1
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Borek WE, Nobre L, Pedicona SF, Campbell AE, Christopher JA, Nawaz N, Perkins DN, Moreno-Cardoso P, Kelsall J, Ferguson HR, Patel B, Gallipoli P, Arruda A, Ambinder AJ, Thompson A, Williamson A, Ghiaur G, Minden MD, Gribben JG, Britton DJ, Cutillas PR, Dokal AD. Phosphoproteomics predict response to midostaurin plus chemotherapy in independent cohorts of FLT3-mutated acute myeloid leukaemia. EBioMedicine 2024; 108:105316. [PMID: 39293215 PMCID: PMC11424955 DOI: 10.1016/j.ebiom.2024.105316] [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: 04/17/2024] [Revised: 08/14/2024] [Accepted: 08/14/2024] [Indexed: 09/20/2024] Open
Abstract
BACKGROUND Acute myeloid leukaemia (AML) is a bone marrow malignancy with poor prognosis. One of several treatments for AML is midostaurin combined with intensive chemotherapy (MIC), currently approved for FLT3 mutation-positive (FLT3-MP) AML. However, many patients carrying FLT3 mutations are refractory or experience an early relapse following MIC treatment, and might benefit more from receiving a different treatment. Development of a stratification method that outperforms FLT3 mutational status in predicting MIC response would thus benefit a large number of patients. METHODS We employed mass spectrometry phosphoproteomics to analyse 71 diagnosis samples of 47 patients with FLT3-MP AML who subsequently received MIC. We then used machine learning to identify biomarkers of response to MIC, and validated the resulting predictive model in two independent validation cohorts (n = 20). FINDINGS We identified three distinct phosphoproteomic AML subtypes amongst long-term survivors. The subtypes showed similar duration of MIC response, but different modulation of AML-implicated pathways, and exhibited distinct, highly-predictive biomarkers of MIC response. Using these biomarkers, we built a phosphoproteomics-based predictive model of MIC response, which we called MPhos. When applied to two retrospective real-world patient test cohorts (n = 20), MPhos predicted MIC response with 83% sensitivity and 100% specificity (log-rank p < 7∗10-5, HR = 0.005 [95% CI: 0-0.31]). INTERPRETATION In validation, MPhos outperformed the currently-used FLT3-based stratification method. Our findings have the potential to transform clinical decision-making, and highlight the important role that phosphoproteomics is destined to play in precision oncology. FUNDING This work was funded by Innovate UK grants (application numbers: 22217 and 10054602) and by Kinomica Ltd.
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Affiliation(s)
| | - Luis Nobre
- Kinomica Ltd, Alderley Park, Macclesfield, United Kingdom
| | | | - Amy E Campbell
- Kinomica Ltd, Alderley Park, Macclesfield, United Kingdom
| | | | - Nazrath Nawaz
- Kinomica Ltd, Alderley Park, Macclesfield, United Kingdom
| | | | | | - Janet Kelsall
- Kinomica Ltd, Alderley Park, Macclesfield, United Kingdom
| | | | - Bela Patel
- Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Paolo Gallipoli
- Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Andrea Arruda
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Alex J Ambinder
- Johns Hopkins Sidney Kimmel Comprehensive Cancer Center, Baltimore, USA
| | | | | | - Gabriel Ghiaur
- Johns Hopkins Sidney Kimmel Comprehensive Cancer Center, Baltimore, USA
| | - Mark D Minden
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - John G Gribben
- Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | | | - Pedro R Cutillas
- Kinomica Ltd, Alderley Park, Macclesfield, United Kingdom; Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Arran D Dokal
- Kinomica Ltd, Alderley Park, Macclesfield, United Kingdom.
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2
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Piersma SR, Valles-Marti A, Rolfs F, Pham TV, Henneman AA, Jiménez CR. Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications. MASS SPECTROMETRY REVIEWS 2024; 43:725-751. [PMID: 36156810 DOI: 10.1002/mas.21808] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.
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Affiliation(s)
- Sander R Piersma
- OncoProteomics Laboratory Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Andrea Valles-Marti
- OncoProteomics Laboratory Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Frank Rolfs
- OncoProteomics Laboratory Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Thang V Pham
- OncoProteomics Laboratory Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Alex A Henneman
- OncoProteomics Laboratory Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Connie R Jiménez
- OncoProteomics Laboratory Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
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3
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Giudice G, Chen H, Koutsandreas T, Petsalaki E. phuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets. Mol Cell Proteomics 2024; 23:100771. [PMID: 38642805 PMCID: PMC11134849 DOI: 10.1016/j.mcpro.2024.100771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 04/08/2024] [Accepted: 04/17/2024] [Indexed: 04/22/2024] Open
Abstract
Signaling networks are critical for virtually all cell functions. Our current knowledge of cell signaling has been summarized in signaling pathway databases, which, while useful, are highly biased toward well-studied processes, and do not capture context specific network wiring or pathway cross-talk. Mass spectrometry-based phosphoproteomics data can provide a more unbiased view of active cell signaling processes in a given context, however, it suffers from low signal-to-noise ratio and poor reproducibility across experiments. While progress in methods to extract active signaling signatures from such data has been made, there are still limitations with respect to balancing bias and interpretability. Here we present phuEGO, which combines up-to-three-layer network propagation with ego network decomposition to provide small networks comprising active functional signaling modules. PhuEGO boosts the signal-to-noise ratio from global phosphoproteomics datasets, enriches the resulting networks for functional phosphosites and allows the improved comparison and integration across datasets. We applied phuEGO to five phosphoproteomics data sets from cell lines collected upon infection with SARS CoV2. PhuEGO was better able to identify common active functions across datasets and to point to a subnetwork enriched for known COVID-19 targets. Overall, phuEGO provides a flexible tool to the community for the improved functional interpretation of global phosphoproteomics datasets.
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Affiliation(s)
- Girolamo Giudice
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom
| | - Haoqi Chen
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom
| | - Thodoris Koutsandreas
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom
| | - Evangelia Petsalaki
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom.
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4
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Rosenberger G, Li W, Turunen M, He J, Subramaniam PS, Pampou S, Griffin AT, Karan C, Kerwin P, Murray D, Honig B, Liu Y, Califano A. Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis. Nat Commun 2024; 15:3909. [PMID: 38724493 PMCID: PMC11082183 DOI: 10.1038/s41467-024-47957-3] [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: 03/18/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.
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Affiliation(s)
- George Rosenberger
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Mikko Turunen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jing He
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Prem S Subramaniam
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sergey Pampou
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron T Griffin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Patrick Kerwin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA.
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA.
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA.
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
- Chan Zuckerberg Biohub New York, New York, NY, USA.
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5
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Stephenson EH, Higgins JMG. Pharmacological approaches to understanding protein kinase signaling networks. Front Pharmacol 2023; 14:1310135. [PMID: 38164473 PMCID: PMC10757940 DOI: 10.3389/fphar.2023.1310135] [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: 10/09/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024] Open
Abstract
Protein kinases play vital roles in controlling cell behavior, and an array of kinase inhibitors are used successfully for treatment of disease. Typical drug development pipelines involve biological studies to validate a protein kinase target, followed by the identification of small molecules that effectively inhibit this target in cells, animal models, and patients. However, it is clear that protein kinases operate within complex signaling networks. These networks increase the resilience of signaling pathways, which can render cells relatively insensitive to inhibition of a single kinase, and provide the potential for pathway rewiring, which can result in resistance to therapy. It is therefore vital to understand the properties of kinase signaling networks in health and disease so that we can design effective multi-targeted drugs or combinations of drugs. Here, we outline how pharmacological and chemo-genetic approaches can contribute to such knowledge, despite the known low selectivity of many kinase inhibitors. We discuss how detailed profiling of target engagement by kinase inhibitors can underpin these studies; how chemical probes can be used to uncover kinase-substrate relationships, and how these tools can be used to gain insight into the configuration and function of kinase signaling networks.
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Affiliation(s)
| | - Jonathan M. G. Higgins
- Faculty of Medical Sciences, Biosciences Institute, Newcastle University, Newcastle uponTyne, United Kingdom
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6
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Basanta CDLAC, Bazzi M, Hijazi M, Bessant C, Cutillas PR. Community detection in empirical kinase networks identifies new potential members of signalling pathways. PLoS Comput Biol 2023; 19:e1010459. [PMID: 37352361 PMCID: PMC10325051 DOI: 10.1371/journal.pcbi.1010459] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 07/06/2023] [Accepted: 06/05/2023] [Indexed: 06/25/2023] Open
Abstract
Phosphoproteomics allows one to measure the activity of kinases that drive the fluxes of signal transduction pathways involved in biological processes such as immune function, senescence and cell growth. However, deriving knowledge of signalling network circuitry from these data is challenging due to a scarcity of phosphorylation sites that define kinase-kinase relationships. To address this issue, we previously identified around 6,000 phosphorylation sites as markers of kinase-kinase relationships (that may be conceptualised as network edges), from which empirical cell-model-specific weighted kinase networks may be reconstructed. Here, we assess whether the application of community detection algorithms to such networks can identify new components linked to canonical signalling pathways. Phosphoproteomics data from acute myeloid leukaemia (AML) cells treated separately with PI3K, AKT, MEK and ERK inhibitors were used to reconstruct individual kinase networks. We used modularity maximisation to detect communities in each network, and selected the community containing the main target of the inhibitor used to treat cells. These analyses returned communities that contained known canonical signalling components. Interestingly, in addition to canonical PI3K/AKT/mTOR members, the community assignments returned TTK (also known as MPS1) as a likely component of PI3K/AKT/mTOR signalling. We drew similar insights from an external phosphoproteomics dataset from breast cancer cells treated with rapamycin and oestrogen. We confirmed this observation with wet-lab laboratory experiments showing that TTK phosphorylation was decreased in AML cells treated with AKT and MTOR inhibitors. This study illustrates the application of community detection algorithms to the analysis of empirical kinase networks to uncover new members linked to canonical signalling pathways.
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Affiliation(s)
- Celia De Los Angeles Colomina Basanta
- Cell signaling and Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Marya Bazzi
- Warwick Mathematics Institute, University of Warwick, Coventry, United Kingdom
- The Alan Turing Institute, London, United Kingdom
| | - Maruan Hijazi
- Cell signaling and Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Conrad Bessant
- The Alan Turing Institute, London, United Kingdom
- School of Biological and Chemical Sciences, Queen Mary University of London, London, United Kingdom
| | - Pedro R. Cutillas
- Cell signaling and Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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7
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Higgins L, Gerdes H, Cutillas PR. Principles of phosphoproteomics and applications in cancer research. Biochem J 2023; 480:403-420. [PMID: 36961757 PMCID: PMC10212522 DOI: 10.1042/bcj20220220] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/24/2023] [Accepted: 02/28/2023] [Indexed: 03/25/2023]
Abstract
Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.
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Affiliation(s)
- Luke Higgins
- Cell Signaling and Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, U.K
| | - Henry Gerdes
- Cell Signaling and Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, U.K
| | - Pedro R. Cutillas
- Cell Signaling and Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, U.K
- Alan Turing Institute, The British Library, London, U.K
- Digital Environment Research Institute, Queen Mary University of London, London, U.K
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8
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Sriraja LO, Werhli A, Petsalaki E. Phosphoproteomics data-driven signalling network inference: Does it work? Comput Struct Biotechnol J 2022; 21:432-443. [PMID: 36618990 PMCID: PMC9798138 DOI: 10.1016/j.csbj.2022.12.010] [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: 09/07/2022] [Revised: 11/16/2022] [Accepted: 12/06/2022] [Indexed: 12/23/2022] Open
Abstract
The advent of global phosphoproteome profiling has led to wide phosphosite coverage and therefore the opportunity to predict kinase-substrate associations from these datasets. However, the regulatory kinase is unknown for most substrates, due to biased and incomplete database annotations. In this study we compare the performance of six pairwise measures to predict kinase-substrate associations using a data driven approach on publicly available time resolved and perturbation mass spectrometry-based phosphoproteome data. First, we validated the performance of these measures using as a reference both a literature-based phosphosite-specific protein interaction network and a predicted kinase-substrate (KS) interactions set. The overall performance in predicting kinase-substrate associations using pairwise measures across both these reference sets was poor. To expand into the wider interactome space, we applied the approach on a network comprising pairs of substrates regulated by the same kinase (substrate-substrate associations) but found the performance to be equally poor. However, the addition of a sequence similarity filter for substrate-substrate associations led to a significant boost in performance. Our findings imply that the use of a filter to reduce the search space, such as a sequence similarity filter, can be used prior to the application of network inference methods to reduce noise and boost the signal. We also find that the current gold standard for reference sets is not adequate for evaluation as it is limited and context-agnostic. Therefore, there is a need for additional evaluation methods that have increased coverage and take into consideration the context-specific nature of kinase-substrate associations.
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Affiliation(s)
- Lourdes O. Sriraja
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Adriano Werhli
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
- Centro de Ciências Computacionais - Universidade Federal do Rio Grande - FURG, Avenida Itália, km 8, s/n, Campus Carreiros, 96203-900 Rio Grande, Rio Grande do Sul, Brazil2
| | - Evangelia Petsalaki
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
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9
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Crowl S, Jordan BT, Ahmed H, Ma CX, Naegle KM. KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data. Nat Commun 2022; 13:4283. [PMID: 35879309 PMCID: PMC9314348 DOI: 10.1038/s41467-022-32017-5] [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: 07/15/2021] [Accepted: 07/13/2022] [Indexed: 01/09/2023] Open
Abstract
Kinase inhibitors as targeted therapies have played an important role in improving cancer outcomes. However, there are still considerable challenges, such as resistance, non-response, patient stratification, polypharmacology, and identifying combination therapy where understanding a tumor kinase activity profile could be transformative. Here, we develop a graph- and statistics-based algorithm, called KSTAR, to convert phosphoproteomic measurements of cells and tissues into a kinase activity score that is generalizable and useful for clinical pipelines, requiring no quantification of the phosphorylation sites. In this work, we demonstrate that KSTAR reliably captures expected kinase activity differences across different tissues and stimulation contexts, allows for the direct comparison of samples from independent experiments, and is robust across a wide range of dataset sizes. Finally, we apply KSTAR to clinical breast cancer phosphoproteomic data and find that there is potential for kinase activity inference from KSTAR to complement the current clinical diagnosis of HER2 status in breast cancer patients.
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Affiliation(s)
- Sam Crowl
- grid.27755.320000 0000 9136 933XUniversity of Virginia, Department of Biomedical Engineering and the Center for Public Health Genomics, Charlottesville, VA 22903 USA
| | - Ben T. Jordan
- grid.27755.320000 0000 9136 933XUniversity of Virginia, Department of Biomedical Engineering and the Center for Public Health Genomics, Charlottesville, VA 22903 USA
| | - Hamza Ahmed
- grid.27755.320000 0000 9136 933XUniversity of Virginia, Department of Biomedical Engineering and the Center for Public Health Genomics, Charlottesville, VA 22903 USA
| | - Cynthia X. Ma
- grid.4367.60000 0001 2355 7002Department of Medicine and Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63108 USA
| | - Kristen M. Naegle
- grid.27755.320000 0000 9136 933XUniversity of Virginia, Department of Biomedical Engineering and the Center for Public Health Genomics, Charlottesville, VA 22903 USA
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10
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Hijazi M, Casado P, Akhtar N, Alvarez-Teijeiro S, Rajeeve V, Cutillas PR. eEF2K Activity Determines Synergy to Cotreatment of Cancer Cells With PI3K and MEK Inhibitors. Mol Cell Proteomics 2022; 21:100240. [PMID: 35513296 PMCID: PMC9184568 DOI: 10.1016/j.mcpro.2022.100240] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 03/17/2022] [Accepted: 04/25/2022] [Indexed: 10/31/2022] Open
Abstract
PI3K-mammalian target of rapamycin and MAPK/ERK kinase (MEK)/mitogen-activated protein kinase (MAPK) are the most frequently dysregulated signaling pathways in cancer. A problem that limits the success of therapies that target individual PI3K-MAPK members is that these pathways converge to regulate downstream functions and often compensate each other, leading to drug resistance and transient responses to therapy. In order to overcome resistance, therapies based on cotreatments with PI3K/AKT and MEK/MAPK inhibitors are now being investigated in clinical trials, but the mechanisms of sensitivity to cotreatment are not fully understood. Using LC-MS/MS-based phosphoproteomics, we found that eukaryotic elongation factor 2 kinase (eEF2K), a key convergence point downstream of MAPK and PI3K pathways, mediates synergism to cotreatment with trametinib plus pictilisib (which target MEK1/2 and PI3Kα/δ, respectively). Inhibition of eEF2K by siRNA or with a small molecule inhibitor reversed the antiproliferative effects of the cotreatment with PI3K plus MEK inhibitors in a cell model-specific manner. Systematic analysis in 12 acute myeloid leukemia cell lines revealed that eEF2K activity was increased in cells for which PI3K plus MEKi cotreatment is synergistic, while PKC potentially mediated resistance to such cotreatment. Together, our study uncovers eEF2K activity as a key mediator of responses to PI3Ki plus MEKi and as a potential biomarker to predict synergy to cotreatment in cancer cells.
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Affiliation(s)
- Maruan Hijazi
- Signalling & Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.
| | - Pedro Casado
- Signalling & Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Nosheen Akhtar
- Signalling & Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Saul Alvarez-Teijeiro
- Signalling & Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Vinothini Rajeeve
- Signalling & Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
| | - Pedro R Cutillas
- Signalling & Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom; The Alan Turing Institute, British Library, London, United Kingdom.
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11
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Roy-Luzarraga M, Reynolds LE, de Luxán-Delgado B, Maiques O, Wisniewski L, Newport E, Rajeeve V, Drake RJ, Gómez-Escudero J, Richards FM, Weller C, Dormann C, Meng YM, Vermeulen PB, Saur D, Sanz-Moreno V, Wong PP, Géraud C, Cutillas PR, Hodivala-Dilke K. Suppression of Endothelial Cell FAK Expression Reduces Pancreatic Ductal Adenocarcinoma Metastasis after Gemcitabine Treatment. Cancer Res 2022; 82:1909-1925. [PMID: 35350066 PMCID: PMC9381116 DOI: 10.1158/0008-5472.can-20-3807] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 02/07/2022] [Accepted: 03/25/2022] [Indexed: 02/02/2023]
Abstract
Despite substantial advances in the treatment of solid cancers, resistance to therapy remains a major obstacle to prolonged progression-free survival. Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive cancers, with a high level of liver metastasis. Primary PDAC is highly hypoxic, and metastases are resistant to first-line treatment, including gemcitabine. Recent studies have indicated that endothelial cell (EC) focal adhesion kinase (FAK) regulates DNA-damaging therapy-induced angiocrine factors and chemosensitivity in primary tumor models. Here, we show that inducible loss of EC-FAK in both orthotopic and spontaneous mouse models of PDAC is not sufficient to affect primary tumor growth but reduces liver and lung metastasis load and improves survival rates in gemcitabine-treated, but not untreated, mice. EC-FAK loss did not affect primary tumor angiogenesis, tumor blood vessel leakage, or early events in metastasis, including the numbers of circulating tumor cells, tumor cell homing, or metastatic seeding. Phosphoproteomics analysis showed a downregulation of the MAPK, RAF, and PAK signaling pathways in gemcitabine-treated FAK-depleted ECs compared with gemcitabine-treated wild-type ECs. Moreover, low levels of EC-FAK correlated with increased survival and reduced relapse in gemcitabine-treated patients with PDAC, supporting the clinical relevance of these findings. Altogether, we have identified a new role of EC-FAK in regulating PDAC metastasis upon gemcitabine treatment that impacts outcome. SIGNIFICANCE These findings establish the potential utility of combinatorial endothelial cell FAK targeting together with gemcitabine in future clinical applications to control metastasis in patients with pancreatic ductal adenocarcinoma.
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Affiliation(s)
- Marina Roy-Luzarraga
- Barts Cancer Institute—A CR-UK Center of Excellence, Queen Mary University of London, John Vane Science Center, Charterhouse Square, London, United Kingdom
| | - Louise E. Reynolds
- Barts Cancer Institute—A CR-UK Center of Excellence, Queen Mary University of London, John Vane Science Center, Charterhouse Square, London, United Kingdom
| | - Beatriz de Luxán-Delgado
- Barts Cancer Institute—A CR-UK Center of Excellence, Queen Mary University of London, John Vane Science Center, Charterhouse Square, London, United Kingdom
| | - Oscar Maiques
- Barts Cancer Institute—A CR-UK Center of Excellence, Queen Mary University of London, John Vane Science Center, Charterhouse Square, London, United Kingdom
| | - Laura Wisniewski
- Barts Cancer Institute—A CR-UK Center of Excellence, Queen Mary University of London, John Vane Science Center, Charterhouse Square, London, United Kingdom
| | - Emma Newport
- Barts Cancer Institute—A CR-UK Center of Excellence, Queen Mary University of London, John Vane Science Center, Charterhouse Square, London, United Kingdom
| | - Vinothini Rajeeve
- Barts Cancer Institute—A CR-UK Center of Excellence, Queen Mary University of London, John Vane Science Center, Charterhouse Square, London, United Kingdom
| | - Rebecca J.G. Drake
- Barts Cancer Institute—A CR-UK Center of Excellence, Queen Mary University of London, John Vane Science Center, Charterhouse Square, London, United Kingdom
| | - Jesús Gómez-Escudero
- Barts Cancer Institute—A CR-UK Center of Excellence, Queen Mary University of London, John Vane Science Center, Charterhouse Square, London, United Kingdom
| | - Frances M. Richards
- Translational Medicine Operations, Astrazeneca Oncology, Darwin Building, Cambridge Science Park, Milton Road, Cambridge, United Kingdom
| | - Céline Weller
- Department of Dermatology, Section of Clinical and Molecular Dermatology, Venereology and Allergology, University Medical Center and European Center for Angioscience, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Christof Dormann
- Department of Dermatology, Section of Clinical and Molecular Dermatology, Venereology and Allergology, University Medical Center and European Center for Angioscience, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Ya-Ming Meng
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Peter B. Vermeulen
- Department of Oncological Research, Translational Cancer Research Unit, Oncology Center GZA—GZA Hospitals St. Augustinus and University of Antwerp, Antwerp, Belgium
| | - Dieter Saur
- Division of Translational Cancer Research, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg and Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum rechts der Isar, School of Medicine, Technische Universität München, München, Germany
| | - Victoria Sanz-Moreno
- Barts Cancer Institute—A CR-UK Center of Excellence, Queen Mary University of London, John Vane Science Center, Charterhouse Square, London, United Kingdom
| | - Ping-Pui Wong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Cyrill Géraud
- Department of Dermatology, Section of Clinical and Molecular Dermatology, Venereology and Allergology, University Medical Center and European Center for Angioscience, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Pedro R. Cutillas
- Barts Cancer Institute—A CR-UK Center of Excellence, Queen Mary University of London, John Vane Science Center, Charterhouse Square, London, United Kingdom
| | - Kairbaan Hodivala-Dilke
- Barts Cancer Institute—A CR-UK Center of Excellence, Queen Mary University of London, John Vane Science Center, Charterhouse Square, London, United Kingdom
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12
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Invergo BM. Accurate, high-coverage assignment of in vivo protein kinases to phosphosites from in vitro phosphoproteomic specificity data. PLoS Comput Biol 2022; 18:e1010110. [PMID: 35560139 PMCID: PMC9132282 DOI: 10.1371/journal.pcbi.1010110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 05/25/2022] [Accepted: 04/15/2022] [Indexed: 12/03/2022] Open
Abstract
Phosphoproteomic experiments routinely observe thousands of phosphorylation sites. To understand the intracellular signaling processes that generated this data, one or more causal protein kinases must be assigned to each phosphosite. However, limited knowledge of kinase specificity typically restricts assignments to a small subset of a kinome. Starting from a statistical model of a high-throughput, in vitro kinase-substrate assay, I have developed an approach to high-coverage, multi-label kinase-substrate assignment called IV-KAPhE (“In vivo-Kinase Assignment for Phosphorylation Evidence”). Tested on human data, IV-KAPhE outperforms other methods of similar scope. Such computational methods generally predict a densely connected kinase-substrate network, with most sites targeted by multiple kinases, pointing either to unaccounted-for biochemical constraints or significant cross-talk and signaling redundancy. I show that such predictions can potentially identify biased kinase-site misannotations within families of closely related kinase isozymes and they provide a robust basis for kinase activity analysis. Proteins can pass around information inside cells about changes in the environment. This process, called intracellular signaling, helps to trigger appropriate cellular responses to environmental changes. One of the main ways information is passed to proteins is through chemical “tagging,” called phosphorylation, by enzymes called protein kinases. We can measure the phosphorylation state of practically all proteins in a cell at any moment. Starting from known cases of phosphorylation by a kinase, many computational methods have been developed to predict if the kinase might tag a certain spot on another protein or if an observed tag was attached by the kinase, with different models for each kinase. I have developed a new method that instead uses a single model to assign one or more kinases to each observed tag, built from the latest large-scale experimental data. This change in focus and unbiased training data allows my method to be significantly more accurate than past methods. I also explored useful applications for my method. For example, I used it to show that much of our knowledge about which kinase is responsible for each tag is probably inaccurately biased towards the commonly studied ones.
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Affiliation(s)
- Brandon M. Invergo
- Translational Research Exchange @ Exeter, University of Exeter, Exeter, United Kingdom
- * E-mail:
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13
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Casado P, Hijazi M, Gerdes H, Cutillas PR. Implementation of Clinical Phosphoproteomics and Proteomics for Personalized Medicine. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2420:87-106. [PMID: 34905168 DOI: 10.1007/978-1-0716-1936-0_8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The identification of biomarkers for companion diagnostics is revolutionizing the development of treatments tailored to individual patients in different disease areas including cancer. Precision medicine is most frequently based on the detection of genomic markers that correlate with the efficacy of selected targeted therapies. However, since nongenetic mechanisms also contribute to disease biology, there is a considerable interest of using proteomic techniques as additional source of biomarkers to personalize therapies. In this chapter, we describe label-free mass spectrometry methods for proteomic and phosphoproteomic analysis compatible with routine analysis of clinical samples. We also outline bioinformatic pipelines based on statistical learning that use these proteomics datasets as input to quantify kinase activities and predict drug responses in cancer cells.
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Affiliation(s)
- Pedro Casado
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK
| | - Maruan Hijazi
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK
| | - Henry Gerdes
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK
| | - Pedro R Cutillas
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK.
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14
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Heydt Q, Xintaropoulou C, Clear A, Austin M, Pislariu I, Miraki-Moud F, Cutillas P, Korfi K, Calaminici M, Cawthorn W, Suchacki K, Nagano A, Gribben JG, Smith M, Cavenagh JD, Oakervee H, Castleton A, Taussig D, Peck B, Wilczynska A, McNaughton L, Bonnet D, Mardakheh F, Patel B. Adipocytes disrupt the translational programme of acute lymphoblastic leukaemia to favour tumour survival and persistence. Nat Commun 2021; 12:5507. [PMID: 34535653 PMCID: PMC8448863 DOI: 10.1038/s41467-021-25540-4] [Citation(s) in RCA: 9] [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: 09/03/2020] [Accepted: 08/17/2021] [Indexed: 11/09/2022] Open
Abstract
The specific niche adaptations that facilitate primary disease and Acute Lymphoblastic Leukaemia (ALL) survival after induction chemotherapy remain unclear. Here, we show that Bone Marrow (BM) adipocytes dynamically evolve during ALL pathogenesis and therapy, transitioning from cellular depletion in the primary leukaemia niche to a fully reconstituted state upon remission induction. Functionally, adipocyte niches elicit a fate switch in ALL cells towards slow-proliferation and cellular quiescence, highlighting the critical contribution of the adipocyte dynamic to disease establishment and chemotherapy resistance. Mechanistically, adipocyte niche interaction targets posttranscriptional networks and suppresses protein biosynthesis in ALL cells. Treatment with general control nonderepressible 2 inhibitor (GCN2ib) alleviates adipocyte-mediated translational repression and rescues ALL cell quiescence thereby significantly reducing the cytoprotective effect of adipocytes against chemotherapy and other extrinsic stressors. These data establish how adipocyte driven restrictions of the ALL proteome benefit ALL tumours, preventing their elimination, and suggest ways to manipulate adipocyte-mediated ALL resistance.
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Affiliation(s)
- Q Heydt
- Centre for Haemato-Oncology, Barts Cancer Institute, John Vane Science Centre, Charterhouse Square, Queen Mary University of London, London, UK
| | - C Xintaropoulou
- Centre for Haemato-Oncology, Barts Cancer Institute, John Vane Science Centre, Charterhouse Square, Queen Mary University of London, London, UK
| | - A Clear
- Centre for Haemato-Oncology, Barts Cancer Institute, John Vane Science Centre, Charterhouse Square, Queen Mary University of London, London, UK
| | - M Austin
- Centre for Haemato-Oncology, Barts Cancer Institute, John Vane Science Centre, Charterhouse Square, Queen Mary University of London, London, UK
| | - I Pislariu
- Centre for Haemato-Oncology, Barts Cancer Institute, John Vane Science Centre, Charterhouse Square, Queen Mary University of London, London, UK
| | - F Miraki-Moud
- Centre for Haemato-Oncology, Barts Cancer Institute, John Vane Science Centre, Charterhouse Square, Queen Mary University of London, London, UK
| | - P Cutillas
- Centre for Haemato-Oncology, Barts Cancer Institute, John Vane Science Centre, Charterhouse Square, Queen Mary University of London, London, UK
| | - K Korfi
- Centre for Haemato-Oncology, Barts Cancer Institute, John Vane Science Centre, Charterhouse Square, Queen Mary University of London, London, UK
| | - M Calaminici
- Centre for Haemato-Oncology, Barts Cancer Institute, John Vane Science Centre, Charterhouse Square, Queen Mary University of London, London, UK
| | - W Cawthorn
- BHF Centre for Cardiovascular Science, The Queen's Medical Research Institute, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, Scotland, UK
| | - K Suchacki
- BHF Centre for Cardiovascular Science, The Queen's Medical Research Institute, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, Scotland, UK
| | - A Nagano
- Centre for Molecular Oncology, Barts Cancer Institute, John Vane Science Centre, Charterhouse Square, Queen Mary University of London, London, UK
| | - J G Gribben
- Centre for Haemato-Oncology, Barts Cancer Institute, John Vane Science Centre, Charterhouse Square, Queen Mary University of London, London, UK
| | - M Smith
- Department of Haemato-Oncology, St Bartholomew's Hospital, West Smithfield, London, UK
| | - J D Cavenagh
- Department of Haemato-Oncology, St Bartholomew's Hospital, West Smithfield, London, UK
| | - H Oakervee
- Department of Haemato-Oncology, St Bartholomew's Hospital, West Smithfield, London, UK
| | - A Castleton
- Christie NHS Foundation Trust, Manchester, UK
| | - D Taussig
- Haemato-oncology Unit, The Royal Marsden Hospital, Sutton, UK
| | - B Peck
- Centre for Tumour Biology, Barts Cancer Institute, John Vane Science Centre, Charterhouse Square, Queen Mary University of London, London, UK
| | - A Wilczynska
- CRUK Beatson Institute, Glasgow, UK
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - L McNaughton
- Haematopoietic Stem Cell Laboratory, The Francis Crick Institute, London, UK
| | - D Bonnet
- Haematopoietic Stem Cell Laboratory, The Francis Crick Institute, London, UK
| | - F Mardakheh
- Centre for Molecular Oncology, Barts Cancer Institute, John Vane Science Centre, Charterhouse Square, Queen Mary University of London, London, UK
| | - B Patel
- Centre for Haemato-Oncology, Barts Cancer Institute, John Vane Science Centre, Charterhouse Square, Queen Mary University of London, London, UK.
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15
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Badodi S, Pomella N, Zhang X, Rosser G, Whittingham J, Niklison-Chirou MV, Lim YM, Brandner S, Morrison G, Pollard SM, Bennett CD, Clifford SC, Peet A, Basson MA, Marino S. Inositol treatment inhibits medulloblastoma through suppression of epigenetic-driven metabolic adaptation. Nat Commun 2021; 12:2148. [PMID: 33846320 PMCID: PMC8042111 DOI: 10.1038/s41467-021-22379-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 03/12/2021] [Indexed: 12/11/2022] Open
Abstract
Deregulation of chromatin modifiers plays an essential role in the pathogenesis of medulloblastoma, the most common paediatric malignant brain tumour. Here, we identify a BMI1-dependent sensitivity to deregulation of inositol metabolism in a proportion of medulloblastoma. We demonstrate mTOR pathway activation and metabolic adaptation specifically in medulloblastoma of the molecular subgroup G4 characterised by a BMI1High;CHD7Low signature and show this can be counteracted by IP6 treatment. Finally, we demonstrate that IP6 synergises with cisplatin to enhance its cytotoxicity in vitro and extends survival in a pre-clinical BMI1High;CHD7Low xenograft model.
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Affiliation(s)
- Sara Badodi
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Nicola Pomella
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Xinyu Zhang
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Gabriel Rosser
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - John Whittingham
- Centre for Craniofacial and Regenerative Biology, King's College London, London, UK
| | - Maria Victoria Niklison-Chirou
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Centre for Therapeutic Innovation (CTI-Bath), Department of Pharmacy & Pharmacology, University of Bath, Bath, UK
| | - Yau Mun Lim
- UCL Queen Square Institute of Neurology and The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Sebastian Brandner
- UCL Queen Square Institute of Neurology and The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Gillian Morrison
- Centre for Regenerative Medicine & Cancer Research UK Edinburgh Centre, The University of Edinburgh, Edinburgh, UK
| | - Steven M Pollard
- Centre for Regenerative Medicine & Cancer Research UK Edinburgh Centre, The University of Edinburgh, Edinburgh, UK
| | - Christopher D Bennett
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Women and Children's Hospital, Birmingham, UK
| | - Steven C Clifford
- Newcastle University Centre for Cancer, Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle upon Tyne, UK
| | - Andrew Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Women and Children's Hospital, Birmingham, UK
| | - M Albert Basson
- Centre for Craniofacial and Regenerative Biology, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Silvia Marino
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
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16
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Gerdes H, Casado P, Dokal A, Hijazi M, Akhtar N, Osuntola R, Rajeeve V, Fitzgibbon J, Travers J, Britton D, Khorsandi S, Cutillas PR. Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs. Nat Commun 2021; 12:1850. [PMID: 33767176 PMCID: PMC7994645 DOI: 10.1038/s41467-021-22170-8] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 02/26/2021] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman's rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies.
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Affiliation(s)
- Henry Gerdes
- Cell Signalling & Proteomics Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, UK
| | - Pedro Casado
- Cell Signalling & Proteomics Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, UK
| | - Arran Dokal
- Cell Signalling & Proteomics Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, UK
- Kinomica Ltd, Alderley Park, Alderley Edge, Macclesfield, UK
| | - Maruan Hijazi
- Cell Signalling & Proteomics Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, UK
| | - Nosheen Akhtar
- Cell Signalling & Proteomics Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, UK
- Department of Biological Sciences, National University of Medical Sciences, Rawalpindi, Pakistan
| | - Ruth Osuntola
- Mass spectrometry Laboratory, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, UK
| | - Vinothini Rajeeve
- Mass spectrometry Laboratory, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, UK
| | - Jude Fitzgibbon
- Personalised Medicine Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, UK
| | - Jon Travers
- Astra Zeneca Ltd, 1 Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge, UK
| | - David Britton
- Cell Signalling & Proteomics Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, UK
- Kinomica Ltd, Alderley Park, Alderley Edge, Macclesfield, UK
| | | | - Pedro R Cutillas
- Cell Signalling & Proteomics Group, Centre for Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, UK.
- Mass spectrometry Laboratory, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, UK.
- The Alan Turing Institute, The British Library, 2QR, London, UK.
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17
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Gjerga E, Dugourd A, Tobalina L, Sousa A, Saez-Rodriguez J. PHONEMeS: Efficient Modeling of Signaling Networks Derived from Large-Scale Mass Spectrometry Data. J Proteome Res 2021; 20:2138-2144. [PMID: 33682416 DOI: 10.1021/acs.jproteome.0c00958] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Post-translational modifications of proteins play an important role in the regulation of cellular processes. The mass spectrometry analysis of proteome modifications offers huge potential for the study of how protein inhibitors affect the phosphosignaling mechanisms inside the cells. We have recently proposed PHONEMeS, a method that uses high-content shotgun phosphoproteomic data to build logical network models of signal perturbation flow. However, in its original implementation, PHONEMeS was computationally demanding and was only used to model signaling in a perturbation context. We have reformulated PHONEMeS as an Integer Linear Program (ILP) that is orders of magnitude more efficient than the original one. We have also expanded the scenarios that can be analyzed. PHONEMeS can model data upon perturbation on not only a known target but also deregulated pathways upstream and downstream of any set of deregulated kinases. Finally, PHONEMeS can now analyze data sets with multiple time points, which helps us to obtain better insight into the dynamics of the propagation of signals. We illustrate the value of the new approach on various data sets of medical relevance, where we shed light on signaling mechanisms and drug modes of action.
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Affiliation(s)
- Enio Gjerga
- Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF267, Heidelberg University, 69120 Heidelberg, Germany.,Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, 52074 Aachen, Germany
| | - Aurelien Dugourd
- Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF267, Heidelberg University, 69120 Heidelberg, Germany.,Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, 52074 Aachen, Germany
| | - Luis Tobalina
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, 52074 Aachen, Germany
| | - Abel Sousa
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, United Kingdom.,Institute for Research and Innovation in Health (i3s), Rua Alfredo Allen 208, 4200-135 Porto, Portugal
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, INF267, Heidelberg University, 69120 Heidelberg, Germany.,Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, 52074 Aachen, Germany
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18
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Yang C, Sheng Y, Shi X, Liu Y, He Y, Du Y, Zhang G, Gao F. CD44/HA signaling mediates acquired resistance to a PI3Kα inhibitor. Cell Death Dis 2020; 11:831. [PMID: 33024087 PMCID: PMC7538592 DOI: 10.1038/s41419-020-03037-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 09/17/2020] [Accepted: 09/21/2020] [Indexed: 12/20/2022]
Abstract
Most luminal breast carcinomas (BrCas) bearing PIK3CA mutations initially respond to phosphoinositide-3-kinase (PI3K)-α inhibitors, but many eventually become resistant. The underlying mechanisms of this resistance remain obscure. In this work, we showed that a CD44high state due to aberrant isoform splicing was acquired from adaptive resistance to a PI3Kα inhibitor (BLY719) in luminal BrCas. Notably, the expression of CD44 was positively correlated with estrogen receptor (ER) activity in PIK3CA-mutant breast cancers, and ER-dependent transcription upon PI3Kα pathway inhibition was in turn mediated by CD44. Furthermore, the interaction of CD44 with the ligand hyaluronan (HA) initiated the Src-ERK signaling cascade, which subsequently maintained AKT and mTOR activity in the presence of a PI3Kα inhibitor. Activation of this pathway was prevented by disruption of the CD44/HA interaction, which in turn restored sensitivity to BLY719. Our results revealed that an ER-CD44-HA signaling circuit that mediates robust compensatory activation of the Src-ERK signaling cascade may contribute to the development of acquired resistance to PI3Kα inhibitors. This study provides new insight into the mechanism of adaptive resistance to PI3Kα inhibition therapy.
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Affiliation(s)
- Cuixia Yang
- Department of Molecular Biology Laboratory, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China.,Department of Clinical Laboratory, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China
| | - Yumeng Sheng
- Department of Molecular Biology Laboratory, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China
| | - Xiaoxing Shi
- Department of Laboratory Medicine, Shanghai Wujing General Hospital, Shanghai, China
| | - Yiwen Liu
- Department of Molecular Biology Laboratory, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China
| | - Yiqing He
- Department of Molecular Biology Laboratory, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China
| | - Yan Du
- Department of Molecular Biology Laboratory, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China
| | - Guoliang Zhang
- Department of Molecular Biology Laboratory, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China
| | - Feng Gao
- Department of Molecular Biology Laboratory, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China. .,Department of Clinical Laboratory, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China.
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19
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Investigation of cancer drug resistance mechanisms by phosphoproteomics. Pharmacol Res 2020; 160:105091. [PMID: 32712320 DOI: 10.1016/j.phrs.2020.105091] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 07/20/2020] [Accepted: 07/20/2020] [Indexed: 12/23/2022]
Abstract
Cancer cell mutations can be identified by genomic and transcriptomic techniques. However, they are not sufficient to understand the full complexity of cancer heterogeneity. Analyses of proteins expressed in cancers and their modification profiles show how these mutations could be translated at the functional level. Protein phosphorylation is a major post-translational modification critical for regulating several cellular functions. The covalent addition of phosphate groups to serine, threonine, and tyrosine is catalyzed by protein kinases. Over the past years, kinases were strongly associated with cancer, thus inhibition of protein kinases emanated as novel cancer treatment. However, cancers frequently develop drug resistance. Therefore, a better understanding of drug effects on tumors is urgently needed. In this perspective, phosphoproteomics arose as advanced tool to monitor cancer therapies and to discover novel drugs. This review highlights the role of phosphoproteomics in predicting sensitivity or resistance of cancers towards tyrosine kinase inhibitors and cytotoxic drugs. It also shows the importance of phosphoproteomics in identifying biomarkers that could be applied in clinical diagnostics to predict responses to drugs.
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20
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Savage SR, Zhang B. Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources. Clin Proteomics 2020; 17:27. [PMID: 32676006 PMCID: PMC7353784 DOI: 10.1186/s12014-020-09290-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 07/04/2020] [Indexed: 12/19/2022] Open
Abstract
Mass spectrometry-based phosphoproteomics is becoming an essential methodology for the study of global cellular signaling. Numerous bioinformatics resources are available to facilitate the translation of phosphopeptide identification and quantification results into novel biological and clinical insights, a critical step in phosphoproteomics data analysis. These resources include knowledge bases of kinases and phosphatases, phosphorylation sites, kinase inhibitors, and sequence variants affecting kinase function, and bioinformatics tools that can predict phosphorylation sites in addition to the kinase that phosphorylates them, infer kinase activity, and predict the effect of mutations on kinase signaling. However, these resources exist in silos and it is challenging to select among multiple resources with similar functions. Therefore, we put together a comprehensive collection of resources related to phosphoproteomics data interpretation, compared the use of tools with similar functions, and assessed the usability from the standpoint of typical biologists or clinicians. Overall, tools could be improved by standardization of enzyme names, flexibility of data input and output format, consistent maintenance, and detailed manuals.
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Affiliation(s)
- Sara R. Savage
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN USA
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX USA
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21
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Invergo BM, Petursson B, Akhtar N, Bradley D, Giudice G, Hijazi M, Cutillas P, Petsalaki E, Beltrao P. Prediction of Signed Protein Kinase Regulatory Circuits. Cell Syst 2020; 10:384-396.e9. [DOI: 10.1016/j.cels.2020.04.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 01/24/2020] [Accepted: 04/20/2020] [Indexed: 01/18/2023]
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22
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Hijazi M, Smith R, Rajeeve V, Bessant C, Cutillas PR. Reconstructing kinase network topologies from phosphoproteomics data reveals cancer-associated rewiring. Nat Biotechnol 2020; 38:493-502. [PMID: 31959955 DOI: 10.1038/s41587-019-0391-9] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 12/11/2019] [Indexed: 12/11/2022]
Abstract
Understanding how oncogenic mutations rewire regulatory-protein networks is important for rationalizing the mechanisms of oncogenesis and for individualizing anticancer treatments. We report a chemical phosphoproteomics method to elucidate the topology of kinase-signaling networks in mammalian cells. We identified >6,000 protein phosphorylation sites that can be used to infer >1,500 kinase-kinase interactions and devised algorithms that can reconstruct kinase network topologies from these phosphoproteomics data. Application of our methods to primary acute myeloid leukemia and breast cancer tumors quantified the relationship between kinase expression and activity, and enabled the identification of hitherto unknown kinase network topologies associated with drug-resistant phenotypes or specific genetic mutations. Using orthogonal methods we validated that PIK3CA wild-type cells adopt MAPK-dependent circuitries in breast cancer cells and that the kinase TTK is important in acute myeloid leukemia. Our phosphoproteomic signatures of network circuitry can identify kinase topologies associated with both phenotypes and genotypes of cancer cells.
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Affiliation(s)
- Maruan Hijazi
- Signalling and Proteomics Group, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Ryan Smith
- School of Biological and Chemical Sciences, Queen Mary University of London, London, UK
| | - Vinothini Rajeeve
- Signalling and Proteomics Group, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Conrad Bessant
- School of Biological and Chemical Sciences, Queen Mary University of London, London, UK
- The Alan Turing Institute, British Library, London, UK
| | - Pedro R Cutillas
- Signalling and Proteomics Group, Barts Cancer Institute, Queen Mary University of London, London, UK.
- The Alan Turing Institute, British Library, London, UK.
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23
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Demircioglu F, Wang J, Candido J, Costa ASH, Casado P, de Luxan Delgado B, Reynolds LE, Gomez-Escudero J, Newport E, Rajeeve V, Baker AM, Roy-Luzarraga M, Graham TA, Foster J, Wang Y, Campbell JJ, Singh R, Zhang P, Schall TJ, Balkwill FR, Sosabowski J, Cutillas PR, Frezza C, Sancho P, Hodivala-Dilke K. Cancer associated fibroblast FAK regulates malignant cell metabolism. Nat Commun 2020; 11:1290. [PMID: 32157087 PMCID: PMC7064590 DOI: 10.1038/s41467-020-15104-3] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 02/18/2020] [Indexed: 12/19/2022] Open
Abstract
Emerging evidence suggests that cancer cell metabolism can be regulated by cancer-associated fibroblasts (CAFs), but the mechanisms are poorly defined. Here we show that CAFs regulate malignant cell metabolism through pathways under the control of FAK. In breast and pancreatic cancer patients we find that low FAK expression, specifically in the stromal compartment, predicts reduced overall survival. In mice, depletion of FAK in a subpopulation of CAFs regulates paracrine signals that increase malignant cell glycolysis and tumour growth. Proteomic and phosphoproteomic analysis in our mouse model identifies metabolic alterations which are reflected at the transcriptomic level in patients with low stromal FAK. Mechanistically we demonstrate that FAK-depletion in CAFs increases chemokine production, which via CCR1/CCR2 on cancer cells, activate protein kinase A, leading to enhanced malignant cell glycolysis. Our data uncover mechanisms whereby stromal fibroblasts regulate cancer cell metabolism independent of genetic mutations in cancer cells.
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Affiliation(s)
- Fevzi Demircioglu
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Jun Wang
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Juliana Candido
- Centre for Cancer and Inflammation, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Ana S H Costa
- MRC Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Cambridge Biomedical Campus, Cambridge, CB2 0XZ, UK
| | - Pedro Casado
- Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Beatriz de Luxan Delgado
- Centre for Stem Cells in Cancer and Ageing, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Louise E Reynolds
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Jesus Gomez-Escudero
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Emma Newport
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Vinothini Rajeeve
- Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Ann-Marie Baker
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Marina Roy-Luzarraga
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Trevor A Graham
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Julie Foster
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Yu Wang
- ChemoCentryx Inc., 850 Maude Ave, Mountain View, CA94043, USA
| | | | - Rajinder Singh
- ChemoCentryx Inc., 850 Maude Ave, Mountain View, CA94043, USA
| | - Penglie Zhang
- ChemoCentryx Inc., 850 Maude Ave, Mountain View, CA94043, USA
| | - Thomas J Schall
- ChemoCentryx Inc., 850 Maude Ave, Mountain View, CA94043, USA
| | - Frances R Balkwill
- Centre for Cancer and Inflammation, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Jane Sosabowski
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Pedro R Cutillas
- Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
| | - Christian Frezza
- MRC Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Cambridge Biomedical Campus, Cambridge, CB2 0XZ, UK
| | - Patricia Sancho
- Centre for Stem Cells in Cancer and Ageing, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK
- IIS Aragon, Hospital Universitario Miguel Servet, Zaragoza, 50009, Spain
| | - Kairbaan Hodivala-Dilke
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK.
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24
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Abstract
Specificity in signal transduction is determined by the ability of cells to "encode" and subsequently "decode" different environmental signals. Akin to computer software, this "signaling code" governs context-dependent execution of cellular programs through modulation of signaling dynamics and can be corrupted by disease-causing mutations. Class IA phosphoinositide 3-kinase (PI3K) signaling is critical for normal growth and development and is dysregulated in human disorders such as benign overgrowth syndromes, cancer, primary immune deficiency, and metabolic syndrome. Despite decades of PI3K research, understanding of context-dependent regulation of the PI3K pathway and of the underlying signaling code remains rudimentary. Here, we review current knowledge on context-specific PI3K signaling and how technological advances now make it possible to move from a qualitative to quantitative understanding of this pathway. Insight into how cellular PI3K signaling is encoded or decoded may open new avenues for rational pharmacological targeting of PI3K-associated diseases. The principles of PI3K context-dependent signal encoding and decoding described here are likely applicable to most, if not all, major cell signaling pathways.
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Affiliation(s)
- Ralitsa R Madsen
- UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London WC1E 6DD, UK.
| | - Bart Vanhaesebroeck
- UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London WC1E 6DD, UK.
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25
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Arshad OA, Danna V, Petyuk VA, Piehowski PD, Liu T, Rodland KD, McDermott JE. An Integrative Analysis of Tumor Proteomic and Phosphoproteomic Profiles to Examine the Relationships Between Kinase Activity and Phosphorylation. Mol Cell Proteomics 2019; 18:S26-S36. [PMID: 31227600 PMCID: PMC6692771 DOI: 10.1074/mcp.ra119.001540] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 06/18/2019] [Indexed: 12/18/2022] Open
Abstract
Phosphorylation of proteins is a key way cells regulate function, both at the individual protein level and at the level of signaling pathways. Kinases are responsible for phosphorylation of substrates, generally on serine, threonine, or tyrosine residues. Though particular sequence patterns can be identified that dictate whether a residue will be phosphorylated by a specific kinase, these patterns are not highly predictive of phosphorylation. The availability of large scale proteomic and phosphoproteomic data sets generated using mass-spectrometry-based approaches provides an opportunity to study the important relationship between kinase activity, substrate specificity, and phosphorylation. In this study, we analyze relationships between protein abundance and phosphopeptide abundance across more than 150 tumor samples and show that phosphorylation at specific phosphosites is not well correlated with overall kinase abundance. However, individual kinases show a clear and statistically significant difference in correlation among known phosphosite targets for that kinase and randomly selected phosphosites. We further investigate relationships between phosphorylation of known activating or inhibitory sites on kinases and phosphorylation of their target phosphosites. Combined with motif-based analysis, this approach can predict novel kinase targets and show which subsets of a kinase's target repertoire are specifically active in one condition versus another.
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Affiliation(s)
- Osama A Arshad
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352
| | - Vincent Danna
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352
| | - Vladislav A Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352
| | - Paul D Piehowski
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352; School of Medicine, Oregon Health & Sciences University, Portland, OR 97239
| | - Jason E McDermott
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352; School of Medicine, Oregon Health & Sciences University, Portland, OR 97239.
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26
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Beekhof R, van Alphen C, Henneman AA, Knol JC, Pham TV, Rolfs F, Labots M, Henneberry E, Le Large TY, de Haas RR, Piersma SR, Vurchio V, Bertotti A, Trusolino L, Verheul HM, Jimenez CR. INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases. Mol Syst Biol 2019; 15:e8250. [PMID: 30979792 PMCID: PMC6461034 DOI: 10.15252/msb.20188250] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 03/15/2019] [Accepted: 03/20/2019] [Indexed: 12/19/2022] Open
Abstract
Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label-free kinase-centric and substrate-centric information in an Integrative Inferred Kinase Activity (INKA) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase-substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild-type versus mutant, +/- drug), (iii) pre- and on-treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient-derived tumor xenografts with INKA-guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate-based single-sample tool KARP, and underscore target potential of high-ranking kinases, encouraging further exploration of INKA's functional and clinical value.
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Affiliation(s)
- Robin Beekhof
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Carolien van Alphen
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Alex A Henneman
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jaco C Knol
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Thang V Pham
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frank Rolfs
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mariette Labots
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Evan Henneberry
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Tessa Ys Le Large
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Richard R de Haas
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sander R Piersma
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Valentina Vurchio
- Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy
| | - Andrea Bertotti
- Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy
| | - Livio Trusolino
- Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy
| | - Henk Mw Verheul
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Connie R Jimenez
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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27
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Rudolph JD, Cox J. A Network Module for the Perseus Software for Computational Proteomics Facilitates Proteome Interaction Graph Analysis. J Proteome Res 2019; 18:2052-2064. [PMID: 30931570 PMCID: PMC6578358 DOI: 10.1021/acs.jproteome.8b00927] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Proteomics data analysis strongly benefits from not studying single proteins in isolation but taking their multivariate interdependence into account. We introduce PerseusNet, the new Perseus network module for the biological analysis of proteomics data. Proteomics is commonly used to generate networks, e.g., with affinity purification experiments, but networks are also used to explore proteomics data. PerseusNet supports the biomedical researcher for both modes of data analysis with a multitude of activities. For affinity purification, a volcano-plot-based statistical analysis method for network generation is featured which is scalable to large numbers of baits. For posttranslational modifications of proteins, such as phosphorylation, a collection of dedicated network analysis tools helps in elucidating cellular signaling events. Co-expression network analysis of proteomics data adopts established tools from transcriptome co-expression analysis. PerseusNet is extensible through a plugin architecture in a multi-lingual way, integrating analyses in C#, Python, and R, and is freely available at http://www.perseus-framework.org .
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Affiliation(s)
- Jan Daniel Rudolph
- Computational Systems Biochemistry , Max-Planck Institute of Biochemistry , Am Klopferspitz 18 , 82152 Martinsried , Germany
| | - Jürgen Cox
- Computational Systems Biochemistry , Max-Planck Institute of Biochemistry , Am Klopferspitz 18 , 82152 Martinsried , Germany.,Department of Biological and Medical Psychology , University of Bergen , Jonas Liesvei 91 , 5009 Bergen , Norway
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28
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Hu Q, Li C, Wang S, Li Y, Wen B, Zhang Y, Liang K, Yao J, Ye Y, Hsiao H, Nguyen TK, Park PK, Egranov SD, Hawke DH, Marks JR, Han L, Hung MC, Zhang B, Lin C, Yang L. LncRNAs-directed PTEN enzymatic switch governs epithelial-mesenchymal transition. Cell Res 2019; 29:286-304. [PMID: 30631154 PMCID: PMC6461864 DOI: 10.1038/s41422-018-0134-3] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 12/07/2018] [Indexed: 02/07/2023] Open
Abstract
Despite the structural conservation of PTEN with dual-specificity phosphatases, there have been no reports regarding the regulatory mechanisms that underlie this potential dual-phosphatase activity. Here, we report that K27-linked polyubiquitination of PTEN at lysines 66 and 80 switches its phosphoinositide/protein tyrosine phosphatase activity to protein serine/threonine phosphatase activity. Mechanistically, high glucose, TGF-β, CTGF, SHH, and IL-6 induce the expression of a long non-coding RNA, GAEA (Glucose Aroused for EMT Activation), which associates with an RNA-binding E3 ligase, MEX3C, and enhances its enzymatic activity, leading to the K27-linked polyubiquitination of PTEN. The MEX3C-catalyzed PTENK27-polyUb activates its protein serine/threonine phosphatase activity and inhibits its phosphatidylinositol/protein tyrosine phosphatase activity. With this altered enzymatic activity, PTENK27-polyUb dephosphorylates the phosphoserine/threonine residues of TWIST1, SNAI1, and YAP1, leading to accumulation of these master regulators of EMT. Animals with genetic inhibition of PTENK27-polyUb, by a single nucleotide mutation generated using CRISPR/Cas9 (PtenK80R/K80R), exhibit inhibition of EMT markers during mammary gland morphogenesis in pregnancy/lactation and during cutaneous wound healing processes. Our findings illustrate an unexpected paradigm in which the lncRNA-dependent switch in PTEN protein serine/threonine phosphatase activity is important for physiological homeostasis and disease development.
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Affiliation(s)
- Qingsong Hu
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Chunlai Li
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Shouyu Wang
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
- Department of hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, Jiangsu Province, China
| | - Yajuan Li
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Bo Wen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Yanyan Zhang
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
- Institute of Immunology, Third Military Medical University, 400038, Chongqing, China
| | - Ke Liang
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Jun Yao
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Youqiong Ye
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGroven Medical School, Houston, TX, 77030, USA
| | - Heidi Hsiao
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Tina K Nguyen
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Peter K Park
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Sergey D Egranov
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - David H Hawke
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Jeffrey R Marks
- Department of Surgery, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Leng Han
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGroven Medical School, Houston, TX, 77030, USA
| | - Mien-Chie Hung
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
- Program in Cancer Biology, Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Bing Zhang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Chunru Lin
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
- Program in Cancer Biology, Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
| | - Liuqing Yang
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
- Program in Cancer Biology, Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
- Center for RNA Interference and Non-Coding RNAs, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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29
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Sacco F, Perfetto L, Cesareni G. Combining Phosphoproteomics Datasets and Literature Information to Reveal the Functional Connections in a Cell Phosphorylation Network. Proteomics 2019; 18:e1700311. [PMID: 29280302 DOI: 10.1002/pmic.201700311] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 12/11/2017] [Indexed: 01/08/2023]
Abstract
Protein phosphorylation modulates many biological processes. However, the characterization of the complex regulatory circuits underlying cell response to external and internal stimuli is still limited by our inability to describe the phosphorylation network on a global scale. Modern MS-based phosphoproteomics allows monitoring tens of thousands of phosphorylation sites in multiple conditions, making the approach ideal to explore signaling pathways mediated by phosphorylation. Here, we review recent advances in phosphoproteomics and discuss some of the computational approaches developed to facilitate extraction of signaling information from these datasets. Finally, this review focuses on approaches that integrate prior literature information with unbiased phosphoproteomics experiments.
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Affiliation(s)
- Francesca Sacco
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy
| | - Livia Perfetto
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy
| | - Gianni Cesareni
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy
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30
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Needham EJ, Parker BL, Burykin T, James DE, Humphrey SJ. Illuminating the dark phosphoproteome. Sci Signal 2019; 12:12/565/eaau8645. [PMID: 30670635 DOI: 10.1126/scisignal.aau8645] [Citation(s) in RCA: 178] [Impact Index Per Article: 35.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Protein phosphorylation is a major regulator of protein function and biological outcomes. This was first recognized through functional biochemical experiments, and in the past decade, major technological advances in mass spectrometry have enabled the study of protein phosphorylation on a global scale. This rapidly growing field of phosphoproteomics has revealed that more than 100,000 distinct phosphorylation events occur in human cells, which likely affect the function of every protein. Phosphoproteomics has improved the understanding of the function of even the most well-characterized protein kinases by revealing new downstream substrates and biology. However, current biochemical and bioinformatic approaches have only identified kinases for less than 5% of the phosphoproteome, and functional assignments of phosphosites are almost negligible. Notably, our understanding of the relationship between kinases and their substrates follows a power law distribution, with almost 90% of phosphorylation sites currently assigned to the top 20% of kinases. In addition, more than 150 kinases do not have a single known substrate. Despite a small group of kinases dominating biomedical research, the number of substrates assigned to a kinase does not correlate with disease relevance as determined by pathogenic human mutation prevalence and mouse model phenotypes. Improving our understanding of the substrates targeted by all kinases and functionally annotating the phosphoproteome will be broadly beneficial. Advances in phosphoproteomics technologies, combined with functional screening approaches, should make it feasible to illuminate the connectivity and functionality of the entire phosphoproteome, providing enormous opportunities for discovering new biology, therapeutic targets, and possibly diagnostics.
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Affiliation(s)
- Elise J Needham
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia.,Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia
| | - Benjamin L Parker
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia.,Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia
| | - Timur Burykin
- Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia
| | - David E James
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia. .,Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia.,Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia
| | - Sean J Humphrey
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia. .,Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia
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31
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Schwill M, Tamaskovic R, Gajadhar AS, Kast F, White FM, Plückthun A. Systemic analysis of tyrosine kinase signaling reveals a common adaptive response program in a HER2-positive breast cancer. Sci Signal 2019; 12:12/565/eaau2875. [PMID: 30670633 DOI: 10.1126/scisignal.aau2875] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Drug-induced compensatory signaling and subsequent rewiring of the signaling pathways that support cell proliferation and survival promote the development of acquired drug resistance in tumors. Here, we sought to analyze the adaptive kinase response in cancer cells after distinct treatment with agents targeting human epidermal growth factor receptor 2 (HER2), specifically those that induce either only temporary cell cycle arrest or, alternatively, apoptosis in HER2-overexpressing cancers. We compared trastuzumab, ARRY380, the combination thereof, and a biparatopic, HER2-targeted designed ankyrin repeat protein (DARPin; specifically, 6L1G) and quantified the phosphoproteome by isobaric tagging using tandem mass tag liquid chromatography/tandem mass spectrometry (TMT LC-MS/MS). We found a specific signature of persistently phosphorylated tyrosine peptides after the nonapoptotic treatments, which we used to distinguish between different treatment-induced cancer cell fates. Next, we analyzed the activation of serine/threonine and tyrosine kinases after treatment using a bait peptide chip array and predicted the corresponding active kinases. Through a combined system-wide analysis, we identified a common adaptive kinase response program that involved the activation of focal adhesion kinase 1 (FAK1), protein kinase C-δ (PRKCD), and Ephrin (EPH) family receptors. These findings reveal potential targets to prevent adaptive resistance to HER2-targeted therapies.
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Affiliation(s)
- Martin Schwill
- Department of Biochemistry, University of Zurich, Winterthurerstr. 190, 8057 Zurich, Switzerland
| | - Rastislav Tamaskovic
- Department of Biochemistry, University of Zurich, Winterthurerstr. 190, 8057 Zurich, Switzerland
| | - Aaron S Gajadhar
- Department of Biological Engineering, Koch Institute for Integrative Cancer Research, Center for Precision Cancer Medicine, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Florian Kast
- Department of Biochemistry, University of Zurich, Winterthurerstr. 190, 8057 Zurich, Switzerland
| | - Forest M White
- Department of Biological Engineering, Koch Institute for Integrative Cancer Research, Center for Precision Cancer Medicine, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Andreas Plückthun
- Department of Biochemistry, University of Zurich, Winterthurerstr. 190, 8057 Zurich, Switzerland.
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32
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Arnandis T, Monteiro P, Adams SD, Bridgeman VL, Rajeeve V, Gadaleta E, Marzec J, Chelala C, Malanchi I, Cutillas PR, Godinho SA. Oxidative Stress in Cells with Extra Centrosomes Drives Non-Cell-Autonomous Invasion. Dev Cell 2018; 47:409-424.e9. [PMID: 30458137 PMCID: PMC6251975 DOI: 10.1016/j.devcel.2018.10.026] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 10/10/2018] [Accepted: 10/23/2018] [Indexed: 01/07/2023]
Abstract
Centrosomal abnormalities, in particular centrosome amplification, are recurrent features of human tumors. Enforced centrosome amplification in vivo plays a role in tumor initiation and progression. However, centrosome amplification occurs only in a subset of cancer cells, and thus, partly due to this heterogeneity, the contribution of centrosome amplification to tumors is unknown. Here, we show that supernumerary centrosomes induce a paracrine-signaling axis via the secretion of proteins, including interleukin-8 (IL-8), which leads to non-cell-autonomous invasion in 3D mammary organoids and zebrafish models. This extra centrosomes-associated secretory phenotype (ECASP) promotes invasion of human mammary cells via HER2 signaling activation. Further, we demonstrate that centrosome amplification induces an early oxidative stress response via increased NOX-generated reactive oxygen species (ROS), which in turn mediates secretion of pro-invasive factors. The discovery that cells with extra centrosomes can manipulate the surrounding cells highlights unexpected and far-reaching consequences of these abnormalities in cancer.
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Affiliation(s)
- Teresa Arnandis
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Pedro Monteiro
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Sophie D Adams
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | | | - Vinothini Rajeeve
- Integrative Cell Signalling and Proteomics, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Emanuela Gadaleta
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Jacek Marzec
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Claude Chelala
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Ilaria Malanchi
- Tumour Host Interaction Laboratory, The Francis Crick Institute, 1 Midland Rd, London NW1 1AT, UK
| | - Pedro R Cutillas
- Integrative Cell Signalling and Proteomics, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Susana A Godinho
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK.
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33
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Reconstructing phosphorylation signalling networks from quantitative phosphoproteomic data. Essays Biochem 2018; 62:525-534. [PMID: 30072490 PMCID: PMC6204553 DOI: 10.1042/ebc20180019] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 06/25/2018] [Accepted: 06/26/2018] [Indexed: 12/25/2022]
Abstract
Cascades of phosphorylation between protein kinases comprise a core mechanism in the integration and propagation of intracellular signals. Although we have accumulated a wealth of knowledge around some such pathways, this is subject to study biases and much remains to be uncovered. Phosphoproteomics, the identification and quantification of phosphorylated proteins on a proteomic scale, provides a high-throughput means of interrogating the state of intracellular phosphorylation, both at the pathway level and at the whole-cell level. In this review, we discuss methods for using human quantitative phosphoproteomic data to reconstruct the underlying signalling networks that generated it. We address several challenges imposed by the data on such analyses and we consider promising advances towards reconstructing unbiased, kinome-scale signalling networks.
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34
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Role for ERK1/2-dependent activation of FCHSD2 in cancer cell-selective regulation of clathrin-mediated endocytosis. Proc Natl Acad Sci U S A 2018; 115:E9570-E9579. [PMID: 30249660 DOI: 10.1073/pnas.1810209115] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Clathrin-mediated endocytosis (CME) regulates the uptake of cell-surface receptors as well as their downstream signaling activities. We recently reported that signaling can reciprocally regulate CME in cancer cells and that this crosstalk can contribute to cancer progression. To further explore the nature and extent of the crosstalk between signaling and CME in cancer cell biology, we analyzed a panel of oncogenic signaling kinase inhibitors for their effects on CME across a panel of normal and cancerous cells. Inhibition of several kinases selectively affected CME in cancer cells, including inhibition of ERK1/2, which selectively inhibited CME by decreasing the rate of clathrin-coated pit (CCP) initiation. We identified an ERK1/2 substrate, the FCH/F-BAR and SH3 domain-containing protein FCHSD2, as being essential for the ERK1/2-dependent effects on CME and CCP initiation. Our data suggest that ERK1/2 phosphorylation activates FCHSD2 and regulates EGF receptor (EGFR) endocytic trafficking as well as downstream signaling activities. Loss of FCHSD2 activity in nonsmall cell lung cancer (NSCLC) cells leads to increased cell-surface expression and altered signaling downstream of EGFR, resulting in enhanced cell proliferation and migration. The expression level of FCHSD2 is positively correlated with higher NSCLC patient survival rates, suggesting that FCHSD2 can negatively affect cancer progression. These findings provide insight into the mechanisms and consequences of the reciprocal regulation of signaling and CME in cancer cells.
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35
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Proteomic and genomic integration identifies kinase and differentiation determinants of kinase inhibitor sensitivity in leukemia cells. Leukemia 2018; 32:1818-1822. [PMID: 29626197 PMCID: PMC5949212 DOI: 10.1038/s41375-018-0032-1] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 11/20/2017] [Accepted: 11/24/2017] [Indexed: 12/11/2022]
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36
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Hernandez-Armenta C, Ochoa D, Gonçalves E, Saez-Rodriguez J, Beltrao P. Benchmarking substrate-based kinase activity inference using phosphoproteomic data. Bioinformatics 2018; 33:1845-1851. [PMID: 28200105 PMCID: PMC5870625 DOI: 10.1093/bioinformatics/btx082] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 02/09/2017] [Indexed: 11/13/2022] Open
Abstract
Motivation Phosphoproteomic experiments are increasingly used to study the changes in signaling occurring across different conditions. It has been proposed that changes in phosphorylation of kinase target sites can be used to infer when a kinase activity is under regulation. However, these approaches have not yet been benchmarked due to a lack of appropriate benchmarking strategies. Results We used curated phosphoproteomic experiments and a gold standard dataset containing a total of 184 kinase-condition pairs where regulation is expected to occur to benchmark and compare different kinase activity inference strategies: Z-test, Kolmogorov Smirnov test, Wilcoxon rank sum test, gene set enrichment analysis (GSEA), and a multiple linear regression model. We also tested weighted variants of the Z-test and GSEA that include information on kinase sequence specificity as proxy for affinity. Finally, we tested how the number of known substrates and the type of evidence (in vivo, in vitro or in silico) supporting these influence the predictions. Conclusions Most models performed well with the Z-test and the GSEA performing best as determined by the area under the ROC curve (Mean AUC = 0.722). Weighting kinase targets by the kinase target sequence preference improves the results marginally. However, the number of known substrates and the evidence supporting the interactions has a strong effect on the predictions. Availability and Implementation The KSEA implementation is available in https://github.com/ evocellnet/ksea. Additional data is available in http://phosfate.com Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - David Ochoa
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Emanuel Gonçalves
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.,RWTH Aachen University, Faculty of Medicine, Joint Research Center for Computational Biomedicine (JRC-COMBINE), Wendlingweg 2, Aachen, Germany
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
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37
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Fearon AE, Carter EP, Clayton NS, Wilkes EH, Baker AM, Kapitonova E, Bakhouche BA, Tanner Y, Wang J, Gadaleta E, Chelala C, Moore KM, Marshall JF, Chupin J, Schmid P, Jones JL, Lockley M, Cutillas PR, Grose RP. PHLDA1 Mediates Drug Resistance in Receptor Tyrosine Kinase-Driven Cancer. Cell Rep 2018; 22:2469-2481. [PMID: 29490281 PMCID: PMC5848852 DOI: 10.1016/j.celrep.2018.02.028] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 11/09/2017] [Accepted: 02/06/2018] [Indexed: 11/09/2022] Open
Abstract
Development of resistance causes failure of drugs targeting receptor tyrosine kinase (RTK) networks and represents a critical challenge for precision medicine. Here, we show that PHLDA1 downregulation is critical to acquisition and maintenance of drug resistance in RTK-driven cancer. Using fibroblast growth factor receptor (FGFR) inhibition in endometrial cancer cells, we identify an Akt-driven compensatory mechanism underpinned by downregulation of PHLDA1. We demonstrate broad clinical relevance of our findings, showing that PHLDA1 downregulation also occurs in response to RTK-targeted therapy in breast and renal cancer patients, as well as following trastuzumab treatment in HER2+ breast cancer cells. Crucially, knockdown of PHLDA1 alone was sufficient to confer de novo resistance to RTK inhibitors and induction of PHLDA1 expression re-sensitized drug-resistant cancer cells to targeted therapies, identifying PHLDA1 as a biomarker for drug response and highlighting the potential of PHLDA1 reactivation as a means of circumventing drug resistance.
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Affiliation(s)
- Abbie E Fearon
- Centre for Tumour Biology, Barts Cancer Institute-a CRUK Centre of Excellence, Queen Mary University of London, London EC1M 6BQ, UK
| | - Edward P Carter
- Centre for Tumour Biology, Barts Cancer Institute-a CRUK Centre of Excellence, Queen Mary University of London, London EC1M 6BQ, UK
| | - Natasha S Clayton
- Centre for Tumour Biology, Barts Cancer Institute-a CRUK Centre of Excellence, Queen Mary University of London, London EC1M 6BQ, UK
| | - Edmund H Wilkes
- Integrative Cell Signalling and Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, London EC1M 6BQ, UK
| | - Ann-Marie Baker
- Centre for Tumour Biology, Barts Cancer Institute-a CRUK Centre of Excellence, Queen Mary University of London, London EC1M 6BQ, UK
| | - Ekaterina Kapitonova
- Centre for Tumour Biology, Barts Cancer Institute-a CRUK Centre of Excellence, Queen Mary University of London, London EC1M 6BQ, UK
| | - Bakhouche A Bakhouche
- Centre for Tumour Biology, Barts Cancer Institute-a CRUK Centre of Excellence, Queen Mary University of London, London EC1M 6BQ, UK
| | - Yasmine Tanner
- Centre for Tumour Biology, Barts Cancer Institute-a CRUK Centre of Excellence, Queen Mary University of London, London EC1M 6BQ, UK
| | - Jun Wang
- Centre for Molecular Oncology, Barts Cancer Institute, London EC1M 6BQ, UK
| | - Emanuela Gadaleta
- Centre for Molecular Oncology, Barts Cancer Institute, London EC1M 6BQ, UK
| | - Claude Chelala
- Centre for Molecular Oncology, Barts Cancer Institute, London EC1M 6BQ, UK
| | - Kate M Moore
- Centre for Tumour Biology, Barts Cancer Institute-a CRUK Centre of Excellence, Queen Mary University of London, London EC1M 6BQ, UK
| | - John F Marshall
- Centre for Tumour Biology, Barts Cancer Institute-a CRUK Centre of Excellence, Queen Mary University of London, London EC1M 6BQ, UK
| | - Juliette Chupin
- Centre for Experimental Cancer Medicine, Barts Cancer Institute, London EC1M 6BQ, UK
| | - Peter Schmid
- Centre for Experimental Cancer Medicine, Barts Cancer Institute, London EC1M 6BQ, UK
| | - J Louise Jones
- Centre for Tumour Biology, Barts Cancer Institute-a CRUK Centre of Excellence, Queen Mary University of London, London EC1M 6BQ, UK
| | - Michelle Lockley
- Centre for Molecular Oncology, Barts Cancer Institute, London EC1M 6BQ, UK
| | - Pedro R Cutillas
- Integrative Cell Signalling and Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, London EC1M 6BQ, UK
| | - Richard P Grose
- Centre for Tumour Biology, Barts Cancer Institute-a CRUK Centre of Excellence, Queen Mary University of London, London EC1M 6BQ, UK.
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38
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Abstract
Cellular signaling, predominantly mediated by phosphorylation through protein kinases, is found to be deregulated in most cancers. Accordingly, protein kinases have been subject to intense investigations in cancer research, to understand their role in oncogenesis and to discover new therapeutic targets. Despite great advances, an understanding of kinase dysfunction in cancer is far from complete.A powerful tool to investigate phosphorylation is mass-spectrometry (MS)-based phosphoproteomics, which enables the identification of thousands of phosphorylated peptides in a single experiment. Since every phosphorylation event results from the activity of a protein kinase, high-coverage phosphoproteomics data should indirectly contain comprehensive information about the activity of protein kinases.In this chapter, we discuss the use of computational methods to predict kinase activity scores from MS-based phosphoproteomics data. We start with a short explanation of the fundamental features of the phosphoproteomics data acquisition process from the perspective of the computational analysis. Next, we briefly review the existing databases with experimentally verified kinase-substrate relationships and present a set of bioinformatic tools to discover novel kinase targets. We then introduce different methods to infer kinase activities from phosphoproteomics data and these kinase-substrate relationships. We illustrate their application with a detailed protocol of one of the methods, KSEA (Kinase Substrate Enrichment Analysis). This method is implemented in Python within the framework of the open-source Kinase Activity Toolbox (kinact), which is freely available at http://github.com/saezlab/kinact/ .
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Affiliation(s)
- Jakob Wirbel
- Joint Research Center for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, MTZ Pauwelsstrasse 19, D-52074, Aachen, Germany
- Institute for Pharmacy and Molecular Biotechnology (IPMB), University of Heidelberg, 69120, Heidelberg, Germany
| | - Pedro Cutillas
- Barts Cancer Institute, Queen Mary University of London, London, UK.
| | - Julio Saez-Rodriguez
- Joint Research Center for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, MTZ Pauwelsstrasse 19, D-52074, Aachen, Germany.
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK.
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39
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Dermit M, Dokal A, Cutillas PR. Approaches to identify kinase dependencies in cancer signalling networks. FEBS Lett 2017; 591:2577-2592. [DOI: 10.1002/1873-3468.12748] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 06/27/2017] [Accepted: 07/03/2017] [Indexed: 12/18/2022]
Affiliation(s)
- Maria Dermit
- Cell Signalling & Proteomics Group; Barts Cancer Institute (CRUK Centre); Queen Mary University of London; UK
| | - Arran Dokal
- Cell Signalling & Proteomics Group; Barts Cancer Institute (CRUK Centre); Queen Mary University of London; UK
| | - Pedro R. Cutillas
- Cell Signalling & Proteomics Group; Barts Cancer Institute (CRUK Centre); Queen Mary University of London; UK
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40
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Wilkes EH, Casado P, Rajeeve V, Cutillas PR. Kinase activity ranking using phosphoproteomics data (KARP) quantifies the contribution of protein kinases to the regulation of cell viability. Mol Cell Proteomics 2017; 16:1694-1704. [PMID: 28674151 DOI: 10.1074/mcp.o116.064360] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 02/05/2017] [Indexed: 12/17/2022] Open
Abstract
Cell survival is regulated by a signaling network driven by the activity of protein kinases; however, determining the contribution that each kinase in the network makes to such regulation remains challenging. Here, we report a computational approach that uses mass spectrometry-based phosphoproteomics data to rank protein kinases based on their contribution to cell regulation. We found that the scores returned by this algorithm, which we have termed kinase activity ranking using phosphoproteomics data (KARP), were a quantitative measure of the contribution that individual kinases make to the signaling output. Application of KARP to the analysis of eight hematological cell lines revealed that cyclin-dependent kinase (CDK) 1/2, casein kinase (CK) 2, extracellular signal-related kinase (ERK), and p21-activated kinase (PAK) were the most frequently highly ranked kinases in these cell models. The patterns of kinase activation were cell-line specific yet showed a significant association with cell viability as a function of kinase inhibitor treatment. Thus, our study exemplifies KARP as an untargeted approach to empirically and systematically identify regulatory kinases within signaling networks.
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Affiliation(s)
- Edmund H Wilkes
- From the ‡Integrative Cell Signalling & Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ
| | - Pedro Casado
- From the ‡Integrative Cell Signalling & Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ
| | - Vinothini Rajeeve
- From the ‡Integrative Cell Signalling & Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ
| | - Pedro R Cutillas
- From the ‡Integrative Cell Signalling & Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ
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41
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Labots M, van der Mijn JC, Beekhof R, Piersma SR, de Goeij-de Haas RR, Pham TV, Knol JC, Dekker H, van Grieken NC, Verheul HM, Jiménez CR. Phosphotyrosine-based-phosphoproteomics scaled-down to biopsy level for analysis of individual tumor biology and treatment selection. J Proteomics 2017; 162:99-107. [DOI: 10.1016/j.jprot.2017.04.014] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Revised: 02/28/2017] [Accepted: 04/12/2017] [Indexed: 12/17/2022]
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42
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Czubaty A, Piekiełko-Witkowska A. Protein kinases that phosphorylate splicing factors: Roles in cancer development, progression and possible therapeutic options. Int J Biochem Cell Biol 2017; 91:102-115. [PMID: 28552434 DOI: 10.1016/j.biocel.2017.05.024] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Revised: 05/16/2017] [Accepted: 05/18/2017] [Indexed: 11/30/2022]
Abstract
Disturbed alternative splicing is a common feature of human tumors. Splicing factors that control alternative splicing are phosphorylated by multiple kinases, including these that specifically add phosphoryl groups to serine-arginine rich proteins (e.g. SR-protein kinases, cdc2-like kinases, topoisomerase 1), and protein kinases that govern key cellular signaling pathways (i.e. AKT). Phosphorylation of splicing factors regulates their subcellular localization and interactions with target transcripts and protein partners, and thus significantly contributes the final result of splicing reactions. In this review we aim to summarize the current knowledge on the role of splicing kinases in cancer. Published studies and recently released data of The Cancer Genome Atlas demonstrate that expressions and activities of splicing kinases are commonly disturbed in cancers. Aberrant functioning of splicing kinases results in changed alternative splicing of tumor suppressors (e.g. p53) and regulators of cell signaling (e.g. MAPKs), apoptosis (e.g. MCL), and angiogenesis (VEGF). Splicing kinases act in complicated regulatory networks in which they mutually affect each other's activity to provide tight control of cellular signaling. Dysregulation of these regulatory networks contributes to oncogenic transformation, uncontrolled proliferation, enhanced migration and invasion. Furthermore, the activities of splicing kinases significantly contribute to cellular responses to genotoxic stress. In conclusion, published data provide strong evidence that splicing kinases emerge as important regulators of key processes governing malignant transformation, progression, and response to therapeutic treatments, suggesting their potential as clinically relevant targets.
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Affiliation(s)
- Alicja Czubaty
- Department of Molecular Biology, Faculty of Biology, University of Warsaw, ul. Miecznikowa 1, 02-096 Warsaw, Poland
| | - Agnieszka Piekiełko-Witkowska
- Department of Biochemistry and Molecular Biology, Centre of Postgraduate Medical Education, ul. Marymoncka 99/103, 01-813 Warsaw, Poland.
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43
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Moniz LS, Surinova S, Ghazaly E, Velasco LG, Haider S, Rodríguez-Prados JC, Berenjeno IM, Chelala C, Vanhaesebroeck B. Phosphoproteomic comparison of Pik3ca and Pten signalling identifies the nucleotidase NT5C as a novel AKT substrate. Sci Rep 2017; 7:39985. [PMID: 28059163 PMCID: PMC5216349 DOI: 10.1038/srep39985] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 11/30/2016] [Indexed: 12/16/2022] Open
Abstract
To identify novel effectors and processes regulated by PI3K pathway activation, we performed an unbiased phosphoproteomic screen comparing two common events of PI3K deregulation in cancer: oncogenic Pik3ca mutation (Pik3caH1047R) and deletion of Pten. Using mouse embryonic fibroblast (MEF) models that generate inducible, low-level pathway activation as observed in cancer, we quantified 7566 unique phosphopeptides from 3279 proteins. A number of proteins were found to be differentially-regulated by Pik3caH1047R and Pten loss, suggesting unique roles for these two events in processes such as vesicular trafficking, DNA damage repair and RNA splicing. We also identified novel PI3K effectors that were commonly-regulated, including putative AKT substrates. Validation of one of these hits, confirmed NT5C (5',3'-Nucleotidase, Cytosolic) as a novel AKT substrate, with an unexpected role in actin cytoskeleton regulation via an interaction with the ARP2/3 complex. This study has produced a comprehensive data resource and identified a new link between PI3K pathway activation and actin regulation.
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Affiliation(s)
- Larissa S. Moniz
- UCL Cancer Institute, Paul O’Gorman Building, University College London, 72 Huntley Street London WC1E 6DD, UK
| | - Silvia Surinova
- UCL Cancer Institute, Paul O’Gorman Building, University College London, 72 Huntley Street London WC1E 6DD, UK
| | - Essam Ghazaly
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Lorena Gonzalez Velasco
- UCL Cancer Institute, Paul O’Gorman Building, University College London, 72 Huntley Street London WC1E 6DD, UK
| | - Syed Haider
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | | | - Inma M. Berenjeno
- UCL Cancer Institute, Paul O’Gorman Building, University College London, 72 Huntley Street London WC1E 6DD, UK
| | - Claude Chelala
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Bart Vanhaesebroeck
- UCL Cancer Institute, Paul O’Gorman Building, University College London, 72 Huntley Street London WC1E 6DD, UK
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44
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Tan AC, Vyse S, Huang PH. Exploiting receptor tyrosine kinase co-activation for cancer therapy. Drug Discov Today 2017; 22:72-84. [PMID: 27452454 PMCID: PMC5346155 DOI: 10.1016/j.drudis.2016.07.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Revised: 06/15/2016] [Accepted: 07/15/2016] [Indexed: 01/04/2023]
Abstract
Studies over the past decade have shown that many cancers have evolved receptor tyrosine kinase (RTK) co-activation as a mechanism to drive tumour progression and limit the lethal effects of therapy. This review summarises the general principles of RTK co-activation and discusses approaches to exploit this phenomenon in cancer therapy and drug discovery. Computational strategies to predict kinase co-dependencies by integrating drug screening data and kinase inhibitor selectivity profiles will also be described. We offer a perspective on the implications of RTK co-activation on tumour heterogeneity and cancer evolution and conclude by surveying emerging computational and experimental approaches that will provide insights into RTK co-activation biology and deliver new developments in effective cancer therapies.
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Affiliation(s)
- Aik-Choon Tan
- Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Simon Vyse
- Division of Cancer Biology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Paul H Huang
- Division of Cancer Biology, The Institute of Cancer Research, London SW3 6JB, UK.
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45
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Abstract
Phosphoproteomics is a powerful platform for the unbiased profiling of kinase-driven signaling pathways. Quantitation of phosphorylation can be performed by means of either labeling or label-free mass spectrometry (MS) methods. Because of their simplicity and universality, label-free methodology is gaining acceptance and popularity in molecular biology research. Analytical workflows for label-free quantification of phosphorylation, however, need to overcome several hurdles for the technique to be accurate and precise. These include the use of biochemical extraction procedures that efficiently and reproducibly isolate phosphopeptides from complex peptide matrices and an analytical strategy that can cope with missing MS/MS phosphopeptide spectra in a subset of the samples being compared. Testing the accuracy of the developed workflows is an essential prerequisite in the analysis of small molecules by MS, and this is achieved by constructing calibration curves to demonstrate linearity of quantification for each analyte. This level of analytical rigor is rarely shown in large-scale quantification of proteins using either label-based or label-free techniques. In this chapter we show an approach to test linearity of quantification of each phosphopeptide quantified by liquid chromatography (LC)-MS without the need to synthesize standards or label proteins. We further describe the appropriate sample handling techniques required for the reproducible recovery of phosphopeptides and explore the essential algorithmic features that enable the handling of missing MS/MS spectra and thus make label-free data suitable for such analyses. The combined technology described in this chapter expands the applicability of phosphoproteomics to questions not previously tractable with other methodologies.
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46
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Casado P, Hijazi M, Britton D, Cutillas PR. Impact of phosphoproteomics in the translation of kinase-targeted therapies. Proteomics 2016; 17. [DOI: 10.1002/pmic.201600235] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2016] [Revised: 09/29/2016] [Accepted: 10/20/2016] [Indexed: 12/29/2022]
Affiliation(s)
- Pedro Casado
- Cell Signalling and Proteomics Group; Centre for Haemato-Oncology; Barts Cancer Institute; Queen Mary University of London; UK
| | - Maruan Hijazi
- Cell Signalling and Proteomics Group; Centre for Haemato-Oncology; Barts Cancer Institute; Queen Mary University of London; UK
| | - David Britton
- Cell Signalling and Proteomics Group; Centre for Haemato-Oncology; Barts Cancer Institute; Queen Mary University of London; UK
| | - Pedro R. Cutillas
- Cell Signalling and Proteomics Group; Centre for Haemato-Oncology; Barts Cancer Institute; Queen Mary University of London; UK
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47
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Dermit M, Casado P, Rajeeve V, Wilkes EH, Foxler DE, Campbell H, Critchlow S, Sharp TV, Gribben JG, Unwin R, Cutillas PR. Oxidative stress downstream of mTORC1 but not AKT causes a proliferative defect in cancer cells resistant to PI3K inhibition. Oncogene 2016; 36:2762-2774. [PMID: 27991931 PMCID: PMC5362070 DOI: 10.1038/onc.2016.435] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Revised: 10/10/2016] [Accepted: 10/10/2016] [Indexed: 12/19/2022]
Abstract
Compounds targeting phosphatidylinositol-3-kinase/mammalian target of rapamycin (PI3K/mTOR) signaling are being investigated in multiple clinical settings, but drug resistance may reduce their benefit. Compound rechallenge after drug holidays can overcome such resistance, yet little is known about the impact of drug holidays on cell biochemistry. We found that PI3K inhibitor (PI3Ki)-resistant cells cultured in the absence of PI3Ki developed a proliferative defect, increased oxygen consumption and accumulated reactive oxygen species (ROS), leading to lactate production through hypoxia-inducible factor-1α. This metabolic imbalance was reversed by mammalian target of rapamycin complex 1 (mTORC1) inhibitors. Interestingly, neither AKT nor c-MYC was involved in mediating the metabolic phenotype, despite the latter contributing to resistant cells' proliferation. These data suggest that an AKT-independent PI3K/mTORC1 axis operates in these cells. The excessive ROS hampered cell division, and the metabolic phenotype made resistant cells more sensitive to hydrogen peroxide and nutrient starvation. Thus, the proliferative defect of PI3Ki-resistant cells during drug holidays is caused by defective metabolic adaptation to chronic PI3K/mTOR pathway inhibition. This metabolic imbalance may open the therapeutic window for challenge with metabolic drugs during drug holidays.
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Affiliation(s)
- M Dermit
- Cell Signalling & Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - P Casado
- Cell Signalling & Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - V Rajeeve
- Cell Signalling & Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - E H Wilkes
- Cell Signalling & Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - D E Foxler
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - H Campbell
- AstraZeneca, Oncology iMED, Cheshire, UK
| | | | - T V Sharp
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - J G Gribben
- Cancer Immunology Group, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - R Unwin
- UCL Centre for Nephrology, Royal Free Campus and Hospital, University College London, London, UK
| | - P R Cutillas
- Cell Signalling & Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London, UK
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48
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Rudolph JD, de Graauw M, van de Water B, Geiger T, Sharan R. Elucidation of Signaling Pathways from Large-Scale Phosphoproteomic Data Using Protein Interaction Networks. Cell Syst 2016; 3:585-593.e3. [DOI: 10.1016/j.cels.2016.11.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 08/24/2016] [Accepted: 11/09/2016] [Indexed: 01/01/2023]
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49
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Ochoa D, Jonikas M, Lawrence RT, El Debs B, Selkrig J, Typas A, Villén J, Santos SD, Beltrao P. An atlas of human kinase regulation. Mol Syst Biol 2016; 12:888. [PMID: 27909043 PMCID: PMC5199121 DOI: 10.15252/msb.20167295] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The coordinated regulation of protein kinases is a rapid mechanism that integrates diverse cues and swiftly determines appropriate cellular responses. However, our understanding of cellular decision‐making has been limited by the small number of simultaneously monitored phospho‐regulatory events. Here, we have estimated changes in activity in 215 human kinases in 399 conditions derived from a large compilation of phosphopeptide quantifications. This atlas identifies commonly regulated kinases as those that are central in the signaling network and defines the logic relationships between kinase pairs. Co‐regulation along the conditions predicts kinase–complex and kinase–substrate associations. Additionally, the kinase regulation profile acts as a molecular fingerprint to identify related and opposing signaling states. Using this atlas, we identified essential mediators of stem cell differentiation, modulators of Salmonella infection, and new targets of AKT1. This provides a global view of human phosphorylation‐based signaling and the necessary context to better understand kinase‐driven decision‐making.
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Affiliation(s)
- David Ochoa
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Mindaugas Jonikas
- Quantitative Cell Biology Group, MRC Clinical Sciences Centre, Imperial College, London, UK
| | - Robert T Lawrence
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Bachir El Debs
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Joel Selkrig
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Athanasios Typas
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Judit Villén
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Silvia Dm Santos
- Quantitative Cell Biology Group, MRC Clinical Sciences Centre, Imperial College, London, UK
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
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50
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Villoria MT, Ramos F, Dueñas E, Faull P, Cutillas PR, Clemente-Blanco A. Stabilization of the metaphase spindle by Cdc14 is required for recombinational DNA repair. EMBO J 2016; 36:79-101. [PMID: 27852625 PMCID: PMC5210157 DOI: 10.15252/embj.201593540] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Revised: 10/05/2016] [Accepted: 10/18/2016] [Indexed: 11/24/2022] Open
Abstract
Cells are constantly threatened by multiple sources of genotoxic stress that cause DNA damage. To maintain genome integrity, cells have developed a coordinated signalling network called DNA damage response (DDR). While multiple kinases have been thoroughly studied during DDR activation, the role of protein dephosphorylation in the damage response remains elusive. Here, we show that the phosphatase Cdc14 is essential to fulfil recombinational DNA repair in budding yeast. After DNA double‐strand break (DSB) generation, Cdc14 is transiently released from the nucleolus and activated. In this state, Cdc14 targets the spindle pole body (SPB) component Spc110 to counterbalance its phosphorylation by cyclin‐dependent kinase (Cdk). Alterations in the Cdk/Cdc14‐dependent phosphorylation status of Spc110, or its inactivation during the induction of a DNA lesion, generate abnormal oscillatory SPB movements that disrupt DSB‐SPB interactions. Remarkably, these defects impair DNA repair by homologous recombination indicating that SPB integrity is essential during the repair process. Together, these results show that Cdc14 promotes spindle stability and DSB‐SPB tethering during DNA repair, and imply that metaphase spindle maintenance is a critical feature of the repair process.
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Affiliation(s)
- María Teresa Villoria
- Cell Cycle and Genome Stability Group, Instituto de Biología Funcional y Genómica Consejo Superior de Investigaciones Científicas (CSIC) Universidad de Salamanca (USAL), Salamanca, Spain
| | - Facundo Ramos
- Cell Cycle and Genome Stability Group, Instituto de Biología Funcional y Genómica Consejo Superior de Investigaciones Científicas (CSIC) Universidad de Salamanca (USAL), Salamanca, Spain
| | - Encarnación Dueñas
- Cell Cycle and Genome Stability Group, Instituto de Biología Funcional y Genómica Consejo Superior de Investigaciones Científicas (CSIC) Universidad de Salamanca (USAL), Salamanca, Spain
| | - Peter Faull
- Biological Mass Spectrometry and Proteomics Laboratory, Medical Research Council Clinical Science Centre Imperial College, London, UK
| | - Pedro Rodríguez Cutillas
- Biological Mass Spectrometry and Proteomics Laboratory, Medical Research Council Clinical Science Centre Imperial College, London, UK
| | - Andrés Clemente-Blanco
- Cell Cycle and Genome Stability Group, Instituto de Biología Funcional y Genómica Consejo Superior de Investigaciones Científicas (CSIC) Universidad de Salamanca (USAL), Salamanca, Spain
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