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Pokhriyall M, Shukla N, Singh TR, Suravajhala P. Proteogenomic Approaches for Diseasome Studies. Methods Mol Biol 2025; 2859:253-264. [PMID: 39436606 DOI: 10.1007/978-1-0716-4152-1_14] [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] [Indexed: 10/23/2024]
Abstract
During the last three decades, technological advancements in high-throughput next-generation sequencing have resulted in an increased understanding of proteomic and genomic data, aptly termed proteogenomics. Efforts in developing such approaches have not only been limited but also focused on protein identification and subcellular localization. These approaches, however, have also been explored for their broad understanding of how genomics/transcriptomics data have yielded measures, for example, gene expression regulation/signal cascading and diseasome studies. In this review, we discuss methods and tools developed through sequence-centric integrative modeling of proteogenomic approaches.
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Affiliation(s)
- Medhavi Pokhriyall
- Centre of Excellence in Healthcare Technologies and Informatics (CEHTI), Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology (JUIT), Solan, India
| | - Nidhi Shukla
- Dr. B. Lal Institute of Biotechnology, Jaipur, India
| | - Tiratha Raj Singh
- Centre of Excellence in Healthcare Technologies and Informatics (CEHTI), Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology (JUIT), Solan, India
| | - Prashanth Suravajhala
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeethaam, Amritapuri, Clappana, Kerala, India.
- Bioclues.org, Hyderabad, India.
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2
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Che Y, Zhao M, Gao Y, Zhang Z, Zhang X. Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases. Front Mol Biosci 2024; 11:1483326. [PMID: 39741929 PMCID: PMC11685090 DOI: 10.3389/fmolb.2024.1483326] [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: 08/23/2024] [Accepted: 12/02/2024] [Indexed: 01/03/2025] Open
Abstract
Thyroid diseases, including functional and neoplastic diseases, bring a huge burden to people's health. Therefore, a timely and accurate diagnosis is necessary. Mass spectrometry (MS) based multi-omics has become an effective strategy to reveal the complex biological mechanisms of thyroid diseases. The exponential growth of biomedical data has promoted the applications of machine learning (ML) techniques to address new challenges in biology and clinical research. In this review, we presented the detailed review of applications of ML for MS-based multi-omics in thyroid disease. It is primarily divided into two sections. In the first section, MS-based multi-omics, primarily proteomics and metabolomics, and their applications in clinical diseases are briefly discussed. In the second section, several commonly used unsupervised learning and supervised algorithms, such as principal component analysis, hierarchical clustering, random forest, and support vector machines are addressed, and the integration of ML techniques with MS-based multi-omics data and its application in thyroid disease diagnosis is explored.
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Affiliation(s)
- Yanan Che
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China
| | - Meng Zhao
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China
| | - Yan Gao
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China
| | - Zhibin Zhang
- Department of General Surgery, Tianjin First Central Hospital, Tianjin, China
| | - Xiangyang Zhang
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China
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Upadhyay AK, Nag DS, Jena S, Sinha N, Lodh D. Newer Biomarkers in Gallbladder Carcinoma: A Scoping Review. Cureus 2024; 16:e75142. [PMID: 39759612 PMCID: PMC11700022 DOI: 10.7759/cureus.75142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/05/2024] [Indexed: 01/07/2025] Open
Abstract
Biomarkers have the potential to play a crucial role in managing gallbladder cancer post-surgery. They can identify patients more likely to experience a recurrence, allowing oncologists to tailor a more intensive surveillance plan and consider additional therapies. Some biomarkers can even predict how well a patient will respond to specific chemotherapy or targeted treatments. By monitoring these biomarkers, clinicians can track how effective the ongoing treatment is and detect any signs of early recurrence. Various biomarkers, like tumor markers, genetic markers, and genomic and epigenetic markers, are being investigated. The goal is to find the most reliable and accurate biomarkers to enhance patient care and outcomes. Integrating biomarker data into treatment plans can help personalize therapy and make better informed decisions. By identifying which patients are likely to benefit from specific treatments, biomarkers have the potential to improve long-term survival rates significantly. This scoping review discusses newer biomarkers in gallbladder carcinoma; some of them are in clinical use, while most of them are used in research settings. This provides a broad insight to practicing clinicians about the present biomarkers and the futuristic biomarkers.
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Affiliation(s)
| | | | | | - Neetesh Sinha
- Surgical Oncology, Tata Main Hospital, Jamshedpur, IND
| | - Dona Lodh
- Anesthesiology, Tata Main Hospital, Jamshedpur, IND
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4
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Bhushan V, Nita-Lazar A. Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology. J Proteome Res 2024; 23:2700-2722. [PMID: 38451675 PMCID: PMC11296931 DOI: 10.1021/acs.jproteome.3c00839] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.
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Affiliation(s)
- Vanya Bhushan
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Aleksandra Nita-Lazar
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
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van der Wijngaart H, Beekhof R, Knol JC, Henneman AA, de Goeij-de Haas R, Piersma SR, Pham TV, Jimenez CR, Verheul HMW, Labots M. Candidate biomarkers for treatment benefit from sunitinib in patients with advanced renal cell carcinoma using mass spectrometry-based (phospho)proteomics. Clin Proteomics 2023; 20:49. [PMID: 37940875 PMCID: PMC10631096 DOI: 10.1186/s12014-023-09437-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 10/11/2023] [Indexed: 11/10/2023] Open
Abstract
The tyrosine kinase inhibitor sunitinib is an effective first-line treatment for patients with advanced renal cell carcinoma (RCC). Hypothesizing that a functional read-out by mass spectrometry-based (phospho, p-)proteomics will identify predictive biomarkers for treatment outcome of sunitinib, tumor tissues of 26 RCC patients were analyzed. Eight patients had primary resistant (RES) and 18 sensitive (SENS) RCC. A 78 phosphosite signature (p < 0.05, fold-change > 2) was identified; 22 p-sites were upregulated in RES (unique in RES: BCAR3, NOP58, EIF4A2, GDI1) and 56 in SENS (35 unique). EIF4A1/EIF4A2 were differentially expressed in RES at the (p-)proteome and, in an independent cohort, transcriptome level. Inferred kinase activity of MAPK3 (p = 0.026) and EGFR (p = 0.045) as determined by INKA was higher in SENS. Posttranslational modifications signature enrichment analysis showed that different p-site-centric signatures were enriched (p < 0.05), of which FGF1 and prolactin pathways in RES and, in SENS, vanadate and thrombin treatment pathways, were most significant. In conclusion, the RCC (phospho)proteome revealed differential p-sites and kinase activities associated with sunitinib resistance and sensitivity. Independent validation is warranted to develop an assay for upfront identification of patients who are intrinsically resistant to sunitinib.
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Affiliation(s)
- Hanneke van der Wijngaart
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Robin Beekhof
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Jaco C Knol
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Alex A Henneman
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Richard de Goeij-de Haas
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Sander R Piersma
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Thang V Pham
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Connie R Jimenez
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Henk M W Verheul
- Department of Medical Oncology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Mariette Labots
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
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Li L, Song X, Chen G, Zhang Z, Zheng B, Zhang Q, Wang S, Xie L. Plasma exosomal protein PLG and SERPINA1 in colorectal cancer diagnosis and coagulation abnormalities. J Cancer Res Clin Oncol 2023; 149:8507-8519. [PMID: 37093347 DOI: 10.1007/s00432-023-04776-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 04/10/2023] [Indexed: 04/25/2023]
Abstract
PURPOSE Early diagnosis of colorectal cancer (CRC) is critical to patient prognosis; however, there is lack of non-invasive biomarkers that are extremely sensitive and specific for early screening and diagnosis. Exosomes are a novel tool applied to the diagnosis and treatment of cancer. Changes in plasma exosomal proteins have a certain relationship with the development of various diseases including tumors. Here, we aimed to find exosomal biomarkers for early diagnosis of CRC. METHODS Exosomes obtained by ultracentrifugation from CRC patients and healthy donors were characterized by transmission electron microscopy (TEM), qNano and western blotting. Proteomic and functional enrichment analyses confirmed differences in the specific expression of exosomal proteins in plasma between CRC patients and healthy donors. Western blotting with enzyme-linked immunosorbent assay (ELISA) was used to verify the difference proteins. Statistical methods were used to analyze the relationship between protein levels and CRC. RESULTS The expression levels of serpin peptidase inhibitor clade A member 1 (SERPINA1) and fibrinogen (PLG) in CRC patients were significantly higher than those in healthy groups. Receptor operating characteristic (ROC) curves analysis was superior to CEA and CA19-9 for the diagnosis of colorectal cancer and early-stage colorectal cancer. The two were related to TNM staging and coagulation, and the difference was statistically significant. CONCLUSION The results of this study have potential value in advancing the clinical diagnosis of colorectal cancer.
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Affiliation(s)
- Lei Li
- School of Medical Laboratory, Weifang Medical University, Weifang, China
- Shandong Cancer Hospital and Institute, Shandong Academy of Medical Sciences, Shandong First Medical University, 440 Ji-Yan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Xingguo Song
- Shandong Cancer Hospital and Institute, Shandong Academy of Medical Sciences, Shandong First Medical University, 440 Ji-Yan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Guanxuan Chen
- Department of Intensive Care Unit, Shandong Cancer Hospital and Institute, Shandong Academy of Medical Sciences, Shandong First Medical University, Jinan, Shandong, People's Republic of China
| | - Zhe Zhang
- Shandong Cancer Hospital and Institute, Shandong Academy of Medical Sciences, Shandong First Medical University, 440 Ji-Yan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Baibing Zheng
- Shandong Cancer Hospital and Institute, Shandong Academy of Medical Sciences, Shandong First Medical University, 440 Ji-Yan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Qianru Zhang
- Shandong Cancer Hospital and Institute, Shandong Academy of Medical Sciences, Shandong First Medical University, 440 Ji-Yan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Shiwen Wang
- Shandong Cancer Hospital and Institute, Shandong Academy of Medical Sciences, Shandong First Medical University, 440 Ji-Yan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Li Xie
- Shandong Cancer Hospital and Institute, Shandong Academy of Medical Sciences, Shandong First Medical University, 440 Ji-Yan Road, Jinan, 250117, Shandong Province, People's Republic of China.
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7
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Birhanu AG. Mass spectrometry-based proteomics as an emerging tool in clinical laboratories. Clin Proteomics 2023; 20:32. [PMID: 37633929 PMCID: PMC10464495 DOI: 10.1186/s12014-023-09424-x] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 08/03/2023] [Indexed: 08/28/2023] Open
Abstract
Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.
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Beekhof R, Bertotti A, Böttger F, Vurchio V, Cottino F, Zanella ER, Migliardi G, Viviani M, Grassi E, Lupo B, Henneman AA, Knol JC, Pham TV, de Goeij-de Haas R, Piersma SR, Labots M, Verheul HMW, Trusolino L, Jimenez CR. Phosphoproteomics of patient-derived xenografts identifies targets and markers associated with sensitivity and resistance to EGFR blockade in colorectal cancer. Sci Transl Med 2023; 15:eabm3687. [PMID: 37585503 DOI: 10.1126/scitranslmed.abm3687] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 07/25/2023] [Indexed: 08/18/2023]
Abstract
Epidermal growth factor receptor (EGFR) is a well-exploited therapeutic target in metastatic colorectal cancer (mCRC). Unfortunately, not all patients benefit from current EGFR inhibitors. Mass spectrometry-based proteomics and phosphoproteomics were performed on 30 genomically and pharmacologically characterized mCRC patient-derived xenografts (PDXs) to investigate the molecular basis of response to EGFR blockade and identify alternative drug targets to overcome resistance. Both the tyrosine and global phosphoproteome as well as the proteome harbored distinctive response signatures. We found that increased pathway activity related to mitogen-activated protein kinase (MAPK) inhibition and abundant tyrosine phosphorylation of cell junction proteins, such as CXADR and CLDN1/3, in sensitive tumors, whereas epithelial-mesenchymal transition and increased MAPK and AKT signaling were more prevalent in resistant tumors. Furthermore, the ranking of kinase activities in single samples confirmed the driver activity of ERBB2, EGFR, and MET in cetuximab-resistant tumors. This analysis also revealed high kinase activity of several members of the Src and ephrin kinase family in 2 CRC PDX models with genomically unexplained resistance. Inhibition of these hyperactive kinases, alone or in combination with cetuximab, resulted in growth inhibition of ex vivo PDX-derived organoids and in vivo PDXs. Together, these findings highlight the potential value of phosphoproteomics to improve our understanding of anti-EGFR treatment and response prediction in mCRC and bring to the forefront alternative drug targets in cetuximab-resistant tumors.
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Affiliation(s)
- Robin Beekhof
- Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, OncoProteomics Laboratory, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
| | - Andrea Bertotti
- Candiolo Cancer Institute - FPO IRCCS, Candiolo, 10060 Torino, Italy
- Department of Oncology, University of Torino, Candiolo, 10060 Torino, Italy
| | - Franziska Böttger
- Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, OncoProteomics Laboratory, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Oncode Institute, 1066 CX Amsterdam, Netherlands
| | - Valentina Vurchio
- Candiolo Cancer Institute - FPO IRCCS, Candiolo, 10060 Torino, Italy
- Department of Oncology, University of Torino, Candiolo, 10060 Torino, Italy
| | - Francesca Cottino
- Candiolo Cancer Institute - FPO IRCCS, Candiolo, 10060 Torino, Italy
| | - Eugenia R Zanella
- Candiolo Cancer Institute - FPO IRCCS, Candiolo, 10060 Torino, Italy
| | - Giorgia Migliardi
- Candiolo Cancer Institute - FPO IRCCS, Candiolo, 10060 Torino, Italy
- Department of Oncology, University of Torino, Candiolo, 10060 Torino, Italy
| | - Marco Viviani
- Candiolo Cancer Institute - FPO IRCCS, Candiolo, 10060 Torino, Italy
- Department of Oncology, University of Torino, Candiolo, 10060 Torino, Italy
| | - Elena Grassi
- Candiolo Cancer Institute - FPO IRCCS, Candiolo, 10060 Torino, Italy
- Department of Oncology, University of Torino, Candiolo, 10060 Torino, Italy
| | - Barbara Lupo
- Candiolo Cancer Institute - FPO IRCCS, Candiolo, 10060 Torino, Italy
| | - Alex A Henneman
- Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, OncoProteomics Laboratory, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
| | - Jaco C Knol
- Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, OncoProteomics Laboratory, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
| | - Thang V Pham
- Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, OncoProteomics Laboratory, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
| | - Richard de Goeij-de Haas
- Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, OncoProteomics Laboratory, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
| | - Sander R Piersma
- Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, OncoProteomics Laboratory, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
| | - Mariette Labots
- Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
| | - Henk M W Verheul
- Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
- Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, Netherlands
| | - Livio Trusolino
- Candiolo Cancer Institute - FPO IRCCS, Candiolo, 10060 Torino, Italy
- Department of Oncology, University of Torino, Candiolo, 10060 Torino, Italy
| | - Connie R Jimenez
- Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, OncoProteomics Laboratory, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, Netherlands
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Barker AD, Alba MM, Mallick P, Agus DB, Lee JSH. An Inflection Point in Cancer Protein Biomarkers: What Was and What's Next. Mol Cell Proteomics 2023:100569. [PMID: 37196763 PMCID: PMC10388583 DOI: 10.1016/j.mcpro.2023.100569] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 04/26/2023] [Accepted: 05/08/2023] [Indexed: 05/19/2023] Open
Abstract
Biomarkers remain the highest value proposition in cancer medicine today - especially protein biomarkers. Yet despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last two decades have seen an explosion of multi-omics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single cell analysis, artificial intelligence (machine and deep learning) for data analysis and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking towards viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to re-define biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.
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Affiliation(s)
- Anna D Barker
- Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA; Complex Adaptive Systems Initiative and School of Life Sciences, Arizona State University, Tempe, Arizona
| | - Mario M Alba
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA
| | - Parag Mallick
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA; Department of Radiology, Stanford University, Stanford, CA
| | - David B Agus
- Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA; Keck School of Medicine, University of Southern California, Los Angeles, CA; Viterbi School of Engineering, University of Southern California, Los Angeles, CA
| | - Jerry S H Lee
- Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA; Keck School of Medicine, University of Southern California, Los Angeles, CA; Viterbi School of Engineering, University of Southern California, Los Angeles, CA
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10
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Nisar N, Mir SA, Kareem O, Pottoo FH. Proteomics approaches in the identification of cancer biomarkers and drug discovery. Proteomics 2023. [DOI: 10.1016/b978-0-323-95072-5.00001-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
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11
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Introduction to Mass Spectrometry for Bimolecular Analysis in a Clinical Laboratory. Methods Mol Biol 2022; 2546:1-12. [PMID: 36127573 DOI: 10.1007/978-1-0716-2565-1_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
Mass spectrometry is a technique that identifies analytes based on mass-to-charge (m/z) ratio and structural fragments. Although this technique has been used in research and specialized clinical laboratories for decades, only in recent years has mass spectrometry become popular in routine clinical laboratories. Mass spectrometry, especially when coupled with gas chromatography or liquid chromatography, provides very specific and often sensitive analysis of many analytes. Other advantages of mass spectrometry include simultaneous analysis of multiple analytes (>100) and generally limited requirement for specialized reagents. Commonly measured analytes by mass spectrometry include metabolites, drugs, hormones, and proteins.
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12
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van Linde ME, Labots M, Brahm CG, Hovinga KE, De Witt Hamer PC, Honeywell RJ, de Goeij-de Haas R, Henneman AA, Knol JC, Peters GJ, Dekker H, Piersma SR, Pham TV, Vandertop WP, Jiménez CR, Verheul HM. Tumor Drug Concentration and Phosphoproteomic Profiles After Two Weeks of Treatment With Sunitinib in Patients with Newly Diagnosed Glioblastoma. Clin Cancer Res 2022; 28:1595-1602. [PMID: 35165100 PMCID: PMC9365363 DOI: 10.1158/1078-0432.ccr-21-1933] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 09/14/2021] [Accepted: 02/09/2022] [Indexed: 01/07/2023]
Abstract
PURPOSE Tyrosine kinase inhibitors (TKI) have poor efficacy in patients with glioblastoma (GBM). Here, we studied whether this is predominantly due to restricted blood-brain barrier penetration or more to biological characteristics of GBM. PATIENTS AND METHODS Tumor drug concentrations of the TKI sunitinib after 2 weeks of preoperative treatment was determined in 5 patients with GBM and compared with its in vitro inhibitory concentration (IC50) in GBM cell lines. In addition, phosphotyrosine (pTyr)-directed mass spectrometry (MS)-based proteomics was performed to evaluate sunitinib-treated versus control GBM tumors. RESULTS The median tumor sunitinib concentration of 1.9 μmol/L (range 1.0-3.4) was 10-fold higher than in concurrent plasma, but three times lower than sunitinib IC50s in GBM cell lines (median 5.4 μmol/L, 3.0-8.5; P = 0.01). pTyr-phosphoproteomic profiles of tumor samples from 4 sunitinib-treated versus 7 control patients revealed 108 significantly up- and 23 downregulated (P < 0.05) phosphopeptides for sunitinib treatment, resulting in an EGFR-centered signaling network. Outlier analysis of kinase activities as a potential strategy to identify drug targets in individual tumors identified nine kinases, including MAPK10 and INSR/IGF1R. CONCLUSIONS Achieved tumor sunitinib concentrations in patients with GBM are higher than in plasma, but lower than reported for other tumor types and insufficient to significantly inhibit tumor cell growth in vitro. Therefore, alternative TKI dosing to increase intratumoral sunitinib concentrations might improve clinical benefit for patients with GBM. In parallel, a complex profile of kinase activity in GBM was found, supporting the potential of (phospho)proteomic analysis for the identification of targets for (combination) treatment.
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Affiliation(s)
- Myra E. van Linde
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Mariette Labots
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Cyrillo G. Brahm
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Koos E. Hovinga
- Department of Neurosurgery, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Philip C. De Witt Hamer
- Department of Neurosurgery, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Richard J. Honeywell
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Pharmacy, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Richard de Goeij-de Haas
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Alex A. Henneman
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Jaco C. Knol
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Godefridus J. Peters
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Biochemistry, Medical University of Gdansk, Gdansk, Poland
| | - Henk Dekker
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Sander R. Piersma
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Thang V. Pham
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - William P. Vandertop
- Department of Neurosurgery, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Connie R. Jiménez
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Henk M.W. Verheul
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Medical Oncology, Radboud UMC, Nijmegen, the Netherlands
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13
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Safabakhsh S, Panwar P, Barichello S, Sangha SS, Hanson PJ, Van Petegem F, Laksman Z. THE ROLE OF PHOSPHORYLATION IN ATRIAL FIBRILLATION: A FOCUS ON MASS SPECTROMETRY APPROACHES. Cardiovasc Res 2021; 118:1205-1217. [PMID: 33744917 DOI: 10.1093/cvr/cvab095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 03/16/2021] [Indexed: 11/14/2022] Open
Abstract
Atrial fibrillation (AF) is the most common arrhythmia worldwide. It is associated with significant increases in morbidity in the form of stroke and heart failure, and a doubling in all-cause mortality. The pathophysiology of AF is incompletely understood, and this has contributed to a lack of effective treatments and disease-modifying therapies. An important cellular process that may explain how risk factors give rise to AF includes post-translational modification (PTM) of proteins. As the most commonly occurring PTM, protein phosphorylation is especially relevant. Although many methods exist for studying protein phosphorylation, a common and highly resolute technique is mass spectrometry (MS). This review will discuss recent evidence surrounding the role of protein phosphorylation in the pathogenesis of AF. MS-based technology to study phosphorylation and uses of MS in other areas of medicine such as oncology will also be presented. Based on these data, future goals and experiments will be outlined that utilize MS technology to better understand the role of phosphorylation in AF and elucidate its role in AF pathophysiology. This may ultimately allow for the development of more effective AF therapies.
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Affiliation(s)
- Sina Safabakhsh
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Pankaj Panwar
- AbCellera Biologicals Inc., Vancouver, British Columbia, Canada
| | - Scott Barichello
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sarabjit S Sangha
- Cellular and Regenerative Medicine Centre, BC Children's Hospital Research Institute, 950 West 28th Avenue, Vancouver, British Columbia, Canada.,Molecular Cardiac Physiology Group, Departments of Biomedical Physiology and Kinesiology and Molecular Biology and Biochemistry, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia, Canada
| | - Paul J Hanson
- UBC Heart Lung Innovation Centre, Vancouver, British Columbia, Canada.,UBC Department of Pathology and Laboratory Medicine, Vancouver, British Columbia, Canada
| | - Filip Van Petegem
- Department of Biochemistry and Molecular Biology, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada
| | - Zachary Laksman
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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14
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Proteomics in thyroid cancer and other thyroid-related diseases: A review of the literature. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2020; 1868:140510. [DOI: 10.1016/j.bbapap.2020.140510] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 06/26/2020] [Accepted: 07/19/2020] [Indexed: 12/21/2022]
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15
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Tang J, Wang Y, Luo Y, Fu J, Zhang Y, Li Y, Xiao Z, Lou Y, Qiu Y, Zhu F. Computational advances of tumor marker selection and sample classification in cancer proteomics. Comput Struct Biotechnol J 2020; 18:2012-2025. [PMID: 32802273 PMCID: PMC7403885 DOI: 10.1016/j.csbj.2020.07.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/06/2020] [Accepted: 07/08/2020] [Indexed: 12/11/2022] Open
Abstract
Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.
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Key Words
- ANN, Artificial Neural Network
- ANOVA, Analysis of Variance
- CFS, Correlation-based Feature Selection
- Cancer proteomics
- Computational methods
- DAPC, Discriminant Analysis of Principal Component
- DT, Decision Trees
- EDA, Estimation of Distribution Algorithm
- FC, Fold Change
- GA, Genetic Algorithms
- GR, Gain Ratio
- HC, Hill Climbing
- HCA, Hierarchical Cluster Analysis
- IG, Information Gain
- LDA, Linear Discriminant Analysis
- LIMMA, Linear Models for Microarray Data
- MBF, Markov Blanket Filter
- MWW, Mann–Whitney–Wilcoxon test
- OPLS-DA, Orthogonal Partial Least Squares Discriminant Analysis
- PCA, Principal Component Analysis
- PLS-DA, Partial Least Square Discriminant Analysis
- RF, Random Forest
- RF-RFE, Random Forest with Recursive Feature Elimination
- SA, Simulated Annealing
- SAM, Significance Analysis of Microarrays
- SBE, Sequential Backward Elimination
- SFS, and Sequential Forward Selection
- SOM, Self-organizing Map
- SU, Symmetrical Uncertainty
- SVM, Support Vector Machine
- SVM-RFE, Support Vector Machine with Recursive Feature Elimination
- Sample classification
- Tumor marker selection
- sPLSDA, Sparse Partial Least Squares Discriminant Analysis
- t-SNE, Student t Distribution
- χ2, Chi-square
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Affiliation(s)
- Jing Tang
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yang Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
| | - Yi Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ziyu Xiao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Yunqing Qiu
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Feng Zhu
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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16
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Jeong S, Oh MJ, Kim U, Lee J, Kim JH, An HJ. Glycosylation of serum haptoglobin as a marker of gastric cancer: an overview for clinicians. Expert Rev Proteomics 2020; 17:109-117. [DOI: 10.1080/14789450.2020.1740091] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Seunghyup Jeong
- Asia-pacific Glycomics Reference Site, Chungnam National University, Daejeon, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, Republic of Korea
| | - Myung Jin Oh
- Asia-pacific Glycomics Reference Site, Chungnam National University, Daejeon, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, Republic of Korea
| | - Unyong Kim
- Biocomplete Inc, Seoul, Republic of Korea
| | - Jua Lee
- Asia-pacific Glycomics Reference Site, Chungnam National University, Daejeon, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, Republic of Korea
| | - Jae-Han Kim
- Department of Food and Nutrition, Chungnam National University, Daejeon, Republic of Korea
| | - Hyun Joo An
- Asia-pacific Glycomics Reference Site, Chungnam National University, Daejeon, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, Republic of Korea
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17
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Labots M, Pham TV, Honeywell RJ, Knol JC, Beekhof R, de Goeij-de Haas R, Dekker H, Neerincx M, Piersma SR, van der Mijn JC, van der Peet DL, Meijerink MR, Peters GJ, van Grieken NC, Jiménez CR, Verheul HM. Kinase Inhibitor Treatment of Patients with Advanced Cancer Results in High Tumor Drug Concentrations and in Specific Alterations of the Tumor Phosphoproteome. Cancers (Basel) 2020; 12:330. [PMID: 32024067 PMCID: PMC7072422 DOI: 10.3390/cancers12020330] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 01/20/2020] [Accepted: 01/29/2020] [Indexed: 12/22/2022] Open
Abstract
Identification of predictive biomarkers for targeted therapies requires information on drug exposure at the target site as well as its effect on the signaling context of a tumor. To obtain more insight in the clinical mechanism of action of protein kinase inhibitors (PKIs), we studied tumor drug concentrations of protein kinase inhibitors (PKIs) and their effect on the tyrosine-(pTyr)-phosphoproteome in patients with advanced cancer. Tumor biopsies were obtained from 31 patients with advanced cancer before and after 2 weeks of treatment with sorafenib (SOR), erlotinib (ERL), dasatinib (DAS), vemurafenib (VEM), sunitinib (SUN) or everolimus (EVE). Tumor concentrations were determined by LC-MS/MS. pTyr-phosphoproteomics was performed by pTyr-immunoprecipitation followed by LC-MS/MS. Median tumor concentrations were 2-10 µM for SOR, ERL, DAS, SUN, EVE and >1 mM for VEM. These were 2-178 × higher than median plasma concentrations. Unsupervised hierarchical clustering of pTyr-phosphopeptide intensities revealed patient-specific clustering of pre- and on-treatment profiles. Drug-specific alterations of peptide phosphorylation was demonstrated by marginal overlap of robustly up- and downregulated phosphopeptides. These findings demonstrate that tumor drug concentrations are higher than anticipated and result in drug specific alterations of the phosphoproteome. Further development of phosphoproteomics-based personalized medicine is warranted.
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Affiliation(s)
- Mariette Labots
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (M.L.); (T.V.P.); (R.J.H.); (J.C.K.); (R.B.); (R.d.G.-d.H.); (H.D.); (M.N.); (S.R.P.); (J.C.v.d.M.); (G.J.P.)
| | - Thang V. Pham
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (M.L.); (T.V.P.); (R.J.H.); (J.C.K.); (R.B.); (R.d.G.-d.H.); (H.D.); (M.N.); (S.R.P.); (J.C.v.d.M.); (G.J.P.)
| | - Richard J. Honeywell
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (M.L.); (T.V.P.); (R.J.H.); (J.C.K.); (R.B.); (R.d.G.-d.H.); (H.D.); (M.N.); (S.R.P.); (J.C.v.d.M.); (G.J.P.)
| | - Jaco C. Knol
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (M.L.); (T.V.P.); (R.J.H.); (J.C.K.); (R.B.); (R.d.G.-d.H.); (H.D.); (M.N.); (S.R.P.); (J.C.v.d.M.); (G.J.P.)
| | - Robin Beekhof
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (M.L.); (T.V.P.); (R.J.H.); (J.C.K.); (R.B.); (R.d.G.-d.H.); (H.D.); (M.N.); (S.R.P.); (J.C.v.d.M.); (G.J.P.)
| | - Richard de Goeij-de Haas
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (M.L.); (T.V.P.); (R.J.H.); (J.C.K.); (R.B.); (R.d.G.-d.H.); (H.D.); (M.N.); (S.R.P.); (J.C.v.d.M.); (G.J.P.)
| | - Henk Dekker
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (M.L.); (T.V.P.); (R.J.H.); (J.C.K.); (R.B.); (R.d.G.-d.H.); (H.D.); (M.N.); (S.R.P.); (J.C.v.d.M.); (G.J.P.)
| | - Maarten Neerincx
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (M.L.); (T.V.P.); (R.J.H.); (J.C.K.); (R.B.); (R.d.G.-d.H.); (H.D.); (M.N.); (S.R.P.); (J.C.v.d.M.); (G.J.P.)
| | - Sander R. Piersma
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (M.L.); (T.V.P.); (R.J.H.); (J.C.K.); (R.B.); (R.d.G.-d.H.); (H.D.); (M.N.); (S.R.P.); (J.C.v.d.M.); (G.J.P.)
| | - Johannes C. van der Mijn
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (M.L.); (T.V.P.); (R.J.H.); (J.C.K.); (R.B.); (R.d.G.-d.H.); (H.D.); (M.N.); (S.R.P.); (J.C.v.d.M.); (G.J.P.)
| | - Donald L. van der Peet
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands;
| | - Martijn R. Meijerink
- Department of Radiology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands;
| | - Godefridus J. Peters
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (M.L.); (T.V.P.); (R.J.H.); (J.C.K.); (R.B.); (R.d.G.-d.H.); (H.D.); (M.N.); (S.R.P.); (J.C.v.d.M.); (G.J.P.)
| | - Nicole C.T. van Grieken
- Department of Pathology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands;
| | - Connie R. Jiménez
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (M.L.); (T.V.P.); (R.J.H.); (J.C.K.); (R.B.); (R.d.G.-d.H.); (H.D.); (M.N.); (S.R.P.); (J.C.v.d.M.); (G.J.P.)
| | - Henk M.W. Verheul
- Department of Medical Oncology, RadboudUMC, Radboud University, Geert Grooteplein Zuid 8, 6525 GA Nijmegen, The Netherlands
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18
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Selheim F, Aasebø E, Ribas C, Aragay AM. An Overview on G Protein-coupled Receptor-induced Signal Transduction in Acute Myeloid Leukemia. Curr Med Chem 2019; 26:5293-5316. [PMID: 31032748 DOI: 10.2174/0929867326666190429153247] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 03/22/2019] [Accepted: 04/05/2019] [Indexed: 02/06/2023]
Abstract
BACKGROUND Acute Myeloid Leukemia (AML) is a genetically heterogeneous disease characterized by uncontrolled proliferation of precursor myeloid-lineage cells in the bone marrow. AML is also characterized by patients with poor long-term survival outcomes due to relapse. Many efforts have been made to understand the biological heterogeneity of AML and the challenges to develop new therapies are therefore enormous. G Protein-coupled Receptors (GPCRs) are a large attractive drug-targeted family of transmembrane proteins, and aberrant GPCR expression and GPCR-mediated signaling have been implicated in leukemogenesis of AML. This review aims to identify the molecular players of GPCR signaling, focusing on the hematopoietic system, which are involved in AML to help developing novel drug targets and therapeutic strategies. METHODS We undertook an exhaustive and structured search of bibliographic databases for research focusing on GPCR, GPCR signaling and expression in AML. RESULTS AND CONCLUSION Many scientific reports were found with compelling evidence for the involvement of aberrant GPCR expression and perturbed GPCR-mediated signaling in the development of AML. The comprehensive analysis of GPCR in AML provides potential clinical biomarkers for prognostication, disease monitoring and therapeutic guidance. It will also help to provide marker panels for monitoring in AML. We conclude that GPCR-mediated signaling is contributing to leukemogenesis of AML, and postulate that mass spectrometrybased protein profiling of primary AML cells will accelerate the discovery of potential GPCR related biomarkers for AML.
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Affiliation(s)
- Frode Selheim
- The Proteomics Unit at the University of Bergen, Department of Biomedicine, University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway
| | - Elise Aasebø
- The Proteomics Unit at the University of Bergen, Department of Biomedicine, University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway.,Department of Clinical Science, University of Bergen, Jonas Lies vei 87, 5021 Bergen, Norway
| | - Catalina Ribas
- Departamento de Biología Molecular and Centro de Biología Molecular "Severo Ochoa" (UAM-CSIC), 28049 Madrid, Spain.,Instituto de Investigación Sanitaria La Princesa, 28006 Madrid, Spain.,CIBER de Enfermedades Cardiovasculares, ISCIII (CIBERCV), 28029 Madrid, Spain
| | - Anna M Aragay
- Departamento de Biologia Celular. Instituto de Biología Molecular de Barcelona (IBMB-CSIC), Spanish National Research Council (CSIC), Baldiri i Reixac, 15, 08028 Barcelona, Spain
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19
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van der Zwet JCG, Cordo' V, Canté-Barrett K, Meijerink JPP. Multi-omic approaches to improve outcome for T-cell acute lymphoblastic leukemia patients. Adv Biol Regul 2019; 74:100647. [PMID: 31523030 DOI: 10.1016/j.jbior.2019.100647] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 08/20/2019] [Accepted: 08/23/2019] [Indexed: 06/10/2023]
Abstract
In the last decade, tremendous progress in curative treatment has been made for T-ALL patients using high-intensive, risk-adapted multi-agent chemotherapy. Further treatment intensification to improve the cure rate is not feasible as it will increase the number of toxic deaths. Hence, about 20% of pediatric patients relapse and often die due to acquired therapy resistance. Personalized medicine is of utmost importance to further increase cure rates and is achieved by targeting specific initiation, maintenance or resistance mechanisms of the disease. Genomic sequencing has revealed mutations that characterize genetic subtypes of many cancers including T-ALL. However, leukemia may have various activated pathways that are not accompanied by the presence of mutations. Therefore, screening for mutations alone is not sufficient to identify all molecular targets and leukemic dependencies for therapeutic inhibition. We review the extent of the driving type A and the secondary type B genomic mutations in pediatric T-ALL that may be targeted by specific inhibitors. Additionally, we review the need for additional screening methods on the transcriptional and protein levels. An integrated 'multi-omic' screening will identify potential targets and biomarkers to establish significant progress in future individualized treatment of T-ALL patients.
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Affiliation(s)
| | - Valentina Cordo'
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
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20
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Beekhof R, van Alphen C, Henneman AA, Knol JC, Pham TV, Rolfs F, Labots M, Henneberry E, Le Large TY, de Haas RR, Piersma SR, Vurchio V, Bertotti A, Trusolino L, Verheul HM, Jimenez CR. INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases. Mol Syst Biol 2019; 15:e8250. [PMID: 30979792 PMCID: PMC6461034 DOI: 10.15252/msb.20188250] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 03/15/2019] [Accepted: 03/20/2019] [Indexed: 12/19/2022] Open
Abstract
Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label-free kinase-centric and substrate-centric information in an Integrative Inferred Kinase Activity (INKA) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase-substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild-type versus mutant, +/- drug), (iii) pre- and on-treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient-derived tumor xenografts with INKA-guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate-based single-sample tool KARP, and underscore target potential of high-ranking kinases, encouraging further exploration of INKA's functional and clinical value.
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Affiliation(s)
- Robin Beekhof
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Carolien van Alphen
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Alex A Henneman
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jaco C Knol
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Thang V Pham
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frank Rolfs
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mariette Labots
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Evan Henneberry
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Tessa Ys Le Large
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Richard R de Haas
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sander R Piersma
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Valentina Vurchio
- Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy
| | - Andrea Bertotti
- Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy
| | - Livio Trusolino
- Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy
| | - Henk Mw Verheul
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Connie R Jimenez
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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21
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Panis C, Corrêa S, Binato R, Abdelhay E. The Role of Proteomics in Cancer Research. ONCOGENOMICS 2019:31-55. [DOI: 10.1016/b978-0-12-811785-9.00003-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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22
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Acland M, Mittal P, Lokman NA, Klingler-Hoffmann M, Oehler MK, Hoffmann P. Mass Spectrometry Analyses of Multicellular Tumor Spheroids. Proteomics Clin Appl 2018; 12:e1700124. [PMID: 29227035 DOI: 10.1002/prca.201700124] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Revised: 11/13/2017] [Indexed: 12/21/2022]
Abstract
Multicellular tumor spheroids (MCTS) are a powerful biological in vitro model, which closely mimics the 3D structure of primary avascularized tumors. Mass spectrometry (MS) has established itself as a powerful analytical tool, not only to better understand and describe the complex structure of MCTS, but also to monitor their response to cancer therapeutics. The first part of this review focuses on traditional mass spectrometry approaches with an emphasis on elucidating the molecular characteristics of these structures. Then the mass spectrometry imaging (MSI) approaches used to obtain spatially defined information from MCTS is described. Finally the analysis of primary spheroids, such as those present in ovarian cancer, and the great potential that mass spectrometry analysis of these structures has for improved understanding of cancer progression and for personalized in vitro therapeutic testing is discussed.
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Affiliation(s)
- Mitchell Acland
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia.,Institute of Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, South Australia, Australia
| | - Parul Mittal
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia.,Institute of Photonics and Advanced Sensing (IPAS), University of Adelaide, Adelaide, South Australia, Australia
| | - Noor A Lokman
- Discipline of Obstetrics and Gynaecology, School of Medicine, Robinson Research Institute, University of Adelaide, Adelaide, South Australia, Australia
| | - Manuela Klingler-Hoffmann
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia.,Future Industries Institute, University of South Australia, Adelaide, South Australia, Australia
| | - Martin K Oehler
- Discipline of Obstetrics and Gynaecology, School of Medicine, Robinson Research Institute, University of Adelaide, Adelaide, South Australia, Australia.,Department of Gynaecological Oncology, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Peter Hoffmann
- Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia.,Future Industries Institute, University of South Australia, Adelaide, South Australia, Australia
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23
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Awan MG, Saeed F. An Out-of-Core GPU based dimensionality reduction algorithm for Big Mass Spectrometry Data and its application in bottom-up Proteomics. ACM-BCB ... ... : THE ... ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE. ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE 2017; 2017:550-555. [PMID: 28868521 DOI: 10.1145/3107411.3107466] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and computational operations inside GPU. These novel data structures include Binary Spectra and Quantized Indexed Spectra (QIS). The former helps in communicating essential information between CPU and GPU using minimum amount of data while latter enables us to store and process complex 3-D data structure into a 1-D array structure while maintaining the integrity of MS data. Our proposed algorithm also takes into account the limited memory of GPUs and switches between in-core and out-of-core modes based upon the size of input data. G-MSR achieves a peak speed-up of 386x over its sequential counterpart and is shown to process over a million spectra in just 32 seconds. The code for this algorithm is available as a GPL open-source at GitHub at the following link: https://github.com/pcdslab/G-MSR.
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Affiliation(s)
- Muaaz Gul Awan
- Department of Computer Science, Western Michigan University, 4601 Campus Drive, Kalamazoo, Michigan 49009,
| | - Fahad Saeed
- Department of Computer Science, Western Michigan University, 4601 Campus Drive, Kalamazoo, Michigan 49009,
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24
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Qin X, Guo Y, Du H, Zhong Y, Zhang J, Li X, Yu H, Zhang Z, Jia Z, Li Z. Comparative Analysis for Glycopatterns and Complex-Type N-Glycans of Glycoprotein in Sera from Chronic Hepatitis B- and C-Infected Patients. Front Physiol 2017; 8:596. [PMID: 28871230 PMCID: PMC5566988 DOI: 10.3389/fphys.2017.00596] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Accepted: 08/02/2017] [Indexed: 12/25/2022] Open
Abstract
Background: Chronic infection with HBV (CHB) or HCV (CHC) is the most common chronic viral hepatitis that can lead to cirrhosis and hepatocellular carcinoma in humans, their infections have distinct pathogenic processes, however, little is known about the difference of glycoprotein glycopatterns in serum between hepatitis B virus (HBV)- and hepatitis C virus (HCV)-infected patients. Methods: A method combining the lectin microarrays, letin-mediated affinity capture glycoproteins, and MALDI-TOF/TOF-MS was employed to analyze serum protein glycopatterns and identify the glycan structures from patients with CHB (n = 54) or CHC(n = 47), and healthy volunteers (HV, n = 35). Lectin blotting was further utilized to validate and assess the expression levels of their serum glycopatterns. Finally, the differences of the glycoprotein glycopatterns were systematically compared between CHB and CHC patients. Conclusions: As a result, there were 11 lectins (e.g., HHL, GSL-II, and EEL) exhibited significantly increased expression levels, and three lectins (LCA, VVA, and ACA) exhibited significantly decreased expression levels of serum protein glycopatterns only in the CHB patients. However, DBA exhibited significantly decreased expression levels, and two lectins (WGA and SNA) exhibited significantly increased expression levels of serum glycopatterns only in the CHC patients. Furthermore, LEL and MAL-I showed a coincidentally increasing trend in both CHC and CHB patients compared with the HV. The individual analysis demonstrated that eight lectins (MPL, GSL-I, PTL-II, UEA-I, WGA, LEL, VVA, and MAL-I) exhibited a high degree of consistency with the pooled serum samples of HV, CHB, and CHC patients. Besides, a complex-type N-glycans binder PHA-E+L exhibited significantly decreased NFIs in the CHB compared with HV and CHC subjects (p < 0.01). The MALDI-TOF/TOF-MS results of N-linked glycans from the serum glycoproteins isolated by PHA-E+L-magnetic particle conjugates showed that there was an overlap of 23 N-glycan peaks (e.g., m/z 1419.743, 1663.734, and 1743.581) between CHB, and CHC patients, 5 glycan peaks (e.g., m/z 1850.878, 1866.661, and 2037.750) were presented in virus-infected hepatitis patients compared with HV, 3 glycan peaks (1460.659, 2069.740, and 2174.772) were observed only in CHC patients. Our data provide useful information to find new biomarkers for distinguishing CHB and CHC patients based on the precision alteration of their serum glycopatterns.
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Affiliation(s)
- Xinmin Qin
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest UniversityXi'an, China
| | - Yonghong Guo
- Department of Infectious Diseases, Second Affiliated Hospital of Xi'an Jiaotong UniversityXi'an, China
| | - Haoqi Du
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest UniversityXi'an, China
| | - Yaogang Zhong
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest UniversityXi'an, China
| | - Jiaxu Zhang
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest UniversityXi'an, China
| | - Xuetian Li
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest UniversityXi'an, China
| | - Hanjie Yu
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest UniversityXi'an, China
| | - Zhiwei Zhang
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest UniversityXi'an, China
| | - Zhansheng Jia
- Center of Infectious Diseases, Tangdu Hospital, Fourth Military Medical UniversityXi'an, China
| | - Zheng Li
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest UniversityXi'an, China
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25
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Duran-Ortiz S, Brittain AL, Kopchick JJ. The impact of growth hormone on proteomic profiles: a review of mouse and adult human studies. Clin Proteomics 2017; 14:24. [PMID: 28670222 PMCID: PMC5492507 DOI: 10.1186/s12014-017-9160-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 06/20/2017] [Indexed: 12/17/2022] Open
Abstract
Growth hormone (GH) is a protein that is known to stimulate postnatal growth, counter regulate insulin's action and induce expression of insulin-like growth factor-1. GH exerts anabolic or catabolic effects depending upon on the targeted tissue. For instance, GH increases skeletal muscle and decreases adipose tissue mass. Our laboratory has spent the past two decades studying these effects, including the effects of GH excess and depletion, on the proteome of several mouse and human tissues. This review first discusses proteomic techniques that are commonly used for these types of studies. We then examine the proteomic differences found in mice with excess circulating GH (bGH mice) or mice with disruption of the GH receptor gene (GHR-/-). We also describe the effects of increased and decreased GH action on the proteome of adult patients with either acromegaly, GH deficiency or patients after short-term GH treatment. Finally, we explain how these proteomic studies resulted in the discovery of potential biomarkers for GH action, particularly those related with the effects of GH on aging, glucose metabolism and body composition.
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Affiliation(s)
- Silvana Duran-Ortiz
- Edison Biotechnology Institute, Ohio University, Athens, OH USA.,Department of Biological Sciences, College of Arts and Sciences, Ohio University, Athens, OH USA.,Molecular and Cellular Biology Program, Ohio University, Athens, OH USA
| | - Alison L Brittain
- Edison Biotechnology Institute, Ohio University, Athens, OH USA.,Department of Biological Sciences, College of Arts and Sciences, Ohio University, Athens, OH USA.,Molecular and Cellular Biology Program, Ohio University, Athens, OH USA.,Department of Biomedical Sciences, Heritage College of Osteopathic Medicine, Ohio University, Athens, OH 45701 USA
| | - John J Kopchick
- Edison Biotechnology Institute, Ohio University, Athens, OH USA.,Molecular and Cellular Biology Program, Ohio University, Athens, OH USA.,Department of Biomedical Sciences, Heritage College of Osteopathic Medicine, Ohio University, Athens, OH 45701 USA
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26
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Bijnsdorp IV, Maxouri O, Kardar A, Schelfhorst T, Piersma SR, Pham TV, Vis A, van Moorselaar RJ, Jimenez CR. Feasibility of urinary extracellular vesicle proteome profiling using a robust and simple, clinically applicable isolation method. J Extracell Vesicles 2017; 6:1313091. [PMID: 28717416 PMCID: PMC5505003 DOI: 10.1080/20013078.2017.1313091] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 03/26/2017] [Indexed: 12/31/2022] Open
Abstract
Extracellular vesicles (EVs) secreted by prostate cancer (PCa) cells contain specific biomarkers and can be isolated from urine. Collection of urine is not invasive, and therefore urinary EVs represent a liquid biopsy for diagnostic and prognostic testing for PCa. In this study, we optimised urinary EV isolation using a method based on heat shock proteins and compared it to gold-standard ultracentrifugation. The urinary EV isolation protocol using the Vn96-peptide is easier, time convenient (≈1.5 h) and no special equipment is needed, in contrast to ultracentrifugation protocol (>3.5 h), making this protocol clinically feasible. We compared the isolated vesicles of both ultracentrifugation and Vn96-peptide by proteome profiling using mass spectrometry-based proteomics (n = 4 per method). We reached a depth of >3000 proteins, with 2400 proteins that were commonly detected in urinary EVs from different donors. We show a large overlap (>85%) between proteins identified in EVs isolated by ultracentrifugation and Vn96-peptide. Addition of the detergent NP40 to Vn96-peptide EV isolations reduced levels of background proteins and highly increased the levels of the EV-markers TSG101 and PDCD6IP, indicative of an increased EV yield. Thus, the Vn96-peptide-based EV isolation procedure is clinically feasibly and allows large-scale protein profiling of urinary EV biomarkers.
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Affiliation(s)
- Irene V Bijnsdorp
- Department of Urology, VU University Medical Centre, Amsterdam, The Netherlands
| | - Olga Maxouri
- Department of Urology, VU University Medical Centre, Amsterdam, The Netherlands.,Department of Medical Oncology, VU University Medical Centre, Amsterdam, The Netherlands
| | - Aarzo Kardar
- Department of Urology, VU University Medical Centre, Amsterdam, The Netherlands.,Department of Medical Oncology, VU University Medical Centre, Amsterdam, The Netherlands
| | - Tim Schelfhorst
- Department of Medical Oncology, VU University Medical Centre, Amsterdam, The Netherlands
| | - Sander R Piersma
- Department of Medical Oncology, VU University Medical Centre, Amsterdam, The Netherlands
| | - Thang V Pham
- Department of Medical Oncology, VU University Medical Centre, Amsterdam, The Netherlands
| | - Andre Vis
- Department of Urology, VU University Medical Centre, Amsterdam, The Netherlands
| | | | - Connie R Jimenez
- Department of Medical Oncology, VU University Medical Centre, Amsterdam, The Netherlands
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27
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Hmmier A, O'Brien ME, Lynch V, Clynes M, Morgan R, Dowling P. Proteomic analysis of bronchoalveolar lavage fluid (BALF) from lung cancer patients using label-free mass spectrometry. BBA CLINICAL 2017; 7:97-104. [PMID: 28331811 PMCID: PMC5357681 DOI: 10.1016/j.bbacli.2017.03.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Revised: 02/08/2017] [Accepted: 03/01/2017] [Indexed: 02/07/2023]
Abstract
BACKGROUND Lung cancer is the leading cause of cancer-related mortality in both men and women throughout the world. The need to detect lung cancer at an early, potentially curable stage, is essential and may reduce mortality by 20%. The aim of this study was to identify distinct proteomic profiles in bronchoalveolar fluid (BALF) and plasma that are able to discriminate individuals with benign disease from those with non-small cell lung cancer (NSCLC). METHODS Using label-free mass spectrometry analysis of BALF during discovery-phase analysis, a significant number of proteins were found to have different abundance levels when comparing control to adenocarcinoma (AD) or squamous cell lung carcinoma (SqCC). Validation of candidate biomarkers identified in BALF was performed in a larger cohort of plasma samples by detection with enzyme-linked immunoassay. RESULTS Four proteins (Cystatin-C, TIMP-1, Lipocalin-2 and HSP70/HSPA1A) were selected as a representative group from discovery phase mass spectrometry BALF analysis. Plasma levels of TIMP-1, Lipocalin-2 and Cystatin-C were found to be significantly elevated in AD and SqCC compared to control. CONCLUSION The results presented in this study indicate that BALF is an important proximal biofluid for the discovery and identification of candidate lung cancer biomarkers. GENERAL SIGNIFICANCE There is good correlation between the trend of protein abundance levels in BALF and that of plasma which validates this approach to develop a blood biomarker to aid lung cancer diagnosis, particularly in the era of lung cancer screening. The protein signatures identified also provide insight into the molecular mechanisms associated with lung malignancy.
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Affiliation(s)
- Abduladim Hmmier
- Department of Biology, Maynooth University, Maynooth, Co. Kildare, Ireland; BioNano Integration Research Group, Biotechnology Research Centre, Tripoli, Libya
| | | | - Vincent Lynch
- National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Martin Clynes
- National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Ross Morgan
- Department of Respiratory Medicine, Beaumont Hospital, Dublin 9, Ireland
| | - Paul Dowling
- Department of Biology, Maynooth University, Maynooth, Co. Kildare, Ireland
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28
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Mezghani N, Ouakrim Y, Fuentes A, Mitiche A, Hagemeister N, Vendittoli PA, de Guise JA. Mechanical biomarkers of medial compartment knee osteoarthritis diagnosis and severity grading: Discovery phase. J Biomech 2017; 52:106-112. [DOI: 10.1016/j.jbiomech.2016.12.022] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Revised: 12/12/2016] [Accepted: 12/19/2016] [Indexed: 11/25/2022]
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29
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Kailemia MJ, Park D, Lebrilla CB. Glycans and glycoproteins as specific biomarkers for cancer. Anal Bioanal Chem 2017; 409:395-410. [PMID: 27590322 PMCID: PMC5203967 DOI: 10.1007/s00216-016-9880-6] [Citation(s) in RCA: 257] [Impact Index Per Article: 32.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Revised: 07/28/2016] [Accepted: 08/12/2016] [Indexed: 12/12/2022]
Abstract
Protein glycosylation and other post-translational modifications are involved in potentially all aspects of human growth and development. Defective glycosylation has adverse effects on human physiological conditions and accompanies many chronic and infectious diseases. Altered glycosylation can occur at the onset and/or during tumor progression. Identifying these changes at early disease stages may aid in making decisions regarding treatments, as early intervention can greatly enhance survival. This review highlights some of the efforts being made to identify N- and O-glycosylation profile shifts in cancer using mass spectrometry. The analysis of single or panels of potential glycoprotein cancer markers are covered. Other emerging technologies such as global glycan release and site-specific glycosylation analysis and quantitation are also discussed. Graphical Abstract Steps involved in the biomarker discovery.
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Affiliation(s)
- Muchena J Kailemia
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - Dayoung Park
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - Carlito B Lebrilla
- Department of Chemistry, University of California, Davis, CA, 95616, USA.
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30
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Zhao L, Chen Y, Bajaj AO, Eblimit A, Xu M, Soens ZT, Wang F, Ge Z, Jung SY, He F, Li Y, Wensel TG, Qin J, Chen R. Integrative subcellular proteomic analysis allows accurate prediction of human disease-causing genes. Genome Res 2016; 26:660-9. [PMID: 26912414 PMCID: PMC4864458 DOI: 10.1101/gr.198911.115] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2015] [Accepted: 02/19/2016] [Indexed: 12/04/2022]
Abstract
Proteomic profiling on subcellular fractions provides invaluable information regarding both protein abundance and subcellular localization. When integrated with other data sets, it can greatly enhance our ability to predict gene function genome-wide. In this study, we performed a comprehensive proteomic analysis on the light-sensing compartment of photoreceptors called the outer segment (OS). By comparing with the protein profile obtained from the retina tissue depleted of OS, an enrichment score for each protein is calculated to quantify protein subcellular localization, and 84% accuracy is achieved compared with experimental data. By integrating the protein OS enrichment score, the protein abundance, and the retina transcriptome, the probability of a gene playing an essential function in photoreceptor cells is derived with high specificity and sensitivity. As a result, a list of genes that will likely result in human retinal disease when mutated was identified and validated by previous literature and/or animal model studies. Therefore, this new methodology demonstrates the synergy of combining subcellular fractionation proteomics with other omics data sets and is generally applicable to other tissues and diseases.
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Affiliation(s)
- Li Zhao
- Structural and Computational Biology and Molecular Biophysics Graduate Program, Baylor College of Medicine, Houston, Texas 77030, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Yiyun Chen
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Amol Onkar Bajaj
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Aiden Eblimit
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Mingchu Xu
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Zachry T Soens
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Feng Wang
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Zhongqi Ge
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Sung Yun Jung
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Feng He
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Yumei Li
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Theodore G Wensel
- Structural and Computational Biology and Molecular Biophysics Graduate Program, Baylor College of Medicine, Houston, Texas 77030, USA; Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Jun Qin
- Structural and Computational Biology and Molecular Biophysics Graduate Program, Baylor College of Medicine, Houston, Texas 77030, USA; Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Rui Chen
- Structural and Computational Biology and Molecular Biophysics Graduate Program, Baylor College of Medicine, Houston, Texas 77030, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
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31
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Abstract
Mass spectrometry (MS) is a technique that can identify analytes on the basis of mass-to-charge (m/z) ratio. Although this technique has been used in research and specialized clinical laboratories for decades, however, in recent years, MS has been increasingly used in routine clinical laboratories. MS, especially when coupled to gas chromatography or liquid chromatography, provides very specific and often sensitive analysis of many analytes. Other advantages of MS include simultaneous analysis of multiple analytes (>100) and generally without need for specialized reagents. Commonly measured analytes by MS include drugs, hormones, and proteins.
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Affiliation(s)
- Uttam Garg
- Department of Pathology and Laboratory Medicine, Children's Mercy Hospitals and Clinics, 2401 Gillham Road, Kansas City, MO, 64108, USA.
- University of Missouri School of Medicine, Kansas City, MO, USA.
| | - Yan Victoria Zhang
- Department of Pathology and Laboratory Medicine, University of Rochester, Rochester, NY, USA
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32
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Garg U, Zhang YV. Mass Spectrometry in Clinical Laboratory: Applications in Therapeutic Drug Monitoring and Toxicology. Methods Mol Biol 2016; 1383:1-10. [PMID: 26660168 DOI: 10.1007/978-1-4939-3252-8_1] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Mass spectrometry (MS) has been used in research and specialized clinical laboratories for decades as a very powerful technology to identify and quantify compounds. In recent years, application of MS in routine clinical laboratories has increased significantly. This is mainly due to the ability of MS to provide very specific identification, high sensitivity, and simultaneous analysis of multiple analytes (>100). The coupling of tandem mass spectrometry with gas chromatography (GC) or liquid chromatography (LC) has enabled the rapid expansion of this technology. While applications of MS are used in many clinical areas, therapeutic drug monitoring, drugs of abuse, and clinical toxicology are still the primary focuses of the field. It is not uncommon to see mass spectrometry being used in routine clinical practices for those applications.
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Affiliation(s)
- Uttam Garg
- Department of Pathology and Laboratory Medicine, Children's Mercy Hospitals and Clinics, 2401 Gillham Road, Kansas City, MO, USA.
| | - Yan Victoria Zhang
- Department of Pathology and Laboratory Medicine, University of Rochester, Rochester, NY, USA
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33
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Langley G, Austin CP, Balapure AK, Birnbaum LS, Bucher JR, Fentem J, Fitzpatrick SC, Fowle JR, Kavlock RJ, Kitano H, Lidbury BA, Muotri AR, Peng SQ, Sakharov D, Seidle T, Trez T, Tonevitsky A, van de Stolpe A, Whelan M, Willett C. Lessons from Toxicology: Developing a 21st-Century Paradigm for Medical Research. ENVIRONMENTAL HEALTH PERSPECTIVES 2015; 123:A268-72. [PMID: 26523530 PMCID: PMC4629751 DOI: 10.1289/ehp.1510345] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Biomedical developments in the 21st century provide an unprecedented opportunity to gain a dynamic systems-level and human-specific understanding of the causes and pathophysiologies of disease. This understanding is a vital need, in view of continuing failures in health research, drug discovery, and clinical translation. The full potential of advanced approaches may not be achieved within a 20th-century conceptual framework dominated by animal models. Novel technologies are being integrated into environmental health research and are also applicable to disease research, but these advances need a new medical research and drug discovery paradigm to gain maximal benefits. We suggest a new conceptual framework that repurposes the 21st-century transition underway in toxicology. Human disease should be conceived as resulting from integrated extrinsic and intrinsic causes, with research focused on modern human-specific models to understand disease pathways at multiple biological levels that are analogous to adverse outcome pathways in toxicology. Systems biology tools should be used to integrate and interpret data about disease causation and pathophysiology. Such an approach promises progress in overcoming the current roadblocks to understanding human disease and successful drug discovery and translation. A discourse should begin now to identify and consider the many challenges and questions that need to be solved.
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Affiliation(s)
- Gill Langley
- Research and Toxicology Department, Humane Society International, London, United Kingdom
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34
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Qi Y, Fan P, Hao Y, Han B, Fang Y, Feng M, Cui Z, Li J. Phosphoproteomic Analysis of Protein Phosphorylation Networks in the Hypopharyngeal Gland of Honeybee Workers (Apis mellifera ligustica). J Proteome Res 2015; 14:4647-61. [DOI: 10.1021/acs.jproteome.5b00530] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Yuping Qi
- Institute
of Apicultural Research, Chinese Academy of Agricultural Science, No. 1 Beigou Xiangshan, Beijing 100093, China
| | - Pei Fan
- Institute
of Apicultural Research, Chinese Academy of Agricultural Science, No. 1 Beigou Xiangshan, Beijing 100093, China
- College
of Bioengineering, Henan University of Technology, No. 100 of Science Road, Zhengzhou 450001, China
| | - Yue Hao
- Institute
of Apicultural Research, Chinese Academy of Agricultural Science, No. 1 Beigou Xiangshan, Beijing 100093, China
| | - Bin Han
- Institute
of Apicultural Research, Chinese Academy of Agricultural Science, No. 1 Beigou Xiangshan, Beijing 100093, China
| | - Yu Fang
- Institute
of Apicultural Research, Chinese Academy of Agricultural Science, No. 1 Beigou Xiangshan, Beijing 100093, China
| | - Mao Feng
- Institute
of Apicultural Research, Chinese Academy of Agricultural Science, No. 1 Beigou Xiangshan, Beijing 100093, China
| | - Ziyou Cui
- Institute
of Apicultural Research, Chinese Academy of Agricultural Science, No. 1 Beigou Xiangshan, Beijing 100093, China
- Department
of Pediatrics, Medical School, and Lillehei Heart Institute, University of Minnesota, Twin Cities 4-240 CCRB, 2231 Sixth Street SE, Minneapolis, Minnesota 55455, United States
| | - Jianke Li
- Institute
of Apicultural Research, Chinese Academy of Agricultural Science, No. 1 Beigou Xiangshan, Beijing 100093, China
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35
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Bohnenberger H, Ströbel P, Mohr S, Corso J, Berg T, Urlaub H, Lenz C, Serve H, Oellerich T. Quantitative mass spectrometric profiling of cancer-cell proteomes derived from liquid and solid tumors. J Vis Exp 2015:e52435. [PMID: 25867170 PMCID: PMC4401153 DOI: 10.3791/52435] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
In-depth analyses of cancer cell proteomes are needed to elucidate oncogenic pathomechanisms, as well as to identify potential drug targets and diagnostic biomarkers. However, methods for quantitative proteomic characterization of patient-derived tumors and in particular their cellular subpopulations are largely lacking. Here we describe an experimental set-up that allows quantitative analysis of proteomes of cancer cell subpopulations derived from either liquid or solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry followed by bioinformatic data analysis. To enrich specific cellular subsets, liquid tumors are first immunophenotyped by flow cytometry followed by FACS-sorting; for solid tumors, laser-capture microdissection is used to purify specific cellular subpopulations. In a second step, proteins are extracted from the purified cells and subsequently combined with a tumor-specific, SILAC-labeled spike-in standard that enables protein quantification. The resulting protein mixture is subjected to either gel electrophoresis or Filter Aided Sample Preparation (FASP) followed by tryptic digestion. Finally, tryptic peptides are analyzed using a hybrid quadrupole-orbitrap mass spectrometer, and the data obtained are processed with bioinformatic software suites including MaxQuant. By means of the workflow presented here, up to 8,000 proteins can be identified and quantified in patient-derived samples, and the resulting protein expression profiles can be compared among patients to identify diagnostic proteomic signatures or potential drug targets.
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Affiliation(s)
| | - Philipp Ströbel
- Institute of Pathology, University Medical Center, Göttingen
| | - Sebastian Mohr
- Department of Hematology/Oncology, Goethe University of Frankfurt
| | - Jasmin Corso
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry
| | - Tobias Berg
- Department of Hematology/Oncology, Goethe University of Frankfurt
| | - Henning Urlaub
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry; Bioanalytics, Institute of Clinical Chemistry, University Medical Center, Göttingen
| | - Christof Lenz
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry; Bioanalytics, Institute of Clinical Chemistry, University Medical Center, Göttingen
| | - Hubert Serve
- Department of Hematology/Oncology, Goethe University of Frankfurt; German Cancer Consortium; German Cancer Research Center
| | - Thomas Oellerich
- Department of Hematology/Oncology, Goethe University of Frankfurt; German Cancer Consortium; German Cancer Research Center;
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36
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Shurtleff AC, Whitehouse CA, Ward MD, Cazares LH, Bavari S. Pre-symptomatic diagnosis and treatment of filovirus diseases. Front Microbiol 2015; 6:108. [PMID: 25750638 PMCID: PMC4335271 DOI: 10.3389/fmicb.2015.00108] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Accepted: 01/28/2015] [Indexed: 01/01/2023] Open
Abstract
Filoviruses are virulent human pathogens which cause severe illness with high case fatality rates and for which there are no available FDA-approved vaccines or therapeutics. Diagnostic tools including antibody- and molecular-based assays, mass spectrometry, and next-generation sequencing are continually under development. Assays using the polymerase chain reaction (PCR) have become the mainstay for the detection of filoviruses in outbreak settings. In many cases, real-time reverse transcriptase-PCR allows for the detection of filoviruses to be carried out with minimal manipulation and equipment and can provide results in less than 2 h. In cases of novel, highly diverse filoviruses, random-primed pyrosequencing approaches have proved useful. Ideally, diagnostic tests would allow for diagnosis of filovirus infection as early as possible after infection, either before symptoms begin, in the event of a known exposure or epidemiologic outbreak, or post-symptomatically. If tests could provide an early definitive diagnosis, then this information may be used to inform the choice of possible therapeutics. Several exciting new candidate therapeutics have been described recently; molecules that have therapeutic activity when administered to animal models of infection several days post-exposure, once signs of disease have begun. The latest data for candidate nucleoside analogs, small interfering RNA (siRNA) molecules, phosphorodiamidate (PMO) molecules, as well as antibody and blood-product therapeutics and therapeutic vaccines are discussed. For filovirus researchers and government agencies interested in making treatments available for a nation's defense as well as its general public, having the right diagnostic tools to identify filovirus infections, as well as a panel of available therapeutics for treatment when needed, is a high priority. Additional research in both areas is required for ultimate success, but significant progress is being made to reach these goals.
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Affiliation(s)
- Amy C Shurtleff
- Molecular and Translational Sciences Division, United States Army Medical Research Institute of Infectious Diseases Fort Detrick, MD, USA
| | - Chris A Whitehouse
- Molecular and Translational Sciences Division, United States Army Medical Research Institute of Infectious Diseases Fort Detrick, MD, USA
| | - Michael D Ward
- Molecular and Translational Sciences Division, United States Army Medical Research Institute of Infectious Diseases Fort Detrick, MD, USA
| | - Lisa H Cazares
- Molecular and Translational Sciences Division, United States Army Medical Research Institute of Infectious Diseases Fort Detrick, MD, USA
| | - Sina Bavari
- Molecular and Translational Sciences Division, United States Army Medical Research Institute of Infectious Diseases Fort Detrick, MD, USA
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Kumar A, Baycin-Hizal D, Shiloach J, Bowen MA, Betenbaugh MJ. Coupling enrichment methods with proteomics for understanding and treating disease. Proteomics Clin Appl 2015; 9:33-47. [PMID: 25523641 DOI: 10.1002/prca.201400097] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2014] [Revised: 11/12/2014] [Accepted: 12/15/2014] [Indexed: 12/17/2022]
Abstract
Owing to recent advances in proteomics analytical methods and bioinformatics capabilities there is a growing trend toward using these capabilities for the development of drugs to treat human disease, including target and drug evaluation, understanding mechanisms of drug action, and biomarker discovery. Currently, the genetic sequences of many major organisms are available, which have helped greatly in characterizing proteomes in model animal systems and humans. Through proteomics, global profiles of different disease states can be characterized (e.g. changes in types and relative levels as well as changes in PTMs such as glycosylation or phosphorylation). Although intracellular proteomics can provide a broad overview of physiology of cells and tissues, it has been difficult to quantify the low abundance proteins which can be important for understanding the diseased states and treatment progression. For this reason, there is increasing interest in coupling comparative proteomics methods with subcellular fractionation and enrichment techniques for membranes, nucleus, phosphoproteome, glycoproteome as well as low abundance serum proteins. In this review, we will provide examples of where the utilization of different proteomics-coupled enrichment techniques has aided target and biomarker discovery, understanding the drug targeting mechanism, and mAb discovery. Taken together, these improvements will help to provide a better understanding of the pathophysiology of various diseases including cancer, autoimmunity, inflammation, cardiovascular disease, and neurological conditions, and in the design and development of better medicines for treating these afflictions.
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Affiliation(s)
- Amit Kumar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA; Antibody Discovery and Protein Engineering, MedImmune LLC, One MedImmune Way, Gaithersburg, MD, USA; Biotechnology Core Laboratory, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
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