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Wang F, Tao L, Wang J, Zhang X, Li J, Zhen B, Bian S. PBI6 Research on CAR-T’S Market Access and Reimbursement Strategy in China. Value Health Reg Issues 2020. [DOI: 10.1016/j.vhri.2020.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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2
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Ni X, Tan Z, Ding C, Zhang C, Song L, Yang S, Liu M, Jia R, Zhao C, Song L, Liu W, Zhou Q, Gong T, Li X, Tai Y, Zhu W, Shi T, Wang Y, Xu J, Zhen B, Qin J. A region-resolved mucosa proteome of the human stomach. Nat Commun 2019; 10:39. [PMID: 30604760 PMCID: PMC6318339 DOI: 10.1038/s41467-018-07960-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Accepted: 12/06/2018] [Indexed: 12/13/2022] Open
Abstract
The human gastric mucosa is the most active layer of the stomach wall, involved in food digestion, metabolic processes and gastric carcinogenesis. Anatomically, the human stomach is divided into seven regions, but the protein basis for cellular specialization is not well understood. Here we present a global analysis of protein profiles of 82 apparently normal mucosa samples obtained from living individuals by endoscopic stomach biopsy. We identify 6,258 high-confidence proteins and estimate the ranges of protein expression in the seven stomach regions, presenting a region-resolved proteome reference map of the near normal, human stomach. Furthermore, we measure mucosa protein profiles of tumor and tumor nearby tissues (TNT) from 58 gastric cancer patients, enabling comparisons between tumor, TNT, and normal tissue. These datasets provide a rich resource for the gastrointestinal tract research community to investigate the molecular basis for region-specific functions in mucosa physiology and pathology including gastric cancer. The human stomach is divided into seven anatomically distinct regions but their protein composition is largely unknown. Here, the authors present a region-resolved map of the healthy human stomach mucosa as well as mucosa proteomes of tumor and tumor nearby tissue from gastric cancer patients.
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Affiliation(s)
- Xiaotian Ni
- Department of Gastrointestinal Oncology, The Fifth Medical Center, General Hospital of PLA, Beijing, 100071, China.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China.,Center for Bioinformatics, East China Normal University, Shanghai, 200241, China
| | - Zhaoli Tan
- Department of Gastrointestinal Oncology, The Fifth Medical Center, General Hospital of PLA, Beijing, 100071, China
| | - Chen Ding
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China.,State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Chunchao Zhang
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Lan Song
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China.,Department of Bioinformatics, College of Life Science, Hebei University, Baoding, 071002, China
| | - Shuai Yang
- Department of Gastrointestinal Oncology, The Fifth Medical Center, General Hospital of PLA, Beijing, 100071, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China
| | - Ru Jia
- Department of Gastrointestinal Oncology, The Fifth Medical Center, General Hospital of PLA, Beijing, 100071, China
| | - Chuanhua Zhao
- Department of Gastrointestinal Oncology, The Fifth Medical Center, General Hospital of PLA, Beijing, 100071, China
| | - Lei Song
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China
| | - Wanlin Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China
| | - Quan Zhou
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China
| | - Tongqing Gong
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China
| | - Xianju Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China
| | - Yanhong Tai
- Department of Gastrointestinal Oncology, The Fifth Medical Center, General Hospital of PLA, Beijing, 100071, China
| | - Weimin Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China
| | - Tieliu Shi
- Center for Bioinformatics, East China Normal University, Shanghai, 200241, China
| | - Yi Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China.,Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Jianming Xu
- Department of Gastrointestinal Oncology, The Fifth Medical Center, General Hospital of PLA, Beijing, 100071, China.
| | - Bei Zhen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China.
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Institute of lifeomics, Beijing, 102206, China. .,State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. .,Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.
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3
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Li X, Zhang C, Gong T, Ni X, Li J, Zhan D, Liu M, Song L, Ding C, Xu J, Zhen B, Wang Y, Qin J. A time-resolved multi-omic atlas of the developing mouse stomach. Nat Commun 2018; 9:4910. [PMID: 30464175 PMCID: PMC6249217 DOI: 10.1038/s41467-018-07463-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 10/30/2018] [Indexed: 02/07/2023] Open
Abstract
The mammalian stomach is structurally highly diverse and its organ functionality critically depends on a normal embryonic development. Although there have been several studies on the morphological changes during stomach development, a system-wide analysis of the underlying molecular changes is lacking. Here, we present a comprehensive, temporal proteome and transcriptome atlas of the mouse stomach at multiple developmental stages. Quantitative analysis of 12,108 gene products allows identifying three distinct phases based on changes in proteins and RNAs and the gain of stomach functions on a longitudinal time scale. The transcriptome indicates functionally important isoforms relevant to development and identifies several functionally unannotated novel splicing junction transcripts that we validate at the peptide level. Importantly, many proteins differentially expressed in stomach development are also significantly overexpressed in diffuse-type gastric cancer. Overall, our study provides a resource to understand stomach development and its connection to gastric cancer tumorigenesis.
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Affiliation(s)
- Xianju Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China
| | - Chunchao Zhang
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Tongqing Gong
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China
| | - Xiaotian Ni
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China.,Department of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Jin'e Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China
| | - Dongdong Zhan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China.,Department of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China
| | - Lei Song
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China
| | - Chen Ding
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Jianming Xu
- Department of Gastrointestinal Oncology, Affiliated Hospital Cancer Center, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Bei Zhen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China
| | - Yi Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China. .,Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, 102206, China. .,Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA. .,State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
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4
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Liu W, Wei L, Sun J, Feng J, Guo G, Liang L, Fu T, Liu M, Li K, Huang Y, Zhu W, Zhen B, Wang Y, Ding C, Qin J. A reference peptide database for proteome quantification based on experimental mass spectrum response curves. Bioinformatics 2018; 34:2766-2772. [PMID: 29617941 PMCID: PMC6084618 DOI: 10.1093/bioinformatics/bty201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 03/29/2018] [Indexed: 11/18/2022] Open
Abstract
Motivation Mass spectrometry (MS) based quantification of proteins/peptides has become a powerful tool in biological research with high sensitivity and throughput. The accuracy of quantification, however, has been problematic as not all peptides are suitable for quantification. Several methods and tools have been developed to identify peptides that response well in mass spectrometry and they are mainly based on predictive models, and rarely consider the linearity of the response curve, limiting the accuracy and applicability of the methods. An alternative solution is to select empirically superior peptides that offer satisfactory MS response intensity and linearity in a wide dynamic range of peptide concentration. Results We constructed a reference database for proteome quantification based on experimental mass spectrum response curves. The intensity and dynamic range of over 2 647 773 transitions from 121 318 peptides were obtained from a set of dilution experiments, covering 11 040 gene products. These transitions and peptides were evaluated and presented in a database named SCRIPT-MAP. We showed that the best-responder (BR) peptide approach for quantification based on SCRIPT-MAP database is robust, repeatable and accurate in proteome-scale protein quantification. This study provides a reference database as well as a peptides/transitions selection method for quantitative proteomics. Availability and implementation SCRIPT-MAP database is available at http://www.firmiana.org/responders/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wanlin Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Lai Wei
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Jianan Sun
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Jinwen Feng
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Gaigai Guo
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Lizhu Liang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Tianyi Fu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Kai Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Yin Huang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Weimin Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Bei Zhen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China
| | - Yi Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Chen Ding
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing, China.,Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
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5
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Wang Y, Song L, Liu M, Ge R, Zhou Q, Liu W, Li R, Qie J, Zhen B, Wang Y, He F, Qin J, Ding C. A proteomics landscape of circadian clock in mouse liver. Nat Commun 2018; 9:1553. [PMID: 29674717 PMCID: PMC5908788 DOI: 10.1038/s41467-018-03898-2] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 03/20/2018] [Indexed: 01/07/2023] Open
Abstract
As a circadian organ, liver executes diverse functions in different phase of the circadian clock. This process is believed to be driven by a transcription program. Here, we present a transcription factor (TF) DNA-binding activity-centered multi-dimensional proteomics landscape of the mouse liver, which includes DNA-binding profiles of different TFs, phosphorylation, and ubiquitylation patterns, the nuclear sub-proteome, the whole proteome as well as the transcriptome, to portray the hierarchical circadian clock network of this tissue. The TF DNA-binding activity indicates diurnal oscillation in four major pathways, namely the immune response, glucose metabolism, fatty acid metabolism, and the cell cycle. We also isolate the mouse liver Kupffer cells and measure their proteomes during the circadian cycle to reveal a cell-type resolved circadian clock. These comprehensive data sets provide a rich data resource for the understanding of mouse hepatic physiology around the circadian clock. As a circadian organ, liver functions are regulated by circadian clock. Here, the authors present a comprehensive proteomics landscape of the mouse liver, including transcription factor binding profiles, phosphorylation and ubiquitylation patterns, nuclear and whole proteome, and the transcriptome.
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Affiliation(s)
- Yunzhi Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Lei Song
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206, China
| | - Rui Ge
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Quan Zhou
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206, China
| | - Wanlin Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206, China
| | - Ruiyang Li
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jingbo Qie
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Bei Zhen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206, China
| | - Yi Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206, China.,Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Fuchu He
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. .,School of Life Sciences, Tsinghua University, Beijing, 100084, China. .,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206, China.
| | - Jun Qin
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. .,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, 102206, China. .,Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Chen Ding
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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6
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Zhang C, Leng W, Sun C, Lu T, Chen Z, Men X, Wang Y, Wang G, Zhen B, Qin J. Urine Proteome Profiling Predicts Lung Cancer from Control Cases and Other Tumors. EBioMedicine 2018; 30:120-128. [PMID: 29576497 PMCID: PMC5952250 DOI: 10.1016/j.ebiom.2018.03.009] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 03/01/2018] [Accepted: 03/09/2018] [Indexed: 12/31/2022] Open
Abstract
Development of noninvasive, reliable biomarkers for lung cancer diagnosis has many clinical benefits knowing that most of lung cancer patients are diagnosed at the late stage. For this purpose, we conducted proteomic analyses of 231 human urine samples in healthy individuals (n = 33), benign pulmonary diseases (n = 40), lung cancer (n = 33), bladder cancer (n = 17), cervical cancer (n = 25), colorectal cancer (n = 22), esophageal cancer (n = 14), and gastric cancer (n = 47) patients collected from multiple medical centers. By random forest modeling, we nominated a list of urine proteins that could separate lung cancers from other cases. With a feature selection algorithm, we selected a panel of five urinary biomarkers (FTL: Ferritin light chain; MAPK1IP1L: Mitogen-Activated Protein Kinase 1 Interacting Protein 1 Like; FGB: Fibrinogen Beta Chain; RAB33B: RAB33B, Member RAS Oncogene Family; RAB15: RAB15, Member RAS Oncogene Family) and established a combinatorial model that can correctly classify the majority of lung cancer cases both in the training set (n = 46) and the test sets (n = 14–47 per set) with an AUC ranging from 0.8747 to 0.9853. A combination of five urinary biomarkers not only discriminates lung cancer patients from control groups but also differentiates lung cancer from other common tumors. The biomarker panel and the predictive model, when validated by more samples in a multi-center setting, may be used as an auxiliary diagnostic tool along with imaging technology for lung cancer detection. A case-control study of biomarker discovery for lung cancer diagnosis was conducted. Human urine profiles in control cases and cancers were characterized. A list of candidate biomarkers was nominated and evaluated. A panel of urinary biomarkers was established and tumor-specificity was evaluated.
Cancer diagnosis with a noninvasive method at the early stage of the disease is highly desirable. Here, we analyzed hundreds of human urine samples from healthy individuals, patients with benign pulmonary diseases, and 6 types of cancers by proteomics and developed a panel of five urinary proteins that can separate the lung cancer from benign pulmonary diseases as well as the other 5 cancers (bladder, cervical, colorectal, esophageal and gastric) with a good sensitivity and disease specificity. Further validation experiments with expanded sample numbers are required to investigate whether this method can be applied in a clinical setting for the diagnosis of lung cancer.
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Affiliation(s)
- Chunchao Zhang
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wenchuan Leng
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China
| | - Changqing Sun
- Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China
| | - Tianyuan Lu
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China
| | - Zhengang Chen
- Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China
| | - Xuebo Men
- Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China
| | - Yi Wang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China; Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Guangshun Wang
- Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China.
| | - Bei Zhen
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China.
| | - Jun Qin
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China; Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
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7
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Ge S, Xia X, Ding C, Zhen B, Zhou Q, Feng J, Yuan J, Chen R, Li Y, Ge Z, Ji J, Zhang L, Wang J, Li Z, Lai Y, Hu Y, Li Y, Li Y, Gao J, Chen L, Xu J, Zhang C, Jung SY, Choi JM, Jain A, Liu M, Song L, Liu W, Guo G, Gong T, Huang Y, Qiu Y, Huang W, Shi T, Zhu W, Wang Y, He F, Shen L, Qin J. A proteomic landscape of diffuse-type gastric cancer. Nat Commun 2018; 9:1012. [PMID: 29520031 PMCID: PMC5843664 DOI: 10.1038/s41467-018-03121-2] [Citation(s) in RCA: 150] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 01/18/2018] [Indexed: 12/19/2022] Open
Abstract
The diffuse-type gastric cancer (DGC) is a subtype of gastric cancer with the worst prognosis and few treatment options. Here we present a dataset from 84 DGC patients, composed of a proteome of 11,340 gene products and mutation information of 274 cancer driver genes covering paired tumor and nearby tissue. DGC can be classified into three subtypes (PX1-3) based on the altered proteome alone. PX1 and PX2 exhibit dysregulation in the cell cycle and PX2 features an additional EMT process; PX3 is enriched in immune response proteins, has the worst survival, and is insensitive to chemotherapy. Data analysis revealed four major vulnerabilities in DGC that may be targeted for treatment, and allowed the nomination of potential immunotherapy targets for DGC patients, particularly for those in PX3. This dataset provides a rich resource for information and knowledge mining toward altered signaling pathways in DGC and demonstrates the benefit of proteomic analysis in cancer molecular subtyping.
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Affiliation(s)
- Sai Ge
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Xia Xia
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Chen Ding
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Bei Zhen
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Quan Zhou
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Jinwen Feng
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
- Center for Bioinformatics, East China Normal University, Shanghai, 200241, China
| | - Jiajia Yuan
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Rui Chen
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Yumei Li
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Zhongqi Ge
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Jiafu Ji
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Lianhai Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Jiayuan Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Zhongwu Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Yumei Lai
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Ying Hu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Yanyan Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Yilin Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Jing Gao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Lin Chen
- General Hospital of Chinese People's Liberation Army, Beijing, 100853, China
| | - Jianming Xu
- Affiliated Hospital of Academy of Military Medical Sciences, Beijing, 100071, China
| | - Chunchao Zhang
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Sung Yun Jung
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Jong Min Choi
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Antrix Jain
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Mingwei Liu
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Lei Song
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Wanlin Liu
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Gaigai Guo
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Tongqing Gong
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Yin Huang
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Yang Qiu
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Wenwen Huang
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Tieliu Shi
- Center for Bioinformatics, East China Normal University, Shanghai, 200241, China
| | - Weimin Zhu
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
| | - Yi Wang
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Fuchu He
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China.
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
| | - Lin Shen
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China.
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, 100142, China.
| | - Jun Qin
- The Joint Laboratory of Translational Medicine, National Center for Protein Sciences (Beijing) and Peking University Cancer Hospital, State Key Laboratory of Proteomics, Institute of Lifeomics, Beijing, 102206, China.
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.
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8
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Peng C, Zhu Y, Zhang W, Liao Q, Chen Y, Zhao X, Guo Q, Shen P, Zhen B, Qian X, Yang D, Zhang JS, Xiao D, Qin W, Pei H. Regulation of the Hippo-YAP Pathway by Glucose Sensor O-GlcNAcylation. Mol Cell 2017; 68:591-604.e5. [PMID: 29100056 DOI: 10.1016/j.molcel.2017.10.010] [Citation(s) in RCA: 171] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2017] [Revised: 06/07/2017] [Accepted: 10/06/2017] [Indexed: 01/01/2023]
Abstract
The Hippo pathway is crucial in organ size control and tissue homeostasis, with deregulation leading to cancer. An extracellular nutrition signal, such as glucose, regulates the Hippo pathway activation. However, the mechanisms are still not clear. Here, we found that the Hippo pathway is directly regulated by the hexosamine biosynthesis pathway (HBP) in response to metabolic nutrients. Mechanistically, the core component of Hippo pathway (YAP) is O-GlcNAcylated by O-GlcNAc transferase (OGT) at serine 109. YAP O-GlcNAcylation disrupts its interaction with upstream kinase LATS1, prevents its phosphorylation, and activates its transcriptional activity. And this activation is not dependent on AMPK. We also identified OGT as a YAP-regulated gene that forms a feedback loop. Finally, we confirmed that glucose-induced YAP O-GlcNAcylation and activation promoted tumorigenesis. Together, our data establish a molecular mechanism and functional significance of the HBP in directly linking extracellular glucose signal to the Hippo-YAP pathway and tumorigenesis.
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Affiliation(s)
- Changmin Peng
- Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Industrial Microbiology Key Lab, College of Biotechnology, Tianjin University of Science and Technology, No 29, 13ST. TEDA, Tianjin 300457, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yue Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China; Anhui Medical University, Hefei 230032, China; Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Wanjun Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Qinchao Liao
- Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Industrial Microbiology Key Lab, College of Biotechnology, Tianjin University of Science and Technology, No 29, 13ST. TEDA, Tianjin 300457, China
| | - Yali Chen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Xinyuan Zhao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Qiang Guo
- Cell Signaling and Epigenetics Laboratory, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, Zhejiang, China
| | - Pan Shen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Bei Zhen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Xiaohong Qian
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Dong Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Jin-San Zhang
- Cell Signaling and Epigenetics Laboratory, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, Zhejiang, China
| | - Dongguang Xiao
- Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Industrial Microbiology Key Lab, College of Biotechnology, Tianjin University of Science and Technology, No 29, 13ST. TEDA, Tianjin 300457, China
| | - Weijie Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
| | - Huadong Pei
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China; Department of Biochemistry and Molecular Medicine, George Washington University School of Medicine and Health Science, 2300 Eye Street, N.W., Washington, DC 20037, USA.
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9
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Ge S, Xia X, Ding C, Zhen B, Zhou Q, Feng J, Yuan J, Chen R, Li Y, Ge Z, Ji J, Zhang L, Wang J, Li Z, Lai Y, Hu Y, Li Y, Li Y, Gao J, Chen L, Xu J, Zhang C, Jung SY, Liu M, Song L, Liu W, Guo G, Gong T, Huang Y, Qiu Y, Shi T, Zhu W, Wang Y, He F, Shen L, Qin J. Abstract 2204: A proteomic landscape of diffuse-type gastric cancer. Cancer Res 2017. [DOI: 10.1158/1538-7445.am2017-2204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Gastric cancer is a heterogeneous disease characterized by poor clinical outcomes and limited targeted treatment options. Among them, diffuse-type gastric cancer (DGC) is the subtype with worst prognosis. Here we describe the first proteomic landscape of DGC. We carried out proteome profiling and targeted exome DNA sequencing of 84 DGC samples. We analyzed the 1,008 (168 x 6) raw files together for uniformed quality control and protein identification with 1% global protein false discovery rate (FDR), which resulted in the identification of 11,340 gene products (GPs). A SAM (significance analysis of microarray) analysis identified 1,641 proteins as differentially expressed between T (tumor) and N (nearby) with statistical significance (FDR q value<0.01 by SAM and differential expression ratio>0.5/ < -0.5), including 1,211 up-regulated and 430 down-regulated GPs. Gene Ontology annotation indicated that tumor proteomes were significantly enriched in cell cycle, DNA replication, checkpoint, E2F, WNT, p53 signaling, epithelial mesenchymal transition (EMT), and inflammation/cytokine-receptor interaction pathways, and the proteomes of the nearby tissues are enriched in metabolism pathways, such as fatty acid metabolism, oxidative phosphorylation, and amino acid metabolism. Notably, many gastric makers (ANXA10, VSIG1, CLDN18, CTSE, TFF2, MUC5AC and MUC6) and signature proteins for stomach functions, including digestion, absorption, secretion, and stomach acid generation (PGC, GIF, GAST, and ATP4A), were lost in tumors. Based on proteome profiling alone, DGC can be subtyped into 3 major classes (PX1-3) that exhibit distinct proteome features and correlate with distinct clinical outcomes (Gehan-Breslow-Wilcoxon P = 0.024). PX1 exhibits proteome stability and the best overall survival; PX2 exhibits dysregulation in DNA replication and cell cycle, and is most sensitive to chemotherapy; PX3 features hyper-activated immune response and is not responsive to chemotherapy. We identified seven-marker proteins that can stratify DGC patients into these three subtypes, opening a door for proteome subtyping in clinical application and intervention. Furthermore, we nominated drug target candidates taking into consideration both the altered DGC proteome and association data with patients’ overall survival. This study revealed the altered signaling pathways in DGC and demonstrated the advantage of proteomic approach in molecular subtyping of cancer.
Citation Format: Sai Ge, Xia Xia, Chen Ding, Bei Zhen, Quan Zhou, Jinwen Feng, Jiajia Yuan, Rui Chen, Yumei Li, Zhongqi Ge, Jiafu Ji, Lianhai Zhang, Jiayuan Wang, Zhongwu Li, Yumei Lai, Ying Hu, Yanyan Li, Yilin Li, Jing Gao, Lin Chen, Jianming Xu, Chunchao Zhang, Sung Yun Jung, Mingwei Liu, Lei Song, Wanlin Liu, Gaigai Guo, Tongqing Gong, Yin Huang, Yang Qiu, Tieliu Shi, Weimin Zhu, Yi Wang, Fuchu He, Lin Shen, Jun Qin, CNHPP. A proteomic landscape of diffuse-type gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2204. doi:10.1158/1538-7445.AM2017-2204
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Affiliation(s)
- Sai Ge
- 1Peking University Cancer Hospital & Institute, Beijing, China
| | - Xia Xia
- 2National Center for Protein Sciences (The PHOENIX center), Beijing, China
| | | | - Bei Zhen
- 2National Center for Protein Sciences (The PHOENIX center), Beijing, China
| | - Quan Zhou
- 2National Center for Protein Sciences (The PHOENIX center), Beijing, China
| | - Jinwen Feng
- 4East China Normal University, Shanghai, China
| | - Jiajia Yuan
- 1Peking University Cancer Hospital & Institute, Beijing, China
| | - Rui Chen
- 5Baylor College of Medicine, Houston, TX
| | - Yumei Li
- 5Baylor College of Medicine, Houston, TX
| | - Zhongqi Ge
- 5Baylor College of Medicine, Houston, TX
| | - Jiafu Ji
- 1Peking University Cancer Hospital & Institute, Beijing, China
| | - Lianhai Zhang
- 1Peking University Cancer Hospital & Institute, Beijing, China
| | - Jiayuan Wang
- 1Peking University Cancer Hospital & Institute, Beijing, China
| | - Zhongwu Li
- 1Peking University Cancer Hospital & Institute, Beijing, China
| | - Yumei Lai
- 1Peking University Cancer Hospital & Institute, Beijing, China
| | - Ying Hu
- 1Peking University Cancer Hospital & Institute, Beijing, China
| | - Yanyan Li
- 1Peking University Cancer Hospital & Institute, Beijing, China
| | - Yilin Li
- 1Peking University Cancer Hospital & Institute, Beijing, China
| | - Jing Gao
- 1Peking University Cancer Hospital & Institute, Beijing, China
| | - Lin Chen
- 6General Hospital of Chinese People's Liberation Army, Beijing, China
| | - Jianming Xu
- 7Affiliated Hospital of Academy of Military Medical Sciences, Beijing, China
| | | | | | - Mingwei Liu
- 2National Center for Protein Sciences (The PHOENIX center), Beijing, China
| | - Lei Song
- 2National Center for Protein Sciences (The PHOENIX center), Beijing, China
| | - Wanlin Liu
- 2National Center for Protein Sciences (The PHOENIX center), Beijing, China
| | - Gaigai Guo
- 2National Center for Protein Sciences (The PHOENIX center), Beijing, China
| | - Tongqing Gong
- 2National Center for Protein Sciences (The PHOENIX center), Beijing, China
| | - Yin Huang
- 2National Center for Protein Sciences (The PHOENIX center), Beijing, China
| | - Yang Qiu
- 2National Center for Protein Sciences (The PHOENIX center), Beijing, China
| | - Tieliu Shi
- 4East China Normal University, Shanghai, China
| | - Weimin Zhu
- 2National Center for Protein Sciences (The PHOENIX center), Beijing, China
| | - Yi Wang
- 5Baylor College of Medicine, Houston, TX
| | - Fuchu He
- 2National Center for Protein Sciences (The PHOENIX center), Beijing, China
| | - Lin Shen
- 1Peking University Cancer Hospital & Institute, Beijing, China
| | - Jun Qin
- 2National Center for Protein Sciences (The PHOENIX center), Beijing, China
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10
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Leng W, Ni X, Sun C, Lu T, Malovannaya A, Jung SY, Huang Y, Qiu Y, Sun G, Holt MV, Ding C, Sun W, Men X, Shi T, Zhu W, Wang Y, He F, Zhen B, Wang G, Qin J. Proof-of-Concept Workflow for Establishing Reference Intervals of Human Urine Proteome for Monitoring Physiological and Pathological Changes. EBioMedicine 2017; 18:300-310. [PMID: 28396014 PMCID: PMC5405183 DOI: 10.1016/j.ebiom.2017.03.028] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Revised: 03/20/2017] [Accepted: 03/20/2017] [Indexed: 12/24/2022] Open
Abstract
Urine as a true non-invasive sampling source holds great potential for biomarker discovery. While approximately 2000 proteins can be detected by mass spectrometry in urine from healthy people, the amount of these proteins vary considerably. A systematic evaluation of a large number of samples is needed to determine the range of the variations. Current biomarker studies often measure limited number of urine samples in the discovery phase, which makes it difficult to determine whether proteins differentially expressed between control and disease groups represent actual difference, or are just physiological variations among the individuals, leads to failures in the validation phase with the increased sample numbers. Here, we report a streamlined workflow with capacity of measuring 8 urine proteomes per day at the coverage of >1500 proteins. With this workflow, we evaluated variations in 497 urine proteomes from 167 healthy donors, establishing reference intervals (RIs) that covered urine protein variations. We demonstrated that RIs could be used to monitor physiological changes by detecting transient outlier proteins. Furthermore, we provided a RIs-based algorithm for biomarker discovery and validation to screen for diseases such as cancer. This study provided a proof-of-principle workflow for the use of urine proteome for health monitoring and disease screening.
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Affiliation(s)
- Wenchuan Leng
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China
| | - Xiaotian Ni
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; Center for Bioinformatics, East China Normal University, Shanghai 200241, China
| | - Changqing Sun
- Joint Center for Translational Medicine, Tianjin Baodi Hospital, Tianjin 301800, China
| | - Tianyuan Lu
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China
| | - Anna Malovannaya
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sung Yun Jung
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yin Huang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; School of Life Science and Technology, Tongji University, Shanghai 200092, China
| | - Yang Qiu
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China
| | - Guannan Sun
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China
| | - Matthew V Holt
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Chen Ding
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; Joint Center for Translational Medicine, Tianjin Baodi Hospital, Tianjin 301800, China
| | - Wei Sun
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China
| | - Xuebo Men
- Joint Center for Translational Medicine, Tianjin Baodi Hospital, Tianjin 301800, China
| | - Tieliu Shi
- Center for Bioinformatics, East China Normal University, Shanghai 200241, China
| | - Weimin Zhu
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; Joint Center for Translational Medicine, Tianjin Baodi Hospital, Tianjin 301800, China
| | - Yi Wang
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Fuchu He
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China
| | - Bei Zhen
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; Joint Center for Translational Medicine, Tianjin Baodi Hospital, Tianjin 301800, China.
| | - Guangshun Wang
- Joint Center for Translational Medicine, Tianjin Baodi Hospital, Tianjin 301800, China.
| | - Jun Qin
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; Joint Center for Translational Medicine, Tianjin Baodi Hospital, Tianjin 301800, China; Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
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11
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Liu M, Ge R, Liu W, Liu Q, Xia X, Lai M, Liang L, Li C, Song L, Zhen B, Qin J, Ding C. Differential proteomics profiling identifies LDPs and biological functions in high-fat diet-induced fatty livers. J Lipid Res 2017; 58:681-694. [PMID: 28179399 PMCID: PMC5392744 DOI: 10.1194/jlr.m071407] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2016] [Revised: 02/06/2017] [Indexed: 01/20/2023] Open
Abstract
Eukaryotic cells store neutral lipids in cytoplasmic lipid droplets (LDs) enclosed in a monolayer of phospholipids and associated proteins [LD proteins (LDPs)]. Growing evidence has demonstrated that LDPs play important roles in the pathogenesis of liver diseases. However, the composition of liver LDPs and the role of their alterations in hepatosteatosis are not well-understood. In this study, we performed liver proteome and LD sub-proteome profiling to identify enriched proteins in LDs as LDPs, and quantified their changes in a high-fat diet (HFD)-induced fatty liver model. Among 5,000 quantified liver proteins, 101 were enriched by greater than 10-fold in the LD sub-proteome and were classified as LDPs. Differential profiling of LDPs in HFD-induced fatty liver provided a list of candidate LDPs for functional investigation. We tested the function of an upregulated LDP, S100a10, in vivo with adenovirus-mediated gene silencing and found, unexpectedly, that knockdown of S100a10 accelerated progression of HFD-induced liver steatosis. The S100A10 interactome revealed a connection between S100A10 and lipid transporting proteins, suggesting that S100A10 regulates the development and formation of LDs by transporting and trafficking. This study identified LD-enriched sub-proteome in homeostatic as well as HFD-induced fatty livers, providing a rich resource for the LDP research field.
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Affiliation(s)
- Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (PHOENIX Center), Beijing 102206, China
| | - Rui Ge
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Wanlin Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (PHOENIX Center), Beijing 102206, China
| | - Qiongming Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (PHOENIX Center), Beijing 102206, China
| | - Xia Xia
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (PHOENIX Center), Beijing 102206, China
| | - Mi Lai
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (PHOENIX Center), Beijing 102206, China
| | - Lizhu Liang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (PHOENIX Center), Beijing 102206, China
| | - Chen Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (PHOENIX Center), Beijing 102206, China
| | - Lei Song
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (PHOENIX Center), Beijing 102206, China
| | - Bei Zhen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (PHOENIX Center), Beijing 102206, China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (PHOENIX Center), Beijing 102206, China; Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030.
| | - Chen Ding
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (PHOENIX Center), Beijing 102206, China; State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200433, China.
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12
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Shi W, Li K, Song L, Liu M, Wang Y, Liu W, Xia X, Qin Z, Zhen B, Wang Y, He F, Qin J, Ding C. Transcription Factor Response Elements on Tip: A Sensitive Approach for Large-Scale Endogenous Transcription Factor Quantitative Identification. Anal Chem 2016; 88:11990-11994. [DOI: 10.1021/acs.analchem.6b03150] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Wenhao Shi
- School of Life Sciences, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Proteomics, Beijing Proteome Research Center,
Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing 102206, China
| | - Kai Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center,
Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing 102206, China
- Department of Pathogeny Biology, School
of Basic Medical Sciences, North China University of Science and Technology, Tangshan 063009, Hebei, China
| | - Lei Song
- School of Life Sciences, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Proteomics, Beijing Proteome Research Center,
Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing 102206, China
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center,
Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing 102206, China
| | - Yunzhi Wang
- State Key Laboratory of Genetic Engineering
and Collaborative Innovation Center for Genetics and Development,
School of Life Sciences, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Wanlin Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center,
Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing 102206, China
| | - Xia Xia
- State Key Laboratory of Proteomics, Beijing Proteome Research Center,
Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing 102206, China
| | - Zhaoyu Qin
- State Key Laboratory of Genetic Engineering
and Collaborative Innovation Center for Genetics and Development,
School of Life Sciences, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Bei Zhen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center,
Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing 102206, China
| | - Yi Wang
- Alkek Center for Molecular Discovery, Verna and Marrs
McLean Department of Biochemistry and Molecular Biology, Department
of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, United States
| | - Fuchu He
- School of Life Sciences, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Proteomics, Beijing Proteome Research Center,
Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing 102206, China
- State Key Laboratory of Genetic Engineering
and Collaborative Innovation Center for Genetics and Development,
School of Life Sciences, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center,
Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing 102206, China
- State Key Laboratory of Genetic Engineering
and Collaborative Innovation Center for Genetics and Development,
School of Life Sciences, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
- Alkek Center for Molecular Discovery, Verna and Marrs
McLean Department of Biochemistry and Molecular Biology, Department
of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, United States
| | - Chen Ding
- State Key Laboratory of Proteomics, Beijing Proteome Research Center,
Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing 102206, China
- State Key Laboratory of Genetic Engineering
and Collaborative Innovation Center for Genetics and Development,
School of Life Sciences, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
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13
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Ding C, Li Y, Guo F, Jiang Y, Ying W, Li D, Yang D, Xia X, Liu W, Zhao Y, He Y, Li X, Sun W, Liu Q, Song L, Zhen B, Zhang P, Qian X, Qin J, He F. A Cell-type-resolved Liver Proteome. Mol Cell Proteomics 2016; 15:3190-3202. [PMID: 27562671 PMCID: PMC5054343 DOI: 10.1074/mcp.m116.060145] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Indexed: 01/16/2023] Open
Abstract
Parenchymatous organs consist of multiple cell types, primarily defined as parenchymal cells (PCs) and nonparenchymal cells (NPCs). The cellular characteristics of these organs are not well understood. Proteomic studies facilitate the resolution of the molecular details of different cell types in organs. These studies have significantly extended our knowledge about organogenesis and organ cellular composition. Here, we present an atlas of the cell-type-resolved liver proteome. In-depth proteomics identified 6000 to 8000 gene products (GPs) for each cell type and a total of 10,075 GPs for four cell types. This data set revealed features of the cellular composition of the liver: (1) hepatocytes (PCs) express the least GPs, have a unique but highly homogenous proteome pattern, and execute fundamental liver functions; (2) the division of labor among PCs and NPCs follows a model in which PCs make the main components of pathways, but NPCs trigger the pathways; and (3) crosstalk among NPCs and PCs maintains the PC phenotype. This study presents the liver proteome at cell resolution, serving as a research model for dissecting the cell type constitution and organ features at the molecular level.
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Affiliation(s)
- Chen Ding
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China; **State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Fudan University, Shanghai 200433, China
| | - Yanyan Li
- ¶School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Feifei Guo
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Ying Jiang
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Wantao Ying
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Dong Li
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Dong Yang
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Xia Xia
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Wanlin Liu
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Yan Zhao
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Yangzhige He
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China; ¶School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xianyu Li
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Wei Sun
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Qiongming Liu
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Lei Song
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Bei Zhen
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Pumin Zhang
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China
| | - Xiaohong Qian
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China;
| | - Jun Qin
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China; ‖Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030; **State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Fudan University, Shanghai 200433, China
| | - Fuchu He
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100039, China; §National Center for Protein Sciences (The PHOENIX center, Beijing), Beijing 102206, China; **State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Fudan University, Shanghai 200433, China
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14
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Lai M, Liang L, Chen J, Qiu N, Ge S, Ji S, Shi T, Zhen B, Liu M, Ding C, Wang Y, Qin J. Multidimensional Proteomics Reveals a Role of UHRF2 in the Regulation of Epithelial-Mesenchymal Transition (EMT). Mol Cell Proteomics 2016; 15:2263-78. [PMID: 27114453 DOI: 10.1074/mcp.m115.057448] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Indexed: 01/07/2023] Open
Abstract
UHRF1 is best known for its positive role in the maintenance of DNMT1-mediated DNA methylation and is implicated in a variety of tumor processes. In this paper, we provided evidence to demonstrate a role of UHRF2 in cell motility and invasion through the regulation of the epithelial-mesenchymal transition (EMT) process by acting as a transcriptional co-regulator of the EMT-transcription factors (TFs). We ectopically expressed UHRF2 in gastric cancer cell lines and performed multidimensional proteomics analyses. Proteome profiling analysis suggested a role of UHRF2 in repression of cell-cell adhesion; analysis of proteome-wide TF DNA binding activities revealed the up-regulation of many EMT-TFs in UHRF2-overexpressing cells. These data suggest that UHRF2 is a regulator of cell motility and the EMT program. Indeed, cell invasion experiments demonstrated that silencing of UHRF2 in aggressive cells impaired their abilities of migration and invasion in vitro Further ChIP-seq identified UHRF2 genomic binding motifs that coincide with several TF binding motifs including EMT-TFs, and the binding of UHRF2 to CDH1 promoter was validated by ChIP-qPCR. Moreover, the interactome analysis with IP-MS uncovered the interaction of UHRF2 with TFs including TCF7L2 and several protein complexes that regulate chromatin remodeling and histone modifications, suggesting that UHRF2 is a transcription co-regulator for TFs such as TCF7L2 to regulate the EMT process. Taken together, our study identified a role of UHRF2 in EMT and tumor metastasis and demonstrated an effective approach to obtain clues of UHRF2 function without prior knowledge through combining evidence from multidimensional proteomics analyses.
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Affiliation(s)
- Mi Lai
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine; National Center for Protein Sciences (Beijing); National Engineering Research Center for Protein Drugs, Beijing 102206, China
| | - Lizhu Liang
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine; National Center for Protein Sciences (Beijing); National Engineering Research Center for Protein Drugs, Beijing 102206, China
| | - Jiwei Chen
- §Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, College of Life Science, East China Normal University, Shanghai, China
| | - Naiqi Qiu
- §Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, College of Life Science, East China Normal University, Shanghai, China
| | - Sai Ge
- ¶Department of Gastrointestinal Oncology, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Shuhui Ji
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine; National Center for Protein Sciences (Beijing); National Engineering Research Center for Protein Drugs, Beijing 102206, China
| | - Tieliu Shi
- §Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, College of Life Science, East China Normal University, Shanghai, China
| | - Bei Zhen
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine; National Center for Protein Sciences (Beijing); National Engineering Research Center for Protein Drugs, Beijing 102206, China
| | - Mingwei Liu
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine; National Center for Protein Sciences (Beijing); National Engineering Research Center for Protein Drugs, Beijing 102206, China
| | - Chen Ding
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine; National Center for Protein Sciences (Beijing); National Engineering Research Center for Protein Drugs, Beijing 102206, China
| | - Yi Wang
- ‖Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030
| | - Jun Qin
- From the ‡State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine; National Center for Protein Sciences (Beijing); National Engineering Research Center for Protein Drugs, Beijing 102206, China; ‖Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030
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15
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Zhang Y, Li Q, Wu F, Zhou R, Qi Y, Su N, Chen L, Xu S, Jiang T, Zhang C, Cheng G, Chen X, Kong D, Wang Y, Zhang T, Zi J, Wei W, Gao Y, Zhen B, Xiong Z, Wu S, Yang P, Wang Q, Wen B, He F, Xu P, Liu S. Tissue-Based Proteogenomics Reveals that Human Testis Endows Plentiful Missing Proteins. J Proteome Res 2015; 14:3583-94. [DOI: 10.1021/acs.jproteome.5b00435] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Yao Zhang
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
- Institute of Microbiology, Chinese Academy of Science, Beijing 100101, China
- Graduate University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Qidan Li
- CAS
Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- BGI-Shenzhen, Shenzhen 518083, China
- Graduate University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Feilin Wu
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
- Life Science
College, Southwest Forestry University, Kunming 650224, P. R, China
| | - Ruo Zhou
- BGI-Shenzhen, Shenzhen 518083, China
| | - Yingzi Qi
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Na Su
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Lingsheng Chen
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
- State
Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning 530005, China
| | | | - Tao Jiang
- BGI-Shenzhen, Shenzhen 518083, China
| | - Chengpu Zhang
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | | | - Xinguo Chen
- Institute of Organ Transportation, General Hospital of Chinese People’s Armed Police Forces, Beijing 100039, China
| | - Degang Kong
- General
Surgery Dept., Capital Medical University Affiliated Beijing YouAn Hospital, Beijing 100069, China
| | | | - Tao Zhang
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Jin Zi
- BGI-Shenzhen, Shenzhen 518083, China
| | - Wei Wei
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Yuan Gao
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Bei Zhen
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Zhi Xiong
- Life Science
College, Southwest Forestry University, Kunming 650224, P. R, China
| | - Songfeng Wu
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Pengyuan Yang
- Institutes
of Biomedical Sciences, Department of Chemistry and Zhongshan Hospital, Fudan University, 130 DongAn Road, Shanghai 200032, China
| | - Quanhui Wang
- CAS
Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- BGI-Shenzhen, Shenzhen 518083, China
- Graduate University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Bo Wen
- BGI-Shenzhen, Shenzhen 518083, China
| | - Fuchu He
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Ping Xu
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
- Key
Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan
University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan 430071, China
| | - Siqi Liu
- CAS
Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- BGI-Shenzhen, Shenzhen 518083, China
- Graduate University of the Chinese Academy of Sciences, Beijing 100049, China
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16
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Su N, Zhang C, Zhang Y, Wang Z, Fan F, Zhao M, Wu F, Gao Y, Li Y, Chen L, Tian M, Zhang T, Wen B, Sensang N, Xiong Z, Wu S, Liu S, Yang P, Zhen B, Zhu Y, He F, Xu P. Special Enrichment Strategies Greatly Increase the Efficiency of Missing Proteins Identification from Regular Proteome Samples. J Proteome Res 2015; 14:3680-92. [DOI: 10.1021/acs.jproteome.5b00481] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Na Su
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Chengpu Zhang
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Yao Zhang
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
- Institute
of Microbiology, Chinese Academy of Science, Beijing 100101, China
| | - Zhiqiang Wang
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
- Key
Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan
University), Ministry of Education , and Wuhan University School of Pharmaceutical Sciences, Wuhan 430071, China
| | - Fengxu Fan
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
- Anhui Medical University, Hefei 230032, Anhui China
| | - Mingzhi Zhao
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Feilin Wu
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
- Life
Science College, Southwest Forestry University, Kunming 650224, China
| | - Yuan Gao
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Yanchang Li
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Lingsheng Chen
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
- State Key
Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning 530005, China
| | - Miaomiao Tian
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Tao Zhang
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Bo Wen
- BGI-Shenzhen, Shenzhen 518083, China
| | - Na Sensang
- Inner Mongolia Medical University, Hohhot 010110, Inner Mongolia China
| | - Zhi Xiong
- Life
Science College, Southwest Forestry University, Kunming 650224, China
| | - Songfeng Wu
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Siqi Liu
- BGI-Shenzhen, Shenzhen 518083, China
| | - Pengyuan Yang
- Institute
of Biomedical Sciences, Department of Chemistry, and Zhongshan Hospital, Fudan University, 130 DongAn Road, Shanghai 200032, China
| | - Bei Zhen
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Yunping Zhu
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Fuchu He
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Ping Xu
- State
Key Laboratory of Proteomics, Beijing Proteome Research Center, National
Engineering Research Center for Protein Drugs, National Center for
Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 102206, China
- Key
Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan
University), Ministry of Education , and Wuhan University School of Pharmaceutical Sciences, Wuhan 430071, China
- Anhui Medical University, Hefei 230032, Anhui China
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17
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Liu Y, Wang Y, Liu J, Zuo W, Hao L, Zhang L, Zhen B. High throughput monoclonal antibody generation by immunizing multiple antigens. Sci China Life Sci 2014; 57:710-7. [PMID: 24950620 DOI: 10.1007/s11427-014-4688-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2014] [Accepted: 04/19/2014] [Indexed: 11/30/2022]
Abstract
Recognizing proteins via the production of highly specific monoclonal antibodies (mAbs) is crucial to identifying proteins for proteomic research. However, traditional mAb generation is time-consuming with low efficiency. In this study, we assessed the high throughput method of producing mAbs by immunizing mice with multiple antigens in order to obtain hybridomas against these multiple antigens in one cell fusion. We selected eight proteins that play important roles in human physiological or pathological processes. These proteins were mixed and simultaneously administered to one mouse. We observed the immunizing period for 10 d, and determined the effect of liquid medium and semi-solid medium in hybridoma generation. As a result, all eight immunogens induced antibodies in the immunized mouse in one cell fusion, we obtained hybridomas specific to all eight proteins by enzyme-linked immuno sorbent assay (ELISA) screening, hybridomas against five out of eight showed specific positive in Western-blotting assays. This indicates that we generated mAbs specific to eight proteins in one cell fusion, greatly increasing the efficiency of mAb generation. Furthermore, we observed that hybridomas selected from the liquid medium and semi-solid medium showed different reactivity to antigens. Our study established high-throughput and time-saving methods for production of mAbs. These results provide alternative approaches for increasing the efficacy of mAb generation.
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Affiliation(s)
- Ying Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing, 102206, China,
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18
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Ding C, Jiang J, Wei J, Liu W, Zhang W, Liu M, Fu T, Lu T, Song L, Ying W, Chang C, Zhang Y, Ma J, Wei L, Malovannaya A, Jia L, Zhen B, Wang Y, He F, Qian X, Qin J. A fast workflow for identification and quantification of proteomes. Mol Cell Proteomics 2013; 12:2370-80. [PMID: 23669031 DOI: 10.1074/mcp.o112.025023] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
The current in-depth proteomics makes use of long chromatography gradient to get access to more peptides for protein identification, resulting in covering of as many as 8000 mammalian gene products in 3 days of mass spectrometer running time. Here we report a fast sequencing (Fast-seq) workflow of the use of dual reverse phase high performance liquid chromatography - mass spectrometry (HPLC-MS) with a short gradient to achieve the same proteome coverage in 0.5 day. We adapted this workflow to a quantitative version (Fast quantification, Fast-quan) that was compatible to large-scale protein quantification. We subjected two identical samples to the Fast-quan workflow, which allowed us to systematically evaluate different parameters that impact the sensitivity and accuracy of the workflow. Using the statistics of significant test, we unraveled the existence of substantial falsely quantified differential proteins and estimated correlation of false quantification rate and parameters that are applied in label-free quantification. We optimized the setting of parameters that may substantially minimize the rate of falsely quantified differential proteins, and further applied them on a real biological process. With improved efficiency and throughput, we expect that the Fast-seq/Fast-quan workflow, allowing pair wise comparison of two proteomes in 1 day may make MS available to the masses and impact biomedical research in a positive way.
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Affiliation(s)
- Chen Ding
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China
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19
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Wu S, Li N, Ma J, Shen H, Jiang D, Chang C, Zhang C, Li L, Zhang H, Jiang J, Xu Z, Ping L, Chen T, Zhang W, Zhang T, Xing X, Yi T, Li Y, Fan F, Li X, Zhong F, Wang Q, Zhang Y, Wen B, Yan G, Lin L, Yao J, Lin Z, Wu F, Xie L, Yu H, Liu M, Lu H, Mu H, Li D, Zhu W, Zhen B, Qian X, Qin J, Liu S, Yang P, Zhu Y, Xu P, He F. First Proteomic Exploration of Protein-Encoding Genes on Chromosome 1 in Human Liver, Stomach, and Colon. J Proteome Res 2012; 12:67-80. [PMID: 23256928 DOI: 10.1021/pr3008286] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Songfeng Wu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Ning Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Jie Ma
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Huali Shen
- Institutes of Biomedical Sciences and Department of Chemistry, 130 DongAn Road, Fudan University, Shanghai 200032, China
| | | | - Cheng Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Chengpu Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Liwei Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Hongxing Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Jing Jiang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Zhongwei Xu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Lingyan Ping
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Tao Chen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Wei Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Tao Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Xiaohua Xing
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Tailong Yi
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Yanchang Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Fengxu Fan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Xiaoqian Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Fan Zhong
- Institutes of Biomedical Sciences and Department of Chemistry, 130 DongAn Road, Fudan University, Shanghai 200032, China
| | - Quanhui Wang
- BGI-Shenzhen, ShenZhen 518083, China
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yang Zhang
- Institutes of Biomedical Sciences and Department of Chemistry, 130 DongAn Road, Fudan University, Shanghai 200032, China
| | - Bo Wen
- BGI-Shenzhen, ShenZhen 518083, China
| | - Guoquan Yan
- Institutes of Biomedical Sciences and Department of Chemistry, 130 DongAn Road, Fudan University, Shanghai 200032, China
| | - Liang Lin
- BGI-Shenzhen, ShenZhen 518083, China
| | - Jun Yao
- Institutes of Biomedical Sciences and Department of Chemistry, 130 DongAn Road, Fudan University, Shanghai 200032, China
| | | | - Feifei Wu
- Institutes of Biomedical Sciences and Department of Chemistry, 130 DongAn Road, Fudan University, Shanghai 200032, China
| | - Liqi Xie
- Institutes of Biomedical Sciences and Department of Chemistry, 130 DongAn Road, Fudan University, Shanghai 200032, China
| | - Hongxiu Yu
- Institutes of Biomedical Sciences and Department of Chemistry, 130 DongAn Road, Fudan University, Shanghai 200032, China
| | - Mingqi Liu
- Institutes of Biomedical Sciences and Department of Chemistry, 130 DongAn Road, Fudan University, Shanghai 200032, China
| | - Haojie Lu
- Institutes of Biomedical Sciences and Department of Chemistry, 130 DongAn Road, Fudan University, Shanghai 200032, China
| | - Hong Mu
- State Key Laboratory of Molecular Oncology, Cancer Institute & Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Dong Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Weimin Zhu
- Taicang Institute for Life Sciences Information, Taicang 215400, China
| | - Bei Zhen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Xiaohong Qian
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Siqi Liu
- BGI-Shenzhen, ShenZhen 518083, China
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China
| | - Pengyuan Yang
- Institutes of Biomedical Sciences and Department of Chemistry, 130 DongAn Road, Fudan University, Shanghai 200032, China
| | - Yunping Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Ping Xu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206,
China
- National Engineering Research Center for Protein Drugs, Beijing
102206, China
- Institutes of Biomedical Sciences and Department of Chemistry, 130 DongAn Road, Fudan University, Shanghai 200032, China
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20
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Liu Q, Ding C, Liu W, Song L, Liu M, Qi L, Fu T, Malovannaya A, Wang Y, Qin J, Zhen B. In-depth proteomic characterization of endogenous nuclear receptors in mouse liver. Mol Cell Proteomics 2012. [PMID: 23197792 DOI: 10.1074/mcp.m112.022319] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Nuclear receptors (NRs) are a superfamily of transcription factors that, upon binding to ligands, bind specific DNA sequences and regulate a transcriptional program governing cell proliferation, differentiation, and metabolism. In the liver, by sensing lipid-soluble hormones and dietary lipids and governing the expression of key liver metabolic genes, NR proteins direct a large array of key hepatic functions that include lipid and glucose metabolism, bile secretion, and bile acid homeostasis. Although much has been learned about the physiology of NRs, little is known about their protein expression and DNA binding activity in the liver because of their low abundance and the lack of high-throughput methods for detection at the protein level. Here we report a method for profiling the DNA binding activity of the NR transcription factor superfamily in mouse liver. We use DNA constructs of hormone response elements (HREs) as affinity reagents to enrich NR proteins from nuclear extracts of mouse liver and then identify them using mass spectrometry. We evaluated 20 DNA constructs containing various combinations of HREs for their ability to enrich endogenous NR proteins and found that two different HREs are sufficient to achieve isolation and identification of nearly all endogenous NR proteins from one mouse liver. We have detected proteins for 35 members of the NR family out of 41 that are expressed in mouse liver at mRNA level. Thus, this method allows coverage of most of the whole NR proteome and establishes a practical assay for the investigation of NR actions in mouse liver. We anticipate that this method will find widespread use in future investigations of NR actions in liver biology and pathology. Furthermore, this workflow is a useful tool for NR biologists interested in measuring NR expression, DNA binding, post-translational modifications, cellular localization, and other functional aspects of NRs in organs under normal physiological and pathological conditions, as well as during pharmacological intervention.
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Affiliation(s)
- Qiongming Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, 100850, China
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21
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Wang XW, Li JS, Guo TK, Zhen B, Kong QX, Yi B, Li Z, Song N, Jin M, Xiao WJ, Zhu XM, Gu CQ, Yin J, Wei W, Yao W, Liu C, Li JF, Ou GR, Wang MN, Fang TY, Wang GJ, Qiu YH, Wu HH, Chao FH, Li JW. Corrigendum to “Concentration and detection of SARS coronavirus in sewage from Xiao Tang Shan Hospital and the 309th Hospital” [J. Virol. Methods 128 (2005) 156–161]. J Virol Methods 2005. [PMCID: PMC7119657 DOI: 10.1016/j.jviromet.2005.08.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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22
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Wang XW, Li JS, Jin M, Zhen B, Kong QX, Song N, Xiao WJ, Yin J, Wei W, Wang GJ, Si BY, Guo BZ, Liu C, Ou GR, Wang MN, Fang TY, Chao FH, Li JW. Study on the resistance of severe acute respiratory syndrome-associated coronavirus. J Virol Methods 2005; 126:171-7. [PMID: 15847934 PMCID: PMC7112909 DOI: 10.1016/j.jviromet.2005.02.005] [Citation(s) in RCA: 219] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2004] [Revised: 02/03/2005] [Accepted: 02/07/2005] [Indexed: 12/13/2022]
Abstract
In this study, the persistence of severe acute respiratory syndrome-associated coronavirus (SARS-CoV) was observed in feces, urine and water. In addition, the inactivation of SARS-CoV in wastewater with sodium hypochlorite and chlorine dioxide was also studied. In vitro experiments demonstrated that the virus could only persist for 2 days in hospital wastewater, domestic sewage and dechlorinated tap water, while 3 days in feces, 14 days in PBS and 17 days in urine at 20 degrees C. However, at 4 degrees C, the SARS-CoV could persist for 14 days in wastewater and at least 17 days in feces or urine. SARS-CoV is more susceptible to disinfectants than Escherichia coli and f2 phage. Free chlorine was found to inactivate SARS-CoV better than chlorine dioxide. Free residue chlorine over 0.5 mg/L for chlorine or 2.19 mg/L for chlorine dioxide in wastewater ensures complete inactivation of SARS-CoV while it does not inactivate completely E. coli and f2 phage.
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Affiliation(s)
- Xin-Wei Wang
- Tianjin Institute of Environment and Health, 1 Da Li Road, Tianjin 300050, PR China
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23
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Wang XW, Li JS, Guo TK, Zhen B, Kong QX, Yi B, Li Z, Song N, Jin M, Xiao WJ, Zhu XM, Gu CQ, Yin J, Wei W, Yao W, Liu C, Li JF, Ou GR, Wang MN, Fang TY, Wang GJ, Qiu YH, Wu HH, Chao FH, Li JW. Concentration and detection of SARS coronavirus in sewage from Xiao Tang Shan Hospital and the 309th Hospital. J Virol Methods 2005; 128:156-61. [PMID: 15964082 PMCID: PMC7112879 DOI: 10.1016/j.jviromet.2005.03.022] [Citation(s) in RCA: 91] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2004] [Revised: 03/21/2005] [Accepted: 03/22/2005] [Indexed: 12/17/2022]
Abstract
The transmission of severe acute respiratory syndrome-associated coronavirus (SARS-CoV) is associated with close contact to SARS patients and droplet secretions of those patients. The finding of positive RT-PCR results from stools of SARS patients suggests that stools of SARS patients or sewage containing stools of patients could transmit SARS-CoV. We used a novel style of electropositive filter media particle to concentrate the SARS-CoV from the sewage of two hospitals receiving SARS patients in Beijing. We also used cell culture, RT-PCR and gene sequencing to detect and identify the viruses from sewage. No infectious SARS-CoV contamination was found in any of the samples collected, but the nucleic acid of SARS-CoV could be detected in the sewage from the two hospitals before disinfection. While the RNA was only detected in three samples from the 309th Hospital, the others were negative after disinfection. These findings provide strong evidence that SARS-CoV can be excreted through the stool/urine of patients into sewage system, thus making the sewage system a possible route of transmission.
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Affiliation(s)
- Xin-Wei Wang
- Tianjin Institute of Environment and Health, 1 Da Li Road, Tianjin 300050, PR China
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24
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Wang XW, Li JS, Guo TK, Zhen B, Kong QX, Yi B, Li Z, Song N, Jin M, Wu XM, Xiao WJ, Zhu XM, Gu CQ, Yin J, Wei W, Yao W, Liu C, Li JF, Ou GR, Wang MN, Fang TY, Wang GJ, Qiu YH, Wu HH, Chao FH, Li JW. Excretion and detection of SARS coronavirus and its nucleic acid from digestive system. World J Gastroenterol 2005; 11:4390-5. [PMID: 16038039 PMCID: PMC4434667 DOI: 10.3748/wjg.v11.i28.4390] [Citation(s) in RCA: 34] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
AIM: To study whether severe acute respiratory syndrome coronavirus (SARS-CoV) could be excreted from digestive system.
METHODS: Cell culture and semi-nested RT-PCR were used to detect SARS-CoV and its RNA from 21 stool and urine samples, and a kind of electropositive filter media particles was used to concentrate the virus in 10 sewage samples from two hospitals receiving SARS patients in Beijing in China.
RESULTS: It was demonstrated that there was no live SARS-CoV in all samples collected, but the RNA of SARS-CoV could be detected in seven stool samples from SARS patients with any one of the symptoms of fever, malaise, cough, or dyspnea, in 10 sewage samples before disinfection and 3 samples after disinfection from the two hospitals. The RNA could not be detected in urine and stool samples from patients recovered from SARS.
CONCLUSION: Nucleic acid of SARS-CoV can be excreted through the stool of patients into sewage system, and the possibility of SARS-CoV transmitting through digestive system cannot be excluded.
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Affiliation(s)
- Xin-Wei Wang
- Tianjin Institute of Environment and Health, Tianjin 300050, China
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25
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Wang XW, Li J, Guo T, Zhen B, Kong Q, Yi B, Li Z, Song N, Jin M, Xiao W, Zhu X, Gu C, Yin J, Wei W, Yao W, Liu C, Li J, Ou G, Wang M, Fang T, Wang G, Qiu Y, Wu H, Chao F, Li J. Concentration and detection of SARS coronavirus in sewage from Xiao Tang Shan Hospital and the 309th Hospital of the Chinese People's Liberation Army. Water Sci Technol 2005; 52:213-221. [PMID: 16312970 DOI: 10.2166/wst.2005.0266] [Citation(s) in RCA: 98] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
A worldwide outbreak of severe acute respiratory syndrome (SARS) had been reported. Over 8439 SARS cases and 812 SARS-related deaths were reported to the World Health Organization from 32 countries around the world up to 5 July 2003. The mechanism of transmission of SARS-CoV has been limited only to close contacts with patients. Attention was focused on possible transmission by the sewage system because laboratory studies showed that patients excreted coronavirus RNA in their stools in Amoy Gardens in Hong Kong. To explore whether the stool of SARS patients or the sewage containing the stool of patients would transmit SARS-CoV or not, we used a style of electropositive filter media particle to concentrate the SARS-CoV from the sewage of two hospitals receiving SARS patients in Beijing, as well as cell culture, semi-nested RT-PCR and sequencing of genes to detect and identify the viruses from sewage. There was no live SARS-CoV detected in the sewage in these assays. The nucleic acid of SARS-CoV was found in the sewage before disinfection from both hospitals by PCR. After disinfection, SARS-CoV RNA could be detected from some samples from the 309th Hospital of the Chinese People's Liberation Army, but not from Xiao Tang Shan Hospital after disinfection. In this study, we found that the virus can survive for 14 days in sewage at 4 degrees C, 2 days at 20 degrees C, and its RNA can be detected for 8 days though the virus had been inactivated. In conclusion, this study demonstrates that the RNA of SARS-CoV could be detected from the concentrates of sewage of both hospitals receiving SARS patients before disinfection and occasionally after disinfection though there was no live SARS-CoV; thus much attention should be paid to the treatment of stools of patients and the sewage of hospitals receiving SARS patients.
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Affiliation(s)
- X W Wang
- Institute of Hygiene and Environmental Medicine, Academy of Military Medical Sciences, Tianjin, PR China
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26
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Wang XW, Li JS, Guo TK, Zhen B, Kong QX, Yi B, Li Z, Song N, Jin M, Xiao WJ, Zhu XM, Gu CQ, Yin J, Wei W, Yao W, Liu C, Li JF, Ou GR, Wang MN, Fang TY, Wang GJ, Qiu YH, Wu HH, Chao FH, Li JW. [Detection of RNA of SARS coronavirus in hospital sewage]. Zhonghua Yu Fang Yi Xue Za Zhi 2004; 38:257-60. [PMID: 15312586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
OBJECTIVE In order to explore the existence of SARS coronavirus (Co-V) and/or its RNA in sewage of hospitals administered SARS patients. METHODS A novel electropositive filter was used to concentrate the SARS-CoV from the sewage of two hospitals administered SARS patients in Beijing, including twelve 2,500 ml sewage samples from the hospitals before disinfection, and ten 25,000 ml samples after disinfection; as well as cell culture, RT-PCR and sequencing of gene to detect and identify the viruses from sewage. RESULTS There was no live SARS-CoV detected in the sewage in this study. The nucleic acid of SARS-CoV had been found in the 12 sewage samples before disinfection from both hospitals by semi-nested PCR. After disinfection, SARS-CoV RNA could only be detected from the samples from the 309th Hospital, and the others were negative. CONCLUSION It provides evidence that there is no live SARS-Cov in the sewage from hospitals with SARS patients though SARS-CoV RNA can be detected.
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Affiliation(s)
- Xin-Wei Wang
- Institute of Hygiene and Environmental Medicine, Academy of Military Medical Sciences, 300050 Tianjin, China
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27
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Zhen B, Song YJ, Guo ZB, Wang J, Zhang ML, Yu SY, Yang RF. [In vitro selection and affinity function of the aptamers to Bacillus anthracis spores by SELEX]. Sheng Wu Hua Xue Yu Sheng Wu Wu Li Xue Bao (Shanghai) 2002; 34:635-42. [PMID: 12198569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
To obtain oligonucleotide aptamers, specifically binding to Bacillus anthracis spores, and to find the relationship between the structures and the affinities, and to determine whether the aptamers can be used as a novel molecule for spore detection, a synthetic 35 mer random DNA library was subjected to 18 rounds of selection by using SELEX (systematic evolution of ligands by exponential enrichment) protocol against spores of Bacillus anthracis vaccine strain A. 16R. The selected aptamers were cloned and sequenced. Software packages CLUSTALX (1.8) and DNASIS v2.5 were employed to analyze the conserved sequences and second structures of the aptamers, respectively. Affinities of aptamers to the spores were visualized by biotin streptavidin horseradish peroxidase system. DAB was used to visualize signals, as an assay method. A membrane-based hybrid sandwich assay was developed for detecting Bacillus anthracic spores by using a 5'-biotinylated ssDNA aptamers and anti-spore antibodies. PCR amplification band pattern of the first round selection was different from that of the ninth round. The binding assay demonstrated that the affinity of the eighteenth round pool increased thirty-seven folds more than that of the first round pool. The affinities of the aptamers were different: the highest A at 450 nm was 1.20, and the lowest was 0.20. The secondary structure analysis revealed possible stem-loop and hairpin structures for binding to the spores. The colorimetry on the immuno-membrane got the best signal with a ratio of 16 microgram aptamer to 4x10(7) spores. A set of aptamers with considerable binding affinity to Bacillus anthracis spores was successfully selected from the initial random ssDNA pool. The stem-loop and hairpin at 5' end of the aptamers worked as the main motif in the interaction between oligonucleotides and spores, while the neighbor bases of the triple structure might affect the stability. Therefore ssDNA aptamers seem to be a type of potential diagnostic molecule.
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Affiliation(s)
- Bei Zhen
- Institute of Microbiology and Epidemiology, the Academy of Military Medical Sciences, Beijing 100071, China.
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28
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Schwartzberg L, Weaver C, Lewkow L, McAneny B, Zhen B, Birch R, West W, Tauer K, Buckner C. High-dose chemotherapy with peripheral blood stem cell support for stage IIIB inflammatory carcinoma of the breast. Bone Marrow Transplant 1999; 24:981-7. [PMID: 10556957 DOI: 10.1038/sj.bmt.1701965] [Citation(s) in RCA: 22] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The purpose of this study was to determine outcomes for 56 patients with inflammatory breast cancer (IBC) receiving high-dose chemotherapy (HDC) with cyclophosphamide, thiotepa and carboplatin (CTCb) with peripheral blood stem cell (PBSC) support. All patients received the same total amount of chemotherapy but there were differences in the sequence of therapy: 15 received induction chemotherapy, chemotherapy mobilization of PBSC and CTCb after surgery (adjuvant group) while 41 received induction chemotherapy with (n = 17) or without (n = 24) chemotherapy for mobilization of PBSC prior to surgery and CTCb after surgery (neoadjuvant group). Median time from diagnosis to HDC was 5.5 months (range 3.5-12.5). Fifty-one patients (91%) required admission to the hospital following HDC for a median of 11 days (range 5-25). There were two (4%) infectious deaths after HDC. Twenty-four patients (43%) have relapsed at a median of 18 months (range 8-50) from diagnosis resulting in death in 34%. The probabilities of overall (OS) and event-free survival (EFS) at 3 years for all 56 patients were 0.72 and 0.53, respectively, with a median follow-up of 44 months (range 15-76) from diagnosis. There were no differences in OS, EFS or patterns of relapse between patients in the adjuvant or neoadjuvant groups. These sequences of combined modality therapy incorporating HDC are comparable or superior to other intensive approaches for the treatment of IBC. Further improvements will be necessary to decrease systemic recurrences.
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Affiliation(s)
- L Schwartzberg
- Clinical Research Division of Response Oncology, Inc., Memphis, TN, USA
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29
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Schulman KA, Birch R, Zhen B, Pania N, Weaver CH. Effect of CD34(+) cell dose on resource utilization in patients after high-dose chemotherapy with peripheral-blood stem-cell support. J Clin Oncol 1999; 17:1227. [PMID: 10561183 DOI: 10.1200/jco.1999.17.4.1227] [Citation(s) in RCA: 58] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE The mean time to neutrophil and platelet recovery for patients receiving high-dose chemotherapy (HDC) supported with peripheral-blood stem cells (PBSCs) is related to the dose of CD34(+) cells infused. The effect of cell dose on resource utilization after transplantation has not been previously reported. MATERIALS AND METHODS We assessed CD34(+) cell dose and resource utilization for 1,317 patients undergoing transplantation with PBSCs from April 1991 to June 1997. PBSCs were collected after mobilization with chemotherapy and recombinant human granulocyte colony-stimulating factor (rhG-CSF). Daily measurement of the CD34(+) content of the PBSC collection was performed by a central laboratory using a single CD34(+) analysis technique. Resource utilization included engraftment parameters, length of stay, and transfusion requirements for 100 days posttransplantation. Analysis included descriptive statistics and multiple regression. RESULTS Mean patient age was 47 years, and 86% of patients were female. Median cell dose was 3.6 x 10(6)/kg and 13.2 x 10(6)/kg for patients receiving less than 5.0 x 10(6) CD34(+) cells/kg and 5.0 x 10(6) or more CD34(+) cells/kg, respectively. Patients receiving less than 5. 0 x 10(6) CD34(+) cells/kg were more likely to have metastatic breast cancer or non-Hodgkin's lymphoma and required more platelet and RBC transfusions, 3.3 more hospital days, and increased antibiotic and antifungal use. In univariate analysis, the cost of care was $41,516 (+/-$20,876 SD) and $32,382 (+/-$16,353 SD) for patients with less than 5.0 x 10(6) CD34(+) cells/kg and 5.0 x 10(6) or more CD34(+) cells/kg, respectively. In multivariate analysis, patients with less than 5.0 x 10(6) CD34(+) cells/kg had an increase in costs of $5,062 (+/- $1,262 SE). CONCLUSION Infusion of more than 5.0 x 10(6) CD34(+) cells/kg was associated with a reduction in resource utilization. Achieving a target of 5.0 x 10(6) CD34(+) cells/kg should have important clinical and economic benefits for patients.
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Affiliation(s)
- K A Schulman
- Clinical Economics Research Unit, Department of Medicine, Georgetown University Medical Center, Washington, DC 20007, USA.
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Weaver CH, Schwartzberg LS, Zhen B, Franco C, Moore M, Smith R, White L, Van Amburg A, Hazelton B, Buckner CD. Mobilization of peripheral blood stem cells with docetaxel and cyclophosphamide (CY) in patients with metastatic breast cancer: a randomized trial of 3 vs 4 g/m2 of CY. Bone Marrow Transplant 1999; 23:421-5. [PMID: 10100554 DOI: 10.1038/sj.bmt.1701599] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The purpose of this study was to develop a regimen of docetaxel, cyclophosphamide (CY) and filgrastim for mobilization of peripheral blood stem cells (PBSC) in patients with metastatic breast cancer (n = 66). A phase I trial of CY 2, 3 or 4 g/m2 with docetaxel 100 mg/m2, in consecutive cohorts of four patients each, did not reveal any dose-limiting toxicities and subsequent patients were randomized to receive 3 or 4 g/m2 of CY. The median yield of CD34+ cells from all patients was 11.06x10(6)/kg (range, 0.03-84.77) from a median of two aphereses (range, 1-7); 6.52x10(6) CD34+ cells/kg/apheresis (range, 0.01-52.07). Target CD34+ cell doses > or =2.5 and > or =5.0x10(6)/kg were achieved in 89% and 79%, respectively. There were no statistically significant differences in CD34+ cell yields or target CD34+ cell doses achieved following 3 or 4 g/m2 of CY. Patients with only one prior chemotherapy regimen yielded a median of 12.82x10(6) CD34+ cells/kg/apheresis compared to 5.85 for those receiving > or =2 regimens (P = 0.03). It was concluded that the combination of docetaxel, 100 mg/m2, CY 3 g/m2 without mesna could be administered with acceptable toxicity with collection of adequate quantities of PBSC from the majority of patients.
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Affiliation(s)
- C H Weaver
- Clinical Research Division of Response Oncology, Inc, Memphis, TN, USA
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Zhen B, Zhou Y, Wang S. [Blood pressure examination using oscillometric method]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 1999; 16:42-5. [PMID: 12553274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/28/2023]
Abstract
The effect of pressure sensor, filter and blood pressure algorithm upon oscillometric method is discussed in this paper. The design principle and parameters configuration used in portable blood pressure HOLTER are presented.
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Affiliation(s)
- B Zhen
- Department of Biomedical Engineering, Xi'an Jiaotong University, Xi'an 710049
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Weaver CH, Zhen B, Schwartzberg LS, Leff R, Magee M, Geier L, Deaton K, Lewkow L, Buckner CD. Phase I-II evaluation of rapid sequence tandem high-dose melphalan with peripheral blood stem cell support in patients with multiple myeloma. Bone Marrow Transplant 1998; 22:245-51. [PMID: 9720737 DOI: 10.1038/sj.bmt.1701324] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This study was designed to determine the maximum tolerated dose (MTD) of high-dose melphalan (HDM), with peripheral blood stem cell support, that could be given twice within 90 days to patients with multiple myeloma. Twenty patients received tandem HDM at 160, 180 or 200 mg/m2 and a total of 55 were treated at the estimated MTD of 200 mg/m2. Seventeen of 55 (31%) did not receive cycle 2; six because of low CD34+ cell yields, three because of severe (n = 1) or fatal toxicities (n = 2) and eight for other reasons. The median interval between doses for 38 patients was 70 days (range 41-225). Three of 55 patients (5%) died of treatment-related causes. In patients completing two cycles of HDM, at any dose level, the complete remission rate improved from 15% following cycle 1 to 55% following cycle 2. The probabilities of survival, event-free survival and relapse or progression at 18 months for the 55 patients treated at the MTD were 0.84, 0.76 and 0.20, respectively, with a median follow-up of 19 months (range 9-36) from mobilization chemotherapy. It was concluded that two cycles of HDM, 200 mg/m2, could be administered to approximately 70% of patients under the age of 66 with multiple myeloma in a median interval of 70 days, with improvement in CR rates.
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Affiliation(s)
- C H Weaver
- Clinical Research Division of Response Oncology, Inc., Memphis, TN, USA
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Weaver CH, Zhen B, Schwartzberg L, Walker C, Upton S, Buckner CD. A randomized trial of mobilization of peripheral blood stem cells with cyclophosphamide, etoposide, and granulocyte colony-stimulating factor with or without cisplatin in patients with malignant lymphoma receiving high-dose chemotherapy. Am J Clin Oncol 1998; 21:408-12. [PMID: 9708644 DOI: 10.1097/00000421-199808000-00019] [Citation(s) in RCA: 18] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
The purpose of this study was to evaluate the addition of cisplatin to cyclophosphamide, etoposide, and granulocyte colony-stimulating factor (G-CSF) for the mobilization of peripheral blood stem cells (PBSC). Eighty-one patients with malignant lymphoma were randomized to receive either cyclophosphamide 4 g/m2 and etoposide 600 mg/m2 (CE), and G-CSF 6 microg/kg/day (n = 41), or the same drugs with cisplatin 105 mg/m2 (CEP; n = 40) followed by collection of PBSC. Seventy-eight of 81 patients (96%) had apheresis performed and 70 (86%) received high-dose chemotherapy (HDC) with PBSC support. The median number of CD34+ cells collected after CE was 19.77 compared with 9.39 x 10(6)/kg after CEP (p = 0.09). More patients receiving CEP had grade 3-4 gastrointestinal (p = 0.03) and neurologic toxicities (p = 0.05), had significant delays in recovery of neutrophils (p = 0.0001) and platelets (p = 0.009), and received more red blood cell (p = 0.03) and platelet (p = 0.08) transfusions than patients receiving CE. There were no significant differences in treatment-related deaths, relapse, survival, or event-free survival between patients receiving CE or CEP when all 81 patients or the 70 patients receiving HDC were evaluated. It was concluded that the addition of cisplatin to CE did not improve CD34+ cell yields, was associated with more morbidity and resource utilization, and was not associated with improvement in outcomes.
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Affiliation(s)
- C H Weaver
- Clinical Research Division of Response Oncology, Inc., Memphis, Tennessee, USA
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Abstract
BACKGROUND The Denver International Airport construction project provided a rare opportunity to identify risk factors for injury on a large construction project for which 769 contractors were hired to complete 2,843 construction contracts. Workers' compensation claims and payroll data for individual contracts were recorded in an administrative database developed by the project's Owner-Controlled Insurance Program. METHODS From claims andy payroll data linked with employee demographic information, we calculated injury rates per 200,000 person-hours by contract and over contract characteristics of interest. We used Poisson regression models to examine contract-specific risk factors in relation to total injuries, lost-work-time (LWT), and non-LWT injuries. We included contract-specific expected loss rates (ELRs) in the model to control for prevailing risk of work and used logistic regression methods to determine the association between LWT and non-LWT injuries on contracts. RESULTS Injury rates were highest during the first year of construction, at the beginning of contracts, and among older workers. Risk for total and non-LWT injuries was elevated for building construction contracts, contract for special trades companies (SIC 17), contracts with payrolls over $1 million, and those with overtime payrolls greater than 20%. Risk for LWT injuries only was increased for site development contracts and contract starting in the first year of construction. Contracts experiencing one or more minor injuries were four times as likely to have at least one major injury (OR = 4.0, 95% CI (2.9, 5.5)). CONCLUSIONS Enhancement of DIA's safety infrastructure during the second year of construction appears to have been effective in reducing serious (LWT) injures. The absence of correlation between injury rates among contracts belonging to the same company suggest that targeting of safety resources at the level of the contract may be an effective approach to injury prevention. Interventions focused on high-risk contracts, including those with considerable overtime work, contracts held by special trades contractors (SIC 17), and contracts belonging to small and mid-sized companies, and on high-risk workers, such as those new to a construction site or new to a contract may reduce injury burden on large construction sites. The join occurrence of minor and major injuries on a contract level suggests that surveillance of minor injuries may be useful in identifying opportunities for prevention of major injures.
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Affiliation(s)
- J T Lowery
- Department of Preventive Medicine and Biometrics, University of Colorado School of Medicine, Denver 80262, USA
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Weaver CH, Zhen B, Buckner CD. Treatment of patients with malignant lymphoma with Mini-BEAM reduces the yield of CD34+ peripheral blood stem cells. Bone Marrow Transplant 1998; 21:1169-70. [PMID: 9645585 DOI: 10.1038/sj.bmt.1701254] [Citation(s) in RCA: 23] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Weaver CH, Moss T, Schwartzberg LS, Zhen B, West J, Rhinehart S, Campos L, Beeker T, Lautersztain L, Messino M, Buckner CD. High-dose chemotherapy in patients with breast cancer: evaluation of infusing peripheral blood stem cells containing occult tumor cells. Bone Marrow Transplant 1998; 21:1117-24. [PMID: 9645574 DOI: 10.1038/sj.bmt.1701247] [Citation(s) in RCA: 21] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The purpose of this study was to evaluate the frequency of detecting occult tumor cells in peripheral blood stem cell (PBSC) harvests and to determine the impact of infusing such cells on relapses after high-dose chemotherapy (HDC). Peripheral blood stem cell harvests from 223 patients with breast cancer were examined by an immunocytochemistry (ICC) method for detection of occult tumor cells, and infused after HDC without consideration of test results. Two hundred and four patients, 114 with stage II-III and 90 with stage IV disease who received only PBSC, that were tested by ICC were evaluated for time to relapse. Five hundred and eighty-one of 619 PBSC harvests (94%) from 223 patients were tested. Fifty-three of 581 harvests (9%), 8% from stage II-III and 10% from stage IV patients, were positive by ICC (P = 0.68). Forty-one of 223 patients (18%), 17/122 (14%) with stage II-III and 24/101 (24%) with stage IV disease, had positive harvests (P = 0.06). Eleven percent of patients who had 1-2 harvests tested were positive as compared to 32% of patients who had > or =3 PBSC harvests tested (P < 0.001). Nineteen patients who were infused with a mixture of ICC negative and untested PBSC harvests were excluded from analyses of relapse. The probabilities of relapse at 18 months for the 97 patients with stage II-III disease infused with ICC-negative and the 17 with ICC-positive PBSC were 0.19 and 0.13, respectively (P = 0.48). The probabilities of relapse at 18 months for patients achieving a CR or a CR in non-bone sites and improvement in bone lesions were 0.55 for the ICC-negative group (n = 30) and 0.45 for the ICC-positive group (n = 11) (P = 0.60). It was concluded that occult tumor cells were detected by ICC in PBSC harvests from a relatively small fraction of women with breast cancer, but were not associated with a significant increase in the probability of early relapse or progression when infused after HDC.
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Affiliation(s)
- C H Weaver
- Clinical Research Division of Response Oncology, Inc, Memphis, TN, USA
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37
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Weaver CH, Tauer K, Zhen B, Schwartzberg LS, Hazelton B, Weaver Z, Lewkow L, Allen C, Longin K, Buckner CD. Second attempts at mobilization of peripheral blood stem cells in patients with initial low CD34+ cell yields. J Hematother 1998; 7:241-9. [PMID: 9621257 DOI: 10.1089/scd.1.1998.7.241] [Citation(s) in RCA: 56] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
The purpose of this study was to determine the effectiveness of second mobilization strategies in patients who yielded < 2.5 x 10(6) CD34+ PBSC/kg after initial mobilization. Repeat mobilization attempts were made with chemotherapy and G-CSF (n = 61) or G-CSF alone (n = 58) in patients who failed initial mobilization with chemotherapy and G-CSF (n = 92) or G-CSF alone (n = 27). A median of 0.27 x 10(6) CD34+ cells/kg per apheresis was collected after the second mobilization, compared with 0.16 with initial harvests (p = 0.0001). Forty-eight percent achieved a target CD34+ cell dose > or = 2.5 x 10(6)/kg when harvests from the first and second mobilizations were combined. Fifteen of 17 patients (88%) with > or = 1.5 x 10(6) CD34+ cells/kg harvested after first mobilization had > or = 2.5 x 10(6) CD34+ cells/kg collected when first and second harvests were combined, as compared with 42 of 102 (41%) achieving < 1.5 x 10(6) CD34+ cells/kg with first PBSC harvests (p = 0.0001). Second mobilizations with chemotherapy and G-CSF or G-CSF alone resulted in similar CD34+ cell yields. Toxicities of second mobilizations were comparable with those of first mobilizations. Seventy-nine patients (66%) received high-dose chemotherapy with PBSC support, with recovery of neutrophils and platelets in a median of 11 and 15 days, respectively. Transplant-related mortality was 4%, and event-free survival at 2 years was 0.34. It was concluded that second mobilization attempts in patients who fail to achieve > or = 2.5 x 10(6) CD34+ cells/kg on initial mobilization were successful in 48% of patients. G-CSF alone was as effective as chemotherapy plus G-CSF in mobilizing CD34+ cells and was associated with less morbidity.
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Affiliation(s)
- C H Weaver
- Clinical Research Division of Response Oncology, Inc., Memphis, TN 38117, USA
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38
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Weaver CH, Schwartzberg L, Rhinehart S, West J, Zhen B, West WH, Buckner CD. High-dose chemotherapy with BUCY or BEAC and unpurged peripheral blood stem cell infusion in patients with low-grade non-Hodgkin's lymphoma. Bone Marrow Transplant 1998; 21:383-9. [PMID: 9509973 DOI: 10.1038/sj.bmt.1701101] [Citation(s) in RCA: 22] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Forty-nine patients with low-grade non-Hodgkin's lymphoma (NHL) received high-dose chemotherapy (HDC) with busulfan and cyclophosphamide (BUCY) or carmustine, etoposide, cytarabine and CY (BEAC) followed by unpurged autologous peripheral blood stem (PBSC) infusion. All patients had failed initial chemotherapy or progressed after an initial remission. Peripheral blood stem cells were mobilized with CY alone (n = 1), CY, etoposide (n = 19), or CY, etoposide and cisplatin (n = 29) followed by granulocyte colony-stimulating factor. Twenty-two patients received BU, 16 mg/kg, and CY, 120 mg/kg. Twenty-seven patients received carmustine 300 mg/m2, etoposide 600 mg/m2, cytarabine 600 mg/m2, and CY 140 mg/kg. Four patients (8%) died of non-relapse causes, two (9%) in the BUCY group and two (7%) in the BEAC group. Twenty-seven patients (55%) relapsed or progressed at a median of 9.4 months (2-38) from PBSC infusion. Ten patients who relapsed are alive a median of 31 months (range, 6-47) after relapse. The probabilities of relapse at 3.6 years for patients receiving BUCY or BEAC were 0.57 and 0.70, respectively (P = 0.92). Twenty-seven patients (55%) are alive at a median of 3.6 years (range, 1-5). The probabilities of survival at 3.6 years for patients receiving BUCY or BEAC were 0.58 and 0.55, respectively (P = 0.72). The probabilities of EFS at 3.6 years for patients receiving BUCY or BEAC were 0.36 and 0.28, respectively (P = 0.82). It was concluded that BUCY is an active regimen for the treatment of patients with low-grade NHL.
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Affiliation(s)
- C H Weaver
- Clinical Research Division of Response Oncology, Inc, Memphis, TN 98122, USA
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39
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Weaver CH, Schwartzberg L, Zhen B, Mangum M, Leff R, Tauer K, Rosenberg A, Pendergrass K, Kaywin P, Hainsworth J, Greco FA, West WH, Buckner CD. High-dose chemotherapy and peripheral blood stem cell infusion in patients with non-Hodgkin's lymphoma: results of outpatient treatment in community cancer centers. Bone Marrow Transplant 1997; 20:753-60. [PMID: 9384477 DOI: 10.1038/sj.bmt.1700975] [Citation(s) in RCA: 21] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The outcomes for patients with non-Hodgkin's lymphoma (NHL) treated with high-dose chemotherapy (HDC) and peripheral blood stem cell (PBSC) infusion by practicing oncologists in community cancer centers in the United States were determined. Eighty-three patients with NHL, who had failed conventional chemotherapy, underwent mobilization of PBSC with chemotherapy and a recombinant growth factor in an outpatient facility. At a median of 40 days (range 26-119) after mobilization chemotherapy all received carmustine (300 mg/m2 x 1), etoposide (150 mg/m2 twice a day x 4 days), cytarabine (100 mg/m2 twice a day x 4 days) and cyclophosphamide (35 mg/kg x 4 days) (BEAC) followed by infusion of unmanipulated PBSC in an outpatient facility. The probabilities of treatment-related mortality, relapse/progression, overall survival (OS) and event-free survival (EFS) at 3 years for all 83 patients were 0.07, 0.57, 0.49 and 0.38, respectively. The probabilities of relapse/progression, OS and EFS at 3 years for 28 patients who had failed primary induction chemotherapy were 0.55, 0.42 and 0.38, respectively. The probabilities of OS and EFS for 27 patients in untreated first relapse were 0.52 and 0.44, respectively, as compared to 0.56 and 0.32, respectively, for 18 patients who had reinduction attempts prior to receiving mobilization chemotherapy (P = 0.81 for OS and 0.99 for EFS). No significant risk factors for the outcomes of TRM, relapse/progression, OS or EFS could be identified. These data demonstrate that approximately 40% of patients with NHL who have failed conventional chemotherapy become long-term disease-free survivors after mobilization chemotherapy, high-dose BEAC and PBSC infusion administered in an outpatient setting in community cancer centers, with the major cause of failure being relapse. Results obtained in this study are comparable to published data in similar patient populations receiving therapy as inpatients, suggesting that clinical trials involving well-tested HDC regimens can be carried out safely in this setting.
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Affiliation(s)
- C H Weaver
- Clinical Research Division of Response Oncology, Inc., Memphis, TN, USA
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Weaver CH, Greco FA, Hainsworth JD, Zhen B, Baldwin P, Wittlin F, Lewis M, West WH, Schwartzberg L, Buckner CD. A phase I-II study of high-dose melphalan, mitoxantrone and carboplatin with peripheral blood stem cell support in patients with advanced ovarian or breast carcinoma. Bone Marrow Transplant 1997; 20:847-53. [PMID: 9404925 DOI: 10.1038/sj.bmt.1700976] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The purpose of this study was to develop a high-dose chemotherapy (HDC) and peripheral blood stem cell (PBSC) regimen for treatment of patients with ovarian carcinoma that could be administered in an outpatient setting. Fourteen patients with advanced ovarian (n = 9) or breast (n = 5) carcinoma, who had failed conventional chemotherapy, were entered into a dose-escalation trial to determine the maximum tolerated dose (MTD) of carboplatin that could be administered with fixed doses of melphalan (160 mg/m2) and mitoxantrone (50 mg/m2). Twenty-five additional patients were included in a phase II trial at the MTD. Two of two patients had grade 4 severe regimen-related toxicities (RRT), one fatal, at a dose level of 1600 mg/m2. Two of 29 patients (6.9%) treated at the MTD (carboplatin, 1400 mg/m2) died of RRT. All three patients who died of toxicity had a calculated AUC for carboplatin >30 mg/ml/min. Thirty-one patients with ovarian cancer who had failed chemotherapy were treated, 24 at the MTD. Fourteen of 20 patients (70%) with ovarian carcinoma with evaluable disease achieved a CR and seven (35%) are alive disease-free a median of 20 months (range, 7-26). Five of seven patients with ovarian cancer who had failed chemotherapy but were rendered clinically disease-free following surgery survive without progression a median of 13 months (range, 9-19). Eight of 16 (50%) platinum-resistant and 4/12 (33%) platinum-sensitive patients with ovarian cancer survive disease-free.
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Affiliation(s)
- C H Weaver
- Clinical Research Division of Response Oncology, Inc., Memphis, TN, USA
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Abstract
Prevalence of berylliosis, a lung disorder driven by the activation of beryllium-specific T cells, is associated with a major histocompatibility complex (MHC) class II marker (HLA-DPB1Glu69) and with the type of industrial exposure. We evaluated the interaction between marker and exposure in a beryllium-exposed population in which the prevalence of berylliosis was associated with machining beryllium. The presence of the marker was associated with higher prevalence (HLA-DPB1Glu69-positive machinists 25%; HLA-DPB1Glu69-negative machinists 3.2%, P = 0.05) and predicted berylliosis independent of machining history (odds ratios 11.8 and 10.1). The study shows that in berylliosis the carrier status of a genetic susceptibility factor adds to the effect of process-related risk factors.
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Affiliation(s)
- L Richeldi
- Department of Medical Sciences, University of Modena, Italy
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42
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Kreiss K, Mroz MM, Zhen B, Wiedemann H, Barna B. Risks of beryllium disease related to work processes at a metal, alloy, and oxide production plant. Occup Environ Med 1997; 54:605-12. [PMID: 9326165 PMCID: PMC1128986 DOI: 10.1136/oem.54.8.605] [Citation(s) in RCA: 121] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To describe relative hazards in sectors of the beryllium industry, risk factors of beryllium disease and sensitisation related to work process were sought in a beryllium manufacturing plant producing pure metal, oxide, alloys, and ceramics. METHODS All 646 active employees were interviewed; beryllium sensitisation was ascertained with the beryllium lymphocyte proliferation blood test on 627 employees; clinical evaluation and bronchoscopy were offered to people with abnormal test results; and industrial hygiene measurements related to work processes taken in 1984-93 were reviewed. RESULTS 59 employees (9.4%) had abnormal blood tests, 47 of whom underwent bronchoscopy. 24 new cases of beryllium disease were identified, resulting in a beryllium disease prevalence of 4.6%, including five known cases (29/632). Employees who had worked in ceramics had the highest prevalence of beryllium disease (9.0%). Employees in the pebble plant (producing beryllium metal) who had been employed after 1983 also had increased risk, with a prevalence of beryllium disease of 6.4%, compared with 1.3% of other workers hired in the same period, and a prevalence of abnormal blood tests of 19.2%. Logistic regression modelling confirmed these two risk factors for beryllium disease related to work processes and the dependence on time of the risk at the pebble plant. The pebble plant was not associated with the highest gravimetric industrial hygiene measurements available since 1984. CONCLUSION Further characterisation of exposures in beryllium metal production may be important to understanding how beryllium exposures confer high contemporary risk of beryllium disease.
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Affiliation(s)
- K Kreiss
- Occupational and Environmental Medicine Division, National Jewish Center for Immunology and Respiratory Medicine, University of Colorado Health Sciences Center, Denver, USA
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43
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Weaver CH, Potz J, Redmond J, Tauer K, Schwartzberg LS, Kaywin P, Drapkin R, Grant B, Unger P, Allen C, Longin K, Zhen B, Hazelton B, Buckner CD. Engraftment and outcomes of patients receiving myeloablative therapy followed by autologous peripheral blood stem cells with a low CD34+ cell content. Bone Marrow Transplant 1997; 19:1103-10. [PMID: 9193753 DOI: 10.1038/sj.bmt.1700808] [Citation(s) in RCA: 49] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Engraftment kinetics after high-dose chemotherapy (HDC) were evaluated in patients receiving autologous peripheral blood stem cell (PBSC) infusions with a low CD34+ cell content. Forty-eight patients were infused with < 2.5 x 10(6) CD34+ cells/kg; 36 because of poor harvests and 12 because they electively received only a fraction of their harvested cells. A median of 2.12 x 10(6) CD34+ cells/kg (range, 1.17-2.48) were infused following one of seven different HDC regimens. All patients achieved absolute neutrophil counts > or = 0.5 x 10(9)/l at a median of day 11 (range, 9-16). Forty-seven patients achieved platelet counts > or = 20 x 10(9)/l at a median of day 14 (range, 8-250). Nine of 47 (19%) had platelet recovery after day 21, 4/47 (9%) after day 100 and one died on day 240 without platelet recovery. Twenty-six patients (54%) died of progressive disease in 51-762 days; 22 (46%) are alive at a median of 450 days (range, 94-1844), 17 (35%) of whom are surviving disease-free at a median of 494 days (range, 55-1263). No patient died as a direct consequence of low blood cell counts. These data demonstrate that PBSC products containing 1.17-2.48 x 10(6) CD34+ cells/kg resulted in relatively prompt neutrophil recovery in all patients but approximately 10% had delayed platelet recovery.
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Affiliation(s)
- C H Weaver
- Clinical Trials Division, Response Oncology Inc, Memphis, TN, USA
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44
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Wen Y, Qian M, Gong X, Zhen B, Wan M. [Causes and influential factors of spectral broadening in Doppler flow signal]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 1997; 14:118-23. [PMID: 9817638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
Signals received are the resultant of the blood flow phasors in CW Doppler system and there are spectral broadening phenomena in it, in other words, frequency of signals corresponding to a moving RBC is not single. A model is presented that enables the detailed effects of spectral broadening to be calculated for a CW Doppler system by using geometric boundary argument. Results are given for the circular geometry.
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Affiliation(s)
- Y Wen
- Dept. of Biomedical Engineering, Xi'an Jiaotong University
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45
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Wen Y, Qian M, Gong X, Zhen B, Wan M. [Effects of a high-pass filter on the Doppler blood flow signal]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 1997; 14:15-20. [PMID: 9817658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
A high-pass filter is used to remove the large signals scattered or reflected from stationary and slow moving objects in many clinical CW Doppler units. theoretical results are presented that such a filter will lead to overestimate of the mean velocity of Doppler blood flow. As for different frequency signals, high-pass filter have different amplitudes and phase responses, consequently, nonlinear phase shift is produced. It is proved by computer experiment that nonlinear phase shift caused by high-pass filter hardly has any effects on Doppler flow power spectrum if the high-pass filter does not affect the amplitudes of signals.
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Affiliation(s)
- Y Wen
- Dept of Biomedical Engineering, Xi'an Jiaotong University
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46
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Abstract
We investigated exposure-response relations for silicosis among 134 men over age 40 who had been identified in a previous community-based random sample study in a mining town. Thirty-two percent of the 100 dust-exposed subjects had radiologic profusions of small opacities of I/O or greater at a mean time since first silica exposure of 36.1 years. Of miners with cumulative silica exposures of 2 mg/m3-years or less, 20% had silicosis; of miners accumulating > 2 mg/m3 years, 63% had silicosis. Average silica exposure was also strongly associated with silicosis prevalence rates, with 13% silicotics among those with average exposure of 0.025-0.05 mg/m3, 34% among those with exposures of > 0.05-0.1 mg/m3, and 75% among those with average exposures > 0.1 mg/m3. Logistic regression models demonstrated that time since last silica exposure and either cumulative silica exposure or a combination of average silica exposure and duration of exposure predicted silicosis risk. Exposure-response relations were substantially higher using measured silica exposures than using estimated silica exposures based on measured dust exposures assuming a constant silica proportion of dust, consistent with less exposure misclassification. The risk of silicosis found in this study is higher than has been found in workforce studies having no follow-up of those leaving the mining industry and in studies without job title-specific silica measurements, but comparable to several recent studies of dust exposure-response relationships which suggest that a permissible exposure limit of 0.1 mg/m3 for silica does not protect against radiologic silicosis.
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Affiliation(s)
- K Kreiss
- Department of Medicine, National Jewish Center for Immunology and Respiratory Medicine, University of Colorado school of Medicine, Denver, USA
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47
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Abstract
We examined the prevalence of beryllium sensitization in relation to work process and beryllium exposure measurements in a beryllia ceramics plant that had operated since 1980. We interviewed 136 employees (97.8% of the workforce), ascertained beryllium sensitization with the beryllium lymphocyte proliferation blood test, and reviewed historical industrial hygiene measurements. Of eight beryllium-sensitized employees (5.9%), six (4.4% of participating employees) had granulomatous disease on transbronchial lung biopsy. Machinists had a sensitization rate of 14.3% compared to a rate of 1.2% among other employees. Machining had significantly higher general area and breathing zone measurements than did other processes in the time period in which most beryllium-sensitized cases had started machining work. Daily weighted average (DWA) estimates of exposure for matching processes also exceeded estimates for other work processes in that time period, with a median DWA of 0.9 microgram/m3. Machining process DWAs accounted for the majority of DWAs exceeding the 2.0 micrograms/m3 OSHA standard, with 8.1% of machining DWAs above the standard. We conclude that lowering machining process-related exposures may be important to lowering risk of beryllium disease.
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Affiliation(s)
- K Kreiss
- Occupational and Environmental Medicine Division, National Jewish Center for Immunology and Respiratory Medicine, Denver CO 80206, USA
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Leonard CE, Wood ME, Zhen B, Rankin J, Waitz DA, Norton L, Howell K, Sedlacek S. Does administration of chemotherapy before radiotherapy in breast cancer patients treated with conservative surgery negatively impact local control? J Clin Oncol 1995; 13:2906-15. [PMID: 8523054 DOI: 10.1200/jco.1995.13.12.2906] [Citation(s) in RCA: 57] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
PURPOSE To determine if a delay of irradiation to the intact breast for administration of adjuvant chemotherapy results in increased local recurrence in breast cancer. PATIENTS AND METHODS The records of 262 women with 264 cases of breast cancer were reviewed. Group I contained 105 patients treated with conservative surgery, chemotherapy, and radiotherapy. Group II contained 157 patients (used as a concurrent control) treated with conservative surgery and radiotherapy only. Eighty-nine percent of subjects in group I received all chemotherapy before radiotherapy. Fifty-eight percent of patients received hormone therapy. Seventy-one percent of patients had negative surgical margins, and 74% had negative lymph nodes. For group I, conservative surgery-radiotherapy intervals in months were less than 1 (five, 5%), > or = 1 to less than 3 (10, 9%), > or = 1 to less 6 (48, 46%), and > or = 6 (42, 40%), mean of 5. For group II, the intervals were less than 1 (20, 13%), > or = 1 to less than 3 (123, 79%), > or = 3 to less than 6 (11, 7%), and > or = 6 (two, 1%), mean of 1.5. RESULTS Thirty patients (11.5%) have disease recurrence (19 distant [6%] and 12 local [5%]). There were no significant differences in local recurrence (group I, four [4%]; group II, eight [5%]; difference not significant). There were no significant differences in local recurrence in any surgery-radiotherapy interval within each group. Although we found marginal increases in the percentage of local recurrences in group I patients (with prolonged surgery-radiotherapy intervals) who had positive margins, positive lymph nodes, and tumor size more than 2 cm versus group II (without prolonged surgery-radiotherapy intervals), these results were not significant. CONCLUSION We could not identify any surgery-radiotherapy interval that resulted in increased local recurrence if radiotherapy was delayed for administration of adjuvant chemotherapy in breast cancer patients. Because of the heterogenous population of breast cancer patients, our results also support the need for further study to determine the optimum integration of radiotherapy and chemotherapy in the management of the conservatively treated breast.
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Affiliation(s)
- C E Leonard
- Department of Radiation Oncology, Swedish Medical Center, Englewood, CO 80110, USA
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Epling CA, Rose CS, Martyny JW, Zhen B, Alexander W, Waldron JA, Kreiss K. Endemic work-related febrile respiratory illness among construction workers. Am J Ind Med 1995; 28:193-205. [PMID: 8585517 DOI: 10.1002/ajim.4700280205] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Construction workers building Denver International Airport (DIA) reported work-related respiratory and flulike symptoms of several months duration. We performed a cross-sectional interview study of 495 randomly selected DIA workers from six contractors in comparison with preplacement workers. We defined cases as workers with two work-attributed lower respiratory symptoms and one work-attributed systemic symptom. Case rates were significantly higher among DIA workers (34%) compared with those who had never worked at DIA (2%). Risk factors for illness included exposure to fireproofing (OR, 4.21; 95% CI, 1.95-9.08), work in tunnels and adjoining areas (OR, 3.07; 95% CI, 1.84-5.12), length of DIA employment (OR, 0.65; 95% CI, 0.46-0.92), and preexisting bronchitis (OR, 2.43; 95% CI, 1.17-5.05). Our industrial hygiene investigation revealed alkaline dust (pH 11) present at a worksite associated with elevated risk of illness, and we identified airborne Penicillium mold widely distributed indoors at DIA. Clinical evaluation of 26 self-identified symptomatic DIA employees, including bronchoalveolar lavage and biopsy in 10, revealed work-related asthma in three workers and histologic evidence of chronic bronchitis in four who had never smoked. We concluded that future investigations of endemic work-related febrile respiratory illness among construction workers should evaluate its association with indoor exposure to dusts from alkaline fireproofing, Penicillium mold, mycotoxins, and bacterial bioaerosols.
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Affiliation(s)
- C A Epling
- Occupational and Environmental Medicine Division, National Jewish Center for Immunology and Respiratory Medicine, Denver, CO 80206, USA
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Leonard C, Corkill M, Tompkin J, Zhen B, Waitz D, Norton L, Kinzie J. Are axillary recurrence and overall survival affected by axillary extranodal tumor extension in breast cancer? Implications for radiation therapy. J Clin Oncol 1995; 13:47-53. [PMID: 7799041 DOI: 10.1200/jco.1995.13.1.47] [Citation(s) in RCA: 65] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
PURPOSE To determine the overall survival and local recurrence significance of axillary lymph node extranodal tumor extension (ETE) and whether axillary/chest-wall irradiation influenced any of these outcomes. MATERIALS AND METHODS The records of 81 breast cancer patients treated with radical or modified radical mastectomy at a single surgical practice were eligible for study. Thirty-four patients had ETE: 17 with focal ETE (< 10 x high-power field) and 17 with extensive ETE (> 10 x high-power field). RESULTS With a median follow-up duration of 92 months, only two patients had an axillary recurrence (2%): one had focal ETE and one had no ETE. Neither of these patients received axillary radiation therapy. Overall survival and recurrence-free survival were significantly decreased with ETE in patients whether axillary radiation therapy had been administered or not. Analysis showed that the age of the patient correlated significantly with extensive ETE (P = .04) and that the number of positive lymph nodes (< or = three v > three) correlated significantly with ETE (whether focal or extensive) (P = .0001). A multivariate analysis of extranodal tumor extension and number of positive lymph nodes showed that ETE was associated with decreased survival (P = .05), although to a lesser degree than number of positive lymph nodes (P = .003). CONCLUSION These results show that ETE is associated with decreased survival and increased recurrence rates regardless of the extent of the radiation therapy field. Also, ETE does not necessarily indicate a significantly increased incidence of axillary recurrence. Therefore, axillary irradiation based on this pathologic finding may not be indicated.
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Affiliation(s)
- C Leonard
- Department of Radiology, University of Colorado Health Sciences Center, Denver
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