1
|
Wang S, Hao X, Dai L, Lou N, Fan G, Gao R, Yang M, Xing P, Liu Y, Wang L, Zhang Z, Yao J, Tang L, Shi Y, Han X. Longitudinal plasma proteomic profiling of EML4-ALK positive lung cancer receiving ALK-TKIs therapy. Lung Cancer 2024; 189:107503. [PMID: 38359741 DOI: 10.1016/j.lungcan.2024.107503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/03/2024] [Accepted: 02/05/2024] [Indexed: 02/17/2024]
Abstract
BACKGROUND Anaplastic lymphoma kinase-tyrosine kinase inhibitors (ALK-TKIs) has demonstrated remarkable therapeutic effects in ALK-positive non-small cell lung cancer (NSCLC) patients. Identifying prognostic biomarkers can enhance the clinical efficacy of relapsed or refractory patients. METHODS We profiled 737 plasma proteins from 159 pre-treatment and on-treatment plasma samples of 63 ALK-positive NSCLC patients using data-independent acquisition-mass spectrometry (DIA-MS). The consensus clustering algorithm was used to identify subtypes with distinct biological features. A plasma-based prognostic model was constructed using the LASSO-Cox method. We performed the Mfuzz analysis to classify the patterns of longitudinal changes in plasma proteins during treatment. 52 baseline plasma samples from another independent ALK-TKI treatment cohort were collected to validate the potential prognostic markers using ELISA. RESULTS We identified three subtypes of ALK-positive NSCLC with distinct biological features and clinical efficacy. Patients in subgroup 1 exhibited activated humoral immunity and inflammatory responses, increased expression of positive acute-phase response proteins, and the worst prognosis. Then we constructed and verified a prognostic model that predicts the efficacy of ALK-TKI therapy using the expression levels of five plasma proteins (SERPINA4, ATRN, APOA4, TF, and MYOC) at baseline. Next, we explored the longitudinal changes in plasma protein expression during treatment and identified four distinct change patterns (Clusters 1-4). The longitudinal changes of acute-phase proteins during treatment can reflect the treatment status and tumor progression of patients. Finally, we validated the prognostic efficacy of baseline plasma CRP, SAA1, AHSG, SERPINA4, and TF in another independent NSCLC cohort undergoing ALK-TKI treatment. CONCLUSIONS This study contributes to the search for prognostic and drug-resistance biomarkers in plasma samples for ALK-TKI therapy and provides new insights into the mechanism of drug resistance and the selection of follow-up treatment.
Collapse
Affiliation(s)
- Shasha Wang
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Xuezhi Hao
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing 100021, China
| | - Liyuan Dai
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Ning Lou
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - Guangyu Fan
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing 100021, China
| | - Ruyun Gao
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing 100021, China
| | - Mengwei Yang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing 100021, China
| | - Puyuan Xing
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing 100021, China
| | - Yutao Liu
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing 100021, China
| | - Lin Wang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing 100021, China
| | - Zhishang Zhang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing 100021, China
| | - Jiarui Yao
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing 100021, China
| | - Le Tang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing 100021, China
| | - Yuankai Shi
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing 100021, China.
| | - Xiaohong Han
- Clinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.
| |
Collapse
|
2
|
Wang M, Dai X, Yang X, Jin B, Xie Y, Xu C, Liu Q, Wang L, Ying L, Lu W, Chen Q, Fu T, Su D, Liu Y, Tan W. Serum Protein Fishing for Machine Learning-Boosted Diagnostic Classification of Small Nodules of Lung. ACS NANO 2024; 18:4038-4055. [PMID: 38270088 DOI: 10.1021/acsnano.3c07217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
Diagnosis of benign and malignant small nodules of the lung remains an unmet clinical problem which is leading to serious false positive diagnosis and overtreatment. Here, we developed a serum protein fishing-based spectral library (ProteoFish) for data independent acquisition analysis and a machine learning-boosted protein panel for diagnosis of early Non-Small Cell Lung Cancer (NSCLC) and classification of benign and malignant small nodules. We established an extensive NSCLC protein bank consisting of 297 clinical subjects. After testing 5 feature extraction algorithms and six machine learning models, the Lasso algorithm for a 15-key protein panel selection and Random Forest was chosen for diagnostic classification. Our random forest classifier achieved 91.38% accuracy in benign and malignant small nodule diagnosis, which is superior to the existing clinical assays. By integrating with machine learning, the 15-key protein panel may provide insights to multiplexed protein biomarker fishing from serum for facile cancer screening and tackling the current clinical challenge in prospective diagnostic classification of small nodules of the lung.
Collapse
Affiliation(s)
- Mengjie Wang
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Bio-Sensing and Chemometrics, College of Chemistry and Chemical Engineering, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha 410082, Hunan, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
| | - Xin Dai
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
| | - Xu Yang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
| | - Baichuan Jin
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
| | - Yueli Xie
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
- School of Life Sciences, Tianjin University, Tianjin 300072, China
| | - Chenlu Xu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
- Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin 300072, China
| | - Qiqi Liu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
| | - Lichao Wang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
| | - Lisha Ying
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
| | - Weishan Lu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
| | - Qixun Chen
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
| | - Ting Fu
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Bio-Sensing and Chemometrics, College of Chemistry and Chemical Engineering, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha 410082, Hunan, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
| | - Dan Su
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
| | - Yuan Liu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
| | - Weihong Tan
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Bio-Sensing and Chemometrics, College of Chemistry and Chemical Engineering, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha 410082, Hunan, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China
| |
Collapse
|
3
|
Wang L, Zhu M, Li Y, Yan P, Li Z, Chen X, Yang J, Pan X, Zhao H, Wang S, Yuan H, Zhao M, Sun X, Wan R, Li F, Wang X, Yu H, Rosas I, Ding C, Yu G. Serum proteomics identify biomarkers associated with the pathogenesis of idiopathic pulmonary fibrosis. Mol Cell Proteomics 2023; 22:100524. [PMID: 36870568 PMCID: PMC10113895 DOI: 10.1016/j.mcpro.2023.100524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 01/31/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
The heterogeneity of idiopathic pulmonary fibrosis (IPF) limits its diagnosis and treatment. The association between the pathophysiological features and the serum protein signatures of IPF currently remains unclear. The present study analyzed the specific proteins and patterns associated with the clinical parameters of IPF based on a serum proteomic dataset by Data-Independent Acquisition (DIA) using mass spectrometry. Differentiated proteins in sera distinguished in IPF patients into three subgroups in signal pathways and overall survival. Aging-associated signatures by WGCNA coincidently provided clear and direct evidence that aging is a critical risk factor for IPF rather than a single biomarker. LDHA and CCT6A expression, which were associated with glucose metabolic reprogramming, were correlated with high serum lactic acid content in the patients with IPF. Cross-model analysis and machine learning showed that a combinatorial biomarker accurately distinguished IPF patients from healthy subjects with an AUC of 0.848 (95% CI = 0.684-0.941) and validated from another cohort and ELISA assay. This serum proteomic profile provides rigorous evidence that enables understanding of the heterogeneity of IPF and protein alterations that could help in its diagnosis and treatment decisions.
Collapse
Affiliation(s)
- Lan Wang
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan center for outstanding overseas scientists of pulmonary fibrosis, College of Life Sciences, Institute of Biomedical Science, Henan Normal University, Xinxiang, Henan 453007, China
| | - Minghui Zhu
- Henan Provincial Chest Hospital, Zhengzhou, Henan 450003, China
| | - Yan Li
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Peishuo Yan
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan center for outstanding overseas scientists of pulmonary fibrosis, College of Life Sciences, Institute of Biomedical Science, Henan Normal University, Xinxiang, Henan 453007, China
| | - Zhongzheng Li
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan center for outstanding overseas scientists of pulmonary fibrosis, College of Life Sciences, Institute of Biomedical Science, Henan Normal University, Xinxiang, Henan 453007, China
| | - Xiuping Chen
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Juntang Yang
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan center for outstanding overseas scientists of pulmonary fibrosis, College of Life Sciences, Institute of Biomedical Science, Henan Normal University, Xinxiang, Henan 453007, China
| | - Xin Pan
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan center for outstanding overseas scientists of pulmonary fibrosis, College of Life Sciences, Institute of Biomedical Science, Henan Normal University, Xinxiang, Henan 453007, China
| | - Huabin Zhao
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan center for outstanding overseas scientists of pulmonary fibrosis, College of Life Sciences, Institute of Biomedical Science, Henan Normal University, Xinxiang, Henan 453007, China
| | - Shenghui Wang
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan center for outstanding overseas scientists of pulmonary fibrosis, College of Life Sciences, Institute of Biomedical Science, Henan Normal University, Xinxiang, Henan 453007, China
| | - Hongmei Yuan
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan center for outstanding overseas scientists of pulmonary fibrosis, College of Life Sciences, Institute of Biomedical Science, Henan Normal University, Xinxiang, Henan 453007, China
| | - Mengxia Zhao
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan center for outstanding overseas scientists of pulmonary fibrosis, College of Life Sciences, Institute of Biomedical Science, Henan Normal University, Xinxiang, Henan 453007, China
| | - Xiaogang Sun
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan center for outstanding overseas scientists of pulmonary fibrosis, College of Life Sciences, Institute of Biomedical Science, Henan Normal University, Xinxiang, Henan 453007, China
| | - Ruyan Wan
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan center for outstanding overseas scientists of pulmonary fibrosis, College of Life Sciences, Institute of Biomedical Science, Henan Normal University, Xinxiang, Henan 453007, China
| | - Fei Li
- Henan Provincial Chest Hospital, Zhengzhou, Henan 450003, China
| | - Xiaobo Wang
- Henan Provincial Chest Hospital, Zhengzhou, Henan 450003, China
| | - Hongtao Yu
- Henan Provincial Chest Hospital, Zhengzhou, Henan 450003, China
| | - Ivan Rosas
- Division of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Chen Ding
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institutes of Biomedical Sciences, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai 200433, China.
| | - Guoying Yu
- State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan center for outstanding overseas scientists of pulmonary fibrosis, College of Life Sciences, Institute of Biomedical Science, Henan Normal University, Xinxiang, Henan 453007, China.
| |
Collapse
|
4
|
Voronina L, Leonardo C, Mueller‐Reif JB, Geyer PE, Huber M, Trubetskov M, Kepesidis KV, Behr J, Mann M, Krausz F, Žigman M. Molecular Origin of Blood‐Based Infrared Spectroscopic Fingerprints**. Angew Chem Int Ed Engl 2021. [DOI: 10.1002/ange.202103272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Liudmila Voronina
- Department of Physics Ludwig Maximilian University of Munich 85748 Garching Germany
- Max Planck Institute of Quantum Optics 85748 Garching Germany
| | - Cristina Leonardo
- Department of Physics Ludwig Maximilian University of Munich 85748 Garching Germany
- Max Planck Institute of Quantum Optics 85748 Garching Germany
| | - Johannes B. Mueller‐Reif
- Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry 82152 Martinsried Germany
- OmicEra Diagnostics GmbH 82152 Planegg Germany
| | - Philipp E. Geyer
- Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry 82152 Martinsried Germany
- Novo Nordisk Foundation Center for Protein Research Faculty of Health Sciences University of Copenhagen 2200 Copenhagen Denmark
- OmicEra Diagnostics GmbH 82152 Planegg Germany
| | - Marinus Huber
- Department of Physics Ludwig Maximilian University of Munich 85748 Garching Germany
- Max Planck Institute of Quantum Optics 85748 Garching Germany
| | | | - Kosmas V. Kepesidis
- Department of Physics Ludwig Maximilian University of Munich 85748 Garching Germany
| | - Jürgen Behr
- Comprehensive Pneumology Center Department of Internal Medicine V Clinic of the Ludwig Maximilians University Munich (LMU), Member of the German Center for Lung Research Germany
| | - Matthias Mann
- Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry 82152 Martinsried Germany
- Novo Nordisk Foundation Center for Protein Research Faculty of Health Sciences University of Copenhagen 2200 Copenhagen Denmark
| | - Ferenc Krausz
- Department of Physics Ludwig Maximilian University of Munich 85748 Garching Germany
- Max Planck Institute of Quantum Optics 85748 Garching Germany
| | - Mihaela Žigman
- Department of Physics Ludwig Maximilian University of Munich 85748 Garching Germany
- Max Planck Institute of Quantum Optics 85748 Garching Germany
| |
Collapse
|
5
|
Voronina L, Leonardo C, Mueller‐Reif JB, Geyer PE, Huber M, Trubetskov M, Kepesidis KV, Behr J, Mann M, Krausz F, Žigman M. Molecular Origin of Blood-Based Infrared Spectroscopic Fingerprints*. Angew Chem Int Ed Engl 2021; 60:17060-17069. [PMID: 33881784 PMCID: PMC8361728 DOI: 10.1002/anie.202103272] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 03/30/2021] [Indexed: 12/17/2022]
Abstract
Infrared spectroscopy of liquid biopsies is a time- and cost-effective approach that may advance biomedical diagnostics. However, the molecular nature of disease-related changes of infrared molecular fingerprints (IMFs) remains poorly understood, impeding the method's applicability. Here we probe 148 human blood sera and reveal the origin of the variations in their IMFs. To that end, we supplemented infrared spectroscopy with biochemical fractionation and proteomic profiling, providing molecular information about serum composition. Using lung cancer as an example of a medical condition, we demonstrate that the disease-related differences in IMFs are dominated by contributions from twelve highly abundant proteins-that, if used as a pattern, may be instrumental for detecting malignancy. Tying proteomic to spectral information and machine learning advances our understanding of the infrared spectra of liquid biopsies, a framework that could be applied to probing of any disease.
Collapse
Affiliation(s)
- Liudmila Voronina
- Department of PhysicsLudwig Maximilian University of Munich85748GarchingGermany
- Max Planck Institute of Quantum Optics85748GarchingGermany
| | - Cristina Leonardo
- Department of PhysicsLudwig Maximilian University of Munich85748GarchingGermany
- Max Planck Institute of Quantum Optics85748GarchingGermany
| | - Johannes B. Mueller‐Reif
- Department of Proteomics and Signal TransductionMax Planck Institute of Biochemistry82152MartinsriedGermany
- OmicEra Diagnostics GmbH82152PlaneggGermany
| | - Philipp E. Geyer
- Department of Proteomics and Signal TransductionMax Planck Institute of Biochemistry82152MartinsriedGermany
- Novo Nordisk Foundation Center for Protein ResearchFaculty of Health SciencesUniversity of Copenhagen2200CopenhagenDenmark
- OmicEra Diagnostics GmbH82152PlaneggGermany
| | - Marinus Huber
- Department of PhysicsLudwig Maximilian University of Munich85748GarchingGermany
- Max Planck Institute of Quantum Optics85748GarchingGermany
| | | | - Kosmas V. Kepesidis
- Department of PhysicsLudwig Maximilian University of Munich85748GarchingGermany
| | - Jürgen Behr
- Comprehensive Pneumology CenterDepartment of Internal Medicine VClinic of the Ludwig Maximilians University Munich (LMU), Member of the German Center for Lung ResearchGermany
| | - Matthias Mann
- Department of Proteomics and Signal TransductionMax Planck Institute of Biochemistry82152MartinsriedGermany
- Novo Nordisk Foundation Center for Protein ResearchFaculty of Health SciencesUniversity of Copenhagen2200CopenhagenDenmark
| | - Ferenc Krausz
- Department of PhysicsLudwig Maximilian University of Munich85748GarchingGermany
- Max Planck Institute of Quantum Optics85748GarchingGermany
| | - Mihaela Žigman
- Department of PhysicsLudwig Maximilian University of Munich85748GarchingGermany
- Max Planck Institute of Quantum Optics85748GarchingGermany
| |
Collapse
|
6
|
Park HM, Kim H, Kim DW, Yoon JH, Kim BG, Cho JY. Common plasma protein marker LCAT in aggressive human breast cancer and canine mammary tumor. BMB Rep 2020. [PMID: 33298249 PMCID: PMC7781914 DOI: 10.5483/bmbrep.2020.53.12.238] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Breast cancer is one of the most frequently diagnosed cancers. Although biomarkers are continuously being discovered, few specific markers, rather than classification markers, representing the aggressiveness and invasiveness of breast cancer are known. In this study, we used samples from canine mammary tumors in a comparative approach. We subjected 36 fractions of both canine normal and mammary tumor plasmas to high-performance quantitative proteomics analysis. Among the identified proteins, LCAT was selectively expressed in mixed tumor samples. With further MRM and Western blot validation, we discovered that the LCAT protein is an indicator of aggressive mammary tumors, an advanced stage of cancer, possibly highly metastatic. Interestingly, we also found that LCAT is overexpressed in high-grade and lymphnode-positive breast cancer in silico data. We also demonstrated that LCAT is highly expressed in the sera of advanced-stage human breast cancers within the same classification. In conclusion, we identified a possible common plasma protein biomarker, LCAT, that is highly expressed in aggressive human breast cancer and canine mammary tumor.
Collapse
Affiliation(s)
- Hyoung-Min Park
- Department of Biochemistry, BK21 Plus and Research Institute for Veterinary Science, School of Veterinary Medicine, Seoul National University, Seoul 08826, Korea
- The Canine Cancer Research Center, Seoul National University, Seoul 08826, Korea
| | - HuiSu Kim
- Department of Biochemistry, BK21 Plus and Research Institute for Veterinary Science, School of Veterinary Medicine, Seoul National University, Seoul 08826, Korea
- The Canine Cancer Research Center, Seoul National University, Seoul 08826, Korea
| | - Dong Wook Kim
- Department of Biochemistry, BK21 Plus and Research Institute for Veterinary Science, School of Veterinary Medicine, Seoul National University, Seoul 08826, Korea
- The Canine Cancer Research Center, Seoul National University, Seoul 08826, Korea
| | - Jong-Hyuk Yoon
- Neurodegenerative Disease Research Group, Korea Brain Research Institute, Daegu 41062, Korea
| | - Byung-Gyu Kim
- Center for Genomic Integrity, Institute for Basic Science, UNIST, Ulsan 44919, Korea
| | - Je-Yoel Cho
- Department of Biochemistry, BK21 Plus and Research Institute for Veterinary Science, School of Veterinary Medicine, Seoul National University, Seoul 08826, Korea
- The Canine Cancer Research Center, Seoul National University, Seoul 08826, Korea
| |
Collapse
|
7
|
Silva‐Costa LC, Garcia‐Rosa S, Smith BJ, Baldasso PA, Steiner J, Martins‐de‐Souza D. Blood plasma high abundant protein depletion unintentionally carries over 100 proteins. SEPARATION SCIENCE PLUS 2019. [DOI: 10.1002/sscp.201900057] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Affiliation(s)
- Licia C. Silva‐Costa
- Laboratory of NeuroproteomicsInstitute of BiologyDepartment of Biochemistry and Tissue BiologyUniversity of Campinas (UNICAMP) Campinas Brazil
| | - Sheila Garcia‐Rosa
- Laboratory of NeuroproteomicsInstitute of BiologyDepartment of Biochemistry and Tissue BiologyUniversity of Campinas (UNICAMP) Campinas Brazil
| | - Bradley J. Smith
- Laboratory of NeuroproteomicsInstitute of BiologyDepartment of Biochemistry and Tissue BiologyUniversity of Campinas (UNICAMP) Campinas Brazil
| | - Paulo A. Baldasso
- Laboratory of NeuroproteomicsInstitute of BiologyDepartment of Biochemistry and Tissue BiologyUniversity of Campinas (UNICAMP) Campinas Brazil
| | - Johann Steiner
- Department of PsychiatryUniversity of Magdeburg Magdeburg Germany
| | - Daniel Martins‐de‐Souza
- Laboratory of NeuroproteomicsInstitute of BiologyDepartment of Biochemistry and Tissue BiologyUniversity of Campinas (UNICAMP) Campinas Brazil
- Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION)Conselho Nacional de Desenvolvimento Científico e Tecnológico São Paulo Brazil
| |
Collapse
|
8
|
Zhang C, Leng W, Sun C, Lu T, Chen Z, Men X, Wang Y, Wang G, Zhen B, Qin J. Urine Proteome Profiling Predicts Lung Cancer from Control Cases and Other Tumors. EBioMedicine 2018; 30:120-128. [PMID: 29576497 PMCID: PMC5952250 DOI: 10.1016/j.ebiom.2018.03.009] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 03/01/2018] [Accepted: 03/09/2018] [Indexed: 12/31/2022] Open
Abstract
Development of noninvasive, reliable biomarkers for lung cancer diagnosis has many clinical benefits knowing that most of lung cancer patients are diagnosed at the late stage. For this purpose, we conducted proteomic analyses of 231 human urine samples in healthy individuals (n = 33), benign pulmonary diseases (n = 40), lung cancer (n = 33), bladder cancer (n = 17), cervical cancer (n = 25), colorectal cancer (n = 22), esophageal cancer (n = 14), and gastric cancer (n = 47) patients collected from multiple medical centers. By random forest modeling, we nominated a list of urine proteins that could separate lung cancers from other cases. With a feature selection algorithm, we selected a panel of five urinary biomarkers (FTL: Ferritin light chain; MAPK1IP1L: Mitogen-Activated Protein Kinase 1 Interacting Protein 1 Like; FGB: Fibrinogen Beta Chain; RAB33B: RAB33B, Member RAS Oncogene Family; RAB15: RAB15, Member RAS Oncogene Family) and established a combinatorial model that can correctly classify the majority of lung cancer cases both in the training set (n = 46) and the test sets (n = 14–47 per set) with an AUC ranging from 0.8747 to 0.9853. A combination of five urinary biomarkers not only discriminates lung cancer patients from control groups but also differentiates lung cancer from other common tumors. The biomarker panel and the predictive model, when validated by more samples in a multi-center setting, may be used as an auxiliary diagnostic tool along with imaging technology for lung cancer detection. A case-control study of biomarker discovery for lung cancer diagnosis was conducted. Human urine profiles in control cases and cancers were characterized. A list of candidate biomarkers was nominated and evaluated. A panel of urinary biomarkers was established and tumor-specificity was evaluated.
Cancer diagnosis with a noninvasive method at the early stage of the disease is highly desirable. Here, we analyzed hundreds of human urine samples from healthy individuals, patients with benign pulmonary diseases, and 6 types of cancers by proteomics and developed a panel of five urinary proteins that can separate the lung cancer from benign pulmonary diseases as well as the other 5 cancers (bladder, cervical, colorectal, esophageal and gastric) with a good sensitivity and disease specificity. Further validation experiments with expanded sample numbers are required to investigate whether this method can be applied in a clinical setting for the diagnosis of lung cancer.
Collapse
Affiliation(s)
- Chunchao Zhang
- Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wenchuan Leng
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China
| | - Changqing Sun
- Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China
| | - Tianyuan Lu
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China
| | - Zhengang Chen
- Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China
| | - Xuebo Men
- Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China
| | - Yi Wang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China; Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Guangshun Wang
- Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China.
| | - Bei Zhen
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China.
| | - Jun Qin
- State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; Joint Center for Translational Medicine, Tianjin, Baodi Hospital, Tianjin 301800, China; Alkek Center for Molecular Discovery, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
| |
Collapse
|
9
|
Aldonza MBD, Son YS, Sung HJ, Ahn JM, Choi YJ, Kim YI, Cho S, Cho JY. Paraoxonase-1 (PON1) induces metastatic potential and apoptosis escape via its antioxidative function in lung cancer cells. Oncotarget 2017; 8:42817-42835. [PMID: 28467805 PMCID: PMC5522108 DOI: 10.18632/oncotarget.17069] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Accepted: 03/20/2017] [Indexed: 01/19/2023] Open
Abstract
Paraoxonase-1 (PON1) gene polymorphisms have been closely associated with the development of advanced cancers while PON1 secretion to the serum is linked with inhibition of oxidized high-density lipoprotein by its antioxidative function. Our group previously demonstrated that post-translational modification of serum PON1 in form of fucosylated PON1 is a potential biomarker of small cell lung cancer. Here, we interrogated the role of PON1 in the pathobiology of lung cancer (LC) by addressing cell-autonomous mechanisms using gain-of-function and loss-of-function approaches and protein expression profiling of tissue samples in our clinical biobank. PON1 expression in LC patient tissues varied between overexpression in squamous cell carcinoma and minimal loss in adenocarcinoma sub-types. Simultaneous overexpression of PON1 both at the gene and protein stability levels induced pro-oncogenic characteristics in LC cells and xenografts. PON1 overexpression supported metastatic progression of LC by decreasing G1/S ratio and LC cell senescence involving p21Waf1/Cip1. PON1 suppressed drug- and ligand-induced cell death and protected LC cells from genotoxic damages with maintained ATP levels, requiring p53-directed signals. PON1 promoted ROS deregulation protecting the mitochondria from dysregulation. PON1 knockdown resulted in the blockage of its antioxidant function in LC cells through Akt signaling with reduced invasive signature as a consequence of scant expression. Targeted glycolysis stimulated PON1 antioxidant activity regulating phosphorylation of AMPK-α. The functional data imply that exploitation of the antioxidative function of PON1 is consequential in driving LC pathogenesis at the cell-autonomous mechanistic level with consequences on tumor growth.
Collapse
Affiliation(s)
- Mark Borris D. Aldonza
- Department of Biochemistry, BK21 PLUS Program for Creative Veterinary Science Research and Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea
- Current address: Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Yeon Sung Son
- Department of Biochemistry, BK21 PLUS Program for Creative Veterinary Science Research and Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea
| | - Hye-Jin Sung
- Department of Biochemistry, BK21 PLUS Program for Creative Veterinary Science Research and Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea
| | - Jung Mo Ahn
- Department of Biochemistry, BK21 PLUS Program for Creative Veterinary Science Research and Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea
- Current address: Bio Center, Incheon Technopark, Incheon, Republic of Korea
| | - Young-Jin Choi
- Department of Biochemistry, BK21 PLUS Program for Creative Veterinary Science Research and Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea
- Current address: College of Medicine, University of Ulsan, Seoul, Republic of Korea
| | - Yong-In Kim
- Department of Biochemistry, BK21 PLUS Program for Creative Veterinary Science Research and Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea
| | - Sukki Cho
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seoungnam-Si, Gyeonggi-Do, Republic of Korea
| | - Je-Yoel Cho
- Department of Biochemistry, BK21 PLUS Program for Creative Veterinary Science Research and Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea
| |
Collapse
|
10
|
Abstract
Lung cancer is the most common cause of cancer-related death worldwide, less than 7% of patients survive 10 years following diagnosis across all stages of lung cancer. Late stage of diagnosis and lack of effective and personalized medicine reflect the need for a better understanding of the mechanisms that underlie lung cancer progression. Quantitative proteomics provides the relative different protein abundance in normal and cancer patients which offers the information for molecular interactions, signaling pathways, and biomarker identification. Here we introduce both theoretical and practical applications in the use of quantitative proteomics approaches, with principles of current technologies and methodologies including gel-based, label free, stable isotope labeling as well as targeted proteomics. Predictive markers of drug resistance, candidate biomarkers for diagnosis, and prognostic markers in lung cancer have also been discovered and analyzed by quantitative proteomic analysis. Moreover, construction of protein networks enables to provide an opportunity to interpret disease pathway and improve our understanding in cancer therapeutic strategies, allowing the discovery of molecular markers and new therapeutic targets for lung cancer.
Collapse
Affiliation(s)
| | - Hsueh-Fen Juan
- Institute of Molecular and Cellular Biology, National Taiwan University, Taipei, Taiwan. .,Department of Life Science, National Taiwan University, Taipei, Taiwan. .,Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan.
| |
Collapse
|
11
|
Fujii K, Nakamura H, Nishimura T. Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer, COPD, and asthma. Expert Rev Proteomics 2017; 14:373-386. [PMID: 28271730 DOI: 10.1080/14789450.2017.1304215] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
INTRODUCTION Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges. Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma. Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.
Collapse
Affiliation(s)
- Kiyonaga Fujii
- a Department of Translational Medicine Informatics , St. Marianna University School of Medicine, Miyamae-ku , Kawasaki , Japan
| | - Haruhiko Nakamura
- a Department of Translational Medicine Informatics , St. Marianna University School of Medicine, Miyamae-ku , Kawasaki , Japan.,b Department of Chest Surgery , St. Marianna University School of Medicine, Miyamae-ku , Kawasaki , Japan
| | - Toshihide Nishimura
- a Department of Translational Medicine Informatics , St. Marianna University School of Medicine, Miyamae-ku , Kawasaki , Japan
| |
Collapse
|
12
|
Exosomal Proteins in Lung Cancer: The Last Frontier in Liquid Biopsies. J Thorac Oncol 2016; 11:1609-11. [DOI: 10.1016/j.jtho.2016.08.122] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Accepted: 08/08/2016] [Indexed: 11/23/2022]
|