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Malyutina A, Tang J, Amiryousefi A. Resolving network clusters disparity based on dissimilarity measurements with nonmetric analysis of variance. iScience 2023; 26:108354. [PMID: 38026214 PMCID: PMC10663764 DOI: 10.1016/j.isci.2023.108354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 06/22/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Classic ANOVA (cA) tests the explanatory power of a partitioning on a set of objects. More fit for clusters proximity analysis, nonparametric ANOVA (npA) extends to a case where instead of the object values themselves, their mutual distances are available. However, extending the cA applicability, the metric conditions in npA are limiting. Based on the central limit theorem (CLT), here we introduce nonmetric ANOVA (nmA) that by relaxing the metric properties between objects, allows an ANOVA-like statistical testing of a network clusters disparity. We present a parametric test statistic which under the null hypothesis of no differences between the competing clusters means, follows an exact F-distribution. We apply our method on three diverse biological examples, discuss its parallel performance, and note the specific use of each method tailored by the inherent data properties. The R code is provided at github.com/AmiryousefiLab/nmANOVA.
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Affiliation(s)
- Alina Malyutina
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Ali Amiryousefi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
- Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
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Kalliokoski T, Kettunen H, Kumpulainen E, Kettunen E, Thieulin-Pardo G, Neumann L, Thomsen M, Paul R, Malyutina A, Georgiadou M. Discovery of novel methionine adenosyltransferase 2A (MAT2A) allosteric inhibitors by structure-based virtual screening. Bioorg Med Chem Lett 2023; 94:129450. [PMID: 37591318 DOI: 10.1016/j.bmcl.2023.129450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/31/2023] [Accepted: 08/14/2023] [Indexed: 08/19/2023]
Abstract
Methionine adenosyltransferase 2A (MAT2A) has been indicated as a drug target for oncology indications. Clinical trials with MAT2A inhibitors are currently on-going. Here, a structure-based virtual screening campaign was performed on the commercially available chemical space which yielded two novel MAT2A-inhibitor chemical series. The binding modes of the compounds were confirmed with X-ray crystallography. Both series have acceptable physicochemical properties and show nanomolar activity in the biochemical MAT2A inhibition assay and single-digit micromolar activity in the proliferation assay (MTAP -/- cell line). The identified compounds and the relating structural data could be helpful in related drug discovery projects.
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Affiliation(s)
| | | | | | | | | | - Lars Neumann
- Proteros Biostructures GmbH, Bunsenstraβe 7a, D - 82152 Martinsried, Germany
| | - Maren Thomsen
- Proteros Biostructures GmbH, Bunsenstraβe 7a, D - 82152 Martinsried, Germany
| | - Ralf Paul
- Orion Pharma, Tengströminkatu 8, 20380 Turku, Finland
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Malyutina A, Tang J, Pessia A. drda: An R Package for Dose-Response Data Analysis Using Logistic Functions. J Stat Softw 2023. [DOI: 10.18637/jss.v106.i04] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023] Open
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4
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Zheng S, Wang W, Aldahdooh J, Malyutina A, Shadbahr T, Tanoli Z, Pessia A, Tang J. SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets. Genomics Proteomics Bioinformatics 2022; 20:587-596. [PMID: 35085776 PMCID: PMC9801064 DOI: 10.1016/j.gpb.2022.01.004] [Citation(s) in RCA: 119] [Impact Index Per Article: 59.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 12/20/2021] [Accepted: 12/24/2021] [Indexed: 01/26/2023]
Abstract
Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the SynergyFinder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated SynergyFinder R package includes five main innovations. 1) We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape. 2) We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values. 3) We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy. 4) We evaluate drug combination synergy and sensitivity to provide an unbiased interpretation of the clinical potential. 5) We enable fast annotation of drugs and cell lines, including their chemical and target information. These annotations will improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.synergyfinderplus.org as a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.
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Zhu Y, Orre LM, Zhou Tran Y, Mermelekas G, Johansson HJ, Malyutina A, Anders S, Lehtiö J. DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis. Mol Cell Proteomics 2020; 19:1047-1057. [PMID: 32205417 PMCID: PMC7261819 DOI: 10.1074/mcp.tir119.001646] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 03/20/2020] [Indexed: 12/19/2022] Open
Abstract
Quantitative proteomics by mass spectrometry is widely used in biomarker research and basic biology research for investigation of phenotype level cellular events. Despite the wide application, the methodology for statistical analysis of differentially expressed proteins has not been unified. Various methods such as t test, linear model and mixed effect models are used to define changes in proteomics experiments. However, none of these methods consider the specific structure of MS-data. Choices between methods, often originally developed for other types of data, are based on compromises between features such as statistical power, general applicability and user friendliness. Furthermore, whether to include proteins identified with one peptide in statistical analysis of differential protein expression varies between studies. Here we present DEqMS, a robust statistical method developed specifically for differential protein expression analysis in mass spectrometry data. In all data sets investigated there is a clear dependence of variance on the number of PSMs or peptides used for protein quantification. DEqMS takes this feature into account when assessing differential protein expression. This allows for a more accurate data-dependent estimation of protein variance and inclusion of single peptide identifications without increasing false discoveries. The method was tested in several data sets including E. coli proteome spike-in data, using both label-free and TMT-labeled quantification. Compared with previous statistical methods used in quantitative proteomics, DEqMS showed consistently better accuracy in detecting altered protein levels compared with other statistical methods in both label-free and labeled quantitative proteomics data. DEqMS is available as an R package in Bioconductor.
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Affiliation(s)
- Yafeng Zhu
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - Lukas M Orre
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - Yan Zhou Tran
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - Georgios Mermelekas
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - Henrik J Johansson
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - Alina Malyutina
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
| | - Simon Anders
- Centre for Molecular Biology of Heidelberg University (ZMBH), Heidelberg, Germany
| | - Janne Lehtiö
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden.
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Zagidullin B, Aldahdooh J, Zheng S, Wang W, Wang Y, Saad J, Malyutina A, Jafari M, Tanoli Z, Pessia A, Tang J. DrugComb: an integrative cancer drug combination data portal. Nucleic Acids Res 2020; 47:W43-W51. [PMID: 31066443 PMCID: PMC6602441 DOI: 10.1093/nar/gkz337] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 04/13/2019] [Accepted: 04/26/2019] [Indexed: 12/25/2022] Open
Abstract
Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users’ own drug combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.
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Affiliation(s)
- Bulat Zagidullin
- Institute for Molecular Medicine Finland, Helsinki Life Science Institute, University of Helsinki, Finland
| | - Jehad Aldahdooh
- Institute for Molecular Medicine Finland, Helsinki Life Science Institute, University of Helsinki, Finland
| | - Shuyu Zheng
- Institute for Molecular Medicine Finland, Helsinki Life Science Institute, University of Helsinki, Finland
| | - Wenyu Wang
- Institute for Molecular Medicine Finland, Helsinki Life Science Institute, University of Helsinki, Finland
| | - Yinyin Wang
- Institute for Molecular Medicine Finland, Helsinki Life Science Institute, University of Helsinki, Finland
| | - Joseph Saad
- Institute for Molecular Medicine Finland, Helsinki Life Science Institute, University of Helsinki, Finland
| | - Alina Malyutina
- Institute for Molecular Medicine Finland, Helsinki Life Science Institute, University of Helsinki, Finland
| | - Mohieddin Jafari
- Institute for Molecular Medicine Finland, Helsinki Life Science Institute, University of Helsinki, Finland
| | - Ziaurrehman Tanoli
- Institute for Molecular Medicine Finland, Helsinki Life Science Institute, University of Helsinki, Finland
| | - Alberto Pessia
- Institute for Molecular Medicine Finland, Helsinki Life Science Institute, University of Helsinki, Finland
| | - Jing Tang
- Institute for Molecular Medicine Finland, Helsinki Life Science Institute, University of Helsinki, Finland.,Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Finland
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Wang W, Malyutina A, Pessia A, Saarela J, Heckman CA, Tang J. Corrigendum to 'Combined gene essentiality scoring improves the prediction of cancer dependency maps' [EBioMedicine 50 (2019) 66-79]. EBioMedicine 2020; 51:102594. [PMID: 31901864 PMCID: PMC6948202 DOI: 10.1016/j.ebiom.2019.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Affiliation(s)
- Wenyu Wang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, FI-0 0 014 Helsinki, Finland
| | - Alina Malyutina
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, FI-0 0 014 Helsinki, Finland
| | - Alberto Pessia
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, FI-0 0 014 Helsinki, Finland
| | - Jani Saarela
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Tukholmankatu 8, FI-0 0 014 Helsinki, Finland
| | - Caroline A Heckman
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Tukholmankatu 8, FI-0 0 014 Helsinki, Finland
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, FI-0 0 014 Helsinki, Finland.
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Majumder MM, Leppä AM, Hellesøy M, Dowling P, Malyutina A, Kopperud R, Bazou D, Andersson E, Parsons A, Tang J, Kallioniemi O, Mustjoki S, O'Gorman P, Wennerberg K, Porkka K, Gjertsen BT, Heckman CA. Multi-parametric single cell evaluation defines distinct drug responses in healthy hematologic cells that are retained in corresponding malignant cell types. Haematologica 2019; 105:1527-1538. [PMID: 31439679 PMCID: PMC7271564 DOI: 10.3324/haematol.2019.217414] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 08/22/2019] [Indexed: 01/22/2023] Open
Abstract
Innate drug sensitivity in healthy cells aids identification of lineage specific anti-cancer therapies and reveals off-target effects. To characterize the diversity in drug responses in the major hematopoietic cell types, we simultaneously assessed their sensitivity to 71 small molecules utilizing a multi-parametric flow cytometry assay and mapped their proteomic and basal signaling profiles. Unsupervised hierarchical clustering identified distinct drug responses in healthy cell subsets based on their cellular lineage. Compared to other cell types, CD19+/B and CD56+/NK cells were more sensitive to dexamethasone, venetoclax and midostaurin, while monocytes were more sensitive to trametinib. Venetoclax exhibited dose-dependent cell selectivity that inversely correlated to STAT3 phosphorylation. Lineage specific effect of midostaurin was similarly detected in CD19+/B cells from healthy, acute myeloid leukemia and chronic lymphocytic leukemia samples. Comparison of drug responses in healthy and neoplastic cells showed that healthy cell responses are predictive of the corresponding malignant cell response. Taken together, understanding drug sensitivity in the healthy cell-of-origin provides opportunities to obtain a new level of therapy precision and avoid off-target toxicity.
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Affiliation(s)
- Muntasir M Majumder
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Aino-Maija Leppä
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Monica Hellesøy
- Hematology Section, Department of Internal Medicine, Haukeland University Hospital, Bergen, Norway
| | - Paul Dowling
- Department of Biology, National University of Ireland, Maynooth, Ireland
| | - Alina Malyutina
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Reidun Kopperud
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Despina Bazou
- Department of Hematology, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Emma Andersson
- Department of Clinical Chemistry and Hematology, University of Helsinki, Finland
| | - Alun Parsons
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Jing Tang
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Olli Kallioniemi
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.,Science for Life Laboratory, Department of Oncology and Pathology, Karolinska Institute, Solna, Sweden
| | - Satu Mustjoki
- Department of Clinical Chemistry and Hematology, University of Helsinki, Finland.,Hematology Research Unit Helsinki, University of Helsinki, Helsinki, Finland
| | - Peter O'Gorman
- Department of Hematology, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Krister Wennerberg
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.,BRIC-Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen, Denmark
| | - Kimmo Porkka
- Hematology Research Unit Helsinki, University of Helsinki, Helsinki, Finland.,Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
| | - Bjørn T Gjertsen
- Hematology Section, Department of Internal Medicine, Haukeland University Hospital, Bergen, Norway.,Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Caroline A Heckman
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
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Malyutina A, Majumder MM, Wang W, Pessia A, Heckman CA, Tang J. Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer. PLoS Comput Biol 2019; 15:e1006752. [PMID: 31107860 PMCID: PMC6544320 DOI: 10.1371/journal.pcbi.1006752] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Revised: 05/31/2019] [Accepted: 04/13/2019] [Indexed: 11/23/2022] Open
Abstract
High-throughput drug screening has facilitated the discovery of drug combinations in cancer. Many existing studies adopted a full matrix design, aiming for the characterization of drug pair effects for cancer cells. However, the full matrix design may be suboptimal as it requires a drug pair to be combined at multiple concentrations in a full factorial manner. Furthermore, many of the computational tools assess only the synergy but not the sensitivity of drug combinations, which might lead to false positive discoveries. We proposed a novel cross design to enable a more cost-effective and simultaneous testing of drug combination sensitivity and synergy. We developed a drug combination sensitivity score (CSS) to determine the sensitivity of a drug pair, and showed that the CSS is highly reproducible between the replicates and thus supported its usage as a robust metric. We further showed that CSS can be predicted using machine learning approaches which determined the top pharmaco-features to cluster cancer cell lines based on their drug combination sensitivity profiles. To assess the degree of drug interactions using the cross design, we developed an S synergy score based on the difference between the drug combination and the single drug dose-response curves. We showed that the S score is able to detect true synergistic and antagonistic drug combinations at an accuracy level comparable to that using the full matrix design. Taken together, we showed that the cross design coupled with the CSS sensitivity and S synergy scoring methods may provide a robust and accurate characterization of both drug combination sensitivity and synergy levels, with minimal experimental materials required. Our experimental-computational approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput drug combination screening, particularly for primary patient samples which are difficult to obtain. Cancer is one of the main causes of death worldwide. Although new treatment strategies have been achieved, they still have limited efficacy as cancer cells can easily develop drug resistance. To achieve more sustainable therapies to treat cancer, we need multi-targeted drug combinations that can inhibit cancer cells more effectively and synergistically. However, the increasing number of possible drug combinations makes a full matrix design unfeasible, even with automated drug screening instruments. Therefore, we proposed a novel cross design to access drug combinations more efficiently. We further developed a drug combination sensitivity score (CSS) that is tailored for the cross design to quantify the efficacy of a drug combination. Using public datasets, we showed that the CSS is a robust metric and highly predictive with an accuracy comparable to the experimental replicates. We also developed a CSS-based synergy score to assess the degree of drug interaction and showed its capability to correctly identify synergistic and antagonistic drug combinations. Taken together, we showed that the cross design and its scoring methods allow a more systematic and cost-effective evaluation of drug combinations. The proposed experimental and computational techniques are expected to be widely applicable in the field of drug combination discovery.
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Affiliation(s)
- Alina Malyutina
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Muntasir Mamun Majumder
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Wenyu Wang
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Alberto Pessia
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Caroline A. Heckman
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Jing Tang
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- * E-mail:
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Tanoli Z, Alam Z, Vähä-Koskela M, Ravikumar B, Malyutina A, Jaiswal A, Tang J, Wennerberg K, Aittokallio T. Drug Target Commons 2.0: a community platform for systematic analysis of drug-target interaction profiles. Database (Oxford) 2018; 2018:1-13. [PMID: 30219839 PMCID: PMC6146131 DOI: 10.1093/database/bay083] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 06/27/2018] [Accepted: 07/18/2018] [Indexed: 12/20/2022]
Abstract
Drug Target Commons (DTC) is a web platform (database with user interface) for community-driven bioactivity data integration and standardization for comprehensive mapping, reuse and analysis of compound-target interaction profiles. End users can search, upload, edit, annotate and export expert-curated bioactivity data for further analysis, using an application programmable interface, database dump or tab-delimited text download options. To guide chemical biology and drug-repurposing applications, DTC version 2.0 includes updated clinical development information for the compounds and target gene-disease associations, as well as cancer-type indications for mutant protein targets, which are critical for precision oncology developments.
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Affiliation(s)
- ZiaurRehman Tanoli
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Zaid Alam
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Markus Vähä-Koskela
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Balaguru Ravikumar
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Alina Malyutina
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Alok Jaiswal
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Jing Tang
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Krister Wennerberg
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Biotech Research & Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
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Rupchev G, Vinogradova M, Malyutina A, Tkhostov A, Ryzhov A. Psychological Traits of Skin Picking Disorder and Psychogenic Itch. Eur Psychiatry 2017. [DOI: 10.1016/j.eurpsy.2017.02.120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
IntroductionDespite the intense discussion of psychiatric comorbidity in psychodermatology, research on psychological components of skin picking and psychogenic itch is limited, especially when it concerns patients’ representation of skin perception and their attitude towards disease.ObjectivesTo characterize psychological traits of skin picking and psychogenic itch disorder by comparing aspects of bodily experience.AimsTo reveal internal relations of different components of bodily experience in skin picking and psychogenic itch.MethodsThirty patients with skin picking disorder (L98.1) and 18 patients with psychogenic itch (F45.8) participated in the study. The psychosemantic method “Classification of sensations” was used to assess bodily experience. It includes estimation of 80 descriptors from 6 classes of bodily sensations: skin (ex. “itch”), inner body (ex. “sickness”), receptor (ex. “sticky”), emotional (ex. “anxiety”), dynamics (ex. “exhaustion”) and attitudinal descriptors (ex. “bad”). Cluster and factor analysis were performed.ResultsThe most significant aspect of bodily experience in skin picking was its dynamics as a transition from irritation to calmness connected with the sensation of itch opposed to all other sensations (there were opposite signs of factor loadings of these variables and they were included in the factor explaining 45% of total variance). In contrast, in psychogenic itch these relations are diffuse and consist of connections between skin sensations and inner bodily sensations and descriptors of emotions reflecting functional origin of disorder.ConclusionTraits of psychological components in skin picking disorder and psychogenic itch should be concerned in the complex (psychiatric, psychological and dermatological) treatment of these disorders.Disclosure of interestThe authors have not supplied their declaration of competing interest.
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