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Jia D, Zhao S, Liu H, Zhan X, Zhou Z, Lv M, Tang X, Guo W, Li H, Sun L, Zhong Y, Tian B, Yuan D, Tang X, Fan Q. ICG-labeled PD-L1-antagonistic affibody dimer for tumor imaging and enhancement of tumor photothermal-immunotherapy. Int J Biol Macromol 2024; 269:132058. [PMID: 38704065 DOI: 10.1016/j.ijbiomac.2024.132058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/25/2024] [Accepted: 05/01/2024] [Indexed: 05/06/2024]
Abstract
In clinical practice, tumor-targeting diagnosis and immunotherapy against programmed death ligand 1 (PD-L1) have a significant impact. In this research, a PD-L1-antagonistic affibody dimer (ZPD-L1) was successfully prepared through Escherichia coli expression system, and conjugated with the photosensitizer of ICG via N-hydroxysuccinimide (NHS) ester to develop a novel tumor-targeting agent (ICG-ZPD-L1) for both tumor imaging diagnosis and photothermal-immunotherapy simultaneously. In vitro, ZPD-L1 could specifically bind to PD-L1-positive LLC and MC38 tumor cells, and ICG-ZPD-L1-mediated photothermal therapy (PTT) also showed excellent phototoxicity to these tumor cells. In vivo, ICG-ZPD-L1 selectively enriched into the PD-L1-positive MC38 tumor tissues, and the high-contrast optical imaging of tumors was obtained. ICG-ZPD-L1-mediated PTT exhibited a potent anti-tumor effect in vivo due to its remarkable photothermal properties. Furthermore, ICG-ZPD-L1-mediated PTT significantly induced the immunogenic cell death (ICD) of primary tumors, promoted maturation of dendritic cells (DCs), up-regulated anti-tumor immune response, enhanced immunotherapy, and superiorly inhibited the growth of metastatic tumors. In addition, ICG-ZPD-L1 showed favorable biosafety throughout the brief duration of treatment. In summary, these results suggest that ICG-ZPD-L1 is a multifunctional tumor-targeting drug integrating tumor imaging diagnosis and photothermal-immunotherapy, and has great guiding significance for the diagnosis and treatment of clinical PD-L1-positive tumor patients.
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Affiliation(s)
- Dianlong Jia
- Laboratory of Drug Discovery and Design, School of Pharmaceutical Sciences, Liaocheng University, Liaocheng 252000, PR China
| | - Shiqi Zhao
- Department of Pharmacy (Shandong Provincinal Key Traditional Chinese Medical Discipline of Clinical Chinese Pharmacy), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, PR China
| | - Huimin Liu
- The Second Hospital of Coal Mining Group, Xuzhou 221011, PR China
| | - Xinyu Zhan
- Laboratory of Drug Discovery and Design, School of Pharmaceutical Sciences, Liaocheng University, Liaocheng 252000, PR China
| | - Zhongxia Zhou
- Department of Pharmacy (Shandong Provincinal Key Traditional Chinese Medical Discipline of Clinical Chinese Pharmacy), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, PR China
| | - Mingjia Lv
- Laboratory of Drug Discovery and Design, School of Pharmaceutical Sciences, Liaocheng University, Liaocheng 252000, PR China
| | - Xiufeng Tang
- Department of Pharmacy (Shandong Provincinal Key Traditional Chinese Medical Discipline of Clinical Chinese Pharmacy), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, PR China
| | - Wen Guo
- Laboratory of Drug Discovery and Design, School of Pharmaceutical Sciences, Liaocheng University, Liaocheng 252000, PR China
| | - Hui Li
- Department of Pharmacy (Shandong Provincinal Key Traditional Chinese Medical Discipline of Clinical Chinese Pharmacy), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, PR China
| | - Lilan Sun
- Department of Pharmacy (Shandong Provincinal Key Traditional Chinese Medical Discipline of Clinical Chinese Pharmacy), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, PR China
| | - Yidong Zhong
- Department of Pharmacy (Shandong Provincinal Key Traditional Chinese Medical Discipline of Clinical Chinese Pharmacy), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, PR China
| | - Baoqing Tian
- Department of Pharmacy (Shandong Provincinal Key Traditional Chinese Medical Discipline of Clinical Chinese Pharmacy), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, PR China
| | - Dandan Yuan
- Department of Pharmacy (Shandong Provincinal Key Traditional Chinese Medical Discipline of Clinical Chinese Pharmacy), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, PR China
| | - Xiaohui Tang
- Department of Pharmacy (Shandong Provincinal Key Traditional Chinese Medical Discipline of Clinical Chinese Pharmacy), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, PR China.
| | - Qing Fan
- Department of Pharmacy (Shandong Provincinal Key Traditional Chinese Medical Discipline of Clinical Chinese Pharmacy), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, PR China.
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Xiao J, Yu X, Meng F, Zhang Y, Zhou W, Ren Y, Li J, Sun Y, Sun H, Chen G, He K, Lu L. Integrating spatial and single-cell transcriptomics reveals tumor heterogeneity and intercellular networks in colorectal cancer. Cell Death Dis 2024; 15:326. [PMID: 38729966 PMCID: PMC11087651 DOI: 10.1038/s41419-024-06598-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 02/20/2024] [Accepted: 03/07/2024] [Indexed: 05/12/2024]
Abstract
Single cell RNA sequencing (scRNA-seq), a powerful tool for studying the tumor microenvironment (TME), does not preserve/provide spatial information on tissue morphology and cellular interactions. To understand the crosstalk between diverse cellular components in proximity in the TME, we performed scRNA-seq coupled with spatial transcriptomic (ST) assay to profile 41,700 cells from three colorectal cancer (CRC) tumor-normal-blood pairs. Standalone scRNA-seq analyses revealed eight major cell populations, including B cells, T cells, Monocytes, NK cells, Epithelial cells, Fibroblasts, Mast cells, Endothelial cells. After the identification of malignant cells from epithelial cells, we observed seven subtypes of malignant cells that reflect heterogeneous status in tumor, including tumor_CAV1, tumor_ATF3_JUN | FOS, tumor_ZEB2, tumor_VIM, tumor_WSB1, tumor_LXN, and tumor_PGM1. By transferring the cellular annotations obtained by scRNA-seq to ST spots, we annotated four regions in a cryosection from CRC patients, including tumor, stroma, immune infiltration, and colon epithelium regions. Furthermore, we observed intensive intercellular interactions between stroma and tumor regions which were extremely proximal in the cryosection. In particular, one pair of ligands and receptors (C5AR1 and RPS19) was inferred to play key roles in the crosstalk of stroma and tumor regions. For the tumor region, a typical feature of TMSB4X-high expression was identified, which could be a potential marker of CRC. The stroma region was found to be characterized by VIM-high expression, suggesting it fostered a stromal niche in the TME. Collectively, single cell and spatial analysis in our study reveal the tumor heterogeneity and molecular interactions in CRC TME, which provides insights into the mechanisms underlying CRC progression and may contribute to the development of anticancer therapies targeting on non-tumor components, such as the extracellular matrix (ECM) in CRC. The typical genes we identified may facilitate to new molecular subtypes of CRC.
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Affiliation(s)
- Jing Xiao
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People's Hospital, (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China
- Centre of Reproduction, Development and Aging, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Xinyang Yu
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People's Hospital, (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China
| | - Fanlin Meng
- CapitalBio Technology Corporation, Beijing, China
| | - Yuncong Zhang
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People's Hospital, (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China
| | - Wenbin Zhou
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People's Hospital, (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China
| | - Yonghong Ren
- CapitalBio Technology Corporation, Beijing, China
| | - Jingxia Li
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People's Hospital, (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China
| | - Yimin Sun
- CapitalBio Technology Corporation, Beijing, China
| | - Hongwei Sun
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People's Hospital, (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China
| | - Guokai Chen
- Centre of Reproduction, Development and Aging, Faculty of Health Sciences, University of Macau, Macau SAR, China.
- Zhuhai UM Science & Technology Research Institute, Zhuhai, Guangdong, China.
| | - Ke He
- Minimally Invasive Tumor Therapies Center, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong, China.
| | - Ligong Lu
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People's Hospital, (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China.
- Guangzhou First People's Hospital, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China.
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Zhou FY, Yapp C, Shang Z, Daetwyler S, Marin Z, Islam MT, Nanes B, Jenkins E, Gihana GM, Chang BJ, Weems A, Dustin M, Morrison S, Fiolka R, Dean K, Jamieson A, Sorger PK, Danuser G. A general algorithm for consensus 3D cell segmentation from 2D segmented stacks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.03.592249. [PMID: 38766074 PMCID: PMC11100681 DOI: 10.1101/2024.05.03.592249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Cell segmentation is the fundamental task. Only by segmenting, can we define the quantitative spatial unit for collecting measurements to draw biological conclusions. Deep learning has revolutionized 2D cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation and computation. However 3D cell segmentation, which requires dense annotation of 2D slices still poses significant challenges. Labelling every cell in every 2D slice is prohibitive. Moreover it is ambiguous, necessitating cross-referencing with other orthoviews. Lastly, there is limited ability to unambiguously record and visualize 1000's of annotated cells. Here we develop a theory and toolbox, u-Segment3D for 2D-to-3D segmentation, compatible with any 2D segmentation method. Given optimal 2D segmentations, u-Segment3D generates the optimal 3D segmentation without data training, as demonstrated on 11 real life datasets, >70,000 cells, spanning single cells, cell aggregates and tissue.
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Affiliation(s)
- Felix Y. Zhou
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Clarence Yapp
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, 02115, USA
| | - Zhiguo Shang
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Stephan Daetwyler
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Zach Marin
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Md Torikul Islam
- Children’s Research Institute and Department of Pediatrics, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Benjamin Nanes
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Edward Jenkins
- Kennedy Institute of Rheumatology, University of Oxford, OX3 7FY UK
| | - Gabriel M. Gihana
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Bo-Jui Chang
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Andrew Weems
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Michael Dustin
- Kennedy Institute of Rheumatology, University of Oxford, OX3 7FY UK
| | - Sean Morrison
- Children’s Research Institute and Department of Pediatrics, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Reto Fiolka
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Kevin Dean
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Andrew Jamieson
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Peter K. Sorger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, 02115, USA
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Gaudenz Danuser
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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An M, Mehta A, Min BH, Heo YJ, Wright SJ, Parikh M, Bi L, Lee H, Kim TJ, Lee SY, Moon J, Park RJ, Strickland MR, Park WY, Kang WK, Kim KM, Kim ST, Klempner SJ, Lee J. Early Immune Remodeling Steers Clinical Response to First-Line Chemoimmunotherapy in Advanced Gastric Cancer. Cancer Discov 2024; 14:766-785. [PMID: 38319303 PMCID: PMC11061611 DOI: 10.1158/2159-8290.cd-23-0857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/28/2023] [Accepted: 02/02/2024] [Indexed: 02/07/2024]
Abstract
Adding anti-programmed cell death protein 1 (anti-PD-1) to 5-fluorouracil (5-FU)/platinum improves survival in some advanced gastroesophageal adenocarcinomas (GEA). To understand the effects of chemotherapy and immunotherapy, we conducted a phase II first-line trial (n = 47) sequentially adding pembrolizumab to 5-FU/platinum in advanced GEA. Using serial biopsy of the primary tumor at baseline, after one cycle of 5-FU/platinum, and after the addition of pembrolizumab, we transcriptionally profiled 358,067 single cells to identify evolving multicellular tumor microenvironment (TME) networks. Chemotherapy induced early on-treatment multicellular hubs with tumor-reactive T-cell and M1-like macrophage interactions in slow progressors. Faster progression featured increased MUC5A and MSLN containing treatment resistance programs in tumor cells and M2-like macrophages with immunosuppressive stromal interactions. After pembrolizumab, we observed increased CD8 T-cell infiltration and development of an immunity hub involving tumor-reactive CXCL13 T-cell program and epithelial interferon-stimulated gene programs. Strategies to drive increases in antitumor immune hub formation could expand the portion of patients benefiting from anti-PD-1 approaches. SIGNIFICANCE The benefit of 5-FU/platinum with anti-PD-1 in first-line advanced gastric cancer is limited to patient subgroups. Using a trial with sequential anti-PD-1, we show coordinated induction of multicellular TME hubs informs the ability of anti-PD-1 to potentiate T cell-driven responses. Differential TME hub development highlights features that underlie clinical outcomes. This article is featured in Selected Articles from This Issue, p. 695.
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Affiliation(s)
- Minae An
- Experimental Therapeutics Development Center, Samsung Medical Center, Seoul, Korea
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Arnav Mehta
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, Division of Hematology-Oncology, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Byung Hoon Min
- Department of Medicine, Division of Gastroenterology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | | | - Samuel J. Wright
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Milan Parikh
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, Division of Hematology-Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Lynn Bi
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, Division of Hematology-Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Hyuk Lee
- Department of Medicine, Division of Gastroenterology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Tae Jun Kim
- Department of Medicine, Division of Gastroenterology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Song-Yi Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jeonghyeon Moon
- Departments of Neurology and Immunology, Yale School of Medicine, New Haven, Connecticut
| | - Ryan J. Park
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Division of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Matthew R. Strickland
- Department of Medicine, Division of Hematology-Oncology, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | | | - Won Ki Kang
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Kyoung-Mee Kim
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seung Tae Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Samuel J. Klempner
- Department of Medicine, Division of Hematology-Oncology, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Jeeyun Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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Warchol S, Troidl J, Muhlich J, Krueger R, Hoffer J, Lin T, Beyer J, Glassman E, Sorger P, Pfister H. psudo: Exploring Multi-Channel Biomedical Image Data with Spatially and Perceptually Optimized Pseudocoloring. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.11.589087. [PMID: 38659870 PMCID: PMC11042212 DOI: 10.1101/2024.04.11.589087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Over the past century, multichannel fluorescence imaging has been pivotal in myriad scientific breakthroughs by enabling the spatial visualization of proteins within a biological sample. With the shift to digital methods and visualization software, experts can now flexibly pseudocolor and combine image channels, each corresponding to a different protein, to explore their spatial relationships. We thus propose psudo, an interactive system that allows users to create optimal color palettes for multichannel spatial data. In psudo, a novel optimization method generates palettes that maximize the perceptual differences between channels while mitigating confusing color blending in overlapping channels. We integrate this method into a system that allows users to explore multi-channel image data and compare and evaluate color palettes for their data. An interactive lensing approach provides on-demand feedback on channel overlap and a color confusion metric while giving context to the underlying channel values. Color palettes can be applied globally or, using the lens, to local regions of interest. We evaluate our palette optimization approach using three graphical perception tasks in a crowdsourced user study with 150 participants, showing that users are more accurate at discerning and comparing the underlying data using our approach. Additionally, we showcase psudo in a case study exploring the complex immune responses in cancer tissue data with a biologist.
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Affiliation(s)
- S Warchol
- Harvard School of Engineering and Applied Sciences
- Harvard Medical School
| | - J Troidl
- Harvard School of Engineering and Applied Sciences
| | | | - R Krueger
- Harvard School of Engineering and Applied Sciences
- Harvard Medical School
- New York University
| | | | - T Lin
- Harvard School of Engineering and Applied Sciences
| | - J Beyer
- Harvard School of Engineering and Applied Sciences
| | - E Glassman
- Harvard School of Engineering and Applied Sciences
| | | | - H Pfister
- Harvard School of Engineering and Applied Sciences
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Tian J, Quek C. Understanding the Tumor Microenvironment in Melanoma Patients with In-Transit Metastases and Its Impacts on Immune Checkpoint Immunotherapy Responses. Int J Mol Sci 2024; 25:4243. [PMID: 38673829 PMCID: PMC11050678 DOI: 10.3390/ijms25084243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Melanoma is the leading cause of global skin cancer-related death and currently ranks as the third most commonly diagnosed cancer in Australia. Melanoma patients with in-transit metastases (ITM), a type of locoregional metastasis located close to the primary tumor site, exhibit a high likelihood of further disease progression and poor survival outcomes. Immunotherapies, particularly immune checkpoint inhibitors (ICI), have demonstrated remarkable efficacy in ITM patients with reduced occurrence of further metastases and prolonged survival. The major challenge of immunotherapeutic efficacy lies in the limited understanding of melanoma and ITM biology, hindering our ability to identify patients who likely respond to ICIs effectively. In this review, we provided an overview of melanoma and ITM disease. We outlined the key ICI therapies and the critical immune features associated with therapy response or resistance. Lastly, we dissected the underlying biological components, including the cellular compositions and their communication networks within the tumor compartment, to enhance our understanding of the interactions between immunotherapy and melanoma, providing insights for future investigation and the development of drug targets and predictive biomarkers.
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Affiliation(s)
| | - Camelia Quek
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia;
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Vargas GM, Shafique N, Xu X, Karakousis G. Tumor-infiltrating lymphocytes as a prognostic and predictive factor for Melanoma. Expert Rev Mol Diagn 2024; 24:299-310. [PMID: 38314660 PMCID: PMC11134288 DOI: 10.1080/14737159.2024.2312102] [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: 07/27/2023] [Accepted: 01/17/2024] [Indexed: 02/06/2024]
Abstract
INTRODUCTION Tumor-infiltrating lymphocytes (TILs) have been investigated as prognostic factors in melanoma. Recent advancements in assessing the tumor microenvironment in the setting of more widespread use of immune checkpoint blockade have reignited interest in identifying predictive biomarkers. This review examines the function and significance of TILs in cutaneous melanoma, evaluating their potential as prognostic and predictive markers. AREAS COVERED A literature search was conducted on papers covering tumor infiltrating lymphocytes in cutaneous melanoma available online in PubMed and Web of Science from inception to 1 December 2023, supplemented by citation searching. This article encompasses the assessment of TILs, the role of TILs in the immune microenvironment, TILs as a prognostic factor, TILs as a predictive factor for immunotherapy response, and clinical applications of TILs in the treatment of cutaneous melanoma. EXPERT OPINION Tumor-infiltrating lymphocytes play a heterogeneous role in cutaneous melanoma. While they have historically been associated with improved survival, their status as independent prognostic or predictive factors remains uncertain. Novel methods of TIL assessment, such as determination of TIL subtypes and molecular signaling, demonstrate potential for predicting therapeutic response. Further, while their clinical utility in risk-stratification in melanoma treatment shows promise, a lack of consensus data hinders standardized application.
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Affiliation(s)
| | - Neha Shafique
- Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Xiaowei Xu
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Giorgos Karakousis
- Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
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Li Y, Jiang B, Chen B, Zou Y, Wang Y, Liu Q, Song B, Yu B. Integrative analysis of bulk and single-cell RNA-seq reveals the molecular characterization of the immune microenvironment and oxidative stress signature in melanoma. Heliyon 2024; 10:e28244. [PMID: 38560689 PMCID: PMC10979206 DOI: 10.1016/j.heliyon.2024.e28244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 03/11/2024] [Accepted: 03/14/2024] [Indexed: 04/04/2024] Open
Abstract
Background The immune microenvironment and oxidative stress of melanoma show significant heterogeneity, which affects tumor growth, invasion and treatment response. Single-cell and bulk RNA-seq data were used to explore the heterogeneity of the immune microenvironment and oxidative stress of melanoma. Methods The R package Seurat facilitated the analysis of the single-cell dataset, while Harmony, another R package, was employed for batch effect correction. Cell types were classified using Uniform Manifold Approximation and Projection (UMAP). The Secreted Signaling algorithm from CellChatDB.human was applied to elucidate cell-to-cell communication patterns within the single-cell data. Consensus clustering analysis for the skin cutaneous melanoma (SKCM) samples was executed with the R package ConsensusClusterPlus. To quantify immune infiltrating cells, we utilized CIBERSORT, ESTIMATE, and TIMERxCell algorithms provided by the R package Immuno-Oncology Biological Research (IOBR). Single nucleotide variant (SNV) analysis was conducted using Maftools, an R package specifically designed for this purpose. Subsequently, the expression levels of PXDN and PAPSS2 genes were assessed in melanoma tissues compared to adjacent normal tissues. Furthermore, in vitro experiments were conducted to evaluate the proliferation and reactive oxygen species expression in melanoma cells following transfection with siRNA targeting PXDN and PAPSS2. Results Malignant tumor cell populations were reclassified based on a comprehensive single-cell dataset analysis, which yielded six distinct tumor subsets. The specific marker genes identified for these subgroups were then used to interrogate the Cancer Genome Atlas Skin Cutaneous Melanoma (TCGA-SKCM) cohort, derived from bulk RNA sequencing data, resulting in the delineation of two immune molecular subtypes. Notably, patients within the cluster2 (C2) subtype exhibited a significantly more favorable prognosis compared to those in the cluster1 (C1) subtype. An alignment of immune characteristics was observed between the C2 subtype and unique immune functional tumor cell subsets. Genes differentially expressed across these subtypes were subsequently leveraged to construct a predictive risk model. In vitro investigations further revealed elevated expression levels of PXDN and PAPSS2 in melanoma tissue samples. Functional assays indicated that modulation of PXDN and PAPSS2 expression could influence the production of reactive oxygen species (ROS) and the proliferative capacity of melanoma cells. Conclusion The constructed six-gene signature can be used as an immune response and an oxidative stress marker to guide the clinical diagnosis and treatment of melanoma.
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Affiliation(s)
- Yaling Li
- Department of Dermatology, Institute of Dermatology, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, China
- Institute of Biomedical and Health Engineering, Shen Zhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, 518055, Guangdong, China
- Department of Dermatology, the First Hospital of China Medical University, Shenyang, 110001, Liaoning, China
| | - Bin Jiang
- Department of Dermatology, Institute of Dermatology, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, China
| | - Bancheng Chen
- Department of Dermatology, Institute of Dermatology, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, China
| | - Yanfen Zou
- Department of Dermatology, Institute of Dermatology, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, China
| | - Yan Wang
- Institute of Biomedical and Health Engineering, Shen Zhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, 518055, Guangdong, China
| | - Qian Liu
- Institute of Biomedical and Health Engineering, Shen Zhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, 518055, Guangdong, China
| | - Bing Song
- Institute of Biomedical and Health Engineering, Shen Zhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, 518055, Guangdong, China
- Department of Dermatology, the First Hospital of China Medical University, Shenyang, 110001, Liaoning, China
| | - Bo Yu
- Department of Dermatology, Institute of Dermatology, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, China
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9
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Yapp C, Nirmal AJ, Zhou F, Maliga Z, Tefft JB, Llopis PM, Murphy GF, Lian CG, Danuser G, Santagata S, Sorger PK. Multiplexed 3D Analysis of Immune States and Niches in Human Tissue. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.10.566670. [PMID: 38014052 PMCID: PMC10680601 DOI: 10.1101/2023.11.10.566670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Tissue homeostasis and the emergence of disease are controlled by changes in the proportions of resident and recruited cells, their organization into cellular neighbourhoods, and their interactions with acellular tissue components. Highly multiplexed tissue profiling (spatial omics)1 makes it possible to study this microenvironment in situ, usually in 4-5 micron thick sections (the standard histopathology format)2. Microscopy-based tissue profiling is commonly performed at a resolution sufficient to determine cell types but not to detect subtle morphological features associated with cytoskeletal reorganisation, juxtracrine signalling, or membrane trafficking3. Here we describe a high-resolution 3D imaging approach able to characterize a wide variety of organelles and structures at sub-micron scale while simultaneously quantifying millimetre-scale spatial features. This approach combines cyclic immunofluorescence (CyCIF) imaging4 of over 50 markers with confocal microscopy of archival human tissue thick enough (30-40 microns) to fully encompass two or more layers of intact cells. 3D imaging of entire cell volumes substantially improves the accuracy of cell phenotyping and allows cell proximity to be scored using plasma membrane apposition, not just nuclear position. In pre-invasive melanoma in situ5, precise phenotyping shows that adjacent melanocytic cells are plastic in state and participate in tightly localised niches of interferon signalling near sites of initial invasion into the underlying dermis. In this and metastatic melanoma, mature and precursor T cells engage in an unexpectedly diverse array of juxtracrine and membrane-membrane interactions as well as looser "neighbourhood" associations6 whose morphologies reveal functional states. These data provide new insight into the transitions occurring during early tumour formation and immunoediting and demonstrate the potential for phenotyping of tissues at a level of detail previously restricted to cultured cells and organoids.
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Affiliation(s)
- Clarence Yapp
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Centre at Harvard, Harvard Medical School, Boston, MA, 02115, USA
| | - Ajit J. Nirmal
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Centre at Harvard, Harvard Medical School, Boston, MA, 02115, USA
- Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Felix Zhou
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Zoltan Maliga
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | - Juliann B. Tefft
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Centre at Harvard, Harvard Medical School, Boston, MA, 02115, USA
| | - Paula Montero Llopis
- Microscopy Resources on the North Quad (MicRoN), Harvard Medical School, Boston, MA 02115, USA
| | - George F. Murphy
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Christine G. Lian
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Gaudenz Danuser
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Sandro Santagata
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Centre at Harvard, Harvard Medical School, Boston, MA, 02115, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Peter K. Sorger
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Centre at Harvard, Harvard Medical School, Boston, MA, 02115, USA
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
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10
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Wan G, Maliga Z, Yan B, Vallius T, Shi Y, Khattab S, Chang C, Nirmal AJ, Yu KH, Liu D, Lian CG, DeSimone MS, Sorger PK, Semenov YR. SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data. Brief Bioinform 2024; 25:bbae189. [PMID: 38701421 PMCID: PMC11066940 DOI: 10.1093/bib/bbae189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 03/01/2024] [Accepted: 04/12/2024] [Indexed: 05/05/2024] Open
Abstract
Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal and other cells within the tumor microenvironment (TME). Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize molecular, cellular and spatial properties of TMEs for various malignancies. This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of TMEs using multiplexed single-cell data. The source code and tutorials are available at https://semenovlab.github.io/SpatialCells. SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion and metastasis.
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Affiliation(s)
- Guihong Wan
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Zoltan Maliga
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Boshen Yan
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tuulia Vallius
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
| | - Yingxiao Shi
- Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sara Khattab
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Crystal Chang
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ajit J Nirmal
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
- Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - David Liu
- Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Christine G Lian
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Mia S DeSimone
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Yevgeniy R Semenov
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
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11
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Baker GJ, Novikov E, Zhao Z, Vallius T, Davis JA, Lin JR, Muhlich JL, Mittendorf EA, Santagata S, Guerriero JL, Sorger PK. Quality Control for Single Cell Analysis of High-plex Tissue Profiles using CyLinter. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.565120. [PMID: 37961235 PMCID: PMC10634977 DOI: 10.1101/2023.11.01.565120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artefacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly, and feature extraction. We show that these artefacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artefacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years prior to data collection, such as those from clinical trials.
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Affiliation(s)
- Gregory J. Baker
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
| | - Edward Novikov
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Ziyuan Zhao
- Systems, Synthetic, and Quantitative Biology Program, Harvard University, Cambridge, MA
| | - Tuulia Vallius
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Janae A. Davis
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA
| | - Jia-Ren Lin
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Jeremy L. Muhlich
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Elizabeth A. Mittendorf
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA
- Breast Oncology Program, Dana-Farber/Brigham and Women’s Cancer Center, Boston, MA
- Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA
| | - Sandro Santagata
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Jennifer L. Guerriero
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA
- Breast Oncology Program, Dana-Farber/Brigham and Women’s Cancer Center, Boston, MA
- Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA
| | - Peter K. Sorger
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
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12
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Launonen IM, Erkan EP, Niemiec I, Junquera A, Hincapié-Otero M, Afenteva D, Liang Z, Salko M, Szabo A, Perez-Villatoro F, Falco MM, Li Y, Micoli G, Nagaraj A, Haltia UM, Kahelin E, Oikkonen J, Hynninen J, Virtanen A, Nirmal AJ, Vallius T, Hautaniemi S, Sorger P, Vähärautio A, Färkkilä A. Chemotherapy induces myeloid-driven spatial T-cell exhaustion in ovarian cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585657. [PMID: 38562799 PMCID: PMC10983974 DOI: 10.1101/2024.03.19.585657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
To uncover the intricate, chemotherapy-induced spatiotemporal remodeling of the tumor microenvironment, we conducted integrative spatial and molecular characterization of 97 high-grade serous ovarian cancer (HGSC) samples collected before and after chemotherapy. Using single-cell and spatial analyses, we identify increasingly versatile immune cell states, which form spatiotemporally dynamic microcommunities at the tumor-stroma interface. We demonstrate that chemotherapy triggers spatial redistribution and exhaustion of CD8+ T cells due to prolonged antigen presentation by macrophages, both within interconnected myeloid networks termed "Myelonets" and at the tumor stroma interface. Single-cell and spatial transcriptomics identifies prominent TIGIT-NECTIN2 ligand-receptor interactions induced by chemotherapy. Using a functional patient-derived immuno-oncology platform, we show that CD8+T-cell activity can be boosted by combining immune checkpoint blockade with chemotherapy. Our discovery of chemotherapy-induced myeloid-driven spatial T-cell exhaustion paves the way for novel immunotherapeutic strategies to unleash CD8+ T-cell-mediated anti-tumor immunity in HGSC.
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Affiliation(s)
- Inga-Maria Launonen
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | | | - Iga Niemiec
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Ada Junquera
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | | | - Daria Afenteva
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Zhihan Liang
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Matilda Salko
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Angela Szabo
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | | | - Matias M Falco
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Yilin Li
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Giulia Micoli
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Ashwini Nagaraj
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Ulla-Maija Haltia
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Obstetrics and Gynecology, Department of Oncology, Clinical trials unit, Comprehensive Cancer Center, Helsinki University Hospital, Helsinki, Finland
| | - Essi Kahelin
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital
| | - Jaana Oikkonen
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Johanna Hynninen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, Finland
| | - Anni Virtanen
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital
| | - Ajit J Nirmal
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, USA
| | - Tuulia Vallius
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, USA
- Ludwig Center at Harvard
| | - Sampsa Hautaniemi
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Peter Sorger
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, USA
| | - Anna Vähärautio
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Foundation for the Finnish Cancer Institute, Finland
| | - Anniina Färkkilä
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Obstetrics and Gynecology, Department of Oncology, Clinical trials unit, Comprehensive Cancer Center, Helsinki University Hospital, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- Institute for Molecular Medicine Finland, Helsinki Institute for Life Sciences, University of Helsinki, Finland
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13
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de Souza N, Zhao S, Bodenmiller B. Multiplex protein imaging in tumour biology. Nat Rev Cancer 2024; 24:171-191. [PMID: 38316945 DOI: 10.1038/s41568-023-00657-4] [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: 12/08/2023] [Indexed: 02/07/2024]
Abstract
Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.
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Affiliation(s)
- Natalie de Souza
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Systems Biology, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Shan Zhao
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Bernd Bodenmiller
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland.
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland.
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14
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Ji S, Shi Y, Yin B. Macrophage barrier in the tumor microenvironment and potential clinical applications. Cell Commun Signal 2024; 22:74. [PMID: 38279145 PMCID: PMC10811890 DOI: 10.1186/s12964-023-01424-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 12/05/2023] [Indexed: 01/28/2024] Open
Abstract
The tumor microenvironment (TME) constitutes a complex microenvironment comprising a diverse array of immune cells and stromal components. Within this intricate context, tumor-associated macrophages (TAMs) exhibit notable spatial heterogeneity. This heterogeneity contributes to various facets of tumor behavior, including immune response modulation, angiogenesis, tissue remodeling, and metastatic potential. This review summarizes the spatial distribution of macrophages in both the physiological environment and the TME. Moreover, this paper explores the intricate interactions between TAMs and diverse immune cell populations (T cells, dendritic cells, neutrophils, natural killer cells, and other immune cells) within the TME. These bidirectional exchanges form a complex network of immune interactions that influence tumor immune surveillance and evasion strategies. Investigating TAM heterogeneity and its intricate interactions with different immune cell populations offers potential avenues for therapeutic interventions. Additionally, this paper discusses therapeutic strategies targeting macrophages, aiming to uncover novel approaches for immunotherapy. Video Abstract.
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Affiliation(s)
- Shuai Ji
- Department of Urinary Surgery, The Shengjing Hospital of China Medical University, Shenyang, 110022, China
| | - Yuqing Shi
- Department of Respiratory Medicine, Shenyang 10th People's Hospital, Shenyang, 110096, China
| | - Bo Yin
- Department of Urinary Surgery, The Shengjing Hospital of China Medical University, Shenyang, 110022, China.
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15
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Magrill J, Moldoveanu D, Gu J, Lajoie M, Watson IR. Mapping the single cell spatial immune landscapes of the melanoma microenvironment. Clin Exp Metastasis 2024:10.1007/s10585-023-10252-4. [PMID: 38217840 DOI: 10.1007/s10585-023-10252-4] [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: 07/15/2023] [Accepted: 11/27/2023] [Indexed: 01/15/2024]
Abstract
Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.
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Affiliation(s)
- Jamie Magrill
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada
- Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Dan Moldoveanu
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada
| | - Jiayao Gu
- Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Mathieu Lajoie
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada
| | - Ian R Watson
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada.
- Department of Human Genetics, McGill University, Montréal, QC, Canada.
- Department of Biochemistry, McGill University, Montréal, QC, Canada.
- Research Institute of the McGill University Health Centre, Montréal, QC, Canada.
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16
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Herzberger L, Hadwiger M, Kruger R, Sorger P, Pfister H, Groller E, Beyer J. Residency Octree: A Hybrid Approach for Scalable Web-Based Multi-Volume Rendering. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024; 30:1380-1390. [PMID: 37889813 PMCID: PMC10840607 DOI: 10.1109/tvcg.2023.3327193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/29/2023]
Abstract
We present a hybrid multi-volume rendering approach based on a novel Residency Octree that combines the advantages of out-of-core volume rendering using page tables with those of standard octrees. Octree approaches work by performing hierarchical tree traversal. However, in octree volume rendering, tree traversal and the selection of data resolution are intrinsically coupled. This makes fine-grained empty-space skipping costly. Page tables, on the other hand, allow access to any cached brick from any resolution. However, they do not offer a clear and efficient strategy for substituting missing high-resolution data with lower-resolution data. We enable flexible mixed-resolution out-of-core multi-volume rendering by decoupling the cache residency of multi-resolution data from a resolution-independent spatial subdivision determined by the tree. Instead of one-to-one node-to-brick correspondences, each residency octree node is mapped to a set of bricks from different resolution levels. This makes it possible to efficiently and adaptively choose and mix resolutions, adapt sampling rates, and compensate for cache misses. At the same time, residency octrees support fine-grained empty-space skipping, independent of the data subdivision used for caching. Finally, to facilitate collaboration and outreach, and to eliminate local data storage, our implementation is a web-based, pure client-side renderer using WebGPU and WebAssembly. Our method is faster than prior approaches and efficient for many data channels with a flexible and adaptive choice of data resolution.
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17
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Navrazhina K, Garcet S, Williams SC, Gulati N, Kiecker F, Frew JW, Mitsui H, Krueger JG. Laser capture microdissection provides a novel molecular profile of human primary cutaneous melanoma. Pigment Cell Melanoma Res 2024; 37:81-89. [PMID: 37776566 PMCID: PMC10841058 DOI: 10.1111/pcmr.13121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 08/08/2023] [Accepted: 08/16/2023] [Indexed: 10/02/2023]
Abstract
Melanoma accounts for the majority of skin cancer-related mortality, highlighting the need to better understand melanoma initiation and progression. In-depth molecular analysis of neoplastic melanocytes in whole tissue biopsies may be diluted by inflammatory infiltration, which may obscure gene signatures specific to neoplastic cells. Thus, a method is needed to precisely uncover molecular changes specific to tumor cells from a limited sample of primary melanomas. Here, we performed laser capture microdissection (LCM) and gene expression profiling of patient-derived frozen sections of pigmented lesions and primary cutaneous melanoma. Compared to bulk tissue analysis, analysis of LCM-derived samples identified 9528 additional differentially expressed genes (DEGs) including melanocyte-specific genes like PMEL and TYR, with enriched of pathways related to cell proliferation. LCM methodology also identified potentially targetable kinases specific to melanoma cells that were not detected by bulk tissue analysis. Taken together, our data demonstrate that there are marked differences in gene expression profiles depending on the method of sample isolation. We found that LCM captured higher expression of melanoma-related genes while whole tissue biopsy identified a wider range of inflammatory markers. Taken together, our data demonstrate that LCM is a valid approach to identify melanoma-specific changes using a relatively small amount of primary patient-derived melanoma sample.
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Affiliation(s)
- Kristina Navrazhina
- Laboratory of Investigative Dermatology, The Rockefeller University, New York, NY, USA
- Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD program, New York, NY
| | - Sandra Garcet
- Laboratory of Investigative Dermatology, The Rockefeller University, New York, NY, USA
| | - Samuel C. Williams
- Laboratory of Investigative Dermatology, The Rockefeller University, New York, NY, USA
- Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD program, New York, NY
| | - Nicholas Gulati
- Laboratory of Investigative Dermatology, The Rockefeller University, New York, NY, USA
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Felix Kiecker
- Department of Dermatology and Allergy, Skin Cancer Center, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - John W. Frew
- Laboratory of Investigative Dermatology, The Rockefeller University, New York, NY, USA
| | - Hiroshi Mitsui
- Laboratory of Investigative Dermatology, The Rockefeller University, New York, NY, USA
- Department of Dermatology, Faculty of Medicine, University of Yamanashi, Yamanashi, Japan
| | - James G. Krueger
- Laboratory of Investigative Dermatology, The Rockefeller University, New York, NY, USA
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18
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Schrom EC, McCaffrey EF, Radtke AJ, Speranza E, Arakkal L, Thakur N, Grant S, Germain RN. Spatial Patterning Analysis of Cellular Ensembles (SPACE) enables statistically robust discovery of complex spatial organization at the cell and tissue level. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.08.570837. [PMID: 38168288 PMCID: PMC10760187 DOI: 10.1101/2023.12.08.570837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Spatial patterns of cells and other entities drive both physiologic and pathologic processes within tissues. While many imaging and transcriptomic methods document tissue organization, discerning these patterns is challenging, especially when they involve multiple entities in complex arrangements. To address this challenge, we present Spatial Patterning Analysis of Cellular Ensembles (SPACE), an R package for analysis of high-plex tissue images generated using any collection modality. Unlike existing platforms, SPACE detects context-dependent associations, quantitative gradients and orientations, and other organizational complexities. Via a robust information theoretic framework, SPACE explores all possible ensembles - single entities, pairs, triplets, and so on - and ranks the strongest patterns of tissue organization. Using lymph node images for which ground truth has been defined, we validate SPACE and demonstrate its advantages. We then use SPACE to reanalyze a public dataset of human tuberculosis granulomas, verifying known patterns and discovering new patterns with possible insights into disease progression.
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Affiliation(s)
- Edward C Schrom
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Erin F McCaffrey
- T-Lymphocyte Biology Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Andrea J Radtke
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
- Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Emily Speranza
- Florida Research and Innovation Center, Cleveland Clinic Lerner Research Institute, Port Saint Lucie, FL 34987, USA
| | - Leanne Arakkal
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Nishant Thakur
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
- Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Spencer Grant
- Center for Alzheimer's and Related Dementias, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Ronald N Germain
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
- Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
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19
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Nirmal AJ, Yapp C, Santagata S, Sorger PK. Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.15.567196. [PMID: 38014110 PMCID: PMC10680730 DOI: 10.1101/2023.11.15.567196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Highly multiplexed tissue imaging and in situ spatial profiling aim to extract single-cell data from specimens containing closely packed cells of diverse morphology. This is challenging due to the difficulty of accurately assigning boundaries between cells (segmentation) and then generating per-cell staining intensities. Existing methods use gating to convert per-cell intensity data to positive and negative scores; this is a common approach in flow cytometry, but one that is problematic in imaging. In contrast, human experts identify cells in crowded environments using morphological, neighborhood, and intensity information. Here we describe a computational approach (Cell Spotter or CSPOT) that uses supervised machine learning in combination with classical segmentation to perform automated cell type calling. CSPOT is robust to artifacts that commonly afflict tissue imaging and can replace conventional gating. The end-to-end Python implementation of CSPOT can be integrated into cloud-based image processing pipelines to substantially improve the speed, accuracy, and reproducibility of single-cell spatial data.
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Affiliation(s)
- Ajit J. Nirmal
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Clarence Yapp
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Sandro Santagata
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Peter K. Sorger
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
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20
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Wan G, Maliga Z, Yan B, Vallius T, Shi Y, Khattab S, Chang C, Nirmal AJ, Yu KH, Liu D, Lian CG, DeSimone MS, Sorger PK, Semenov YR. SpatialCells: Automated Profiling of Tumor Microenvironments with Spatially Resolved Multiplexed Single-Cell Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.10.566378. [PMID: 38014067 PMCID: PMC10680639 DOI: 10.1101/2023.11.10.566378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Background Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal, and other cells within the tumor microenvironment. Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize the molecular, cellular, and spatial properties of tumor microenvironments for various malignancies. Results This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of tumor microenvironments using multiplexed single-cell data. Conclusions SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion, and metastasis.
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Affiliation(s)
- Guihong Wan
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Zoltan Maliga
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Boshen Yan
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tuulia Vallius
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
| | - Yingxiao Shi
- Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sara Khattab
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Crystal Chang
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ajit J. Nirmal
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - David Liu
- Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Christine G. Lian
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Mia S. DeSimone
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter K. Sorger
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Yevgeniy R. Semenov
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
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21
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Coy S, Cheng B, Lee JS, Rashid R, Browning L, Xu Y, Chakrabarty SS, Yapp C, Chan S, Tefft JB, Scott E, Spektor A, Ligon KL, Baker GJ, Pellman D, Sorger PK, Santagata S. 2D and 3D multiplexed subcellular profiling of nuclear instability in human cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.07.566063. [PMID: 37986801 PMCID: PMC10659270 DOI: 10.1101/2023.11.07.566063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Nuclear atypia, including altered nuclear size, contour, and chromatin organization, is ubiquitous in cancer cells. Atypical primary nuclei and micronuclei can rupture during interphase; however, the frequency, causes, and consequences of nuclear rupture are unknown in most cancers. We demonstrate that nuclear envelope rupture is surprisingly common in many human cancers, particularly glioblastoma. Using highly-multiplexed 2D and super-resolution 3D-imaging of glioblastoma tissues and patient-derived xenografts and cells, we link primary nuclear rupture with reduced lamin A/C and micronuclear rupture with reduced lamin B1. Moreover, ruptured glioblastoma cells activate cGAS-STING-signaling involved in innate immunity. We observe that local patterning of cell states influences tumor spatial organization and is linked to both lamin expression and rupture frequency, with neural-progenitor-cell-like states exhibiting the lowest lamin A/C levels and greatest susceptibility to primary nuclear rupture. Our study reveals that nuclear instability is a core feature of cancer, and links nuclear integrity, cell state, and immune signaling.
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Affiliation(s)
- Shannon Coy
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Brian Cheng
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Jong Suk Lee
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Rumana Rashid
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Lindsay Browning
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Yilin Xu
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Sankha S. Chakrabarty
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Clarence Yapp
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Sabrina Chan
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Juliann B. Tefft
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Emily Scott
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Alexander Spektor
- Department of Radiation Oncology, Brigham and Women’s Hospital and Dana Farber Cancer Institute, Boston, MA, USA
| | - Keith L. Ligon
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Gregory J. Baker
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - David Pellman
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Peter K. Sorger
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Sandro Santagata
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
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22
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Velleuer E, Domínguez-Hüttinger E, Rodríguez A, Harris LA, Carlberg C. Concepts of multi-level dynamical modelling: understanding mechanisms of squamous cell carcinoma development in Fanconi anemia. Front Genet 2023; 14:1254966. [PMID: 38028610 PMCID: PMC10652399 DOI: 10.3389/fgene.2023.1254966] [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: 07/07/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Fanconi anemia (FA) is a rare disease (incidence of 1:300,000) primarily based on the inheritance of pathogenic variants in genes of the FA/BRCA (breast cancer) pathway. These variants ultimately reduce the functionality of different proteins involved in the repair of DNA interstrand crosslinks and DNA double-strand breaks. At birth, individuals with FA might present with typical malformations, particularly radial axis and renal malformations, as well as other physical abnormalities like skin pigmentation anomalies. During the first decade of life, FA mostly causes bone marrow failure due to reduced capacity and loss of the hematopoietic stem and progenitor cells. This often makes hematopoietic stem cell transplantation necessary, but this therapy increases the already intrinsic risk of developing squamous cell carcinoma (SCC) in early adult age. Due to the underlying genetic defect in FA, classical chemo-radiation-based treatment protocols cannot be applied. Therefore, detecting and treating the multi-step tumorigenesis process of SCC in an early stage, or even its progenitors, is the best option for prolonging the life of adult FA individuals. However, the small number of FA individuals makes classical evidence-based medicine approaches based on results from randomized clinical trials impossible. As an alternative, we introduce here the concept of multi-level dynamical modelling using large, longitudinally collected genome, proteome- and transcriptome-wide data sets from a small number of FA individuals. This mechanistic modelling approach is based on the "hallmarks of cancer in FA", which we derive from our unique database of the clinical history of over 750 FA individuals. Multi-omic data from healthy and diseased tissue samples of FA individuals are to be used for training constituent models of a multi-level tumorigenesis model, which will then be used to make experimentally testable predictions. In this way, mechanistic models facilitate not only a descriptive but also a functional understanding of SCC in FA. This approach will provide the basis for detecting signatures of SCCs at early stages and their precursors so they can be efficiently treated or even prevented, leading to a better prognosis and quality of life for the FA individual.
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Affiliation(s)
- Eunike Velleuer
- Department of Cytopathology, Heinrich Heine University, Düsseldorf, Germany
- Center for Child and Adolescent Health, Helios Klinikum, Krefeld, Germany
| | - Elisa Domínguez-Hüttinger
- Departamento Düsseldorf Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad México, Mexico
| | - Alfredo Rodríguez
- Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad México, Mexico
- Instituto Nacional de Pediatría, Ciudad México, Mexico
| | - Leonard A. Harris
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR, United States
- Interdisciplinary Graduate Program in Cell and Molecular Biology, University of Arkansas, Fayetteville, AR, United States
- Cancer Biology Program, Winthrop P Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Carsten Carlberg
- Institute of Animal Reproduction and Food Research, Polish Academy of Sciences, Olsztyn, Poland
- School of Medicine, Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
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23
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Park YM, Lin DC. Moving closer towards a comprehensive view of tumor biology and microarchitecture using spatial transcriptomics. Nat Commun 2023; 14:7017. [PMID: 37919364 PMCID: PMC10622517 DOI: 10.1038/s41467-023-42960-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/27/2023] [Indexed: 11/04/2023] Open
Affiliation(s)
- Young Min Park
- Center for Craniofacial Molecular Biology, Herman Ostrow School of Dentistry, and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, South Korea
| | - De-Chen Lin
- Center for Craniofacial Molecular Biology, Herman Ostrow School of Dentistry, and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA.
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24
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Windhager J, Zanotelli VRT, Schulz D, Meyer L, Daniel M, Bodenmiller B, Eling N. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc 2023; 18:3565-3613. [PMID: 37816904 DOI: 10.1038/s41596-023-00881-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 06/23/2023] [Indexed: 10/12/2023]
Abstract
Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell-cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5-6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/ .
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Affiliation(s)
- Jonas Windhager
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
- SciLifeLab BioImage Informatics Facility and Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Vito Riccardo Tomaso Zanotelli
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Division of Metabolism and Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Daniel Schulz
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Lasse Meyer
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Michelle Daniel
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
| | - Nils Eling
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
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25
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Gray GK, Girnius N, Kuiken HJ, Henstridge AZ, Brugge JS. Single-cell and spatial analyses reveal a tradeoff between murine mammary proliferation and lineage programs associated with endocrine cues. Cell Rep 2023; 42:113293. [PMID: 37858468 PMCID: PMC10840493 DOI: 10.1016/j.celrep.2023.113293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/25/2023] [Accepted: 09/29/2023] [Indexed: 10/21/2023] Open
Abstract
Although distinct epithelial cell types have been distinguished in glandular tissues such as the mammary gland, the extent of heterogeneity within each cell type and the degree of endocrine control of this diversity across development are incompletely understood. By combining mass cytometry and cyclic immunofluorescence, we define a rich array of murine mammary epithelial cell subtypes associated with puberty, the estrous cycle, and sex. These subtypes are differentially proliferative and spatially segregate distinctly in adult versus pubescent glands. Further, we identify systematic suppression of lineage programs at the protein and RNA levels as a common feature of mammary epithelial expansion during puberty, the estrous cycle, and gestation and uncover a pervasive enrichment of ribosomal protein genes in luminal cells elicited specifically during progesterone-dominant expansionary periods. Collectively, these data expand our knowledge of murine mammary epithelial heterogeneity and connect endocrine-driven epithelial expansion with lineage suppression.
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Affiliation(s)
- G Kenneth Gray
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Nomeda Girnius
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; The Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Hendrik J Kuiken
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Aylin Z Henstridge
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Joan S Brugge
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
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26
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Adeuyan O, Gordon ER, Kenchappa D, Bracero Y, Singh A, Espinoza G, Geskin LJ, Saenger YM. An update on methods for detection of prognostic and predictive biomarkers in melanoma. Front Cell Dev Biol 2023; 11:1290696. [PMID: 37900283 PMCID: PMC10611507 DOI: 10.3389/fcell.2023.1290696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 10/04/2023] [Indexed: 10/31/2023] Open
Abstract
The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.
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Affiliation(s)
- Oluwaseyi Adeuyan
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Emily R. Gordon
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Divya Kenchappa
- Albert Einstein College of Medicine, Bronx, NY, United States
| | - Yadriel Bracero
- Albert Einstein College of Medicine, Bronx, NY, United States
| | - Ajay Singh
- Albert Einstein College of Medicine, Bronx, NY, United States
| | | | - Larisa J. Geskin
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, United States
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27
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Wee Y, Wang J, Wilson EC, Rich CP, Rogers A, Tong Z, DeGroot E, Gopal YV, Davies MA, Ekiz HA, Tay JK, Stubben C, Boucher KM, Oviedo JM, Fairfax KC, Williams MA, Holmen SL, Wolff RK, Grossmann AH. ARF6-dependent endocytic trafficking of the Interferon-γ receptor drives adaptive immune resistance in cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.29.560199. [PMID: 37873189 PMCID: PMC10592860 DOI: 10.1101/2023.09.29.560199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Adaptive immune resistance (AIR) is a protective process used by cancer to escape elimination by CD8+ T cells. Inhibition of immune checkpoints PD-1 and CTLA-4 specifically target Interferon-gamma (IFNγ)-driven AIR. AIR begins at the plasma membrane where tumor cell-intrinsic cytokine signaling is initiated. Thus, plasma membrane remodeling by endomembrane trafficking could regulate AIR. Herein we report that the trafficking protein ADP-Ribosylation Factor 6 (ARF6) is critical for IFNγ-driven AIR. ARF6 prevents transport of the receptor to the lysosome, augmenting IFNγR expression, tumor intrinsic IFNγ signaling and downstream expression of immunosuppressive genes. In murine melanoma, loss of ARF6 causes resistance to immune checkpoint blockade (ICB). Likewise, low expression of ARF6 in patient tumors correlates with inferior outcomes with ICB. Our data provide new mechanistic insights into tumor immune escape, defined by ARF6-dependent AIR, and support that ARF6-dependent endomembrane trafficking of the IFNγ receptor influences outcomes of ICB.
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Affiliation(s)
- Yinshen Wee
- Department of Pathology, University of Utah, Salt Lake City, Utah
- Huntsman Cancer Institute, Salt Lake City, Utah
- These authors contributed equally
- current contact information: School of Dentistry, Taipei Medical University, Taiwan
| | - Junhua Wang
- Department of Pathology, University of Utah, Salt Lake City, Utah
- Huntsman Cancer Institute, Salt Lake City, Utah
- These authors contributed equally
| | - Emily C. Wilson
- Department of Pathology, University of Utah, Salt Lake City, Utah
- Huntsman Cancer Institute, Salt Lake City, Utah
| | - Coulson P. Rich
- Department of Pathology, University of Utah, Salt Lake City, Utah
- Huntsman Cancer Institute, Salt Lake City, Utah
| | - Aaron Rogers
- Department of Pathology, University of Utah, Salt Lake City, Utah
- Huntsman Cancer Institute, Salt Lake City, Utah
| | - Zongzhong Tong
- Department of Pathology, University of Utah, Salt Lake City, Utah
| | - Evelyn DeGroot
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Y.N. Vashisht Gopal
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Michael A. Davies
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - H. Atakan Ekiz
- Department of Molecular Biology and Genetics, Izmir institute of Technology, Gulbahce, Urla, 35430, Izmir, Turkey
| | - Joshua K.H. Tay
- Department of Pathology, University of Utah, Salt Lake City, Utah
- Huntsman Cancer Institute, Salt Lake City, Utah
| | - Chris Stubben
- Bioinformatics Shared Resource, Huntsman Cancer Institute, Salt Lake City, Utah
| | - Kenneth M. Boucher
- Cancer Biostatistics Shared Resource, Huntsman Cancer Institute, Salt Lake City, Utah
| | - Juan M. Oviedo
- Department of Pathology, University of Utah, Salt Lake City, Utah
| | - Keke C. Fairfax
- Department of Pathology, University of Utah, Salt Lake City, Utah
| | - Matthew A. Williams
- Department of Pathology, University of Utah, Salt Lake City, Utah
- Huntsman Cancer Institute, Salt Lake City, Utah
| | - Sheri L. Holmen
- Huntsman Cancer Institute, Salt Lake City, Utah
- Department of Surgery, University of Utah, Salt Lake City, Utah
| | - Roger K. Wolff
- Department of Pathology, University of Utah, Salt Lake City, Utah
- Huntsman Cancer Institute, Salt Lake City, Utah
| | - Allie H. Grossmann
- Department of Pathology, University of Utah, Salt Lake City, Utah
- Huntsman Cancer Institute, Salt Lake City, Utah
- Lead contact
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28
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Nordmann TM, Schweizer L, Metousis A, Thielert M, Rodriguez E, Rahbek-Gjerdrum LM, Stadler PC, Bzorek M, Mund A, Rosenberger FA, Mann M. A Standardized and Reproducible Workflow for Membrane Glass Slides in Routine Histology and Spatial Proteomics. Mol Cell Proteomics 2023; 22:100643. [PMID: 37683827 PMCID: PMC10565769 DOI: 10.1016/j.mcpro.2023.100643] [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: 07/06/2023] [Revised: 08/28/2023] [Accepted: 09/05/2023] [Indexed: 09/10/2023] Open
Abstract
Defining the molecular phenotype of single cells in situ is key for understanding tissue architecture in health and disease. Advanced imaging platforms have recently been joined by spatial omics technologies, promising unparalleled insights into the molecular landscape of biological samples. Furthermore, high-precision laser microdissection (LMD) of tissue on membrane glass slides is a powerful method for spatial omics technologies and single-cell type spatial proteomics in particular. However, current histology protocols have not been compatible with glass membrane slides and LMD for automated staining platforms and routine histology procedures. This has prevented the combination of advanced staining procedures with LMD. In this study, we describe a novel method for handling glass membrane slides that enables automated eight-color multiplexed immunofluorescence staining and high-quality imaging followed by precise laser-guided extraction of single cells. The key advance is the glycerol-based modification of heat-induced epitope retrieval protocols, termed "G-HIER." We find that this altered antigen-retrieval solution prevents membrane distortion. Importantly, G-HIER is fully compatible with current antigen retrieval workflows and mass spectrometry-based proteomics and does not affect proteome depth or quality. To demonstrate the versatility of G-HIER for spatial proteomics, we apply the recently introduced deep visual proteomics technology to perform single-cell type analysis of adjacent suprabasal and basal keratinocytes of human skin. G-HIER overcomes previous incompatibility of standard and advanced staining protocols with membrane glass slides and enables robust integration with routine histology procedures, high-throughput multiplexed imaging, and sophisticated downstream spatial omics technologies.
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Affiliation(s)
- Thierry M Nordmann
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Lisa Schweizer
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Andreas Metousis
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Marvin Thielert
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Edwin Rodriguez
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | | | | | - Michael Bzorek
- Department of Pathology, Zealand University Hospital, Roskilde, Denmark
| | - Andreas Mund
- Proteomics Program, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Florian A Rosenberger
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
| | - Matthias Mann
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; Proteomics Program, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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29
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Zhao C, Germain RN. Multiplex imaging in immuno-oncology. J Immunother Cancer 2023; 11:e006923. [PMID: 37899132 PMCID: PMC10619056 DOI: 10.1136/jitc-2023-006923] [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: 06/15/2023] [Indexed: 10/31/2023] Open
Abstract
Multiplex imaging has emerged as an invaluable tool for immune-oncologists and translational researchers, enabling them to examine intricate interactions among immune cells, stroma, matrix, and malignant cells within the tumor microenvironment (TME). It holds significant promise in the quest to discover improved biomarkers for treatment stratification and identify novel therapeutic targets. Nonetheless, several challenges exist in the realms of study design, experiment optimization, and data analysis. In this review, our aim is to present an overview of the utilization of multiplex imaging in immuno-oncology studies and inform novice researchers about the fundamental principles at each stage of the imaging and analysis process.
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Affiliation(s)
- Chen Zhao
- Thoracic and GI Malignancies Branch, CCR, NCI, Bethesda, Maryland, USA
- Lymphocyte Biology Section, Laboratory of Immune System Biology, NIAID, Bethesda, Maryland, USA
| | - Ronald N Germain
- Lymphocyte Biology Section, Laboratory of Immune System Biology, NIAID, Bethesda, Maryland, USA
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30
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Tang Z, Liu X, Li Z, Zhang T, Yang B, Su J, Song Q. SpaRx: elucidate single-cell spatial heterogeneity of drug responses for personalized treatment. Brief Bioinform 2023; 24:bbad338. [PMID: 37798249 PMCID: PMC10555713 DOI: 10.1093/bib/bbad338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 08/08/2023] [Accepted: 09/07/2023] [Indexed: 10/07/2023] Open
Abstract
Spatial cellular authors heterogeneity contributes to differential drug responses in a tumor lesion and potential therapeutic resistance. Recent emerging spatial technologies such as CosMx, MERSCOPE and Xenium delineate the spatial gene expression patterns at the single cell resolution. This provides unprecedented opportunities to identify spatially localized cellular resistance and to optimize the treatment for individual patients. In this work, we present a graph-based domain adaptation model, SpaRx, to reveal the heterogeneity of spatial cellular response to drugs. SpaRx transfers the knowledge from pharmacogenomics profiles to single-cell spatial transcriptomics data, through hybrid learning with dynamic adversarial adaption. Comprehensive benchmarking demonstrates the superior and robust performance of SpaRx at different dropout rates, noise levels and transcriptomics coverage. Further application of SpaRx to the state-of-the-art single-cell spatial transcriptomics data reveals that tumor cells in different locations of a tumor lesion present heterogenous sensitivity or resistance to drugs. Moreover, resistant tumor cells interact with themselves or the surrounding constituents to form an ecosystem for drug resistance. Collectively, SpaRx characterizes the spatial therapeutic variability, unveils the molecular mechanisms underpinning drug resistance and identifies personalized drug targets and effective drug combinations.
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Affiliation(s)
- Ziyang Tang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Xiang Liu
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
| | - Zuotian Li
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
- Department of Computer Graphics Technology, Purdue University, Indiana, USA
| | - Tonglin Zhang
- Department of Statistics, Purdue University, Indiana, USA
| | - Baijian Yang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Jing Su
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
| | - Qianqian Song
- Department of Cancer Biology, Wake Forest University School of Medicine, North Carolina, USA
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Florida, USA
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31
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XIE FANGMEI, XI NAITE, HAN ZEPING, LUO WENFENG, SHEN JIAN, LUO JINGGENG, TANG XINGKUI, PANG TING, LV YUBING, LIANG JIABING, LIAO LIYIN, ZHANG HAOYU, JIANG YONG, LI YUGUANG, HE JINHUA. Progress in research on tumor microenvironment-based spatial omics technologies. Oncol Res 2023; 31:877-885. [PMID: 37744276 PMCID: PMC10513957 DOI: 10.32604/or.2023.029494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 06/21/2023] [Indexed: 09/26/2023] Open
Abstract
Spatial omics technology integrates the concept of space into omics research and retains the spatial information of tissues or organs while obtaining molecular information. It is characterized by the ability to visualize changes in molecular information and yields intuitive and vivid visual results. Spatial omics technologies include spatial transcriptomics, spatial proteomics, spatial metabolomics, and other technologies, the most widely used of which are spatial transcriptomics and spatial proteomics. The tumor microenvironment refers to the surrounding microenvironment in which tumor cells exist, including the surrounding blood vessels, immune cells, fibroblasts, bone marrow-derived inflammatory cells, various signaling molecules, and extracellular matrix. A key issue in modern tumor biology is the application of spatial omics to the study of the tumor microenvironment, which can reveal problems that conventional research techniques cannot, potentially leading to the development of novel therapeutic agents for cancer. This paper summarizes the progress of research on spatial transcriptomics and spatial proteomics technologies for characterizing the tumor immune microenvironment.
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Affiliation(s)
- FANGMEI XIE
- Central Laboratory, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - NAITE XI
- Central Laboratory, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - ZEPING HAN
- Central Laboratory, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - WENFENG LUO
- Central Laboratory, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - JIAN SHEN
- Central Laboratory, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - JINGGENG LUO
- Department of General Surgery, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - XINGKUI TANG
- Department of General Surgery, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - TING PANG
- Central Laboratory, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - YUBING LV
- Central Laboratory, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - JIABING LIANG
- Central Laboratory, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - LIYIN LIAO
- Central Laboratory, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - HAOYU ZHANG
- Central Laboratory, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - YONG JIANG
- Central Laboratory, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - YUGUANG LI
- Administrating Office, He Xian Memorial Hospital, Southern Medical University, Guangzhou, China
| | - JINHUA HE
- Central Laboratory, Panyu Central Hospital of Guangzhou, Guangzhou, China
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32
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Centofanti E, Wang C, Iyer S, Krichevsky O, Oyler-Yaniv A, Oyler-Yaniv J. The spread of interferon-γ in melanomas is highly spatially confined, driving nongenetic variability in tumor cells. Proc Natl Acad Sci U S A 2023; 120:e2304190120. [PMID: 37603742 PMCID: PMC10468618 DOI: 10.1073/pnas.2304190120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 07/12/2023] [Indexed: 08/23/2023] Open
Abstract
Interferon-γ (IFNγ) is a critical antitumor cytokine that has varied effects on different cell types. The global effect of IFNγ in the tumor depends on which cells it acts upon and the spatial extent of its spread. Reported measurements of IFNγ spread vary dramatically in different contexts, ranging from nearest-neighbor signaling to perfusion throughout the entire tumor. Here, we apply theoretical considerations to experiments both in vitro and in vivo to study the spread of IFNγ in melanomas. We observe spatially confined niches of IFNγ signaling in 3-D mouse melanoma cultures and human tumors that generate cellular heterogeneity in gene expression and alter the susceptibility of affected cells to T cell killing. Widespread IFNγ signaling only occurs when niches overlap due to high local densities of IFNγ-producing T cells. We measured length scales of ~30 to 40 μm for IFNγ spread in B16 mouse melanoma cultures and human primary cutaneous melanoma. Our results are consistent with IFNγ spread being governed by a simple diffusion-consumption model and offer insight into how the spatial organization of T cells contributes to intratumor heterogeneity in inflammatory signaling, gene expression, and immune-mediated clearance. Solid tumors are often viewed as collections of diverse cellular "neighborhoods": Our work provides a general explanation for such nongenetic cellular variability due to confinement in the spread of immune mediators.
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Affiliation(s)
- Edoardo Centofanti
- The Department of Systems Biology at Harvard Medical School, Boston, MA02115
| | - Chad Wang
- The Systems, Synthetic, and Quantitative Biology Graduate Program at Harvard Medical School, Boston, MA02115
| | - Sandhya Iyer
- The Department of Systems Biology at Harvard Medical School, Boston, MA02115
| | - Oleg Krichevsky
- The Department of Physics at Ben Gurion University of the Negev, Beer-Sheva8410501, Israel
| | - Alon Oyler-Yaniv
- The Department of Systems Biology at Harvard Medical School, Boston, MA02115
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33
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Gunawan I, Vafaee F, Meijering E, Lock JG. An introduction to representation learning for single-cell data analysis. CELL REPORTS METHODS 2023; 3:100547. [PMID: 37671013 PMCID: PMC10475795 DOI: 10.1016/j.crmeth.2023.100547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
Single-cell-resolved systems biology methods, including omics- and imaging-based measurement modalities, generate a wealth of high-dimensional data characterizing the heterogeneity of cell populations. Representation learning methods are routinely used to analyze these complex, high-dimensional data by projecting them into lower-dimensional embeddings. This facilitates the interpretation and interrogation of the structures, dynamics, and regulation of cell heterogeneity. Reflecting their central role in analyzing diverse single-cell data types, a myriad of representation learning methods exist, with new approaches continually emerging. Here, we contrast general features of representation learning methods spanning statistical, manifold learning, and neural network approaches. We consider key steps involved in representation learning with single-cell data, including data pre-processing, hyperparameter optimization, downstream analysis, and biological validation. Interdependencies and contingencies linking these steps are also highlighted. This overview is intended to guide researchers in the selection, application, and optimization of representation learning strategies for current and future single-cell research applications.
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Affiliation(s)
- Ihuan Gunawan
- School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- School of Computer Science and Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, Australia
| | - Fatemeh Vafaee
- School of Biotechnology and Biomolecular Sciences, Faculty of Science, University of New South Wales, Sydney, NSW, Australia
- UNSW Data Science Hub, University of New South Wales, Sydney, NSW, Australia
| | - Erik Meijering
- School of Computer Science and Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, Australia
| | - John George Lock
- School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- UNSW Data Science Hub, University of New South Wales, Sydney, NSW, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
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34
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Tang Z, Liu X, Li Z, Zhang T, Yang B, Su J, Song Q. SpaRx: Elucidate single-cell spatial heterogeneity of drug responses for personalized treatment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.03.551911. [PMID: 37577665 PMCID: PMC10418183 DOI: 10.1101/2023.08.03.551911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Spatial cellular heterogeneity contributes to differential drug responses in a tumor lesion and potential therapeutic resistance. Recent emerging spatial technologies such as CosMx SMI, MERSCOPE, and Xenium delineate the spatial gene expression patterns at the single cell resolution. This provides unprecedented opportunities to identify spatially localized cellular resistance and to optimize the treatment for individual patients. In this work, we present a graph-based domain adaptation model, SpaRx, to reveal the heterogeneity of spatial cellular response to drugs. SpaRx transfers the knowledge from pharmacogenomics profiles to single-cell spatial transcriptomics data, through hybrid learning with dynamic adversarial adaption. Comprehensive benchmarking demonstrates the superior and robust performance of SpaRx at different dropout rates, noise levels, and transcriptomics coverage. Further application of SpaRx to the state-of-art single-cell spatial transcriptomics data reveals that tumor cells in different locations of a tumor lesion present heterogenous sensitivity or resistance to drugs. Moreover, resistant tumor cells interact with themselves or the surrounding constituents to form an ecosystem for drug resistance. Collectively, SpaRx characterizes the spatial therapeutic variability, unveils the molecular mechanisms underpinning drug resistance, and identifies personalized drug targets and effective drug combinations. Key Points We have developed a novel graph-based domain adaption model named SpaRx, to reveal the heterogeneity of spatial cellular response to different types of drugs, which bridges the gap between pharmacogenomics knowledgebase and single-cell spatial transcriptomics data.SpaRx is developed tailored for single-cell spatial transcriptomics data and is provided available as a ready-to-use open-source software, which demonstrates high accuracy and robust performance.SpaRx uncovers that tumor cells located in different areas within tumor lesion exhibit varying levels of sensitivity or resistance to drugs. Moreover, SpaRx reveals that tumor cells interact with themselves and the surrounding microenvironment to form an ecosystem capable of drug resistance.
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Affiliation(s)
- Ziyang Tang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Xiang Liu
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
| | - Zuotian Li
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
- Department of Computer Graphics Technology, Purdue University, Indiana, USA
| | - Tonglin Zhang
- Department of Statistics, Purdue University, Indiana, USA
| | - Baijian Yang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Jing Su
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
| | - Qianqian Song
- Center for Cancer Genomics and Precision Oncology, Atrium Health Wake Forest Baptist Comprehensive Cancer Center, North Carolina, USA
- Department of Cancer Biology, Wake Forest University School of Medicine, North Carolina, USA
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35
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Chen Z, Lau KS. Advances in Mapping Tumor Progression from Precancer Atlases. Cancer Prev Res (Phila) 2023; 16:439-447. [PMID: 37167978 PMCID: PMC10523872 DOI: 10.1158/1940-6207.capr-22-0473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/22/2023] [Accepted: 04/11/2023] [Indexed: 05/13/2023]
Abstract
Tissue profiling technologies present opportunities for understanding transition from precancerous lesions to malignancy, which may impact risk stratification, prevention, and even cancer treatment. A human precancer atlas building effort is ongoing to tackle the significant challenge of decoding the heterogeneity among cells, specimens, and patients. Here, we discuss the findings resulting from atlases built across precancer types, including those found in colon, breast, lung, stomach, cervix, and skin, using bulk, single-cell, and spatial profiling strategies. We highlight two main themes that emerge across precancer types: the ordering of molecular events that occur during tumor progression and the fluctuation of microenvironmental response during precancer progression. We further highlight the key challenges of data integration across large cohorts of patients, and the need for computational tools to reliably annotate and quality control high-volume, high-dimensional data.
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Affiliation(s)
- Zhengyi Chen
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Ken S. Lau
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
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36
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Langenbach M, Giesler S, Richtsfeld S, Costa-Pereira S, Rindlisbacher L, Wertheimer T, Braun LM, Andrieux G, Duquesne S, Pfeifer D, Woessner NM, Menssen HD, Taromi S, Duyster J, Börries M, Brummer T, Blazar BR, Minguet S, Turko P, Levesque MP, Becher B, Zeiser R. MDM2 Inhibition Enhances Immune Checkpoint Inhibitor Efficacy by Increasing IL15 and MHC Class II Production. Mol Cancer Res 2023; 21:849-864. [PMID: 37071397 PMCID: PMC10524444 DOI: 10.1158/1541-7786.mcr-22-0898] [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: 11/09/2022] [Revised: 03/12/2023] [Accepted: 04/13/2023] [Indexed: 04/19/2023]
Abstract
The treatment of patients with metastatic melanoma with immune checkpoint inhibitors (ICI) leads to impressive response rates but primary and secondary resistance to ICI reduces progression-free survival. Novel strategies that interfere with resistance mechanisms are key to further improve patient outcome during ICI therapy. P53 is often inactivated by mouse-double-minute-2 (MDM2), which may decrease immunogenicity of melanoma cells. We analyzed primary patient-derived melanoma cell lines, performed bulk sequencing analysis of patient-derived melanoma samples, and used melanoma mouse models to investigate the role of MDM2-inhibition for enhanced ICI therapy. We found increased expression of IL15 and MHC-II in murine melanoma cells upon p53 induction by MDM2-inhibition. MDM2-inhibitor induced MHC-II and IL15-production, which was p53 dependent as Tp53 knockdown blocked the effect. Lack of IL15-receptor in hematopoietic cells or IL15 neutralization reduced the MDM2-inhibition/p53-induction-mediated antitumor immunity. P53 induction by MDM2-inhibition caused anti-melanoma immune memory as T cells isolated from MDM2-inhibitor-treated melanoma-bearing mice exhibited anti-melanoma activity in secondary melanoma-bearing mice. In patient-derived melanoma cells p53 induction by MDM2-inhibition increased IL15 and MHC-II. IL15 and CIITA expressions were associated with a more favorable prognosis in patients bearing WT but not TP53-mutated melanoma. IMPLICATIONS MDM2-inhibition represents a novel strategy to enhance IL15 and MHC-II-production, which disrupts the immunosuppressive tumor microenvironment. On the basis of our findings, a clinical trial combining MDM2-inhibition with anti-PD-1 immunotherapy for metastatic melanoma is planned.
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Affiliation(s)
- Marlene Langenbach
- Department of Medicine I - Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
- Faculty of Biology, Albert-Ludwigs-University, Freiburg, Germany
| | - Sophie Giesler
- Department of Medicine I - Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Stefan Richtsfeld
- Department of Medicine I - Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Sara Costa-Pereira
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | - Lukas Rindlisbacher
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | - Tobias Wertheimer
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | - Lukas M. Braun
- Department of Medicine I - Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Geoffroy Andrieux
- Institute of Medical Bioinformatics and Systems Medicine, Medical Center - University of Freiburg, Germany. German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sandra Duquesne
- Department of Medicine I - Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Dietmar Pfeifer
- Department of Medicine I - Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Nadine M. Woessner
- Signalling Research Centres BIOSS and CIBSS – Centre for Integrative Biological Signalling Studies, University of Freiburg
- Spemann Graduate School of Biology and Medicine (SGBM), Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | | | - Sanaz Taromi
- Department of Medicine I - Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Justus Duyster
- Department of Medicine I - Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Melanie Börries
- Institute of Medical Bioinformatics and Systems Medicine, Medical Center - University of Freiburg, Germany. German Cancer Research Center (DKFZ), Heidelberg, Germany
- Spemann Graduate School of Biology and Medicine (SGBM), Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Tilman Brummer
- German Cancer Consortium (DKTK), partner site Freiburg, and German Cancer Research Center (DKFZ) Heidelberg, Germany
- Institute of Molecular Medicine and Cell Research (IMMZ), Faculty of Medicine, University of Freiburg, Freiburg, Germany Germany
| | - Bruce R. Blazar
- Masonic Cancer Center and Department of Pediatrics, Division of Blood and Marrow Transplant & Cellular Therapy, University of Minnesota, Minneapolis, Minnesota, USA
| | - Susana Minguet
- Signalling Research Centres BIOSS and CIBSS – Centre for Integrative Biological Signalling Studies, University of Freiburg
| | - Patrick Turko
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
| | | | - Burkhard Becher
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | - Robert Zeiser
- Department of Medicine I - Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
- Signalling Research Centres BIOSS and CIBSS – Centre for Integrative Biological Signalling Studies, University of Freiburg
- Spemann Graduate School of Biology and Medicine (SGBM), Albert-Ludwigs-University Freiburg, Freiburg, Germany
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37
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Zidane M, Makky A, Bruhns M, Rochwarger A, Babaei S, Claassen M, Schürch CM. A review on deep learning applications in highly multiplexed tissue imaging data analysis. FRONTIERS IN BIOINFORMATICS 2023; 3:1159381. [PMID: 37564726 PMCID: PMC10410935 DOI: 10.3389/fbinf.2023.1159381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 07/12/2023] [Indexed: 08/12/2023] Open
Abstract
Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.
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Affiliation(s)
- Mohammed Zidane
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Ahmad Makky
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Matthias Bruhns
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Alexander Rochwarger
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Sepideh Babaei
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
| | - Manfred Claassen
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Christian M. Schürch
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
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38
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Bai Y, Zhu B, Oliveria JP, Cannon BJ, Feyaerts D, Bosse M, Vijayaragavan K, Greenwald NF, Phillips D, Schürch CM, Naik SM, Ganio EA, Gaudilliere B, Rodig SJ, Miller MB, Angelo M, Bendall SC, Rovira-Clavé X, Nolan GP, Jiang S. Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology. Nat Commun 2023; 14:4013. [PMID: 37419873 PMCID: PMC10329015 DOI: 10.1038/s41467-023-39616-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/16/2023] [Indexed: 07/09/2023] Open
Abstract
Cellular organization and functions encompass multiple scales in vivo. Emerging high-plex imaging technologies are limited in resolving subcellular biomolecular features. Expansion Microscopy (ExM) and related techniques physically expand samples for enhanced spatial resolution, but are challenging to be combined with high-plex imaging technologies to enable integrative multiscaled tissue biology insights. Here, we introduce Expand and comPRESS hydrOgels (ExPRESSO), an ExM framework that allows high-plex protein staining, physical expansion, and removal of water, while retaining the lateral tissue expansion. We demonstrate ExPRESSO imaging of archival clinical tissue samples on Multiplexed Ion Beam Imaging and Imaging Mass Cytometry platforms, with detection capabilities of > 40 markers. Application of ExPRESSO on archival human lymphoid and brain tissues resolved tissue architecture at the subcellular level, particularly that of the blood-brain barrier. ExPRESSO hence provides a platform for extending the analysis compatibility of hydrogel-expanded biospecimens to mass spectrometry, with minimal modifications to protocols and instrumentation.
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Affiliation(s)
- Yunhao Bai
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - John-Paul Oliveria
- Department of Translational Medicine, Genentech, Inc., South San Francisco, CA, USA
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Bryan J Cannon
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Dorien Feyaerts
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Marc Bosse
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | | | - Darci Phillips
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Christian M Schürch
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Samuel M Naik
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Edward A Ganio
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael B Miller
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics and Genomics, Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Sean C Bendall
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Xavier Rovira-Clavé
- Department of Pathology, Stanford University, Stanford, CA, USA.
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, USA.
| | - Sizun Jiang
- Department of Pathology, Stanford University, Stanford, CA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, USA.
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39
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Lin JR, Chen YA, Campton D, Cooper J, Coy S, Yapp C, Tefft JB, McCarty E, Ligon KL, Rodig SJ, Reese S, George T, Santagata S, Sorger PK. High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers. NATURE CANCER 2023; 4:1036-1052. [PMID: 37349501 PMCID: PMC10368530 DOI: 10.1038/s43018-023-00576-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 05/08/2023] [Indexed: 06/24/2023]
Abstract
Precision medicine is critically dependent on better methods for diagnosing and staging disease and predicting drug response. Histopathology using hematoxylin and eosin (H&E)-stained tissue (not genomics) remains the primary diagnostic method in cancer. Recently developed highly multiplexed tissue imaging methods promise to enhance research studies and clinical practice with precise, spatially resolved single-cell data. Here, we describe the 'Orion' platform for collecting H&E and high-plex immunofluorescence images from the same cells in a whole-slide format suitable for diagnosis. Using a retrospective cohort of 74 colorectal cancer resections, we show that immunofluorescence and H&E images provide human experts and machine learning algorithms with complementary information that can be used to generate interpretable, multiplexed image-based models predictive of progression-free survival. Combining models of immune infiltration and tumor-intrinsic features achieves a 10- to 20-fold discrimination between rapid and slow (or no) progression, demonstrating the ability of multimodal tissue imaging to generate high-performance biomarkers.
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Affiliation(s)
- Jia-Ren Lin
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Yu-An Chen
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | | | | | - Shannon Coy
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Clarence Yapp
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Juliann B Tefft
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | | | - Keith L Ligon
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | - Sandro Santagata
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA.
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Pizzurro GA, Bridges K, Jiang X, Vidyarthi A, Miller-Jensen K, Colegio OR. Functionally and Metabolically Divergent Melanoma-Associated Macrophages Originate from Common Bone-Marrow Precursors. Cancers (Basel) 2023; 15:3330. [PMID: 37444440 DOI: 10.3390/cancers15133330] [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: 06/03/2023] [Revised: 06/16/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
Tumor-associated macrophages (TAMs) can be widely heterogeneous, based on their ontogeny and function, and driven by the tissue-specific niche. TAMs are highly abundant in the melanoma tumor microenvironment (TME), usually correlating with worse prognoses. However, the understanding of their diversity may be harnessed for therapeutic purposes. Here, we used the clinically relevant YUMM1.7 model to study melanoma TAM origin and dynamics during tumor progression. In i.d. YUMM1.7 tumors, we identified distinct TAM subsets based on F4/80 expression, with the F4/80high fraction increasing over time and displaying a tissue-resident-like phenotype. While skin-resident macrophages showed mixed ontogeny, F4/80+ TAM subsets in the melanoma TME originated almost exclusively from bone-marrow precursors. A multiparametric analysis of the macrophage phenotype showed a temporal divergence of the F4/80+ TAM subpopulations, which also differed from the skin-resident subsets and their monocytic precursors. Overall, the F4/80+ TAMs displayed co-expressions of M1- and M2-like canonical markers, while RNA sequencing showed differential immunosuppressive and metabolic profiles. Gene-set enrichment analysis (GSEA) revealed F4/80high TAMs to rely on oxidative phosphorylation, with increased proliferation and protein secretion, while F4/80low cells had high pro-inflammatory and intracellular signaling pathways, with lipid and polyamine metabolism. Overall, we provide an in-depth characterization of and compelling evidence for the BM-dependency of melanoma TAMs. Interestingly, the transcriptomic analysis of these BM-derived TAMs matched macrophage subsets with mixed ontogeny, which have been observed in other tumor models. Our findings may serve as a guide for identifying potential ways of targeting specific immunosuppressive TAMs in melanoma.
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Affiliation(s)
- Gabriela A Pizzurro
- Department of Biomedical Engineering, School of Engineering and Applied Science, Yale University, New Haven, CT 06511, USA
| | - Kate Bridges
- Department of Biomedical Engineering, School of Engineering and Applied Science, Yale University, New Haven, CT 06511, USA
| | - Xiaodong Jiang
- Department of Immunobiology, School of Medicine, Yale University, New Haven, CT 06511, USA
| | - Aurobind Vidyarthi
- Department of Immunobiology, School of Medicine, Yale University, New Haven, CT 06511, USA
| | - Kathryn Miller-Jensen
- Department of Biomedical Engineering, School of Engineering and Applied Science, Yale University, New Haven, CT 06511, USA
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA
| | - Oscar R Colegio
- Department of Dermatology, School of Medicine, Yale University, New Haven, CT 06511, USA
- Department of Dermatology, Roswell Park Cancer Comprehensive Center, Buffalo, NY 14203, USA
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41
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Tanaka M, Lum L, Hu K, Ledezma-Soto C, Samad B, Superville D, Ng K, Adams Z, Kersten K, Fong L, Combes AJ, Krummel M, Reeves M. Tumor cell heterogeneity drives spatial organization of the intratumoral immune response in squamous cell skin carcinoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.25.538140. [PMID: 37162860 PMCID: PMC10168251 DOI: 10.1101/2023.04.25.538140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Intratumoral heterogeneity (ITH)-defined as genetic and cellular diversity within a tumor-is linked to failure of immunotherapy and an inferior anti-tumor immune response. The underlying mechanism of this association is unknown. To address this question, we modeled heterogeneous tumors comprised of a pro-inflammatory ("hot") and an immunosuppressive ("cold") tumor population, labeled with YFP and RFP tags respectively to enable precise spatial tracking. The resulting mixed-population tumors exhibited distinct regions comprised of YFP+ (hot) cells, RFP+ (cold) cells, or a mixture. We found that tumor regions occupied by hot tumor cells (YFP+) harbored more total T cells and a higher frequency of Th1 cells and IFNγ+ CD8 T cells compared to regions occupied by cold tumor cells (RFP+), whereas immunosuppressive macrophages showed the opposite spatial pattern. We identified the chemokine CX3CL1, produced at higher levels by our cold tumors, as a mediator of intratumoral macrophage accumulation, particularly immunosuppressive CD206Hi macrophages. Furthermore, we examined the response of heterogeneous tumors to a therapeutic combination of PD-1 blockade and CD40 agonist on a region-by-region basis. While the combination successfully increases Th1 abundance in "cold" tumor regions, it fails to bring overall T cell activity to the same level as seen in "hot" regions. The presence of the "cold" cells thus ultimately leads to a failure of the therapy to induce tumor rejection. Collectively, our results demonstrate that the organization of heterogeneous tumor cells has a profound impact on directing the spatial organization and function of tumor-infiltrating immune cells as well as on responses to immunotherapy.
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Affiliation(s)
- Miho Tanaka
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA
| | - Lotus Lum
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA
| | - Kenneth Hu
- UCSF CoLabs, University of California San Francisco, San Francisco, CA, USA
| | - Cecilia Ledezma-Soto
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA
| | - Bushra Samad
- UCSF CoLabs, University of California San Francisco, San Francisco, CA, USA
| | - Daphne Superville
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA
| | - Kenneth Ng
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA
| | - Zoe Adams
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA
| | - Kelly Kersten
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
| | - Lawrence Fong
- Division of Hematology and Oncology, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
- ImmunoX Initiative, University of California San Francisco, San Francisco, CA, USA
| | - Alexis J Combes
- UCSF CoLabs, University of California San Francisco, San Francisco, CA, USA
- ImmunoX Initiative, University of California San Francisco, San Francisco, CA, USA
- Division of Gastroenterology, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Matthew Krummel
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
- ImmunoX Initiative, University of California San Francisco, San Francisco, CA, USA
| | - Melissa Reeves
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Pathology, University of Utah, Salt Lake City, UT, USA
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42
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Kaur SD, Singh AD, Kapoor DN. Current perspectives on Vaxinia virus: an immuno-oncolytic vector in cancer therapy. Med Oncol 2023; 40:205. [PMID: 37318642 DOI: 10.1007/s12032-023-02068-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 05/14/2023] [Indexed: 06/16/2023]
Abstract
Viruses are being researched as cutting-edge therapeutic agents in cancer due to their selective oncolytic action against malignancies. Immuno-oncolytic viruses are a potential category of anticancer treatments because they have natural features that allow viruses to efficiently infect, replicate, and destroy cancer cells. Oncolytic viruses may be genetically modified; engineers can use them as a platform to develop additional therapy modalities that overcome the limitations of current treatment approaches. In recent years, researchers have made great strides in the understanding relationship between cancer and the immune system. An increasing corpus of research is functioning on the immunomodulatory functions of oncolytic virus (OVs). Several clinical studies are currently underway to determine the efficacy of these immuno-oncolytic viruses. These studies are exploring the design of these platforms to elicit the desired immune response and to supplement the available immunotherapeutic modalities to render immune-resistant malignancies amenable to treatment. This review will discuss current research and clinical developments on Vaxinia immuno-oncolytic virus.
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Affiliation(s)
- Simran Deep Kaur
- School of Pharmaceutical Sciences, Shoolini University of Biotechnology and Management Sciences, Solan, Himachal Pradesh, 173229, India
| | - Aman Deep Singh
- Department of Pharmaceutics, ISF College of Pharmacy, Moga, Punjab, 142048, India
| | - Deepak N Kapoor
- School of Pharmaceutical Sciences, Shoolini University of Biotechnology and Management Sciences, Solan, Himachal Pradesh, 173229, India.
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43
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Andhari MD, Antoranz A, De Smet F, Bosisio FM. Recent advancements in tumour microenvironment landscaping for target selection and response prediction in immune checkpoint therapies achieved through spatial protein multiplexing analysis. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2023; 382:207-237. [PMID: 38225104 DOI: 10.1016/bs.ircmb.2023.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Immune checkpoint therapies have significantly advanced cancer treatment. Nevertheless, the high costs and potential adverse effects associated with these therapies highlight the need for better predictive biomarkers to identify patients who are most likely to benefit from treatment. Unfortunately, the existing biomarkers are insufficient to identify such patients. New high-dimensional spatial technologies have emerged as a valuable tool for discovering novel biomarkers by analysing multiple protein markers at a single-cell resolution in tissue samples. These technologies provide a more comprehensive map of tissue composition, cell functionality, and interactions between different cell types in the tumour microenvironment. In this review, we provide an overview of how spatial protein-based multiplexing technologies have fuelled biomarker discovery and advanced the field of immunotherapy. In particular, we will focus on how these technologies contributed to (i) characterise the tumour microenvironment, (ii) understand the role of tumour heterogeneity, (iii) study the interplay of the immune microenvironment and tumour progression, (iv) discover biomarkers for immune checkpoint therapies (v) suggest novel therapeutic strategies.
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Affiliation(s)
- Madhavi Dipak Andhari
- 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
| | - 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
| | - Francesca Maria Bosisio
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.
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44
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Pizzurro GA, Bridges K, Jiang X, Vidyarthi A, Miller-Jensen K, Colegio OR. Functionally and metabolically divergent melanoma-associated macrophages originate from common bone-marrow precursors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.02.543515. [PMID: 37333194 PMCID: PMC10274732 DOI: 10.1101/2023.06.02.543515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Melanomas display high numbers of tumor-associated macrophages (TAMs), which correlate with worse prognosis. Harnessing macrophages for therapeutic purposes has been particularly challenging due to their heterogeneity, based on their ontogeny and function and driven by the tissue-specific niche. In the present study, we used the YUMM1.7 model to better understand melanoma TAM origin and dynamics during tumor progression, with potential therapeutic implications. We identified distinct TAM subsets based on F4/80 expression, with the F4/80 high fraction increasing over time and displaying tissue-resident-like phenotype. While skin-resident macrophages showed mixed on-togeny, F4/80 + TAM subsets in i.d. YUMM1.7 tumors originated almost exclusively from bone-marrow precursors. Mul-tiparametric analysis of macrophage phenotype showed a temporal divergence of F4/80 + TAM subpopulations, which also differed from skin-resident subsets, and from their monocytic precursors. Overall, F4/80 + TAMs displayed co-ex-pression of M1- and M2-like canonical markers, while RNA-seq and pathway analysis showed differential immunosup-pressive and metabolic profiles. GSEA showed F4/80 high TAMs to rely on oxidative phosphorylation, with increased proliferation and protein secretion while F4/80 low cells had high pro-inflammatory and intracellular signaling pathways, with lipid and polyamine metabolism. Overall, the present in-depth characterization provides further evidence of the ontogeny of the evolving melanoma TAMs, whose gene expression profiles matched recently-identified TAM clusters in other tumor models and human cancers. These findings provide evidence for potentially targeting specific immunosup-pressive TAMs in advanced tumor stages.
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45
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Wang V, Liu Z, Martinek J, Zhou J, Boruchov H, Ray K, Palucka K, Chuang J. Computational immune synapse analysis reveals T-cell interactions in distinct tumor microenvironments. RESEARCH SQUARE 2023:rs.3.rs-2968528. [PMID: 37398220 PMCID: PMC10312981 DOI: 10.21203/rs.3.rs-2968528/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
The tumor microenvironment (TME) and the cellular interactions within it can be critical to tumor progression and treatment response. Although technologies to generate multiplex images of the TME are advancing, the many ways in which TME imaging data can be mined to elucidate cellular interactions are only beginning to be realized. Here, we present a novel approach for multipronged computational immune synapse analysis (CISA) that reveals T-cell synaptic interactions from multiplex images. CISA enables automated discovery and quantification of immune synapse interactions based on the localization of proteins on cell membranes. We first demonstrate the ability of CISA to detect T-cell:APC (antigen presenting cell) synaptic interactions in two independent human melanoma imaging mass cytometry (IMC) tissue microarray datasets. We then generate melanoma histocytometry whole slide images and verify that CISA can detect similar interactions across data modalities. Interestingly, CISA histoctyometry analysis also reveals that T-cell:macrophage synapse formation is associated with T-cell proliferation. We next show the generality of CISA by extending it to breast cancer IMC images, finding that CISA quantifications of T-cell:B-cell synapses are predictive of improved patient survival. Our work demonstrates the biological and clinical significance of spatially resolving cell-cell synaptic interactions in the TME and provides a robust method to do so across imaging modalities and cancer types.
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Affiliation(s)
| | - Zichao Liu
- 1The Jackson Laboratory for Genomic Medicine
| | | | - Jie Zhou
- The Jackson Laboratory for Genomic Medicine
| | | | - Kelly Ray
- The Jackson Laboratory for Genomic Medicine
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Yan C, Huang H, Zheng Z, Ma X, Zhao G, Zhang T, Chen X, Cao F, Wei H, Dong J, Tang P, Jiang H, Wang M, Wang P, Pang Q, Zhang W. Spatial distribution of tumor-infiltrating T cells indicated immune response status under chemoradiotherapy plus PD-1 blockade in esophageal cancer. Front Immunol 2023; 14:1138054. [PMID: 37275884 PMCID: PMC10235618 DOI: 10.3389/fimmu.2023.1138054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 05/04/2023] [Indexed: 06/07/2023] Open
Abstract
Background The spatial distribution of tumor-infiltrating T cells and its dynamics during chemoradiotherapy combined with PD-1 blockade is little known in esophageal squamous cell carcinoma (ESCC). Methods We applied the multiplex immunofluorescence method to identify T cells (CD4+, CD8+ T cells, and their PD-1- or PD-1+ subsets) and myeloid-derived cells (CD11c+ dendritic cells, CD68+ macrophages, and their PD-L1+ subpopulations) in paired tumor biopsies (n = 36) collected at baseline and during combination (40 Gy of radiation) from a phase Ib trial (NCT03671265) of ESCC patients treated with first-line chemoradiotherapy plus anti-PD-1 antibody camrelizumab. We used the FoundationOne CDx assay to evaluate tumor mutational burden (TMB) in baseline tumor biopsies (n = 14). We dynamically assessed the nearest distance and proximity of T-cell subsets to tumor cells under combination and estimated the association between T-cell spatial distribution and combination outcome, myeloid-derived subsets, TMB, and patient baseline characteristics. Findings We found that the tumor compartment had lower T-cell subsets than the stromal compartment but maintained a comparable level under combination. Both before and under combination, PD-1- T cells were located closer than PD-1+ T cells to tumor cells; T cells, dendritic cells, and macrophages showed the highest accumulation in the 5-10-μm distance. Higher CD4+ T cells in the tumor compartment and a shorter nearest distance of T-cell subsets at baseline predicted poor OS. Higher baseline CD4+ T cells, dendritic cells, and macrophages were associated with worse OS in less than 10-μm distance to tumor cells, but related with better OS in the farther distance. Higher on-treatment PD-1-positive-expressed CD4+ and CD8+ T cells within the 100-μm distance to tumor cells predicted longer OS. T cells, dendritic cells, and macrophages showed a positive spatial correlation. Both high TMB and smoking history were associated with a closer location of T cells to tumor cells at baseline. Conclusions We firstly illustrated the T-cell spatial distribution in ESCC. Combining chemoradiotherapy with PD-1 blockade could improve the antitumor immune microenvironment, which benefits the treatment outcome. Further understanding the precision spatiality of tumor-infiltrating T cells would provide new evidence for the tumor immune microenvironment and for the combination treatment with immunotherapy.
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Affiliation(s)
- Cihui Yan
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Immunology and Biotherapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Hui Huang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Zhunhao Zheng
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Xiaoxue Ma
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Gang Zhao
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Tian Zhang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Xi Chen
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Fuliang Cao
- Department of Endoscopy Diagnosis and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Hui Wei
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Jie Dong
- Department of Nutrition Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Peng Tang
- Department of Esophageal Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Hongjing Jiang
- Department of Esophageal Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Meng Wang
- Department of Lung Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Ping Wang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Qingsong Pang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Wencheng Zhang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
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Srivastava RM, Thounaojam M, Marincola FM, Shanker A. Editorial: Lymphocyte functional crosstalk and regulation, volume II. Front Immunol 2023; 14:1214843. [PMID: 37266417 PMCID: PMC10231030 DOI: 10.3389/fimmu.2023.1214843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 05/05/2023] [Indexed: 06/03/2023] Open
Affiliation(s)
- Raghvendra M. Srivastava
- Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, United States
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Menaka Thounaojam
- Department of Ophthalmology, Augusta University, Augusta, GA, United States
| | | | - Anil Shanker
- Department of Biochemistry, Cancer Biology, Neuroscience and Pharmacology, School of Medicine, Meharry Medical College, Nashville, TN, United States
- Host-Tumor Interactions Research Program, Vanderbilt-Ingram Comprehensive Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, United States
- Vanderbilt Center for Immunobiology, Vanderbilt University School of Medicine, Nashville, TN, United States
- Vanderbilt Institute for Infection, Immunology and Inflammation, Vanderbilt University School of Medicine, Nashville, TN, United States
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48
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Eng J, Bucher E, Hu Z, Sanders M, Chakravarthy B, Gonzalez P, Pietenpol JA, Gibbs SL, Sears RC, Chin K. Robust biomarker discovery through multiplatform multiplex image analysis of breast cancer clinical cohorts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.31.525753. [PMID: 36778343 PMCID: PMC9915596 DOI: 10.1101/2023.01.31.525753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Spatial profiling of tissues promises to elucidate tumor-microenvironment interactions and enable development of spatial biomarkers to predict patient response to immunotherapy and other therapeutics. However, spatial biomarker discovery is often carried out on a single patient cohort or imaging technology, limiting statistical power and increasing the likelihood of technical artifacts. In order to analyze multiple patient cohorts profiled on different platforms, we developed methods for comparative data analysis from three disparate multiplex imaging technologies: 1) cyclic immunofluorescence data we generated from 102 breast cancer patients with clinical follow-up, in addition to publicly available 2) imaging mass cytometry and 3) multiplex ion-beam imaging data. We demonstrate similar single-cell phenotyping results across breast cancer patient cohorts imaged with these three technologies and identify cellular abundance and proximity-based biomarkers with prognostic value across platforms. In multiple platforms, we identified lymphocyte infiltration as independently associated with longer survival in triple negative and high-proliferation breast tumors. Then, a comparison of nine spatial analysis methods revealed robust spatial biomarkers. In estrogen receptor-positive disease, quiescent stromal cells close to tumor were more abundant in good prognosis tumors while tumor neighborhoods of mixed fibroblast phenotypes were enriched in poor prognosis tumors. In triple-negative breast cancer (TNBC), macrophage proximity to tumor and B cell proximity to T cells were greater in good prognosis tumors, while tumor neighborhoods of vimentin-positive fibroblasts were enriched in poor prognosis tumors. We also tested previously published spatial biomarkers in our ensemble cohort, reproducing the positive prognostic value of isolated lymphocytes and lymphocyte occupancy and failing to reproduce the prognostic value of tumor-immune mixing score in TNBC. In conclusion, we demonstrate assembly of larger clinical cohorts from diverse platforms to aid in prognostic spatial biomarker identification and validation.
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Affiliation(s)
- Jennifer Eng
- Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, OR, 97239, USA
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Elmar Bucher
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Zhi Hu
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Melinda Sanders
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Bapsi Chakravarthy
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA, USA
| | - Paula Gonzalez
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA, USA
| | - Jennifer A. Pietenpol
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA, USA
- Department of Biochemistry, Vanderbilt University, Nashville, TN, 37232, USA
| | - Summer L. Gibbs
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Rosalie C. Sears
- Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Koei Chin
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
- Center for Early Detection Advanced Research, Oregon Health and Science University, Portland, OR, 97239, USA
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Tekkela S, Theocharidis G, McGrath JA, Onoufriadis A. Spatial transcriptomics in human skin research. Exp Dermatol 2023. [PMID: 37150587 DOI: 10.1111/exd.14827] [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: 02/28/2023] [Revised: 04/17/2023] [Accepted: 04/21/2023] [Indexed: 05/09/2023]
Abstract
Spatial transcriptomics is a revolutionary technique that enables researchers to characterise tissue architecture and localisation of gene expression. A plethora of technologies that map gene expression are currently being developed, aiming to facilitate spatially resolved, high-dimensional assessment of gene transcription in the context of human skin research. Knowing which gene is expressed by which cell and in which location within skin, facilitates understanding of skin function and dysfunction in both health and disease. In this review, we summarise the available spatial transcriptomic methods and we describe their application to a broad spectrum of dermatological diseases.
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Affiliation(s)
- Stavroula Tekkela
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Georgios Theocharidis
- Joslin-Beth Israel Deaconess Foot Center and The Rongxiang Xu, MD, Center for Regenerative Therapeutics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - John A McGrath
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Alexandros Onoufriadis
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
- Laboratory of Medical Biology and Genetics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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50
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Howell R, Davies J, Clarke MA, Appios A, Mesquita I, Jayal Y, Ringham-Terry B, Boned Del Rio I, Fisher J, Bennett CL. Localized immune surveillance of primary melanoma in the skin deciphered through executable modeling. SCIENCE ADVANCES 2023; 9:eadd1992. [PMID: 37043573 PMCID: PMC10096595 DOI: 10.1126/sciadv.add1992] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
While skin is a site of active immune surveillance, primary melanomas often escape detection. Here, we have developed an in silico model to determine the local cross-talk between melanomas and Langerhans cells (LCs), the primary antigen-presenting cells at the site of melanoma development. The model predicts that melanomas fail to activate LC migration to lymph nodes until tumors reach a critical size, which is determined by a positive TNF-α feedback loop within melanomas, in line with our observations of murine tumors. In silico drug screening, supported by subsequent experimental testing, shows that treatment of primary tumors with MAPK pathway inhibitors may further prevent LC migration. In addition, our in silico model predicts treatment combinations that bypass LC dysfunction. In conclusion, our combined approach of in silico and in vivo studies suggests a molecular mechanism that explains how early melanomas develop under the radar of immune surveillance by LC.
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Affiliation(s)
| | | | - Matthew A. Clarke
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Anna Appios
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Inês Mesquita
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Yashoda Jayal
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Ben Ringham-Terry
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Isabel Boned Del Rio
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
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