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Vallés-Martí A, de Goeij-de Haas RR, Henneman AA, Piersma SR, Pham TV, Knol JC, Verheij J, Dijk F, Halfwerk H, Giovannetti E, Jiménez CR, Bijlsma MF. Kinase activities in pancreatic ductal adenocarcinoma with prognostic and therapeutic avenues. Mol Oncol 2024. [PMID: 38650175 DOI: 10.1002/1878-0261.13625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 12/12/2023] [Accepted: 02/21/2024] [Indexed: 04/25/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited number of known driver mutations but considerable cancer cell heterogeneity. Phosphoproteomics provides a direct read-out of aberrant signaling and the resultant clinically relevant phenotype. Mass spectrometry (MS)-based proteomics and phosphoproteomics were applied to 42 PDAC tumors. Data encompassed over 19 936 phosphoserine or phosphothreonine (pS/T; in 5412 phosphoproteins) and 1208 phosphotyrosine (pY; in 501 phosphoproteins) sites and a total of 3756 proteins. Proteome data identified three distinct subtypes with tumor intrinsic and stromal features. Subsequently, three phospho-subtypes were apparent: two tumor intrinsic (Phos1/2) and one stromal (Phos3), resembling known PDAC molecular subtypes. Kinase activity was analyzed by the Integrative iNferred Kinase Activity (INKA) scoring. Phospho-subtypes displayed differential phosphorylation signals and kinase activity, such as FGR and GSK3 activation in Phos1, SRC kinase family and EPHA2 in Phos2, and EGFR, INSR, MET, ABL1, HIPK1, JAK, and PRKCD in Phos3. Kinase activity analysis of an external PDAC cohort supported our findings and underscored the importance of PI3K/AKT and ERK pathways, among others. Interestingly, unfavorable patient prognosis correlated with higher RTK, PAK2, STK10, and CDK7 activity and high proliferation, whereas long survival was associated with MYLK and PTK6 activity, which was previously unknown. Subtype-associated activity profiles can guide therapeutic combination approaches in tumor and stroma-enriched tissues, and emphasize the critical role of parallel signaling pathways. In addition, kinase activity profiling identifies potential disease markers with prognostic significance.
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Affiliation(s)
- Andrea Vallés-Martí
- Department of Medical Oncology, Amsterdam University Medical Center, VU University, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, The Netherlands
- Cancer Biology, Cancer Center Amsterdam, The Netherlands
- Pharmacology Laboratory, Cancer Center Amsterdam, The Netherlands
| | - Richard R de Goeij-de Haas
- Department of Medical Oncology, Amsterdam University Medical Center, VU University, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, The Netherlands
| | - Alex A Henneman
- Department of Medical Oncology, Amsterdam University Medical Center, VU University, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, The Netherlands
| | - Sander R Piersma
- Department of Medical Oncology, Amsterdam University Medical Center, VU University, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, The Netherlands
| | - Thang V Pham
- Department of Medical Oncology, Amsterdam University Medical Center, VU University, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, The Netherlands
| | - Jaco C Knol
- Department of Medical Oncology, Amsterdam University Medical Center, VU University, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, The Netherlands
| | - Joanne Verheij
- Department of Pathology, Amsterdam University Medical Center, The Netherlands
| | - Frederike Dijk
- Department of Pathology, Amsterdam University Medical Center, The Netherlands
| | - Hans Halfwerk
- Department of Pathology, Amsterdam University Medical Center, The Netherlands
| | - Elisa Giovannetti
- Department of Medical Oncology, Amsterdam University Medical Center, VU University, Amsterdam, The Netherlands
- Pharmacology Laboratory, Cancer Center Amsterdam, The Netherlands
- Cancer Pharmacology Lab, AIRC Start-Up Unit, Fondazione Pisana per la Scienza, San Giuliano Terme, Italy
| | - Connie R Jiménez
- Department of Medical Oncology, Amsterdam University Medical Center, VU University, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, The Netherlands
| | - Maarten F Bijlsma
- Cancer Biology, Cancer Center Amsterdam, The Netherlands
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Amsterdam University Medical Center, University of Amsterdam, The Netherlands
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2
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Joshi SK, Piehowski P, Liu T, Gosline SJC, McDermott JE, Druker BJ, Traer E, Tyner JW, Agarwal A, Tognon CE, Rodland KD. Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine. Annu Rev Pharmacol Toxicol 2024; 64:455-479. [PMID: 37738504 PMCID: PMC10950354 DOI: 10.1146/annurev-pharmtox-022723-113921] [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] [Indexed: 09/24/2023]
Abstract
Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.
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Affiliation(s)
- Sunil K Joshi
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Paul Piehowski
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Tao Liu
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Sara J C Gosline
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Jason E McDermott
- Pacific Northwest National Laboratory, Richland, Washington, USA
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, Oregon, USA
| | - Brian J Druker
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Elie Traer
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Jeffrey W Tyner
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, Oregon, USA
| | - Anupriya Agarwal
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, Oregon, USA
| | - Cristina E Tognon
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Karin D Rodland
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Pacific Northwest National Laboratory, Richland, Washington, USA
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3
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Zou J, Qin Z, Li R, Yan X, Huang H, Yang B, Zhou F, Zhang L. iProPhos: A Web-Based Interactive Platform for Integrated Proteome and Phosphoproteome Analysis. Mol Cell Proteomics 2024; 23:100693. [PMID: 38097182 PMCID: PMC10828474 DOI: 10.1016/j.mcpro.2023.100693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 11/06/2023] [Accepted: 12/11/2023] [Indexed: 01/29/2024] Open
Abstract
Large-scale omics studies have generated a wealth of mass spectrometry-based proteomics data, which provide additional insights into disease biology spanning genomic boundaries. However, there is a notable lack of web-based analysis and visualization tools that facilitate the reutilization of these data. Given this challenge, we present iProPhos, a user-friendly web server to deliver interactive and customizable functionalities. iProPhos incorporates a large number of samples, including 1444 tumor samples and 746 normal samples across 12 cancer types, sourced from the Clinical Proteomic Tumor Analysis Consortium. Additionally, users can also upload their own proteomics/phosphoproteomics data for analysis and visualization. In iProPhos, users can perform profiling plotting and differential expression, patient survival, clinical feature-related, and correlation analyses, including protein-protein, mRNA-protein, and kinase-substrate correlations. Furthermore, functional enrichment, protein-protein interaction network, and kinase-substrate enrichment analyses are accessible. iProPhos displays the analytical results in interactive figures and tables with various selectable parameters. It is freely accessible at http://longlab-zju.cn/iProPhos without login requirement. We present two case studies to demonstrate that iProPhos can identify potential drug targets and upstream kinases contributing to site-specific phosphorylation. Ultimately, iProPhos allows end-users to leverage the value of big data in cancer proteomics more effectively and accelerates the discovery of novel therapeutic targets.
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Affiliation(s)
- Jing Zou
- The Second Affiliated Hospital and Life Sciences Institute and School of Medicine, The MOE Key Laboratory of Biosystems Homeostasis and Protection and Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Zhejiang University, Hangzhou, China
| | - Ziran Qin
- The Second Affiliated Hospital and Life Sciences Institute and School of Medicine, The MOE Key Laboratory of Biosystems Homeostasis and Protection and Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Zhejiang University, Hangzhou, China
| | - Ran Li
- School of Medicine, Hangzhou City University, Hangzhou, China.
| | - Xiaohua Yan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Nanchang University, Nanchang, China
| | - Huizhe Huang
- The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bing Yang
- The Second Affiliated Hospital and Life Sciences Institute and School of Medicine, The MOE Key Laboratory of Biosystems Homeostasis and Protection and Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Zhejiang University, Hangzhou, China; Department of Pharmaceutical Chemistry and the Cardiovascular Research Institute, University of California, San Francisco, California, USA
| | - Fangfang Zhou
- Institutes of Biology and Medical Sciences, Soochow University, Suzhou, China.
| | - Long Zhang
- The Second Affiliated Hospital and Life Sciences Institute and School of Medicine, The MOE Key Laboratory of Biosystems Homeostasis and Protection and Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Zhejiang University, Hangzhou, China; Cancer Center, Zhejiang University, Hangzhou, China.
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4
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Schraink T, Blumenberg L, Hussey G, George S, Miller B, Mathew N, González-Robles TJ, Sviderskiy V, Papagiannakopoulos T, Possemato R, Fenyö D, Ruggles KV. PhosphoDisco: A Toolkit for Co-regulated Phosphorylation Module Discovery in Phosphoproteomic Data. Mol Cell Proteomics 2023; 22:100596. [PMID: 37394063 PMCID: PMC10416063 DOI: 10.1016/j.mcpro.2023.100596] [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: 09/28/2022] [Revised: 04/20/2023] [Accepted: 06/12/2023] [Indexed: 07/04/2023] Open
Abstract
Kinases are key players in cancer-relevant pathways and are the targets of many successful precision cancer therapies. Phosphoproteomics is a powerful approach to study kinase activity and has been used increasingly for the characterization of tumor samples leading to the identification of novel chemotherapeutic targets and biomarkers. Finding co-regulated phosphorylation sites which represent potential kinase-substrate sets or members of the same signaling pathway allows us to harness these data to identify clinically relevant and targetable alterations in signaling cascades. Unfortunately, studies have found that databases of co-regulated phosphorylation sites are only experimentally supported in a small number of substrate sets. To address the inherent challenge of defining co-regulated phosphorylation modules relevant to a given dataset, we developed PhosphoDisco, a toolkit for determining co-regulated phosphorylation modules. We applied this approach to tandem mass spectrometry based phosphoproteomic data for breast and non-small cell lung cancer and identified canonical as well as putative new phosphorylation site modules. Our analysis identified several interesting modules in each cohort. Among these was a new cell cycle checkpoint module enriched in basal breast cancer samples and a module of PRKC isozymes putatively co-regulated by CDK12 in lung cancer. We demonstrate that modules defined by PhosphoDisco can be used to further personalized cancer treatment strategies by establishing active signaling pathways in a given patient tumor or set of tumors, and in providing new ways to classify tumors based on signaling activity.
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Affiliation(s)
- Tobias Schraink
- Division of Precision Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, New York, USA; Institute for Systems Genetics, New York University Grossman School of Medicine, New York, New York, USA; Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, New York, USA
| | - Lili Blumenberg
- Division of Precision Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, New York, USA; Institute for Systems Genetics, New York University Grossman School of Medicine, New York, New York, USA
| | - Grant Hussey
- Division of Precision Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, New York, USA; Institute for Systems Genetics, New York University Grossman School of Medicine, New York, New York, USA; Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, New York, USA
| | - Sabrina George
- Division of Precision Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, New York, USA; Institute for Systems Genetics, New York University Grossman School of Medicine, New York, New York, USA
| | - Brecca Miller
- Division of Precision Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, New York, USA; Institute for Systems Genetics, New York University Grossman School of Medicine, New York, New York, USA
| | - Nithu Mathew
- Division of Precision Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, New York, USA; Institute for Systems Genetics, New York University Grossman School of Medicine, New York, New York, USA
| | - Tania J González-Robles
- Division of Precision Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, New York, USA; Institute for Systems Genetics, New York University Grossman School of Medicine, New York, New York, USA; Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, New York, USA
| | - Vladislav Sviderskiy
- Department of Pathology, New York University Grossman School of Medicine, New York, New York, USA
| | | | - Richard Possemato
- Department of Pathology, New York University Grossman School of Medicine, New York, New York, USA
| | - David Fenyö
- Institute for Systems Genetics, New York University Grossman School of Medicine, New York, New York, USA; Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, New York, USA
| | - Kelly V Ruggles
- Division of Precision Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, New York, USA; Institute for Systems Genetics, New York University Grossman School of Medicine, New York, New York, USA.
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5
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Yang G, Zuo C, Lin Y, Zhou X, Wen P, Zhang C, Xiao H, Jiang M, Fujita M, Gao XD, Fu F. Comprehensive proteome, phosphoproteome and kinome characterization of luminal A breast cancer. Front Oncol 2023; 13:1127446. [PMID: 37064116 PMCID: PMC10102592 DOI: 10.3389/fonc.2023.1127446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/08/2023] [Indexed: 04/03/2023] Open
Abstract
BackgroundBreast cancer is one of the most frequently occurring malignant cancers worldwide. Invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) are the two most common histological subtypes of breast cancer. In this study, we aimed to deeply explore molecular characteristics and the relationship between IDC and ILC subtypes in luminal A subgroup of breast cancer using comprehensive proteomics and phosphoproteomics analysis.MethodsCancer tissues and noncancerous adjacent tissues (NATs) with the luminal A subtype (ER- and PR-positive, HER2-negative) were obtained from paired IDC and ILC patients respectively. Label-free quantitative proteomics and phosphoproteomics methods were used to detect differential proteins and the phosphorylation status between 10 paired breast cancer and NATs. Then, the difference in protein expression and its phosphorylation between IDC and ILC subtypes were explored. Meanwhile, the activation of kinases and their substrates was also revealed by Kinase-Substrate Enrichment Analysis (KSEA).ResultsIn the luminal A breast cancer, a total of 5,044 high-confidence proteins and 3,808 phosphoproteins were identified from 10 paired tissues. The protein phosphorylation level in ILC tissues was higher than that in IDC tissues. Histone H1.10 was significantly increased in IDC but decreased in ILC, Conversely, complement C4-B and Crk-like protein were significantly decreased in IDC but increased in ILC. Moreover, the increased protein expression of Septin-2, Septin-9, Heterogeneous nuclear ribonucleoprotein A1 and Kinectin but reduce of their phosphorylation could clearly distinguish IDC from ILC. In addition, IDC was primarily related to energy metabolism and MAPK pathway, while ILC was more closely involved in the AMPK and p53/p21 pathways. Furthermore, the kinomes in IDC were primarily significantly activated in the CMGC groups.ConclusionsOur research provides insights into the molecular characterization of IDC and ILC and contributes to discovering novel targets for further drug development and targeted treatment.
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Affiliation(s)
- Ganglong Yang
- The Key Laboratory of Carbohydrate Chemistry & Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China
| | - Chenyang Zuo
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China
| | - Yuxiang Lin
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
- Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian, China
| | - Xiaoman Zhou
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China
| | - Piaopiao Wen
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China
| | - Chairui Zhang
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China
| | - Han Xiao
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Meichen Jiang
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Morihisa Fujita
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China
| | - Xiao-Dong Gao
- The Key Laboratory of Carbohydrate Chemistry & Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China
- *Correspondence: Xiao-Dong Gao, ; Fangmeng Fu,
| | - Fangmeng Fu
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
- Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian, China
- *Correspondence: Xiao-Dong Gao, ; Fangmeng Fu,
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6
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Sousa A, Dugourd A, Memon D, Petursson B, Petsalaki E, Saez-Rodriguez J, Beltrao P. Pan-Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival. Mol Syst Biol 2023; 19:e10631. [PMID: 36688815 PMCID: PMC9996241 DOI: 10.15252/msb.202110631] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 01/06/2023] [Accepted: 01/09/2023] [Indexed: 01/24/2023] Open
Abstract
Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours have been extensively characterised, the measurements of protein activities have been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours and 77 cell lines that we used to estimate activity changes in 218 kinases and 292 TFs. Co-regulation of kinase and TF activities reflects previously known regulatory relationships and allows us to dissect genetic drivers of signalling changes in cancer. We find that loss-of-function mutations are not often associated with the dysregulation of downstream targets, suggesting frequent compensatory mechanisms. Finally, we identified the activities most differentially regulated in cancer subtypes and showed how these can be linked to differences in patient survival. Our results provide broad insights into the dysregulation of protein activities in cancer and their contribution to disease severity.
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Affiliation(s)
- Abel Sousa
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.,Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3s), Porto, Portugal.,Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto, Portugal.,Graduate Program in Areas of Basic and Applied Biology (GABBA), Abel Salazar Biomedical Sciences Institute, University of Porto, Porto, Portugal
| | - Aurelien Dugourd
- Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany.,Faculty of Medicine, Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Danish Memon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Borgthor Petursson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Evangelia Petsalaki
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Julio Saez-Rodriguez
- Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.,Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
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7
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Gosline SJC, Tognon C, Nestor M, Joshi S, Modak R, Damnernsawad A, Posso C, Moon J, Hansen JR, Hutchinson-Bunch C, Pino JC, Gritsenko MA, Weitz KK, Traer E, Tyner J, Druker B, Agarwal A, Piehowski P, McDermott JE, Rodland K. Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML. Clin Proteomics 2022; 19:30. [PMID: 35896960 PMCID: PMC9327422 DOI: 10.1186/s12014-022-09367-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 06/22/2022] [Indexed: 11/23/2022] Open
Abstract
Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemic cells with diverse mutation spectra, which eventually leads to disease relapse. This genetic heterogeneity drives the activation of complex signaling pathways that is reflected at the protein level. This diversity makes it difficult to treat AML with targeted therapy, requiring custom patient treatment protocols tailored to each individual's leukemia. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify genomic signatures that could predict patient-specific drug responses. However, there are inherent weaknesses in using only genetic and transcriptomic measurements as surrogates of drug response, particularly the absence of direct information about phosphorylation-mediated signal transduction. As a member of the Clinical Proteomic Tumor Analysis Consortium, we have extended the molecular characterization of this cohort by collecting proteomic and phosphoproteomic measurements from a subset of these patient samples (38 in total) to evaluate the hypothesis that proteomic signatures can improve the ability to predict response to 26 drugs in AML ex vivo samples. In this work we describe our systematic, multi-omic approach to evaluate proteomic signatures of drug response and compare protein levels to other markers of drug response such as mutational patterns. We explore the nuances of this approach using two drugs that target key pathways activated in AML: quizartinib (FLT3) and trametinib (Ras/MEK), and show how patient-derived signatures can be interpreted biologically and validated in cell lines. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to assess drug sensitivity in AML.
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Affiliation(s)
| | - Cristina Tognon
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | | | - Sunil Joshi
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Rucha Modak
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Alisa Damnernsawad
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
- Department of Biology, Faculty of Science, Mahidol University, Bangkok, Thailand
| | - Camilo Posso
- Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Jamie Moon
- Pacific Northwest National Laboratory, Seattle, WA, USA
| | | | | | - James C Pino
- Pacific Northwest National Laboratory, Seattle, WA, USA
| | | | - Karl K Weitz
- Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Elie Traer
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Jeffrey Tyner
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Brian Druker
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Anupriya Agarwal
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
- Division of Oncological Sciences, Oregon Health & Science University, Portland, OR, USA
- Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR, USA
| | | | - Jason E McDermott
- Pacific Northwest National Laboratory, Seattle, WA, USA
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR, USA
| | - Karin Rodland
- Pacific Northwest National Laboratory, Seattle, WA, USA.
- Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR, USA.
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8
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Ayati M, Yılmaz S, Chance MR, Koyuturk M. Functional Characterization of Co-Phosphorylation Networks. Bioinformatics 2022; 38:3785-3793. [PMID: 35731218 PMCID: PMC9344848 DOI: 10.1093/bioinformatics/btac406] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 03/16/2022] [Accepted: 06/18/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Protein phosphorylation is a ubiquitous regulatory mechanism that plays a central role in cellular signaling. According to recent estimates, up to 70% of human proteins can be phosphorylated. Therefore, characterization of phosphorylation dynamics is critical for understanding a broad range of biological and biochemical processes. Technologies based on mass spectrometry are rapidly advancing to meet the needs for high-throughput screening of phosphorylation. These technologies enable untargeted quantification of thousands of phosphorylation sites in a given sample. Many labs are already utilizing these technologies to comprehensively characterize signaling landscapes by examining perturbations with drugs and knockdown approaches, or by assessing diverse phenotypes in cancers, neuro-degerenational diseases, infectious diseases, and normal development. RESULTS We comprehensively investigate the concept of "co-phosphorylation", defined as the correlated phosphorylation of a pair of phosphosites across various biological states. We integrate nine publicly available phosphoproteomics datasets for various diseases (including breast cancer, ovarian cancer and Alzheimer's disease) and utilize functional data related to sequence, evolutionary histories, kinase annotations, and pathway annotations to investigate the functional relevance of co-phosphorylation. Our results across a broad range of studies consistently show that functionally associated sites tend to exhibit significant positive or negative co-phosphorylation. Specifically, we show that co-phosphorylation can be used to predict with high precision the sites that are on the same pathway or that are targeted by the same kinase. Overall, these results establish co-phosphorylation as a useful resource for analyzing phosphoproteins in a network context, which can help extend our knowledge on cellular signaling and its dysregulation.
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Affiliation(s)
- Marzieh Ayati
- Department of Computer Science, University of Texas Rio Grande Valley, Edinburg, TX, 78531, USA
| | - Serhan Yılmaz
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Mark R Chance
- Department of Nutrition, Case Western Reserve University, Cleveland, OH.,Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH.,Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH
| | - Mehmet Koyuturk
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA.,Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH.,Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH
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9
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Plubell DL, Käll L, Webb-Robertson BJM, Bramer LM, Ives A, Kelleher NL, Smith LM, Montine TJ, Wu CC, MacCoss MJ. Putting Humpty Dumpty Back Together Again: What Does Protein Quantification Mean in Bottom-Up Proteomics? J Proteome Res 2022; 21:891-898. [PMID: 35220718 PMCID: PMC8976764 DOI: 10.1021/acs.jproteome.1c00894] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Bottom-up proteomics provides peptide measurements and has been invaluable for moving proteomics into large-scale analyses. Commonly, a single quantitative value is reported for each protein-coding gene by aggregating peptide quantities into protein groups following protein inference or parsimony. However, given the complexity of both RNA splicing and post-translational protein modification, it is overly simplistic to assume that all peptides that map to a singular protein-coding gene will demonstrate the same quantitative response. By assuming that all peptides from a protein-coding sequence are representative of the same protein, we may miss the discovery of important biological differences. To capture the contributions of existing proteoforms, we need to reconsider the practice of aggregating protein values to a single quantity per protein-coding gene.
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Affiliation(s)
- Deanna L. Plubell
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195 USA
| | - Lukas Käll
- Science for Life Laboratory, KTH - Royal Institute of Technology, Box 1031, 17121, Solna, Sweden
| | | | - Lisa M. Bramer
- Pacific Northwest National Laboratory, Richland, WA 99352
| | - Ashley Ives
- Proteomics Center of Excellence & Departments of Chemistry and Molecular Biosciences, Northwestern University, Evanston, IL 60208
| | - Neil L. Kelleher
- Proteomics Center of Excellence & Departments of Chemistry and Molecular Biosciences, Northwestern University, Evanston, IL 60208
| | - Lloyd M. Smith
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706
| | | | - Christine C. Wu
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195 USA
| | - Michael J. MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195 USA
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10
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Alganem K, Hamoud AR, Creeden JF, Henkel ND, Imami AS, Joyce AW, Ryan V WG, Rethman JB, Shukla R, O'Donovan SM, Meller J, McCullumsmith R. The active kinome: The modern view of how active protein kinase networks fit in biological research. Curr Opin Pharmacol 2022; 62:117-129. [PMID: 34968947 PMCID: PMC9438800 DOI: 10.1016/j.coph.2021.11.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 10/21/2021] [Accepted: 11/12/2021] [Indexed: 02/03/2023]
Abstract
Biological regulatory networks are dynamic, intertwined, and complex systems making them challenging to study. While quantitative measurements of transcripts and proteins are key to investigate the state of a biological system, they do not inform the "active" state of regulatory networks. In consideration of that fact, "functional" proteomics assessments are needed to decipher active regulatory processes. Phosphorylation, a key post-translation modification, is a reversible regulatory mechanism that controls the functional state of proteins. Recent advancements of high-throughput protein kinase activity profiling platforms allow for a broad assessment of protein kinase networks in complex biological systems. In conjunction with sophisticated computational modeling techniques, these profiling platforms provide datasets that inform the active state of regulatory systems in disease models and highlight potential drug targets. Taken together, system-wide profiling of protein kinase activity has become a critical component of modern molecular biology research and presents a promising avenue for drug discovery.
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Affiliation(s)
- Khaled Alganem
- Department of Neurosciences, University of Toledo College of Medicine, Toledo, OH, USA
| | - Abdul-Rizaq Hamoud
- Department of Neurosciences, University of Toledo College of Medicine, Toledo, OH, USA
| | - Justin F Creeden
- Department of Neurosciences, University of Toledo College of Medicine, Toledo, OH, USA
| | - Nicholas D Henkel
- Department of Neurosciences, University of Toledo College of Medicine, Toledo, OH, USA
| | - Ali S Imami
- Department of Neurosciences, University of Toledo College of Medicine, Toledo, OH, USA
| | - Alex W Joyce
- Department of Neurosciences, University of Toledo College of Medicine, Toledo, OH, USA
| | - William G Ryan V
- Department of Neurosciences, University of Toledo College of Medicine, Toledo, OH, USA
| | - Jacob B Rethman
- Department of Neurosciences, University of Toledo College of Medicine, Toledo, OH, USA
| | - Rammohan Shukla
- Department of Neurosciences, University of Toledo College of Medicine, Toledo, OH, USA
| | - Sinead M O'Donovan
- Department of Neurosciences, University of Toledo College of Medicine, Toledo, OH, USA
| | - Jarek Meller
- Department of Biomedical Informatics, University of Cincinnati, Cincinnati, OH, USA; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, USA; Department of Pharmacology and System Biology, College of Medicine, University of Cincinnati, Cincinnati, OH, USA; Department of Electrical Engineering and Computer Science, College of Engineering and Applied Sciences, University of Cincinnati, Cincinnati, OH, USA
| | - Robert McCullumsmith
- Department of Neurosciences, University of Toledo College of Medicine, Toledo, OH, USA; Neurosciences Institute, ProMedica, Toledo, OH, USA.
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11
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Ino Y, Kinoshita E, Kinoshita-Kikuta E, Akiyama T, Nakai Y, Nishino K, Osada M, Ryo A, Hirano H, Koike T, Kimura Y. Evaluation of four phosphopeptide enrichment strategies for mass spectrometry-based proteomic analysis. Proteomics 2021; 22:e2100216. [PMID: 34932266 DOI: 10.1002/pmic.202100216] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 12/07/2021] [Accepted: 12/14/2021] [Indexed: 11/07/2022]
Abstract
Information about phosphorylation status can be used to prioritize and characterize biological processes in the cell. Various analytical strategies have been proposed to address the complexity of phosphorylation status and comprehensively identify phosphopeptides. In this study, we evaluated four strategies for phosphopeptide enrichment, using titanium dioxide (TiO2 ) and Phos-tag ligand particles from in-gel or in-solution digests prior to mass spectrometry-based analysis. Using TiO2 and Phos-tag magnetic beads, it was possible to enrich phosphopeptides from in-gel digests of phosphorylated ovalbumin separated by Phos-tag SDS-PAGE or in-solution serum digests, while minimizing non-specific adsorption. The tip-column strategy with TiO2 particles enabled enrichment of phosphopeptides from in-solution digests of whole-cell lysates with high efficiency and selectivity. However, the tip-column strategy with Phos-tag agarose beads yielded the greatest number of identified phosphopeptides. The strategies using both types of tip columns had a high degree of overlap, although there were differences in selectivity between the identified phosphopeptides. Together, our results indicate that multi-enrichment strategies using TiO2 particles and Phos-tag agarose beads are useful for comprehensive phosphoproteomic analysis.
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Affiliation(s)
- Yoko Ino
- Advanced Medical Research Center, Yokohama City University, Yokohama, Japan.,Graduate School of Health Sciences, Gunma Paz University, Takasaki City, Gunma, Japan
| | - Eiji Kinoshita
- Department of Human Nutrition, Faculty of Human Sciences, Hiroshima Bunkyo University, Hiroshima, Japan
| | - Emiko Kinoshita-Kikuta
- Department of Functional Molecular Science, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Tomoko Akiyama
- Advanced Medical Research Center, Yokohama City University, Yokohama, Japan
| | - Yusuke Nakai
- Advanced Medical Research Center, Yokohama City University, Yokohama, Japan
| | - Kohei Nishino
- Kuramoto Division, Technical Support Department, Tokushima University, Tokushima, Japan
| | - Makoto Osada
- Graduate School of Health Sciences, Gunma Paz University, Takasaki City, Gunma, Japan
| | - Akihide Ryo
- Advanced Medical Research Center, Yokohama City University, Yokohama, Japan.,Department of Microbiology, Yokohama City University School of Medicine, Yokohama, Japan
| | - Hisashi Hirano
- Advanced Medical Research Center, Yokohama City University, Yokohama, Japan
| | - Tohru Koike
- Department of Functional Molecular Science, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yayoi Kimura
- Advanced Medical Research Center, Yokohama City University, Yokohama, Japan
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12
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[Affinity chromatography based phosphoproteome research on lung cancer cells and its application]. Se Pu 2021; 39:77-86. [PMID: 34227361 PMCID: PMC9274851 DOI: 10.3724/sp.j.1123.2020.07041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
磷酸化是蛋白质翻译后修饰的重要形式之一,其异常往往会导致细胞内信号通路的紊乱和疾病的发生。固定化金属离子亲和色谱(IMAC)是磷酸化肽段的高效富集技术,在磷酸化蛋白质组研究方面应用广泛。该研究以金属钛离子(Ti4+)螯合IMAC材料(Ti4+-IMAC)为载体,进行磷酸化肽段富集。比较了10 μm Ti4+-IMAC通过振荡法和固相萃取法(SPE)富集磷酸肽的效果,发现振荡法可以富集到更多的磷酸肽;对比了两种尺寸(10 μm和30 μm)Ti4+-IMAC在磷酸化肽段富集中的差异,发现小尺寸材料富集效果更佳。进一步采用优化的策略比较了不同转移能力肺癌细胞的磷酸化蛋白质组,免标记定量蛋白质组学结果表明,优化的Ti4+-IMAC方法可以从正常的肺成纤维细胞MRC5、低转移肺癌细胞95C和高转移肺癌细胞95D中分别鉴定到510、863和1108种磷酸化蛋白质,其中317种为3组所共有。该研究共鉴定到1268种磷酸化蛋白质上的7560个磷酸化位点,其中1130个为差异磷酸化位点,文献报道显示部分异常表达的激酶与癌症转移密切相关。通过生信对比分析发现,异常表达的磷酸化蛋白质主要与细胞侵袭、迁移和死亡等细胞迁移方面的功能有关。通过优化磷酸化肽富集策略,初步阐明了磷酸化蛋白质网络的异常与肺癌转移之间的相关性,该方法有望用于肺癌进展相关的磷酸化位点、磷酸化蛋白质及其信号通路研究。
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13
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Safabakhsh S, Panwar P, Barichello S, Sangha SS, Hanson PJ, Van Petegem F, Laksman Z. THE ROLE OF PHOSPHORYLATION IN ATRIAL FIBRILLATION: A FOCUS ON MASS SPECTROMETRY APPROACHES. Cardiovasc Res 2021; 118:1205-1217. [PMID: 33744917 DOI: 10.1093/cvr/cvab095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 03/16/2021] [Indexed: 11/14/2022] Open
Abstract
Atrial fibrillation (AF) is the most common arrhythmia worldwide. It is associated with significant increases in morbidity in the form of stroke and heart failure, and a doubling in all-cause mortality. The pathophysiology of AF is incompletely understood, and this has contributed to a lack of effective treatments and disease-modifying therapies. An important cellular process that may explain how risk factors give rise to AF includes post-translational modification (PTM) of proteins. As the most commonly occurring PTM, protein phosphorylation is especially relevant. Although many methods exist for studying protein phosphorylation, a common and highly resolute technique is mass spectrometry (MS). This review will discuss recent evidence surrounding the role of protein phosphorylation in the pathogenesis of AF. MS-based technology to study phosphorylation and uses of MS in other areas of medicine such as oncology will also be presented. Based on these data, future goals and experiments will be outlined that utilize MS technology to better understand the role of phosphorylation in AF and elucidate its role in AF pathophysiology. This may ultimately allow for the development of more effective AF therapies.
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Affiliation(s)
- Sina Safabakhsh
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Pankaj Panwar
- AbCellera Biologicals Inc., Vancouver, British Columbia, Canada
| | - Scott Barichello
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sarabjit S Sangha
- Cellular and Regenerative Medicine Centre, BC Children's Hospital Research Institute, 950 West 28th Avenue, Vancouver, British Columbia, Canada.,Molecular Cardiac Physiology Group, Departments of Biomedical Physiology and Kinesiology and Molecular Biology and Biochemistry, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia, Canada
| | - Paul J Hanson
- UBC Heart Lung Innovation Centre, Vancouver, British Columbia, Canada.,UBC Department of Pathology and Laboratory Medicine, Vancouver, British Columbia, Canada
| | - Filip Van Petegem
- Department of Biochemistry and Molecular Biology, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada
| | - Zachary Laksman
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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14
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Danna V, Mitchell H, Anderson L, Godinez I, Gosline SJC, Teeguarden J, McDermott JE. leapR: An R Package for Multiomic Pathway Analysis. J Proteome Res 2021; 20:2116-2121. [PMID: 33703901 DOI: 10.1021/acs.jproteome.0c00963] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A generalized goal of many high-throughput data studies is to identify functional mechanisms that underlie observed biological phenomena, whether they be disease outcomes or metabolic output. Increasingly, studies that rely on multiple sources of high-throughput data (genomic, transcriptomic, proteomic, metabolomic) are faced with a challenge of summarizing the data to generate testable hypotheses. However, this requires a time-consuming process to evaluate numerous statistical methods across numerous data sources. Here, we introduce the leapR package, a framework to rapidly assess biological pathway activity using diverse statistical tests and data sources, allowing facile integration of multisource data. The leapR package with a user manual and example workflow is available for download from GitHub (https://github.com/biodataganache/leapR).
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Affiliation(s)
- Vincent Danna
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Hugh Mitchell
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Lindsey Anderson
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Iobani Godinez
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Sara J C Gosline
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Justin Teeguarden
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Jason E McDermott
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.,Department of Molecular Microbiology and Immunology, Oregon Health & Sciences University, Portland, Oregon 97201, United States
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15
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Yılmaz S, Ayati M, Schlatzer D, Çiçek AE, Chance MR, Koyutürk M. Robust inference of kinase activity using functional networks. Nat Commun 2021; 12:1177. [PMID: 33608514 PMCID: PMC7895941 DOI: 10.1038/s41467-021-21211-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 01/15/2021] [Indexed: 12/17/2022] Open
Abstract
Mass spectrometry enables high-throughput screening of phosphoproteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimer’s disease and Parkinson’s disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io. Kinases drive fundamental changes in cell state, but predicting kinase activity based on substrate-level changes can be challenging. Here the authors introduce a computational framework that utilizes similarities between substrates to robustly infer kinase activity.
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Affiliation(s)
- Serhan Yılmaz
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
| | - Marzieh Ayati
- Department of Computer Science, University of Texas Rio Grande Valley, Edinburg, TX, USA
| | - Daniela Schlatzer
- Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, USA
| | - A Ercüment Çiçek
- Department of Computer Engineering, Bilkent University, Ankara, Turkey.,Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Mark R Chance
- Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, USA.,Department of Nutrition, Case Western Reserve University, Cleveland, OH, USA
| | - Mehmet Koyutürk
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.,Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, USA
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16
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Lim Kam Sian TCC, Chüeh AC, Daly RJ. Proteomics-based interrogation of the kinome and its implications for precision oncology. Proteomics 2021; 21:e2000161. [PMID: 33547865 DOI: 10.1002/pmic.202000161] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/22/2021] [Accepted: 01/27/2021] [Indexed: 12/11/2022]
Abstract
The identification of specific protein kinases as oncogenic drivers in a variety of cancer types, coupled with the clinical success of particular kinase-directed targeted therapies, has cemented the human kinome as an attractive source of "actionable" targets for cancer therapy. However, "mining" of the human kinome for precision oncology applications has yet to yield its full potential. This reflects a variety of issues, including oncogenic kinase dysregulation at levels not detectable by genomic sequencing and the uncharacterized nature of a considerable fraction of the kinome. In addition, selective therapeutic targeting of specific kinases requires efficient mapping of total kinome space impacted by candidate small molecule drugs. Fortunately, recent developments in proteomics techniques, particularly in mass spectrometry-based phosphoproteomics and kinomics, provide the necessary technology platforms to address these impediments. Moreover, initiatives such as the Clinical Proteomic Tumour Analysis Consortium have enabled the generation, deposition and integration of genomic, transcriptomic and (phospho)proteomic data for many cancer types, providing unprecedented insights into oncogenic kinases and cancer cell signalling generally. These multi-omic data are identifying novel therapeutic targets, highlighting opportunities for drug re-purposing, and helping assign optimal therapies to specific tumour subtypes, heralding a new era of "enhanced" precision oncology.
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Affiliation(s)
- Terry C C Lim Kam Sian
- Cancer Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Anderly C Chüeh
- Cancer Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Roger J Daly
- Cancer Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
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17
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Bramer LM, Irvahn J, Piehowski PD, Rodland KD, Webb-Robertson BJM. A Review of Imputation Strategies for Isobaric Labeling-Based Shotgun Proteomics. J Proteome Res 2020; 20:1-13. [PMID: 32929967 DOI: 10.1021/acs.jproteome.0c00123] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is different than unlabeled studies, which prompts the need for this review to compare the efficacy of nine imputation methods on large isobaric-labeled proteomics data sets to guide researchers on the appropriateness of various imputation methods. Imputation methods were evaluated by accuracy, statistical hypothesis test inference, and run time. In general, expectation maximization and random forest imputation methods yielded the best performance, and constant-based methods consistently performed poorly across all data set sizes and percentages of missing values. For data sets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. On the basis of the findings in this review, there are core imputation methods that perform better for isobaric-labeled proteomics data, but great care and consideration as to whether imputation is the optimal strategy should be given for data sets comprised of a small number of samples.
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Affiliation(s)
- Lisa M Bramer
- Computing & Analytics Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Jan Irvahn
- Boeing, Seattle, Washington 98055, United States
| | - Paul D Piehowski
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, Washington 99354, United States
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, Washington 99354, United States
| | - Bobbie-Jo M Webb-Robertson
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, Washington 99354, United States
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18
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Affinity chromatography assisted comprehensive phosphoproteomics analysis of human saliva for lung cancer. Anal Chim Acta 2020; 1111:103-113. [PMID: 32312387 DOI: 10.1016/j.aca.2020.03.043] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 03/18/2020] [Accepted: 03/20/2020] [Indexed: 12/13/2022]
Abstract
Affinity chromatography is a powerful technology for phosphopeptide enrichment from body fluids. Saliva is a non-invasive body fluid for disease diagnosis, while few studies applied affinity enrichment for saliva phosphoproteome. In this study, we tested two kinds of affinity chromatography materials, Ti4+-IMAC (immobilized metal affinity chromatography) and CaTiO3, for the enrichment of phosphopeptides. Through comparison, Ti4+-IMAC method was demonstrated as the superior one, which was utilized for the comprehensive analysis of salivary phosphoproteome. More than 360 phosphoproteins were specifically extracted and identified from human saliva. Ti4+-IMAC method was further applied to compare the phosphoprotein profiling in the saliva of lung cancer group and normal control group through label-free quantification. Accordingly, 477 and 699 phosphopeptides were enriched, respectively, which corresponded to 339 and 466 proteins. In total, 796 unique phosphopeptides were revealed for 517 saliva phosphoproteins. In particular, 709 phosphorylation sites were identified, among which 26 were up-regulated (>1.5) and 149 were down-regulated (<0.66) in lung cancer. Their corresponding proteins were mainly associated with cancer promotion, system disorder, and organismal injury. Our data collectively demonstrated that salivary phosphopeptides can be comprehensively characterized through Ti4+-IMAC method. These discovered phosphoprotein candidates might be used for lung cancer detection through salivary diagnostics.
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19
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Zhang B, Kuster B. Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems. Mol Cell Proteomics 2019; 18:S1-S4. [PMID: 31399542 PMCID: PMC6692779 DOI: 10.1074/mcp.e119.001693] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Indexed: 12/18/2022] Open
Affiliation(s)
- Bing Zhang
- ‡Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas
- §Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Bernhard Kuster
- ¶Chair of Proteomics and Bioanalytics, Technische Universitat Munchen, Freising, Germany
- ‖Bavarian Biomolecular Mass Spectrometry Center, Technische Universitat Munchen, Freising, Germany
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20
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Haymond A, Davis JB, Espina V. Proteomics for cancer drug design. Expert Rev Proteomics 2019; 16:647-664. [PMID: 31353977 PMCID: PMC6736641 DOI: 10.1080/14789450.2019.1650025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Accepted: 07/26/2019] [Indexed: 12/29/2022]
Abstract
Introduction: Signal transduction cascades drive cellular proliferation, apoptosis, immune, and survival pathways. Proteins have emerged as actionable drug targets because they are often dysregulated in cancer, due to underlying genetic mutations, or dysregulated signaling pathways. Cancer drug development relies on proteomic technologies to identify potential biomarkers, mechanisms-of-action, and to identify protein binding hot spots. Areas covered: Brief summaries of proteomic technologies for drug discovery include mass spectrometry, reverse phase protein arrays, chemoproteomics, and fragment based screening. Protein-protein interface mapping is presented as a promising method for peptide therapeutic development. The topic of biosimilar therapeutics is presented as an opportunity to apply proteomic technologies to this new class of cancer drug. Expert opinion: Proteomic technologies are indispensable for drug discovery. A suite of technologies including mass spectrometry, reverse phase protein arrays, and protein-protein interaction mapping provide complimentary information for drug development. These assays have matured into well controlled, robust technologies. Recent regulatory approval of biosimilar therapeutics provides another opportunity to decipher the molecular nuances of their unique mechanisms of action. The ability to identify previously hidden protein hot spots is expanding the gamut of potential drug targets. Proteomic profiling permits lead compound evaluation beyond the one drug, one target paradigm.
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Affiliation(s)
- Amanda Haymond
- Center for Applied Proteomics and Molecular Medicine, George Mason University , Manassas , VA , USA
| | - Justin B Davis
- Center for Applied Proteomics and Molecular Medicine, George Mason University , Manassas , VA , USA
| | - Virginia Espina
- Center for Applied Proteomics and Molecular Medicine, George Mason University , Manassas , VA , USA
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