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Lukacova E, Hanzlikova Z, Podlesnyi P, Sedlackova T, Szemes T, Grendar M, Samec M, Hurtova T, Malicherova B, Leskova K, Budis J, Burjanivova T. Novel liquid biopsy CNV biomarkers in malignant melanoma. Sci Rep 2024; 14:15786. [PMID: 38982214 PMCID: PMC11233564 DOI: 10.1038/s41598-024-65928-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 06/25/2024] [Indexed: 07/11/2024] Open
Abstract
Malignant melanoma (MM) is known for its abundance of genetic alterations and a tendency for rapid metastasizing. Identification of novel plasma biomarkers may enhance non-invasive diagnostics and disease monitoring. Initially, we examined copy number variations (CNV) in CDK genes (CDKN2A, CDKN2B, CDK4) using MLPA (gDNA) and ddPCR (ctDNA) analysis. Subsequently, low-coverage whole genome sequencing (lcWGS) was used to identify the most common CNV in plasma samples, followed by ddPCR verification of chosen biomarkers. CNV alterations in CDK genes were identified in 33.3% of FFPE samples (Clark IV, V only). Detection of the same genes in MM plasma showed no significance, neither compared to healthy plasmas nor between pre- versus post-surgery plasma. Sequencing data showed the most common CNV occurring in 6q27, 4p16.1, 10p15.3, 10q22.3, 13q34, 18q23, 20q11.21-q13.12 and 22q13.33. CNV in four chosen genes (KIF25, E2F1, DIP2C and TFG) were verified by ddPCR using 2 models of interpretation. Model 1 was concordant with lcWGS results in 54% of samples, for model 2 it was 46%. Although CDK genes have not been proven to be suitable CNV liquid biopsy biomarkers, lcWGS defined the most frequently affected chromosomal regions by CNV. Among chosen genes, DIP2C demonstrated a potential for further analysis.
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Affiliation(s)
- E Lukacova
- Department of Molecular Biology and Genomics, Comenius University in Bratislava, Jessenius Faculty of Medicine in Martin (JFM CU), Martin, Slovakia
| | | | - P Podlesnyi
- Instituto de Investigaciones Biomedicas de Barcelona (IIBB), CSIC /Centro Investigacion Biomedica en Red Enfermedades Neurodegenerativas (CiberNed), Barcelona, Spain
| | - T Sedlackova
- Geneton Ltd., Bratislava, Slovakia
- Science Park, Comenius University in Bratislava, Bratislava, Slovakia
| | - T Szemes
- Geneton Ltd., Bratislava, Slovakia
- Science Park, Comenius University in Bratislava, Bratislava, Slovakia
| | - M Grendar
- Laboratory of Bioinformatics and Biostatistics, Biomedical Center Martin, Comenius University in Bratislava, Jessenius Faculty of Medicine in Martin (JFM CU), Martin, Slovakia
| | - M Samec
- Department of Medical Biology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia
| | - T Hurtova
- Department of Dermatovenereology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia
| | - B Malicherova
- Department of Clinical Biochemistry, University Hospital in Martin and Jessenius Faculty of Medicine, Comenius University, Martin, Slovakia
| | - K Leskova
- Department of Pathological Anatomy, Jessenius Faculty of Medicine and University Hospital in Martin, Comenius University, Martin, Slovakia
| | - J Budis
- Geneton Ltd., Bratislava, Slovakia
- Science Park, Comenius University in Bratislava, Bratislava, Slovakia
| | - T Burjanivova
- Department of Molecular Biology and Genomics, Comenius University in Bratislava, Jessenius Faculty of Medicine in Martin (JFM CU), Martin, Slovakia.
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Shen Y, Zhao X, Chen L, Wang X, Wang D, Zhang H, Zheng Z, Huang W, Zheng C, Chen Y, Chen C, Chen Q. A modified HSV-1 oncolytic virus reconciles antiviral and antitumor immunity via promoting IFNβ expression and inhibiting PKR. Int J Biol Macromol 2024; 274:133297. [PMID: 38925170 DOI: 10.1016/j.ijbiomac.2024.133297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/17/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024]
Abstract
Type I interferon (IFN-I) is a potent immune modulator intricately involved in regulating tumor immunity. Meanwhile, the integrity of the IFN-I signaling pathway is essential for radiotherapy, chemotherapy, targeted therapy, and immunotherapy. However, the clinical application of IFN-I remains challenging due to its non-specific cytotoxicity and limited half-life. To overcome these limitations, we developed a gene delivery platform, CRISPR-V, enabling the rapid creation of novel HSV-1 oncolytic viruses. Utilizing this platform, we created an oncolytic virus, OVH-IFNβ, in which the IFNβ gene was incorporated into the HSV-1 genome. However, exogenous IFNβ expression significantly inhibited OVH-IFNβ replication. Through transcriptome data analyses, we identified several ISG genes inhibiting OVH-IFNβ replication. By gene knockout and functional studies of the downstream effectors, we confirmed the prominent antiviral activities of protein kinase R (PKR). To balance the antitumor and antiviral immunity of IFNβ, we developed a novel HSV-1 oncolytic virus, OVH-IFNβ-iPKR, which can express IFNβ while inhibiting PKR, leading to a potent antitumor immunity while reducing the antiviral capacity of IFNβ. OVH-IFNβ-iPKR shows a strong ability to induce immunogenic cell death and activate tumor-specific CD8+ T cells, leading to de novo immune responses and providing a novel strategy for tumor immunotherapy.
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Affiliation(s)
- Yangkun Shen
- Fujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, China
| | - Xiangqian Zhao
- Fujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, China
| | - Lizhu Chen
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Xin Wang
- Fuzhou Hospital of Traditional Chinese Medicine Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Dawei Wang
- Fujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, China
| | - Hucheng Zhang
- Fujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, China
| | - Zuda Zheng
- Fujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, China
| | - Weiwei Huang
- Department of Medical Oncology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Chunfu Zheng
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Yu Chen
- Department of Medical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
| | - Chuanben Chen
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
| | - Qi Chen
- Fujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, China.
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3
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Wohlfarth J, Kosnopfel C, Faber D, Berthold M, Siedel C, Bernhardt M, Schlosser A, Aprati T, Liu D, Schrama D, Houben R, Schadendorf D, Goebeler M, Meierjohann S, Schilling B. Loss of p14 diminishes immunogenicity in melanoma via non-canonical Wnt signaling by reducing the peptide surface density. Mol Oncol 2024. [PMID: 38807304 DOI: 10.1002/1878-0261.13660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/04/2024] [Accepted: 04/26/2024] [Indexed: 05/30/2024] Open
Abstract
Immunotherapy has achieved tremendous success in melanoma. However, only around 50% of advanced melanoma patients benefit from immunotherapy. Cyclin-dependent kinase inhibitor 2A (CDKN2A), encoding the two tumor-suppressor proteins p14ARF and p16INK4a, belongs to the most frequently inactivated gene loci in melanoma and leads to decreased T cell infiltration. While the role of p16INK4a has been extensively investigated, knowledge about p14ARF in melanoma is scarce. In this study, we elucidate the impact of reduced p14ARF expression on melanoma immunogenicity. Knockdown of p14ARF in melanoma cell lines diminished their recognition and killing by melanoma differentiation antigen (MDA)-specific T cells. Resistance was caused by a reduction of the peptide surface density of presented MDAs. Immunopeptidomic analyses revealed that antigen presentation via human leukocyte antigen class I (HLA-I) molecules was enhanced upon p14ARF downregulation in general, but absolute and relative expression of cognate peptides was decreased. However, this phenotype is associated with a favorable outcome for melanoma patients. Limiting Wnt5a signaling reverted this phenotype, suggesting an involvement of non-canonical Wnt signaling. Taken together, our data indicate a new mechanism limiting MDA-specific T cell responses by decreasing both absolute and relative MDA-peptide presentation in melanoma.
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Affiliation(s)
- Jonas Wohlfarth
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, Germany
| | - Corinna Kosnopfel
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, Germany
| | - Dominic Faber
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, Germany
| | - Marion Berthold
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, Germany
| | - Claudia Siedel
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, Germany
| | - Melissa Bernhardt
- Rudolf-Virchow-Centre for Integrative and Translational Bioimaging, University of Würzburg, Germany
| | - Andreas Schlosser
- Rudolf-Virchow-Centre for Integrative and Translational Bioimaging, University of Würzburg, Germany
| | - Tyler Aprati
- Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Cambridge, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - David Liu
- Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Cambridge, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - David Schrama
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, Germany
| | - Roland Houben
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, Germany
| | - Dirk Schadendorf
- Department of Dermatology, Comprehensive Cancer Center (Westdeutsches Tumorzentrum), German Cancer Consortium (DKTK, partner site Essen) and University Hospital Essen, Germany
| | - Matthias Goebeler
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, Germany
| | | | - Bastian Schilling
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, Germany
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Azuma I, Mizuno T, Kusuhara H. GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution. Brief Bioinform 2024; 25:bbae315. [PMID: 38982642 PMCID: PMC11233176 DOI: 10.1093/bib/bbae315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 05/21/2024] [Accepted: 06/14/2024] [Indexed: 07/11/2024] Open
Abstract
Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.
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Affiliation(s)
- Iori Azuma
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Bunkyo-ku 113-0033, Japan
| | - Tadahaya Mizuno
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Bunkyo-ku 113-0033, Japan
| | - Hiroyuki Kusuhara
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1, Bunkyo-ku 113-0033, Japan
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Wang L, Wei Y, Jin Z, Liu F, Li X, Zhang X, Bai X, Jia Q, Zhu B, Chu Q. IFN-α/β/IFN-γ/IL-15 pathways identify GBP1-expressing tumors with an immune-responsive phenotype. Clin Exp Med 2024; 24:102. [PMID: 38758367 PMCID: PMC11101573 DOI: 10.1007/s10238-024-01328-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 03/09/2024] [Indexed: 05/18/2024]
Abstract
Immunotherapy is widely used in cancer treatment; however, only a subset of patients responds well to it. Significant efforts have been made to identify patients who will benefit from immunotherapy. Successful anti-tumor immunity depends on an intact cancer-immunity cycle, especially long-lasting CD8+ T-cell responses. Interferon (IFN)-α/β/IFN-γ/interleukin (IL)-15 pathways have been reported to be involved in the development of CD8+ T cells. And these pathways may predict responses to immunotherapy. Herein, we aimed to analyze multiple public databases to investigate whether IFN-α/β/IFN-γ/IL-15 pathways could be used to predict the response to immunotherapy. Results showed that IFN-α/β/IFN-γ/IL-15 pathways could efficiently predict immunotherapy response, and guanylate-binding protein 1 (GBP1) could represent the IFN-α/β/IFN-γ/IL-15 pathways. In public and private cohorts, we further demonstrated that GBP1 could efficiently predict the response to immunotherapy. Functionally, GBP1 was mainly expressed in macrophages and strongly correlated with chemokines involved in T-cell migration. Therefore, our study comprehensively investigated the potential role of GBP1 in immunotherapy, which could serve as a novel biomarker for immunotherapy and a target for drug development.
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Affiliation(s)
- Lei Wang
- Department of Oncology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, People's Republic of China
| | - Yuxuan Wei
- Department of Oncology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, People's Republic of China
| | - Zheng Jin
- Institute of Life Sciences, Chongqing Medical University, Chongqing, 400032, People's Republic of China
- Research Institute, GloriousMed Clinical Laboratory (Shanghai) Co., Ltd, Shanghai, 201318, People's Republic of China
| | - Fangfang Liu
- Department of Oncology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, People's Republic of China
| | - Xuchang Li
- Department of Oncology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, People's Republic of China
| | - Xiao Zhang
- Army 953 Hospital, Shigatse Branch of Xinqiao Hospital, Army Medical University, Shigatse, 857000, People's Republic of China
| | - Xiumei Bai
- Army 953 Hospital, Shigatse Branch of Xinqiao Hospital, Army Medical University, Shigatse, 857000, People's Republic of China
| | - Qingzhu Jia
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing, 400037, People's Republic of China
- Chongqing Key Laboratory of Immunotherapy, Chongqing, 400037, People's Republic of China
| | - Bo Zhu
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing, 400037, People's Republic of China
- Chongqing Key Laboratory of Immunotherapy, Chongqing, 400037, People's Republic of China
| | - Qian Chu
- Department of Oncology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, People's Republic of China.
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6
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Ghosh S, Mohanty R, Santra A, Saha A, Agrawal A, Shrivastava S, Roy C, Mazumder I, Das D, Mahmood SH. Unlocking the genetic tapestry of autoimmune diseases: Unveiling common genes across multiple conditions. Int J Rheum Dis 2024; 27:e15185. [PMID: 38742742 DOI: 10.1111/1756-185x.15185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/16/2024] [Accepted: 04/28/2024] [Indexed: 05/16/2024]
Abstract
OBJECTIVES This study aimed to unravel the complexities of autoimmune diseases by conducting a comprehensive analysis of gene expression data across 10 conditions, including systemic lupus erythematosus (SLE), psoriasis, Sjögren's syndrome, sclerosis, immune-associated diseases, osteoarthritis, cystic fibrosis, inflammatory bowel disease (IBD), type 1 diabetes, and Guillain-Barré syndrome. METHODS Gene expression profiles were rigorously examined to identify both upregulated and downregulated genes specific to each autoimmune disease. The study employed visual representation techniques such as heatmaps, volcano plots, and contour-MA plots to provide an intuitive understanding of the complex gene expression patterns in these conditions. RESULTS Distinct gene expression profiles for each autoimmune condition were uncovered, with psoriasis and osteoarthritis standing out due to a multitude of both upregulated and downregulated genes, indicating intricate molecular interplays in these disorders. Notably, common upregulated and downregulated genes were identified across various autoimmune conditions, with genes like SELENBP1, MMP9, BNC1, and COL1A1 emerging as pivotal players. CONCLUSION This research contributes valuable insights into the molecular signatures of autoimmune diseases, highlighting the unique gene expression patterns characterizing each condition. The identification of common genes shared among different autoimmune conditions, and their potential role in mitigating the risk of rare diseases in patients with more prevalent conditions, underscores the growing significance of genetics in healthcare and the promising future of personalized medicine.
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Affiliation(s)
- Soujanya Ghosh
- School of Biotechnology, KIIT University, Bhubaneswar, Odisha, India
| | - Rupali Mohanty
- School of Biotechnology, KIIT University, Bhubaneswar, Odisha, India
| | - Arunava Santra
- School of Biotechnology, KIIT University, Bhubaneswar, Odisha, India
| | - Anisha Saha
- School of Biotechnology, KIIT University, Bhubaneswar, Odisha, India
| | - Anubha Agrawal
- School of Biotechnology, KIIT University, Bhubaneswar, Odisha, India
| | | | - Chandrashish Roy
- School of Biotechnology, KIIT University, Bhubaneswar, Odisha, India
| | - Ishanee Mazumder
- School of Biotechnology, KIIT University, Bhubaneswar, Odisha, India
| | - Debarup Das
- School of Biotechnology, KIIT University, Bhubaneswar, Odisha, India
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7
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Meng G, Pan Y, Tang W, Zhang L, Cui Y, Schumacher FR, Wang M, Wang R, He S, Krischer J, Li Q, Feng H. imply: improving cell-type deconvolution accuracy using personalized reference profiles. Genome Med 2024; 16:65. [PMID: 38685057 PMCID: PMC11057104 DOI: 10.1186/s13073-024-01338-z] [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: 09/26/2023] [Accepted: 04/18/2024] [Indexed: 05/02/2024] Open
Abstract
Using computational tools, bulk transcriptomics can be deconvoluted to estimate the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, ignoring person-to-person heterogeneity. Here, we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson's disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/ .
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Affiliation(s)
- Guanqun Meng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Yue Pan
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA
| | - Wen Tang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Lijun Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ying Cui
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
| | - Fredrick R Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ming Wang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Rui Wang
- Department of Surgery, Division of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, 44106, OH, USA
| | - Sijia He
- Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Jeffrey Krischer
- Health Informatics Institute, University of South Florida, Tampa, 38105, FL, USA
| | - Qian Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA.
| | - Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA.
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8
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Li G, Zhao X, Zheng Z, Zhang H, Wu Y, Shen Y, Chen Q. cGAS-STING pathway mediates activation of dendritic cell sensing of immunogenic tumors. Cell Mol Life Sci 2024; 81:149. [PMID: 38512518 PMCID: PMC10957617 DOI: 10.1007/s00018-024-05191-6] [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: 12/22/2023] [Revised: 02/09/2024] [Accepted: 02/28/2024] [Indexed: 03/23/2024]
Abstract
Type I interferons (IFN-I) play pivotal roles in tumor therapy for three decades, underscoring the critical importance of maintaining the integrity of the IFN-1 signaling pathway in radiotherapy, chemotherapy, targeted therapy, and immunotherapy. However, the specific mechanism by which IFN-I contributes to these therapies, particularly in terms of activating dendritic cells (DCs), remains unclear. Based on recent studies, aberrant DNA in the cytoplasm activates the cyclic GMP-AMP synthase (cGAS)- stimulator of interferon genes (STING) signaling pathway, which in turn produces IFN-I, which is essential for antiviral and anticancer immunity. Notably, STING can also enhance anticancer immunity by promoting autophagy, inflammation, and glycolysis in an IFN-I-independent manner. These research advancements contribute to our comprehension of the distinctions between IFN-I drugs and STING agonists in the context of oncology therapy and shed light on the challenges involved in developing STING agonist drugs. Thus, we aimed to summarize the novel mechanisms underlying cGAS-STING-IFN-I signal activation in DC-mediated antigen presentation and its role in the cancer immune cycle in this review.
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Affiliation(s)
- Guohao Li
- Fujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, China
| | - Xiangqian Zhao
- Fujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, China
| | - Zuda Zheng
- Fujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, China
| | - Hucheng Zhang
- Fujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, China
| | - Yundi Wu
- Fujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, China
| | - Yangkun Shen
- Fujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, China.
| | - Qi Chen
- Fujian Key Laboratory of Innate Immune Biology, Biomedical Research Center of South China, College of Life Science, Fujian Normal University, Fuzhou, China.
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9
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Azuma I, Mizuno T, Morita K, Suzuki Y, Kusuhara H. Investigation of the usefulness of liver-specific deconvolution method by establishing a liver benchmark dataset. NAR Genom Bioinform 2024; 6:lqad111. [PMID: 38187088 PMCID: PMC10768887 DOI: 10.1093/nargab/lqad111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 10/31/2023] [Accepted: 12/16/2023] [Indexed: 01/09/2024] Open
Abstract
Immune responses in the liver are related to the development and progression of liver failure, and precise prediction of their behavior is important. Deconvolution is a methodology for estimating the immune cell proportions from the transcriptome, and it is mainly applied to blood-derived samples and tumor tissues. However, the influence of tissue-specific modeling on the estimation results has rarely been investigated. Here, we constructed a system to evaluate the performance of the deconvolution method on liver transcriptome data. We prepared seven mouse liver injury models using small-molecule compounds and established a benchmark dataset with corresponding liver bulk RNA-Seq and immune cell proportions. RNA-Seq expression for nine leukocyte subsets and four liver-associated cell types were obtained from the Gene Expression Omnibus to provide a reference. We found that the combination of reference cell sets affects the estimation results of reference-based deconvolution methods and established a liver-specific deconvolution by optimizing the reference cell set for each cell to be estimated. We applied this model to independent datasets and showed that liver-specific modeling is highly extrapolatable. We expect that this approach will enable sophisticated estimation from rich tissue data accumulated in public databases and to obtain information on aggregated immune cell trafficking.
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Affiliation(s)
- Iori Azuma
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo, Tokyo 113-0033, Japan
| | - Tadahaya Mizuno
- To whom correspondence should be addressed. Tel: +81 3 5841 4771; Fax: +81 3 5841 4766;
| | - Katsuhisa Morita
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo, Tokyo 113-0033, Japan
| | - Yutaka Suzuki
- Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan
| | - Hiroyuki Kusuhara
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo, Tokyo 113-0033, Japan
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10
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Feng S, Calinawan A, Pugliese P, Wang P, Ceccarelli M, Petralia F, Gosline SJC. Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution. CELL REPORTS METHODS 2024; 4:100708. [PMID: 38412834 PMCID: PMC10921018 DOI: 10.1016/j.crmeth.2024.100708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 10/23/2023] [Accepted: 01/18/2024] [Indexed: 02/29/2024]
Abstract
Tumor deconvolution enables the identification of diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold-standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA sequencing) rather than protein levels. Despite the popularity of gene expression datasets, protein levels often provide a more accurate view of rare cell types. To facilitate the use, development, and reproducibility of multiomic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic datasets. Decomprolute incorporates the large-scale multiomic datasets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. http://pnnl-compbio.github.io/decomprolute.
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Affiliation(s)
- Song Feng
- Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Anna Calinawan
- Icahn School of Medicine at Mount Sinai School, New York, NY, USA
| | | | - Pei Wang
- Icahn School of Medicine at Mount Sinai School, New York, NY, USA
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11
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Pardo-Cea MA, Farré X, Esteve A, Palade J, Espín R, Mateo F, Alsop E, Alorda M, Blay N, Baiges A, Shabbir A, Comellas F, Gómez A, Arnan M, Teulé A, Salinas M, Berrocal L, Brunet J, Rofes P, Lázaro C, Conesa M, Rojas JJ, Velten L, Fendler W, Smyczynska U, Chowdhury D, Zeng Y, He HH, Li R, Van Keuren-Jensen K, de Cid R, Pujana MA. Biological basis of extensive pleiotropy between blood traits and cancer risk. Genome Med 2024; 16:21. [PMID: 38308367 PMCID: PMC10837955 DOI: 10.1186/s13073-024-01294-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 01/22/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND The immune system has a central role in preventing carcinogenesis. Alteration of systemic immune cell levels may increase cancer risk. However, the extent to which common genetic variation influences blood traits and cancer risk remains largely undetermined. Here, we identify pleiotropic variants and predict their underlying molecular and cellular alterations. METHODS Multivariate Cox regression was used to evaluate associations between blood traits and cancer diagnosis in cases in the UK Biobank. Shared genetic variants were identified from the summary statistics of the genome-wide association studies of 27 blood traits and 27 cancer types and subtypes, applying the conditional/conjunctional false-discovery rate approach. Analysis of genomic positions, expression quantitative trait loci, enhancers, regulatory marks, functionally defined gene sets, and bulk- and single-cell expression profiles predicted the biological impact of pleiotropic variants. Plasma small RNAs were sequenced to assess association with cancer diagnosis. RESULTS The study identified 4093 common genetic variants, involving 1248 gene loci, that contributed to blood-cancer pleiotropism. Genomic hotspots of pleiotropism include chromosomal regions 5p15-TERT and 6p21-HLA. Genes whose products are involved in regulating telomere length are found to be enriched in pleiotropic variants. Pleiotropic gene candidates are frequently linked to transcriptional programs that regulate hematopoiesis and define progenitor cell states of immune system development. Perturbation of the myeloid lineage is indicated by pleiotropic associations with defined master regulators and cell alterations. Eosinophil count is inversely associated with cancer risk. A high frequency of pleiotropic associations is also centered on the regulation of small noncoding Y-RNAs. Predicted pleiotropic Y-RNAs show specific regulatory marks and are overabundant in the normal tissue and blood of cancer patients. Analysis of plasma small RNAs in women who developed breast cancer indicates there is an overabundance of Y-RNA preceding neoplasm diagnosis. CONCLUSIONS This study reveals extensive pleiotropism between blood traits and cancer risk. Pleiotropism is linked to factors and processes involved in hematopoietic development and immune system function, including components of the major histocompatibility complexes, and regulators of telomere length and myeloid lineage. Deregulation of Y-RNAs is also associated with pleiotropism. Overexpression of these elements might indicate increased cancer risk.
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Affiliation(s)
- Miguel Angel Pardo-Cea
- ProCURE, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain
| | - Xavier Farré
- Genomes for Life - GCAT Lab Group, Institut Germans Trias i Pujol (IGTP), Badalona, 08916, Barcelona, Catalonia, Spain
| | - Anna Esteve
- Badalona Applied Research Group in Oncology (B-ARGO), Catalan Institute of Oncology, Institut Germans Trias i Pujol (IGTP), Badalona, 08916, Barcelona, Catalonia, Spain
| | - Joanna Palade
- Cancer and Cell Biology, Translational Genomics Research Institute (TGen), Arizona, Phoenix, AZ, 85004, USA
| | - Roderic Espín
- ProCURE, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain
| | - Francesca Mateo
- ProCURE, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain
| | - Eric Alsop
- Cancer and Cell Biology, Translational Genomics Research Institute (TGen), Arizona, Phoenix, AZ, 85004, USA
| | - Marc Alorda
- ProCURE, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain
| | - Natalia Blay
- Genomes for Life - GCAT Lab Group, Institut Germans Trias i Pujol (IGTP), Badalona, 08916, Barcelona, Catalonia, Spain
| | - Alexandra Baiges
- ProCURE, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain
| | - Arzoo Shabbir
- ProCURE, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain
| | - Francesc Comellas
- Department of Mathematics, Technical University of Catalonia, Castelldefels, 08860, Barcelona, Catalonia, Spain
| | - Antonio Gómez
- Department of Biosciences, Faculty of Sciences and Technology (FCT), University of Vic - Central University of Catalonia (UVic-UCC), Vic, 08500, Barcelona, Catalonia, Spain
| | - Montserrat Arnan
- Department of Hematology, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain
| | - Alex Teulé
- Hereditary Cancer Program, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain
| | - Monica Salinas
- Hereditary Cancer Program, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain
| | - Laura Berrocal
- OncoGir, Catalan Institute of Oncology, Girona Biomedical Research Institute (IDIBGI), 17190, Salt, Catalonia, Spain
| | - Joan Brunet
- Hereditary Cancer Program, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain
- OncoGir, Catalan Institute of Oncology, Girona Biomedical Research Institute (IDIBGI), 17190, Salt, Catalonia, Spain
- Biomedical Research Network Centre in Cancer (CIBERONC), Instituto de Salud Carlos III, 28222, Madrid, Spain
| | - Paula Rofes
- Hereditary Cancer Program, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain
- Biomedical Research Network Centre in Cancer (CIBERONC), Instituto de Salud Carlos III, 28222, Madrid, Spain
| | - Conxi Lázaro
- Hereditary Cancer Program, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain
- Biomedical Research Network Centre in Cancer (CIBERONC), Instituto de Salud Carlos III, 28222, Madrid, Spain
| | - Miquel Conesa
- Department of Pathology and Experimental Therapies, University of Barcelona (UB), Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain
| | - Juan Jose Rojas
- Department of Pathology and Experimental Therapies, University of Barcelona (UB), Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain
| | - Lars Velten
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), 08003, Barcelona, Spain
- University Pompeu Fabra (UPF), 08002, Barcelona, Spain
| | - Wojciech Fendler
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215, Lodz, Poland
| | - Urszula Smyczynska
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215, Lodz, Poland
| | - Dipanjan Chowdhury
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Center for BRCA and Related Genes, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Yong Zeng
- Princess Margaret Cancer Center, University Health Network, Toronto, ON, M5G 2C4, Canada
| | - Housheng Hansen He
- Princess Margaret Cancer Center, University Health Network, Toronto, ON, M5G 2C4, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada
| | - Rong Li
- Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, 20052, USA
| | - Kendall Van Keuren-Jensen
- Cancer and Cell Biology, Translational Genomics Research Institute (TGen), Arizona, Phoenix, AZ, 85004, USA.
| | - Rafael de Cid
- Genomes for Life - GCAT Lab Group, Institut Germans Trias i Pujol (IGTP), Badalona, 08916, Barcelona, Catalonia, Spain.
| | - Miquel Angel Pujana
- ProCURE, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, 08908, Barcelona, Catalonia, Spain.
- Biomedical Research Network Centre in Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, 28222, Madrid, Spain.
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12
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Yang P, Hubert SM, Futreal PA, Song X, Zhang J, Lee JJ, Wistuba I, Yuan Y, Zhang J, Li Z. A novel Bayesian model for assessing intratumor heterogeneity of tumor infiltrating leukocytes with multi-region gene expression sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.24.563820. [PMID: 37961165 PMCID: PMC10634795 DOI: 10.1101/2023.10.24.563820] [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
Intratumor heterogeneity (ITH) of tumor-infiltrated leukocytes (TILs) is an important phenomenon of cancer biology with potentially profound clinical impacts. Multi-region gene expression sequencing data provide a promising opportunity that allows for explorations of TILs and their intratumor heterogeneity for each subject. Although several existing methods are available to infer the proportions of TILs, considerable methodological gaps exist for evaluating intratumor heterogeneity of TILs with multi-region gene expression data. Here, we develop ICeITH, immune cell estimation reveals intratumor heterogeneity, a Bayesian hierarchical model that borrows cell type profiles as prior knowledge to decompose mixed bulk data while accounting for the within-subject correlations among tumor samples. ICeITH quantifies intratumor heterogeneity by the variability of targeted cellular compositions. Through extensive simulation studies, we demonstrate that ICeITH is more accurate in measuring relative cellular abundance and evaluating intratumor heterogeneity compared with existing methods. We also assess the ability of ICeITH to stratify patients by their intratumor heterogeneity score and associate the estimations with the survival outcomes. Finally, we apply ICeITH to two multi-region gene expression datasets from lung cancer studies to classify patients into different risk groups according to the ITH estimations of targeted TILs that shape either pro- or anti-tumor processes. In conclusion, ICeITH is a useful tool to evaluate intratumor heterogeneity of TILs from multi-region gene expression data.
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Affiliation(s)
- Peng Yang
- Department of Statistics, Rice University, Houston, Texas 77005, U.S.A
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Texas 77030, U.S.A
| | - Shawna M. Hubert
- Department of Thoracic Head Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - P. Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xingzhi Song
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jianhua Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Texas 77030, U.S.A
| | - Ignacio Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Texas 77030, U.S.A
| | - Jianjun Zhang
- Department of Thoracic Head Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ziyi Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Texas 77030, U.S.A
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13
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Patkar S, Mannheimer J, Harmon S, Mazcko C, Choyke P, Brown GT, Turkbey B, LeBlanc A, Beck J. Large Scale Comparative Deconvolution Analysis of the Canine and Human Osteosarcoma Tumor Microenvironment Uncovers Conserved Clinically Relevant Subtypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.27.559797. [PMID: 37808704 PMCID: PMC10557692 DOI: 10.1101/2023.09.27.559797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Osteosarcoma is a relatively rare but aggressive cancer of the bones with a shortage of effective biomarkers. Although less common in humans, Osteosarcomas are fairly common in adult pet dogs and have been shown to share many similarities with their human analogs. In this work, we analyze bulk transcriptomic data of 213 primary and 100 metastatic Osteosarcoma samples from 210 pet dogs enrolled in nation-wide clinical trials to uncover three Tumor Microenvironment (TME)-based subtypes: Immune Enriched (IE), Immune Enriched Dense Extra-Cellular Matrix-like (IE-ECM) and Immune Desert (ID) with distinct cell type compositions, oncogenic pathway activity and chromosomal instability. Furthermore, leveraging bulk transcriptomic data of canine primary tumors and their matched metastases from different sites, we characterize how the Osteosarcoma TME evolves from primary to metastatic disease in a standard of care clinical setting and assess its overall impact on clinical outcomes of canines. Most importantly, we find that TME-based subtypes of canine Osteosarcomas are conserved in humans and predictive of progression free survival outcomes of human patients, independently of known prognostic biomarkers such as presence of metastatic disease at diagnosis and percent necrosis following chemotherapy. In summary, these results demonstrate the power of using canines to model the human Osteosarcoma TME and discover novel biomarkers for clinical translation.
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Affiliation(s)
- Sushant Patkar
- Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Josh Mannheimer
- Comparative Oncology Program, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Stephanie Harmon
- Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Christina Mazcko
- Comparative Oncology Program, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Peter Choyke
- Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - G Tom Brown
- Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Baris Turkbey
- Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Amy LeBlanc
- Comparative Oncology Program, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Jessica Beck
- Comparative Oncology Program, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
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14
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Meng G, Pan Y, Tang W, Zhang L, Cui Y, Schumacher FR, Wang M, Wang R, He S, Krischer J, Li Q, Feng H. imply: improving cell-type deconvolution accuracy using personalized reference profiles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.27.559579. [PMID: 37808714 PMCID: PMC10557724 DOI: 10.1101/2023.09.27.559579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, which ignores person-to-person heterogeneity. Here we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. imply can borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkinson's disease. Our proposed tool imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/.
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Affiliation(s)
- Guanqun Meng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Yue Pan
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, 38105, TN, USA
| | - Wen Tang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Lijun Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ying Cui
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
| | - Fredrick R. Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ming Wang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Rui Wang
- Department of Surgery, Division of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, 44106, OH, USA
| | - Sijia He
- Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Jeffrey Krischer
- Health Informatics Institute, University of South Florida, Tampa, 38105, FL, USA
| | - Qian Li
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, 38105, TN, USA
| | - Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
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15
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Pividori M, Lu S, Li B, Su C, Johnson ME, Wei WQ, Feng Q, Namjou B, Kiryluk K, Kullo IJ, Luo Y, Sullivan BD, Voight BF, Skarke C, Ritchie MD, Grant SFA, Greene CS. Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms. Nat Commun 2023; 14:5562. [PMID: 37689782 PMCID: PMC10492839 DOI: 10.1038/s41467-023-41057-4] [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: 09/05/2021] [Accepted: 08/18/2023] [Indexed: 09/11/2023] Open
Abstract
Genes act in concert with each other in specific contexts to perform their functions. Determining how these genes influence complex traits requires a mechanistic understanding of expression regulation across different conditions. It has been shown that this insight is critical for developing new therapies. Transcriptome-wide association studies have helped uncover the role of individual genes in disease-relevant mechanisms. However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. Here we introduce PhenoPLIER, a computational approach that maps gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. This representation is based on modules of genes with similar expression patterns across the same conditions. We observe that diseases are significantly associated with gene modules expressed in relevant cell types, and our approach is accurate in predicting known drug-disease pairs and inferring mechanisms of action. Furthermore, using a CRISPR screen to analyze lipid regulation, we find that functionally important players lack associations but are prioritized in trait-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets missed by single-gene strategies.
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Affiliation(s)
- Milton Pividori
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Sumei Lu
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Binglan Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Chun Su
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Matthew E Johnson
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Wei-Qi Wei
- Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Qiping Feng
- Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Bahram Namjou
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Krzysztof Kiryluk
- Department of Medicine, Division of Nephrology, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, 10032, USA
| | | | - Yuan Luo
- Northwestern University, Chicago, IL, 60611, USA
| | - Blair D Sullivan
- Kahlert School of Computing, University of Utah, Salt Lake City, UT, 84112, USA
| | - Benjamin F Voight
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Carsten Skarke
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Struan F A Grant
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Casey S Greene
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
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16
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Ner-Gaon H, Peleg R, Gazit R, Reiner-Benaim A, Shay T. Mapping the splicing landscape of the human immune system. Front Immunol 2023; 14:1116392. [PMID: 37711610 PMCID: PMC10499523 DOI: 10.3389/fimmu.2023.1116392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 08/14/2023] [Indexed: 09/16/2023] Open
Abstract
Most human genes code for more than one transcript. Different ratios of transcripts of the same gene can be found in different cell types or states, indicating differential use of transcription start sites or differential splicing. Such differential transcript use (DTUs) events provide an additional layer of regulation and protein diversity. With the exceptions of PTPRC and CIITA, there are very few reported cases of DTU events in the immune system. To rigorously map DTUs between different human immune cell types, we leveraged four publicly available RNA sequencing datasets. We identified 282 DTU events between five human healthy immune cell types that appear in at least two datasets. The patterns of the DTU events were mostly cell-type-specific or lineage-specific, in the context of the five cell types tested. DTUs correlated with the expression pattern of potential regulators, namely, splicing factors and transcription factors. Of the several immune related conditions studied, only sepsis affected the splicing of more than a few genes and only in innate immune cells. Taken together, we map the DTUs landscape in human peripheral blood immune cell types, and present hundreds of genes whose transcript use changes between cell types or upon activation.
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Affiliation(s)
- Hadas Ner-Gaon
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ronnie Peleg
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Roi Gazit
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Anat Reiner-Benaim
- Department of Epidemiology, Biostatistics and Community Health Sciences, School of Public Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Tal Shay
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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17
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Jacobsen LM, Diggins K, Blanchfield L, McNichols J, Perry DJ, Brant J, Dong X, Bacher R, Gersuk VH, Schatz DA, Atkinson MA, Mathews CE, Haller MJ, Long SA, Linsley PS, Brusko TM. Responders to low-dose ATG induce CD4+ T cell exhaustion in type 1 diabetes. JCI Insight 2023; 8:e161812. [PMID: 37432736 PMCID: PMC10543726 DOI: 10.1172/jci.insight.161812] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 07/06/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUNDLow-dose anti-thymocyte globulin (ATG) transiently preserves C-peptide and lowers HbA1c in individuals with recent-onset type 1 diabetes (T1D); however, the mechanisms of action and features of the response remain unclear. Here, we characterized the post hoc immunological outcomes of ATG administration and their potential use as biomarkers of metabolic response to therapy (i.e., improved preservation of endogenous insulin production).METHODSWe assessed gene and protein expression, targeted gene methylation, and cytokine concentrations in peripheral blood following treatment with ATG (n = 29), ATG plus granulocyte colony-stimulating factor (ATG/G-CSF, n = 28), or placebo (n = 31).RESULTSTreatment with low-dose ATG preserved regulatory T cells (Tregs), as measured by stable methylation of FOXP3 Treg-specific demethylation region (TSDR) and increased proportions of CD4+FOXP3+ Tregs (P < 0.001) identified by flow cytometry. While treatment effects were consistent across participants, not all maintained C-peptide. Responders exhibited a transient rise in IL-6, IP-10, and TNF-α (P < 0.05 for all) 2 weeks after treatment and a durable CD4+ exhaustion phenotype (increased PD-1+KLRG1+CD57- on CD4+ T cells [P = 0.011] and PD1+CD4+ Temra MFI [P < 0.001] at 12 weeks, following ATG and ATG/G-CSF, respectively). ATG nonresponders displayed higher proportions of senescent T cells (at baseline and after treatment) and increased methylation of EOMES (i.e., less expression of this exhaustion marker).CONCLUSIONAltogether in these exploratory analyses, Th1 inflammation-associated serum and CD4+ exhaustion transcript and cellular phenotyping profiles may be useful for identifying signatures of clinical response to ATG in T1D.TRIAL REGISTRATIONClinicalTrials.gov NCT02215200.FUNDINGThe Leona M. and Harry B. Helmsley Charitable Trust (2019PG-T1D011), the NIH (R01 DK106191 Supplement, K08 DK128628), NIH TrialNet (U01 DK085461), and the NIH NIAID (P01 AI042288).
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Affiliation(s)
- Laura M. Jacobsen
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
| | - Kirsten Diggins
- Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA
| | - Lori Blanchfield
- Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA
| | - James McNichols
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
| | - Daniel J. Perry
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
| | - Jason Brant
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
| | - Xiaoru Dong
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Rhonda Bacher
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Vivian H. Gersuk
- Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA
| | - Desmond A. Schatz
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Mark A. Atkinson
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
| | - Clayton E. Mathews
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
| | - Michael J. Haller
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - S. Alice Long
- Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA
| | - Peter S. Linsley
- Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA
| | - Todd M. Brusko
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, Florida, USA
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18
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Farrel A, Li P, Veenbergen S, Patel K, Maris JM, Leonard WJ. ROGUE: an R Shiny app for RNA sequencing analysis and biomarker discovery. BMC Bioinformatics 2023; 24:303. [PMID: 37516886 PMCID: PMC10386769 DOI: 10.1186/s12859-023-05420-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 07/18/2023] [Indexed: 07/31/2023] Open
Abstract
BACKGROUND The growing power and ever decreasing cost of RNA sequencing (RNA-Seq) technologies have resulted in an explosion of RNA-Seq data production. Comparing gene expression values within RNA-Seq datasets is relatively easy for many interdisciplinary biomedical researchers; however, user-friendly software applications increase the ability of biologists to efficiently explore available datasets. RESULTS Here, we describe ROGUE (RNA-Seq Ontology Graphic User Environment, https://marisshiny. RESEARCH chop.edu/ROGUE/ ), a user-friendly R Shiny application that allows a biologist to perform differentially expressed gene analysis, gene ontology and pathway enrichment analysis, potential biomarker identification, and advanced statistical analyses. We use ROGUE to identify potential biomarkers and show unique enriched pathways between various immune cells. CONCLUSIONS User-friendly tools for the analysis of next generation sequencing data, such as ROGUE, will allow biologists to efficiently explore their datasets, discover expression patterns, and advance their research by allowing them to develop and test hypotheses.
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Affiliation(s)
- Alvin Farrel
- Laboratory of Molecular Immunology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
- Immunology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
| | - Peng Li
- Laboratory of Molecular Immunology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Immunology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sharon Veenbergen
- Laboratory of Molecular Immunology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Laboratory of Pediatric Gastroenterology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Laboratory of Medical Immunology, Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Khushbu Patel
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - John M Maris
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Warren J Leonard
- Laboratory of Molecular Immunology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
- Immunology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
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19
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Suomi T, Starskaia I, Kalim UU, Rasool O, Jaakkola MK, Grönroos T, Välikangas T, Brorsson C, Mazzoni G, Bruggraber S, Overbergh L, Dunger D, Peakman M, Chmura P, Brunak S, Schulte AM, Mathieu C, Knip M, Lahesmaa R, Elo LL. Gene expression signature predicts rate of type 1 diabetes progression. EBioMedicine 2023; 92:104625. [PMID: 37224769 DOI: 10.1016/j.ebiom.2023.104625] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/06/2023] [Accepted: 05/09/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND Type 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes. METHODS Whole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diagnosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respectively, using only complete pairs of observations. FINDINGS We found that genes and pathways related to innate immunity were downregulated during the first year after diagnosis. Significant associations of the gene expression changes were found with ZnT8A autoantibody positivity. Rate of change in the expression of 16 genes between baseline and 12 months was found to predict the decline in C-peptide at 24 months. Interestingly and consistent with earlier reports, increased B cell levels and decreased neutrophil levels were associated with the rapid progression. INTERPRETATION There is considerable individual variation in the rate of progression from appearance of type 1 diabetes-specific autoantibodies to clinical disease. Patient stratification and prediction of disease progression can help in developing more personalised therapeutic strategies for different disease endotypes. FUNDING A full list of funding bodies can be found under Acknowledgments.
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Affiliation(s)
- Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Inna Starskaia
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland; Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland
| | - Ubaid Ullah Kalim
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Omid Rasool
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Maria K Jaakkola
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Toni Grönroos
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Tommi Välikangas
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Caroline Brorsson
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Gianluca Mazzoni
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Lut Overbergh
- Katholieke Universiteit Leuven/Universitaire Ziekenhuizen, Leuven, Belgium
| | - David Dunger
- Department of Paediatrics, University of Cambridge, Cambridge, England, UK
| | - Mark Peakman
- Immunology & Inflammation Research Therapeutic Area, Sanofi, MA, USA
| | - Piotr Chmura
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Chantal Mathieu
- Katholieke Universiteit Leuven/Universitaire Ziekenhuizen, Leuven, Belgium
| | - Mikael Knip
- Paediatric Research Centre, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Tampere Centre for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland; Institute of Biomedicine, University of Turku, FI-20520, Turku, Finland.
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland; Institute of Biomedicine, University of Turku, FI-20520, Turku, Finland.
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20
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Hamann MV, Adiba M, Lange UC. Confounding factors in profiling of locus-specific human endogenous retrovirus (HERV) transcript signatures in primary T cells using multi-study-derived datasets. BMC Med Genomics 2023; 16:68. [PMID: 37013607 PMCID: PMC10068191 DOI: 10.1186/s12920-023-01486-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/11/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND Human endogenous retroviruses (HERV) are repetitive sequence elements and a substantial part of the human genome. Their role in development has been well documented and there is now mounting evidence that dysregulated HERV expression also contributes to various human diseases. While research on HERV elements has in the past been hampered by their high sequence similarity, advanced sequencing technology and analytical tools have empowered the field. For the first time, we are now able to undertake locus-specific HERV analysis, deciphering expression patterns, regulatory networks and biological functions of these elements. To do so, we inevitable rely on omics datasets available through the public domain. However, technical parameters inevitably differ, making inter-study analysis challenging. We here address the issue of confounding factors for profiling locus-specific HERV transcriptomes using datasets from multiple sources. METHODS We collected RNAseq datasets of CD4 and CD8 primary T cells and extracted HERV expression profiles for 3220 elements, resembling most intact, near full-length proviruses. Looking at sequencing parameters and batch effects, we compared HERV signatures across datasets and determined permissive features for HERV expression analysis from multiple-source data. RESULTS We could demonstrate that considering sequencing parameters, sequencing-depth is most influential on HERV signature outcome. Sequencing samples deeper broadens the spectrum of expressed HERV elements. Sequencing mode and read length are secondary parameters. Nevertheless, we find that HERV signatures from smaller RNAseq datasets do reliably reveal most abundantly expressed HERV elements. Overall, HERV signatures between samples and studies overlap substantially, indicating a robust HERV transcript signature in CD4 and CD8 T cells. Moreover, we find that measures of batch effect reduction are critical to uncover genic and HERV expression differences between cell types. After doing so, differences in the HERV transcriptome between ontologically closely related CD4 and CD8 T cells became apparent. CONCLUSION In our systematic approach to determine sequencing and analysis parameters for detection of locus-specific HERV expression, we provide evidence that analysis of RNAseq datasets from multiple studies can aid confidence of biological findings. When generating de novo HERV expression datasets we recommend increased sequence depth ( > = 100 mio reads) compared to standard genic transcriptome pipelines. Finally, batch effect reduction measures need to be implemented to allow for differential expression analysis.
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Affiliation(s)
| | - Maisha Adiba
- Leibniz Institute of Virology (LIV), Hamburg, Germany
| | - Ulrike C Lange
- Leibniz Institute of Virology (LIV), Hamburg, Germany.
- Institute for Infection Research and Vaccine Development, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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21
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Holicek P, Truxova I, Rakova J, Salek C, Hensler M, Kovar M, Reinis M, Mikyskova R, Pasulka J, Vosahlikova S, Remesova H, Valentova I, Lysak D, Holubova M, Kaspar P, Prochazka J, Kasikova L, Spisek R, Galluzzi L, Fucikova J. Type I interferon signaling in malignant blasts contributes to treatment efficacy in AML patients. Cell Death Dis 2023; 14:209. [PMID: 36964168 PMCID: PMC10039058 DOI: 10.1038/s41419-023-05728-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/28/2023] [Accepted: 03/09/2023] [Indexed: 03/26/2023]
Abstract
While type I interferon (IFN) is best known for its key role against viral infection, accumulating preclinical and clinical data indicate that robust type I IFN production in the tumor microenvironment promotes cancer immunosurveillance and contributes to the efficacy of various antineoplastic agents, notably immunogenic cell death inducers. Here, we report that malignant blasts from patients with acute myeloid leukemia (AML) release type I IFN via a Toll-like receptor 3 (TLR3)-dependent mechanism that is not driven by treatment. While in these patients the ability of type I IFN to stimulate anticancer immune responses was abolished by immunosuppressive mechanisms elicited by malignant blasts, type I IFN turned out to exert direct cytostatic, cytotoxic and chemosensitizing activity in primary AML blasts, leukemic stem cells from AML patients and AML xenograft models. Finally, a genetic signature of type I IFN signaling was found to have independent prognostic value on relapse-free survival and overall survival in a cohort of 132 AML patients. These findings delineate a clinically relevant, therapeutically actionable and prognostically informative mechanism through which type I IFN mediates beneficial effects in patients with AML.
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Affiliation(s)
- Peter Holicek
- Sotio Biotech, Prague, Czech Republic
- Department of Immunology, Charles University, 2nd Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
| | | | | | - Cyril Salek
- Institute of Hematology and Blood Transfusion, Prague, Czech Republic
- Institute of Clinical and Experimental Hematology, 1st Faculty of Medicine, Charles University, Prague, Czech Republic
| | | | - Marek Kovar
- Laboratory of Tumor Immunology, Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - Milan Reinis
- Laboratory of Immunological and Tumour Models, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Romana Mikyskova
- Laboratory of Immunological and Tumour Models, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | | | | | - Hana Remesova
- Institute of Hematology and Blood Transfusion, Prague, Czech Republic
| | - Iva Valentova
- Sotio Biotech, Prague, Czech Republic
- Department of Immunology, Charles University, 2nd Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
| | - Daniel Lysak
- Department of Hematology and Oncology, Faculty Hospital in Pilsen, Pilsen, Czech Republic
| | - Monika Holubova
- Biomedical Center, Medical Faculty in Pilsen, Charles University, Pilsen, Czech Republic
| | - Petr Kaspar
- Czech Centre for Phenogenomics, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Jan Prochazka
- Czech Centre for Phenogenomics, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | | | - Radek Spisek
- Sotio Biotech, Prague, Czech Republic
- Department of Immunology, Charles University, 2nd Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
| | - Lorenzo Galluzzi
- Department of Radiation Oncology, Weill Cornell Medical College, New York, NY, USA.
- Sandra and Edward Meyer Cancer Center, New York, NY, USA.
- Caryl and Israel Englander Institute for Precision Medicine, New York, NY, USA.
| | - Jitka Fucikova
- Sotio Biotech, Prague, Czech Republic.
- Department of Immunology, Charles University, 2nd Faculty of Medicine and University Hospital Motol, Prague, Czech Republic.
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22
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Olson NE, Ragan SP, Reiss DJ, Thorpe J, Kim Y, Abramson JS, McCoy C, Newhall KJ, Fox BA. Exploration of Tumor Biopsy Gene Signatures to Understand the Role of the Tumor Microenvironment in Outcomes to Lisocabtagene Maraleucel. Mol Cancer Ther 2023; 22:406-418. [PMID: 36595660 PMCID: PMC9978882 DOI: 10.1158/1535-7163.mct-21-0506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 12/17/2021] [Accepted: 12/21/2022] [Indexed: 01/05/2023]
Abstract
In the TRANSCEND NHL 001 study, 53% of patients with relapsed/refractory large B-cell lymphoma (LBCL) treated with lisocabtagene maraleucel (liso-cel) achieved a complete response (CR). To determine characteristics of patients who did and did not achieve a CR, we examined the tumor biology and microenvironment from lymph node tumor biopsies. LBCL biopsies from liso-cel-treated patients were taken pretreatment and ∼11 days posttreatment for RNA sequencing (RNA-seq) and multiplex immunofluorescence (mIF). We analyzed gene expression data from pretreatment biopsies (N = 78) to identify gene sets enriched in patients who achieved a CR to those with progressive disease. Pretreatment biopsies from month-3 CR patients displayed higher expression levels of T-cell and stroma-associated genes, and lower expression of cell-cycle genes. To interpret whether LBCL samples were "follicular lymphoma (FL)-like," we constructed an independent gene expression signature and found that patients with a higher "FL-like" gene expression score had longer progression-free survival (PFS). Cell of origin was not associated with response or PFS, but double-hit gene expression was associated with shorter PFS. The day 11 posttreatment samples (RNA-seq, N = 73; mIF, N = 53) had higher levels of chimeric antigen receptor (CAR) T-cell densities and CAR gene expression, general immune infiltration, and immune activation in patients with CR. Further, the majority of T cells in the day 11 samples were endogenous. Gene expression signatures in liso-cel-treated patients with LBCL can inform the development of combination therapies and next-generation CAR T-cell therapies.
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Affiliation(s)
| | | | | | | | | | - Jeremy S Abramson
- Massachusetts General Hospital Cancer Center, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
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23
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Muir V, Sagadiev S, Liu S, Holder U, Armendariz AM, Suchland E, Meitlis I, Camp N, Giltiay N, Tam JM, Garner EC, Wivagg CN, Shows D, James RG, Lacy-Hulbert A, Acharya M. Transcriptomic analysis of pathways associated with ITGAV/alpha(v) integrin-dependent autophagy in human B cells. Autophagy 2023; 19:926-942. [PMID: 36016494 PMCID: PMC9980515 DOI: 10.1080/15548627.2022.2113296] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Macroautophagy/autophagy proteins have been linked with the development of immune-mediated diseases including lupus, but the mechanisms for this are unclear due to the complex roles of these proteins in multiple immune cell types. We have previously shown that a form of noncanonical autophagy induced by ITGAV/alpha(v) integrins regulates B cell activation by viral and self-antigens, in mice. Here, we investigate the involvement of this pathway in B cells from human tissues. Our data reveal that autophagy is specifically induced in the germinal center and memory B cell subpopulations of human tonsils and spleens. Transcriptomic analysis show that the induction of autophagy is related to unique aspects of activated B cells such as mitochondrial metabolism. To understand the function of ITGAV/alpha(v) integrin-dependent autophagy in human B cells, we used CRISPR-mediated knockdown of autophagy genes. Integrating data from primary B cells and knockout cells, we found that ITGAV/alpha(v)-dependent autophagy limits activation of specific pathways related to B cell responses, while promoting others. These data provide new mechanistic links for autophagy and B-cell-mediated immune dysregulation in diseases such as lupus.
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Affiliation(s)
- Virginia Muir
- Immunology Program, Benaroya Research Institute at Virginia Mason, Seattle, WA, USA
| | - Sara Sagadiev
- Immunology Program, Benaroya Research Institute at Virginia Mason, Seattle, WA, USA.,Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA
| | - Shuozhi Liu
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA
| | - Ursula Holder
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA
| | - Andrea M Armendariz
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA
| | - Emmaline Suchland
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA
| | - Iana Meitlis
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA
| | - Nathan Camp
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA
| | - Natalia Giltiay
- Departments of Rheumatology, University of Washington, Seattle, WA, USA
| | - Jenny M Tam
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.,Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Ethan C Garner
- Department of Molecular and Cell Biology, Harvard University, Cambridge, MA, USA
| | - Carl N Wivagg
- Department of Molecular and Cell Biology, Harvard University, Cambridge, MA, USA
| | - Donna Shows
- Immunology Program, Benaroya Research Institute at Virginia Mason, Seattle, WA, USA
| | - Richard G James
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA.,Department of Pediatric, University of Washington, Seattle, WA, USA.,Department of Pharmacology, University of Washington, Seattle, WA, USA
| | - Adam Lacy-Hulbert
- Immunology Program, Benaroya Research Institute at Virginia Mason, Seattle, WA, USA.,Department of Immunology, University of Washington, Seattle, WA, USA
| | - Mridu Acharya
- Immunology Program, Benaroya Research Institute at Virginia Mason, Seattle, WA, USA.,Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA.,Department of Pediatric, University of Washington, Seattle, WA, USA
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24
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Chen L, Li Z, Wu H. CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data. Genome Biol 2023; 24:37. [PMID: 36855165 PMCID: PMC9972684 DOI: 10.1186/s13059-023-02857-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 01/17/2023] [Indexed: 03/02/2023] Open
Abstract
Bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell type-specific inferences from bulk data. Our real data exploration suggests that differential expression or methylation status is often correlated among cell types. Based on this observation, we develop a novel statistical method named CeDAR to incorporate the cell type hierarchy in cell type-specific differential analyses of bulk data. Extensive simulation and real data analyses demonstrate that this approach significantly improves the accuracy and power in detecting cell type-specific differential signals compared with existing methods, especially in low-abundance cell types.
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Affiliation(s)
- Luxiao Chen
- Department of Biostatistics and Bioinformatics, Emory University, GA 30322 Atlanta, USA
| | - Ziyi Li
- Department of Biostatistics, The University of MD Anderson Cancer Center, 77030 Houston, TX, USA
| | - Hao Wu
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055 P.R. China
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25
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Meng G, Tang W, Huang E, Li Z, Feng H. A comprehensive assessment of cell type-specific differential expression methods in bulk data. Brief Bioinform 2023; 24:bbac516. [PMID: 36472568 PMCID: PMC9851321 DOI: 10.1093/bib/bbac516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/08/2022] [Accepted: 10/29/2022] [Indexed: 12/12/2022] Open
Abstract
Accounting for cell type compositions has been very successful at analyzing high-throughput data from heterogeneous tissues. Differential gene expression analysis at cell type level is becoming increasingly popular, yielding biomarker discovery in a finer granularity within a particular cell type. Although several computational methods have been developed to identify cell type-specific differentially expressed genes (csDEG) from RNA-seq data, a systematic evaluation is yet to be performed. Here, we thoroughly benchmark six recently published methods: CellDMC, CARseq, TOAST, LRCDE, CeDAR and TCA, together with two classical methods, csSAM and DESeq2, for a comprehensive comparison. We aim to systematically evaluate the performance of popular csDEG detection methods and provide guidance to researchers. In simulation studies, we benchmark available methods under various scenarios of baseline expression levels, sample sizes, cell type compositions, expression level alterations, technical noises and biological dispersions. Real data analyses of three large datasets on inflammatory bowel disease, lung cancer and autism provide evaluation in both the gene level and the pathway level. We find that csDEG calling is strongly affected by effect size, baseline expression level and cell type compositions. Results imply that csDEG discovery is a challenging task itself, with room to improvements on handling low signal-to-noise ratio and low expression genes.
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Affiliation(s)
- Guanqun Meng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, Ohio, USA
| | - Wen Tang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, Ohio, USA
| | - Emina Huang
- Department of Surgery, The University of Texas Southwestern Medical Center, Dallas, 75390, Texas, USA
| | - Ziyi Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 77030, Texas, USA
| | - Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, Ohio, USA
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26
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Shembrey C, Foroutan M, Hollande F. A new natural killer cell-specific gene signature predicting recurrence in colorectal cancer patients. Front Immunol 2023; 13:1011247. [PMID: 36685584 PMCID: PMC9853446 DOI: 10.3389/fimmu.2022.1011247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 11/30/2022] [Indexed: 01/07/2023] Open
Abstract
The protective role of Natural Killer (NK) cell tumour immunosurveillance has long been recognised in colorectal cancer (CRC). However, as most patients show limited intra-tumoral NK cell infiltration, improving our ability to identify those with high NK cell activity might aid in dissecting the molecular features which underlie NK cell sensitivity. Here, a novel CRC-specific NK cell gene signature that infers NK cell load in primary tissue samples was derived and validated in multiple patient CRC cohorts. In contrast with other NK cell gene signatures that have several overlapping genes across different immune cell types, our NK cell signature has been extensively refined to be specific for CRC-infiltrating NK cells. The specificity of the signature is substantiated in tumour-infiltrating NK cells from primary CRC tumours at the single cell level, and the signature includes genes representative of NK cells of different maturation states, activation status and anatomical origin. Our signature also accurately discriminates murine NK cells, demonstrating the applicability of this geneset when mining datasets generated from preclinical studies. Differential gene expression analysis revealed tumour-intrinsic features associated with NK cell inclusion versus exclusion in CRC patients, with those tumours with predicted high NK activity showing strong evidence of enhanced chemotactic and cytotoxic transcriptional programs. Furthermore, survival modelling indicated that NK signature expression is associated with improved survival outcomes in CRC patients. Thus, scoring CRC samples with this refined NK cell signature might aid in identifying patients with high NK cell activity who could be prime candidates for NK cell directed immunotherapies.
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Affiliation(s)
- Carolyn Shembrey
- Department of Clinical Pathology, The University of Melbourne, Victorian Comprehensive Cancer Centre, Melbourne, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Momeneh Foroutan
- Department of Clinical Pathology, The University of Melbourne, Victorian Comprehensive Cancer Centre, Melbourne, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne, VIC, Australia
- Biomedicine Discovery Institute, Monash University, Melbourne, VIC, Australia
| | - Frédéric Hollande
- Department of Clinical Pathology, The University of Melbourne, Victorian Comprehensive Cancer Centre, Melbourne, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne, VIC, Australia
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27
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Groha S, Alaiwi SA, Xu W, Naranbhai V, Nassar AH, Bakouny Z, El Zarif T, Saliby RM, Wan G, Rajeh A, Adib E, Nuzzo PV, Schmidt AL, Labaki C, Ricciuti B, Alessi JV, Braun DA, Shukla SA, Keenan TE, Van Allen E, Awad MM, Manos M, Rahma O, Zubiri L, Villani AC, Fairfax B, Hammer C, Khan Z, Reynolds K, Semenov Y, Schrag D, Kehl KL, Freedman ML, Choueiri TK, Gusev A. Germline variants associated with toxicity to immune checkpoint blockade. Nat Med 2022; 28:2584-2591. [PMID: 36526723 PMCID: PMC10958775 DOI: 10.1038/s41591-022-02094-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 10/18/2022] [Indexed: 12/23/2022]
Abstract
Immune checkpoint inhibitors (ICIs) have yielded remarkable responses but often lead to immune-related adverse events (irAEs). Although germline causes for irAEs have been hypothesized, no individual variant associated with developing irAEs has been identified. We carried out a genome-wide association study of 1,751 patients on ICIs across 12 cancer types. We investigated two irAE phenotypes: (1) high-grade (3-5) and (2) all-grade events. We identified 3 genome-wide significant associations (P < 5 × 10-8) in the discovery cohort associated with all-grade irAEs: rs16906115 near IL7 (combined P = 3.6 × 10-11; hazard ratio (HR) = 2.1); rs75824728 near IL22RA1 (combined P = 3.5 × 10-8; HR = 1.8); and rs113861051 on 4p15 (combined P = 1.2 × 10-8, HR = 2.0); rs16906115 was replicated in 3 independent studies. The association near IL7 colocalized with the gain of a new cryptic exon for IL7, a critical regulator of lymphocyte homeostasis. Patients carrying the IL7 germline variant exhibited significantly increased lymphocyte stability after ICI initiation, which was itself predictive of downstream irAEs and improved survival.
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Affiliation(s)
- Stefan Groha
- Division of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Harvard & MIT, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sarah Abou Alaiwi
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Wenxin Xu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Vivek Naranbhai
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Amin H Nassar
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Ziad Bakouny
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Talal El Zarif
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Renee Maria Saliby
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Guihong Wan
- Harvard Medical School, Boston, MA, USA
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA
| | - Ahmad Rajeh
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA
| | - Elio Adib
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Pier V Nuzzo
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Internal Medicine and Medical Specialties, School of Medicine, University of Genoa, Genoa, Italy
| | - Andrew L Schmidt
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Chris Labaki
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Biagio Ricciuti
- Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Joao Victor Alessi
- Department of Internal Medicine and Medical Specialties, School of Medicine, University of Genoa, Genoa, Italy
| | - David A Braun
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Center of Molecular and Cellular Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Sachet A Shukla
- Broad Institute of Harvard & MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Tanya E Keenan
- Broad Institute of Harvard & MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, USA
| | - Eliezer Van Allen
- Broad Institute of Harvard & MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mark M Awad
- Department of Internal Medicine and Medical Specialties, School of Medicine, University of Genoa, Genoa, Italy
| | - Michael Manos
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Osama Rahma
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Alexandra-Chloe Villani
- Broad Institute of Harvard & MIT, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Zia Khan
- Genentech, South San Francisco, CA, USA
| | - Kerry Reynolds
- Harvard Medical School, Boston, MA, USA
- Division of Medical Oncology, Bartlett, Massachusetts General Hospital, Boston, MA, USA
| | - Yevgeniy Semenov
- Harvard Medical School, Boston, MA, USA
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA
| | - Deborah Schrag
- Division of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kenneth L Kehl
- Division of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Matthew L Freedman
- Broad Institute of Harvard & MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Toni K Choueiri
- Harvard Medical School, Boston, MA, USA
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alexander Gusev
- Division of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute of Harvard & MIT, Cambridge, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA.
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28
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Somatic 9p24.1 alterations in HPV - head and neck squamous cancer dictate immune microenvironment and anti-PD-1 checkpoint inhibitor activity. Proc Natl Acad Sci U S A 2022; 119:e2213835119. [PMID: 36395141 PMCID: PMC9704728 DOI: 10.1073/pnas.2213835119] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Somatic copy number alterations (SCNAs), generally (1) losses containing interferons and interferon-pathway genes, many on chromosome 9p, predict immune-cold, immune checkpoint therapy (ICT)-resistant tumors (2); however, genomic regions mediating these effects are unclear and probably tissue specific. Previously, 9p21.3 loss was found to be an early genetic driver of human papillomavirus-negative (HPV-) head and neck squamous cancer (HNSC), associated with an immune-cold tumor microenvironment (TME) signal, and recent evidence suggested that this TME-cold phenotype was greatly enhanced with 9p21 deletion size, notably encompassing band 9p24.1 (3). Here, we report multi-omic, -threshold and continuous-variable dissection of 9p21 and 9p24 loci (including depth and degree of somatic alteration of each band at each locus, and each gene at each band) and TME of four HPV- HNSC cohorts. Preferential 9p24 deletion, CD8 T-cell immune-cold associations were observed, driven by 9p24.1 loss, and in turn by an essential telomeric regulatory gene element, JAK2-CD274. Surprisingly, same genetic region gains were immune hot. Related 9p21-TME analyses were less evident. Inherent 9p-band-level influences on anti-PD1 ICT survival rates, coincident with TME patterns, were also observed. At a 9p24.1 whole-transcriptome expression threshold of 60th percentile, ICT survival rate exceeded that of lower expression percentiles and of chemotherapy; below this transcript threshold, ICT survival was inferior to chemotherapy, the latter unaffected by 9p24.1 expression level (P-values < 0.01, including in a PD-L1 immunohistochemistry-positive patient subgroup). Whole-exome analyses of 10 solid-tumor types suggest that these 9p-related ICT findings could be relevant to squamous cancers, in which 9p24.1 gain/immune-hot associations exist.
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29
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Chen J, Bie R, Qin Y, Li Y, Ma S. Lq-based robust analytics on ultrahigh and high dimensional data. Stat Med 2022; 41:5220-5241. [PMID: 36098057 DOI: 10.1002/sim.9563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 06/02/2022] [Accepted: 08/02/2022] [Indexed: 11/10/2022]
Abstract
Ultrahigh and high dimensional data are common in regression analysis for various fields, such as omics data, finance, and biological engineering. In addition to the problem of dimension, the data might also be contaminated. There are two main types of contamination: outliers and model misspecification. We develop an unique method that takes into account the ultrahigh or high dimensional issues and both types of contamination. In this article, we propose a framework for feature screening and selection based on the minimum Lq-likelihood estimation (MLqE), which accounts for the model misspecification contamination issue and has also been shown to be robust to outliers. In numerical analysis, we explore the robustness of this framework under different outliers and model misspecification scenarios. To examine the performance of this framework, we conduct real data analysis using the skin cutaneous melanoma data. When comparing with traditional screening and feature selection methods, the proposed method shows superiority in both variable identification effectiveness and parameter estimation accuracy.
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Affiliation(s)
- Jiachen Chen
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Ruofan Bie
- Department of Biostatistics, Brown University, Providence, RI, USA
| | - Yichen Qin
- Department of Operations, Business Analytics and Information Systems, University of Cincinnati, Cincinnati, OH, USA
| | - Yang Li
- Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China.,RSS and China-Re Life Joint Lab on Public Health and Risk Management, Renmin University of China, Beijing, China
| | - Shuangge Ma
- Department of Biostatistics, Boston University, Boston, MA, USA.,RSS and China-Re Life Joint Lab on Public Health and Risk Management, Renmin University of China, Beijing, China
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30
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Single-cell RNA sequencing uncovers the nuclear decoy lincRNA PIRAT as a regulator of systemic monocyte immunity during COVID-19. Proc Natl Acad Sci U S A 2022; 119:e2120680119. [PMID: 35998224 PMCID: PMC9457492 DOI: 10.1073/pnas.2120680119] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
SARS-CoV-2–infected patients often display characteristic changes in the production of immune mediators that trigger life-threatening courses of COVID-19. The underlying molecular mechanisms are not yet fully understood. Here, we used single-cell RNA sequencing to investigate the involvement of the emerging class of long regulatory RNA in COVID-19. Our data reveal that a previously unknown regulatory RNA in the nucleus of immune cells is altered after SARS-CoV-2 infection. The degradation of this RNA removes a natural brake on the production of critical immune mediators that can promote the development of severe COVID-19. We believe that therapeutic intervention in this nuclear RNA circuit could counteract the overproduction of disease-causing immune mediators and protect against severe COVID-19. The systemic immune response to viral infection is shaped by master transcription factors, such as NF-κB, STAT1, or PU.1. Although long noncoding RNAs (lncRNAs) have been suggested as important regulators of transcription factor activity, their contributions to the systemic immunopathologies observed during SARS-CoV-2 infection have remained unknown. Here, we employed a targeted single-cell RNA sequencing approach to reveal lncRNAs differentially expressed in blood leukocytes during severe COVID-19. Our results uncover the lncRNA PIRAT (PU.1-induced regulator of alarmin transcription) as a major PU.1 feedback-regulator in monocytes, governing the production of the alarmins S100A8/A9, key drivers of COVID-19 pathogenesis. Knockout and transgene expression, combined with chromatin-occupancy profiling, characterized PIRAT as a nuclear decoy RNA, keeping PU.1 from binding to alarmin promoters and promoting its binding to pseudogenes in naïve monocytes. NF-κB–dependent PIRAT down-regulation during COVID-19 consequently releases a transcriptional brake, fueling alarmin production. Alarmin expression is additionally enhanced by the up-regulation of the lncRNA LUCAT1, which promotes NF-κB–dependent gene expression at the expense of targets of the JAK-STAT pathway. Our results suggest a major role of nuclear noncoding RNA networks in systemic antiviral responses to SARS-CoV-2 in humans.
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31
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Wu Y, Biswas D, Swanton C. Impact of cancer evolution on immune surveillance and checkpoint inhibitor response. Semin Cancer Biol 2022; 84:89-102. [PMID: 33631295 PMCID: PMC9253787 DOI: 10.1016/j.semcancer.2021.02.013] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/18/2021] [Accepted: 02/19/2021] [Indexed: 12/21/2022]
Abstract
Intratumour heterogeneity (ITH) is pervasive across all cancers studied and may provide the evolving tumour multiple routes to escape immune surveillance. Immune checkpoint inhibitors (CPIs) are rapidly becoming standard of care for many cancers. Here, we discuss recent work investigating the influence of ITH on patient response to immune checkpoint inhibitor (CPI) therapy. At its simplest, ITH may confound the diagnostic accuracy of predictive biomarkers used to stratify patients for CPI therapy. Furthermore, ITH is fuelled by mechanisms of genetic instability that can both engage immune surveillance and drive immune evasion. A greater appreciation of the interplay between ITH and the immune system may hold the key to increasing the proportion of patients experiencing durable responses from CPI therapy.
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Affiliation(s)
- Yin Wu
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, NW1 1AT, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, Paul O'Gorman Building, London, WC1E 6DD, UK; Peter Gorer Department of Immunobiology, School of Immunology & Microbial Sciences, King's College London, London, SE1 9RT, UK
| | - Dhruva Biswas
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, NW1 1AT, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, Paul O'Gorman Building, London, WC1E 6DD, UK; Bill Lyons Informatics Centre, University College London Cancer Institute, Paul O'Gorman Building, London, WC1E 6DD, UK
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, NW1 1AT, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, Paul O'Gorman Building, London, WC1E 6DD, UK.
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32
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Song L, Yang YT, Guo Q, Zhao XM. Cellular transcriptional alterations of peripheral blood in Alzheimer's disease. BMC Med 2022; 20:266. [PMID: 36031604 PMCID: PMC9422129 DOI: 10.1186/s12916-022-02472-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 07/11/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD), a progressive neurodegenerative disease, is the most common cause of dementia worldwide. Accumulating data support the contributions of the peripheral immune system in AD pathogenesis. However, there is a lack of comprehensive understanding about the molecular characteristics of peripheral immune cells in AD. METHODS To explore the alterations of cellular composition and the alterations of intrinsic expression of individual cell types in peripheral blood, we performed cellular deconvolution in a large-scale bulk blood expression cohort and identified cell-intrinsic differentially expressed genes in individual cell types with adjusting for cellular proportion. RESULTS We detected a significant increase and decrease in the proportion of neutrophils and B lymphocytes in AD blood, respectively, which had a robust replicability across other three AD cohorts, as well as using alternative algorithms. The differentially expressed genes in AD neutrophils were enriched for some AD-associated pathways, such as ATP metabolic process and mitochondrion organization. We also found a significant enrichment of protein-protein interaction network modules of leukocyte cell-cell activation, mitochondrion organization, and cytokine-mediated signaling pathway in neutrophils for AD risk genes including CD33 and IL1B. Both changes in cellular composition and expression levels of specific genes were significantly associated with the clinical and pathological alterations. A similar pattern of perturbations on the cellular proportion and gene expression levels of neutrophils could be also observed in mild cognitive impairment (MCI). Moreover, we noticed an elevation of neutrophil abundance in the AD brains. CONCLUSIONS We revealed the landscape of molecular perturbations at the cellular level for AD. These alterations highlight the putative roles of neutrophils in AD pathobiology.
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Affiliation(s)
- Liting Song
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Yucheng T Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China.,MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.,Zhangjiang Fudan International Innovation Center, Shanghai, 200433, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | | | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China. .,MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China. .,Zhangjiang Fudan International Innovation Center, Shanghai, 200433, China. .,International Human Phenome Institutes (Shanghai), Shanghai, 200433, China.
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33
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Bilous M, Tran L, Cianciaruso C, Gabriel A, Michel H, Carmona SJ, Pittet MJ, Gfeller D. Metacells untangle large and complex single-cell transcriptome networks. BMC Bioinformatics 2022; 23:336. [PMID: 35963997 PMCID: PMC9375201 DOI: 10.1186/s12859-022-04861-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 07/23/2022] [Indexed: 12/13/2022] Open
Abstract
Background Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. Results We develop a framework called SuperCell to merge highly similar cells into metacells and perform standard scRNA-seq data analyses at the metacell level. Our systematic benchmarking demonstrates that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop. Conclusions SuperCell is a framework to build and analyze metacells in a way that efficiently preserves the results of scRNA-seq data analyses while significantly accelerating and facilitating them.
Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04861-1.
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Affiliation(s)
- Mariia Bilous
- Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Loc Tran
- Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Chiara Cianciaruso
- Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland
| | - Aurélie Gabriel
- Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Hugo Michel
- Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Santiago J Carmona
- Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Mikael J Pittet
- Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland.,Department of Oncology, Geneva University Hospitals, Geneva, Switzerland.,Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland. .,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
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34
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Zaitsev A, Chelushkin M, Dyikanov D, Cheremushkin I, Shpak B, Nomie K, Zyrin V, Nuzhdina E, Lozinsky Y, Zotova A, Degryse S, Kotlov N, Baisangurov A, Shatsky V, Afenteva D, Kuznetsov A, Paul SR, Davies DL, Reeves PM, Lanuti M, Goldberg MF, Tazearslan C, Chasse M, Wang I, Abdou M, Aslanian SM, Andrewes S, Hsieh JJ, Ramachandran A, Lyu Y, Galkin I, Svekolkin V, Cerchietti L, Poznansky MC, Ataullakhanov R, Fowler N, Bagaev A. Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes. Cancer Cell 2022; 40:879-894.e16. [PMID: 35944503 DOI: 10.1016/j.ccell.2022.07.006] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 05/10/2022] [Accepted: 07/12/2022] [Indexed: 12/21/2022]
Abstract
Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. We present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of >9,400 tissue and blood sorted cell RNA profiles incorporated into millions of artificial transcriptomes to accurately reconstruct the tumor microenvironment (TME). Bioinformatics correction for technical and biological variability, aberrant cancer cell expression inclusion, and accurate quantification and normalization of transcript expression increased Kassandra stability and robustness. Performance was validated on 4,000 H&E slides and 1,000 tissues by comparison with cytometric, immunohistochemical, or single-cell RNA-seq measurements. Kassandra accurately deconvolved TME elements, showing the role of these populations in tumor pathogenesis and other biological processes. Digital TME reconstruction revealed that the presence of PD-1-positive CD8+ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.
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Affiliation(s)
| | | | | | | | - Boris Shpak
- BostonGene, Corp., 95 Sawyer Road, Waltham, MA 02453, USA
| | - Krystle Nomie
- BostonGene, Corp., 95 Sawyer Road, Waltham, MA 02453, USA
| | - Vladimir Zyrin
- BostonGene, Corp., 95 Sawyer Road, Waltham, MA 02453, USA
| | | | | | | | | | - Nikita Kotlov
- BostonGene, Corp., 95 Sawyer Road, Waltham, MA 02453, USA
| | | | | | - Daria Afenteva
- BostonGene, Corp., 95 Sawyer Road, Waltham, MA 02453, USA
| | | | - Susan Raju Paul
- The Vaccine and Immunotherapy Center, Massachusetts General Hospital, Boston, MA, USA
| | - Diane L Davies
- Division of Thoracic Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick M Reeves
- The Vaccine and Immunotherapy Center, Massachusetts General Hospital, Boston, MA, USA
| | - Michael Lanuti
- Division of Thoracic Surgery, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Madison Chasse
- BostonGene, Corp., 95 Sawyer Road, Waltham, MA 02453, USA
| | - Iris Wang
- BostonGene, Corp., 95 Sawyer Road, Waltham, MA 02453, USA
| | - Mary Abdou
- BostonGene, Corp., 95 Sawyer Road, Waltham, MA 02453, USA
| | | | | | - James J Hsieh
- Molecular Oncology, Division of Oncology, Department of Medicine, Washington University, St. Louis, MO, USA
| | - Akshaya Ramachandran
- Molecular Oncology, Division of Oncology, Department of Medicine, Washington University, St. Louis, MO, USA
| | - Yang Lyu
- Molecular Oncology, Division of Oncology, Department of Medicine, Washington University, St. Louis, MO, USA
| | - Ilia Galkin
- BostonGene, Corp., 95 Sawyer Road, Waltham, MA 02453, USA
| | | | - Leandro Cerchietti
- Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, USA
| | - Mark C Poznansky
- The Vaccine and Immunotherapy Center, Massachusetts General Hospital, Boston, MA, USA
| | | | - Nathan Fowler
- BostonGene, Corp., 95 Sawyer Road, Waltham, MA 02453, USA; Department of Lymphoma and Myeloma, MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 429, Houston, TX 77030, USA.
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35
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Lelliott EJ, Ramsbottom KM, Dowling MR, Shembrey C, Noori T, Kearney CJ, Michie J, Parish IA, Jordan MA, Baxter AG, Young ND, Brennan AJ, Oliaro J. NKG7 Enhances CD8+ T Cell Synapse Efficiency to Limit Inflammation. Front Immunol 2022; 13:931630. [PMID: 35874669 PMCID: PMC9299089 DOI: 10.3389/fimmu.2022.931630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 06/06/2022] [Indexed: 12/01/2022] Open
Abstract
Cytotoxic lymphocytes are essential for anti-tumor immunity, and for effective responses to cancer immunotherapy. Natural killer cell granule protein 7 (NKG7) is expressed at high levels in cytotoxic lymphocytes infiltrating tumors from patients treated with immunotherapy, but until recently, the role of this protein in cytotoxic lymphocyte function was largely unknown. Unexpectedly, we found that highly CD8+ T cell-immunogenic murine colon carcinoma (MC38-OVA) tumors grew at an equal rate in Nkg7+/+ and Nkg7-/- littermate mice, suggesting NKG7 may not be necessary for effective CD8+ T cell anti-tumor activity. Mechanistically, we found that deletion of NKG7 reduces the ability of CD8+ T cells to degranulate and kill target cells in vitro. However, as a result of inefficient cytotoxic activity, NKG7 deficient T cells form a prolonged immune synapse with tumor cells, resulting in increased secretion of inflammatory cytokines, including tumor necrosis factor alpha (TNF). By deleting the TNF receptor, TNFR1, from MC38-OVA tumors, we demonstrate that this hyper-secretion of TNF compensates for reduced synapse-mediated cytotoxic activity against MC38-OVA tumors in vivo, via increased TNF-mediated tumor cell death. Taken together, our results demonstrate that NKG7 enhances CD8+ T cell immune synapse efficiency, which may serve as a mechanism to accelerate direct cytotoxicity and limit potentially harmful inflammatory responses.
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Affiliation(s)
- Emily J Lelliott
- Centre for Cancer Immunotherapy, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, Faculty of Medicine, Density and Health Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Kelly M Ramsbottom
- Centre for Cancer Immunotherapy, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Mark R Dowling
- Centre for Cancer Immunotherapy, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, Faculty of Medicine, Density and Health Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Carolyn Shembrey
- Centre for Cancer Immunotherapy, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, Faculty of Medicine, Density and Health Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Tahereh Noori
- Centre for Cancer Immunotherapy, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Conor J Kearney
- Centre for Cancer Immunotherapy, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, Faculty of Medicine, Density and Health Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Jessica Michie
- Centre for Cancer Immunotherapy, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Ian A Parish
- Centre for Cancer Immunotherapy, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, Faculty of Medicine, Density and Health Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Margaret A Jordan
- College of Public Health, Medical & Veterinary Sciences, James Cook University, Townsville, QLD, Australia
| | - Alan G Baxter
- College of Public Health, Medical & Veterinary Sciences, James Cook University, Townsville, QLD, Australia.,Central Clinical School, Monash University, Prahran, VIC, Australia
| | - Neil D Young
- Department of Veterinary Biosciences, Melbourne Veterinary School, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Amelia J Brennan
- Centre for Cancer Immunotherapy, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, Faculty of Medicine, Density and Health Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Jane Oliaro
- Centre for Cancer Immunotherapy, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, Faculty of Medicine, Density and Health Sciences, The University of Melbourne, Parkville, VIC, Australia
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36
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Hu Y, Tang C, Zhu W, Ye H, Lin Y, Wang R, Zhou T, Wen S, Yang J, Fang C. Identification of chromosomal instability-associated genes as hepatocellular carcinoma progression-related biomarkers to guide clinical diagnosis, prognosis and therapy. Comput Biol Med 2022; 148:105896. [PMID: 35868048 DOI: 10.1016/j.compbiomed.2022.105896] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 06/21/2022] [Accepted: 07/16/2022] [Indexed: 11/03/2022]
Abstract
Hepatocellular carcinoma (HCC) is a type of cancer characterized by high heterogeneity and a complex multistep progression process. Significantly-altered biomarkers for HCC need to be identified. Differentially expressed genes and weighted gene co-expression network analyses were used to identify progression-related biomarkers. LASSO-Cox regression and random forest algorithms were used to construct the progression-related prognosis (PRP) score. Three chromosomal instability-associated genes (KIF20A, TOP2A, and TTK) have been identified as progression-related biomarkers. The robustness of the PRP scores were validated using four independent cohorts. Immune status was observed using the single-sample gene set enrichment analysis (ssGSEA). Comprehensive analysis showed that the patients with high PRP score had wider genomic alterations, more malignant phenotypes, and were in a state of immunosuppression. The diagnostic models constructed via logistic regression based on the three genes showed satisfactory performances in distinguishing HCC from cirrhotic tissues or dysplastic nodules. The nomogram combining PRP scores with clinical factors had a better performance in predicting prognosis than the tumor node metastasis classification (TNM) system. We further confirmed that KIF20A, TOP2A, and TTK were highly expressed in HCC tissues than in cirrhotic tissues. Downregulation of all three genes aggravated chromosomal instabilities in HCC and suppressed HCC cells viability both in vitro and in vivo. Overall, our study highlights the important roles of chromosomal instability-associated genes during the progression of HCC and their potential clinical diagnosis and prognostic value and provides promising new ideas for developing therapeutic strategies to improve the outcomes of HCC patients.
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Affiliation(s)
- Yueyang Hu
- Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Institute of Digital Intelligence, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, 510280, China
| | - Chuanyu Tang
- Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Institute of Digital Intelligence, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, 510280, China
| | - Wen Zhu
- Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Institute of Digital Intelligence, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, 510280, China
| | - Hanjie Ye
- Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Institute of Digital Intelligence, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, 510280, China
| | - Yuxing Lin
- Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Institute of Digital Intelligence, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, 510280, China
| | - Ruixuan Wang
- Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Institute of Digital Intelligence, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, 510280, China
| | - Tianjun Zhou
- Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Institute of Digital Intelligence, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, 510280, China
| | - Sai Wen
- Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Institute of Digital Intelligence, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, 510280, China
| | - Jian Yang
- Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Institute of Digital Intelligence, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, 510280, China
| | - Chihua Fang
- Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Institute of Digital Intelligence, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China; Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, 510280, China.
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Immune Cytolytic Activity for Comprehensive Insights of the Immune Landscape in Endometrial Carcinoma. JOURNAL OF ONCOLOGY 2022; 2022:9060243. [PMID: 35898926 PMCID: PMC9313908 DOI: 10.1155/2022/9060243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 06/16/2022] [Indexed: 11/29/2022]
Abstract
Immune checkpoint blockade (ICB) has been explored as a therapeutic strategy to recover the antitumor immune activities against endometrial cancer (EC) escaping from immune surveillance. Increasing evidence has indicated that microsatellite instability (MSI) is a promising biomarker to stratify patients for the ICB therapy. However, even in patients with MSI-High (MSI-H) endometrial cancers, PD-L1 inhibitors, avelumab, and durvalumab have shown only 27% of response rates. Therefore, there is an urgent need to discover new biomarkers for a predictive response to ICB therapy. In this study, we demonstrated that the immune cytolytic activity (CYT) index was significantly correlated with the development and response to immunotherapy in EC. The data showed that higher CYT was significantly associated with better clinical outcome, more antitumor infiltrating immune cells, fewer somatic copy number alterations, but a higher TMB (Tumor mutational burden) status. Furthermore, CYT-high EC was notably relevant to the high expression of various immune checkpoint molecules and showed more effective responses to ICB treatment. Taken together, this study provided new insights into the connection between diverse genetic events and the immune microenvironment in EC and indicated that the CYT status might be a promising biomarker to stratify patients with EC for ICB therapy.
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Kuang X, Li J. Chromosome instability and aneuploidy as context-dependent activators or inhibitors of antitumor immunity. Front Immunol 2022; 13:895961. [PMID: 36003402 PMCID: PMC9393846 DOI: 10.3389/fimmu.2022.895961] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/28/2022] [Indexed: 12/11/2022] Open
Abstract
Chromosome instability (CIN) and its major consequence, aneuploidy, are hallmarks of human cancers. In addition to imposing fitness costs on tumor cells through several cell-intrinsic mechanisms, CIN/aneuploidy also provokes an antitumor immune response. However, as the major contributor to genomic instability, intratumor heterogeneity generated by CIN/aneuploidy helps tumor cells to evolve methods to overcome the antitumor role of the immune system or even convert the immune system to be tumor-promoting. Although the interplay between CIN/aneuploidy and the immune system is complex and context-dependent, understanding this interplay is essential for the success of immunotherapy in tumors exhibiting CIN/aneuploidy, regardless of whether the efficacy of immunotherapy is increased by combination with strategies to promote CIN/aneuploidy or by designing immunotherapies to target CIN/aneuploidy directly.
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Affiliation(s)
- Xiaohong Kuang
- Department of Hematology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
| | - Jian Li
- Department of General Surgery, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
- *Correspondence: Jian Li,
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39
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Identification of a Seven-Differentially Expressed Gene-Based Recurrence-Free Survival Model for Melanoma Patients. DISEASE MARKERS 2022; 2022:3915112. [PMID: 35872694 PMCID: PMC9303152 DOI: 10.1155/2022/3915112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/29/2022] [Indexed: 11/17/2022]
Abstract
Melanoma is a malignant tumor that originates in melanocytes of the skin or mucous membrane, which has a high mortality rate and worse prognosis. Therefore, perspective prognosis evaluation seems more important for patients' treatment. Gene expression profiles of melanoma were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. 130 consistent differentially expressed genes (DEGs) were identified between melanoma and nevus tissues from two GEO cohorts. Prognostic genes were identified by univariate analysis, and 20 of them were regarded to be associated with the recurrence-free survival (RFS) of melanoma patients. Then, the LASSO Cox regression analysis chose seven of them to establish a seven-DEG-based RFS predicting signature. We demonstrated that this model was more powerful to predict RFS risk than other individual clinical features and was able to independently predict the RFS outcomes in different subsets of patients. We attempted to search for the underlying mechanisms by analyzing the coexpression genes of the seven candidates, and the pathway enrichment analyses indicated that immune response-related pathways might play a critical role in melanoma progression. Finally, we establish a robust seven-DEG-based RFS predicting signature, which will facilitate the personalized treatment of melanoma patients.
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40
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Casu A, Grippo PJ, Wasserfall C, Sun Z, Linsley PS, Hamerman JA, Fife BT, Lacy-Hulbert A, Toledo FGS, Hart PA, Papachristou GI, Bellin MD, Yadav D, Laughlin MR, Goodarzi MO, Speake C. Evaluating the Immunopathogenesis of Diabetes After Acute Pancreatitis in the Diabetes RElated to Acute Pancreatitis and Its Mechanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium. Pancreas 2022; 51:580-585. [PMID: 36206462 PMCID: PMC9555855 DOI: 10.1097/mpa.0000000000002076] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
ABSTRACT The association between acute pancreatitis (AP) and diabetes mellitus (DM) has long been established, with the initial descriptions of AP patients presenting with DM after a bout of AP published in the 1940s and 50s. However, the potential mechanisms involved, particularly those components related to the immune system, have not been well defined. The Diabetes RElated to Acute pancreatitis and its Mechanisms (DREAM) study is a multicenter clinical study designed to understand the frequency and phenotype of DM developing after AP. This article describes one objective of the DREAM study: to determine the immunologic mechanisms of DM after AP, including the contribution of β-cell autoimmunity. This component of the study will assess the presence of islet autoimmunity, as well as the magnitude and kinetics of the innate and adaptive immune response at enrollment and during longitudinal follow-up after 1 or more episodes of AP. Finally, DREAM will evaluate the relationship between immune features, DM development, and pancreatitis etiology and severity.
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Affiliation(s)
- Anna Casu
- From the Translational Research Institute, AdventHealth Orlando, Orlando, FL
| | - Paul J Grippo
- Department of Medicine, Division of Gastroenterology and Hepatology, University of Illinois-Chicago, Chicago, IL
| | - Clive Wasserfall
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL
| | - Zhaoli Sun
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Peter S Linsley
- Center for Systems Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA
| | - Jessica A Hamerman
- Center for Fundamental Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA
| | - Brian T Fife
- Department of Medicine, Center for Immunology, University of Minnesota, Minneapolis, MN
| | - Adam Lacy-Hulbert
- Center for Fundamental Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA
| | - Frederico G S Toledo
- Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Phil A Hart
- Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, OH
| | - Georgios I Papachristou
- Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, OH
| | | | - Dhiraj Yadav
- Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh Medical Center, Pittsburgh, PA
| | - Maren R Laughlin
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Cate Speake
- Diabetes Clinical Research Program, Center for Interventional Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA
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41
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Shen Z, Fang M, Sun W, Tang M, Liu N, Zhu L, Liu Q, Li B, Sun R, Shi Y, Guo C, Lin J, Qu K. A transcriptome atlas and interactive analysis platform for autoimmune disease. Database (Oxford) 2022; 2022:6618550. [PMID: 35758882 PMCID: PMC9235372 DOI: 10.1093/database/baac050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/18/2022] [Accepted: 06/09/2022] [Indexed: 11/12/2022]
Abstract
With the rapid development of next-generation sequencing technology, many laboratories have produced a large amount of single-cell transcriptome data of blood and tissue samples from patients with autoimmune diseases, which enables in-depth studies of the relationship between gene transcription and autoimmune diseases. However, there is still a lack of a database that integrates the large amount of autoimmune disease transcriptome sequencing data and conducts effective analysis. In this study, we developed a user-friendly web database tool, Interactive Analysis and Atlas for Autoimmune disease (IAAA), which integrates bulk RNA-seq data of 929 samples of 10 autoimmune diseases and single-cell RNA-seq data of 783 203 cells in 96 samples of 6 autoimmune diseases. IAAA also provides customizable analysis modules, including gene expression, difference, correlation, similar gene detection and cell–cell interaction, and can display results in three formats (plot, table and pdf) through custom parameters. IAAA provides valuable data resources for researchers studying autoimmune diseases and helps users deeply explore the potential value of the current transcriptome data. IAAA is available. Database URL: http://galaxy.ustc.edu.cn/IAAA
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Affiliation(s)
- Zhuoqiao Shen
- School of Data Sciences, University of Science and Technology of China, No. 443, Huangshan Road, Shushan District, Hefei, Anhui 230027, China.,Department of Oncology, The First Affiliated Hospital of USTC, Department of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 17, Lujiang Road, Luyang District, Hefei, Anhui 230021, China.,Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Wangjiang West Road, Shushan District, Hefei, Anhui 230088, China
| | - Minghao Fang
- Department of Oncology, The First Affiliated Hospital of USTC, Department of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 17, Lujiang Road, Luyang District, Hefei, Anhui 230021, China
| | - Wujianan Sun
- Department of Oncology, The First Affiliated Hospital of USTC, Department of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 17, Lujiang Road, Luyang District, Hefei, Anhui 230021, China.,Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Wangjiang West Road, Shushan District, Hefei, Anhui 230088, China.,CAS Center for Excellence in Molecular Cell Sciences, the CAS Key Laboratory of Innate Immunity and Chronic Disease, University of Science and Technology of China, No. 373 Huangshan Road, Shushan District, Hefei, Anhui 230027, China
| | - Meifang Tang
- Department of Oncology, The First Affiliated Hospital of USTC, Department of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 17, Lujiang Road, Luyang District, Hefei, Anhui 230021, China
| | - Nianping Liu
- Department of Oncology, The First Affiliated Hospital of USTC, Department of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 17, Lujiang Road, Luyang District, Hefei, Anhui 230021, China
| | - Lin Zhu
- Department of Oncology, The First Affiliated Hospital of USTC, Department of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 17, Lujiang Road, Luyang District, Hefei, Anhui 230021, China
| | - Qian Liu
- Department of Oncology, The First Affiliated Hospital of USTC, Department of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 17, Lujiang Road, Luyang District, Hefei, Anhui 230021, China
| | - Bin Li
- Department of Oncology, The First Affiliated Hospital of USTC, Department of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 17, Lujiang Road, Luyang District, Hefei, Anhui 230021, China
| | - Ruoming Sun
- Department of Oncology, The First Affiliated Hospital of USTC, Department of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 17, Lujiang Road, Luyang District, Hefei, Anhui 230021, China
| | - Yu Shi
- School of Medicine, China Pharmaceutical University, No. 639, Longmian Avenue, Jiangning District, Nanjing, Jiangsu 211198, China
| | - Chuang Guo
- Department of Oncology, The First Affiliated Hospital of USTC, Department of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 17, Lujiang Road, Luyang District, Hefei, Anhui 230021, China
| | - Jun Lin
- Department of Oncology, The First Affiliated Hospital of USTC, Department of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 17, Lujiang Road, Luyang District, Hefei, Anhui 230021, China.,Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Wangjiang West Road, Shushan District, Hefei, Anhui 230088, China
| | - Kun Qu
- School of Data Sciences, University of Science and Technology of China, No. 443, Huangshan Road, Shushan District, Hefei, Anhui 230027, China.,Department of Oncology, The First Affiliated Hospital of USTC, Department of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 17, Lujiang Road, Luyang District, Hefei, Anhui 230021, China.,Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Wangjiang West Road, Shushan District, Hefei, Anhui 230088, China.,CAS Center for Excellence in Molecular Cell Sciences, the CAS Key Laboratory of Innate Immunity and Chronic Disease, University of Science and Technology of China, No. 373 Huangshan Road, Shushan District, Hefei, Anhui 230027, China
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Differential Regulation of NK Cell Receptors in Acute Lymphoblastic Leukemia. J Immunol Res 2022; 2022:7972039. [PMID: 35652109 PMCID: PMC9150999 DOI: 10.1155/2022/7972039] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/22/2022] [Indexed: 11/18/2022] Open
Abstract
Cancer immunotherapies are preferred over conventional treatments which are highly cytotoxic to normal cells. Focus has been on T cells but natural killer (NK) cells have equal potential. Concepts in cancer control and influence of sex require further investigation to improve successful mobilization of immune cells in cancer patients. Acute lymphoblastic leukemia (ALL) is a hematological malignancy mainly of B cell (B-ALL) and T cell (T-ALL) subtypes. Influence of ALL on NK cell is still unclear. Targeted next-generation sequencing was conducted on 62 activating/inhibitory receptors, ligands, effector, and exhaustion molecules on T-ALL (6 males) and normal controls (NC) (4 males and 4 females). Quantitative PCR (q-PCR) further investigated copy number variation (CNV), methylation index (MI), and mRNA expression of significant genes in T-ALL (14 males), NC (12 males and 12 females), and B-ALL samples (N = 12 males and 12 females). Bioinformatics revealed unique variants particularly rs2253849 (T>C) in KLRC1 and rs1141715 (A>G) in KLRC2 only among T-ALL (allele frequency 0.8-1.0). Gene amplification was highest in female B-ALL compared to male B-ALL (KLRC2, KLRC4, and NCR3, p < 0.05) and lowest in male T-ALL cumulating in deletion of KLRD1 and CD69. MI was higher in male ALL of both subtypes compared to normal (KIR2DL1-2 and 4 and KIR2DS2 and 4, p < 0.05) as well as to female B-ALL (KIR3DL2 and KIR2DS2, p < 0.05). mRNA expressions were low. Thus, ALL subtypes potentially regulated NK cell suppression by different mechanisms which should be considered in future immunotherapies for ALL.
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43
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Detection of Cell Separation-Induced Gene Expression Through a Penalized Deconvolution Approach. STATISTICS IN BIOSCIENCES 2022. [DOI: 10.1007/s12561-022-09344-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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44
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Jensen MR, Stoltze U, Hansen TVO, Bak M, Sehested A, Rechnitzer C, Mathiasen R, Scheie D, Larsen KB, Olsen TE, Muhic A, Skjøth-Rasmussen J, Rossing M, Schmiegelow K, Wadt K. 9p21.3 microdeletion involving CDKN2A/2B in a young patient with multiple primary cancers and review of the literature. Cold Spring Harb Mol Case Stud 2022; 8:mcs.a006164. [PMID: 35422439 PMCID: PMC9235845 DOI: 10.1101/mcs.a006164] [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: 12/07/2021] [Accepted: 04/01/2022] [Indexed: 11/29/2022] Open
Abstract
Germline pathogenic variants in CDKN2A predispose to various cancers, including melanoma, pancreatic cancer, and neural system tumors, whereas CDKN2B variants are associated with renal cell carcinoma. A few case reports have described heterozygous germline deletions spanning both CDKN2A and CDKN2B associated with a cancer predisposition syndrome (CPS) that constitutes a risk of cancer beyond those associated with haploinsufficiency of each gene individually, indicating an additive effect or a contiguous gene deletion syndrome. We report a young woman with a de novo germline 9p21 microdeletion involving the CDKN2A/CDKN2B genes, who developed six primary cancers since childhood, including a very rare extraskeletal osteosarcoma (eOS) at the age of 8. To our knowledge this is the first report of eOS in a patient with CDKN2A/CDKN2B deletion.
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Affiliation(s)
- Marlene Richter Jensen
- Department of Clinical Genetics, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Ulrik Stoltze
- Department of Clinical Genetics, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark, Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Thomas Van Overeem Hansen
- Department of Clinical Genetics, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark, Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Mads Bak
- Department of Clinical Genetics, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Astrid Sehested
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Catherine Rechnitzer
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - René Mathiasen
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - David Scheie
- Department of Pathology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Karen Bonde Larsen
- Department of Pathology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Tina Elisabeth Olsen
- Department of Pathology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Aida Muhic
- Department of Oncology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Jane Skjøth-Rasmussen
- Department of Neurosurgery, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Maria Rossing
- Center for Genomic Medicine, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Kjeld Schmiegelow
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark, Institute of Clinical Medicine, Faculty of Medicine, University of Copenhagen, Denmark
| | - Karin Wadt
- Department of Clinical Genetics, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark;
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Spiliopoulou P, Yang SC, Bruce JP, Wang BX, Berman HK, Pugh TJ, Siu LL. All is not lost: learning from 9p21 loss in cancer. Trends Immunol 2022; 43:379-390. [DOI: 10.1016/j.it.2022.03.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/04/2022] [Accepted: 03/04/2022] [Indexed: 12/11/2022]
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Rangel R, Pickering CR, Sikora AG, Spiotto MT. Genetic Changes Driving Immunosuppressive Microenvironments in Oral Premalignancy. Front Immunol 2022; 13:840923. [PMID: 35154165 PMCID: PMC8829003 DOI: 10.3389/fimmu.2022.840923] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/10/2022] [Indexed: 12/25/2022] Open
Abstract
Oral premalignant lesions (OPLs) are the precursors to oral cavity cancers, and have variable rates of progression to invasive disease. As an intermediate state, OPLs have acquired a subset of the genomic alterations while arising in an oral inflammatory environment. These specific genomic changes may facilitate the transition to an immune microenvironment that permits malignant transformation. Here, we will discuss mechanisms by which OPLs develop an immunosuppressive microenvironment that facilitates progression to invasive cancer. We will describe how genomic alterations and immune microenvironmental changes co-evolve and cooperate to promote OSCC progression. Finally, we will describe how these immune microenvironmental changes provide specific and unique evolutionary vulnerabilities for targeted therapies. Therefore, understanding the genomic changes that drive immunosuppressive microenvironments may eventually translate into novel biomarker and/or therapeutic approaches to limit the progression of OPLs to potential lethal oral cancers.
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Affiliation(s)
- Roberto Rangel
- Department of Head and Neck Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United States
| | - Curtis R Pickering
- Department of Head and Neck Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United States
| | - Andrew G Sikora
- Department of Head and Neck Surgery, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United States
| | - Michael T Spiotto
- Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United States
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47
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Bediaga NG, Garnham AL, Naselli G, Bandala-Sanchez E, Stone NL, Cobb J, Harbison JE, Wentworth JM, Ziegler AG, Couper JJ, Smyth GK, Harrison LC. Cytotoxicity-Related Gene Expression and Chromatin Accessibility Define a Subset of CD4+ T Cells That Mark Progression to Type 1 Diabetes. Diabetes 2022; 71:566-577. [PMID: 35007320 PMCID: PMC8893947 DOI: 10.2337/db21-0612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 12/12/2021] [Indexed: 11/13/2022]
Abstract
Type 1 diabetes in children is heralded by a preclinical phase defined by circulating autoantibodies to pancreatic islet antigens. How islet autoimmunity is initiated and then progresses to clinical diabetes remains poorly understood. Only one study has reported gene expression in specific immune cells of children at risk associated with progression to islet autoimmunity. We analyzed gene expression with RNA sequencing in CD4+ and CD8+ T cells, natural killer (NK) cells, and B cells, and chromatin accessibility by assay for transposase-accessible chromatin sequencing (ATAC-seq) in CD4+ T cells, in five genetically at risk children with islet autoantibodies who progressed to diabetes over a median of 3 years ("progressors") compared with five children matched for sex, age, and HLA-DR who had not progressed ("nonprogressors"). In progressors, differentially expressed genes (DEGs) were largely confined to CD4+ T cells and enriched for cytotoxicity-related genes/pathways. Several top-ranked DEGs were validated in a semi-independent cohort of 13 progressors and 11 nonprogressors. Flow cytometry confirmed that progression was associated with expansion of CD4+ cells with a cytotoxic phenotype. By ATAC-seq, progression was associated with reconfiguration of regulatory chromatin regions in CD4+ cells, some linked to differentially expressed cytotoxicity-related genes. Our findings suggest that cytotoxic CD4+ T cells play a role in promoting progression to type 1 diabetes.
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Affiliation(s)
- Naiara G. Bediaga
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Alexandra L. Garnham
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Gaetano Naselli
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
| | - Esther Bandala-Sanchez
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Natalie L. Stone
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
| | - Joanna Cobb
- Murdoch Children’s Research Institute, Parkville, Australia
| | - Jessica E. Harbison
- Department of Endocrinology and Diabetes, Women’s and Children’s Hospital, North Adelaide, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, Australia
| | - John M. Wentworth
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
- Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Parkville, Australia
| | - Annette-G. Ziegler
- Institute of Diabetes Research, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jennifer J. Couper
- Department of Endocrinology and Diabetes, Women’s and Children’s Hospital, North Adelaide, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, Australia
| | - Gordon K. Smyth
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Australia
| | - Leonard C. Harrison
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
- Corresponding author: Leonard C. Harrison,
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48
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Duong E, Fessenden TB, Lutz E, Dinter T, Yim L, Blatt S, Bhutkar A, Wittrup KD, Spranger S. Type I interferon activates MHC class I-dressed CD11b + conventional dendritic cells to promote protective anti-tumor CD8 + T cell immunity. Immunity 2022; 55:308-323.e9. [PMID: 34800368 PMCID: PMC10827482 DOI: 10.1016/j.immuni.2021.10.020] [Citation(s) in RCA: 123] [Impact Index Per Article: 61.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 07/31/2021] [Accepted: 10/22/2021] [Indexed: 12/12/2022]
Abstract
Tumor-infiltrating dendritic cells (DCs) assume varied functional states that impact anti-tumor immunity. To delineate the DC states associated with productive anti-tumor T cell immunity, we compared spontaneously regressing and progressing tumors. Tumor-reactive CD8+ T cell responses in Batf3-/- mice lacking type 1 DCs (DC1s) were lost in progressor tumors but preserved in regressor tumors. Transcriptional profiling of intra-tumoral DCs within regressor tumors revealed an activation state of CD11b+ conventional DCs (DC2s) characterized by expression of interferon (IFN)-stimulated genes (ISGs) (ISG+ DCs). ISG+ DC-activated CD8+ T cells ex vivo comparably to DC1. Unlike cross-presenting DC1, ISG+ DCs acquired and presented intact tumor-derived peptide-major histocompatibility complex class I (MHC class I) complexes. Constitutive type I IFN production by regressor tumors drove the ISG+ DC state, and activation of MHC class I-dressed ISG+ DCs by exogenous IFN-β rescued anti-tumor immunity against progressor tumors in Batf3-/- mice. The ISG+ DC gene signature is detectable in human tumors. Engaging this functional DC state may present an approach for the treatment of human disease.
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Affiliation(s)
- Ellen Duong
- Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA; Department of Biology, MIT, Cambridge, MA, USA
| | - Tim B Fessenden
- Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA
| | - Emi Lutz
- Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA; Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Teresa Dinter
- Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA; Department of Biology, MIT, Cambridge, MA, USA
| | - Leon Yim
- Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA
| | - Sarah Blatt
- Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA
| | - Arjun Bhutkar
- Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA
| | - Karl Dane Wittrup
- Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA; Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Stefani Spranger
- Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA; Department of Biology, MIT, Cambridge, MA, USA; Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA.
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49
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Au L, Hatipoglu E, Robert de Massy M, Litchfield K, Beattie G, Rowan A, Schnidrig D, Thompson R, Byrne F, Horswell S, Fotiadis N, Hazell S, Nicol D, Shepherd STC, Fendler A, Mason R, Del Rosario L, Edmonds K, Lingard K, Sarker S, Mangwende M, Carlyle E, Attig J, Joshi K, Uddin I, Becker PD, Sunderland MW, Akarca A, Puccio I, Yang WW, Lund T, Dhillon K, Vasquez MD, Ghorani E, Xu H, Spencer C, López JI, Green A, Mahadeva U, Borg E, Mitchison M, Moore DA, Proctor I, Falzon M, Pickering L, Furness AJS, Reading JL, Salgado R, Marafioti T, Jamal-Hanjani M, Kassiotis G, Chain B, Larkin J, Swanton C, Quezada SA, Turajlic S. Determinants of anti-PD-1 response and resistance in clear cell renal cell carcinoma. Cancer Cell 2021; 39:1497-1518.e11. [PMID: 34715028 PMCID: PMC8599450 DOI: 10.1016/j.ccell.2021.10.001] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/19/2021] [Accepted: 10/06/2021] [Indexed: 02/08/2023]
Abstract
ADAPTeR is a prospective, phase II study of nivolumab (anti-PD-1) in 15 treatment-naive patients (115 multiregion tumor samples) with metastatic clear cell renal cell carcinoma (ccRCC) aiming to understand the mechanism underpinning therapeutic response. Genomic analyses show no correlation between tumor molecular features and response, whereas ccRCC-specific human endogenous retrovirus expression indirectly correlates with clinical response. T cell receptor (TCR) analysis reveals a significantly higher number of expanded TCR clones pre-treatment in responders suggesting pre-existing immunity. Maintenance of highly similar clusters of TCRs post-treatment predict response, suggesting ongoing antigen engagement and survival of families of T cells likely recognizing the same antigens. In responders, nivolumab-bound CD8+ T cells are expanded and express GZMK/B. Our data suggest nivolumab drives both maintenance and replacement of previously expanded T cell clones, but only maintenance correlates with response. We hypothesize that maintenance and boosting of a pre-existing response is a key element of anti-PD-1 mode of action.
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Affiliation(s)
- Lewis Au
- Cancer Dynamics Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Emine Hatipoglu
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK; Cancer Immunology Unit, Research Department of Hematology, University College London Cancer Institute, London WC1E 6DD, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK
| | - Marc Robert de Massy
- Cancer Immunology Unit, Research Department of Hematology, University College London Cancer Institute, London WC1E 6DD, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK
| | - Kevin Litchfield
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Gordon Beattie
- Cancer Immunology Unit, Research Department of Hematology, University College London Cancer Institute, London WC1E 6DD, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK
| | - Andrew Rowan
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Desiree Schnidrig
- Cancer Dynamics Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Rachael Thompson
- Retroviral Immunology, The Francis Crick Institute, London NW1 1AT, UK
| | - Fiona Byrne
- Cancer Dynamics Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Stuart Horswell
- Department of Bioinformatics and Biostatistics, The Francis Crick Institute, London NW1 1AT, UK
| | - Nicos Fotiadis
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden Hospital, London SW3 6JJ, UK
| | - Steve Hazell
- Department of Pathology, the Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - David Nicol
- Department of Urology, the Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Scott T C Shepherd
- Cancer Dynamics Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Annika Fendler
- Cancer Dynamics Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Robert Mason
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Lyra Del Rosario
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Kim Edmonds
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Karla Lingard
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Sarah Sarker
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Mary Mangwende
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Eleanor Carlyle
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Jan Attig
- Retroviral Immunology, The Francis Crick Institute, London NW1 1AT, UK
| | - Kroopa Joshi
- Cancer Immunology Unit, Research Department of Hematology, University College London Cancer Institute, London WC1E 6DD, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK
| | - Imran Uddin
- Cancer Immunology Unit, Research Department of Hematology, University College London Cancer Institute, London WC1E 6DD, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK; Division of Infection and Immunity, University College London, London WC1E 6BT, UK
| | - Pablo D Becker
- Cancer Immunology Unit, Research Department of Hematology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Mariana Werner Sunderland
- Cancer Immunology Unit, Research Department of Hematology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Ayse Akarca
- Department of Cellular Pathology, University College London Hospital, London NW1 2BU, UK
| | - Ignazio Puccio
- Department of Cellular Pathology, University College London Hospital, London NW1 2BU, UK
| | - William W Yang
- Department of Cellular Pathology, University College London Hospital, London NW1 2BU, UK
| | - Tom Lund
- Translational Immune Oncology Lab, Centre for Molecular Pathology, The Royal Marsden Hospital, Sutton SM2 5PT, UK
| | - Kim Dhillon
- Department of Cellular Pathology, University College London Hospital, London NW1 2BU, UK
| | - Marcos Duran Vasquez
- Cancer Immunology Unit, Research Department of Hematology, University College London Cancer Institute, London WC1E 6DD, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK
| | - Ehsan Ghorani
- Cancer Immunology Unit, Research Department of Hematology, University College London Cancer Institute, London WC1E 6DD, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK
| | - Hang Xu
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Charlotte Spencer
- Cancer Dynamics Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - José I López
- Department of Pathology, Cruces University Hospital, Biocruces-Bizkaia Institute, 48903 Barakaldo, Bizkaia, Spain
| | - Anna Green
- Department of Cellular Pathology, Guy's & St Thomas' NHS Foundation Trust, St Thomas' Hospital, London SE1 7EH, UK
| | - Ula Mahadeva
- Department of Cellular Pathology, Guy's & St Thomas' NHS Foundation Trust, St Thomas' Hospital, London SE1 7EH, UK
| | - Elaine Borg
- Department of Cellular Pathology, University College London Hospital, London NW1 2BU, UK
| | - Miriam Mitchison
- Department of Cellular Pathology, University College London Hospital, London NW1 2BU, UK
| | - David A Moore
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK; Department of Cellular Pathology, University College London Hospital, London NW1 2BU, UK
| | - Ian Proctor
- Department of Cellular Pathology, University College London Hospital, London NW1 2BU, UK
| | - Mary Falzon
- Department of Cellular Pathology, University College London Hospital, London NW1 2BU, UK
| | - Lisa Pickering
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Andrew J S Furness
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - James L Reading
- Cancer Immunology Unit, Research Department of Hematology, University College London Cancer Institute, London WC1E 6DD, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK
| | - Roberto Salgado
- Division of Research, Peter MacCallum Cancer Centre, Melbourne VIC 300, Australia; Department of Pathology, GZA-ZNA Hospitals, Wilrijk, Antwerp, Belgium
| | - Teresa Marafioti
- Department of Cellular Pathology, University College London Hospital, London NW1 2BU, UK
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK; Cancer Metastasis Laboratory, University College London Cancer Institute, London WC1E 6DD, UK; Department of Medical Oncology, University College London Hospitals, London NW1 2BU, UK
| | - George Kassiotis
- Retroviral Immunology, The Francis Crick Institute, London NW1 1AT, UK
| | - Benny Chain
- Division of Infection and Immunity, University College London, London WC1E 6BT, UK; University College London Cancer Institute, London WC1E 6DD, UK
| | - James Larkin
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK; Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Department of Medical Oncology, University College London Hospitals, London NW1 2BU, UK; University College London Cancer Institute, London WC1E 6DD, UK
| | - Sergio A Quezada
- Cancer Immunology Unit, Research Department of Hematology, University College London Cancer Institute, London WC1E 6DD, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK.
| | - Samra Turajlic
- Cancer Dynamics Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK.
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50
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Aldea M, Benitez JC, Chaput N, Besse B. New Immunotherapy Combinations Enter the Battlefield of Malignant Mesothelioma. Cancer Discov 2021; 11:2674-2676. [PMID: 34725087 DOI: 10.1158/2159-8290.cd-21-1046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Chimeric antigen receptor (CAR) T cells targeting mesothelin plus pembrolizumab, and atezolizumab plus bevacizumab, have recently shown clinical efficacy in phase I trials in malignant pleural and peritoneal mesothelioma. Despite being tested in a highly selected patient population and requiring a complex engineering that can hardly be upscaled, CAR T cells combined with pembrolizumab bring the first proof of efficacy in cold solid tumors with low genomic heterogeneity, while atezolizumab-bevacizumab offers an easy-to-use combination of antiangiogenics and immunotherapy in an orphan disease.See related article by Raghav et al., p. 2738.See related article by Adusumilli et al., p. 2748.
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Affiliation(s)
- Mihaela Aldea
- School of Medicine, Paris-Saclay University, Paris, France.,Cancer Medicine Department, Gustave Roussy, Villejuif, France
| | - Jose Carlos Benitez
- School of Medicine, Paris-Saclay University, Paris, France.,Cancer Medicine Department, Gustave Roussy, Villejuif, France
| | - Nathalie Chaput
- Laboratory of Immunomonitoring in Oncology, Gustave Roussy, Villejuif, France.,School of Pharmacy, Paris-Saclay University, Chatenay-Malabry, France
| | - Benjamin Besse
- School of Medicine, Paris-Saclay University, Paris, France. .,Cancer Medicine Department, Gustave Roussy, Villejuif, France
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