1
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Whiteaker JR, Zhao L, Schoenherr RM, Huang D, Kennedy JJ, Ivey RG, Lin C, Lorentzen TD, Colantonio S, Caceres TW, Roberts RR, Knotts JG, Reading JJ, Perry CD, Garcia-Buntley SS, Bocik W, Hewitt SM, Paulovich AG. Characterization of an expanded set of assays for immunomodulatory proteins using targeted mass spectrometry. Sci Data 2024; 11:682. [PMID: 38918394 PMCID: PMC11199596 DOI: 10.1038/s41597-024-03467-x] [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: 02/01/2024] [Accepted: 06/03/2024] [Indexed: 06/27/2024] Open
Abstract
Immunotherapies are revolutionizing cancer care, but many patients do not achieve durable responses and immune-related adverse events are difficult to predict. Quantifying the hundreds of proteins involved in cancer immunity has the potential to provide biomarkers to monitor and predict tumor response. We previously developed robust, multiplexed quantitative assays for immunomodulatory proteins using targeted mass spectrometry, providing measurements that can be performed reproducibly and harmonized across laboratories. Here, we expand upon those efforts in presenting data from a multiplexed immuno-oncology (IO)-3 assay panel targeting 43 peptides representing 39 immune- and inflammation-related proteins. A suite of novel monoclonal antibodies was generated as assay reagents, and the fully characterized antibodies are made available as a resource to the community. The publicly available dataset contains complete characterization of the assay performance, as well as the mass spectrometer parameters and reagent information necessary for implementation of the assay. Quantification of the proteins will provide benefit to correlative studies in clinical trials, identification of new biomarkers, and improve understanding of the immune response in cancer.
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Affiliation(s)
- Jeffrey R Whiteaker
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Lei Zhao
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Regine M Schoenherr
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Dongqing Huang
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Jacob J Kennedy
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Richard G Ivey
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Chenwei Lin
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Travis D Lorentzen
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Simona Colantonio
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Tessa W Caceres
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Rhonda R Roberts
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Joseph G Knotts
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Joshua J Reading
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Candice D Perry
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Sandra S Garcia-Buntley
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - William Bocik
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Stephen M Hewitt
- Experimental Pathology Laboratory, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD, USA
| | - Amanda G Paulovich
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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2
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Shin D, Kim Y, Park J, Kim Y. High-throughput Proteomics-Guided Biomarker Discovery of Hepatocellular Carcinoma. Biomed J 2024:100752. [PMID: 38901798 DOI: 10.1016/j.bj.2024.100752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 06/07/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024] Open
Abstract
Liver cancer stands as the fifth leading cause of cancer-related deaths globally. Hepatocellular carcinoma (HCC) comprises approximately 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients qualify for curative therapy, primarily due to the absence of reliable tools for early detection and prognosis of HCC. This underscores the critical need for molecular biomarkers for HCC management. Since proteins reflect disease status directly, proteomics has been utilized in biomarker developments for HCC. In particular, proteomics coupled with liquid chromatography-mass spectrometer (LC-MS) methods facilitate the process of discovering biomarker candidates for diagnosis, prognosis, and therapeutic strategies. In this work, we investigated LC-MS-based proteomics methods through recent reference reviews, with a particular focus on sample preparation and LC-MS methods appropriate for the discovery of HCC biomarkers and their clinical applications. We classified proteomics studies of HCC according to sample types, and we examined the coverage of protein biomarker candidates based on LC-MS methods in relation to study scales and goals. Comprehensively, we proposed protein biomarker candidates categorized by sample types and biomarker types for appropriate clinical use. In this review, we summarized recent LC-MS-based proteomics studies on HCC and proposed potential protein biomarkers. Our findings are expected to expand the understanding of HCC pathogenesis and enhance the efficiency of HCC diagnosis and prognosis, thereby contributing to improved patient outcomes.
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Affiliation(s)
- Dongyoon Shin
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea
| | - Yeongshin Kim
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea; Department of Medical Science, School of Medicine, CHA University, Seongnam, Republic of Korea
| | - Junho Park
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea; Department of Pharmacology, School of Medicine, CHA University, Seongnam, Republic of Korea.
| | - Youngsoo Kim
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea; Department of Medical Science, School of Medicine, CHA University, Seongnam, Republic of Korea.
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3
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Sun BB, Suhre K, Gibson BW. Promises and Challenges of populational Proteomics in Health and Disease. Mol Cell Proteomics 2024; 23:100786. [PMID: 38761890 PMCID: PMC11193116 DOI: 10.1016/j.mcpro.2024.100786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024] Open
Abstract
Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.
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Affiliation(s)
- Benjamin B Sun
- Human Genetics, Informatics and Predictive Sciences, Bristol-Myers Squibb, Cambridge, Massachusetts, USA.
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Bradford W Gibson
- Pharmaceutical Chemistry, University of California, San Francisco, California, USA
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4
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Michaud SA, Pětrošová H, Sinclair NJ, Kinnear AL, Jackson AM, McGuire JC, Hardie DB, Bhowmick P, Ganguly M, Flenniken AM, Nutter LMJ, McKerlie C, Smith D, Mohammed Y, Schibli D, Sickmann A, Borchers CH. Multiple reaction monitoring assays for large-scale quantitation of proteins from 20 mouse organs and tissues. Commun Biol 2024; 7:6. [PMID: 38168632 PMCID: PMC10762018 DOI: 10.1038/s42003-023-05687-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 12/07/2023] [Indexed: 01/05/2024] Open
Abstract
Mouse is the mammalian model of choice to study human health and disease due to its size, ease of breeding and the natural occurrence of conditions mimicking human pathology. Here we design and validate multiple reaction monitoring mass spectrometry (MRM-MS) assays for quantitation of 2118 unique proteins in 20 murine tissues and organs. We provide open access to technical aspects of these assays to enable their implementation in other laboratories, and demonstrate their suitability for proteomic profiling in mice by measuring normal protein abundances in tissues from three mouse strains: C57BL/6NCrl, NOD/SCID, and BALB/cAnNCrl. Sex- and strain-specific differences in protein abundances are identified and described, and the measured values are freely accessible via our MouseQuaPro database: http://mousequapro.proteincentre.com . Together, this large library of quantitative MRM-MS assays established in mice and the measured baseline protein abundances represent an important resource for research involving mouse models.
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Affiliation(s)
- Sarah A Michaud
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada.
| | - Helena Pětrošová
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Nicholas J Sinclair
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Andrea L Kinnear
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Angela M Jackson
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Jamie C McGuire
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Darryl B Hardie
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Pallab Bhowmick
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Milan Ganguly
- The Center for Phenogenomics, Toronto, ON, Canada
- The Hospital for Sick Children, Toronto, ON, Canada
| | - Ann M Flenniken
- The Center for Phenogenomics, Toronto, ON, Canada
- Sinai Health Lunenfeld-Tanenbaum Research Institute, Toronto, ON, Canada
| | - Lauryl M J Nutter
- The Center for Phenogenomics, Toronto, ON, Canada
- The Hospital for Sick Children, Toronto, ON, Canada
| | | | - Derek Smith
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Yassene Mohammed
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V, Dortmund, 44139, Germany
- Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - David Schibli
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V, Dortmund, 44139, Germany
| | - Christoph H Borchers
- Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada.
- Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, QC, Canada.
- Department of Experimental Medicine, McGill University, Montreal, QC, Canada.
- Department of Pathology, McGill University, Montreal, QC, Canada.
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5
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Cao X, Tang X, Feng C, Lin J, Zhang H, Liu Q, Zheng Q, Zhuang H, Liu X, Li H, Khan NU, Shen L. A Systematic Investigation of Complement and Coagulation-Related Protein in Autism Spectrum Disorder Using Multiple Reaction Monitoring Technology. Neurosci Bull 2023; 39:1623-1637. [PMID: 37031449 PMCID: PMC10603015 DOI: 10.1007/s12264-023-01055-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 02/02/2023] [Indexed: 04/10/2023] Open
Abstract
Autism spectrum disorder (ASD) is one of the common neurodevelopmental disorders in children. Its etiology and pathogenesis are poorly understood. Previous studies have suggested potential changes in the complement and coagulation pathways in individuals with ASD. In this study, using multiple reactions monitoring proteomic technology, 16 of the 33 proteins involved in this pathway were identified as differentially-expressed proteins in plasma between children with ASD and controls. Among them, CFHR3, C4BPB, C4BPA, CFH, C9, SERPIND1, C8A, F9, and F11 were found to be altered in the plasma of children with ASD for the first time. SERPIND1 expression was positively correlated with the CARS score. Using the machine learning method, we obtained a panel composed of 12 differentially-expressed proteins with diagnostic potential for ASD. We also reviewed the proteins changed in this pathway in the brain and blood of patients with ASD. The complement and coagulation pathways may be activated in the peripheral blood of children with ASD and play a key role in the pathogenesis of ASD.
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Affiliation(s)
- Xueshan Cao
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518060, China
- College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Xiaoxiao Tang
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518060, China
| | - Chengyun Feng
- Maternal and Child Health Hospital of Baoan, Shenzhen, 518100, China
| | - Jing Lin
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518060, China
| | - Huajie Zhang
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518060, China
| | - Qiong Liu
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518060, China
- College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Qihong Zheng
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518060, China
| | - Hongbin Zhuang
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518060, China
| | - Xukun Liu
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518060, China
| | - Haiying Li
- Department of Endocrinology, Guiyang First People's Hospital, Guiyang, 550002, China
| | - Naseer Ullah Khan
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518060, China
| | - Liming Shen
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518060, China.
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China.
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, Shenzhen, 518060, China.
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6
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Wang H, Ma LZ, Sheng ZH, Liu JY, Yuan WY, Guo F, Zhang W, Tan L. Association between cerebrospinal fluid clusterin and biomarkers of Alzheimer's disease pathology in mild cognitive impairment: a longitudinal cohort study. Front Aging Neurosci 2023; 15:1256389. [PMID: 37941999 PMCID: PMC10629112 DOI: 10.3389/fnagi.2023.1256389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/10/2023] [Indexed: 11/10/2023] Open
Abstract
Background Clusterin, a glycoprotein implicated in Alzheimer's disease (AD), remains unclear. The objective of this study was to analyze the effect of cerebrospinal fluid (CSF) clusterin in relation to AD biomarkers using a longitudinal cohort of non-demented individuals. Methods We gathered a sample comprising 86 individuals under cognition normal (CN) and 134 patients diagnosed with MCI via the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. To investigate the correlation of CSF clusterin with cognitive function and markers of key physiological changes, we employed multiple linear regression and mixed-effect models. We undertook a causal mediation analysis to inspect the mediating influence of CSF clusterin on cognitive abilities. Results Pathological characteristics associated with baseline Aβ42, Tau, brain volume, exhibited a correlation with initial CSF clusterin in the general population, Specifically, these correlations were especially prominent in the MCI population; CSF Aβ42 (PCN = 0.001; PMCI = 0.007), T-tau (PCN < 0.001; PMCI < 0.001), and Mid temporal (PCN = 0.033; PMCI = 0.005). Baseline CSF clusterin level was predictive of measurable cognitive shifts in the MCI population, as indicated by MMSE (β = 0.202, p = 0.029), MEM (β = 0.186, p = 0.036), RAVLT immediate recall (β = 0.182, p = 0.038), and EF scores (β = 0.221, p = 0.013). In MCI population, the alterations in brain regions (17.87% of the total effect) mediated the effect of clusterin on cognition. It was found that variables such as age, gender, and presence of APOE ε4 carrier status, influenced some of these connections. Conclusion Our investigation underscored a correlation between CSF clusterin concentrations and pivotal AD indicators, while also highlighting clusterin's potential role as a protective factor for cognitive abilities in MCI patients.
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Affiliation(s)
- Hao Wang
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Ling-Zhi Ma
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ze-Hu Sheng
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jia-Yao Liu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei-Yu Yuan
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Fan Guo
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Zhang
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
- Department of Neurology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, China
| | - Lan Tan
- Department of Neurology, Affiliated Hospital of Weifang Medical University, School of Clinical Medicine, Weifang Medical University, Weifang, China
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
- Department of Neurology, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, China
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7
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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: 1.0] [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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8
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Yu Q, Liu X, Keller MP, Navarrete-Perea J, Zhang T, Fu S, Vaites LP, Shuken SR, Schmid E, Keele GR, Li J, Huttlin EL, Rashan EH, Simcox J, Churchill GA, Schweppe DK, Attie AD, Paulo JA, Gygi SP. Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression. Nat Commun 2023; 14:555. [PMID: 36732331 PMCID: PMC9894840 DOI: 10.1038/s41467-023-36269-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 01/20/2023] [Indexed: 02/04/2023] Open
Abstract
Targeted proteomics enables hypothesis-driven research by measuring the cellular expression of protein cohorts related by function, disease, or class after perturbation. Here, we present a pathway-centric approach and an assay builder resource for targeting entire pathways of up to 200 proteins selected from >10,000 expressed proteins to directly measure their abundances, exploiting sample multiplexing to increase throughput by 16-fold. The strategy, termed GoDig, requires only a single-shot LC-MS analysis, ~1 µg combined peptide material, a list of up to 200 proteins, and real-time analytics to trigger simultaneous quantification of up to 16 samples for hundreds of analytes. We apply GoDig to quantify the impact of genetic variation on protein expression in mice fed a high-fat diet. We create several GoDig assays to quantify the expression of multiple protein families (kinases, lipid metabolism- and lipid droplet-associated proteins) across 480 fully-genotyped Diversity Outbred mice, revealing protein quantitative trait loci and establishing potential linkages between specific proteins and lipid homeostasis.
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Affiliation(s)
- Qing Yu
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Xinyue Liu
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Mark P Keller
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | | | - Tian Zhang
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Sipei Fu
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Laura P Vaites
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Steven R Shuken
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Ernst Schmid
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | | | - Jiaming Li
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Edward L Huttlin
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Edrees H Rashan
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Judith Simcox
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | | | - Devin K Schweppe
- Department of Genome Sciences, University of Washington, Seattle, WA, 98105, USA
| | - Alan D Attie
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Joao A Paulo
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Steven P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA.
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9
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Tang L, Wang ZB, Ma LZ, Cao XP, Tan L, Tan MS. Dynamic changes of CSF clusterin levels across the Alzheimer's disease continuum. BMC Neurol 2022; 22:508. [PMID: 36581903 PMCID: PMC9801612 DOI: 10.1186/s12883-022-03038-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 12/19/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Clusterin is a multifunctional protein, which is associated with the pathogenesis and the development of Alzheimer's disease (AD). Compared with normal controls, inconsistent results have yielded in previous studies for concentration of cerebrospinal fluid (CSF) clusterin in AD patients. We explored CSF clusterin levels in different pathological processes of AD. METHODS Following the National Institute on Aging-Alzheimer's Association (NIA-AA) criteria, we employed on the levels of CSF Aβ42(A), phosphorylated-Tau (T), and total-tau (N). Based on previously published cutoffs and the close correlation between CSF p-tau and t-tau, 276 participants from the publicly available ADNI database with CSF biomarkers were divided into four groups: A-(TN)- (normal Aβ42 and normal p-tau and t-tau; n = 50), A+(TN)- (abnormal Aβ42 and normal p-tau and t-tau; n = 39), A+(TN) + (abnormal Aβ42 and abnormal p-tau or t-tau; n = 147), A-(TN) + (normal Aβ42 and abnormal p-tau or t-tau; n = 40). To assess CSF clusterin levels in AD continuum, intergroup differences in four groups were compared. Pairwise comparisons were conducted as appropriate followed by Bonferroni post hoc analyses. To further study the relationships between CSF clusterin levels and AD core pathological biomarkers, we employed multiple linear regression method in subgroups. RESULTS Compared with the A-(TN)- group, CSF clusterin levels were decreased in A+ (TN)- group (P = 0.002 after Bonferroni correction), but increased in the A+(TN) + group and the A-(TN) + group (both P < 0.001 after Bonferroni correction). Moreover, we found CSF clusterin levels are positively associated with CSF Aβ42 (β = 0.040, P < 0. 001), CSF p-tau (β = 0.325, P < 0.001) and CSF t-tau (β = 0.346, P < 0.001). CONCLUSIONS Our results indicated that there are differences levels of CSF clusterin in different stages of AD pathology. The CSF clusterin level decreased at the early stage are related to abnormal Aβ pathology; and the increased levels are associated with tau pathology and neurodegeneration.
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Affiliation(s)
- Lian Tang
- grid.410645.20000 0001 0455 0905Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Zhi-Bo Wang
- grid.410645.20000 0001 0455 0905Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ling-Zhi Ma
- grid.410645.20000 0001 0455 0905Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Xi-Peng Cao
- grid.410645.20000 0001 0455 0905Clinical Research Center, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Lan Tan
- grid.410645.20000 0001 0455 0905Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Meng-Shan Tan
- grid.410645.20000 0001 0455 0905Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
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10
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Kim JY, Kim J, Lim YS, Gwak GY, Yeo I, Kim Y, Lee J, Shin D, Lee JH, Kim Y. Proteome Multimarker Panel for the Early Detection of Hepatocellular Carcinoma: Multicenter Derivation, Validation, and Comparison. ACS OMEGA 2022; 7:29934-29943. [PMID: 36061641 PMCID: PMC9434733 DOI: 10.1021/acsomega.2c02926] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Abstract
Conventional methods for the surveillance of hepatocellular carcinoma (HCC) by imaging, with and without serum tumor markers, are suboptimal with regard to accuracy. We aimed to develop and validate a reliable serum biomarker panel for the early detection of HCC using a proteomic technique. This multicenter case-control study comprised 727 patients with HCC and patients with risk factors but no HCC. We developed a multiple reaction monitoring-mass spectrometry (MRM-MS) multimarker panel using 17 proteins from the sera of 398 patients. Area under the receiver operating characteristics curve (AUROC) values of this MRM-MS panel with and without α-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist-II (PIVKA-II) were compared. The combination and standalone MRM-MS panels had higher AUROC values than AFP in the training (0.940 and 0.929 vs 0.775, both P < 0.05), test (0.894 and 0.893 vs 0.593, both P < 0.05), and confirmation sets (0.961 and 0.937 vs 0.806, both P < 0.05) in detecting small single HCC. The combination and standalone MRM-MS panels had significantly higher AUROC values than the GALAD score (0.945 and 0.931 vs 0.829, both P < 0.05). Our proteome 17-protein multimarker panel distinguished HCC patients from high-risk controls and had high accuracy in the early detection of HCC.
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Affiliation(s)
- Ju Yeon Kim
- Department
of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Jaenyeon Kim
- Interdisciplinary
Program of Bioengineering, Graduate School,
Seoul National University, Seoul 08826, Republic of Korea
| | - Young-Suk Lim
- Department
of Gastroenterology, Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul 44610, Republic of Korea
| | - Geum-Youn Gwak
- Department
of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic
of Korea
| | - Injoon Yeo
- Interdisciplinary
Program of Bioengineering, Graduate School,
Seoul National University, Seoul 08826, Republic of Korea
| | - Yoseop Kim
- Interdisciplinary
Program of Bioengineering, Graduate School,
Seoul National University, Seoul 08826, Republic of Korea
| | - Jihyeon Lee
- Department
of Biomedical Sciences, Seoul National University
College of Medicine, Seoul 03080, Republic of Korea
| | - Dongyoon Shin
- Department
of Biomedical Sciences, Seoul National University
College of Medicine, Seoul 03080, Republic of Korea
| | - Jeong-Hoon Lee
- Department
of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Youngsoo Kim
- Interdisciplinary
Program of Bioengineering, Graduate School,
Seoul National University, Seoul 08826, Republic of Korea
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11
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The addition of FAIMS increases targeted proteomics sensitivity from FFPE tumor biopsies. Sci Rep 2022; 12:13876. [PMID: 35974054 PMCID: PMC9381555 DOI: 10.1038/s41598-022-16358-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 07/08/2022] [Indexed: 12/02/2022] Open
Abstract
Mass spectrometry-based targeted proteomics allows objective protein quantitation of clinical biomarkers from a single section of formalin-fixed, paraffin-embedded (FFPE) tumor tissue biopsies. We combined high-field asymmetric waveform ion mobility spectrometry (FAIMS) and parallel reaction monitoring (PRM) to increase assay sensitivity. The modular nature of the FAIMS source allowed direct comparison of the performance of FAIMS-PRM to PRM. Limits of quantitation were determined by spiking synthetic peptides into a human spleen matrix. In addition, 20 clinical samples were analyzed using FAIMS-PRM and the quantitation of HER2 was compared with that obtained with the Ventana immunohistochemistry assay. FAIMS-PRM improved the overall signal-to-noise ratio over that from PRM and increased assay sensitivity in FFPE tissue analysis for four (HER2, EGFR, cMET, and KRAS) of five proteins of clinical interest. FAIMS-PRM enabled sensitive quantitation of basal HER2 expression in breast cancer samples classified as HER2 negative by immunohistochemistry. Furthermore, we determined the degree of FAIMS-dependent background reduction and showed that this correlated with an improved lower limit of quantitation with FAIMS. FAIMS-PRM is anticipated to benefit clinical trials in which multiple biomarker questions must be addressed and the availability of tumor biopsy samples is limited.
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12
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Kennedy J, Whiteaker JR, Ivey RG, Burian A, Chowdhury S, Tsai CF, Liu T, Lin C, Murillo OD, Lundeen RA, Jones LA, Gafken PR, Longton G, Rodland KD, Skates SJ, Landua J, Wang P, Lewis MT, Paulovich AG. Internal Standard Triggered-Parallel Reaction Monitoring Mass Spectrometry Enables Multiplexed Quantification of Candidate Biomarkers in Plasma. Anal Chem 2022; 94:9540-9547. [PMID: 35767427 PMCID: PMC9280723 DOI: 10.1021/acs.analchem.1c04382] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Previously, mass spectrometry-based approaches have been used to fill this gap but feature poor quantitative performance and were generally limited to hundreds of proteins. Here, we demonstrate the capability of an internal standard triggered-parallel reaction monitoring (IS-PRM) assay to greatly expand the numbers of candidates that can be tested with improved quantitative performance. The assay couples immunodepletion and fractionation with IS-PRM and was developed and implemented in human plasma to quantify 5176 peptides representing 1314 breast cancer biomarker candidates. Characterization of the IS-PRM assay demonstrated the precision (median % CV of 7.7%), linearity (median R2 > 0.999 over 4 orders of magnitude), and sensitivity (median LLOQ < 1 fmol, approximately) to enable rank-ordering of candidate biomarkers for validation studies. Using three plasma pools from breast cancer patients and three control pools, 893 proteins were quantified, of which 162 candidate biomarkers were verified in at least one of the cancer pools and 22 were verified in all three cancer pools. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.
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Affiliation(s)
- Jacob
J. Kennedy
- Clinical
Research Division, Fred Hutchinson Cancer
Research Center, Seattle, Washington 98109, United States
| | - Jeffrey R. Whiteaker
- Clinical
Research Division, Fred Hutchinson Cancer
Research Center, Seattle, Washington 98109, United States
| | - Richard G. Ivey
- Clinical
Research Division, Fred Hutchinson Cancer
Research Center, Seattle, Washington 98109, United States
| | - Aura Burian
- Clinical
Research Division, Fred Hutchinson Cancer
Research Center, Seattle, Washington 98109, United States
| | - Shrabanti Chowdhury
- Department
of Genetics and Genomic Sciences and Icahn Institute for Data Science
and Genomic Technology, Icahn School of
Medicine at Mount Sinai, New York, New York 10029, United States
| | - Chia-Feng Tsai
- Biological
Sciences Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Tao Liu
- Biological
Sciences Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - ChenWei Lin
- Clinical
Research Division, Fred Hutchinson Cancer
Research Center, Seattle, Washington 98109, United States
| | - Oscar D. Murillo
- Clinical
Research Division, Fred Hutchinson Cancer
Research Center, Seattle, Washington 98109, United States
| | - Rachel A. Lundeen
- Clinical
Research Division, Fred Hutchinson Cancer
Research Center, Seattle, Washington 98109, United States
| | - Lisa A. Jones
- Proteomics
and Metabolomics Shared Resources, Fred
Hutchinson Cancer Research Center, Seattle, Washington 98109, United States
| | - Philip R. Gafken
- Proteomics
and Metabolomics Shared Resources, Fred
Hutchinson Cancer Research Center, Seattle, Washington 98109, United States
| | - Gary Longton
- Public
Health Sciences Division, Fred Hutchinson
Cancer Research Center, Seattle, Washington 98109, United States
| | - Karin D. Rodland
- Biological
Sciences Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Steven J. Skates
- MGH
Biostatistics Center, Harvard Medical School, Boston, Massachusetts 02114, United States
| | - John Landua
- Lester
and Sue Smith Breast Center, Baylor College
of Medicine, Houston, Texas 77030, United States
| | - Pei Wang
- Department
of Genetics and Genomic Sciences, Mount
Sinai Hospital, New York, New York 10065, United States
| | - Michael T. Lewis
- Lester
and Sue Smith Breast Center, Baylor College
of Medicine, Houston, Texas 77030, United States
| | - Amanda G. Paulovich
- Clinical
Research Division, Fred Hutchinson Cancer
Research Center, Seattle, Washington 98109, United States,Phone: 206-667-1912. . Fax: 206-667-2277
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13
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Zhang J, Hu L, Shao H. Research Progress on Quantification Methods of Drug Concentration of Monoclonal Antibodies. CURR PHARM ANAL 2022. [DOI: 10.2174/1573412918666220329110712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
With the development of monoclonal antibodies (mAbs) from the first generation of mice to the fourth generation of human origin, the efficacy and safety in the treatment of many diseases have been continuously improved. MAbs have been widely used in the treatment of cancer, chronic inflammatory diseases, and so on. However, the treatment response of mAbs varies greatly among individuals, and drug exposure may be affected by a variety of physiological and pathological factors, such as combined use of drugs and progression of disease. Therefore, studies tend to recommend therapeutic drug monitoring and individualized treatment strategies.
Objective:
In this paper, the commonly used methods of quantification of monoclonal antibodies were reviewed, especially liquid chromatography- mass spectrometry (LC-MS/MS) and enzyme-linked immunosorbent assay (ELISA), to provide technical support for therapeutic drug detection and individualize dosing for patients.
Conclusion:
For patients achieving mAbs treatment, it is necessary to carry out therapeutic drug monitoring and take it as a routine monitoring index. We recommend that for pharmaceutical laboratories in hospitals, establishing an appropriate assay formats, such as ELISA and LC-MS/MS is critical to determine drug concentration and antidrug antibody (ADA) for mAbs.
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Affiliation(s)
- Jinlu Zhang
- School of Medicine, Southeast University, Nanjing, China
| | - Linlin Hu
- Office of Medication Clinical Institution, Zhongda Hospital, Southeast University, Nanjing, China;
- Department of Pharmacy, Zhongda Hospital, Southeast University, Nanjing, China
| | - Hua Shao
- Department of Pharmacy, Zhongda Hospital, Southeast University, Nanjing, China
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14
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Shu Q, Liu S, Alonzi T, LaCourse SM, Singh DK, Bao D, Wamalwa D, Jiang L, Lyon CJ, John-Stewart G, Kaushal D, Goletti D, Hu T. Assay design for unambiguous identification and quantification of circulating pathogen-derived peptide biomarkers. Theranostics 2022; 12:2948-2962. [PMID: 35401822 PMCID: PMC8965485 DOI: 10.7150/thno.70373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 02/25/2022] [Indexed: 11/05/2022] Open
Abstract
Rationale: Circulating pathogen-derived proteins can serve as useful biomarkers for infections but may be detected with poor sensitivity and specificity by standard immunoassays due to masking effects and cross-reactivity. Mass spectrometry (MS)-read immunoassays for biomarker-derived peptides can resolve these issues, but lack standard workflows to select species-specific peptides with strong MS signal that are suitable for antibody generation. Methods:Using a Mycobacterium tuberculosis (Mtb) protein as an example, candidate peptides were selected by length, species-specificity, MS intensity, and antigenicity score. MS data from spiked healthy serum was employed to define MS feature thresholds, including a novel measure of internal MS data correlation, to produce a peak detection algorithm. Results: This algorithm performed better in rejecting false positive signal than each of its criteria, including those currently employed for this purpose. Analysis of an Mtb peptide biomarker (CFP-10pep) by this approach identified tuberculosis cases not detected by microbiologic assays, including extrapulmonary tuberculosis and tuberculosis cases in children infected with HIV-1. Circulating CFP-10pep levels measured in a non-human primate model of tuberculosis distinguished disease from asymptomatic infection and tended to correspond with Mtb granuloma size, suggesting that it could also serve as a surrogate marker for Mtb burden and possibly treatment response. Conclusions: These biomarker selection and analysis approach appears to have strong potential utility for infectious disease diagnosis, including cryptic infections, and possibly to monitor changes in Mtb burden that may reflect disease progression or a response to treatment, which are critical needs for more effective disease control.
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Affiliation(s)
- Qingbo Shu
- Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Shan Liu
- Sichuan Provincial Key Laboratory for Human Disease Gene Study, Department of Medical Genetics, Department of Laboratory medicine, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Tonino Alonzi
- Translational Research Unit, National Institute for Infectious Diseases Lazzaro Spallanzani-IRCCS, Rome, Italy
| | - Sylvia M. LaCourse
- Departments of Medicine, Division of Allergy and Infectious Diseases, and Global Health, University of Washington, Seattle, USA
| | - Dhiraj Kumar Singh
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, Texas, USA
| | - Duran Bao
- Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Dalton Wamalwa
- Department of Pediatrics and Child Health, University of Nairobi, Nairobi, Kenya
| | - Li Jiang
- Sichuan Provincial Key Laboratory for Human Disease Gene Study, Department of Medical Genetics, Department of Laboratory medicine, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Christopher J. Lyon
- Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Grace John-Stewart
- Departments of Medicine, Division of Allergy and Infectious Diseases, and Global Health, University of Washington, Seattle, USA
| | - Deepak Kaushal
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, Texas, USA
| | - Delia Goletti
- Translational Research Unit, National Institute for Infectious Diseases Lazzaro Spallanzani-IRCCS, Rome, Italy
| | - Tony Hu
- Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine, Tulane University, New Orleans, Louisiana, USA.,✉ Corresponding author: Tony Hu.
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15
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Kim Y, Kim J, Son M, Lee J, Yeo I, Choi KY, Kim H, Kim BC, Lee KH, Kim Y. Plasma protein biomarker model for screening Alzheimer disease using multiple reaction monitoring-mass spectrometry. Sci Rep 2022; 12:1282. [PMID: 35075217 PMCID: PMC8786819 DOI: 10.1038/s41598-022-05384-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 01/11/2022] [Indexed: 12/01/2022] Open
Abstract
Alzheimer disease (AD) is a leading cause of dementia that has gained prominence in our aging society. Yet, the complexity of diagnosing AD and measuring its invasiveness poses an obstacle. To this end, blood-based biomarkers could mitigate the inconveniences that impede an accurate diagnosis. We developed models to diagnose AD and measure the severity of neurocognitive impairment using blood protein biomarkers. Multiple reaction monitoring-mass spectrometry, a highly selective and sensitive approach for quantifying targeted proteins in samples, was used to analyze blood samples from 4 AD groups: cognitive normal control, asymptomatic AD, prodromal AD), and AD dementia. Multimarker models were developed using 10 protein biomarkers and apolipoprotein E genotypes for amyloid beta and 10 biomarkers with Korean Mini-Mental Status Examination (K-MMSE) score for predicting Alzheimer disease progression. The accuracies for the AD classification model and AD progression monitoring model were 84.9% (95% CI 82.8 to 87.0) and 79.1% (95% CI 77.8 to 80.5), respectively. The models were more accurate in diagnosing AD, compared with single APOE genotypes and the K-MMSE score. Our study demonstrates the possibility of predicting AD with high accuracy by blood biomarker analysis as an alternative method of screening for AD.
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Affiliation(s)
- Yeongshin Kim
- Interdisciplinary Program of Bioengineering, Seoul National University College of Engineering, Seoul, Republic of Korea
| | - Jaenyeon Kim
- Interdisciplinary Program of Bioengineering, Seoul National University College of Engineering, Seoul, Republic of Korea
| | - Minsoo Son
- Interdisciplinary Program of Bioengineering, Seoul National University College of Engineering, Seoul, Republic of Korea
| | - Jihyeon Lee
- Department of Biomedical Engineering, Seoul National University College of Medicine, 28 Yongon-Dong, Chongno-Ku, Seoul, 110-799, Republic of Korea
| | - Injoon Yeo
- Department of Biomedical Engineering, Seoul National University College of Medicine, 28 Yongon-Dong, Chongno-Ku, Seoul, 110-799, Republic of Korea
| | - Kyu Yeong Choi
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center and Department of Biomedical Science, Chosun University, Gwangju, 61452, Republic of Korea
| | - Hoowon Kim
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center and Department of Biomedical Science, Chosun University, Gwangju, 61452, Republic of Korea
- Department of Neurology, Chosun University Hospital, Gwangju, 61452, Republic of Korea
| | - Byeong C Kim
- Department of Neurology, Chonnam National University Medical School, Gwangju, 61469, Republic of Korea
| | - Kun Ho Lee
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center and Department of Biomedical Science, Chosun University, Gwangju, 61452, Republic of Korea.
- Department of Biomedical Science, Chosun University, Gwangju, 61452, Republic of Korea.
- Aging Neuroscience Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea.
| | - Youngsoo Kim
- Interdisciplinary Program of Bioengineering, Seoul National University College of Engineering, Seoul, Republic of Korea.
- Department of Biomedical Engineering, Seoul National University College of Medicine, 28 Yongon-Dong, Chongno-Ku, Seoul, 110-799, Republic of Korea.
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16
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Whiteaker JR, Lundeen RA, Zhao L, Schoenherr RM, Burian A, Huang D, Voytovich U, Wang T, Kennedy JJ, Ivey RG, Lin C, Murillo OD, Lorentzen TD, Thiagarajan M, Colantonio S, Caceres TW, Roberts RR, Knotts JG, Reading JJ, Kaczmarczyk JA, Richardson CW, Garcia-Buntley SS, Bocik W, Hewitt SM, Murray KE, Do N, Brophy M, Wilz SW, Yu H, Ajjarapu S, Boja E, Hiltke T, Rodriguez H, Paulovich AG. Targeted Mass Spectrometry Enables Multiplexed Quantification of Immunomodulatory Proteins in Clinical Biospecimens. Front Immunol 2021; 12:765898. [PMID: 34858420 PMCID: PMC8632241 DOI: 10.3389/fimmu.2021.765898] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 10/22/2021] [Indexed: 12/11/2022] Open
Abstract
Immunotherapies are revolutionizing cancer care, producing durable responses and potentially cures in a subset of patients. However, response rates are low for most tumors, grade 3/4 toxicities are not uncommon, and our current understanding of tumor immunobiology is incomplete. While hundreds of immunomodulatory proteins in the tumor microenvironment shape the anti-tumor response, few of them can be reliably quantified. To address this need, we developed a multiplex panel of targeted proteomic assays targeting 52 peptides representing 46 proteins using peptide immunoaffinity enrichment coupled to multiple reaction monitoring-mass spectrometry. We validated the assays in tissue and plasma matrices, where performance figures of merit showed over 3 orders of dynamic range and median inter-day CVs of 5.2% (tissue) and 21% (plasma). A feasibility study in clinical biospecimens showed detection of 48/52 peptides in frozen tissue and 38/52 peptides in plasma. The assays are publicly available as a resource for the research community.
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Affiliation(s)
- Jeffrey R. Whiteaker
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Rachel A. Lundeen
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Lei Zhao
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Regine M. Schoenherr
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Aura Burian
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Dongqing Huang
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Ulianna Voytovich
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Tao Wang
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Jacob J. Kennedy
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Richard G. Ivey
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Chenwei Lin
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Oscar D. Murillo
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Travis D. Lorentzen
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | | | - Simona Colantonio
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Tessa W. Caceres
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Rhonda R. Roberts
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Joseph G. Knotts
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Joshua J. Reading
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Jan A. Kaczmarczyk
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Christopher W. Richardson
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Sandra S. Garcia-Buntley
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - William Bocik
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Stephen M. Hewitt
- Experimental Pathology Laboratory, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD, United States
| | - Karen E. Murray
- Veteran’s Administration (VA) Cooperative Studies Program, Veteran’s Administration (VA) Boston Healthcare System (151MAV), Jamaica Plain, MA, United States
| | - Nhan Do
- Veteran’s Administration (VA) Cooperative Studies Program, Veteran’s Administration (VA) Boston Healthcare System (151MAV), Jamaica Plain, MA, United States
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Mary Brophy
- Veteran’s Administration (VA) Cooperative Studies Program, Veteran’s Administration (VA) Boston Healthcare System (151MAV), Jamaica Plain, MA, United States
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Stephen W. Wilz
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States
- Pathology and Laboratory Medicine Service, Program, Veteran’s Administration (VA) Boston Healthcare System, Jamaica Plain, MA, United States
| | - Hongbo Yu
- Pathology and Laboratory Medicine Service, Program, Veteran’s Administration (VA) Boston Healthcare System, Jamaica Plain, MA, United States
- Department of Pathology, Harvard Medical School, Boston, MA, United States
| | - Samuel Ajjarapu
- Veteran’s Administration (VA) Cooperative Studies Program, Veteran’s Administration (VA) Boston Healthcare System (151MAV), Jamaica Plain, MA, United States
- Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Emily Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD, United States
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD, United States
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD, United States
| | - Amanda G. Paulovich
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
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17
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Lee S, Ju S, Kim SJ, Choi JO, Kim K, Kim D, Jeon ES, Lee C. tipNrich: A Tip-Based N-Terminal Proteome Enrichment Method. Anal Chem 2021; 93:14088-14098. [PMID: 34615347 DOI: 10.1021/acs.analchem.1c01722] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The mass spectrometry-based analysis of protein post-translational modifications requires large amounts of sample, complicating the analysis of samples with limited amounts of proteins such as clinical biopsies. Here, we present a tip-based N-terminal analysis method, tipNrich. The entire procedure is processed in a single pipette tip to minimize sample loss, which is so highly optimized to analyze small amounts of proteins, even femtomole-scale of a single protein. With tipNrich, we investigated various single proteins purified from different organisms using a low-resolution mass spectrometer and identified several N-terminal peptides with different Nt-modifications such as ragged N-termini. Furthermore, we applied matrix-assisted laser desorption ionization time-of-flight mass spectrometry to our method for shortening the analysis time. Moreover, we showed that our method could be utilized in disease diagnosis as exemplified by the characterization of wild-type transthyretin amyloidosis patients compared to the healthy individuals based on N-terminome profiling. In summary, tipNrich will satisfy the need of identifying N-terminal peptides even with highly scarce amounts of proteins and of having faster processing time to check the quality of protein products or to characterize N-terminal proteoform-related diseases.
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Affiliation(s)
- Seonjeong Lee
- Center for Theragnosis, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea.,Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Republic of Korea
| | - Shinyeong Ju
- Center for Theragnosis, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Seok Jin Kim
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul 02792, Korea.,Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 02792, Korea
| | - Jin-Oh Choi
- Division of Cardiology, Department of Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 02792, Korea
| | - Kihyun Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 02792, Korea
| | - Darae Kim
- Division of Cardiology, Department of Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 02792, Korea
| | - Eun-Seok Jeon
- Division of Cardiology, Department of Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 02792, Korea
| | - Cheolju Lee
- Center for Theragnosis, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea.,Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Republic of Korea
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18
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van Bentum M, Selbach M. An Introduction to Advanced Targeted Acquisition Methods. Mol Cell Proteomics 2021; 20:100165. [PMID: 34673283 PMCID: PMC8600983 DOI: 10.1016/j.mcpro.2021.100165] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 10/11/2021] [Accepted: 10/13/2021] [Indexed: 01/13/2023] Open
Abstract
Targeted proteomics via selected reaction monitoring (SRM) or parallel reaction monitoring (PRM) enables fast and sensitive detection of a preselected set of target peptides. However, the number of peptides that can be monitored in conventional targeting methods is usually rather small. Recently, a series of methods has been described that employ intelligent acquisition strategies to increase the efficiency of mass spectrometers to detect target peptides. These methods are based on one of two strategies. First, retention time adjustment-based methods enable intelligent scheduling of target peptide retention times. These include Picky, iRT, as well as spike-in free real-time adjustment methods such as MaxQuant.Live. Second, in spike-in triggered acquisition methods such as SureQuant, Pseudo-PRM, TOMAHAQ, and Scout-MRM, targeted scans are initiated by abundant labeled synthetic peptides added to samples before the run. Both strategies enable the mass spectrometer to better focus data acquisition time on target peptides. This either enables more sensitive detection or a higher number of targets per run. Here, we provide an overview of available advanced targeting methods and highlight their intrinsic strengths and weaknesses and compatibility with specific experimental setups. Our goal is to provide a basic introduction to advanced targeting methods for people starting to work in this field. Advanced acquisition methods improve focus of mass spectrometers on target peptides. This review discusses existing methods based on two strategies. Retention time adjustment-based methods enable intelligent scheduling of peptide RTs. In spike-in triggered acquisition methods targeted scans are initiated by spike-ins.
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Affiliation(s)
- Mirjam van Bentum
- Proteome Dynamics, Max Delbrück Center for Molecular Medicine, Berlin, Germany; Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Matthias Selbach
- Proteome Dynamics, Max Delbrück Center for Molecular Medicine, Berlin, Germany; Charité-Universitätsmedizin Berlin, Berlin, Germany.
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19
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Picard C, Nilsson N, Labonté A, Auld D, Rosa-Neto P, Ashton NJ, Zetterberg H, Blennow K, Breitner JCB, Villeneuve S, Poirier J. Apolipoprotein B is a novel marker for early tau pathology in Alzheimer's disease. Alzheimers Dement 2021; 18:875-887. [PMID: 34590423 PMCID: PMC9293308 DOI: 10.1002/alz.12442] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 06/30/2021] [Accepted: 07/05/2021] [Indexed: 01/08/2023]
Abstract
INTRODUCTION We examine the role of brain apolipoprotein B (apoB) as a putative marker of early tau pathology and cognitive decline. METHODS Cerebrospinal fluid (CSF) samples from cognitively normal and Alzheimer's disease (AD) participants were collected to measure protein levels of apoB and AD biomarkers amyloid beta (Aβ), t-tau and p-tau, as well as synaptic markers GAP43, SYNAPTOTAGMIN-1, synaptosome associated protein 25 (SNAP-25), and NEUROGRANIN. CSF apoB levels were contrasted with positron emission tomography (PET) scan measures of Aβ (18F-NAV4694) and Tau (flortaucipir) along with cognitive assessment alterations over 6 to 8 years. RESULTS CSF apoB levels were elevated in AD participants and correlated with t-tau, p-tau, and the four synaptic markers in pre-symptomatic individuals. In the latter, CSF apoB levels correlated with PET flortaucipir-binding in entorhinal, parahippocampal, and fusiform regions. Baseline CSF apoB levels were associated with longitudinal visuospatial cognitive decline. DISCUSSION CSF apoB markedly associates with early tau dysregulation in asymptomatic subjects and identifies at-risk individuals predisposed to develop visuospatial cognitive decline over time.
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Affiliation(s)
- Cynthia Picard
- Douglas Mental Health University Institute, Montréal, Québec, Canada.,Centre for the Studies in the Prevention of Alzheimer's Disease, Montréal, Québec, Canada
| | - Nathalie Nilsson
- Douglas Mental Health University Institute, Montréal, Québec, Canada.,Centre for the Studies in the Prevention of Alzheimer's Disease, Montréal, Québec, Canada.,McGill University, Montréal, Québec, Canada
| | - Anne Labonté
- Douglas Mental Health University Institute, Montréal, Québec, Canada.,Centre for the Studies in the Prevention of Alzheimer's Disease, Montréal, Québec, Canada
| | | | - Pedro Rosa-Neto
- Douglas Mental Health University Institute, Montréal, Québec, Canada.,Centre for the Studies in the Prevention of Alzheimer's Disease, Montréal, Québec, Canada.,McGill University, Montréal, Québec, Canada
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- Douglas Mental Health University Institute, Montréal, Québec, Canada
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.,UK Dementia Research Institute at UCL, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - John C B Breitner
- Douglas Mental Health University Institute, Montréal, Québec, Canada.,Centre for the Studies in the Prevention of Alzheimer's Disease, Montréal, Québec, Canada.,McGill University, Montréal, Québec, Canada
| | - Sylvia Villeneuve
- Douglas Mental Health University Institute, Montréal, Québec, Canada.,Centre for the Studies in the Prevention of Alzheimer's Disease, Montréal, Québec, Canada.,McGill University, Montréal, Québec, Canada
| | - Judes Poirier
- Douglas Mental Health University Institute, Montréal, Québec, Canada.,Centre for the Studies in the Prevention of Alzheimer's Disease, Montréal, Québec, Canada.,McGill University, Montréal, Québec, Canada
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- Douglas Mental Health University Institute, Montréal, Québec, Canada
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20
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Nakayasu ES, Gritsenko M, Piehowski PD, Gao Y, Orton DJ, Schepmoes AA, Fillmore TL, Frohnert BI, Rewers M, Krischer JP, Ansong C, Suchy-Dicey AM, Evans-Molina C, Qian WJ, Webb-Robertson BJM, Metz TO. Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation. Nat Protoc 2021; 16:3737-3760. [PMID: 34244696 PMCID: PMC8830262 DOI: 10.1038/s41596-021-00566-6] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 04/26/2021] [Indexed: 02/06/2023]
Abstract
Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.
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Affiliation(s)
- Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
| | - Marina Gritsenko
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Paul D Piehowski
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Yuqian Gao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Daniel J Orton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Athena A Schepmoes
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Thomas L Fillmore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Brigitte I Frohnert
- Barbara Davis Center for Diabetes, School of Medicine, University of Colorado, Aurora, CO, USA
| | - Marian Rewers
- Barbara Davis Center for Diabetes, School of Medicine, University of Colorado, Aurora, CO, USA
| | - Jeffrey P Krischer
- Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Charles Ansong
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Astrid M Suchy-Dicey
- Elson S. Floyd College of Medicine, Washington State University, Seattle, WA, USA
| | - Carmella Evans-Molina
- Center for Diabetes and Metabolic Diseases and the Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Bobbie-Jo M Webb-Robertson
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
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21
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Whiteaker JR, Wang T, Zhao L, Schoenherr RM, Kennedy JJ, Voytovich U, Ivey RG, Huang D, Lin C, Colantonio S, Caceres TW, Roberts RR, Knotts JG, Kaczmarczyk JA, Blonder J, Reading JJ, Richardson CW, Hewitt SM, Garcia-Buntley SS, Bocik W, Hiltke T, Rodriguez H, Harrington EA, Barrett JC, Lombardi B, Marco-Casanova P, Pierce AJ, Paulovich AG. Targeted Mass Spectrometry Enables Quantification of Novel Pharmacodynamic Biomarkers of ATM Kinase Inhibition. Cancers (Basel) 2021; 13:cancers13153843. [PMID: 34359745 PMCID: PMC8345163 DOI: 10.3390/cancers13153843] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/20/2021] [Accepted: 07/23/2021] [Indexed: 11/16/2022] Open
Abstract
The ATM serine/threonine kinase (HGNC: ATM) is involved in initiation of repair of DNA double-stranded breaks, and ATM inhibitors are currently being tested as anti-cancer agents in clinical trials, where pharmacodynamic (PD) assays are crucial to help guide dose and scheduling and support mechanism of action studies. To identify and quantify PD biomarkers of ATM inhibition, we developed and analytically validated a 51-plex assay (DDR-2) quantifying protein expression and DNA damage-responsive phosphorylation. The median lower limit of quantification was 1.28 fmol, the linear range was over 3 orders of magnitude, the median inter-assay variability was 11% CV, and 86% of peptides were stable for storage prior to analysis. Use of the assay was demonstrated to quantify signaling following ionizing radiation-induced DNA damage in both immortalized lymphoblast cell lines and primary human peripheral blood mononuclear cells, identifying PD biomarkers for ATM inhibition to support preclinical and clinical studies.
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Affiliation(s)
- Jeffrey R. Whiteaker
- Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA 98109, USA; (J.R.W.); (T.W.); (L.Z.); (R.M.S.); (J.J.K.); (U.V.); (R.G.I.); (D.H.); (C.L.)
| | - Tao Wang
- Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA 98109, USA; (J.R.W.); (T.W.); (L.Z.); (R.M.S.); (J.J.K.); (U.V.); (R.G.I.); (D.H.); (C.L.)
| | - Lei Zhao
- Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA 98109, USA; (J.R.W.); (T.W.); (L.Z.); (R.M.S.); (J.J.K.); (U.V.); (R.G.I.); (D.H.); (C.L.)
| | - Regine M. Schoenherr
- Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA 98109, USA; (J.R.W.); (T.W.); (L.Z.); (R.M.S.); (J.J.K.); (U.V.); (R.G.I.); (D.H.); (C.L.)
| | - Jacob J. Kennedy
- Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA 98109, USA; (J.R.W.); (T.W.); (L.Z.); (R.M.S.); (J.J.K.); (U.V.); (R.G.I.); (D.H.); (C.L.)
| | - Ulianna Voytovich
- Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA 98109, USA; (J.R.W.); (T.W.); (L.Z.); (R.M.S.); (J.J.K.); (U.V.); (R.G.I.); (D.H.); (C.L.)
| | - Richard G. Ivey
- Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA 98109, USA; (J.R.W.); (T.W.); (L.Z.); (R.M.S.); (J.J.K.); (U.V.); (R.G.I.); (D.H.); (C.L.)
| | - Dongqing Huang
- Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA 98109, USA; (J.R.W.); (T.W.); (L.Z.); (R.M.S.); (J.J.K.); (U.V.); (R.G.I.); (D.H.); (C.L.)
| | - Chenwei Lin
- Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA 98109, USA; (J.R.W.); (T.W.); (L.Z.); (R.M.S.); (J.J.K.); (U.V.); (R.G.I.); (D.H.); (C.L.)
| | - Simona Colantonio
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA; (S.C.); (T.W.C.); (R.R.R.); (J.G.K.); (J.A.K.); (J.B.); (J.J.R.); (C.W.R.); (S.S.G.-B.); (W.B.)
| | - Tessa W. Caceres
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA; (S.C.); (T.W.C.); (R.R.R.); (J.G.K.); (J.A.K.); (J.B.); (J.J.R.); (C.W.R.); (S.S.G.-B.); (W.B.)
| | - Rhonda R. Roberts
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA; (S.C.); (T.W.C.); (R.R.R.); (J.G.K.); (J.A.K.); (J.B.); (J.J.R.); (C.W.R.); (S.S.G.-B.); (W.B.)
| | - Joseph G. Knotts
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA; (S.C.); (T.W.C.); (R.R.R.); (J.G.K.); (J.A.K.); (J.B.); (J.J.R.); (C.W.R.); (S.S.G.-B.); (W.B.)
| | - Jan A. Kaczmarczyk
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA; (S.C.); (T.W.C.); (R.R.R.); (J.G.K.); (J.A.K.); (J.B.); (J.J.R.); (C.W.R.); (S.S.G.-B.); (W.B.)
| | - Josip Blonder
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA; (S.C.); (T.W.C.); (R.R.R.); (J.G.K.); (J.A.K.); (J.B.); (J.J.R.); (C.W.R.); (S.S.G.-B.); (W.B.)
| | - Joshua J. Reading
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA; (S.C.); (T.W.C.); (R.R.R.); (J.G.K.); (J.A.K.); (J.B.); (J.J.R.); (C.W.R.); (S.S.G.-B.); (W.B.)
| | - Christopher W. Richardson
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA; (S.C.); (T.W.C.); (R.R.R.); (J.G.K.); (J.A.K.); (J.B.); (J.J.R.); (C.W.R.); (S.S.G.-B.); (W.B.)
| | - Stephen M. Hewitt
- Experimental Pathology Laboratory, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD 20892, USA;
| | - Sandra S. Garcia-Buntley
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA; (S.C.); (T.W.C.); (R.R.R.); (J.G.K.); (J.A.K.); (J.B.); (J.J.R.); (C.W.R.); (S.S.G.-B.); (W.B.)
| | - William Bocik
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA; (S.C.); (T.W.C.); (R.R.R.); (J.G.K.); (J.A.K.); (J.B.); (J.J.R.); (C.W.R.); (S.S.G.-B.); (W.B.)
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA; (T.H.); (H.R.)
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA; (T.H.); (H.R.)
| | - Elizabeth A. Harrington
- Translational Sciences, Oncology, AstraZeneca, Cambridge CB4 0WG, UK; (E.A.H.); (J.C.B.); (B.L.); (P.M.-C.); (A.J.P.)
| | - J. Carl Barrett
- Translational Sciences, Oncology, AstraZeneca, Cambridge CB4 0WG, UK; (E.A.H.); (J.C.B.); (B.L.); (P.M.-C.); (A.J.P.)
| | - Benedetta Lombardi
- Translational Sciences, Oncology, AstraZeneca, Cambridge CB4 0WG, UK; (E.A.H.); (J.C.B.); (B.L.); (P.M.-C.); (A.J.P.)
| | - Paola Marco-Casanova
- Translational Sciences, Oncology, AstraZeneca, Cambridge CB4 0WG, UK; (E.A.H.); (J.C.B.); (B.L.); (P.M.-C.); (A.J.P.)
| | - Andrew J. Pierce
- Translational Sciences, Oncology, AstraZeneca, Cambridge CB4 0WG, UK; (E.A.H.); (J.C.B.); (B.L.); (P.M.-C.); (A.J.P.)
| | - Amanda G. Paulovich
- Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA 98109, USA; (J.R.W.); (T.W.); (L.Z.); (R.M.S.); (J.J.K.); (U.V.); (R.G.I.); (D.H.); (C.L.)
- Correspondence: ; Tel.: +1-206-667-1912
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22
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Whiteaker JR, Sharma K, Hoffman MA, Kuhn E, Zhao L, Cocco AR, Schoenherr RM, Kennedy JJ, Voytovich U, Lin C, Fang B, Bowers K, Whiteley G, Colantonio S, Bocik W, Roberts R, Hiltke T, Boja E, Rodriguez H, McCormick F, Holderfield M, Carr SA, Koomen JM, Paulovich AG. Targeted mass spectrometry-based assays enable multiplex quantification of receptor tyrosine kinase, MAP Kinase, and AKT signaling. CELL REPORTS METHODS 2021; 1:100015. [PMID: 34671754 PMCID: PMC8525888 DOI: 10.1016/j.crmeth.2021.100015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 02/16/2021] [Accepted: 05/07/2021] [Indexed: 02/07/2023]
Abstract
SUMMARY A primary goal of the US National Cancer Institute's Ras initiative at the Frederick National Laboratory for Cancer Research is to develop methods to quantify RAS signaling to facilitate development of novel cancer therapeutics. We use targeted proteomics technologies to develop a community resource consisting of 256 validated multiple reaction monitoring (MRM)-based, multiplexed assays for quantifying protein expression and phosphorylation through the receptor tyrosine kinase, MAPK, and AKT signaling networks. As proof of concept, we quantify the response of melanoma (A375 and SK-MEL-2) and colorectal cancer (HCT-116 and HT-29) cell lines to BRAF inhibition by PLX-4720. These assays replace over 60 Western blots with quantitative mass spectrometry-based assays of high molecular specificity and quantitative precision, showing the value of these methods for pharmacodynamic measurements and mechanism of action studies. Methods, fit-for-purpose validation, and results are publicly available as a resource for the community at assays.cancer.gov. MOTIVATION A lack of quantitative, multiplexable assays for phosphosignaling limits comprehensive investigation of aberrant signaling in cancer and evaluation of novel treatments. To alleviate this limitation, we sought to develop assays using targeted mass spectrometry for quantifying protein expression and phosphorylation through the receptor tyrosine kinase, MAPK, and AKT signaling networks. The resulting assays provide a resource for replacing over 60 Western blots in examining cancer signaling and tumor biology with high molecular specificity and quantitative rigor.
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Affiliation(s)
- Jeffrey R. Whiteaker
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Kanika Sharma
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA
| | - Melissa A. Hoffman
- Proteomics and Metabolomics Core, Department of Molecular Oncology, and Department of Tumor Biology, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Eric Kuhn
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Lei Zhao
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Alexandra R. Cocco
- Gillings School of Global Public Health, Kenan-Flagler Business School, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Regine M. Schoenherr
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Jacob J. Kennedy
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Ulianna Voytovich
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Chenwei Lin
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Bin Fang
- Proteomics and Metabolomics Core, Department of Molecular Oncology, and Department of Tumor Biology, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Kiah Bowers
- Proteomics and Metabolomics Core, Department of Molecular Oncology, and Department of Tumor Biology, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Gordon Whiteley
- Antibody Characterization Laboratory, Leidos Biochemical Research Inc, Frederick National Laboratory for Cancer Research ATRF, Frederick, MD 21701, USA
| | - Simona Colantonio
- Antibody Characterization Laboratory, Leidos Biochemical Research Inc, Frederick National Laboratory for Cancer Research ATRF, Frederick, MD 21701, USA
| | - William Bocik
- Antibody Characterization Laboratory, Leidos Biochemical Research Inc, Frederick National Laboratory for Cancer Research ATRF, Frederick, MD 21701, USA
| | - Rhonda Roberts
- Antibody Characterization Laboratory, Leidos Biochemical Research Inc, Frederick National Laboratory for Cancer Research ATRF, Frederick, MD 21701, USA
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Emily Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Frank McCormick
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA 94158, USA
| | - Matthew Holderfield
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA
- Department of Biology, Revolution Medicines, Inc., Redwood City, CA 94063, USA
| | - Steven A. Carr
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - John M. Koomen
- Proteomics and Metabolomics Core, Department of Molecular Oncology, and Department of Tumor Biology, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Amanda G. Paulovich
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
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23
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Ahn HS, Ho JY, Yu J, Yeom J, Lee S, Hur SY, Jung Y, Kim K, Choi YJ. Plasma Protein Biomarkers Associated with Higher Ovarian Cancer Risk in BRCA1/2 Carriers. Cancers (Basel) 2021; 13:cancers13102300. [PMID: 34064977 PMCID: PMC8150736 DOI: 10.3390/cancers13102300] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/03/2021] [Accepted: 05/08/2021] [Indexed: 12/22/2022] Open
Abstract
Simple Summary Most hereditary ovarian cancer is associated with BRCA1/2 variants, and risk-reducing salpingo-oophorectomy during the follow-up monitoring of ovarian cancer development in heathy women with the BRCA1/2 variant reduces ovarian cancer incidence. The aim of this study was to identify plasma protein biomarkers that can indicate an increased risk of developing ovarian cancer using a proteomic approach based on a population of genetic variants. Two identified biomarkers among differentially expressed proteins, SPARC and THBS1, had lower plasma concentrations in healthy BRCA1/2 variant carriers than in ovarian cancer patients with the BRCA1/2 variant; concentration of two proteins increased at the onset of ovarian cancer. These protein markers from non-invasive liquid biopsy sampling could be used to help women with the BRCA1/2 variant determine whether to undergo an oophorectomy that could potentially affect the quality of life. Abstract Ovarian cancer (OC) is the most lethal gynecologic malignancy and in-time diagnosis is limited because of the absence of effective biomarkers. Germline BRCA1/2 genetic alterations are risk factors for hereditary OC; risk-reducing salpingo-oophorectomy (RRSO) is pursued for disease prevention. However, not all healthy carriers develop the disease. Therefore, identifying predictive markers in the BRCA1/2 carrier population could help improve the identification of candidates for preventive RRSO. In this study, plasma samples from 20 OC patients (10 patients with BRCA1/2 wild type (wt) and 10 with the BRCA1/2 variant (var)) and 20 normal subjects (10 subjects with BRCA1/2wt and 10 with BRCA1/2var) were analyzed for potential biomarkers of hereditary OC. We applied a bottom-up proteomics approach, using nano-flow LC-MS to analyze depleted plasma proteome quantitatively, and potential plasma protein markers specific to the BRCA1/2 variant were identified from a comparative statistical analysis of the four groups. We obtained 1505 protein candidates from the 40 subjects, and SPARC and THBS1 were verified by enzyme-linked immunosorbent assay. Plasma SPARC and THBS1 concentrations in healthy BRCA1/2 carriers were found to be lower than in OC patients with BRCA1/2var. If plasma SPARC concentrations increase over 337.35 ng/mL or plasma THBS1 concentrations increase over 65.28 μg/mL in a healthy BRCA1/2 carrier, oophorectomy may be suggested.
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Affiliation(s)
- Hee-Sung Ahn
- Asan Medical Center, Asan Institute for Life Sciences, Seoul 05505, Korea; (H.-S.A.); (J.Y.)
| | - Jung Yoon Ho
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (J.Y.H.); (S.L.); (S.Y.H.); (Y.J.)
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
| | - Jiyoung Yu
- Asan Medical Center, Asan Institute for Life Sciences, Seoul 05505, Korea; (H.-S.A.); (J.Y.)
| | - Jeonghun Yeom
- Convergence Medicine Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea;
| | - Sanha Lee
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (J.Y.H.); (S.L.); (S.Y.H.); (Y.J.)
| | - Soo Young Hur
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (J.Y.H.); (S.L.); (S.Y.H.); (Y.J.)
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
| | - Yuyeon Jung
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (J.Y.H.); (S.L.); (S.Y.H.); (Y.J.)
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
| | - Kyunggon Kim
- Asan Medical Center, Asan Institute for Life Sciences, Seoul 05505, Korea; (H.-S.A.); (J.Y.)
- Department of Biomedical Sciences, University of Ulsan College of Medicine, Seoul 05505, Korea
- Convergence Medicine Research Center, Asan Medical Center, Clinical Proteomics Core Laboratory, Seoul 05505, Korea
- Asan Medical Center, Bio-Medical Institute of Technology, Seoul 05505, Korea
- Correspondence: (K.K.); (Y.J.C.); Tel.: +82-2-1688-7575 (K.K.); +82-2-2258-2810 (Y.J.C.)
| | - Youn Jin Choi
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (J.Y.H.); (S.L.); (S.Y.H.); (Y.J.)
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
- Correspondence: (K.K.); (Y.J.C.); Tel.: +82-2-1688-7575 (K.K.); +82-2-2258-2810 (Y.J.C.)
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24
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Shin D, Rhee SJ, Lee J, Yeo I, Do M, Joo EJ, Jung HY, Roh S, Lee SH, Kim H, Bang M, Lee KY, Kwon JS, Ha K, Ahn YM, Kim Y. Quantitative Proteomic Approach for Discriminating Major Depressive Disorder and Bipolar Disorder by Multiple Reaction Monitoring-Mass Spectrometry. J Proteome Res 2021; 20:3188-3203. [PMID: 33960196 DOI: 10.1021/acs.jproteome.1c00058] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.
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Affiliation(s)
| | - Sang Jin Rhee
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea
| | | | | | | | - Eun-Jeong Joo
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon 34824, Republic of Korea.,Department of Psychiatry, Nowon Eulji Medical Center, Eulji University, Seoul 01830, Republic of Korea
| | - Hee Yeon Jung
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul 07061, Republic of Korea.,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, 101 Daehakro, Seoul 30380, Republic of Korea
| | - Sungwon Roh
- Department of Psychiatry, Hanyang University Hospital, Seoul 04763, Republic of Korea.,Department of Psychiatry, Hanyang University College of Medicine, Seoul 04763, Republic of Korea
| | - Sang-Hyuk Lee
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam 13496, Republic of Korea
| | - Hyeyoung Kim
- Department of Psychiatry, Inha University Hospital, Incheon 22332, Republic of Korea
| | - Minji Bang
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam 13496, Republic of Korea
| | - Kyu Young Lee
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon 34824, Republic of Korea.,Department of Psychiatry, Nowon Eulji Medical Center, Eulji University, Seoul 01830, Republic of Korea
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea.,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, 101 Daehakro, Seoul 30380, Republic of Korea
| | - Kyooseob Ha
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea.,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, 101 Daehakro, Seoul 30380, Republic of Korea
| | - Yong Min Ahn
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea.,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, 101 Daehakro, Seoul 30380, Republic of Korea
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25
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Dan K, Lee JE, Han D, Kim SM, Hong S, Kim HJ, Park KH. Proteomic identification of biomarkers in maternal plasma that predict the outcome of rescue cerclage for cervical insufficiency. PLoS One 2021; 16:e0250031. [PMID: 33857242 PMCID: PMC8049309 DOI: 10.1371/journal.pone.0250031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 03/29/2021] [Indexed: 11/18/2022] Open
Abstract
Objective We sought to identify plasma protein biomarkers that are predictive of the outcome of rescue cerclage in patients with cervical insufficiency. Methods This retrospective cohort study included 39 singleton pregnant women undergoing rescue cerclage for cervical insufficiency (17–25 weeks) who gave plasma samples. Three sets of pooled plasma samples from controls (cerclage success, n = 10) and cases (cerclage failure, n = 10, defined as spontaneous preterm delivery at <33 weeks) were labeled with 6-plex tandem mass tag (TMT) reagents and analyzed by liquid chromatography-tandem mass spectrometry. Differentially expressed proteins between the two groups were selected from the TMT-based quantitative analysis. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was further used to verify the candidate proteins of interest in patients with cervical insufficiency in the final cohort (n = 39). Results From MRM-MS analysis of the 40 proteins showing statistically significant changes (P < 0.05) from the TMT-based quantitative analysis, plasma IGFBP-2, PSG4, and PGLYRP2 levels were found to be significantly increased, whereas plasma MET and LXN levels were significantly decreased in women with cerclage failure. Of these, IGFBP-2, PSG4, and LXN levels in plasma were independent of cervical dilatation. A multiple-biomarker panel was developed for the prediction of cerclage failure, using a stepwise regression procedure, which included the plasma IGFBP-2, PSG4, and LXN (area under the curve [AUC] = 0.916). The AUC for this multiple-biomarker panel was significantly greater than the AUC for any single biomarker included in the multi-biomarker model. Conclusions Proteomic analysis identified useful and independent plasma biomarkers (IGFBP-2, PSG4, and LXN; verified by MRM) that predict poor pregnancy outcome following rescue cerclage. Their combined analysis in a multi-biomarker panel significantly improved predictability.
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Affiliation(s)
- Kisoon Dan
- Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
| | - Ji Eun Lee
- Biomedical Research Division, Theragnosis Research Center, Korea Institute of Science and Technology, Seoul, Korea
| | - Dohyun Han
- Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
| | - Sun Min Kim
- Department of Obstetrics and Gynecology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Korea
| | - Subeen Hong
- Department of Obstetrics and Gynecology, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hyeon Ji Kim
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Kyo Hoon Park
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- * E-mail:
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26
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Rodriguez H, Zenklusen JC, Staudt LM, Doroshow JH, Lowy DR. The next horizon in precision oncology: Proteogenomics to inform cancer diagnosis and treatment. Cell 2021; 184:1661-1670. [PMID: 33798439 DOI: 10.1016/j.cell.2021.02.055] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 02/13/2021] [Accepted: 02/26/2021] [Indexed: 12/18/2022]
Abstract
When it comes to precision oncology, proteogenomics may provide better prospects to the clinical characterization of tumors, help make a more accurate diagnosis of cancer, and improve treatment for patients with cancer. This perspective describes the significant contributions of The Cancer Genome Atlas and the Clinical Proteomic Tumor Analysis Consortium to precision oncology and makes the case that proteogenomics needs to be fully integrated into clinical trials and patient care in order for precision oncology to deliver the right cancer treatment to the right patient at the right dose and at the right time.
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Affiliation(s)
- Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Jean Claude Zenklusen
- Center for Cancer Genomics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Louis M Staudt
- Center for Cancer Genomics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - James H Doroshow
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Office of the Director, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Douglas R Lowy
- Office of the Director, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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27
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Addona T, Abbatiello S, Carr SA. From Skepticism to Embrace: The Role of Targeted Mass Spectrometry in Validating Proteomics. Clin Chem 2021; 66:973-974. [PMID: 32628755 DOI: 10.1093/clinchem/hvaa111] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 05/01/2020] [Indexed: 12/12/2022]
Affiliation(s)
| | - Susan Abbatiello
- Department of Chemistry and Chemical Biology, Barnett Institute of Chemical and Biological Analysis, Northeastern University Boston, MA
| | - Steven A Carr
- Proteomics Group, Broad Institute of MIT and Harvard, Cambridge, MA
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28
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Zhou Y, Tan Z, Xue P, Wang Y, Li X, Guan F. High-throughput, in-depth and estimated absolute quantification of plasma proteome using data-independent acquisition/mass spectrometry ("HIAP-DIA"). Proteomics 2021; 21:e2000264. [PMID: 33460299 DOI: 10.1002/pmic.202000264] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/07/2021] [Accepted: 01/07/2021] [Indexed: 01/01/2023]
Abstract
Mass spectrometry-based plasma proteomics has been demonstrated to be a useful tool capable of quantifying hundreds of proteins in a single LC-MS/MS experiment, for biomarker discovery or elucidation of disease mechanisms. We developed a novel data-independent acquisition (DIA)/MS-based workflow for high-throughput, in-depth and estimated absolute quantification of plasma proteins (termed HIAP-DIA), without depleting high-abundant proteins, in a single-shot experiment. In HIAP-DIA workflow, we generated an ultra-deep cumulative undepleted and depleted spectral library which contained 55,157 peptides and 5,328 proteins, optimized column length (50 cm) and gradient (90 min) of liquid chromatography instrumentation, optimized 50 DIA segments with average isolation window 17 Th, and selected reference proteins for estimated absolute quantification of all plasma proteins. A total of 606 proteins were quantified in triplicate, and 427 proteins were quantified with CV <20% in plasma proteome. R-squared value of overlapped 208 endogenous PQ500 estimated protein amounts from HIAP-DIA and absolute quantification with internal standards was 0.82, indicating high quantification accuracy of HIAP-DIA. As a pilot study, the HIAP-DIA approach described here was applied to a myelodysplastic syndromes (MDS) disease cohort. We achieved absolute quantification of 789 plasma proteins in 22 clinical plasma samples, spanning less than six orders of magnitude with quantification limit 10-20 ng/mL, and discovered 95 differentially expressed proteins providing insights into MDS pathophysiology.
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Affiliation(s)
- Yue Zhou
- The Key Laboratory of Carbohydrate Chemistry & Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China
| | - Zengqi Tan
- College of Life Science, Northwest University, Xi'an, China
| | - Peng Xue
- Department of Biology, Institute of Molecular Systems Biology, Zürich, Switzerland
| | - Yi Wang
- Department of Hematology, Provincial People's Hospital, Xi'an, China
| | - Xiang Li
- College of Life Science, Northwest University, Xi'an, China
| | - Feng Guan
- The Key Laboratory of Carbohydrate Chemistry & Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China.,College of Life Science, Northwest University, Xi'an, China
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29
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Han Y, Wood SD, Wright JM, Dostal V, Lau E, Lam MPY. Computation-assisted targeted proteomics of alternative splicing protein isoforms in the human heart. J Mol Cell Cardiol 2021; 154:92-96. [PMID: 33549679 DOI: 10.1016/j.yjmcc.2021.01.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 01/17/2021] [Accepted: 01/19/2021] [Indexed: 02/06/2023]
Abstract
Alternative splicing is prevalent in the heart and implicated in many cardiovascular diseases, but not every alternative transcript is translated and detecting non-canonical isoforms at the protein level remains challenging. Here we show the use of a computation-assisted targeted proteomics workflow to detect protein alternative isoforms in the human heart. We build on a recent strategy to integrate deep RNA-seq and large-scale mass spectrometry data to identify candidate translated isoform peptides. A machine learning approach is then applied to predict their fragmentation patterns and design protein isoform-specific parallel reaction monitoring detection (PRM) assays. As proof-of-principle, we built PRM assays for 29 non-canonical isoform peptides and detected 22 peptides in a human heart lysate. The predictions-aided PRM assays closely mirrored synthetic peptide standards for non-canonical sequences. This approach may be useful for validating non-canonical protein identification and discovering functionally relevant isoforms in the heart.
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Affiliation(s)
- Yu Han
- Department of Medicine-Cardiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, United States of America; Department of Consortium for Fibrosis Research & Translation, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, United States of America
| | - Silas D Wood
- Department of Medicine-Cardiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, United States of America
| | - Julianna M Wright
- Department of Medicine-Cardiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, United States of America
| | - Vishantie Dostal
- Department of Medicine-Cardiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, United States of America; Department of Consortium for Fibrosis Research & Translation, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, United States of America
| | - Edward Lau
- Department of Medicine-Cardiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, United States of America; Department of Consortium for Fibrosis Research & Translation, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, United States of America
| | - Maggie P Y Lam
- Department of Medicine-Cardiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, United States of America; Department of Biochemistry & Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, United States of America; Department of Consortium for Fibrosis Research & Translation, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, United States of America.
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30
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Michaud SA, Pětrošová H, Jackson AM, McGuire JC, Sinclair NJ, Ganguly M, Flenniken AM, Nutter LMJ, McKerlie C, Schibli D, Smith D, Borchers CH. Process and Workflow for Preparation of Disparate Mouse Tissues for Proteomic Analysis. J Proteome Res 2020; 20:305-316. [PMID: 33151080 DOI: 10.1021/acs.jproteome.0c00399] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We investigated the effect of homogenization strategy and protein precipitation on downstream protein quantitation using multiple reaction monitoring mass spectrometry (MRM-MS). Our objective was to develop a workflow capable of processing disparate tissue types with high throughput, minimal variability, and maximum purity. Similar abundances of endogenous proteins were measured in nine different mouse tissues regardless of the homogenization method used; however, protein precipitation had strong positive effects on several targets. The best throughput was achieved by lyophilizing tissues to dryness, followed by homogenization via bead-beating without sample buffer. Finally, the effect of tissue perfusion prior to dissection and collection was explored in 20 mouse tissues. MRM-MS showed decreased abundances of blood-related proteins in perfused tissues; however, complete removal was not achieved. Concentrations of nonblood proteins were largely unchanged, although significantly higher variances were observed for proteins from the perfused lung, indicating that perfusion may not be suitable for this organ. We present a simple yet effective tissue processing workflow consisting of harvest of fresh nonperfused tissue, novel lyophilization and homogenization by bead-beating, and protein precipitation. This workflow can be applied to a range of mouse tissues with the advantages of simplicity, minimal manual manipulation of samples, use of commonly available equipment, and high sample quality.
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Affiliation(s)
- Sarah A Michaud
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria V8Z 7X8, British Columbia, Canada
| | - Helena Pětrošová
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria V8Z 7X8, British Columbia, Canada
| | - Angela M Jackson
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria V8Z 7X8, British Columbia, Canada
| | - Jamie C McGuire
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria V8Z 7X8, British Columbia, Canada
| | - Nicholas J Sinclair
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria V8Z 7X8, British Columbia, Canada
| | - Milan Ganguly
- The Center for Phenogenomics, Toronto M5T 3H7, Ontario, Canada.,The Hospital for Sick Children, Toronto M5G 1X8, Ontario, Canada
| | - Ann M Flenniken
- The Center for Phenogenomics, Toronto M5T 3H7, Ontario, Canada.,Sinai Health Lunenfeld-Tanenbaum Research Institute, Toronto M5G 1X5, Ontario, Canada
| | - Lauryl M J Nutter
- The Center for Phenogenomics, Toronto M5T 3H7, Ontario, Canada.,The Hospital for Sick Children, Toronto M5G 1X8, Ontario, Canada
| | - Colin McKerlie
- The Center for Phenogenomics, Toronto M5T 3H7, Ontario, Canada.,The Hospital for Sick Children, Toronto M5G 1X8, Ontario, Canada
| | - David Schibli
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria V8Z 7X8, British Columbia, Canada
| | - Derek Smith
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria V8Z 7X8, British Columbia, Canada
| | - Christoph H Borchers
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria V8Z 7X8, British Columbia, Canada.,Department of Data Intensive Science and Engineering, Skolkovo Innovation Center, Skolkovo Institute of Science and Technology, Nobel Street, Moscow 143026, Russia.,Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal H3T 1E2, Quebec, Canada.,Gerald Bronfman Department of Oncology, Jewish General Hospital, McGill University, Montreal H3T 1E2, Quebec, Canada
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31
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An Exploratory Pilot Study with Plasma Protein Signatures Associated with Response of Patients with Depression to Antidepressant Treatment for 10 Weeks. Biomedicines 2020; 8:biomedicines8110455. [PMID: 33126421 PMCID: PMC7692261 DOI: 10.3390/biomedicines8110455] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 10/26/2020] [Accepted: 10/26/2020] [Indexed: 12/11/2022] Open
Abstract
Major depressive disorder (MDD) is a leading cause of global disability with a chronic and recurrent course. Recognition of biological markers that could predict and monitor response to drug treatment could personalize clinical decision-making, minimize unnecessary drug exposure, and achieve better outcomes. Four longitudinal plasma samples were collected from each of ten patients with MDD treated with antidepressants for 10 weeks. Plasma proteins were analyzed qualitatively and quantitatively with a nanoflow LC−MS/MS technique. Of 1153 proteins identified in the 40 longitudinal plasma samples, 37 proteins were significantly associated with response/time and clustered into six according to time and response by the linear mixed model. Among them, three early-drug response markers (PHOX2B, SH3BGRL3, and YWHAE) detectable within one week were verified by liquid chromatography-multiple reaction monitoring/mass spectrometry (LC-MRM/MS) in the well-controlled 24 patients. In addition, 11 proteins correlated significantly with two or more psychiatric measurement indices. This pilot study might be useful in finding protein marker candidates that can monitor response to antidepressant treatment during follow-up visits within 10 weeks after the baseline visit.
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32
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Adhikari S, Nice EC, Deutsch EW, Lane L, Omenn GS, Pennington SR, Paik YK, Overall CM, Corrales FJ, Cristea IM, Van Eyk JE, Uhlén M, Lindskog C, Chan DW, Bairoch A, Waddington JC, Justice JL, LaBaer J, Rodriguez H, He F, Kostrzewa M, Ping P, Gundry RL, Stewart P, Srivastava S, Srivastava S, Nogueira FCS, Domont GB, Vandenbrouck Y, Lam MPY, Wennersten S, Vizcaino JA, Wilkins M, Schwenk JM, Lundberg E, Bandeira N, Marko-Varga G, Weintraub ST, Pineau C, Kusebauch U, Moritz RL, Ahn SB, Palmblad M, Snyder MP, Aebersold R, Baker MS. A high-stringency blueprint of the human proteome. Nat Commun 2020; 11:5301. [PMID: 33067450 PMCID: PMC7568584 DOI: 10.1038/s41467-020-19045-9] [Citation(s) in RCA: 127] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 09/25/2020] [Indexed: 02/07/2023] Open
Abstract
The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP's tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases.
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Affiliation(s)
- Subash Adhikari
- Faculty of Medicine, Health and Human Sciences, Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, 2109, Australia
| | - Edouard C Nice
- Faculty of Medicine, Health and Human Sciences, Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, 2109, Australia
- Faculty of Medicine, Nursing and Health Sciences, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia
| | - Eric W Deutsch
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA, 98109, USA
| | - Lydie Lane
- Faculty of Medicine, SIB-Swiss Institute of Bioinformatics and Department of Microbiology and Molecular Medicine, University of Geneva, CMU, Michel-Servet 1, 1211, Geneva, Switzerland
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109-2218, USA
| | - Stephen R Pennington
- UCD Conway Institute of Biomolecular and Biomedical Research, School of Medicine, University College Dublin, Dublin, Ireland
| | - Young-Ki Paik
- Yonsei Proteome Research Center, 50 Yonsei-ro, Sudaemoon-ku, Seoul, 120-749, South Korea
| | | | - Fernando J Corrales
- Functional Proteomics Laboratory, Centro Nacional de Biotecnología-CSIC, Proteored-ISCIII, 28049, Madrid, Spain
| | - Ileana M Cristea
- Department of Molecular Biology, Princeton University, Princeton, NJ, 08544, USA
| | - Jennifer E Van Eyk
- Cedars Sinai Medical Center, Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Los Angeles, CA, 90048, USA
| | - Mathias Uhlén
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 17121, Solna, Sweden
| | - Cecilia Lindskog
- Rudbeck Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, 75185, Uppsala, Sweden
| | - Daniel W Chan
- Department of Pathology and Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21224, USA
| | - Amos Bairoch
- Faculty of Medicine, SIB-Swiss Institute of Bioinformatics and Department of Microbiology and Molecular Medicine, University of Geneva, CMU, Michel-Servet 1, 1211, Geneva, Switzerland
| | - James C Waddington
- UCD Conway Institute of Biomolecular and Biomedical Research, School of Medicine, University College Dublin, Dublin, Ireland
| | - Joshua L Justice
- Department of Molecular Biology, Princeton University, Princeton, NJ, 08544, USA
| | - Joshua LaBaer
- Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Markus Kostrzewa
- Bruker Daltonik GmbH, Microbiology and Diagnostics, Fahrenheitstrasse, 428359, Bremen, Germany
| | - Peipei Ping
- Cardiac Proteomics and Signaling Laboratory, Department of Physiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Rebekah L Gundry
- CardiOmics Program, Center for Heart and Vascular Research, Division of Cardiovascular Medicine and Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Peter Stewart
- Department of Chemical Pathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | | | - Sudhir Srivastava
- Cancer Biomarkers Research Branch, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Suite 5E136, Rockville, MD, 20852, USA
| | - Fabio C S Nogueira
- Proteomics Unit and Laboratory of Proteomics, Institute of Chemistry, Federal University of Rio de Janeiro, Av Athos da Silveria Ramos, 149, 21941-909, Rio de Janeiro, RJ, Brazil
| | - Gilberto B Domont
- Proteomics Unit and Laboratory of Proteomics, Institute of Chemistry, Federal University of Rio de Janeiro, Av Athos da Silveria Ramos, 149, 21941-909, Rio de Janeiro, RJ, Brazil
| | - Yves Vandenbrouck
- University of Grenoble Alpes, Inserm, CEA, IRIG-BGE, U1038, 38000, Grenoble, France
| | - Maggie P Y Lam
- Departments of Medicine-Cardiology and Biochemistry and Molecular Genetics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
- Consortium for Fibrosis Research and Translation, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Sara Wennersten
- Division of Cardiology, Department of Medicine, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Juan Antonio Vizcaino
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Marc Wilkins
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 17121, Solna, Sweden
| | - Emma Lundberg
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 17121, Solna, Sweden
| | - Nuno Bandeira
- Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0404, La Jolla, CA, 92093-0404, USA
| | | | - Susan T Weintraub
- Department of Biochemistry and Structural Biology, University of Texas Health Science Center San Antonio, UT Health, 7703 Floyd Curl Drive, San Antonio, TX, 78229-3900, USA
| | - Charles Pineau
- University of Rennes, Inserm, EHESP, IREST, UMR_S 1085, F-35042, Rennes, France
| | - Ulrike Kusebauch
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA, 98109, USA
| | - Robert L Moritz
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA, 98109, USA
| | - Seong Beom Ahn
- Faculty of Medicine, Health and Human Sciences, Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, 2109, Australia
| | - Magnus Palmblad
- Leiden University Medical Center, Leiden, 2333, The Netherlands
| | - Michael P Snyder
- Department of Genetics, Stanford School of Medicine, Stanford, CA, 94305, USA
| | - Ruedi Aebersold
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA, 98109, USA
- Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Mark S Baker
- Faculty of Medicine, Health and Human Sciences, Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, 2109, Australia.
- Department of Genetics, Stanford School of Medicine, Stanford, CA, 94305, USA.
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33
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Krasny L, Huang PH. Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology. Mol Omics 2020; 17:29-42. [PMID: 33034323 DOI: 10.1039/d0mo00072h] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Data-independent acquisition mass spectrometry (DIA-MS) is a next generation proteomic methodology that generates permanent digital proteome maps offering highly reproducible retrospective analysis of cellular and tissue specimens. The adoption of this technology has ushered a new wave of oncology studies across a wide range of applications including its use in molecular classification, oncogenic pathway analysis, drug and biomarker discovery and unravelling mechanisms of therapy response and resistance. In this review, we provide an overview of the experimental workflows commonly used in DIA-MS, including its current strengths and limitations versus conventional data-dependent acquisition mass spectrometry (DDA-MS). We further summarise a number of key studies to illustrate the power of this technology when applied to different facets of oncology. Finally we offer a perspective of the latest innovations in DIA-MS technology and machine learning-based algorithms necessary for driving the development of high-throughput, in-depth and reproducible proteomic assays that are compatible with clinical diagnostic workflows, which will ultimately enable the delivery of precision cancer medicine to achieve optimal patient outcomes.
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Affiliation(s)
- Lukas Krasny
- Division of Molecular Pathology, The Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK.
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Cerciello F, Choi M, Sinicropi-Yao SL, Lomeo K, Amann JM, Felley-Bosco E, Stahel RA, Robinson BWS, Creaney J, Pass HI, Vitek O, Carbone DP. Verification of a Blood-Based Targeted Proteomics Signature for Malignant Pleural Mesothelioma. Cancer Epidemiol Biomarkers Prev 2020; 29:1973-1982. [PMID: 32732250 PMCID: PMC7541795 DOI: 10.1158/1055-9965.epi-20-0543] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/18/2020] [Accepted: 07/27/2020] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND We have verified a mass spectrometry (MS)-based targeted proteomics signature for the detection of malignant pleural mesothelioma (MPM) from the blood. METHODS A seven-peptide biomarker MPM signature by targeted proteomics in serum was identified in a previous independent study. Here, we have verified the predictive accuracy of a reduced version of that signature, now composed of six-peptide biomarkers. We have applied liquid chromatography-selected reaction monitoring (LC-SRM), also known as multiple-reaction monitoring (MRM), for the investigation of 402 serum samples from 213 patients with MPM and 189 cancer-free asbestos-exposed donors from the United States, Australia, and Europe. RESULTS Each of the biomarkers composing the signature was independently informative, with no apparent functional or physical relation to each other. The multiplexing possibility offered by MS proteomics allowed their integration into a single signature with a higher discriminating capacity than that of the single biomarkers alone. The strategy allowed in this way to increase their potential utility for clinical decisions. The signature discriminated patients with MPM and asbestos-exposed donors with AUC of 0.738. For early-stage MPM, AUC was 0.765. This signature was also prognostic, and Kaplan-Meier analysis showed a significant difference between high- and low-risk groups with an HR of 1.659 (95% CI, 1.075-2.562; P = 0.021). CONCLUSIONS Targeted proteomics allowed the development of a multianalyte signature with diagnostic and prognostic potential for MPM from the blood. IMPACT The proteomic signature represents an additional diagnostic approach for informing clinical decisions for patients at risk for MPM.
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Affiliation(s)
- Ferdinando Cerciello
- James Thoracic Center, James Cancer Center, The Ohio State University Medical Center, Columbus, Ohio.
| | - Meena Choi
- College of Computer and Information Science, Northeastern University, Boston, Massachusetts
| | - Sara L Sinicropi-Yao
- James Thoracic Center, James Cancer Center, The Ohio State University Medical Center, Columbus, Ohio
| | - Katie Lomeo
- James Thoracic Center, James Cancer Center, The Ohio State University Medical Center, Columbus, Ohio
| | - Joseph M Amann
- James Thoracic Center, James Cancer Center, The Ohio State University Medical Center, Columbus, Ohio
| | - Emanuela Felley-Bosco
- Laboratory of Molecular Oncology, Division of Thoracic Surgery, University Hospital Zürich, Zürich, Switzerland
| | - Rolf A Stahel
- Department of Oncology, Center of Hematology and Oncology, Comprehensive Cancer Center Zürich, University Hospital Zürich, Zürich, Switzerland
| | - Bruce W S Robinson
- National Centre for Asbestos Related Disease, University of Western Australia, School of Medicine and Pharmacology, Nedlands, Western Australia
| | - Jenette Creaney
- National Centre for Asbestos Related Disease, University of Western Australia, School of Medicine and Pharmacology, Nedlands, Western Australia
| | - Harvey I Pass
- New York University, Langone Medical Center, New York, New York
| | - Olga Vitek
- College of Computer and Information Science, Northeastern University, Boston, Massachusetts
| | - David P Carbone
- James Thoracic Center, James Cancer Center, The Ohio State University Medical Center, Columbus, Ohio.
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35
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Do M, Kim H, Yeo I, Lee J, Park IA, Ryu HS, Kim Y. Clinical Application of Multiple Reaction Monitoring-Mass Spectrometry to Human Epidermal Growth Factor Receptor 2 Measurements as a Potential Diagnostic Tool for Breast Cancer Therapy. Clin Chem 2020; 66:1339-1348. [DOI: 10.1093/clinchem/hvaa178] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 07/13/2020] [Indexed: 12/23/2022]
Abstract
Abstract
Background
Human epidermal growth factor receptor 2 (HER2) is often overexpressed in breast cancer and correlates with a worse prognosis. Thus, the accurate detection of HER2 is crucial for providing the appropriate measures for patients. However, the current techniques used to detect HER2 status, immunohistochemistry and fluorescence in situ hybridization (FISH), have limitations. Specifically, FISH, which is mandatory for arbitrating 2+ cases, is time-consuming and costly. To address this shortcoming, we established a multiple reaction monitoring-mass spectrometry (MRM-MS) assay that improves on existing methods for differentiating HER2 status.
Methods
We quantified HER2 expression levels in 210 breast cancer formalin-fixed paraffin-embedded (FFPE) tissue samples by MRM-MS. We aimed to improve the accuracy and precision of HER2 quantification by simplifying the sample preparation through predicting the number of FFPE slides required to ensure an adequate amount of protein and using the expression levels of an epithelial cell-specific protein as a normalization factor when measuring HER2 expression levels.
Results
To assess the correlation between MRM-MS and IHC/FISH data, HER2 quantitative data from MRM-MS were divided by the expression levels of junctional adhesion molecule A, an epithelial cell-specific protein, prior to statistical analysis. The normalized HER2 amounts distinguished between HER2 2+/FISH-negative and 2+/FISH-positive groups (AUROC = 0.908), which could not be differentiated by IHC. In addition, all HER2 status were discriminated by MRM-MS.
Conclusions
This MRM-MS assay yields more accurate HER2 expression levels relative to immunohistochemistry and should help to guide clinicians toward the proper treatment for breast cancer patients, based on their HER2 expression.
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Affiliation(s)
- Misol Do
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyunsoo Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Injoon Yeo
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jihyeon Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - In Ae Park
- Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Han Suk Ryu
- Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Youngsoo Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea
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36
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Bouchal P, Schubert OT, Faktor J, Capkova L, Imrichova H, Zoufalova K, Paralova V, Hrstka R, Liu Y, Ebhardt HA, Budinska E, Nenutil R, Aebersold R. Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry. Cell Rep 2020; 28:832-843.e7. [PMID: 31315058 PMCID: PMC6656695 DOI: 10.1016/j.celrep.2019.06.046] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 03/06/2019] [Accepted: 06/12/2019] [Indexed: 01/10/2023] Open
Abstract
Accurate classification of breast tumors is vital for patient management decisions and enables more precise cancer treatment. Here, we present a quantitative proteotyping approach based on sequential windowed acquisition of all theoretical fragment ion spectra (SWATH) mass spectrometry and establish key proteins for breast tumor classification. The study is based on 96 tissue samples representing five conventional breast cancer subtypes. SWATH proteotype patterns largely recapitulate these subtypes; however, they also reveal varying heterogeneity within the conventional subtypes, with triple negative tumors being the most heterogeneous. Proteins that contribute most strongly to the proteotype-based classification include INPP4B, CDK1, and ERBB2 and are associated with estrogen receptor (ER) status, tumor grade status, and HER2 status. Although these three key proteins exhibit high levels of correlation with transcript levels (R > 0.67), general correlation did not exceed R = 0.29, indicating the value of protein-level measurements of disease-regulated genes. Overall, this study highlights how cancer tissue proteotyping can lead to more accurate patient stratification. Proteotyping of 96 breast tumors by SWATH mass spectrometry Three key proteins for breast tumor classification Varying degrees of heterogeneity within conventional breast cancer subtypes Generally modest correlation between protein and transcript levels in tumor tissue
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Affiliation(s)
- Pavel Bouchal
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic; Regional Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic.
| | - Olga T Schubert
- Department of Biology, Institute of Molecular Systems Biology, Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jakub Faktor
- Regional Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Lenka Capkova
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Hana Imrichova
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic; Center for Human Genetics, University of Leuven, Leuven, Belgium
| | - Karolina Zoufalova
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Vendula Paralova
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Roman Hrstka
- Regional Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Yansheng Liu
- Department of Pharmacology, Yale Cancer Biology Institute, Yale University School of Medicine, West Haven, CT, USA
| | - Holger Alexander Ebhardt
- Department of Biology, Institute of Molecular Systems Biology, Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland; Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | - Eva Budinska
- Regional Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic; Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Rudolf Nenutil
- Regional Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland; Faculty of Science, University of Zurich, Zurich, Switzerland.
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37
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Lee TY, Lee J, Lee HJ, Lee Y, Rhee SJ, Park DY, Paek MJ, Kim EY, Kim E, Roh S, Jung HY, Kim M, Kim SH, Han D, Ahn YM, Ha K, Kwon JS. Study Protocol for a Prospective Longitudinal Cohort Study to Identify Proteomic Predictors of Pluripotent Risk for Mental Illness: The Seoul Pluripotent Risk for Mental Illness Study. Front Psychiatry 2020; 11:340. [PMID: 32372992 PMCID: PMC7186772 DOI: 10.3389/fpsyt.2020.00340] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 04/03/2020] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The Seoul Pluripotent Risk for Mental Illness (SPRIM) study was designed to identify predictors leading to mental illness in help-seeking individuals by securing sufficient statistical power through transdiagnostic approaches. The SPRIM study aims to examine the clinical characteristics of high-risk individuals for mental illness and to identify proteomic biomarkers that can predict the onset of mental illness. METHODS This paper describes the study protocol of the SPRIM study. We aim to recruit 150 participants who meet the criteria for high risk for major mental illness, 150 patients with major psychiatric disorders (schizophrenia, bipolar disorder, and major depressive disorder), and 50 matched healthy control subjects for 2 years. Clinical evaluations, self-report measures, and proteomic analyses will be implemented. The assessment points are at baseline, 6, 12, 18, and 24 months. CONCLUSIONS In the present study, we introduced the study protocol of the SPRIM study, which is the first prospective cohort study of transdiagnostic high-risk concepts using proteomic biomarkers. This study has a paradigm that encompasses various diseases without aiming at predicting and preventing the development of a specific mental illness in help-seeking individuals. The transdiagnostic high-risk concept could be extended to provide a perspective for people with various psychopathological tendencies below a threshold, such that they do not meet the existing diagnostic criteria of mental illnesses, to determine what may lead them to a specific disease and help identify appropriate preventative interventions.
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Affiliation(s)
- Tae Young Lee
- Department of Neuropsychiatry, Pusan National University Yangsan Hospital, Yangsan, South Korea
| | - Junhee Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
| | - Hyun Ju Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
| | - Yunna Lee
- Department of Neuropsychiatry, Kosin University Gospel Hospital, Pusan, South Korea
| | - Sang Jin Rhee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
| | - Dong Yeon Park
- Department of Psychiatry, National Center for Mental Health, Seoul, South Korea
| | - Myung Jae Paek
- Department of Psychiatry, The Armed Forces Capital Hospital, Seongnam, South Korea
| | - Eun Young Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
| | - Euitae Kim
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Sungwon Roh
- Department of Neuropsychiatry, Hanyang University Hospital, Seoul, South Korea
| | - Hee Yeon Jung
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, South Korea
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
| | - Se Hyun Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
| | - Dohyun Han
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
| | - Yong Min Ahn
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
| | - Kyooseob Ha
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
| | - Jun Soo Kwon
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
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Collins CJ, Yi F, Dayuha R, Whiteaker JR, Ochs HD, Freeman A, Su HC, Paulovich AG, Segundo GRS, Torgerson T, Hahn SH. Multiplexed Proteomic Analysis for Diagnosis and Screening of Five Primary Immunodeficiency Disorders From Dried Blood Spots. Front Immunol 2020; 11:464. [PMID: 32296420 PMCID: PMC7141245 DOI: 10.3389/fimmu.2020.00464] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 02/28/2020] [Indexed: 12/17/2022] Open
Abstract
Early detection of Primary Immunodeficiencies Disorders (PIDDs) is of paramount importance for effective treatment and disease management. Many PIDDs would be strong candidates for newborn screening (NBS) if robust screening methods could identify patients from dried blood spots (DBS) during the neonatal period. As majority of congenital PIDDs result in the reduction or absence of specific proteins, direct quantification of these target proteins represents an attractive potential screening tool. Unfortunately, detection is often limited by the extremely low protein concentrations in blood cells and limited blood volume present in DBS. We have recently developed a robust novel method for quantification of low abundance proteins in DBS for PIDDs using peptide immunoaffinity enrichment coupled to selected reaction monitoring (immuno-SRM). Here, we further generated a multiplexed Immuno-SRM panel for simultaneous screening of eight signature peptides representing five PIDD-specific and two cell-type specific proteins from DBS. In samples from 28 PIDD patients including two carriers, representing X-Linked Agammaglobulinemia (XLA), Wiskott-Aldrich Syndrome (WAS), X-Linked Chronic Granulomatous Disease (XL-CGD), DOCK8 Deficiency and ADA deficiency, peptides representing each disease are significantly reduced relative to normal controls and patient identification had excellent agreement with clinical and molecular diagnosis. Also included in the multiplex panel are cell specific markers for platelets (CD42) and Natural Killer Cells (CD56). In patients with WAS, CD42 levels were found to be significantly reduced consistent with characteristic thrombocytopenia. A patient with WAS analyzed before and after bone marrow transplant showed normalized WAS protein and platelet CD42 after treatment highlighting the ability of immuno-SRM to monitor the effects of PIDD treatment. The assay was readily reproduced in two separate laboratories with similar analytical performance and complete agreement in patient diagnosis demonstrating the effective standardized methods. A high-throughput Immuno-SRM method screens PIDD-specific peptides in a 2.5-min runtime meeting high volume NBS workflow requirements was also demonstrated in this report. This high-throughput method returned identical results to the standard Immuno-SRM PIDD panel. Immuno-SRM peptide analysis represents a robust potential clinical diagnostic for identifying and studying PIDD patients from easily collected and shipped DBS and supports a significant potential for early PIDD diagnosis through newborn screening.
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Affiliation(s)
| | - Fan Yi
- Seattle Children's Research Institute, Seattle, WA, United States
| | - Remwilyn Dayuha
- Seattle Children's Research Institute, Seattle, WA, United States
| | | | - Hans D. Ochs
- Seattle Children's Research Institute, Seattle, WA, United States
- University of Washington School of Medicine, Seattle, WA, United States
| | - Alexandra Freeman
- National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, United States
| | - Helen C. Su
- National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, United States
| | | | - Gesmar R. S. Segundo
- Department of Pediatrics, Universidade Federal de Uberlândia, Uberlândia, Brazil
| | - Troy Torgerson
- Seattle Children's Research Institute, Seattle, WA, United States
- University of Washington School of Medicine, Seattle, WA, United States
| | - Si Houn Hahn
- Seattle Children's Research Institute, Seattle, WA, United States
- University of Washington School of Medicine, Seattle, WA, United States
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39
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Yeo I, Kim GA, Kim H, Lee JH, Sohn A, Gwak GY, Lee JH, Lim YS, Kim Y. Proteome Multimarker Panel With Multiple Reaction Monitoring-Mass Spectrometry for Early Detection of Hepatocellular Carcinoma. Hepatol Commun 2020; 4:753-768. [PMID: 32363324 PMCID: PMC7193127 DOI: 10.1002/hep4.1500] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 02/05/2020] [Accepted: 02/17/2020] [Indexed: 12/18/2022] Open
Abstract
There is an urgent need for new biomarkers that address the shortcomings of current screening methods which fail to detect a large proportion of cases with hepatocellular carcinoma (HCC) at early stage. To develop a robust, multiple-biomarker panel based on multiple reaction monitoring-mass spectrometry with high performance in detecting early-stage HCC within at-risk populations. In the discovery set, 150 samples were analyzed to identify candidate biomarkers. The resulting list of candidates was tested in the training set (713 samples) to establish a multimarker panel, which was evaluated in the validation set (305 samples). We identified 385 serum HCC biomarker candidates in the discovery set and developed a multimarker panel consisting of 28 peptides that best differentiated HCC from controls. The area under the receiver operating characteristic curve of multimarker panel was significantly higher than alpha-fetoprotein (AFP) in the training (0.976 vs. 0.804; P < 0.001) and validation (0.898 vs. 0.778; P < 0.001) sets. In the validation set, this multimarker panel, compared with AFP, showed significantly greater sensitivity (81.1% vs. 26.8%; P < 0.001) and lower specificity (84.8% vs. 98.8%; P < 0.001) in detecting HCC cases. Combining AFP with the multimarker panel did not significantly improve the area under the receiver operating characteristic curve compared with the panel alone in the training (0.981 vs. 0.976; P = 0.37) and validation set (0.906 vs. 0.898; P = 0.75). Conclusion: The multiple reaction monitoring-mass spectrometry multimarker panel consisting of 28 peptides discriminates HCC cases from at-risk controls with high performance and may have potential for clinical application in HCC surveillance.
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Affiliation(s)
- Injoon Yeo
- Interdisciplinary Program in Bioengineering College of Engineering Seoul National University Seoul Korea
| | - Gi-Ae Kim
- Department of Internal Medicine Kyung Hee University School of Medicine Seoul Korea
| | - Hyunsoo Kim
- Departments of Biomedical Sciences Seoul National University College of Medicine Seoul Korea.,Biomedical Engineering Seoul National University College of Medicine Seoul Korea.,Institute of Medical and Biological Engineering MRC Seoul National University College of Medicine Seoul Korea
| | - Ji Hyeon Lee
- Departments of Biomedical Sciences Seoul National University College of Medicine Seoul Korea
| | - Areum Sohn
- Biomedical Engineering Seoul National University College of Medicine Seoul Korea
| | - Geum-Youn Gwak
- Department of Medicine Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea
| | - Jeong-Hoon Lee
- Department of Internal Medicine and Liver Research Institute Seoul National University College of Medicine Seoul Korea
| | - Young-Suk Lim
- Department of Gastroenterology Liver Center Asan Medical Center University of Ulsan College of Medicine Seoul Korea
| | - Youngsoo Kim
- Interdisciplinary Program in Bioengineering College of Engineering Seoul National University Seoul Korea.,Departments of Biomedical Sciences Seoul National University College of Medicine Seoul Korea.,Biomedical Engineering Seoul National University College of Medicine Seoul Korea.,Institute of Medical and Biological Engineering MRC Seoul National University College of Medicine Seoul Korea
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40
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Nishimura T, Nakamura H, Végvári Á, Marko-Varga G, Furuya N, Saji H. Current status of clinical proteogenomics in lung cancer. Expert Rev Proteomics 2019; 16:761-772. [PMID: 31402712 DOI: 10.1080/14789450.2019.1654861] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.
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Affiliation(s)
- Toshihide Nishimura
- Department of Translational Medicine Informatics, St. Marianna University School of Medicine , Kawasaki, Kanagawa , Japan
| | - Haruhiko Nakamura
- Department of Translational Medicine Informatics, St. Marianna University School of Medicine , Kawasaki, Kanagawa , Japan.,Department of Chest Surgery, St. Marianna University School of Medicine , Kawasaki, Kanagawa , Japan
| | - Ákos Végvári
- Proteomics Biomedicum, Division of Physiological Chemistry I, Department of Medical Biochemistry & Biophysics (MBB), Karolinska Institutet , Solna , Sweden
| | - György Marko-Varga
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University , Lund , Sweden.,Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö , Malmö , Sweden
| | - Naoki Furuya
- Department of Internal Medicine, Division of Respiratory Medicine, St. Marianna University School of Medicine , Kawasaki , Kanagawa , Japan
| | - Hisashi Saji
- Department of Chest Surgery, St. Marianna University School of Medicine , Kawasaki, Kanagawa , Japan
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41
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Bradshaw RA, Hondermarck H, Rodriguez H. Cancer Proteomics and the Elusive Diagnostic Biomarkers. Proteomics 2019; 19:e1800445. [PMID: 31172665 DOI: 10.1002/pmic.201800445] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 06/02/2019] [Indexed: 12/11/2022]
Abstract
Despite progress in genomic and proteomic technology and applications, the validation of cancer biomarkers of use as clinical early detection diagnostics has remained elusive. As described in this brief viewpoint, there are now recognized to be many types of clinical biomarkers and proteomic analyses, particularly when combined with other 'omic analyses, have been effective in many such biomarker identifications. However, in the area of early diagnosis of cancers, the problems associated with the conversion from identification to diagnostic have largely not been overcome. Notably, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) of the National Cancer Institute (NCI), has been particularly successful in refining the analytical steps needed to tackle this challenging issue and has provided positive insight into how to solve many of the underlying problems. The potential for developing clinical diagnostics for early detection of highly lethal cancers and possible new therapeutic strategies through proteomic analyses, as seen through these CPTAC successes, is more promising than ever.
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Affiliation(s)
- Ralph A Bradshaw
- Department of Physiology and Biophysics, University of California, Irvine, CA, 92697, USA.,Department of Pharmacology, University of California, San Diego, CA, 92093, USA
| | - Hubert Hondermarck
- School of Biomedical Sciences and Pharmacy, Hunter Medical Research Institute, University of Newcastle, Callaghan, NSW, 2308, Australia
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
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42
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Kim KH, Kim JY, Yoo JS. Mass spectrometry analysis of glycoprotein biomarkers in human blood of hepatocellular carcinoma. Expert Rev Proteomics 2019; 16:553-568. [DOI: 10.1080/14789450.2019.1626235] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Kwang Hoe Kim
- Biomedical Omics Group, Korea Basic Science Institute, Cheongju, Republic of Korea
| | - Jin Young Kim
- Biomedical Omics Group, Korea Basic Science Institute, Cheongju, Republic of Korea
| | - Jong Shin Yoo
- Biomedical Omics Group, Korea Basic Science Institute, Cheongju, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, Republic of Korea
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Iacobucci C, Piotrowski C, Aebersold R, Amaral BC, Andrews P, Bernfur K, Borchers C, Brodie NI, Bruce JE, Cao Y, Chaignepain S, Chavez JD, Claverol S, Cox J, Davis T, Degliesposti G, Dong MQ, Edinger N, Emanuelsson C, Gay M, Götze M, Gomes-Neto F, Gozzo FC, Gutierrez C, Haupt C, Heck AJR, Herzog F, Huang L, Hoopmann MR, Kalisman N, Klykov O, Kukačka Z, Liu F, MacCoss MJ, Mechtler K, Mesika R, Moritz RL, Nagaraj N, Nesati V, Neves-Ferreira AGC, Ninnis R, Novák P, O'Reilly FJ, Pelzing M, Petrotchenko E, Piersimoni L, Plasencia M, Pukala T, Rand KD, Rappsilber J, Reichmann D, Sailer C, Sarnowski CP, Scheltema RA, Schmidt C, Schriemer DC, Shi Y, Skehel JM, Slavin M, Sobott F, Solis-Mezarino V, Stephanowitz H, Stengel F, Stieger CE, Trabjerg E, Trnka M, Vilaseca M, Viner R, Xiang Y, Yilmaz S, Zelter A, Ziemianowicz D, Leitner A, Sinz A. First Community-Wide, Comparative Cross-Linking Mass Spectrometry Study. Anal Chem 2019; 91:6953-6961. [PMID: 31045356 PMCID: PMC6625963 DOI: 10.1021/acs.analchem.9b00658] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The number of publications in the field of chemical cross-linking combined with mass spectrometry (XL-MS) to derive constraints for protein three-dimensional structure modeling and to probe protein-protein interactions has increased during the last years. As the technique is now becoming routine for in vitro and in vivo applications in proteomics and structural biology there is a pressing need to define protocols as well as data analysis and reporting formats. Such consensus formats should become accepted in the field and be shown to lead to reproducible results. This first, community-based harmonization study on XL-MS is based on the results of 32 groups participating worldwide. The aim of this paper is to summarize the status quo of XL-MS and to compare and evaluate existing cross-linking strategies. Our study therefore builds the framework for establishing best practice guidelines to conduct cross-linking experiments, perform data analysis, and define reporting formats with the ultimate goal of assisting scientists to generate accurate and reproducible XL-MS results.
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Affiliation(s)
- Claudio Iacobucci
- Department of Pharmaceutical Chemistry and Bioanalytics, Institute of Pharmacy, Charles Tanford Protein Center , Martin Luther University Halle-Wittenberg , Kurt-Mothes-Strasse 3a , 06120 Halle/Saale , Germany
| | - Christine Piotrowski
- Department of Pharmaceutical Chemistry and Bioanalytics, Institute of Pharmacy, Charles Tanford Protein Center , Martin Luther University Halle-Wittenberg , Kurt-Mothes-Strasse 3a , 06120 Halle/Saale , Germany
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich , Otto-Stern-Weg 3 , 8093 Zurich , Switzerland.,Faculty of Science , University of Zurich , 8006 Zurich , Switzerland
| | - Bruno C Amaral
- Institute of Chemistry , University of Campinas , Campinas São Paulo 13083-970 , Brazil
| | - Philip Andrews
- Departments of Biological Chemistry, Bioinformatics, and Chemistry , University of Michigan , Ann Arbor , Michigan 48109 , United States
| | - Katja Bernfur
- Department of Biochemistry and Structural Biology, Center for Molecular Protein Science , Lund University , 221 00 Lund , Sweden
| | - Christoph Borchers
- University of Victoria-Genome British Columbia Proteomics Centre , Vancouver Island Technology Park , Victoria , British Columbia V8Z 7X8 , Canada.,Department of Biochemistry and Microbiology , University of Victoria , Petch Building, Room 270d, 3800 Finnerty Road , Victoria , British Columbia V8P 5C2 , Canada.,Gerald Bronfman Department of Oncology, Jewish General Hospital , McGill University , 3755 Côte Ste-Catherine Road , Montréal , Quebec H3T 1E2 , Canada.,Proteomics Centre, Segal Cancer Centre, Lady Davis Institute, Jewish General Hospital , McGill University , 3755 Côte Ste-Catherine Road , Montréal , Quebec H3T 1E2 , Canada
| | - Nicolas I Brodie
- University of Victoria-Genome British Columbia Proteomics Centre , Vancouver Island Technology Park , Victoria , British Columbia V8Z 7X8 , Canada
| | - James E Bruce
- Department of Genome Sciences , University of Washington , Seattle , Washington 98195 , United States
| | - Yong Cao
- National Institute of Biological Sciences , Beijing 7 Science Park Road, ZGC Life Science Park , 102206 Beijing , China
| | - Stéphane Chaignepain
- CBMN, UMR 5248, CNRS , Université de Bordeaux, INP Bordeaux , Pessac 33607 , France
| | - Juan D Chavez
- Department of Genome Sciences , University of Washington , Seattle , Washington 98195 , United States
| | - Stéphane Claverol
- Centre de Génomique Fonctionnelle, Plateforme Protéome , Université de Bordeaux , Bordeaux 33000 , France
| | - Jürgen Cox
- Computational Systems Biochemistry Research Group , Max-Planck-Institute of Biochemistry , Am Klopferspitz 18 , 82152 Martinsried , Germany
| | - Trisha Davis
- Department of Biochemistry , University of Washington , Seattle , Washington 98195 , United States
| | - Gianluca Degliesposti
- MRC Laboratory of Molecular Biology , Cambridge Biomedical Campus , Francis Crick Avenue , Cambridge CB2 0QH , U.K
| | - Meng-Qiu Dong
- National Institute of Biological Sciences , Beijing 7 Science Park Road, ZGC Life Science Park , 102206 Beijing , China
| | - Nufar Edinger
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Safra Campus Givat Ram , The Hebrew University of Jerusalem , Jerusalem 91904 , Israel
| | - Cecilia Emanuelsson
- Department of Biochemistry and Structural Biology, Center for Molecular Protein Science , Lund University , 221 00 Lund , Sweden
| | - Marina Gay
- Institute for Research in Biomedicine (IRB Barcelona) , The Barcelona Institute of Science and Technology (BIST) , Baldiri Reixac 10 , 08028 Barcelona , Spain
| | - Michael Götze
- Institute for Biochemistry and Biotechnology, Charles Tanford Protein Center , Martin Luther University Halle-Wittenberg , Kurt-Mothes-Strasse 3a , 06120 Halle/Saale , Germany
| | - Francisco Gomes-Neto
- Laboratory of Toxinology , Oswaldo Cruz Institute , Fiocruz, Avenida Brasil 4365 (Moorish Castle) , Manguinhos, Rio de Janeiro , Rio de Janeiro 21040-900 , Brazil
| | - Fabio C Gozzo
- Institute of Chemistry , University of Campinas , Campinas São Paulo 13083-970 , Brazil
| | - Craig Gutierrez
- Department of Physiology & Biophysics , University of California , Irvine , California 92697 , United States
| | - Caroline Haupt
- Interdisciplinary Research Center HALOmem, Institute for Biochemistry and Biotechnology, Charles Tanford Protein Center , Martin Luther University Halle-Wittenberg , Kurt-Mothes-Strasse 3a , 06120 Halle/Saale , Germany
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences , University of Utrecht and Netherlands Proteomics Centre , Padualaan 8 , 3584 CH Utrecht , The Netherlands
| | - Franz Herzog
- Gene Center Munich, Department of Biochemistry, Faculty of Chemistry and Pharmacy , Ludwig Maximilians University of Munich , Feodor-Lynen-Strasse 25 , 81377 Munich , Germany
| | - Lan Huang
- Department of Physiology & Biophysics , University of California , Irvine , California 92697 , United States
| | - Michael R Hoopmann
- Institute for Systems Biology , 401 Terry Avenue North , Seattle , Washington 98109 , United States
| | - Nir Kalisman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Safra Campus Givat Ram , The Hebrew University of Jerusalem , Jerusalem 91904 , Israel
| | - Oleg Klykov
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences , University of Utrecht and Netherlands Proteomics Centre , Padualaan 8 , 3584 CH Utrecht , The Netherlands
| | - Zdeněk Kukačka
- Institute of Microbiology , BIOCEV , Prumyslova 595 , 252 50 Vestec , Czech Republic
| | - Fan Liu
- Leibniz Institute of Molecular Pharmacology (FMP) , Robert-Rössle-Strasse 10 , 13125 Berlin , Germany
| | - Michael J MacCoss
- Department of Genome Sciences , University of Washington , Seattle , Washington 98195 , United States
| | - Karl Mechtler
- Protein Chemistry Facility, Research Institute of Molecular Pathology (IMP) and Institute of Molecular Biotechnology (IMBA) , Vienna Biocenter (VBC) , Dr. Bohr-Gasse 3 , 1030 Vienna , Austria
| | - Ravit Mesika
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Safra Campus Givat Ram , The Hebrew University of Jerusalem , Jerusalem 91904 , Israel
| | - Robert L Moritz
- Institute for Systems Biology , 401 Terry Avenue North , Seattle , Washington 98109 , United States
| | - Nagarjuna Nagaraj
- Biochemistry Core Facility , Max-Planck-Institute of Biochemistry , Am Klopferspitz 18 , 82152 Martinsried , Germany
| | - Victor Nesati
- Analytical Biochemistry, CSL Limited , Bio21 Institute , 30 Flemington Road , 3010 Parkville, Melbourne , Australia
| | - Ana G C Neves-Ferreira
- Laboratory of Toxinology , Oswaldo Cruz Institute , Fiocruz, Avenida Brasil 4365 (Moorish Castle) , Manguinhos, Rio de Janeiro , Rio de Janeiro 21040-900 , Brazil
| | - Robert Ninnis
- Analytical Biochemistry, CSL Limited , Bio21 Institute , 30 Flemington Road , 3010 Parkville, Melbourne , Australia
| | - Petr Novák
- Institute of Microbiology , BIOCEV , Prumyslova 595 , 252 50 Vestec , Czech Republic
| | - Francis J O'Reilly
- Chair of Bioanalytics, Institute of Biotechnology Technische Universität Berlin , 13355 Berlin , Germany
| | - Matthias Pelzing
- Analytical Biochemistry, CSL Limited , Bio21 Institute , 30 Flemington Road , 3010 Parkville, Melbourne , Australia
| | - Evgeniy Petrotchenko
- University of Victoria-Genome British Columbia Proteomics Centre , Vancouver Island Technology Park , Victoria , British Columbia V8Z 7X8 , Canada
| | - Lolita Piersimoni
- Departments of Biological Chemistry, Bioinformatics, and Chemistry , University of Michigan , Ann Arbor , Michigan 48109 , United States
| | - Manolo Plasencia
- Departments of Biological Chemistry, Bioinformatics, and Chemistry , University of Michigan , Ann Arbor , Michigan 48109 , United States
| | - Tara Pukala
- Discipline of Chemistry, Faculty of Sciences , University of Adelaide , North Terrace, Adelaide , South Australia 5005 , Australia
| | - Kasper D Rand
- Department of Pharmacy , University of Copenhagen , 2100 Copenhagen , Denmark
| | - Juri Rappsilber
- Chair of Bioanalytics, Institute of Biotechnology Technische Universität Berlin , 13355 Berlin , Germany.,Wellcome Trust Centre for Cell Biology, School of Biological Sciences , University of Edinburgh , EH9 3BF Edinburgh , U.K
| | - Dana Reichmann
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Safra Campus Givat Ram , The Hebrew University of Jerusalem , Jerusalem 91904 , Israel
| | - Carolin Sailer
- University of Konstanz , Department of Biology , Universitätsstrasse 10 , 78457 Konstanz , Germany
| | - Chris P Sarnowski
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich , Otto-Stern-Weg 3 , 8093 Zurich , Switzerland.,PhD Program in Systems Biology , University of Zurich and ETH Zurich , 8092 Zurich , Switzerland
| | - Richard A Scheltema
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences , University of Utrecht and Netherlands Proteomics Centre , Padualaan 8 , 3584 CH Utrecht , The Netherlands
| | - Carla Schmidt
- Interdisciplinary Research Center HALOmem, Institute for Biochemistry and Biotechnology, Charles Tanford Protein Center , Martin Luther University Halle-Wittenberg , Kurt-Mothes-Strasse 3a , 06120 Halle/Saale , Germany
| | - David C Schriemer
- Department of Biochemistry & Molecular Biology, Robson DNA Science Centre , University of Calgary , 3330 Hospital Drive North West , Calgary , Alberta T2N 4N1 , Canada
| | - Yi Shi
- Department of Cell Biology , University of Pittsburgh, School of Medicine , Pittsburgh , Pennsylvania 15213 , United States
| | - J Mark Skehel
- MRC Laboratory of Molecular Biology , Cambridge Biomedical Campus , Francis Crick Avenue , Cambridge CB2 0QH , U.K
| | - Moriya Slavin
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Safra Campus Givat Ram , The Hebrew University of Jerusalem , Jerusalem 91904 , Israel
| | - Frank Sobott
- Department of Chemistry , University of Antwerp , Groenenborgerlaan 171 , 2020 Antwerp , Belgium.,The Astbury Centre for Structural Molecular Biology and School of Molecular and Cellular Biology , University of Leeds , LS2 9JT Leeds , U.K
| | - Victor Solis-Mezarino
- Gene Center Munich, Department of Biochemistry, Faculty of Chemistry and Pharmacy , Ludwig Maximilians University of Munich , Feodor-Lynen-Strasse 25 , 81377 Munich , Germany
| | - Heike Stephanowitz
- Leibniz Institute of Molecular Pharmacology (FMP) , Robert-Rössle-Strasse 10 , 13125 Berlin , Germany
| | - Florian Stengel
- University of Konstanz , Department of Biology , Universitätsstrasse 10 , 78457 Konstanz , Germany
| | - Christian E Stieger
- Protein Chemistry Facility, Research Institute of Molecular Pathology (IMP) and Institute of Molecular Biotechnology (IMBA) , Vienna Biocenter (VBC) , Dr. Bohr-Gasse 3 , 1030 Vienna , Austria
| | - Esben Trabjerg
- Department of Pharmacy , University of Copenhagen , 2100 Copenhagen , Denmark
| | - Michael Trnka
- UCSF Mass Spectrometry Facility , Genentech Hall, 600 16th Street , San Francisco , California 94158 , United States
| | - Marta Vilaseca
- Institute for Research in Biomedicine (IRB Barcelona) , The Barcelona Institute of Science and Technology (BIST) , Baldiri Reixac 10 , 08028 Barcelona , Spain
| | - Rosa Viner
- Thermo Fisher Scientific , 355 River Oaks Parkway , San Jose , California 95134 , United States
| | - Yufei Xiang
- Department of Cell Biology , University of Pittsburgh, School of Medicine , Pittsburgh , Pennsylvania 15213 , United States
| | - Sule Yilmaz
- Computational Systems Biochemistry Research Group , Max-Planck-Institute of Biochemistry , Am Klopferspitz 18 , 82152 Martinsried , Germany
| | - Alex Zelter
- Department of Biochemistry , University of Washington , Seattle , Washington 98195 , United States
| | - Daniel Ziemianowicz
- Department of Biochemistry & Molecular Biology, Robson DNA Science Centre , University of Calgary , 3330 Hospital Drive North West , Calgary , Alberta T2N 4N1 , Canada
| | - Alexander Leitner
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich , Otto-Stern-Weg 3 , 8093 Zurich , Switzerland
| | - Andrea Sinz
- Department of Pharmaceutical Chemistry and Bioanalytics, Institute of Pharmacy, Charles Tanford Protein Center , Martin Luther University Halle-Wittenberg , Kurt-Mothes-Strasse 3a , 06120 Halle/Saale , Germany
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44
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Method Validation by CPTAC Guidelines for Multi-protein Marker Assays Using Multiple Reaction Monitoring-mass Spectrometry. BIOTECHNOL BIOPROC E 2019. [DOI: 10.1007/s12257-018-0454-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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45
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Zhang B, Whiteaker JR, Hoofnagle AN, Baird GS, Rodland KD, Paulovich AG. Clinical potential of mass spectrometry-based proteogenomics. Nat Rev Clin Oncol 2019; 16:256-268. [PMID: 30487530 PMCID: PMC6448780 DOI: 10.1038/s41571-018-0135-7] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.
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Affiliation(s)
- Bing Zhang
- Department of Molecular and Human Genetics, Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
| | - Jeffrey R Whiteaker
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Andrew N Hoofnagle
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine, University of Washington, Seattle, WA, USA
| | - Geoffrey S Baird
- Department of Laboratory Medicine, University of Washington, Seattle, WA, USA
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
- Department of Cell, Development and Cancer Biology, Oregon Health & Sciences University, Portland, OR, USA
| | - Amanda G Paulovich
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Division of Medical Oncology, University of Washington School of Medicine, Seattle, WA, USA.
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46
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Yi X, Liu W, Yu F. A simple MS method to characterize the higher order structures of antibody therapeutics. Eur J Pharm Sci 2019; 131:111-118. [DOI: 10.1016/j.ejps.2019.01.038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 01/30/2019] [Accepted: 01/31/2019] [Indexed: 11/24/2022]
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47
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Chiva C, Pastor O, Trilla-Fuertes L, Gámez-Pozo A, Fresno Vara JÁ, Sabidó E. Isotopologue Multipoint Calibration for Proteomics Biomarker Quantification in Clinical Practice. Anal Chem 2019; 91:4934-4938. [DOI: 10.1021/acs.analchem.8b05802] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Cristina Chiva
- Proteomics Unit, Center for Genomics Regulation, Barcelona Institute of Science and Technology (BIST), 08003, Barcelona, Spain
- Proteomics Unit, Universitat Pompeu Fabra, 08003, Barcelona, Spain
| | - Olga Pastor
- Proteomics Unit, Center for Genomics Regulation, Barcelona Institute of Science and Technology (BIST), 08003, Barcelona, Spain
- Proteomics Unit, Universitat Pompeu Fabra, 08003, Barcelona, Spain
| | | | - Angelo Gámez-Pozo
- Biomedica Molecular Medicine SL, C/Faraday 7, 28049, Madrid, Spain
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain
| | - Juan Ángel Fresno Vara
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain
| | - Eduard Sabidó
- Proteomics Unit, Center for Genomics Regulation, Barcelona Institute of Science and Technology (BIST), 08003, Barcelona, Spain
- Proteomics Unit, Universitat Pompeu Fabra, 08003, Barcelona, Spain
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48
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Development of parallel reaction monitoring assays for cerebrospinal fluid proteins associated with Alzheimer's disease. Clin Chim Acta 2019; 494:79-93. [PMID: 30858094 DOI: 10.1016/j.cca.2019.03.243] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 03/07/2019] [Accepted: 03/07/2019] [Indexed: 12/16/2022]
Abstract
Detailed knowledge of protein changes in cerebrospinal fluid (CSF) across healthy and diseased individuals would provide a better understanding of the onset and progression of neurodegenerative disorders. In this study, we selected 20 brain-enriched proteins previously identified in CSF by antibody suspension bead arrays (SBA) to be potentially biomarkers for Alzheimer's disease (AD) and verified these using an orthogonal approach. We examined the same set of 94 CSF samples from patients affected by AD (including preclinical and prodromal), mild cognitive impairment (MCI), non-AD dementia and healthy individuals, which had previously been analyzed by SBA. Twenty-eight parallel reaction monitoring (PRM) assays were developed and 13 of them could be validated for protein quantification. Antibody profiles were verified by PRM. For seven proteins, the antibody profiles were highly correlated with the PRM results (r > 0.7) and GAP43, VCAM1 and PSAP were identified as potential markers of preclinical AD. In conclusion, we demonstrate the usefulness of targeted mass spectrometry as a tool for the orthogonal verification of antibody profiling data, suggesting that these complementary methods can be successfully applied for comprehensive exploration of CSF protein levels in neurodegenerative disorders.
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49
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Rodriguez H, Pennington SR. Revolutionizing Precision Oncology through Collaborative Proteogenomics and Data Sharing. Cell 2019; 173:535-539. [PMID: 29677503 DOI: 10.1016/j.cell.2018.04.008] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The integration of proteomics into precision oncology presents opportunities that may transform the molecular analysis of cancer and accelerate basic and clinical cancer research. This Commentary discusses the importance of international collaboration and data sharing inspired by the Cancer Moonshot to accelerate the progress of multi-omic precision medicine-an approach that addresses the global diversity of people and of cancers.
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Affiliation(s)
- Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Stephen R Pennington
- School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield Dublin 4, Ireland
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50
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Arora A, Somasundaram K. Targeted Proteomics Comes to the Benchside and the Bedside: Is it Ready for Us? Bioessays 2019; 41:e1800042. [PMID: 30734933 DOI: 10.1002/bies.201800042] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 11/28/2018] [Indexed: 12/22/2022]
Abstract
While mass spectrometry (MS)-based quantification of small molecules has been successfully used for decades, targeted MS has only recently been used by the proteomics community to investigate clinical questions such as biomarker verification and validation. Targeted MS holds the promise of a paradigm shift in the quantitative determination of proteins. Nevertheless, targeted quantitative proteomics requires improvisation in making sample processing, instruments, and data analysis more accessible. In the backdrop of the genomic era reaching its zenith, certain questions arise: is the proteomic era about to come? If we are at the beginning of a new future for protein quantification, are we prepared to incorporate targeted proteomics at the benchside for basic research and at the bedside for the good of patients? Here, an overview of the knowledge required to perform targeted proteomics as well as its applications is provided. A special emphasis is placed on upcoming areas such as peptidomics, proteoform research, and mass spectrometry imaging, where the utilization of targeted proteomics is expected to bring forth new avenues. The limitations associated with the acceptance of this technique for mainstream usage are also highlighted. Also see the video abstract here https://youtu.be/mieB47B8gZw.
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Affiliation(s)
- Anjali Arora
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, 560012, India
| | - Kumaravel Somasundaram
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, 560012, India
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