survival characteristics for Luminal A (LumA) samples. METHODS We combined TCGA and METABRIC cohorts and obtained transcriptome, molecular, and clinical data, which yielded 11,379 gene transcripts in common and 1,178 cases assigned to LumA. We used semi-supervised non-negative matrix factorization (ssNMF) to compute the subtype admixture proportions of the four major subtypes-pLumA, pLumB, pHER2, and pBasal-for each case and measured associations with tumor characteristics, molecular features, and survival. RESULTS Luminal A cases in the lowest versus highest quartile for pLumA transcriptomic proportion had a 27% higher prevalence of stage > 1, nearly a threefold higher prevalence of TP53 mutation, and a hazard ratio of 2.08 for overall mortality. We found positive associations between pHER2 and HER2 positivity by IHC or FISH; between pLumB and PR negativity; and between pBasal and younger age, node positivity, TP53 mutation, and EGFR expression. Predominant basal admixture, in contrast to predominant LumB or HER2 admixture, was not associated with shorter survival. CONCLUSION Bulk sampling for genomic analyses provides an opportunity to expose intratumor heterogeneity, as reflected by subtype admixture. Our results elucidate the striking extent of diversity among LumA cancers and suggest that determining the extent and type of admixture holds promise for refining individualized therapy. LumA cancers with a high degree of basal admixture appear to have distinct biological characteristics that warrant further study.
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Affiliation(s)
- Neeraj Kumar
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Peter H Gann
- Department of Pathology, College of Medicine, University of Illinois Cancer Center, University of Illinois at Chicago, Chicago, IL, USA.
| | - Stephanie M McGregor
- Department of Pathology and Laboratory Medicine, University of Wisconsin Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Amit Sethi
- Department of Pathology, College of Medicine, University of Illinois Cancer Center, University of Illinois at Chicago, Chicago, IL, USA
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
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Bonnett SA, Rosenbloom AB, Ong GT, Conner M, Rininger AB, Newhouse D, New F, Phan CQ, Ilcisin S, Sato H, Lyssand JS, Geiss G, Beechem JM. Ultra High-plex Spatial Proteogenomic Investigation of Giant Cell Glioblastoma Multiforme Immune Infiltrates Reveals Distinct Protein and RNA Expression Profiles. CANCER RESEARCH COMMUNICATIONS 2023; 3:763-779. [PMID: 37377888 PMCID: PMC10155752 DOI: 10.1158/2767-9764.crc-22-0396] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 01/20/2023] [Accepted: 04/04/2023] [Indexed: 06/29/2023]
Abstract
A deeper understanding of complex biological processes, including tumor development and immune response, requires ultra high-plex, spatial interrogation of multiple "omes". Here we present the development and implementation of a novel spatial proteogenomic (SPG) assay on the GeoMx Digital Spatial Profiler platform with next-generation sequencing readout that enables ultra high-plex digital quantitation of proteins (>100-plex) and RNA (whole transcriptome, >18,000-plex) from a single formalin-fixed paraffin-embedded (FFPE) sample. This study highlighted the high concordance, R > 0.85 and <15% change in sensitivity between the SPG assay and the single-analyte assays on various cell lines and tissues from human and mouse. Furthermore, we demonstrate that the SPG assay was reproducible across multiple users. When used in conjunction with advanced cellular neighborhood segmentation, distinct immune or tumor RNA and protein targets were spatially resolved within individual cell subpopulations in human colorectal cancer and non-small cell lung cancer. We used the SPG assay to interrogate 23 different glioblastoma multiforme (GBM) samples across four pathologies. The study revealed distinct clustering of both RNA and protein based on pathology and anatomic location. The in-depth investigation of giant cell glioblastoma multiforme (gcGBM) revealed distinct protein and RNA expression profiles compared with that of the more common GBM. More importantly, the use of spatial proteogenomics allowed simultaneous interrogation of critical protein posttranslational modifications alongside whole transcriptomic profiles within the same distinct cellular neighborhoods. Significance We describe ultra high-plex spatial proteogenomics; profiling whole transcriptome and high-plex proteomics on a single FFPE tissue section with spatial resolution. Investigation of gcGBM versus GBM revealed distinct protein and RNA expression profiles.
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Affiliation(s)
| | | | | | - Mark Conner
- NanoString Technologies, Seattle, Washington
| | | | | | - Felicia New
- NanoString Technologies, Seattle, Washington
| | - Chi Q. Phan
- NanoString Technologies, Seattle, Washington
| | | | - Hiromi Sato
- NanoString Technologies, Seattle, Washington
| | | | - Gary Geiss
- NanoString Technologies, Seattle, Washington
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Carter JM, Chumsri S, Hinerfeld DA, Ma Y, Wang X, Zahrieh D, Hillman DW, Tenner KS, Kachergus JM, Brauer HA, Warren SE, Henderson D, Shi J, Liu Y, Joensuu H, Lindman H, Leon-Ferre RA, Boughey JC, Liu MC, Ingle JN, Kalari KR, Couch FJ, Knutson KL, Goetz MP, Perez EA, Thompson EA. Distinct spatial immune microlandscapes are independently associated with outcomes in triple-negative breast cancer. Nat Commun 2023; 14:2215. [PMID: 37072398 PMCID: PMC10113250 DOI: 10.1038/s41467-023-37806-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 03/30/2023] [Indexed: 04/20/2023] Open
Abstract
The utility of spatial immunobiomarker quantitation in prognostication and therapeutic prediction is actively being investigated in triple-negative breast cancer (TNBC). Here, with high-plex quantitative digital spatial profiling, we map and quantitate intraepithelial and adjacent stromal tumor immune protein microenvironments in systemic treatment-naïve (female only) TNBC to assess the spatial context in immunobiomarker-based prediction of outcome. Immune protein profiles of CD45-rich and CD68-rich stromal microenvironments differ significantly. While they typically mirror adjacent, intraepithelial microenvironments, this is not uniformly true. In two TNBC cohorts, intraepithelial CD40 or HLA-DR enrichment associates with better outcomes, independently of stromal immune protein profiles or stromal TILs and other established prognostic variables. In contrast, intraepithelial or stromal microenvironment enrichment with IDO1 associates with improved survival irrespective of its spatial location. Antigen-presenting and T-cell activation states are inferred from eigenprotein scores. Such scores within the intraepithelial compartment interact with PD-L1 and IDO1 in ways that suggest prognostic and/or therapeutic potential. This characterization of the intrinsic spatial immunobiology of treatment-naïve TNBC highlights the importance of spatial microenvironments for biomarker quantitation to resolve intrinsic prognostic and predictive immune features and ultimately inform therapeutic strategies for clinically actionable immune biomarkers.
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Affiliation(s)
- Jodi M Carter
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Canada
| | - Saranya Chumsri
- Department of Medicine, Division of Hematology and Oncology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Yaohua Ma
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Jacksonville, FL, USA
| | - Xue Wang
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Jacksonville, FL, USA
| | - David Zahrieh
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - David W Hillman
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Kathleen S Tenner
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | - Ji Shi
- Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, USA
| | - Yi Liu
- Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, USA
| | - Heikki Joensuu
- Department of Oncology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Henrik Lindman
- Department of Oncology, University of Uppsala, Uppsala, Sweden
| | - Roberto A Leon-Ferre
- Department of Oncology, Division of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | | | | | - James N Ingle
- Department of Oncology, Division of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | - Krishna R Kalari
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Keith L Knutson
- Department of Immunology, Mayo Clinic, Jacksonville, FL, USA
| | - Matthew P Goetz
- Department of Oncology, Division of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | - Edith A Perez
- Department of Medicine, Division of Hematology and Oncology, Mayo Clinic, Jacksonville, FL, USA
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Bellini D, Milan M, Bordin A, Rizzi R, Rengo M, Vicini S, Onori A, Carbone I, De Falco E. A Focus on the Synergy of Radiomics and RNA Sequencing in Breast Cancer. Int J Mol Sci 2023; 24:ijms24087214. [PMID: 37108377 PMCID: PMC10138689 DOI: 10.3390/ijms24087214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
Radiological imaging is currently employed as the most effective technique for screening, diagnosis, and follow up of patients with breast cancer (BC), the most common type of tumor in women worldwide. However, the introduction of the omics sciences such as metabolomics, proteomics, and molecular genomics, have optimized the therapeutic path for patients and implementing novel information parallel to the mutational asset targetable by specific clinical treatments. Parallel to the "omics" clusters, radiological imaging has been gradually employed to generate a specific omics cluster termed "radiomics". Radiomics is a novel advanced approach to imaging, extracting quantitative, and ideally, reproducible data from radiological images using sophisticated mathematical analysis, including disease-specific patterns, that could not be detected by the human eye. Along with radiomics, radiogenomics, defined as the integration of "radiology" and "genomics", is an emerging field exploring the relationship between specific features extracted from radiological images and genetic or molecular traits of a particular disease to construct adequate predictive models. Accordingly, radiological characteristics of the tissue are supposed to mimic a defined genotype and phenotype and to better explore the heterogeneity and the dynamic evolution of the tumor over the time. Despite such improvements, we are still far from achieving approved and standardized protocols in clinical practice. Nevertheless, what can we learn by this emerging multidisciplinary clinical approach? This minireview provides a focused overview on the significance of radiomics integrated by RNA sequencing in BC. We will also discuss advances and future challenges of such radiomics-based approach.
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Affiliation(s)
- Davide Bellini
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Marika Milan
- UOC Neurology, Fondazione Ca'Granda, Ospedale Maggiore Policlinico, Via F. Sforza, 28, 20122 Milan, Italy
| | - Antonella Bordin
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Roberto Rizzi
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Marco Rengo
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Simone Vicini
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Alessandro Onori
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Iacopo Carbone
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Elena De Falco
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
- Mediterranea Cardiocentro, 80122 Napoli, Italy
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Lee RY, Ng CW, Rajapakse MP, Ang N, Yeong JPS, Lau MC. The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI. Front Oncol 2023; 13:1172314. [PMID: 37197415 PMCID: PMC10183599 DOI: 10.3389/fonc.2023.1172314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/18/2023] [Indexed: 05/19/2023] Open
Abstract
Growing evidence supports the critical role of tumour microenvironment (TME) in tumour progression, metastases, and treatment response. However, the in-situ interplay among various TME components, particularly between immune and tumour cells, are largely unknown, hindering our understanding of how tumour progresses and responds to treatment. While mainstream single-cell omics techniques allow deep, single-cell phenotyping, they lack crucial spatial information for in-situ cell-cell interaction analysis. On the other hand, tissue-based approaches such as hematoxylin and eosin and chromogenic immunohistochemistry staining can preserve the spatial information of TME components but are limited by their low-content staining. High-content spatial profiling technologies, termed spatial omics, have greatly advanced in the past decades to overcome these limitations. These technologies continue to emerge to include more molecular features (RNAs and/or proteins) and to enhance spatial resolution, opening new opportunities for discovering novel biological knowledge, biomarkers, and therapeutic targets. These advancements also spur the need for novel computational methods to mine useful TME insights from the increasing data complexity confounded by high molecular features and spatial resolution. In this review, we present state-of-the-art spatial omics technologies, their applications, major strengths, and limitations as well as the role of artificial intelligence (AI) in TME studies.
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Affiliation(s)
- Ren Yuan Lee
- Singapore Thong Chai Medical Institution, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Chan Way Ng
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | | | - Nicholas Ang
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Joe Poh Sheng Yeong
- Department of Anatomical Pathology, Singapore General Hospital, Singapore, Singapore
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
- *Correspondence: Joe Poh Sheng Yeong, ; Mai Chan Lau,
| | - Mai Chan Lau
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- *Correspondence: Joe Poh Sheng Yeong, ; Mai Chan Lau,
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33
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Wang N, Li X, Ding Z. High-Plex Spatial Profiling of RNA and Protein Using Digital Spatial Profiler. Methods Mol Biol 2023; 2660:69-83. [PMID: 37191791 DOI: 10.1007/978-1-0716-3163-8_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
The rapid emergence of spatial multi-omics technologies in recent years has revolutionized biomedical research. Among these, the Digital Spatial Profiler (DSP, commercialized by nanoString) has become one of the dominant technologies in spatial transcriptomics and proteomics and has assisted in deconvoluting complex biological questions. Based on our practical experience in the past 3 years with DSP, we share here a detailed hands-on protocol and key handling notes that will allow the broader community to optimize their work procedure.
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Affiliation(s)
- Nan Wang
- Mills Institute for Personalized Cancer Care, Fynn Biotechnologies Ltd, Jinan City, Shandong Province, P. R. China
| | - Xia Li
- Mills Institute for Personalized Cancer Care, Fynn Biotechnologies Ltd, Jinan City, Shandong Province, P. R. China
| | - Zhiyong Ding
- Mills Institute for Personalized Cancer Care, Fynn Biotechnologies Ltd, Jinan City, Shandong Province, P. R. China.
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Seferbekova Z, Lomakin A, Yates LR, Gerstung M. Spatial biology of cancer evolution. Nat Rev Genet 2022; 24:295-313. [PMID: 36494509 DOI: 10.1038/s41576-022-00553-x] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2022] [Indexed: 12/13/2022]
Abstract
The natural history of cancers can be understood through the lens of evolution given that the driving forces of cancer development are mutation and selection of fitter clones. Cancer growth and progression are spatial processes that involve the breakdown of normal tissue organization, invasion and metastasis. For these reasons, spatial patterns are an integral part of histological tumour grading and staging as they measure the progression from normal to malignant disease. Furthermore, tumour cells are part of an ecosystem of tumour cells and their surrounding tumour microenvironment. A range of new spatial genomic, transcriptomic and proteomic technologies offers new avenues for the study of cancer evolution with great molecular and spatial detail. These methods enable precise characterizations of the tumour microenvironment, cellular interactions therein and micro-anatomical structures. In conjunction with spatial genomics, it emerges that tumours and microenvironments co-evolve, which helps explain observable patterns of heterogeneity and offers new routes for therapeutic interventions.
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35
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He Y, Kim IK, Bian J, Polyzos A, Di Giammartino DC, Zhang YW, Luo J, Hernandez MO, Kedei N, Cam M, Borczuk AC, Lee T, Han Y, Conner EA, Wong M, Tillo DC, Umemura S, Chen V, Ruan L, White JB, Miranda IC, Awasthi PP, Altorki NK, Divakar P, Elemento O, Apostolou E, Giaccone G. A Knock-In Mouse Model of Thymoma With the GTF2I L424H Mutation. J Thorac Oncol 2022; 17:1375-1386. [PMID: 36049655 PMCID: PMC9691559 DOI: 10.1016/j.jtho.2022.08.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 08/02/2022] [Indexed: 11/29/2022]
Abstract
INTRODUCTION The pathogenesis of thymic epithelial tumors remains largely unknown. We previously identified GTF2I L424H as the most frequently recurrent mutation in thymic epithelial tumors. Nevertheless, the precise role of this mutation in tumorigenesis of thymic epithelial cells is unclear. METHODS To investigate the role of GTF2I L424H mutation in thymic epithelial cells in vivo, we generated and characterized a mouse model in which the Gtf2i L424H mutation was conditionally knocked-in in the Foxn1+ thymic epithelial cells. Digital spatial profiling was performed on thymomas and normal thymic tissues with GeoMx-mouse whole transcriptome atlas. Immunohistochemistry staining was performed using both mouse tissues and human thymic epithelial tumors. RESULTS We observed that the Gtf2i mutation impairs development of the thymic medulla and maturation of medullary thymic epithelial cells in young mice and causes tumor formation in the thymus of aged mice. Cell cycle-related pathways, such as E2F targets and MYC targets, are enriched in the tumor epithelial cells. Results of gene set variation assay analysis revealed that gene signatures of cortical thymic epithelial cells and thymic epithelial progenitor cells are also enriched in the thymomas of the knock-in mice, which mirrors the human counterparts in The Cancer Genome Atlas database. Immunohistochemistry results revealed similar expression pattern of epithelial cell markers between mouse and human thymomas. CONCLUSIONS We have developed and characterized a novel thymoma mouse model. This study improves knowledge of the molecular drivers in thymic epithelial cells and provides a tool for further study of the biology of thymic epithelial tumors and for development of novel therapies.
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Affiliation(s)
- Yongfeng He
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York
| | - In-Kyu Kim
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia
| | - Jing Bian
- CCR Collaborative Bioinformatics Resource, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Alexander Polyzos
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York
| | | | - Yu-Wen Zhang
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia; New address: Department of Cell Biology, University of Virginia, School of Medicine, Charlottesville, Virginia
| | - Ji Luo
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
| | - Maria O Hernandez
- Collaborative Protein Technology Resource, Office of Science and Technology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Noemi Kedei
- Collaborative Protein Technology Resource, Office of Science and Technology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Maggie Cam
- CCR Collaborative Bioinformatics Resource, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Alain C Borczuk
- Department of Pathology, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York; New address: Department of Pathology, Northwell Health, Greenvale, New York
| | - Trevor Lee
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York
| | - Yumin Han
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York
| | | | - Madeline Wong
- CCR Genomics Core, National Cancer Institute, Bethesda, Maryland
| | - Desiree C Tillo
- CCR Genomics Core, National Cancer Institute, Bethesda, Maryland
| | - Shigeki Umemura
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia
| | - Vincent Chen
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia
| | - Lydia Ruan
- CCR Collaborative Bioinformatics Resource, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Jessica B White
- Caryl and Israel Englander Institute for Precision Medicine, New York-Presbyterian Hospital, Weill Cornell Medicine, New York, New York
| | - Ileana C Miranda
- Laboratory of Comparative Pathology, Memorial Sloan Kettering Cancer Center, Weill Cornell Medicine, and The Rockefeller University, New York, New York
| | - Parirokh P Awasthi
- Frederick National Laboratory for Cancer Research, Laboratory Animal Sciences, Mouse Modeling & Cryopreservation, National Cancer Institute, Frederick, Maryland
| | - Nasser K Altorki
- Department of Cardiothoracic Surgery, Weill Cornell Medicine, New York Presbyterian Hospital, New York, New York
| | | | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, New York-Presbyterian Hospital, Weill Cornell Medicine, New York, New York
| | - Effie Apostolou
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York
| | - Giuseppe Giaccone
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York; Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia.
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Chiriboga L, Callis GM, Wang Y, Chlipala E. Guide for collecting and reporting metadata on protocol variables and parameters from slide-based histotechnology assays to enhance reproducibility. J Histotechnol 2022; 45:132-147. [DOI: 10.1080/01478885.2022.2134022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Luis Chiriboga
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
- NYULH Center for Biospecimen Research and Development, New York, NY, USA
| | | | - Yongfu Wang
- Stowers Institute for Medical Research, Kansas, MO, USA
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Kleino I, Frolovaitė P, Suomi T, Elo LL. Computational solutions for spatial transcriptomics. Comput Struct Biotechnol J 2022; 20:4870-4884. [PMID: 36147664 PMCID: PMC9464853 DOI: 10.1016/j.csbj.2022.08.043] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/18/2022] [Accepted: 08/18/2022] [Indexed: 11/18/2022] Open
Abstract
Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.
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Key Words
- AOI, area of illumination
- BICCN, Brain Initiative Cell Census Network
- BOLORAMIS, barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses
- Baysor, Bayesian Segmentation of Spatial Transcriptomics Data
- BinSpect, Binary Spatial Extraction
- CCC, cell–cell communication
- CCI, cell–cell interactions
- CNV, copy-number variation
- Computational biology
- DSP, digital spatial profiling
- DbiT-Seq, Deterministic Barcoding in Tissue for spatial omics sequencing
- FA, factor analysis
- FFPE, formalin-fixed, paraffin-embedded
- FISH, fluorescence in situ hybridization
- FISSEQ, fluorescence in situ sequencing of RNA
- FOV, Field of view
- GRNs, gene regulation networks
- GSEA, gene set enrichment analysis
- GSVA, gene set variation analysis
- HDST, high definition spatial transcriptomics
- HMRF, hidden Markov random field
- ICG, interaction changed genes
- ISH, in situ hybridization
- ISS, in situ sequencing
- JSTA, Joint cell segmentation and cell type annotation
- KNN, k-nearest neighbor
- LCM, Laser Capture Microdissection
- LCM-seq, laser capture microdissection coupled with RNA sequencing
- LOH, loss of heterozygosity analysis
- MC, Molecular Cartography
- MERFISH, multiplexed error-robust FISH
- NMF (NNMF), Non-negative matrix factorization
- PCA, Principal Component Analysis
- PIXEL-seq, Polony (or DNA cluster)-indexed library-sequencing
- PL-lig, padlock ligation
- QC, quality control
- RNAseq, RNA sequencing
- ROI, region of interest
- SCENIC, Single-Cell rEgulatory Network Inference and Clustering
- SME, Spatial Morphological gene Expression normalization
- SPATA, SPAtial Transcriptomic Analysis
- ST Pipeline, Spatial Transcriptomics Pipeline
- ST, Spatial transcriptomics
- STARmap, spatially-resolved transcript amplicon readout mapping
- Single-cell analysis
- Spatial data analysis frameworks
- Spatial deconvolution
- Spatial transcriptomics
- TIVA, Transcriptome in Vivo Analysis
- TMA, tissue microarray
- TME, tumor micro environment
- UMAP, Uniform Manifold Approximation and Projection for Dimension Reduction
- UMI, unique molecular identifier
- ZipSeq, zipcoded sequencing.
- scRNA-seq, single-cell RNA sequencing
- scvi-tools, single-cell variational inference tools
- seqFISH, sequential fluorescence in situ hybridization
- sequ-smFISH, sequential single-molecule fluorescent in situ hybridization
- smFISH, single molecule FISH
- t-SNE, t-distributed stochastic neighbor embedding
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Affiliation(s)
- Iivari Kleino
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Paulina Frolovaitė
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
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Schmitd LB, Perez-Pacheco C, Bellile EL, Wu W, Casper K, Mierzwa M, Rozek LS, Wolf GT, Taylor JM, D'Silva NJ. Spatial and Transcriptomic Analysis of Perineural Invasion in Oral Cancer. Clin Cancer Res 2022; 28:3557-3572. [PMID: 35819260 PMCID: PMC9560986 DOI: 10.1158/1078-0432.ccr-21-4543] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/25/2022] [Accepted: 05/24/2022] [Indexed: 01/07/2023]
Abstract
PURPOSE Perineural invasion (PNI), a common occurrence in oral squamous cell carcinomas, is associated with poor survival. Consequently, these tumors are treated aggressively. However, diagnostic criteria of PNI vary and its role as an independent predictor of prognosis has not been established. To address these knowledge gaps, we investigated spatial and transcriptomic profiles of PNI-positive and PNI-negative nerves. EXPERIMENTAL DESIGN Tissue sections from 142 patients were stained with S100 and cytokeratin antibodies. Nerves were identified in two distinct areas: tumor bulk and margin. Nerve diameter and nerve-to-tumor distance were assessed; survival analyses were performed. Spatial transcriptomic analysis of nerves at varying distances from tumor was performed with NanoString GeoMx Digital Spatial Profiler Transcriptomic Atlas. RESULTS PNI is an independent predictor of poor prognosis among patients with metastasis-free lymph nodes. Patients with close nerve-tumor distance have poor outcomes even if diagnosed as PNI negative using current criteria. Patients with large nerve(s) in the tumor bulk survive poorly, suggesting that even PNI-negative nerves facilitate tumor progression. Diagnostic criteria were supported by spatial transcriptomic analyses of >18,000 genes; nerves in proximity to cancer exhibit stress and growth response changes that diminish with increasing nerve-tumor distance. These findings were validated in vitro and in human tissue. CONCLUSIONS This is the first study in human cancer with high-throughput gene expression analysis in nerves with striking correlations between transcriptomic profile and clinical outcomes. Our work illuminates nerve-cancer interactions suggesting that cancer-induced injury modulates neuritogenesis, and supports reclassification of PNI based on nerve-tumor distance rather than current subjective criteria.
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Affiliation(s)
- Ligia B. Schmitd
- Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, Michigan
| | - Cindy Perez-Pacheco
- Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, Michigan
| | - Emily L. Bellile
- Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Weisheng Wu
- Bioinformatics Core, University of Michigan, Ann Arbor, Michigan
| | - Keith Casper
- Otolaryngology, University of Michigan Medical School, Ann Arbor, Michigan
| | - Michelle Mierzwa
- Radiation Oncology, University of Michigan Medical School, Ann Arbor, Michigan
| | - Laura S. Rozek
- Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Gregory T. Wolf
- Otolaryngology, University of Michigan Medical School, Ann Arbor, Michigan
| | - Jeremy M.G. Taylor
- Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan
- Radiation Oncology, University of Michigan Medical School, Ann Arbor, Michigan
| | - Nisha J. D'Silva
- Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, Michigan
- Pathology, University of Michigan Medical School, Ann Arbor, Michigan
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
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Kuczkiewicz-Siemion O, Sokół K, Puton B, Borkowska A, Szumera-Ciećkiewicz A. The Role of Pathology-Based Methods in Qualitative and Quantitative Approaches to Cancer Immunotherapy. Cancers (Basel) 2022; 14:cancers14153833. [PMID: 35954496 PMCID: PMC9367614 DOI: 10.3390/cancers14153833] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/25/2022] [Accepted: 08/02/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Immunotherapy has become the filar of modern oncological treatment, and programmed death-ligand 1 expression is one of the primary immune markers assessed by pathologists. However, there are still some issues concerning the evaluation of the marker and limited information about the interaction between the tumour and associated immune cells. Recent studies have focused on cancer immunology to try to understand the complex tumour microenvironment, and multiplex imaging methods are more widely used for this purpose. The presented article aims to provide an overall review of a different multiplex in situ method using spectral imaging, supported by automated image-acquisition and software-assisted marker visualisation and interpretation. Multiplex imaging methods could improve the current understanding of complex tumour-microenvironment immunology and could probably help to better match patients to appropriate treatment regimens. Abstract Immune checkpoint inhibitors, including those concerning programmed cell death 1 (PD-1) and its ligand (PD-L1), have revolutionised the cancer therapy approach in the past decade. However, not all patients benefit from immunotherapy equally. The prediction of patient response to this type of therapy is mainly based on conventional immunohistochemistry, which is limited by intraobserver variability, semiquantitative assessment, or single-marker-per-slide evaluation. Multiplex imaging techniques and digital image analysis are powerful tools that could overcome some issues concerning tumour-microenvironment studies. This novel approach to biomarker assessment offers a better understanding of the complicated interactions between tumour cells and their environment. Multiplex labelling enables the detection of multiple markers simultaneously and the exploration of their spatial organisation. Evaluating a variety of immune cell phenotypes and differentiating their subpopulations is possible while preserving tissue histology in most cases. Multiplexing supported by digital pathology could allow pathologists to visualise and understand every cell in a single tissue slide and provide meaning in a complex tumour-microenvironment contexture. This review aims to provide an overview of the different multiplex imaging methods and their application in PD-L1 biomarker assessment. Moreover, we discuss digital imaging techniques, with a focus on slide scanners and software.
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Affiliation(s)
- Olga Kuczkiewicz-Siemion
- Department of Pathology, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
- Diagnostic Hematology Department, Institute of Hematology and Transfusion Medicine, 02-776 Warsaw, Poland
- Correspondence: (O.K.-S.); (A.S.-C.)
| | - Kamil Sokół
- Diagnostic Hematology Department, Institute of Hematology and Transfusion Medicine, 02-776 Warsaw, Poland
| | - Beata Puton
- Department of Pathology, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
| | - Aneta Borkowska
- Department of Soft Tissue/Bone Sarcoma and Melanoma, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
| | - Anna Szumera-Ciećkiewicz
- Department of Pathology, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
- Correspondence: (O.K.-S.); (A.S.-C.)
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40
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Bosisio FM, Van Herck Y, Messiaen J, Bolognesi MM, Marcelis L, Van Haele M, Cattoretti G, Antoranz A, De Smet F. Next-Generation Pathology Using Multiplexed Immunohistochemistry: Mapping Tissue Architecture at Single-Cell Level. Front Oncol 2022; 12:918900. [PMID: 35992810 PMCID: PMC9389457 DOI: 10.3389/fonc.2022.918900] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/20/2022] [Indexed: 01/23/2023] Open
Abstract
Single-cell omics aim at charting the different types and properties of all cells in the human body in health and disease. Over the past years, myriads of cellular phenotypes have been defined by methods that mostly required cells to be dissociated and removed from their original microenvironment, thus destroying valuable information about their location and interactions. Growing insights, however, are showing that such information is crucial to understand complex disease states. For decades, pathologists have interpreted cells in the context of their tissue using low-plex antibody- and morphology-based methods. Novel technologies for multiplexed immunohistochemistry are now rendering it possible to perform extended single-cell expression profiling using dozens of protein markers in the spatial context of a single tissue section. The combination of these novel technologies with extended data analysis tools allows us now to study cell-cell interactions, define cellular sociology, and describe detailed aberrations in tissue architecture, as such gaining much deeper insights in disease states. In this review, we provide a comprehensive overview of the available technologies for multiplexed immunohistochemistry, their advantages and challenges. We also provide the principles on how to interpret high-dimensional data in a spatial context. Similar to the fact that no one can just “read” a genome, pathological assessments are in dire need of extended digital data repositories to bring diagnostics and tissue interpretation to the next level.
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Affiliation(s)
- Francesca Maria Bosisio
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- *Correspondence: Frederik De Smet, ; Francesca Maria Bosisio,
| | | | - Julie Messiaen
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- The Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Department of Pediatrics, University Hospitals Leuven, Leuven, Belgium
| | - Maddalena Maria Bolognesi
- Pathology, Department of Medicine and Surgery, Università di Milano-Bicocca, Monza, Italy
- Department of Pathology, Azienda Socio Sanitaria Territoriale (ASST) Monza, Ospedale San Gerardo, Monza, Italy
| | - Lukas Marcelis
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Matthias Van Haele
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Giorgio Cattoretti
- Pathology, Department of Medicine and Surgery, Università di Milano-Bicocca, Monza, Italy
- Department of Pathology, Azienda Socio Sanitaria Territoriale (ASST) Monza, Ospedale San Gerardo, Monza, Italy
| | - Asier Antoranz
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- The Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Frederik De Smet
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- The Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- *Correspondence: Frederik De Smet, ; Francesca Maria Bosisio,
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41
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Lim Y, Kang SJ, Cho BK, Kim HJ, Mun JH, Roh MR, Gulati N, Yang HJ, Moon JH, Won CH, Park CG. Intra-tumoral heterogeneity and immune escape of melanoma arising from congenital melanocytic nevus revealed by spatial gene expression profiling. J Eur Acad Dermatol Venereol 2022; 36:e1044-e1047. [PMID: 35857376 DOI: 10.1111/jdv.18443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Youngkyoung Lim
- Department of Dermatology, Seoul National University Hospital, Seoul, Korea
| | - Seong-Jun Kang
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea.,Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Beom Keun Cho
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea
| | - Hyun Je Kim
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.,Department of Microbiology and Immunology, Seoul National University College of Medicine, Seoul, Korea.,Genome Medicine Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Je-Ho Mun
- Department of Dermatology, Seoul National University Hospital, Seoul, Korea
| | - Mi Ryung Roh
- Department of Dermatology, Gangnam Severance Hospital, Cutaneous Biology Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Nicholas Gulati
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York
| | - Hee Joo Yang
- Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Ji Hwan Moon
- Samsung Genome Institute, Samsung Medical Center, Seoul, Korea
| | - Chong Hyun Won
- Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Chung-Gyu Park
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea.,Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.,Department of Microbiology and Immunology, Seoul National University College of Medicine, Seoul, Korea
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42
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Hernandez S, Lazcano R, Serrano A, Powell S, Kostousov L, Mehta J, Khan K, Lu W, Solis LM. Challenges and Opportunities for Immunoprofiling Using a Spatial High-Plex Technology: The NanoString GeoMx ® Digital Spatial Profiler. Front Oncol 2022; 12:890410. [PMID: 35847846 PMCID: PMC9277770 DOI: 10.3389/fonc.2022.890410] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
Characterization of the tumor microenvironment through immunoprofiling has become an essential resource for the understanding of the complex immune cell interactions and the assessment of biomarkers for prognosis and prediction of immunotherapy response; however, these studies are often limited by tissue heterogeneity and sample size. The nanoString GeoMx® Digital Spatial Profiler (DSP) is a platform that allows high-plex profiling at the protein and RNA level, providing spatial and temporal assessment of tumors in frozen or formalin-fixed paraffin-embedded limited tissue sample. Recently, high-impact studies have shown the feasibility of using this technology to identify biomarkers in different settings, including predictive biomarkers for immunotherapy in different tumor types. These studies showed that compared to other multiplex and high-plex platforms, the DSP can interrogate a higher number of biomarkers with higher throughput; however, it does not provide single-cell resolution, including co-expression of biomarker or spatial information at the single-cell level. In this review, we will describe the technical overview of the platform, present current evidence of the advantages and limitations of the applications of this technology, and provide important considerations for the experimental design for translational immune-oncology research using this tissue-based high-plex profiling approach.
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Affiliation(s)
- Sharia Hernandez
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Rossana Lazcano
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Alejandra Serrano
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Steven Powell
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Larissa Kostousov
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jay Mehta
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Khaja Khan
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Wei Lu
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Luisa M Solis
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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43
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Elishaev M, Hodonsky CJ, Ghosh SKB, Finn AV, von Scheidt M, Wang Y. Opportunities and Challenges in Understanding Atherosclerosis by Human Biospecimen Studies. Front Cardiovasc Med 2022; 9:948492. [PMID: 35872917 PMCID: PMC9300954 DOI: 10.3389/fcvm.2022.948492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Over the last few years, new high-throughput biotechnologies and bioinformatic methods are revolutionizing our way of deep profiling tissue specimens at the molecular levels. These recent innovations provide opportunities to advance our understanding of atherosclerosis using human lesions aborted during autopsies and cardiac surgeries. Studies on human lesions have been focusing on understanding the relationship between molecules in the lesions with tissue morphology, genetic risk of atherosclerosis, and future adverse cardiovascular events. This review will highlight ways to utilize human atherosclerotic lesions in translational research by work from large cardiovascular biobanks to tissue registries. We will also discuss the opportunities and challenges of working with human atherosclerotic lesions in the era of next-generation sequencing.
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Affiliation(s)
- Maria Elishaev
- Department of Pathology and Laboratory Medicine, Center for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada
| | - Chani J. Hodonsky
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, United States
| | | | - Aloke V. Finn
- Cardiovascular Pathology Institute, Gaithersburg, MD, United States
| | - Moritz von Scheidt
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- Deutsches Zentrum für Herz-Kreislauf-Forschung, Partner Site Munich Heart Alliance, Munich, Germany
| | - Ying Wang
- Department of Pathology and Laboratory Medicine, Center for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada
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44
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Badve SS, Gökmen-Polar Y. Protein Profiling of Breast Cancer for Treatment Decision-Making. Am Soc Clin Oncol Educ Book 2022; 42:1-9. [PMID: 35580295 DOI: 10.1200/edbk_351207] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
The increasing use of neoadjuvant therapy has resulted in therapeutic decisions being made on the basis of diagnostic needle core biopsy. For many patients, this method might yield the only fragment of tumor available for biomarker analysis, necessitating judicious use. Many multiplex protein analytic methods have been developed that employ fluorescence or other tags to overcome the limitations of immunohistochemistry while still retaining the spatial annotation. Interpretation of the data can be difficult because of the limitations of the human eye. Computational deconvolution of the signals may be necessary for some of these methods to enable identification of cell-specific localization and coexpression of biomarkers. Herein, we present the different methods that are coming of age and their application in cancer research, with a focus on breast cancer. We also discuss the limitations, which include high costs and long turnaround times. The methods are also based on the premise that preanalytical factors will have identical impact on all proteins analyzed. There is a need to establish standards to normalize the data and enable cross-sample comparisons. In spite of these limitations, the multiplex technologies are extremely valuable discovery tools and can provide novel insights into the biology of cancer and mechanisms of drug resistance.
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Affiliation(s)
- Sunil S Badve
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA
| | - Yesim Gökmen-Polar
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA
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45
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Kashyap D, Pal D, Sharma R, Garg VK, Goel N, Koundal D, Zaguia A, Koundal S, Belay A. Global Increase in Breast Cancer Incidence: Risk Factors and Preventive Measures. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9605439. [PMID: 35480139 PMCID: PMC9038417 DOI: 10.1155/2022/9605439] [Citation(s) in RCA: 150] [Impact Index Per Article: 75.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/25/2022] [Accepted: 03/21/2022] [Indexed: 02/07/2023]
Abstract
Breast cancer is a global cause for concern owing to its high incidence around the world. The alarming increase in breast cancer cases emphasizes the management of disease at multiple levels. The management should start from the beginning that includes stringent cancer screening or cancer registry to effective diagnostic and treatment strategies. Breast cancer is highly heterogeneous at morphology as well as molecular levels and needs different therapeutic regimens based on the molecular subtype. Breast cancer patients with respective subtype have different clinical outcome prognoses. Breast cancer heterogeneity emphasizes the advanced molecular testing that will help on-time diagnosis and improved survival. Emerging fields such as liquid biopsy and artificial intelligence would help to under the complexity of breast cancer disease and decide the therapeutic regimen that helps in breast cancer management. In this review, we have discussed various risk factors and advanced technology available for breast cancer diagnosis to combat the worst breast cancer status and areas that need to be focused for the better management of breast cancer.
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Affiliation(s)
- Dharambir Kashyap
- Department of Histopathology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Deeksha Pal
- Department of Translational and Regenerative Medicine, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Riya Sharma
- Department of Pulmonary Medicine, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Vivek Kumar Garg
- Department of Medical Laboratory Technology, University Institute of Applied Health Sciences, Chandigarh University (Gharuan), Mohali 140313, India
| | - Neelam Goel
- Department of Information Technology, University Institute of Engineering & Technology, Panjab University, Chandigarh 160014, India
| | - Deepika Koundal
- Department of Systemics, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India
| | - Atef Zaguia
- Department of computer science, College of Computers and Information Technology, Taif University, P.O. BOX 11099, Taif 21944, Saudi Arabia
| | - Shubham Koundal
- Department of Medical Laboratory Technology, University Institute of Applied Health Sciences, Chandigarh University (Gharuan), Mohali 140313, India
| | - Assaye Belay
- Department of Statistics, Mizan-Tepi University, Ethiopia
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46
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Dissecting Tumor-Immune Microenvironment in Breast Cancer at a Spatial and Multiplex Resolution. Cancers (Basel) 2022; 14:cancers14081999. [PMID: 35454904 PMCID: PMC9026731 DOI: 10.3390/cancers14081999] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/09/2022] [Accepted: 04/12/2022] [Indexed: 02/01/2023] Open
Abstract
The tumor immune microenvironment (TIME) is an important player in breast cancer pathophysiology. Surrogates for antitumor immune response have been explored as predictive biomarkers to immunotherapy, though with several limitations. Immunohistochemistry for programmed death ligand 1 suffers from analytical problems, immune signatures are devoid of spatial information and histopathological evaluation of tumor infiltrating lymphocytes exhibits interobserver variability. Towards improved understanding of the complex interactions in TIME, several emerging multiplex in situ methods are being developed and gaining much attention for protein detection. They enable the simultaneous evaluation of multiple targets in situ, detection of cell densities/subpopulations as well as estimations of functional states of immune infiltrate. Furthermore, they can characterize spatial organization of TIME—by cell-to-cell interaction analyses and the evaluation of distribution within different regions of interest and tissue compartments—while digital imaging and image analysis software allow for reproducibility of the various assays. In this review, we aim to provide an overview of the different multiplex in situ methods used in cancer research with special focus on breast cancer TIME at the neoadjuvant, adjuvant and metastatic setting. Spatial heterogeneity of TIME and importance of longitudinal evaluation of TIME changes under the pressure of therapy and metastatic progression are also addressed.
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47
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Brown MS, Muller KE, Pattabiraman DR. Quantifying the Epithelial-to-Mesenchymal Transition (EMT) from Bench to Bedside. Cancers (Basel) 2022; 14:1138. [PMID: 35267444 PMCID: PMC8909103 DOI: 10.3390/cancers14051138] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/04/2022] [Accepted: 02/17/2022] [Indexed: 02/07/2023] Open
Abstract
The epithelial-to-mesenchymal transition (EMT) and its reversal, the mesenchymal-to-epithelial transition (MET) are critical components of the metastatic cascade in breast cancer and many other solid tumor types. Recent work has uncovered the presence of a variety of states encompassed within the EMT spectrum, each of which may play unique roles or work collectively to impact tumor progression. However, defining EMT status is not routinely carried out to determine patient prognosis or dictate therapeutic decision-making in the clinic. Identifying and quantifying the presence of various EMT states within a tumor is a critical first step to scoring patient tumors to aid in determining prognosis. Here, we review the major strides taken towards translating our understanding of EMT biology from bench to bedside. We review previously used approaches including basic immunofluorescence staining, flow cytometry, single-cell sequencing, and multiplexed tumor mapping. Future studies will benefit from the consideration of multiple methods and combinations of markers in designing a diagnostic tool for detecting and measuring EMT in patient tumors.
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Affiliation(s)
- Meredith S. Brown
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA;
| | - Kristen E. Muller
- Department of Pathology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, USA;
| | - Diwakar R. Pattabiraman
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA;
- Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, USA
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48
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A high-throughput technique to map cell images to cell positions using a 3D imaging flow cytometer. Proc Natl Acad Sci U S A 2022; 119:2118068119. [PMID: 35173045 PMCID: PMC8872737 DOI: 10.1073/pnas.2118068119] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/17/2022] [Indexed: 01/02/2023] Open
Abstract
This article demonstrates a high-throughput technique to map cell images to cell positions. The technology uses a three-dimensional (3D) imaging flow cytometer to record multiparameter 3D cell images at a throughput of 1,000 cells/s and a cell placement robot to place the exiting cells from the imaging system on a filter plate in a first-in–first-out manner so the cells on the plate have the same order as the cells that are imaged. Innovative algorithms were developed to match the cell sequences from the imaging and placement modules to detect and eliminate errors to ensure high accuracy. The technology forms an unprecedented bridge between single-cell molecular analysis and single-cell image analysis to connect phenotype and genotype analysis with single-cell resolution. We develop a high-throughput technique to relate positions of individual cells to their three-dimensional (3D) imaging features with single-cell resolution. The technique is particularly suitable for nonadherent cells where existing spatial biology methodologies relating cell properties to their positions in a solid tissue do not apply. Our design consists of two parts, as follows: recording 3D cell images at high throughput (500 to 1,000 cells/s) using a custom 3D imaging flow cytometer (3D-IFC) and dispensing cells in a first-in–first-out (FIFO) manner using a robotic cell placement platform (CPP). To prevent errors due to violations of the FIFO principle, we invented a method that uses marker beads and DNA sequencing software to detect errors. Experiments with human cancer cell lines demonstrate the feasibility of mapping 3D side scattering and fluorescent images, as well as two-dimensional (2D) transmission images of cells to their locations on the membrane filter for around 100,000 cells in less than 10 min. While the current work uses our specially designed 3D imaging flow cytometer to produce 3D cell images, our methodology can support other imaging modalities. The technology and method form a bridge between single-cell image analysis and single-cell molecular analysis.
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Chen HY, Palendira U, Feng CG. Navigating the cellular landscape in tissue: Recent advances in defining the pathogenesis of human disease. Comput Struct Biotechnol J 2022; 20:5256-5263. [PMID: 36212528 PMCID: PMC9519395 DOI: 10.1016/j.csbj.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/04/2022] [Accepted: 09/04/2022] [Indexed: 11/19/2022] Open
Affiliation(s)
- Helen Y. Chen
- Immunology and Host Defence Group, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
- Centenary Institute, The University of Sydney, NSW, Australia
| | - Umaimainthan Palendira
- Immunology and Host Defence Group, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
- Centenary Institute, The University of Sydney, NSW, Australia
| | - Carl G. Feng
- Immunology and Host Defence Group, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
- Centenary Institute, The University of Sydney, NSW, Australia
- Corresponding author at: Level 5 (East) The Charles Perkins Centre (D17), The University of Sydney, NSW, 2006, Australia
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