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Zhu W, Li M, Wang Q, Shen J, Ji J. Quantitative Proteomic Analysis Reveals Functional Alterations of the Peripheral Immune System in Colorectal Cancer. Mol Cell Proteomics 2024; 23:100784. [PMID: 38735538 PMCID: PMC11215959 DOI: 10.1016/j.mcpro.2024.100784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 04/26/2024] [Accepted: 05/09/2024] [Indexed: 05/14/2024] Open
Abstract
Colorectal cancer (CRC) is characterized by high morbidity, high mortality, and limited response to immunotherapies. The peripheral immune system is an important component of tumor immunity, and enhancements of peripheral immunity help to suppress tumor progression. However, the functional alterations of the peripheral immune system in CRC are unclear. Here, we used mass spectrometry-based quantitative proteomics to establish a protein expression atlas for the peripheral immune system in CRC, including plasma and five types of immune cells (CD4+ T cells, CD8+ T cells, monocytes, natural killer cells, and B cells). Synthesizing the results of the multidimensional analysis, we observed an enhanced inflammatory phenotype in CRC, including elevated expression of plasma inflammatory proteins, activation of the inflammatory pathway in monocytes, and increased inflammation-related ligand-receptor interactions. Notably, we observed tumor effects on peripheral T cells, including altered cell subpopulation ratios and suppression of cell function. Suppression of CD4+ T cell function is mainly mediated by high expression levels of protein tyrosine phosphatases. Among them, the expression of protein tyrosine phosphatase receptor type J (PTPRJ) gradually increased with CRC progression; knockdown of PTPRJ in vitro could promote T cell activation, thereby enhancing peripheral immunity. We also found that the combination of leucine-rich α-2 glycoprotein 1 (LRG1) and apolipoprotein A4 (APOA4) had the best predictive ability for colorectal cancer and has the potential to be a biomarker. Overall, this study provides a comprehensive understanding of the peripheral immune system in CRC. It also offers insights regarding the potential clinical utilities of these peripheral immune characteristics as diagnostic indicators and therapeutic targets.
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Affiliation(s)
- Wenyuan Zhu
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China; Department of Biochemistry and Molecular Biology, School of Life Sciences, Peking University, Beijing, China
| | - Minzhe Li
- General Surgery Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Qingsong Wang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China; Department of Biochemistry and Molecular Biology, School of Life Sciences, Peking University, Beijing, China.
| | - Jian Shen
- General Surgery Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
| | - Jianguo Ji
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China; Department of Biochemistry and Molecular Biology, School of Life Sciences, Peking University, Beijing, China.
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2
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Hua H, Wang T, Pan L, Du X, Xia T, Fa Z, Gu L, Gao F, Yu C, Gao F, Liao L, Shen Z. A proteomic classifier panel for early screening of colorectal cancer: a case control study. J Transl Med 2024; 22:188. [PMID: 38383428 PMCID: PMC10880210 DOI: 10.1186/s12967-024-04983-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/12/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Diagnosis of colorectal cancer (CRC) during early stages can greatly improve patient outcome. Although technical advances in the field of genomics and proteomics have identified a number of candidate biomarkers for non-invasive screening and diagnosis, developing more sensitive and specific methods with improved cost-effectiveness and patient compliance has tremendous potential to help combat the disease. METHODS We enrolled three cohorts of 479 subjects, including 226 CRC cases, 197 healthy controls, and 56 advanced precancerous lesions (APC). In the discovery cohort, we used quantitative mass spectrometry to measure the expression profile of plasma proteins and applied machine-learning to select candidate proteins. We then developed a targeted mass spectrometry assay to measure plasma concentrations of seven proteins and a logistic regression classifier to distinguish CRC from healthy subjects. The classifier was further validated using two independent cohorts. RESULTS The seven-protein panel consisted of leucine rich alpha-2-glycoprotein 1 (LRG1), complement C9 (C9), insulin-like growth factor binding protein 2 (IGFBP2), carnosine dipeptidase 1 (CNDP1), inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3), serpin family A member 1 (SERPINA1), and alpha-1-acid glycoprotein 1 (ORM1). The panel classified CRC and healthy subjects with high accuracy, since the area under curve (AUC) of the training and testing cohort reached 0.954 and 0.958. The AUC of the two independent validation cohorts was 0.905 and 0.909. In one validation cohort, the panel had an overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 89.9%, 81.8%, 89.2%, and 82.9%, respectively. In another blinded validation cohort, the panel classified CRC from healthy subjects with a sensitivity of 81.5%, specificity of 97.9%, and overall accuracy of 92.0%. Finally, the panel was able to detect APC with a sensitivity of 49%. CONCLUSIONS This seven-protein classifier is a clear improvement compared to previously published blood-based protein biomarkers for detecting early-stage CRC, and is of translational potential to develop into a clinically useful assay.
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Affiliation(s)
- Hanju Hua
- Department of Colorectal Surgery (H.H), and Department of Gastroenterology (C.Y. and Z.S.), College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, 310006, Zhejiang, China
| | - Tingting Wang
- Durbrain Medical Laboratory, Hangzhou, 310000, Zhejiang, China
| | - Liangxuan Pan
- Durbrain Medical Laboratory, Hangzhou, 310000, Zhejiang, China
| | - Xiaoyao Du
- Durbrain Medical Laboratory, Hangzhou, 310000, Zhejiang, China
| | - Tianxue Xia
- Department of Colorectal Surgery (H.H), and Department of Gastroenterology (C.Y. and Z.S.), College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, 310006, Zhejiang, China
| | - Zhenzhong Fa
- Changzhou Wujin People's Hospital, Changzhou, 213000, Jiangsu, China
| | - Lei Gu
- Department of General Surgery, School of Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China
| | - Fei Gao
- Durbrain Medical Laboratory, Hangzhou, 310000, Zhejiang, China
| | - Chaohui Yu
- Department of Colorectal Surgery (H.H), and Department of Gastroenterology (C.Y. and Z.S.), College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, 310006, Zhejiang, China.
| | - Feng Gao
- Changzhou Wujin People's Hospital, Changzhou, 213000, Jiangsu, China.
| | - Lujian Liao
- Durbrain Medical Laboratory, Hangzhou, 310000, Zhejiang, China.
- Shanghai Key Laboratory of Regulatory Biology, School of Life Sciences, East China Normal University, Shanghai, 200241, China.
| | - Zhe Shen
- Department of Colorectal Surgery (H.H), and Department of Gastroenterology (C.Y. and Z.S.), College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, 310006, Zhejiang, China.
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3
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Ye X, Cui X, Zhang L, Wu Q, Sui X, He A, Zhang X, Xu R, Tian R. Combination of automated sample preparation and micro-flow LC-MS for high-throughput plasma proteomics. Clin Proteomics 2023; 20:3. [PMID: 36611134 PMCID: PMC9824974 DOI: 10.1186/s12014-022-09390-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 12/27/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Non-invasive detection of blood-based markers is a critical clinical need. Plasma has become the main sample type for clinical proteomics research because it is easy to obtain and contains measurable protein biomarkers that can reveal disease-related physiological and pathological changes. Many efforts have been made to improve the depth of its identification, while there is an increasing need to improve the throughput and reproducibility of plasma proteomics analysis in order to adapt to the clinical large-scale sample analysis. METHODS We have developed and optimized a robust plasma analysis workflow that combines an automated sample preparation platform with a micro-flow LC-MS-based detection method. The stability and reproducibility of the workflow were systematically evaluated and the workflow was applied to a proof-of-concept plasma proteome study of 30 colon cancer patients from three age groups. RESULTS This workflow can analyze dozens of samples simultaneously with high reproducibility. Without protein depletion and prefractionation, more than 300 protein groups can be identified in a single analysis with micro-flow LC-MS system on a Orbitrap Exploris 240 mass spectrometer, including quantification of 35 FDA approved disease markers. The quantitative precision of the entire workflow was acceptable with median CV of 9%. The preliminary proteomic analysis of colon cancer plasma from different age groups could be well separated with identification of potential colon cancer-related biomarkers. CONCLUSIONS This workflow is suitable for the analysis of large-scale clinical plasma samples with its simple and time-saving operation, and the results demonstrate the feasibility of discovering significantly changed plasma proteins and distinguishing different patient groups.
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Affiliation(s)
- Xueting Ye
- grid.440218.b0000 0004 1759 7210The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People’s Hospital, Shenzhen, 518020 China ,grid.258164.c0000 0004 1790 3548The First Affiliated Hospital, Jinan University, Guangzhou, 510632 China ,grid.263817.90000 0004 1773 1790Department of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055 China
| | - Xiaozhen Cui
- grid.263817.90000 0004 1773 1790Department of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055 China
| | - Luobin Zhang
- grid.440218.b0000 0004 1759 7210The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People’s Hospital, Shenzhen, 518020 China
| | - Qiong Wu
- grid.263817.90000 0004 1773 1790Department of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055 China
| | - Xintong Sui
- grid.263817.90000 0004 1773 1790Department of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055 China
| | - An He
- grid.263817.90000 0004 1773 1790Department of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055 China
| | - Xinyou Zhang
- grid.440218.b0000 0004 1759 7210The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People’s Hospital, Shenzhen, 518020 China
| | - Ruilian Xu
- grid.440218.b0000 0004 1759 7210The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People’s Hospital, Shenzhen, 518020 China
| | - Ruijun Tian
- grid.263817.90000 0004 1773 1790Department of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055 China
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Lee DH, Yoon W, Lee A, Han Y, Byun Y, Kang JS, Kim H, Kwon W, Suh YA, Choi Y, Namkung J, Han S, Yi SG, Heo JS, Han IW, Park JO, Park JK, Kim SC, Jun E, Kang CM, Lee WJ, Lee HK, Lee H, Lee S, Jeong SY, Lee KE, Han W, Park T, Jang JY. Multi-biomarker panel prediction model for diagnosis of pancreatic cancer. JOURNAL OF HEPATO-BILIARY-PANCREATIC SCIENCES 2023; 30:122-132. [PMID: 33991409 DOI: 10.1002/jhbp.986] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 03/02/2021] [Accepted: 05/02/2021] [Indexed: 01/17/2023]
Abstract
BACKGROUND/PURPOSE The current study aimed to develop a prediction model using a multi-marker panel as a diagnostic screening tool for pancreatic ductal adenocarcinoma. METHODS Multi-center cohort of 1991 blood samples were collected from January 2011 to September 2019, of which 609 were normal, 145 were other cancer (colorectal, thyroid, and breast cancer), 314 were pancreatic benign disease, and 923 were pancreatic ductal adenocarcinoma. The automated multi-biomarker Enzyme-Linked Immunosorbent Assay kit was developed using three potential biomarkers: LRG1, TTR, and CA 19-9. Using a logistic regression model on a training data set, the predicted values for pancreatic ductal adenocarcinoma were obtained, and the result was classification into one of the three risk groups: low, intermediate, and high. The five covariates used to create the model were sex, age, and three biomarkers. RESULTS Participants were categorized into four groups as normal (n = 609), other cancer (n = 145), pancreatic benign disease (n = 314), and pancreatic ductal adenocarcinoma (n = 923). The normal, other cancer, and pancreatic benign disease groups were clubbed into the non-pancreatic ductal adenocarcinoma group (n = 1068). The positive and negative predictive value, sensitivity, and specificity were 94.12, 90.40, 93.81, and 90.86, respectively. CONCLUSIONS This study demonstrates a significant diagnostic performance of the multi-marker panel in distinguishing pancreatic ductal adenocarcinoma from normal and benign pancreatic disease states, as well as patients with other cancers.
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Affiliation(s)
- Doo-Ho Lee
- Department of Surgery and Cancer Research Institute, College of Medicine, Seoul National University, Seoul, Korea
- Department of Surgery, Gachon university Gil medical center, Incheon, Korea
| | - Woongchang Yoon
- Bio-MAX/N-Bio Institute, Seoul National University, Seoul, Korea
| | - Areum Lee
- Department of Surgery and Cancer Research Institute, College of Medicine, Seoul National University, Seoul, Korea
| | - Youngmin Han
- Department of Surgery and Cancer Research Institute, College of Medicine, Seoul National University, Seoul, Korea
| | - Yoonhyeong Byun
- Department of Surgery and Cancer Research Institute, College of Medicine, Seoul National University, Seoul, Korea
| | - Jae Seung Kang
- Department of Surgery and Cancer Research Institute, College of Medicine, Seoul National University, Seoul, Korea
| | - Hongbeom Kim
- Department of Surgery and Cancer Research Institute, College of Medicine, Seoul National University, Seoul, Korea
| | - Wooil Kwon
- Department of Surgery and Cancer Research Institute, College of Medicine, Seoul National University, Seoul, Korea
| | - Young-Ah Suh
- Department of Surgery and Cancer Research Institute, College of Medicine, Seoul National University, Seoul, Korea
| | - Yonghwan Choi
- Immunodiagnostics R&D Team, IVD Business Unit 5, SK Telecom, Seoul, Korea
| | - Junghyun Namkung
- Immunodiagnostics R&D Team, IVD Business Unit 5, SK Telecom, Seoul, Korea
| | - Sangjo Han
- Immunodiagnostics R&D Team, IVD Business Unit 5, SK Telecom, Seoul, Korea
| | - Sung Gon Yi
- Immunodiagnostics R&D Team, IVD Business Unit 5, SK Telecom, Seoul, Korea
| | - Jin Seok Heo
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - In Woong Han
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Joon Oh Park
- Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Joo Kyung Park
- Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Song Cheol Kim
- Department of Surgery, University of Ulsan College of Medicine and Asan Medical Center, Seoul, Korea
| | - Eunsung Jun
- Department of Surgery, University of Ulsan College of Medicine and Asan Medical Center, Seoul, Korea
| | - Chang Moo Kang
- Department of Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Woo Jin Lee
- Center for Liver Cancer, National Cancer Center, Seoul, Korea
| | - Hyeon Kook Lee
- Department of Surgery, Ewha Womans University School of Medicine, Seoul, Korea
| | - Huisong Lee
- Department of Surgery, Ewha Womans University School of Medicine, Seoul, Korea
| | - Seungyeoun Lee
- Department of Mathematics and Statistics, Sejong University, Seoul, Korea
| | - Seung-Yong Jeong
- Department of Surgery and Cancer Research Institute, College of Medicine, Seoul National University, Seoul, Korea
| | - Kyu Eun Lee
- Department of Surgery and Cancer Research Institute, College of Medicine, Seoul National University, Seoul, Korea
| | - Wonshik Han
- Department of Surgery and Cancer Research Institute, College of Medicine, Seoul National University, Seoul, Korea
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul, Korea
| | - Jin-Young Jang
- Department of Surgery and Cancer Research Institute, College of Medicine, Seoul National University, Seoul, Korea
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Detection of factors affecting kidney function using machine learning methods. Sci Rep 2022; 12:21740. [PMID: 36526702 PMCID: PMC9758148 DOI: 10.1038/s41598-022-26160-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
Due to the increasing prevalence of chronic kidney disease and its high mortality rate, study of risk factors affecting the progression of the disease is of great importance. Here in this work, we aim to develop a framework for using machine learning methods to identify factors affecting kidney function. To this end classification methods are trained to predict the serum creatinine level based on numerical values of other blood test parameters in one of the three classes representing different ranges of the variable values. Models are trained using the data from blood test results of healthy and patient subjects including 46 different blood test parameters. The best developed models are random forest and LightGBM. Interpretation of the resulting model reveals a direct relationship between vitamin D and blood creatinine level. The detected analogy between these two parameters is reliable, regarding the relatively high predictive accuracy of the random forest model reaching the AUC of 0.90 and the accuracy of 0.74. Moreover, in this paper we develop a Bayesian network to infer the direct relationships between blood test parameters which have consistent results with the classification models. The proposed framework uses an inclusive set of advanced imputation methods to deal with the main challenge of working with electronic health data, missing values. Hence it can be applied to similar clinical studies to investigate and discover the relationships between the factors under study.
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6
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He B, Huang Z, Huang C, Nice EC. Clinical applications of plasma proteomics and peptidomics: Towards precision medicine. Proteomics Clin Appl 2022; 16:e2100097. [PMID: 35490333 DOI: 10.1002/prca.202100097] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/16/2022] [Accepted: 04/28/2022] [Indexed: 02/05/2023]
Abstract
In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.
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Affiliation(s)
- Bo He
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, P. R. China
| | - Zhao Huang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, P. R. China
| | - Canhua Huang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, P. R. China.,Department of Pharmacology, and Provincial Key Laboratory of Pathophysiology in Ningbo University School of Medicine, Ningbo, Zhejiang, China
| | - Edouard C Nice
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia
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Yu R, Cheng L, Yang S, Liu Y, Zhu Z. iTRAQ-Based Proteomic Analysis Reveals Potential Serum Biomarkers for Pediatric Non-Hodgkin's Lymphoma. Front Oncol 2022; 12:848286. [PMID: 35371990 PMCID: PMC8970600 DOI: 10.3389/fonc.2022.848286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 02/21/2022] [Indexed: 11/20/2022] Open
Abstract
Non-Hodgkin’s lymphoma (NHL) is the third most common malignant tumor among children. However, at initial NHL diagnosis, most cases are at an advanced stage because of nonspecific clinical manifestations and currently limited diagnostic methods. This study aimed to screen and verify potential serum biomarkers of pediatric NHL using isobaric tags for relative and absolute quantification (iTRAQ)-based proteomic analysis. Serum protein expression profiles from children with B-NHL (n=20) and T-NHL (n=20) and healthy controls (n=20) were detected by utilizing iTRAQ in combination with two-dimensional liquid chromatography-tandem mass spectrometry (2D LC–MS/MS) and analyzed by applying Ingenuity Pathway Analysis (IPA). The candidate biomarkers S100A8 and LRG1 were further validated by using enzyme-linked immunosorbent assays (ELISAs). Receiver operating characteristic (ROC) analysis based on ELISA data was used to evaluate diagnostic efficacy. In total, 534 proteins were identified twice using iTRAQ combined with 2D LC–MS/MS. Further analysis identified 79 and 73 differentially expressed proteins in B-NHL and T-NHL serum, respectively, compared with control serum according to our defined criteria; 34 proteins were overexpressed and 45 proteins underexpressed in B-NHL, whereas 45 proteins were overexpressed and 28 proteins underexpressed in T-NHL (p < 0.05). IPA demonstrated a variety of signaling pathways, including acute phase response signaling and liver X receptor/retinoid X receptor (LXR/RXR) activation, to be strongly associated with pediatric NHL. S100A8 and LRG1 were elevated in NHL patients compared to normal controls according to ELISA (p < 0.05), which was consistent with iTRAQ results. The areas under the ROC curves of S100A8, LRG1, and the combination of S100A8 and LRG1 were 0.873, 0.898 and 0.970, respectively. Our findings indicate that analysis of the serum proteome using iTRAQ combined with 2D LC–MS/MS is a feasible approach for biomarker discovery. Serum S100A8 and LRG1 are promising candidate biomarkers for pediatric NHL, and these differential proteins illustrate a novel pathogenesis and may be clinically helpful for NHL diagnosis in the future.
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Affiliation(s)
- Runhong Yu
- Henan Provincial People's Hospital, Institute of Hematology of Henan Provincial People's Hospital, Zhengzhou, China.,Henan Provincial People's Hospital, Henan Key laboratory of Stem Cell Differentiation and Modification, Zhengzhou, China
| | - Linna Cheng
- Henan Provincial People's Hospital, Institute of Hematology of Henan Provincial People's Hospital, Zhengzhou, China.,Henan Provincial People's Hospital, Henan Key laboratory of Stem Cell Differentiation and Modification, Zhengzhou, China
| | - Shiwei Yang
- Henan Provincial People's Hospital, Institute of Hematology of Henan Provincial People's Hospital, Zhengzhou, China.,Henan Provincial People's Hospital, Henan Key laboratory of Stem Cell Differentiation and Modification, Zhengzhou, China
| | - Yufeng Liu
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zunmin Zhu
- Henan Provincial People's Hospital, Institute of Hematology of Henan Provincial People's Hospital, Zhengzhou, China.,Henan Provincial People's Hospital, Henan Key laboratory of Stem Cell Differentiation and Modification, Zhengzhou, China.,Department of Hematology, People's Hospital of Zhengzhou University, Zhengzhou, China
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8
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Visual Analytics for Predicting Disease Outcomes Using Laboratory Test Results. INFORMATICS 2022. [DOI: 10.3390/informatics9010017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Laboratory tests play an essential role in the early and accurate diagnosis of diseases. In this paper, we propose SUNRISE, a visual analytics system that allows the user to interactively explore the relationships between laboratory test results and a disease outcome. SUNRISE integrates frequent itemset mining (i.e., Eclat algorithm) with extreme gradient boosting (XGBoost) to develop more specialized and accurate prediction models. It also includes interactive visualizations to allow the user to interact with the model and track the decision process. SUNRISE helps the user probe the prediction model by generating input examples and observing how the model responds. Furthermore, it improves the user’s confidence in the generated predictions and provides them the means to validate the model’s response by illustrating the underlying working mechanism of the prediction models through visualization representations. SUNRISE offers a balanced distribution of processing load through the seamless integration of analytical methods with interactive visual representations to support the user’s cognitive tasks. We demonstrate the usefulness of SUNRISE through a usage scenario of exploring the association between laboratory test results and acute kidney injury, using large provincial healthcare databases from Ontario, Canada.
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9
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Camilli C, Hoeh AE, De Rossi G, Moss SE, Greenwood J. LRG1: an emerging player in disease pathogenesis. J Biomed Sci 2022; 29:6. [PMID: 35062948 PMCID: PMC8781713 DOI: 10.1186/s12929-022-00790-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 01/11/2022] [Indexed: 12/15/2022] Open
Abstract
The secreted glycoprotein leucine-rich α-2 glycoprotein 1 (LRG1) was first described as a key player in pathogenic ocular neovascularization almost a decade ago. Since then, an increasing number of publications have reported the involvement of LRG1 in multiple human conditions including cancer, diabetes, cardiovascular disease, neurological disease, and inflammatory disorders. The purpose of this review is to provide, for the first time, a comprehensive overview of the LRG1 literature considering its role in health and disease. Although LRG1 is constitutively expressed by hepatocytes and neutrophils, Lrg1-/- mice show no overt phenotypic abnormality suggesting that LRG1 is essentially redundant in development and homeostasis. However, emerging data are challenging this view by suggesting a novel role for LRG1 in innate immunity and preservation of tissue integrity. While our understanding of beneficial LRG1 functions in physiology remains limited, a consistent body of evidence shows that, in response to various inflammatory stimuli, LRG1 expression is induced and directly contributes to disease pathogenesis. Its potential role as a biomarker for the diagnosis, prognosis and monitoring of multiple conditions is widely discussed while dissecting the mechanisms underlying LRG1 pathogenic functions. Emphasis is given to the role that LRG1 plays as a vasculopathic factor where it disrupts the cellular interactions normally required for the formation and maintenance of mature vessels, thereby indirectly contributing to the establishment of a highly hypoxic and immunosuppressive microenvironment. In addition, LRG1 has also been reported to affect other cell types (including epithelial, immune, mesenchymal and cancer cells) mostly by modulating the TGFβ signalling pathway in a context-dependent manner. Crucially, animal studies have shown that LRG1 inhibition, through gene deletion or a function-blocking antibody, is sufficient to attenuate disease progression. In view of this, and taking into consideration its role as an upstream modifier of TGFβ signalling, LRG1 is suggested as a potentially important therapeutic target. While further investigations are needed to fill gaps in our current understanding of LRG1 function, the studies reviewed here confirm LRG1 as a pleiotropic and pathogenic signalling molecule providing a strong rationale for its use in the clinic as a biomarker and therapeutic target.
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Affiliation(s)
- Carlotta Camilli
- Institute of Ophthalmology, University College London, London, UK.
| | - Alexandra E Hoeh
- Institute of Ophthalmology, University College London, London, UK
| | - Giulia De Rossi
- Institute of Ophthalmology, University College London, London, UK
| | - Stephen E Moss
- Institute of Ophthalmology, University College London, London, UK
| | - John Greenwood
- Institute of Ophthalmology, University College London, London, UK
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11
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Ashman N, Bargh JD, Spring DR. Non-internalising antibody–drug conjugates. Chem Soc Rev 2022; 51:9182-9202. [DOI: 10.1039/d2cs00446a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This review introduces non-internalising Antibody–Drug Conjugates (ADCs), highlighting the linker chemistry that enables extracellular payload release.
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Affiliation(s)
- Nicola Ashman
- Yusuf Hamied Department of Chemistry University of Cambridge Lensfield Road, Cambridge, CB2 1EW, UK
| | - Jonathan D. Bargh
- Medicinal Chemistry, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - David R. Spring
- Yusuf Hamied Department of Chemistry University of Cambridge Lensfield Road, Cambridge, CB2 1EW, UK
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12
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Shao D, Huang L, Wang Y, Cui X, Li Y, Wang Y, Ma Q, Du W, Cui J. HBFP: a new repository for human body fluid proteome. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6395039. [PMID: 34642750 PMCID: PMC8516408 DOI: 10.1093/database/baab065] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 09/23/2021] [Accepted: 09/28/2021] [Indexed: 12/15/2022]
Abstract
Body fluid proteome has been intensively studied as a primary source for disease
biomarker discovery. Using advanced proteomics technologies, early research
success has resulted in increasingly accumulated proteins detected in different
body fluids, among which many are promising biomarkers. However, despite a
handful of small-scale and specific data resources, current research is clearly
lacking effort compiling published body fluid proteins into a centralized and
sustainable repository that can provide users with systematic analytic tools. In
this study, we developed a new database of human body fluid proteome (HBFP) that
focuses on experimentally validated proteome in 17 types of human body fluids.
The current database archives 11 827 unique proteins reported by 164
scientific publications, with a maximal false discovery rate of 0.01 on both the
peptide and protein levels since 2001, and enables users to query, analyze and
download protein entries with respect to each body fluid. Three unique features
of this new system include the following: (i) the protein annotation page
includes detailed abundance information based on relative qualitative measures
of peptides reported in the original references, (ii) a new score is calculated
on each reported protein to indicate the discovery confidence and (iii) HBFP
catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of
nine amino acids according to the Human Proteome Project Data Interpretation
Guidelines, while the remaining 4473 proteins have more than two unique peptides
without given sequence information. As an important resource for human protein
secretome, we anticipate that this new HBFP database can be a powerful tool that
facilitates research in clinical proteomics and biomarker discovery. Database URL:https://bmbl.bmi.osumc.edu/HBFP/
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Affiliation(s)
- Dan Shao
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, 122E Avery Hall, 1144 T St., Lincoln, NE 68588, USA.,Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun 130012, China.,Department of Computer Science and Technology, Changchun University, 6543 Weixing Road, Changchun 130022, China
| | - Lan Huang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Yan Wang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Xueteng Cui
- Department of Computer Science and Technology, Changchun University, 6543 Weixing Road, Changchun 130022, China
| | - Yufei Li
- Department of Computer Science and Technology, Changchun University, 6543 Weixing Road, Changchun 130022, China
| | - Yao Wang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 310G Lincoln tower, 1800 cannon drive, Columbus, OH 43210, USA
| | - Wei Du
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Juan Cui
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, 122E Avery Hall, 1144 T St., Lincoln, NE 68588, USA
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13
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Javaid F, Pilotti C, Camilli C, Kallenberg D, Bahou C, Blackburn J, R Baker J, Greenwood J, Moss SE, Chudasama V. Leucine-rich alpha-2-glycoprotein 1 (LRG1) as a novel ADC target. RSC Chem Biol 2021; 2:1206-1220. [PMID: 34458833 PMCID: PMC8341842 DOI: 10.1039/d1cb00104c] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 05/27/2021] [Indexed: 12/20/2022] Open
Abstract
Leucine-rich alpha-2-glycoprotein 1 (LRG1) is present abundantly in the microenvironment of many tumours where it contributes to vascular dysfunction, which impedes the delivery of therapeutics. In this work we demonstrate that LRG1 is predominantly a non-internalising protein. We report the development of a novel antibody-drug conjugate (ADC) comprising the anti-LRG1 hinge-stabilised IgG4 monoclonal antibody Magacizumab coupled to the anti-mitotic payload monomethyl auristatin E (MMAE) via a cleavable dipeptide linker using the site-selective disulfide rebridging dibromopyridazinedione (diBrPD) scaffold. It is demonstrated that this ADC retains binding post-modification, is stable in serum and effective in in vitro cell studies. We show that the extracellular LRG1-targeting ADC provides an increase in survival in vivo when compared against antibody alone and similar anti-tumour activity when compared against standard chemotherapy, but without undesired side-effects. LRG1 targeting through this ADC presents a novel and effective proof-of-concept en route to improving the efficacy of cancer therapeutics.
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Affiliation(s)
- Faiza Javaid
- UCL Department of Chemistry 20 Gordon Street London WC1H 0AJ UK
- UCL Institute of Ophthalmology 11-43 Bath Street London EC1V 9EL UK
| | - Camilla Pilotti
- UCL Institute of Ophthalmology 11-43 Bath Street London EC1V 9EL UK
| | - Carlotta Camilli
- UCL Institute of Ophthalmology 11-43 Bath Street London EC1V 9EL UK
| | - David Kallenberg
- UCL Institute of Ophthalmology 11-43 Bath Street London EC1V 9EL UK
| | - Calise Bahou
- UCL Department of Chemistry 20 Gordon Street London WC1H 0AJ UK
| | - Jack Blackburn
- UCL Institute of Ophthalmology 11-43 Bath Street London EC1V 9EL UK
| | - James R Baker
- UCL Department of Chemistry 20 Gordon Street London WC1H 0AJ UK
| | - John Greenwood
- UCL Institute of Ophthalmology 11-43 Bath Street London EC1V 9EL UK
| | - Stephen E Moss
- UCL Institute of Ophthalmology 11-43 Bath Street London EC1V 9EL UK
| | - Vijay Chudasama
- UCL Department of Chemistry 20 Gordon Street London WC1H 0AJ UK
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Abstract
Secretory proteins in tumor tissues are important components of the tumor microenvironment. Secretory proteins act on tumor cells or stromal cells or mediate interactions between tumor cells and stromal cells, thereby affecting tumor progression and clinical treatment efficacy. In this paper, recent research advances in secretory proteins in malignant tumors are reviewed.
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Affiliation(s)
- Na Zhang
- State Key Laboratory of Molecular Oncology, Center for Cancer Precision Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jiajie Hao
- State Key Laboratory of Molecular Oncology, Center for Cancer Precision Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yan Cai
- State Key Laboratory of Molecular Oncology, Center for Cancer Precision Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Mingrong Wang
- State Key Laboratory of Molecular Oncology, Center for Cancer Precision Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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15
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Gallardo-Gómez M, De Chiara L, Álvarez-Chaver P, Cubiella J. Colorectal cancer screening and diagnosis: omics-based technologies for development of a non-invasive blood-based method. Expert Rev Anticancer Ther 2021; 21:723-738. [PMID: 33507120 DOI: 10.1080/14737140.2021.1882858] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Introduction: Colorectal cancer (CRC) is one of the most important health problems in the Western world. In order to reduce the burden of the disease, two strategies are proposed: screening and prompt detection in symptomatic patients. Although diagnosis and prevention are mainly based on colonoscopy, fecal hemoglobin detection has been widely implemented as a noninvasive strategy. Various studies aiming to discover blood-based biomarkers have recently emerged.Areas covered: The burgeoning omics field provides diverse high-throughput approaches for CRC blood-based biomarker discovery. In this review, we appraise the most robust and commonly used technologies within the fields of genomics, transcriptomics, epigenomics, proteomics, and metabolomics, together with their targeted validation approaches. We summarize the transference process from the discovery phase until clinical translation. Finally, we review the best candidate biomarkers and their potential clinical applicability.Expert opinion: Some available biomarkers are promising, especially in the field of epigenomics: DNA methylation and microRNA. Transference requires the joint collaboration of basic researchers, intellectual property experts, technology transfer officers and clinicians. Blood-based biomarkers will be selected not only based on their diagnostic accuracy and cost but also on their reliability, applicability to clinical analysis laboratories and their acceptance by the population.
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Affiliation(s)
- María Gallardo-Gómez
- Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.,Biomedical Research Center (CINBIO), University of Vigo, Vigo, Spain
| | - Loretta De Chiara
- Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.,Biomedical Research Center (CINBIO), University of Vigo, Vigo, Spain
| | - Paula Álvarez-Chaver
- Proteomics Unit, Service of Structural Determination, Proteomics and Genomics, Center for Scientific and Technological Research Support (CACTI), University of Vigo, Vigo, Spain
| | - Joaquin Cubiella
- Department of Gastroenterology, Hospital Universitario De Ourense, Ourense, Spain.,Instituto De Investigación Sanitaria Galicia Sur, Ourense, Spain.,Centro De Investigación Biomédica En Red Enfermedades Hepáticas Y Digestivas, Ourense, Spain
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16
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Huang L, Shao D, Wang Y, Cui X, Li Y, Chen Q, Cui J. Human body-fluid proteome: quantitative profiling and computational prediction. Brief Bioinform 2021; 22:315-333. [PMID: 32020158 PMCID: PMC7820883 DOI: 10.1093/bib/bbz160] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/22/2019] [Accepted: 10/18/2019] [Indexed: 12/15/2022] Open
Abstract
Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.
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Affiliation(s)
- Lan Huang
- College of Computer Science and Technology in the Jilin University
| | - Dan Shao
- College of Computer Science and Technology in the Jilin University
- College of Computer Science and Technology in Changchun University
| | - Yan Wang
- College of Computer Science and Technology in the Jilin University
| | - Xueteng Cui
- College of Computer Science and Technology in the Changchun University
| | - Yufei Li
- College of Computer Science and Technology in the Changchun University
| | - Qian Chen
- College of Computer Science and Technology in the Jilin University
| | - Juan Cui
- Department of Computer Science and Engineering in the University of Nebraska-Lincoln
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17
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Chen X, Sun J, Wang X, Yuan Y, Cai L, Xie Y, Fan Z, Liu K, Jiao X. A Meta-Analysis of Proteomic Blood Markers of Colorectal Cancer. Curr Med Chem 2021; 28:1176-1196. [PMID: 32338203 DOI: 10.2174/0929867327666200427094054] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 02/23/2020] [Accepted: 03/24/2020] [Indexed: 02/05/2023]
Abstract
BACKGROUND Early diagnosis will significantly improve the survival rate of colorectal cancer (CRC); however, the existing methods for CRC screening were either invasive or inefficient. There is an emergency need for novel markers in CRC's early diagnosis. Serum proteomics has gained great potential in discovering novel markers, providing markers that reflect the early stage of cancer and prognosis prediction of CRC. In this paper, the results of proteomics of CRC studies were summarized through a meta-analysis in order to obtain the diagnostic efficiency of novel markers. METHODS A systematic search on bibliographic databases was performed to collect the studies that explore blood-based markers for CRC applying proteomics. The detection and validation methods, as well as the specificity and sensitivity of the biomarkers in these studies, were evaluated. Newcastle- Ottawa Scale (NOS) case-control studies version was used for quality assessment of included studies. RESULTS Thirty-four studies were selected from 751 studies, in which markers detected by proteomics were summarized. In total, fifty-nine proteins were classified according to their biological function. The sensitivity, specificity, or AUC varied among these markers. Among them, Mammalian STE20-like protein kinase 1/ Serine threonine kinase 4 (MST1/STK4), S100 calcium-binding protein A9 (S100A9), and Tissue inhibitor of metalloproteinases 1 (TIMP1) were suitable for effect sizes merging, and their diagnostic efficiencies were recalculated after merging. MST1/STK4 obtained a sensitivity of 68% and a specificity of 78%. S100A9 achieved a sensitivity of 72%, a specificity of 83%, and an AUC of 0.88. TIMP1 obtained a sensitivity of 42%, a specificity of 88%, and an AUC of 0.71. CONCLUSION MST1/STK4, S100A9, and TIMP1 showed excellent performance for CRC detection. Several other markers also presented optimized diagnostic efficacy for CRC early detection, but further verification is still needed before they are suitable for clinical use. The discovering of more efficient markers will benefit CRC treatment.
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Affiliation(s)
- Xiang Chen
- The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Jiayu Sun
- Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Xue Wang
- Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Yumeng Yuan
- Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Leshan Cai
- The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Yanxuan Xie
- The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Zhiqiang Fan
- Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Kaixi Liu
- Shantou Central Hospital, Shantou, Guangdong 515041, China
| | - Xiaoyang Jiao
- Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China
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18
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Orwoll ES, Wiedrick J, Nielson CM, Jacobs J, Baker ES, Piehowski P, Petyuk V, Gao Y, Shi T, Smith RD, Bauer DC, Cummings SR, Lapidus J. Proteomic assessment of serum biomarkers of longevity in older men. Aging Cell 2020; 19:e13253. [PMID: 33078901 PMCID: PMC7681066 DOI: 10.1111/acel.13253] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 06/30/2020] [Accepted: 08/30/2020] [Indexed: 12/28/2022] Open
Abstract
The biological bases of longevity are not well understood, and there are limited biomarkers for the prediction of long life. We used a high-throughput, discovery-based proteomics approach to identify serum peptides and proteins that were associated with the attainment of longevity in a longitudinal study of community-dwelling men age ≥65 years. Baseline serum in 1196 men were analyzed using liquid chromatography-ion mobility-mass spectrometry, and lifespan was determined during ~12 years of follow-up. Men who achieved longevity (≥90% expected survival) were compared to those who died earlier. Rigorous statistical methods that controlled for false positivity were utilized to identify 25 proteins that were associated with longevity. All these proteins were in lower abundance in long-lived men and included a variety involved in inflammation or complement activation. Lower levels of longevity-associated proteins were also associated with better health status, but as time to death shortened, levels of these proteins increased. Pathway analyses implicated a number of compounds as important upstream regulators of the proteins and implicated shared networks that underlie the observed associations with longevity. Overall, these results suggest that complex pathways, prominently including inflammation, are linked to the likelihood of attaining longevity. This work may serve to identify novel biomarkers for longevity and to understand the biology underlying lifespan.
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Affiliation(s)
| | | | | | - Jon Jacobs
- Biological Science Division Pacific Northwest National Laboratory Richland WA USA
| | - Erin S. Baker
- Department of Chemistry North Carolina State University Raleigh NC USA
| | - Paul Piehowski
- Biological Science Division Pacific Northwest National Laboratory Richland WA USA
| | - Vladislav Petyuk
- Biological Science Division Pacific Northwest National Laboratory Richland WA USA
| | - Yuqian Gao
- Biological Science Division Pacific Northwest National Laboratory Richland WA USA
| | - Tujin Shi
- Biological Science Division Pacific Northwest National Laboratory Richland WA USA
| | - Richard D. Smith
- Biological Science Division Pacific Northwest National Laboratory Richland WA USA
| | - Douglas C. Bauer
- Departments of Medicine and Epidemiology & Biostatistics University of California San Francisco CA USA
| | - Steven R. Cummings
- California Pacific Medical Center Research Institute San Francisco CA USA
| | - Jodi Lapidus
- Oregon Health & Science University Portland OR USA
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19
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Binetti M, Lauro A, Vaccari S, Cervellera M, Tonini V. Proteogenomic biomarkers in colorectal cancers: clinical applications. Expert Rev Proteomics 2020; 17:355-363. [PMID: 32536221 DOI: 10.1080/14789450.2020.1782202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
INTRODUCTION Colorectal cancer (CRC) is one of the leading cancers in terms of incidence and mortality, rate requiring a multidisciplinary approach. The discovery of specific CRC biomarkers has caused a paradigm shift in its clinical management. AREAS COVERED The aim is to illustrate the possible clinical applications of CRC biomarkers through an updated literature review (from 2015 to 2020) based on the PubMed database. A relationship between cancer localization and genetic profile has been identified. Nowadays, the tumor markers are largely used to select patients that could really benefit from a specific type of adjuvant therapy, in order to optimize treatment programs, especially in metastatic patients. This review highlights both CRC biomarkers' advantages and critical issues. EXPERT OPINION New biomarker discoveries allow to set noninvasive tests that could increase patient's compliance with therapy. They also permit a cost-effective early diagnosis, as well as patient-tailored treatments, improving the overall survival. The CRC biomarkers could also have a prognostic value, and usually, they are included in follow-up programs. However, despite the continuous progression of new technologies, their clinical validation is still debated. In this context, additional clinical studies are still necessary to identify, among potential markers, the most effective ones.
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Affiliation(s)
| | - Augusto Lauro
- Emergency Surgery Unit, St. Orsola University Hospital , Bologna, Italy
| | - Samuele Vaccari
- Department of Surgical Sciences, Umberto I University Hospital , Rome, Italy
| | | | - Valeria Tonini
- Emergency Surgery Unit, St. Orsola University Hospital , Bologna, Italy
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20
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Barat A, Smeets D, Moran B, Zhang W, Cao S, Das S, Klinger R, Betge J, Murphy V, Bacon O, Kay EW, Van Grieken NCT, Verheul HMW, Gaiser T, Schulte N, Ebert MP, Fender B, Hennessy BT, McNamara DA, O'Connor D, Gallagher WM, Cremolini C, Loupakis F, Parikh A, Mancao C, Ylstra B, Lambrechts D, Lenz HJ, Byrne AT, Prehn JHM. Combination of variations in inflammation- and endoplasmic reticulum-associated genes as putative biomarker for bevacizumab response in KRAS wild-type colorectal cancer. Sci Rep 2020; 10:9778. [PMID: 32555399 PMCID: PMC7299973 DOI: 10.1038/s41598-020-65869-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 05/05/2020] [Indexed: 12/30/2022] Open
Abstract
Chemotherapy combined with the angiogenesis inhibitor bevacizumab (BVZ) is approved as a first-line treatment in metastatic colorectal cancer (mCRC). Limited clinical benefit underpins the need for improved understanding of resistance mechanisms and the elucidation of novel predictive biomarkers. We assessed germline single-nucleotide polymorphisms (SNPs) in 180 mCRC patients (Angiopredict [APD] cohort) treated with combined BVZ + chemotherapy and investigated previously reported predictive SNPs. We further employed a machine learning approach to identify novel associations. In the APD cohort IL8 rs4073 any A carriers, compared to TT carriers, were associated with worse progression-free survival (PFS) (HR = 1.51, 95% CI:1.03-2.22, p-value = 0.037) and TBK1 rs7486100 TT carriers, compared to any A carriers, were associated with worse PFS in KRAS wild-type (wt) patients (HR = 1.94, 95% CI:1.04-3.61, p-value = 0.037), replicating previous findings. Machine learning identified novel associations in genes encoding the inflammasome protein NLRP1 and the ER protein Sarcalumenin (SRL). A negative association between PFS and carriers of any A at NLRP1 rs12150220 and AA for SRL rs13334970 in APD KRAS wild-type patients (HR = 4.44, 95% CI:1.23-16.13, p-value = 0.005), which validated in two independent clinical cohorts involving BVZ, MAVERICC and TRIBE. Our findings highlight a key role for inflammation and ER signalling underpinning BVZ + chemotherapy responsiveness.
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Affiliation(s)
- Ana Barat
- Centre for Systems Medicine and Department of Physiology & Medical Physics, Royal College of Surgeons in Ireland, Dublin, Ireland.
| | | | - Bruce Moran
- UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Wu Zhang
- USC Norris Comprehensive Cancer Center, Los Angeles, USA
| | - Shu Cao
- USC Norris Comprehensive Cancer Center, Los Angeles, USA
| | - Sudipto Das
- Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Rut Klinger
- UCD, School of Biomolecular and Biomedical Science, Dublin, Ireland
| | - Johannes Betge
- Department of Medicine II, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, Heidelberg, Germany
| | | | - Orna Bacon
- Centre for Systems Medicine and Department of Physiology & Medical Physics, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Elaine W Kay
- Department of Pathology, Beaumont Hospital, Dublin, Ireland
| | | | - Henk M W Verheul
- Department of Medical Oncology, VU University Medical Center, Amsterdam, The Netherlands
| | - Timo Gaiser
- Institute of Pathology, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Nadine Schulte
- Department of Medicine II, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Matthias P Ebert
- Department of Medicine II, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Bozena Fender
- OncoMark Ltd., NovaUCD, Belfield Innovation Park, Dublin, Ireland
| | - Bryan T Hennessy
- Department of Medical Oncology, Beaumont Hospital, Dublin, Ireland
| | | | - Darran O'Connor
- Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland
| | | | - Chiara Cremolini
- Unit of Medical Oncology 2, Department of Translational Research and New Technologies in Medicine and Surgery, Azienda Ospedaliera Universitaria Pisana, Pisa, Italy
| | - Fotios Loupakis
- Oncology Unit, Istituto Oncologico Veneto, IOV-IRCCS, Padua, Italy
| | - Aparna Parikh
- Division of Hematology and Oncology, Massachusetts General Hospital, Boston, USA
| | - Christoph Mancao
- Oncology Biomarker Development, Genentech Inc., San Francisco, USA
| | - Bauke Ylstra
- Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands
| | | | | | - Annette T Byrne
- Centre for Systems Medicine and Department of Physiology & Medical Physics, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Jochen H M Prehn
- Centre for Systems Medicine and Department of Physiology & Medical Physics, Royal College of Surgeons in Ireland, Dublin, Ireland.
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21
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Cha BS, Park KS, Park JS. Signature mRNA markers in extracellular vesicles for the accurate diagnosis of colorectal cancer. J Biol Eng 2020; 14:4. [PMID: 32042310 PMCID: PMC7001337 DOI: 10.1186/s13036-020-0225-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 01/21/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND With the increasing incidence of colorectal cancer (CRC), its accurate diagnosis is critical and in high demand. However, conventional methods are not ideal due to invasiveness and low accuracy. Herein, we aimed to identify efficient CRC mRNA markers in a non-invasive manner using CRC-derived extracellular vesicles (EVs). The expression levels of EV mRNAs from cancer cell lines were compared with those of a normal cell line using quantitative polymerase chain reaction. Eight markers were evaluated in plasma EVs from CRC patients and healthy controls. The diagnostic value of each marker, individually or in combination, was then determined using recessive operating characteristics analyses and the Mann-Whitney U test. RESULTS Eight mRNA markers (MYC, VEGF, CDX2, CD133, CEA, CK19, EpCAM, and CD24) were found to be more abundant in EVs derived from cancer cell lines compared to control cell lines. A combination of VEGF and CD133 showed the highest sensitivity (100%), specificity (80%), and accuracy (93%) and an area under the curve of 0.96; hence, these markers were deemed to be the CRC signature. Moreover, this signature was found to be highly expressed in CRC-derived EVs compared to healthy controls. CONCLUSIONS VEGF and CD133 mRNAs comprise a unique CRC signature in EVs that has the potential to act as a novel, non-invasive, and accurate biomarker that would improve the current diagnostic platform for CRC, while also serving to strengthen the value of EV mRNA as diagnostic markers for myriad of diseases.
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Affiliation(s)
- Byung Seok Cha
- Department of Biological Engineering, College of Engineering, Konkuk University, Seoul, Republic of Korea
| | - Ki Soo Park
- Department of Biological Engineering, College of Engineering, Konkuk University, Seoul, Republic of Korea
| | - Jun Seok Park
- School of Medicine, Kyungpook National University, Daegu, Republic of Korea
- Colorectal Cancer Center, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
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22
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Affiliation(s)
| | - Javid Moslehi
- Division of Cardiovascular MedicineClinical PharmacologyCardio‐Oncology ProgramVanderbilt University Medical Center and Vanderbilt‐Ingram Cancer CenterNashvilleTN
- Division of OncologyVanderbilt University Medical Center and Vanderbilt‐Ingram Cancer CenterNashvilleTN
| | - Rudolf A. de Boer
- Department of CardiologyUniversity Medical Center GroningenUniversity of Groningenthe Netherlands
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Bhardwaj M, Weigl K, Tikk K, Holland-Letz T, Schrotz-King P, Borchers CH, Brenner H. Multiplex quantitation of 270 plasma protein markers to identify a signature for early detection of colorectal cancer. Eur J Cancer 2020; 127:30-40. [PMID: 31972396 DOI: 10.1016/j.ejca.2019.11.021] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 11/19/2019] [Accepted: 11/25/2019] [Indexed: 02/07/2023]
Abstract
Blood-based protein biomarker signatures might be an alternative or supplement to existing methods for early detection of colorectal cancer (CRC) for population-based screening. The objective of this study was to derive a protein biomarker signature for early detection of CRC and its precursor advanced adenoma (AA). In a two-stage design, 270 protein markers were measured by liquid chromatography/multiple reaction monitoring/mass spectrometry in plasma samples of discovery and validation sets. In the discovery set consisting of 100 newly diagnosed CRC cases and 100 age- and sex-matched controls free of neoplasm at screening colonoscopy, the algorithms predicting the presence of early- or late-stage CRC were derived by Lasso regression and .632 + bootstrap. The prediction algorithms were then externally validated in an independent validation set consisting of participants of screening colonoscopy including 56 participants with CRC, 99 with AA and 99 controls without any colorectal neoplasms. Three different signatures for all-, early- and late-stage CRC consisting of five-, three- and eight-protein markers were obtained in the discovery set with areas under the curves (AUCs) after .632 + bootstrap adjustment of 0.85, 0.83 and 0.96, respectively. External validation in the representative screening population yielded AUCs of 0.79 (95% CI, 0.70-0.86), 0.79 (95% CI, 0.66-0.89) and 0.80 (95% CI, 0.70-0.89) for all-, early- and late-stage CRCs, respectively. The three-marker early-stage algorithm yielded an AUC of 0.65 (95% CI, 0.56-0.73) for detection of AA in the validation set. Although not yet competitive with available stool-based tests for CRC early detection, the identified proteins may contribute to the development of powerful blood-based tests for early detection of CRC and its precursors AAs.
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Affiliation(s)
- Megha Bhardwaj
- Division of Preventive Oncology, German Cancer Research Center (DKFZ), National Center for Tumour Diseases (NCT), Heidelberg, Germany; Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, Germany
| | - Korbinian Weigl
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kaja Tikk
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tim Holland-Letz
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Petra Schrotz-King
- Division of Preventive Oncology, German Cancer Research Center (DKFZ), National Center for Tumour Diseases (NCT), Heidelberg, Germany
| | - Christoph H Borchers
- University of Victoria - Genome British Columbia Proteomics Centre, University of Victoria (UVic), Victoria, British Columbia, V8Z 7X8, Canada; Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, V8P 5C2, Canada; Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, H3T 1E2, Canada; Gerald Bronfman Department of Oncology, Jewish General Hospital, McGill University, Montreal, Quebec, H3T 1E2, Canada
| | - Hermann Brenner
- Division of Preventive Oncology, German Cancer Research Center (DKFZ), National Center for Tumour Diseases (NCT), Heidelberg, Germany; Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
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24
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Bhardwaj M, Weigl K, Tikk K, Benner A, Schrotz-King P, Brenner H. Multiplex screening of 275 plasma protein biomarkers to identify a signature for early detection of colorectal cancer. Mol Oncol 2019; 14:8-21. [PMID: 31652396 PMCID: PMC6944100 DOI: 10.1002/1878-0261.12591] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 10/09/2019] [Accepted: 10/24/2019] [Indexed: 02/06/2023] Open
Abstract
Blood-based protein biomarkers may be an attractive option for early detection of colorectal cancer (CRC). Here, we used a two-stage design to measure 275 protein markers by proximity extension assay (PEA), first in plasma samples of a discovery set consisting of 98 newly diagnosed CRC cases and 100 age- and gender-matched controls free of neoplasm at screening colonoscopy. An algorithm predicting the presence of early- or late-stage CRC was derived by least absolute shrinkage and selection operator regression with .632+ bootstrap method, and the algorithms were then validated using PEA again in an independent validation set consisting of participants of screening colonoscopy with and without CRC (n = 56 and 102, respectively). Three different signatures for all-, early-, and late-stage CRC consisting of 9, 12, and 11 protein markers were obtained in the discovery set with areas under the curves (AUCs) after .632 + bootstrap adjustment of 0.92, 0.91, and 0.96, respectively. External validation among participants of screening colonoscopy yielded AUCs of 0.76 [95% confidence interval (95% CI), 0.67-0.84], 0.75 (95% CI, 0.62-0.87), and 0.80 (95% CI, 0.68-0.89) for all-, early-, and late-stage CRC, respectively. Although the identified protein markers are not competitive with the best available stool tests, these proteins may contribute to the development of powerful blood-based tests for CRC early detection in the future.
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Affiliation(s)
- Megha Bhardwaj
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany.,Medical Faculty Heidelberg, University of Heidelberg, Germany
| | - Korbinian Weigl
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kaja Tikk
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Axel Benner
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Petra Schrotz-King
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Hermann Brenner
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany.,Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
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25
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Sajic T, Liu Y, Arvaniti E, Surinova S, Williams EG, Schiess R, Hüttenhain R, Sethi A, Pan S, Brentnall TA, Chen R, Blattmann P, Friedrich B, Niméus E, Malander S, Omlin A, Gillessen S, Claassen M, Aebersold R. Similarities and Differences of Blood N-Glycoproteins in Five Solid Carcinomas at Localized Clinical Stage Analyzed by SWATH-MS. Cell Rep 2019; 23:2819-2831.e5. [PMID: 29847809 DOI: 10.1016/j.celrep.2018.04.114] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Revised: 03/30/2018] [Accepted: 04/26/2018] [Indexed: 02/07/2023] Open
Abstract
Cancer is mostly incurable when diagnosed at a metastatic stage, making its early detection via blood proteins of immense clinical interest. Proteomic changes in tumor tissue may lead to changes detectable in the protein composition of circulating blood plasma. Using a proteomic workflow combining N-glycosite enrichment and SWATH mass spectrometry, we generate a data resource of 284 blood samples derived from patients with different types of localized-stage carcinomas and from matched controls. We observe whether the changes in the patient's plasma are specific to a particular carcinoma or represent a generic signature of proteins modified uniformly in a common, systemic response to many cancers. A quantitative comparison of the resulting N-glycosite profiles discovers that proteins related to blood platelets are common to several cancers (e.g., THBS1), whereas others are highly cancer-type specific. Available proteomics data, including a SWATH library to study N-glycoproteins, will facilitate follow-up biomarker research into early cancer detection.
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Affiliation(s)
- Tatjana Sajic
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland.
| | - Yansheng Liu
- Department of Pharmacology, Cancer Biology Institute, Yale University School of Medicine, West Haven, CT 06516, USA
| | - Eirini Arvaniti
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland; PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | - Evan G Williams
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | | | - Ruth Hüttenhain
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Atul Sethi
- Department of Biomedicine, University of Basel/University Hospital Basel, and Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Sheng Pan
- The Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, 1825 Pressler, Houston, TX 77030, USA
| | - Teresa A Brentnall
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Ru Chen
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Peter Blattmann
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Betty Friedrich
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland; PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Emma Niméus
- Department of Clinical Sciences Lund, Surgery, Oncology and Pathology, Lund University, and Skåne University Hospital, Department of Surgery, Lund, Sweden
| | - Susanne Malander
- Department of Clinical Sciences Lund, Oncology and Pathology, Lund University, and Skåne University Hospital, Department of Oncology, Lund, Sweden
| | - Aurelius Omlin
- Department of Oncology and Hematology, Kantonsspital St. Gallen, St. Gallen, Switzerland; Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Silke Gillessen
- Department of Oncology and Hematology, Kantonsspital St. Gallen, St. Gallen, Switzerland; Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Manfred Claassen
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland; Faculty of Science, University of Zurich, 8057 Zurich, Switzerland.
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Bhardwaj M, Gies A, Weigl K, Tikk K, Benner A, Schrotz-King P, Borchers CH, Brenner H. Evaluation and Validation of Plasma Proteins Using Two Different Protein Detection Methods for Early Detection of Colorectal Cancer. Cancers (Basel) 2019; 11:cancers11101426. [PMID: 31557860 PMCID: PMC6826652 DOI: 10.3390/cancers11101426] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 09/11/2019] [Accepted: 09/17/2019] [Indexed: 12/24/2022] Open
Abstract
Objective: Plasma protein biomarkers could be an efficient alternative for population-based screening for early detection of colorectal cancer (CRC). The objective of this study was to evaluate and validate plasma proteins individually and as a signature for early detection of CRC. Methods: In a three-stage design, proteins were measured firstly by liquid chromatography/multiple reaction monitoring-mass spectrometry (LC/MRM-MS) and later by proximity extension assay (PEA) in a discovery set consisting of 96 newly diagnosed CRC cases and 94 controls free of neoplasms at screening colonoscopy. Two algorithms (one for each measurement method) were derived by Lasso regression and .632+ bootstrap based on 11 proteins that were included in both the LC/MRM-MS and PEA measurements. Additionally, another algorithm was constructed from the same eleven biomarkers plus amphireglin, the most promising protein marker in the PEA measurements that had not been available from the LC/MRM-MS measurements. Lastly the three prediction signatures were validated with PEA in independent samples of participants of screening colonoscopy (CRC (n = 56), advanced adenoma (n = 101), and participants free of neoplasm (n = 102)). Results: The same four proteins were included in all three prediction signatures; mannan binding lectin serine protease 1, osteopontin, serum paraoxonase lactonase 3 and transferrin receptor protein 1, and the third prediction signature additionally included amphiregulin. In the independent validation set from a true screening setting, the five-marker blood-based signature including AREG presented areas under the curves of 0.82 (95% CI, 0.74–0.89), 0.86 (95% CI, 0.77–0.92) and 0.76 (95% CI, 0.64–0.86) for all, early and late stages CRC, respectively. Conclusion: Two different measurement methods consistently identified four protein markers and an algorithm additionally including amphiregulin, a marker measured by PEA only, showed promising performance for detecting early stage CRC in an independent validation in a true screening setting. These proteins may be potential candidates for blood-based tests for early detection of CRC.
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Affiliation(s)
- Megha Bhardwaj
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany.
- Medical Faculty Heidelberg, University of Heidelberg, 69120 Heidelberg, Germany.
| | - Anton Gies
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany.
- Medical Faculty Heidelberg, University of Heidelberg, 69120 Heidelberg, Germany.
| | - Korbinian Weigl
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
| | - Kaja Tikk
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
| | - Axel Benner
- Division of Biostatistics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
| | - Petra Schrotz-King
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany.
| | - Christoph H Borchers
- University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria (UVic), Victoria, BC V8Z 7X8, Canada.
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC V8P 5C2, Canada.
- Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada.
- Gerald Bronfman Department of Oncology, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada.
| | - Hermann Brenner
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany.
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
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27
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Marín‐Vicente C, Mendes M, los Ríos V, Fernández‐Aceñero MJ, Casal JI. Identification and Validation of Stage‐Associated Serum Biomarkers in Colorectal Cancer Using MS‐Based Procedures. Proteomics Clin Appl 2019; 14:e1900052. [DOI: 10.1002/prca.201900052] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 09/03/2019] [Indexed: 12/17/2022]
Affiliation(s)
- Consuelo Marín‐Vicente
- Department of Molecular BiomedicineCentro de Investigaciones Biológicas (CIB‐CSIC) Madrid Spain
- Proteomics facilityCentro de Investigaciones Biológicas (CIB‐CSIC) Madrid Spain
| | - Marta Mendes
- Department of Molecular BiomedicineCentro de Investigaciones Biológicas (CIB‐CSIC) Madrid Spain
| | - Vivian los Ríos
- Proteomics facilityCentro de Investigaciones Biológicas (CIB‐CSIC) Madrid Spain
| | | | - J. Ignacio Casal
- Department of Molecular BiomedicineCentro de Investigaciones Biológicas (CIB‐CSIC) Madrid Spain
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28
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Meijers WC, Maglione M, Bakker SJL, Oberhuber R, Kieneker LM, de Jong S, Haubner BJ, Nagengast WB, Lyon AR, van der Vegt B, van Veldhuisen DJ, Westenbrink BD, van der Meer P, Silljé HHW, de Boer RA. Heart Failure Stimulates Tumor Growth by Circulating Factors. Circulation 2019; 138:678-691. [PMID: 29459363 DOI: 10.1161/circulationaha.117.030816] [Citation(s) in RCA: 222] [Impact Index Per Article: 44.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Heart failure (HF) survival has improved, and nowadays, many patients with HF die of noncardiac causes, including cancer. Our aim was to investigate whether a causal relationship exists between HF and the development of cancer. METHODS HF was induced by inflicting large anterior myocardial infarction in APCmin mice, which are prone to developing precancerous intestinal tumors, and tumor growth was measured. In addition, to rule out hemodynamic impairment, a heterotopic heart transplantation model was used in which an infarcted or sham-operated heart was transplanted into a recipient mouse while the native heart was left in situ. After 6 weeks, tumor number, volume, and proliferation were quantified. Candidate secreted proteins were selected because they were previously associated both with (colon) tumor growth and with myocardial production in post-myocardial infarction proteomic studies. Myocardial gene expression levels of these selected candidates were analyzed, as well as their proliferative effects on HT-29 (colon cancer) cells. We validated these candidates by measuring them in plasma of healthy subjects and patients with HF. Finally, we associated the relation between cardiac specific and inflammatory biomarkers and new-onset cancer in a large, prospective general population cohort. RESULTS The presence of failing hearts, both native and heterotopically transplanted, resulted in significantly increased intestinal tumor load of 2.4-fold in APCmin mice (all P<0.0001). The severity of left ventricular dysfunction and fibrotic scar strongly correlated with tumor growth ( P=0.002 and P=0.016, respectively). We identified several proteins (including serpinA3 and A1, fibronectin, ceruloplasmin, and paraoxonase 1) that were elevated in human patients with chronic HF (n=101) compared with healthy subjects (n=180; P<0.001). Functionally, serpinA3 resulted in marked proliferation effects in human colon cancer (HT-29) cells, associated with Akt-S6 phosphorylation. Finally, elevated cardiac and inflammation biomarkers in apparently healthy humans (n=8319) were predictive of new-onset cancer (n=1124) independently of risk factors for cancer (age, smoking status, and body mass index). CONCLUSIONS We demonstrate that the presence of HF is associated with enhanced tumor growth and that this is independent of hemodynamic impairment and could be caused by cardiac excreted factors. A diagnosis of HF may therefore be considered a risk factor for incident cancer.
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Affiliation(s)
- Wouter C Meijers
- Department of Cardiology (W.C.M., D.J.v.V., B.D.W., P.v.d.M., H.H.W.S., R.A.d.B.)
| | - Manuel Maglione
- Centre of Operative Medicine, Department of Visceral, Transplant and Thoracic Surgery (M.M., R.O.)
| | - Stephan J L Bakker
- Department of Internal Medicine, Division of Nephrology (S.J.L.B., L.M.K.), University Medical Center Groningen, University of Groningen, The Netherlands
| | - Rupert Oberhuber
- Centre of Operative Medicine, Department of Visceral, Transplant and Thoracic Surgery (M.M., R.O.)
| | - Lyanne M Kieneker
- Department of Internal Medicine, Division of Nephrology (S.J.L.B., L.M.K.), University Medical Center Groningen, University of Groningen, The Netherlands
| | | | - Bernhard J Haubner
- Department of Internal Medicine III (Cardiology and Angiology) (B.J.H.), Medical University of Innsbruck, Austria
| | | | - Alexander R Lyon
- National Heart and Lung Institute, Imperial College London and Royal Brompton Hospital, United Kingdom (A.R.L.)
| | | | | | - B Daan Westenbrink
- Department of Cardiology (W.C.M., D.J.v.V., B.D.W., P.v.d.M., H.H.W.S., R.A.d.B.)
| | - Peter van der Meer
- Department of Cardiology (W.C.M., D.J.v.V., B.D.W., P.v.d.M., H.H.W.S., R.A.d.B.)
| | - Herman H W Silljé
- Department of Cardiology (W.C.M., D.J.v.V., B.D.W., P.v.d.M., H.H.W.S., R.A.d.B.)
| | - Rudolf A de Boer
- Department of Cardiology (W.C.M., D.J.v.V., B.D.W., P.v.d.M., H.H.W.S., R.A.d.B.)
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29
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Hüttenhain R, Choi M, Martin de la Fuente L, Oehl K, Chang CY, Zimmermann AK, Malander S, Olsson H, Surinova S, Clough T, Heinzelmann-Schwarz V, Wild PJ, Dinulescu DM, Niméus E, Vitek O, Aebersold R. A Targeted Mass Spectrometry Strategy for Developing Proteomic Biomarkers: A Case Study of Epithelial Ovarian Cancer. Mol Cell Proteomics 2019; 18:1836-1850. [PMID: 31289117 PMCID: PMC6731088 DOI: 10.1074/mcp.ra118.001221] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 05/07/2019] [Indexed: 12/11/2022] Open
Abstract
Protein biomarkers for epithelial ovarian cancer are critical for the early detection of the cancer to improve patient prognosis and for the clinical management of the disease to monitor treatment response and to detect recurrences. Unfortunately, the discovery of protein biomarkers is hampered by the limited availability of reliable and sensitive assays needed for the reproducible quantification of proteins in complex biological matrices such as blood plasma. In recent years, targeted mass spectrometry, exemplified by selected reaction monitoring (SRM) has emerged as a method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing plasma-based protein biomarkers for epithelial ovarian cancer and illustrate how the SRM platform, when combined with rigorous experimental design and statistical analysis, can result in detection of predictive analytes.Our biomarker development strategy first involved a discovery-driven proteomic effort to derive potential N-glycoprotein biomarker candidates for plasma-based detection of human ovarian cancer from a genetically engineered mouse model of endometrioid ovarian cancer, which accurately recapitulates the human disease. Next, 65 candidate markers selected from proteins of different abundance in the discovery dataset were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive a 5-protein signature for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion.
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Affiliation(s)
- Ruth Hüttenhain
- ‡Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland.
| | - Meena Choi
- §Khoury College of Computer Sciences, Northeastern University, Boston, MA
| | | | - Kathrin Oehl
- ‖Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Ching-Yun Chang
- **Department of Statistics, Purdue University, West Lafayette, IN
| | - Anne-Kathrin Zimmermann
- ‖Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Susanne Malander
- ¶Department of Surgery and Oncology, Clinical Sciences, Lund University, Lund, Sweden
| | - Håkan Olsson
- ¶Department of Surgery and Oncology, Clinical Sciences, Lund University, Lund, Sweden
| | - Silvia Surinova
- ‡Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Timothy Clough
- **Department of Statistics, Purdue University, West Lafayette, IN
| | - Viola Heinzelmann-Schwarz
- ‡‡Gynecological Cancer Center, University Hospital Basel, University of Basel, Basel, Switzerland; §§Ovarian Cancer Research, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Peter J Wild
- ¶¶Dr. Senckenberg Institute of Pathology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Daniela M Dinulescu
- ‖‖Department of Pathology, Division of Women's and Perinatal Pathology Brigham and Women's Hospital Harvard Medical School, Boston, MA
| | - Emma Niméus
- ¶Department of Surgery and Oncology, Clinical Sciences, Lund University, Lund, Sweden; ‡‡‡Department of Surgery, Skånes University hospital, Lund, Sweden
| | - Olga Vitek
- §Khoury College of Computer Sciences, Northeastern University, Boston, MA; **Department of Statistics, Purdue University, West Lafayette, IN
| | - Ruedi Aebersold
- ‡Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland; §§§Faculty of Science, University of Zurich, 8057 Zurich, Switzerland
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30
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Ahn SB, Sharma S, Mohamedali A, Mahboob S, Redmond WJ, Pascovici D, Wu JX, Zaw T, Adhikari S, Vaibhav V, Nice EC, Baker MS. Potential early clinical stage colorectal cancer diagnosis using a proteomics blood test panel. Clin Proteomics 2019; 16:34. [PMID: 31467500 PMCID: PMC6712843 DOI: 10.1186/s12014-019-9255-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 08/14/2019] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND One of the most significant challenges in colorectal cancer (CRC) management is the use of compliant early stage population-based diagnostic tests as adjuncts to confirmatory colonoscopy. Despite the near curative nature of early clinical stage surgical resection, mortality remains unacceptably high-as the majority of patients diagnosed by faecal haemoglobin followed by colonoscopy occur at latter stages. Additionally, current population-based screens reliant on fecal occult blood test (FOBT) have low compliance (~ 40%) and tests suffer low sensitivities. Therefore, blood-based diagnostic tests offer survival benefits from their higher compliance (≥ 97%), if they can at least match the sensitivity and specificity of FOBTs. However, discovery of low abundance plasma biomarkers is difficult due to occupancy of a high percentage of proteomic discovery space by many high abundance plasma proteins (e.g., human serum albumin). METHODS A combination of high abundance protein ultradepletion (e.g., MARS-14 and an in-house IgY depletion columns) strategies, extensive peptide fractionation methods (SCX, SAX, High pH and SEC) and SWATH-MS were utilized to uncover protein biomarkers from a cohort of 100 plasma samples (i.e., pools of 20 healthy and 20 stages I-IV CRC plasmas). The differentially expressed proteins were analyzed using ANOVA and pairwise t-tests (p < 0.05; fold-change > 1.5), and further examined with a neural network classification method using in silico augmented 5000 patient datasets. RESULTS Ultradepletion combined with peptide fractionation allowed for the identification of a total of 513 plasma proteins, 8 of which had not been previously reported in human plasma (based on PeptideAtlas database). SWATH-MS analysis revealed 37 protein biomarker candidates that exhibited differential expression across CRC stages compared to healthy controls. Of those, 7 candidates (CST3, GPX3, CFD, MRC1, COMP, PON1 and ADAMDEC1) were validated using Western blotting and/or ELISA. The neural network classification narrowed down candidate biomarkers to 5 proteins (SAA2, APCS, APOA4, F2 and AMBP) that had maintained accuracy which could discern early (I/II) from late (III/IV) stage CRC. CONCLUSION MS-based proteomics in combination with ultradepletion strategies have an immense potential of identifying diagnostic protein biosignature.
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Affiliation(s)
- Seong Beom Ahn
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Level 1, 75 Talavera Road, Sydney, NSW 2109 Australia
| | - Samridhi Sharma
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Level 1, 75 Talavera Road, Sydney, NSW 2109 Australia
| | - Abidali Mohamedali
- Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109 Australia
| | - Sadia Mahboob
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Level 1, 75 Talavera Road, Sydney, NSW 2109 Australia
| | - William J. Redmond
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Level 1, 75 Talavera Road, Sydney, NSW 2109 Australia
| | - Dana Pascovici
- Australian Proteome Analysis Facility (APAF), Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109 Australia
| | - Jemma X. Wu
- Australian Proteome Analysis Facility (APAF), Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109 Australia
| | - Thiri Zaw
- Australian Proteome Analysis Facility (APAF), Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109 Australia
| | - Subash Adhikari
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Level 1, 75 Talavera Road, Sydney, NSW 2109 Australia
| | - Vineet Vaibhav
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Level 1, 75 Talavera Road, Sydney, NSW 2109 Australia
| | - Edouard C. Nice
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, VIC 3800 Australia
| | - Mark S. Baker
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Level 1, 75 Talavera Road, Sydney, NSW 2109 Australia
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31
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Tang J, Wang Y, Li Y, Zhang Y, Zhang R, Xiao Z, Luo Y, Guo X, Tao L, Lou Y, Xue W, Zhu F. Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics. Curr Pharm Des 2019; 25:1536-1553. [PMID: 31258068 DOI: 10.2174/1381612825666190618123306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 06/11/2019] [Indexed: 11/22/2022]
Abstract
Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.
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Affiliation(s)
- Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Yi Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Yang Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Runyuan Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Ziyu Xiao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Xueying Guo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou 310036, China
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Weiwei Xue
- School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
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32
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Zhang B, Whiteaker JR, Hoofnagle AN, Baird GS, Rodland KD, Paulovich AG. Clinical potential of mass spectrometry-based proteogenomics. Nat Rev Clin Oncol 2019; 16:256-268. [PMID: 30487530 PMCID: PMC6448780 DOI: 10.1038/s41571-018-0135-7] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.
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Affiliation(s)
- Bing Zhang
- Department of Molecular and Human Genetics, Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
| | - Jeffrey R Whiteaker
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Andrew N Hoofnagle
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine, University of Washington, Seattle, WA, USA
| | - Geoffrey S Baird
- Department of Laboratory Medicine, University of Washington, Seattle, WA, USA
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
- Department of Cell, Development and Cancer Biology, Oregon Health & Sciences University, Portland, OR, USA
| | - Amanda G Paulovich
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Division of Medical Oncology, University of Washington School of Medicine, Seattle, WA, USA.
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33
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Shou X, Li Y, Hu W, Ye T, Wang G, Xu F, Sui M, Xu Y. Six-gene Assay as a new biomarker in the blood of patients with colorectal cancer: establishment and clinical validation. Mol Oncol 2019; 13:781-791. [PMID: 30556647 PMCID: PMC6441906 DOI: 10.1002/1878-0261.12427] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 11/28/2018] [Accepted: 11/28/2018] [Indexed: 12/23/2022] Open
Abstract
Colorectal cancer (CRC) is the second most common cancer in men and the third most common cancer in women. Although long-term survival has improved over the past 30 years, at least 50% of patients with CRC will develop metastases after diagnosis. In this study, we examined whether quantifying the mRNA of six CRC-related genes in the blood could improve disease assessment through detection of circulating tumor cells (CTC), and thereby improve progression prediction in relapsed CRC patients. Cell spiking assay and RT-PCR were performed with blood samples from healthy volunteers spiked with six CRC cell lines to generate an algorithm, herein called the Six-gene Assay, based on six genes (CEA, EpCAM, CK19, MUC1, EGFR and C-Met) for CTC detection. The CTCs of 50 relapsed CRC patients were then respectively measured by CEA Gene Assay (single-gene assay control) and Six-gene Assay. Subsequently, receiver operating characteristic analysis of the CTC panel performance in diagnosing CRC was conducted for both assays. Moreover, the 2-year progression-free survival (PFS) of all patients was collected, and the application of CEA Gene Assay and Six-gene Assay in predicting PFS was carefully evaluated with different CTC cutoff values. Encouragingly, we successfully constructed the first multiple gene-based algorithm, named the Six-gene Assay, for CTC detection in CRC patients. Six-gene Assay was more sensitive than CEA Gene Assay; for instance, in 50 CRC patients, the positive rate of Six-gene Assay in CTC detection was 82%, whereas that of CEA Gene Assay was only 70%. Moreover, Six-gene Assay was more sensitive and accurate than CEA Gene Assay in diagnosing CRC as well as predicting the 2-year PFS of CRC patients. Statistical analysis demonstrated that CTC numbers measured by Six-gene Assay were significantly associated with 2-year PFS. This novel Six-gene Assay improves the definition of disease status and correlates with PFS in relapsed CRC, and thus holds promise for future clinical applications.
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Affiliation(s)
- Xin Shou
- Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Yong Li
- Department of Medical Oncology, Shanghai Gongli Hospital, Second Military Medical University, Shanghai, China
| | - Weilei Hu
- Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Tingting Ye
- Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Guosheng Wang
- Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Feng Xu
- Department of Medical Oncology, Shanghai Gongli Hospital, Second Military Medical University, Shanghai, China
| | - Meihua Sui
- Center for Cancer Biology and Innovative Therapeutics, Key Laboratory of Tumor Molecular Diagnosis and Individualized Medicine of Zhejiang Province, Clinical Research Institute, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, China
| | - Yibing Xu
- Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, China
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34
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Setting up and exploitation of a nano/technological platform for the evaluation of HMGA1b protein in peripheral blood of cancer patients. NANOMEDICINE-NANOTECHNOLOGY BIOLOGY AND MEDICINE 2019; 15:231-242. [PMID: 30308301 DOI: 10.1016/j.nano.2018.09.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 09/13/2018] [Accepted: 09/27/2018] [Indexed: 01/08/2023]
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35
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Belczacka I, Latosinska A, Metzger J, Marx D, Vlahou A, Mischak H, Frantzi M. Proteomics biomarkers for solid tumors: Current status and future prospects. MASS SPECTROMETRY REVIEWS 2019; 38:49-78. [PMID: 29889308 DOI: 10.1002/mas.21572] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 05/08/2018] [Indexed: 06/08/2023]
Abstract
Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.
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Affiliation(s)
- Iwona Belczacka
- Mosaiques-Diagnostics GmbH, Hannover, Germany
- University Hospital RWTH Aachen, Institute for Molecular Cardiovascular Research (IMCAR), Aachen, Germany
| | | | | | - David Marx
- Hôpitaux Universitaires de Strasbourg, Service de Transplantation Rénale, Strasbourg, France
- Laboratoire de Spectrométrie de Masse BioOrganique (LSMBO), University of Strasbourg, National Center for Scientific Research (CNRS), Institut Pluridisciplinaire Hubert Curien (IPHC) UMR 7178, Strasbourg, France
| | - Antonia Vlahou
- Biotechnology Division, Biomedical Research Foundation, Academy of Athens (BRFAA), Athens, Greece
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36
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Identification of proteinaceous binders in paintings: A targeted proteomic approach for cultural heritage. Microchem J 2019. [DOI: 10.1016/j.microc.2018.09.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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37
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Núñez C. Blood-based protein biomarkers in breast cancer. Clin Chim Acta 2018; 490:113-127. [PMID: 30597138 DOI: 10.1016/j.cca.2018.12.028] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Revised: 12/26/2018] [Accepted: 12/27/2018] [Indexed: 02/07/2023]
Abstract
Breast cancer (BCa) is a significant healthcare problem on women worldwide. Thus, early detection is very important to reduce mortality. Furthermore, better BCa prognosis could improve selection of patients eligible for adjuvant therapy. New markers for early diagnosis, accurate prognosis and prediction of response to treatment are necessary to improve BCa care. The present review summarizes important aspects of the potential usefulness of modern technologies, strategies, and scientific findings in proteomic research for discovery of breast cancer-associated blood-based protein biomarkers in the clinic.
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Affiliation(s)
- Cristina Núñez
- Research Unit, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de Saúde (SERGAS), 27002 Lugo, Spain.
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38
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Lee PY, Chin SF, Low TY, Jamal R. Probing the colorectal cancer proteome for biomarkers: Current status and perspectives. J Proteomics 2018; 187:93-105. [PMID: 29953962 DOI: 10.1016/j.jprot.2018.06.014] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 06/13/2018] [Accepted: 06/23/2018] [Indexed: 02/07/2023]
Abstract
Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide. Biomarkers that can facilitate better clinical management of CRC are in high demand to improve patient outcome and to reduce mortality. In this regard, proteomic analysis holds a promising prospect in the hunt of novel biomarkers for CRC and in understanding the mechanisms underlying tumorigenesis. This review aims to provide an overview of the current progress of proteomic research, focusing on discovery and validation of diagnostic biomarkers for CRC. We will summarize the contributions of proteomic strategies to recent discoveries of protein biomarkers for CRC and also briefly discuss the potential and challenges of different proteomic approaches in biomarker discovery and translational applications.
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Affiliation(s)
- Pey Yee Lee
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000 Kuala Lumpur, Malaysia.
| | - Siok-Fong Chin
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000 Kuala Lumpur, Malaysia
| | - Teck Yew Low
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000 Kuala Lumpur, Malaysia
| | - Rahman Jamal
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000 Kuala Lumpur, Malaysia
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39
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Orwoll ES, Wiedrick J, Jacobs J, Baker ES, Piehowski P, Petyuk V, Gao Y, Shi T, Smith RD, Bauer DC, Cummings SR, Nielson CM, Lapidus J. High-throughput serum proteomics for the identification of protein biomarkers of mortality in older men. Aging Cell 2018; 17. [PMID: 29399943 PMCID: PMC5847880 DOI: 10.1111/acel.12717] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/02/2017] [Indexed: 01/17/2023] Open
Abstract
The biological perturbations associated with incident mortality are not well elucidated, and there are limited biomarkers for the prediction of mortality. We used a novel high‐throughput proteomics approach to identify serum peptides and proteins associated with 5‐year mortality in community‐dwelling men age ≥65 years who participated in a longitudinal observational study of musculoskeletal aging (Osteoporotic Fractures in Men: MrOS). In a discovery phase, serum specimens collected at baseline in 2473 men were analyzed using liquid chromatography–ion mobility–mass spectrometry, and incident mortality in the subsequent 5 years was ascertained by tri‐annual questionnaire. Rigorous statistical methods were utilized to identify 56 peptides (31 proteins) that were associated with 5‐year mortality. In an independent replication phase, selected reaction monitoring was used to examine 21 of those peptides in baseline serum from 750 additional men; 81% of those peptides remained significantly associated with mortality. Mortality‐associated proteins included a variety involved in inflammation or complement activation; several have been previously linked to mortality (e.g., C‐reactive protein, alpha 1‐antichymotrypsin) and others are not previously known to be associated with mortality. Other novel proteins of interest included pregnancy‐associated plasma protein, VE‐cadherin, leucine‐rich α‐2 glycoprotein 1, vinculin, vitronectin, mast/stem cell growth factor receptor, and Saa4. A panel of peptides improved the predictive value of a commonly used clinical predictor of mortality. Overall, these results suggest that complex inflammatory pathways, and proteins in other pathways, are linked to 5‐year mortality risk. This work may serve to identify novel biomarkers for near‐term mortality.
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Affiliation(s)
| | | | - Jon Jacobs
- Biological Science Division; Pacific Northwest National Laboratory; Richland WA USA
| | - Erin S. Baker
- Biological Science Division; Pacific Northwest National Laboratory; Richland WA USA
| | - Paul Piehowski
- Biological Science Division; Pacific Northwest National Laboratory; Richland WA USA
| | - Vladislav Petyuk
- Biological Science Division; Pacific Northwest National Laboratory; Richland WA USA
| | - Yuqian Gao
- Biological Science Division; Pacific Northwest National Laboratory; Richland WA USA
| | - Tujin Shi
- Biological Science Division; Pacific Northwest National Laboratory; Richland WA USA
| | - Richard D. Smith
- Biological Science Division; Pacific Northwest National Laboratory; Richland WA USA
| | - Douglas C. Bauer
- Department of Medicine; University of California; San Francisco CA USA
| | - Steven R Cummings
- California Pacific Medical Center Research Institute; San Francisco CA USA
| | | | - Jodi Lapidus
- Oregon Health & Science University; Portland OR USA
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40
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Liang S, Chang L. Serum matrix metalloproteinase-9 level as a biomarker for colorectal cancer: a diagnostic meta-analysis. Biomark Med 2018; 12:393-402. [PMID: 29575908 DOI: 10.2217/bmm-2017-0206] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
AIM To comprehensively evaluate the diagnostic value of serum matrix metalloproteinase-9 (MMP-9) level for colorectal cancer (CRC). METHODS Both of the relationships between MMP-9 level and CRC and the diagnostic value were evaluated from 12 eligible papers. RESULTS The high MMP-9 level increased CRC risk. The estimated sensitivity and specificity were 69 and 68%, respectively, which signified that the diagnostic value was medium. Diagnostic odds ratio and the area under the receiver operating characteristic curve suggested MMP-9 level has a moderate diagnostic value in CRC. Additionally, the likelihood matrix indicated MMP-9 levels could be considered as a biomarker for the diagnosis of CRC. CONCLUSION Patients with CRC have elevated MMP-9 levels, which is a potential biomarker for CRC diagnosis.
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Affiliation(s)
- Shucai Liang
- Luohe Medical College, Luohe 462002, Henan Province, PR China
| | - Lulin Chang
- Luohe Medical College, Luohe 462002, Henan Province, PR China
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41
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Ruhaak LR, Xu G, Li Q, Goonatilleke E, Lebrilla CB. Mass Spectrometry Approaches to Glycomic and Glycoproteomic Analyses. Chem Rev 2018; 118:7886-7930. [PMID: 29553244 DOI: 10.1021/acs.chemrev.7b00732] [Citation(s) in RCA: 253] [Impact Index Per Article: 42.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Glycomic and glycoproteomic analyses involve the characterization of oligosaccharides (glycans) conjugated to proteins. Glycans are produced through a complicated nontemplate driven process involving the competition of enzymes that extend the nascent chain. The large diversity of structures, the variations in polarity of the individual saccharide residues, and the poor ionization efficiencies of glycans all conspire to make the analysis arguably much more difficult than any other biopolymer. Furthermore, the large number of glycoforms associated with a specific protein site makes it more difficult to characterize than any post-translational modification. Nonetheless, there have been significant progress, and advanced separation and mass spectrometry methods have been at its center and the main reason for the progress. While glycomic and glycoproteomic analyses are still typically available only through highly specialized laboratories, new software and workflow is making it more accessible. This review focuses on the role of mass spectrometry and separation methods in advancing glycomic and glycoproteomic analyses. It describes the current state of the field and progress toward making it more available to the larger scientific community.
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Affiliation(s)
- L Renee Ruhaak
- Department of Clinical Chemistry and Laboratory Medicine , Leiden University Medical Center , 2333 ZA Leiden , The Netherlands
| | - Gege Xu
- Department of Chemistry , University of California, Davis , One Shields Avenue , Davis , California 95616 , United States
| | - Qiongyu Li
- Department of Chemistry , University of California, Davis , One Shields Avenue , Davis , California 95616 , United States
| | - Elisha Goonatilleke
- Department of Chemistry , University of California, Davis , One Shields Avenue , Davis , California 95616 , United States
| | - Carlito B Lebrilla
- Department of Chemistry , University of California, Davis , One Shields Avenue , Davis , California 95616 , United States.,Department of Biochemistry and Molecular Medicine , University of California, Davis , Davis , California 95616 , United States.,Foods for Health Institute , University of California, Davis , Davis , California 95616 , United States
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42
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Lim LC, Lim YM. Proteome Heterogeneity in Colorectal Cancer. Proteomics 2018; 18. [PMID: 29316255 DOI: 10.1002/pmic.201700169] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 12/17/2017] [Indexed: 01/26/2023]
Abstract
Tumor heterogeneity is an important feature of colorectal cancer (CRC) manifested by dynamic changes in gene expression, protein expression, and availability of different tumor subtypes. Recent publications in the past 10 years have revealed proteome heterogeneity between different colorectal tumors and within the same tumor site. This paper reviews recent research works on the proteome heterogeneity in CRC, which includes the heterogeneity within a single tumor (intratumor heterogeneity), between different anatomical sites at the same organ, and between primary and metastatic sites (intertumor heterogeneity). The potential use of proteome heterogeneity in precision medicine and its implications in biomarker discovery and therapeutic outcomes will be discussed. Identification of the unique proteome landscape between and within individual tumors is imperative for understanding cancer biology and the management of CRC patients.
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Affiliation(s)
- Lay Cheng Lim
- Centre for Cancer Research, Faculty of Medicine and Health Sciences, University of Tunku Abdul Rahman, Selangor, Malaysia
| | - Yang Mooi Lim
- Centre for Cancer Research, Faculty of Medicine and Health Sciences, University of Tunku Abdul Rahman, Selangor, Malaysia
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43
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Bhardwaj M, Gies A, Werner S, Schrotz-King P, Brenner H. Blood-Based Protein Signatures for Early Detection of Colorectal Cancer: A Systematic Review. Clin Transl Gastroenterol 2017; 8:e128. [PMID: 29189767 PMCID: PMC5717517 DOI: 10.1038/ctg.2017.53] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Accepted: 10/10/2017] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVES Blood-based proteins might be an attractive option for early detection of colorectal cancer (CRC), but individually they are unlikely to achieve the diagnostic performance required for population based screening. We aimed at summarizing current evidence of diagnostic performance of signatures based on multiple proteins for early detection of CRC. METHODS A systematic literature review adhering to the PRISMA (preferred reporting items for systematic reviews and meta-analysis) guidelines was performed. PubMed and Web of Science databases were searched for potentially relevant studies published until 28th August, 2017. Relevant studies were identified by predefined eligibility criteria. Estimates of indicators of diagnostic performance such as sensitivity, specificity, and the area under the curve (AUC), along with information on validation and other key methodological procedures were extracted. Study quality was assessed by a QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) instrument tool. RESULTS Thirty six eligible studies with numbers of CRC cases ranging from 23 to 512 and the number of proteins included in signatures ranged from 3 to 13 were identified. Reported Youden's Index and AUC ranged from 0.19 to 0.95 and from 0.62 to 0.996, respectively. However most studies, especially those reporting better diagnostic performance, were conducted in clinical rather than screening setting and many studies lacked any internal or external validation of identified algorithm. CONCLUSIONS Blood-based tests using signatures of multiple proteins may be a promising approach for non-invasive CRC screening. However, promising signatures identified in clinical settings still require rigorous evaluation in large studies conducted in true screening setting.
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Affiliation(s)
- Megha Bhardwaj
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Anton Gies
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Simone Werner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Petra Schrotz-King
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Hermann Brenner
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
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44
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Cristobal A, van den Toorn HWP, van de Wetering M, Clevers H, Heck AJR, Mohammed S. Personalized Proteome Profiles of Healthy and Tumor Human Colon Organoids Reveal Both Individual Diversity and Basic Features of Colorectal Cancer. Cell Rep 2017; 18:263-274. [PMID: 28052255 DOI: 10.1016/j.celrep.2016.12.016] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 11/23/2016] [Accepted: 12/06/2016] [Indexed: 12/19/2022] Open
Abstract
Diseases at the molecular level are complex and patient dependent, necessitating development of strategies that enable precision treatment to optimize clinical outcomes. Organoid technology has recently been shown to have the potential to recapitulate the in vivo characteristics of the original individual's tissue in a three-dimensional in vitro culture system. Here, we present a quantitative mass-spectrometry-based proteomic analysis and a comparative transcriptomic analysis of human colorectal tumor and healthy organoids derived, in parallel, from seven patients. Although gene and protein signatures can be derived to distinguish the tumor organoid population from healthy organoids, our data clearly reveal that each patient possesses a distinct organoid signature at the proteomic level. We demonstrate that a personalized patient-specific organoid proteome profile can be related to the diagnosis of a patient and with future development contribute to the generation of personalized therapies.
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Affiliation(s)
- Alba Cristobal
- Biomolecular Mass Spectrometry and Proteomics Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 Utrecht, the Netherlands; Netherlands Proteomics Center, Padualaan 8, 3584 Utrecht, the Netherlands
| | - Henk W P van den Toorn
- Biomolecular Mass Spectrometry and Proteomics Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 Utrecht, the Netherlands; Netherlands Proteomics Center, Padualaan 8, 3584 Utrecht, the Netherlands
| | - Marc van de Wetering
- Princess Maxima Center for Pediatric Oncology, Uppsalalaan 8, 3584 Utrecht, Netherlands
| | - Hans Clevers
- Princess Maxima Center for Pediatric Oncology, Uppsalalaan 8, 3584 Utrecht, Netherlands; Hubrecht Institute, KNAW and University Medical Center Utrecht, Uppsalalaan 8, 3584 Utrecht, Netherlands.
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 Utrecht, the Netherlands; Netherlands Proteomics Center, Padualaan 8, 3584 Utrecht, the Netherlands.
| | - Shabaz Mohammed
- Biomolecular Mass Spectrometry and Proteomics Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 Utrecht, the Netherlands; Netherlands Proteomics Center, Padualaan 8, 3584 Utrecht, the Netherlands; Department of Biochemistry, University of Oxford, New Biochemistry building, South Parks Road, Oxford OX1 3QU, UK; Department of Chemistry, University of Oxford, Chemistry Research Laboratory, Mansfield Road, Oxford OX1 3TA, UK.
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45
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Bhardwaj M, Erben V, Schrotz-King P, Brenner H. Cell Line Secretome and Tumor Tissue Proteome Markers for Early Detection of Colorectal Cancer: A Systematic Review. Cancers (Basel) 2017; 9:cancers9110156. [PMID: 29144439 PMCID: PMC5704174 DOI: 10.3390/cancers9110156] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 11/06/2017] [Accepted: 11/08/2017] [Indexed: 12/12/2022] Open
Abstract
Objective: In order to find low abundant proteins secretome and tumor tissue proteome data have been explored in the last few years for the diagnosis of colorectal cancer (CRC). In this review we aim to summarize the results of studies evaluating markers derived from the secretome and tumor proteome for blood based detection of colorectal cancer. Methods: Observing the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines PubMed and Web of Science databases were searched systematically for relevant studies published up to 18 July 2017. After screening for predefined eligibility criteria a total of 47 studies were identified. Information on diagnostic performance indicators, methodological procedures and validation was extracted. Functions of proteins were identified from the UniProt database and the the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess study quality. Results: Forty seven studies meeting inclusion criteria were identified. Overall, 83 different proteins were identified, with carcinoembryonic Antigen (CEA) being by far the most commonly reported (reported in 24 studies). Evaluation of the markers or marker combinations in blood samples from CRC cases and controls yielded apparently very promising diagnostic performances, with area under the curve >0.9 in several cases, but lack of internal or external validation, overoptimism due to overfitting and spectrum bias due to evaluation in clinical setting rather than screening settings are major concerns. Conclusions: Secretome and tumor proteome-based biomarkers when validated in blood yield promising candidates. However, for discovered protein markers to be clinically applicable as screening tool they have to be specific for early stages and need to be validated externally in larger studies with participants recruited in true screening setting.
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Affiliation(s)
- Megha Bhardwaj
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg 69120, Germany.
| | - Vanessa Erben
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg 69120, Germany.
| | - Petra Schrotz-King
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg 69120, Germany.
| | - Hermann Brenner
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg 69120, Germany.
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
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Alnabulsi A, Murray GI. Proteomics for early detection of colorectal cancer: recent updates. Expert Rev Proteomics 2017; 15:55-63. [PMID: 29064727 DOI: 10.1080/14789450.2018.1396893] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Colorectal cancer (CRC) is a common type of cancer with a relatively poor survival rate. The survival rate of patients could be improved if CRC is detected early. Biomarkers associated with early stages of tumor development might provide useful tools for the early diagnosis of colorectal cancer. Areas covered: Online searches using PubMed and Google Scholar were performed using keywords and with a focus on recent proteomic studies. The aim of this review is to highlight the need for biomarkers to improve the detection rate of early CRC and provide an overview of proteomic technologies used for biomarker discovery and validation. This review will also discuss recent proteomic studies which focus on identifying biomarkers associated with the early stages of CRC development. Expert commentary: A large number of CRC biomarkers are increasingly being identified by proteomics using diverse approaches. However, the clinical relevance and introduction of these markers into clinical practice cannot be determined without a robust validation process. The size of validation cohorts remains a major limitation in many biomarker studies.
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Affiliation(s)
- Abdo Alnabulsi
- a Pathology, School of Medicine, Medical Sciences and Nutrition , University of Aberdeen , Aberdeen , UK
| | - Graeme I Murray
- a Pathology, School of Medicine, Medical Sciences and Nutrition , University of Aberdeen , Aberdeen , UK
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47
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Machine learning in laboratory medicine: waiting for the flood? ACTA ACUST UNITED AC 2017; 56:516-524. [DOI: 10.1515/cclm-2017-0287] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2017] [Accepted: 09/05/2017] [Indexed: 02/04/2023]
Abstract
Abstract
This review focuses on machine learning and on how methods and models combining data analytics and artificial intelligence have been applied to laboratory medicine so far. Although still in its infancy, the potential for applying machine learning to laboratory data for both diagnostic and prognostic purposes deserves more attention by the readership of this journal, as well as by physician-scientists who will want to take advantage of this new computer-based support in pathology and laboratory medicine.
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48
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Park J, Choi Y, Namkung J, Yi SG, Kim H, Yu J, Kim Y, Kwon MS, Kwon W, Oh DY, Kim SW, Jeong SY, Han W, Lee KE, Heo JS, Park JO, Park JK, Kim SC, Kang CM, Lee WJ, Lee S, Han S, Park T, Jang JY, Kim Y. Diagnostic performance enhancement of pancreatic cancer using proteomic multimarker panel. Oncotarget 2017; 8:93117-93130. [PMID: 29190982 PMCID: PMC5696248 DOI: 10.18632/oncotarget.21861] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 08/29/2017] [Indexed: 12/15/2022] Open
Abstract
Due to its high mortality rate and asymptomatic nature, early detection rates of pancreatic ductal adenocarcinoma (PDAC) remain poor. We measured 1000 biomarker candidates in 134 clinical plasma samples by multiple reaction monitoring-mass spectrometry (MRM-MS). Differentially abundant proteins were assembled into a multimarker panel from a training set (n=684) and validated in independent set (n=318) from five centers. The level of panel proteins was also confirmed by immunoassays. The panel including leucine-rich alpha-2 glycoprotein (LRG1), transthyretin (TTR), and CA19-9 had a sensitivity of 82.5% and a specificity of 92.1%. The triple-marker panel exceeded the diagnostic performance of CA19-9 by more than 10% (AUCCA19-9 = 0.826, AUCpanel= 0.931, P < 0.01) in all PDAC samples and by more than 30% (AUCCA19-9 = 0.520, AUCpanel = 0.830, P < 0.001) in patients with normal range of CA19-9 (<37U/mL). Further, it differentiated PDAC from benign pancreatic disease (AUCCA19-9 = 0.812, AUCpanel = 0.892, P < 0.01) and other cancers (AUCCA19-9 = 0.796, AUCpanel = 0.899, P < 0.001). Overall, the multimarker panel that we have developed and validated in large-scale samples by MRM-MS and immunoassay has clinical applicability in the early detection of PDAC.
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Affiliation(s)
- Jiyoung Park
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea.,Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
| | - Yonghwan Choi
- Immunodiagnostics R&D Team, IVD Business Unit 5, SK Telecom, Seoul, Korea
| | - Junghyun Namkung
- Immunodiagnostics R&D Team, IVD Business Unit 5, SK Telecom, Seoul, Korea
| | - Sung Gon Yi
- Immunodiagnostics R&D Team, IVD Business Unit 5, SK Telecom, Seoul, Korea
| | - Hyunsoo Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea.,Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
| | - Jiyoung Yu
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea.,Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
| | - Yongkang Kim
- Department of Statistics, Seoul National University, Seoul, Korea
| | - Min-Seok Kwon
- Department of Statistics, Seoul National University, Seoul, Korea
| | - Wooil Kwon
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Do-Youn Oh
- Department of Internal Medicine and Cancer Research Institute, Seoul National University Hospital, Seoul, Korea
| | - Sun-Whe Kim
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Seung-Yong Jeong
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Wonshik Han
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Kyu Eun Lee
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Jin Seok Heo
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Joon Oh Park
- Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Joo Kyung Park
- Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Song Cheol Kim
- Department of Surgery, University of Ulsan College of Medicine and Asan Medical Center, Seoul, Korea
| | - Chang Moo Kang
- Department of Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Woo Jin Lee
- Center for Liver Cancer, National Cancer Center, Seoul, Korea
| | - Seungyeoun Lee
- Department of Mathematics and Statistics, Sejong University, Seoul, Korea
| | - Sangjo Han
- Immunodiagnostics R&D Team, IVD Business Unit 5, SK Telecom, Seoul, Korea
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul, Korea
| | - Jin-Young Jang
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Youngsoo Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea.,Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
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Ishizaki J, Takemori A, Suemori K, Matsumoto T, Akita Y, Sada KE, Yuzawa Y, Amano K, Takasaki Y, Harigai M, Arimura Y, Makino H, Yasukawa M, Takemori N, Hasegawa H. Targeted proteomics reveals promising biomarkers of disease activity and organ involvement in antineutrophil cytoplasmic antibody-associated vasculitis. Arthritis Res Ther 2017; 19:218. [PMID: 28962592 PMCID: PMC5622475 DOI: 10.1186/s13075-017-1429-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Accepted: 09/15/2017] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Targeted proteomics, which involves quantitative analysis of targeted proteins using selected reaction monitoring (SRM) mass spectrometry, has emerged as a new methodology for discovery of clinical biomarkers. In this study, we used targeted serum proteomics to identify circulating biomarkers for prediction of disease activity and organ involvement in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). METHODS A large-scale SRM assay targeting 135 biomarker candidates was established using a triple-quadrupole mass spectrometer coupled with nanoflow liquid chromatography. Target proteins in serum samples from patients in the active and remission (6 months after treatment) stages were quantified using the established assays. Identified marker candidates were further validated by enzyme-linked immunosorbent assay using serum samples (n = 169) collected in a large-cohort Japanese study (the RemIT-JAV-RPGN study). RESULTS Our proteomic analysis identified the following proteins as biomarkers for discriminating patients with highly active AAV from those in remission or healthy control subjects: tenascin C (TNC), C-reactive protein (CRP), tissue inhibitor of metalloproteinase 1 (TIMP1), leucine-rich alpha-2-glycoprotein 1, S100A8/A9, CD93, matrix metalloproteinase 9, and transketolase (TKT). Of these, TIMP1 was the best-performing marker of disease activity, allowing distinction between mildly active AAV and remission. Moreover, in contrast to CRP, serum levels of TIMP1 in patients with active AAV were significantly higher than those in patients with infectious diseases. The serum levels of TKT and CD93 were higher in patients with renal involvement than in those without, and they predicted kidney outcome. The level of circulating TNC was elevated significantly in patients with lung infiltration. AAV severity was associated with markers reflecting organ involvement (TKT, CD93, and TNC) rather than inflammation. The eight markers and myeloperoxidase (MPO)-ANCA were clustered into three groups: MPO-ANCA, renal involvement (TKT and CD93), and inflammation (the other six markers). CONCLUSIONS We have identified promising biomarkers of disease activity, disease severity, and organ involvement in AAV with a targeted proteomics approach using serum samples obtained from a large-cohort Japanese study. Especially, our analysis demonstrated the effectiveness of TIMP1 as a marker of AAV activity. In addition, we identified TKT and CD93 as novel markers for evaluation of renal involvement and kidney outcome in AAV.
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Affiliation(s)
- Jun Ishizaki
- Department of Hematology, Clinical Immunology, and Infectious Diseases, Ehime University Graduate School of Medicine, Toon, Ehime 791-0295 Japan
| | - Ayako Takemori
- Division of Proteomics Research, Proteo-Science Center, Ehime University, Toon, Ehime 791-0295 Japan
| | - Koichiro Suemori
- Department of Hematology, Clinical Immunology, and Infectious Diseases, Ehime University Graduate School of Medicine, Toon, Ehime 791-0295 Japan
| | - Takuya Matsumoto
- Department of Hematology, Clinical Immunology, and Infectious Diseases, Ehime University Graduate School of Medicine, Toon, Ehime 791-0295 Japan
| | - Yoko Akita
- Department of Hematology, Clinical Immunology, and Infectious Diseases, Ehime University Graduate School of Medicine, Toon, Ehime 791-0295 Japan
| | - Ken-ei Sada
- Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Yukio Yuzawa
- Department of Nephrology, Fujita Health University School of Medicine, Aichi, Japan
| | - Koichi Amano
- Department of Rheumatology and Clinical Immunology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Yoshinari Takasaki
- Department of Rheumatology, Juntendo University Koshigaya Hospital, Saitama, Japan
| | - Masayoshi Harigai
- Division of Epidemiology and Pharmacoepidemiology of Rheumatic Diseases, Institute of Rheumatology, Tokyo Women’s Medical University, Tokyo, Japan
| | - Yoshihiro Arimura
- Nephrology and Rheumatology, First Department of Internal Medicine, Kyorin University School of Medicine, Tokyo, Japan
| | | | - Masaki Yasukawa
- Department of Hematology, Clinical Immunology, and Infectious Diseases, Ehime University Graduate School of Medicine, Toon, Ehime 791-0295 Japan
| | - Nobuaki Takemori
- Division of Proteomics Research, Proteo-Science Center, Ehime University, Toon, Ehime 791-0295 Japan
| | - Hitoshi Hasegawa
- Department of Hematology, Clinical Immunology, and Infectious Diseases, Ehime University Graduate School of Medicine, Toon, Ehime 791-0295 Japan
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50
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ADAM Metalloprotease-Released Cancer Biomarkers. Trends Cancer 2017; 3:482-490. [DOI: 10.1016/j.trecan.2017.05.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Revised: 04/28/2017] [Accepted: 05/03/2017] [Indexed: 12/14/2022]
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