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Munson BP, Chen M, Bogosian A, Kreisberg JF, Licon K, Abagyan R, Kuenzi BM, Ideker T. De novo generation of multi-target compounds using deep generative chemistry. Nat Commun 2024; 15:3636. [PMID: 38710699 DOI: 10.1038/s41467-024-47120-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 03/18/2024] [Indexed: 05/08/2024] Open
Abstract
Polypharmacology drugs-compounds that inhibit multiple proteins-have many applications but are difficult to design. To address this challenge we have developed POLYGON, an approach to polypharmacology based on generative reinforcement learning. POLYGON embeds chemical space and iteratively samples it to generate new molecular structures; these are rewarded by the predicted ability to inhibit each of two protein targets and by drug-likeness and ease-of-synthesis. In binding data for >100,000 compounds, POLYGON correctly recognizes polypharmacology interactions with 82.5% accuracy. We subsequently generate de-novo compounds targeting ten pairs of proteins with documented co-dependency. Docking analysis indicates that top structures bind their two targets with low free energies and similar 3D orientations to canonical single-protein inhibitors. We synthesize 32 compounds targeting MEK1 and mTOR, with most yielding >50% reduction in each protein activity and in cell viability when dosed at 1-10 μM. These results support the potential of generative modeling for polypharmacology.
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Affiliation(s)
- Brenton P Munson
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Michael Chen
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Audrey Bogosian
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jason F Kreisberg
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Katherine Licon
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Brent M Kuenzi
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Trey Ideker
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, 92093, USA.
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2
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Fisher JL, Wilk EJ, Oza VH, Gary SE, Howton TC, Flanary VL, Clark AD, Hjelmeland AB, Lasseigne BN. Signature reversion of three disease-associated gene signatures prioritizes cancer drug repurposing candidates. FEBS Open Bio 2024; 14:803-830. [PMID: 38531616 PMCID: PMC11073506 DOI: 10.1002/2211-5463.13796] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 03/28/2024] Open
Abstract
Drug repurposing is promising because approving a drug for a new indication requires fewer resources than approving a new drug. Signature reversion detects drug perturbations most inversely related to the disease-associated gene signature to identify drugs that may reverse that signature. We assessed the performance and biological relevance of three approaches for constructing disease-associated gene signatures (i.e., limma, DESeq2, and MultiPLIER) and prioritized the resulting drug repurposing candidates for four low-survival human cancers. Our results were enriched for candidates that had been used in clinical trials or performed well in the PRISM drug screen. Additionally, we found that pamidronate and nimodipine, drugs predicted to be efficacious against the brain tumor glioblastoma (GBM), inhibited the growth of a GBM cell line and cells isolated from a patient-derived xenograft (PDX). Our results demonstrate that by applying multiple disease-associated gene signature methods, we prioritized several drug repurposing candidates for low-survival cancers.
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Affiliation(s)
- Jennifer L. Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Elizabeth J. Wilk
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Vishal H. Oza
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Sam E. Gary
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Timothy C. Howton
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Victoria L. Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Amanda D. Clark
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Anita B. Hjelmeland
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
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3
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Li S, Zhang L, Wang L, Ji J, He J, Zheng X, Cao L, Li K. BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs. Molecules 2024; 29:1784. [PMID: 38675604 PMCID: PMC11051887 DOI: 10.3390/molecules29081784] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/08/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
Detecting the unintended adverse reactions of drugs (ADRs) is a crucial concern in pharmacological research. The experimental validation of drug-ADR associations often entails expensive and time-consuming investigations. Thus, a computational model to predict ADRs from known associations is essential for enhanced efficiency and cost-effectiveness. Here, we propose BiMPADR, a novel model that integrates drug gene expression into adverse reaction features using a message passing neural network on a bipartite graph of drugs and adverse reactions, leveraging publicly available data. By combining the computed adverse reaction features with the structural fingerprints of drugs, we predict the association between drugs and adverse reactions. Our models obtained high AUC (area under the receiver operating characteristic curve) values ranging from 0.861 to 0.907 in an external drug validation dataset under differential experiment conditions. The case study on multiple BET inhibitors also demonstrated the high accuracy of our predictions, and our model's exploration of potential adverse reactions for HWD-870 has contributed to its research and development for market approval. In summary, our method would provide a promising tool for ADR prediction and drug safety assessment in drug discovery and development.
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Affiliation(s)
| | | | | | | | | | | | - Lei Cao
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China; (S.L.); (L.Z.); (L.W.); (J.J.); (J.H.); (X.Z.)
| | - Kang Li
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China; (S.L.); (L.Z.); (L.W.); (J.J.); (J.H.); (X.Z.)
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4
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Peidli S, Green TD, Shen C, Gross T, Min J, Garda S, Yuan B, Schumacher LJ, Taylor-King JP, Marks DS, Luna A, Blüthgen N, Sander C. scPerturb: harmonized single-cell perturbation data. Nat Methods 2024; 21:531-540. [PMID: 38279009 DOI: 10.1038/s41592-023-02144-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 12/04/2023] [Indexed: 01/28/2024]
Abstract
Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation-response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.
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Affiliation(s)
- Stefan Peidli
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Tessa D Green
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Ciyue Shen
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | | | - Joseph Min
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Samuele Garda
- Institute of Biology, Humboldt-Universität, Berlin, Germany
- Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bo Yuan
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Linus J Schumacher
- Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Augustin Luna
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
- Computational Biology Branch, National Library of Medicine and Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD, USA.
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Chris Sander
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
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5
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Hu J, Allen BK, Stathias V, Ayad NG, Schürer SC. Kinome-Wide Virtual Screening by Multi-Task Deep Learning. Int J Mol Sci 2024; 25:2538. [PMID: 38473785 PMCID: PMC10932040 DOI: 10.3390/ijms25052538] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/04/2024] [Accepted: 02/17/2024] [Indexed: 03/14/2024] Open
Abstract
Deep learning is a machine learning technique to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform well for applications in drug discovery, utilizing structural features of small molecules to predict activity. Here, we report a large-scale study to predict the activity of small molecules across the human kinome-a major family of drug targets, particularly in anti-cancer agents. While small-molecule kinase inhibitors exhibit impressive clinical efficacy in several different diseases, resistance often arises through adaptive kinome reprogramming or subpopulation diversity. Polypharmacology and combination therapies offer potential therapeutic strategies for patients with resistant diseases. Their development would benefit from a more comprehensive and dense knowledge of small-molecule inhibition across the human kinome. Leveraging over 650,000 bioactivity annotations for more than 300,000 small molecules, we evaluated multiple machine learning methods to predict the small-molecule inhibition of 342 kinases across the human kinome. Our results demonstrated that multi-task deep neural networks outperformed classical single-task methods, offering the potential for conducting large-scale virtual screening, predicting activity profiles, and bridging the gaps in the available data.
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Affiliation(s)
- Jiaming Hu
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA;
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (B.K.A.); (V.S.)
| | - Bryce K. Allen
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (B.K.A.); (V.S.)
- Institute for Data Science & Computing, University of Miami, Miami, FL 33136, USA
| | - Vasileios Stathias
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (B.K.A.); (V.S.)
| | - Nagi G. Ayad
- Center for Therapeutic Innovation Miller School of Medicine, University of Miami, Miami, FL 33136, USA;
- Miami Project to Cure Paralysis, Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Stephan C. Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (B.K.A.); (V.S.)
- Institute for Data Science & Computing, University of Miami, Miami, FL 33136, USA
- Center for Therapeutic Innovation Miller School of Medicine, University of Miami, Miami, FL 33136, USA;
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
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6
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Gonzalez G, Herath I, Veselkov K, Bronstein M, Zitnik M. Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks. bioRxiv 2024:2024.01.03.573985. [PMID: 38260532 PMCID: PMC10802439 DOI: 10.1101/2024.01.03.573985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
As an alternative to target-driven drug discovery, phenotype-driven approaches identify compounds that counteract the overall disease effects by analyzing phenotypic signatures. Our study introduces a novel approach to this field, aiming to expand the search space for new therapeutic agents. We introduce PDGrapher, a causally-inspired graph neural network model designed to predict arbitrary perturbagens - sets of therapeutic targets - capable of reversing disease effects. Unlike existing methods that learn responses to perturbations, PDGrapher solves the inverse problem, which is to infer the perturbagens necessary to achieve a specific response - i.e., directly predicting perturbagens by learning which perturbations elicit a desired response. Experiments across eight datasets of genetic and chemical perturbations show that PDGrapher successfully predicted effective perturbagens in up to 9% additional test samples and ranked therapeutic targets up to 35% higher than competing methods. A key innovation of PDGrapher is its direct prediction capability, which contrasts with the indirect, computationally intensive models traditionally used in phenotypedriven drug discovery that only predict changes in phenotypes due to perturbations. The direct approach enables PDGrapher to train up to 30 times faster, representing a significant leap in efficiency. Our results suggest that PDGrapher can advance phenotype-driven drug discovery, offering a fast and comprehensive approach to identifying therapeutically useful perturbations.
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Affiliation(s)
- Guadalupe Gonzalez
- Imperial College London, London, UK
- Prescient Design, Genentech, South San Francisco, CA, USA
- F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Isuru Herath
- Merck & Co., South San Francisco, CA, USA
- Cornell University, Ithaca, NY, USA
| | | | | | - Marinka Zitnik
- Harvard Medical School, Boston, MA, USA
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Data Science Initiative, Cambridge, MA, USA
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7
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Satish KS, Saravanan KS, Augustine D, Saraswathy GR, V SS, Khan SS, H VC, Chakraborty S, Dsouza PL, N KH, Halawani IF, Alzahrani FM, Alzahrani KJ, Patil S. Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer. Front Oncol 2024; 13:1183766. [PMID: 38234400 PMCID: PMC10792052 DOI: 10.3389/fonc.2023.1183766] [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: 03/30/2023] [Accepted: 11/30/2023] [Indexed: 01/19/2024] Open
Abstract
Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird's eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies.
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Affiliation(s)
- Kshreeraja S. Satish
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Kamatchi Sundara Saravanan
- Department of Pharmacognosy, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Dominic Augustine
- Department of Oral Pathology & Microbiology, Faculty of Dental Sciences, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Ganesan Rajalekshmi Saraswathy
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Sowmya S. V
- Department of Oral Pathology & Microbiology, Faculty of Dental Sciences, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Samar Saeed Khan
- Department of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral and Maxillofacial Pathology, College of Dentistry, Jazan University, Jazan, Saudi Arabia
| | - Vanishri C. H
- Department of Oral Pathology & Microbiology, Faculty of Dental Sciences, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Shreshtha Chakraborty
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Prizvan Lawrence Dsouza
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Kavya H. N
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, MSR Nagar, Bengaluru, India
| | - Ibrahim F. Halawani
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
- Haematology and Immunology Department, Faculty of Medicine, Umm Al-Qura University, AI Abdeyah, Makkah, Saudi Arabia
| | - Fuad M. Alzahrani
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Khalid J. Alzahrani
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Shankargouda Patil
- College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT, United States
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Tshilenge KT, Bons J, Aguirre CG, Geronimo-Olvera C, Shah S, Rose J, Gerencser AA, Mak SK, Ehrlich ME, Bragg DC, Schilling B, Ellerby LM. Proteomic analysis of X-linked dystonia parkinsonism disease striatal neurons reveals altered RNA metabolism and splicing. Neurobiol Dis 2024; 190:106367. [PMID: 38042508 DOI: 10.1016/j.nbd.2023.106367] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 11/16/2023] [Accepted: 11/27/2023] [Indexed: 12/04/2023] Open
Abstract
X-linked dystonia-parkinsonism (XDP) is a rare neurodegenerative disease endemic to the Philippines. The genetic cause for XDP is an insertion of a SINE-VNTR-Alu (SVA)-type retrotransposon within intron 32 of TATA-binding protein associated factor 1 (TAF1) that causes an alteration of TAF1 splicing, partial intron retention, and decreased transcription. Although TAF1 is expressed in all organs, medium spiny neurons (MSNs) within the striatum are one of the cell types most affected in XDP. To define how mutations in the TAF1 gene lead to MSN vulnerability, we carried out a proteomic analysis of human XDP patient-derived neural stem cells (NSCs) and MSNs derived from induced pluripotent stem cells. NSCs and MSNs were grown in parallel and subjected to quantitative proteomic analysis in data-independent acquisition mode on the Orbitrap Eclipse Tribrid mass spectrometer. Subsequent functional enrichment analysis demonstrated that neurodegenerative disease-related pathways, such as Huntington's disease, spinocerebellar ataxia, cellular senescence, mitochondrial function and RNA binding metabolism, were highly represented. We used weighted coexpression network analysis (WGCNA) of the NSC and MSN proteomic data set to uncover disease-driving network modules. Three of the modules significantly correlated with XDP genotype when compared to the non-affected control and were enriched for DNA helicase and nuclear chromatin assembly, mitochondrial disassembly, RNA location and mRNA processing. Consistent with aberrant mRNA processing, we found splicing and intron retention of TAF1 intron 32 in XDP MSN. We also identified TAF1 as one of the top enriched transcription factors, along with YY1, ATF2, USF1 and MYC. Notably, YY1 has been implicated in genetic forms of dystonia. Overall, our proteomic data set constitutes a valuable resource to understand mechanisms relevant to TAF1 dysregulation and to identify new therapeutic targets for XDP.
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Affiliation(s)
| | - Joanna Bons
- The Buck Institute for Research on Aging, Novato, California 94945, USA
| | - Carlos Galicia Aguirre
- The Buck Institute for Research on Aging, Novato, California 94945, USA; University of Southern California, Leonard Davis School of Gerontology, 3715 McClintock Ave, Los Angeles, CA 90893, USA
| | | | - Samah Shah
- The Buck Institute for Research on Aging, Novato, California 94945, USA
| | - Jacob Rose
- The Buck Institute for Research on Aging, Novato, California 94945, USA
| | - Akos A Gerencser
- The Buck Institute for Research on Aging, Novato, California 94945, USA
| | - Sally K Mak
- The Buck Institute for Research on Aging, Novato, California 94945, USA
| | - Michelle E Ehrlich
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - D Cristopher Bragg
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Neurology, The Collaborative Center for X-linked Dystonia-Parkinsonism, Massachusetts General Hospital, Charlestown, MA, USA
| | - Birgit Schilling
- The Buck Institute for Research on Aging, Novato, California 94945, USA; University of Southern California, Leonard Davis School of Gerontology, 3715 McClintock Ave, Los Angeles, CA 90893, USA.
| | - Lisa M Ellerby
- The Buck Institute for Research on Aging, Novato, California 94945, USA; University of Southern California, Leonard Davis School of Gerontology, 3715 McClintock Ave, Los Angeles, CA 90893, USA.
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9
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Yang X, Huang K, Yang D, Zhao W, Zhou X. Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review. Glob Chall 2024; 8:2300163. [PMID: 38223896 PMCID: PMC10784210 DOI: 10.1002/gch2.202300163] [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] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 09/20/2023] [Indexed: 01/16/2024]
Abstract
The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.
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Affiliation(s)
- Xue Yang
- Department of Pancreatic Surgery and West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu610041China
| | - Kexin Huang
- Department of Pancreatic Surgery and West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu610041China
| | - Dewei Yang
- College of Advanced Manufacturing EngineeringChongqing University of Posts and TelecommunicationsChongqingChongqing400000China
| | - Weiling Zhao
- Center for Systems MedicineSchool of Biomedical InformaticsUTHealth at HoustonHoustonTX77030USA
| | - Xiaobo Zhou
- Center for Systems MedicineSchool of Biomedical InformaticsUTHealth at HoustonHoustonTX77030USA
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10
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Jeong E, Yoon S. Current advances in comprehensive omics data mining for oncology and cancer research. Biochim Biophys Acta Rev Cancer 2024; 1879:189030. [PMID: 38008264 DOI: 10.1016/j.bbcan.2023.189030] [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: 02/08/2023] [Revised: 09/05/2023] [Accepted: 11/19/2023] [Indexed: 11/28/2023]
Abstract
The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.
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Affiliation(s)
- Euna Jeong
- Research Institute of Women's Health, Sookmyung Women's University, Seoul 04310, Republic of Korea
| | - Sukjoon Yoon
- Research Institute of Women's Health, Sookmyung Women's University, Seoul 04310, Republic of Korea; Department of Biological Sciences, Sookmyung Women's University, Seoul 04310, Republic of Korea.
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11
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Tang C, Fu S, Jin X, Li W, Xing F, Duan B, Cheng X, Chen X, Wang S, Zhu C, Li G, Chuai G, He Y, Wang P, Liu Q. Personalized tumor combination therapy optimization using the single-cell transcriptome. Genome Med 2023; 15:105. [PMID: 38041202 PMCID: PMC10691165 DOI: 10.1186/s13073-023-01256-6] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 11/13/2023] [Indexed: 12/03/2023] Open
Abstract
BACKGROUND The precise characterization of individual tumors and immune microenvironments using transcriptome sequencing has provided a great opportunity for successful personalized cancer treatment. However, the cancer treatment response is often characterized by in vitro assays or bulk transcriptomes that neglect the heterogeneity of malignant tumors in vivo and the immune microenvironment, motivating the need to use single-cell transcriptomes for personalized cancer treatment. METHODS Here, we present comboSC, a computational proof-of-concept study to explore the feasibility of personalized cancer combination therapy optimization using single-cell transcriptomes. ComboSC provides a workable solution to stratify individual patient samples based on quantitative evaluation of their personalized immune microenvironment with single-cell RNA sequencing and maximize the translational potential of in vitro cellular response to unify the identification of synergistic drug/small molecule combinations or small molecules that can be paired with immune checkpoint inhibitors to boost immunotherapy from a large collection of small molecules and drugs, and finally prioritize them for personalized clinical use based on bipartition graph optimization. RESULTS We apply comboSC to publicly available 119 single-cell transcriptome data from a comprehensive set of 119 tumor samples from 15 cancer types and validate the predicted drug combination with literature evidence, mining clinical trial data, perturbation of patient-derived cell line data, and finally in-vivo samples. CONCLUSIONS Overall, comboSC provides a feasible and one-stop computational prototype and a proof-of-concept study to predict potential drug combinations for further experimental validation and clinical usage using the single-cell transcriptome, which will facilitate and accelerate personalized tumor treatment by reducing screening time from a large drug combination space and saving valuable treatment time for individual patients. A user-friendly web server of comboSC for both clinical and research users is available at www.combosc.top . The source code is also available on GitHub at https://github.com/bm2-lab/comboSC .
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Affiliation(s)
- Chen Tang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Xuan Jin
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Wannian Li
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Bin Duan
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Xiaojie Cheng
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Xiaohan Chen
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Shuguang Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Chenyu Zhu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Gaoyang Li
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Guohui Chuai
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Yayi He
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, China.
| | - Ping Wang
- Tongji University Cancer Center, Shanghai Tenth People's Hospital of Tongji University, Tongji University, Shanghai, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, China.
- Tongji University Cancer Center, Shanghai Tenth People's Hospital of Tongji University, Tongji University, Shanghai, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
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Sarin KY, Kincaid J, Sell B, Shahryari J, Duncton MAJ, Morefield E, Sun W, Prieto K, Chavez-Chiang O, de Moran Segura C, Nguyen J, Bronson RT, Plotkin SR, Kochendoerfer GG, Fenn P, Wootton MA, Powala C, de Souza MP, Tsai KY. Development of a MEK inhibitor, NFX-179, as a chemoprevention agent for squamous cell carcinoma. Sci Transl Med 2023; 15:eade1844. [PMID: 37820007 DOI: 10.1126/scitranslmed.ade1844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 09/19/2023] [Indexed: 10/13/2023]
Abstract
Cutaneous squamous cell carcinoma (cSCC) is the second most common skin cancer. Although cSCC contributes to substantial morbidity and mortality in high-risk individuals, deployment of otherwise effective chemoprevention of cSCC is limited by toxicities. Our systematic computational drug repurposing screen predicted that selumetinib, a MAPK (mitogen-activated protein kinase) kinase inhibitor (MEKi), would reverse transcriptional signatures associated with cSCC development, consistent with our genomic analysis implicating MEK as a chemoprevention target. Although systemic MEKi suppresses the formation of cSCC in mice, systemic MEKi can cause severe adverse effects. Here, we report the development of a metabolically labile MEKi, NFX-179, designed to potently and selectively suppress the MAPK pathway in the skin before rapid metabolism in the systemic circulation. NFX-179 was identified on the basis of its biochemical and cellular potency, selectivity, and rapid metabolism upon systemic absorption. In our ultraviolet-induced cSCC mouse model, topical application of NFX-179 gel reduced the formation of new cSCCs by an average of 60% at doses of 0.1% and greater at 28 days. We further confirmed the localized nature of these effects in an additional split-mouse randomized controlled study where suppression of cSCC was observed only in drug-treated areas. No toxicities were observed. NFX-179 inhibits the growth of human SCC cell lines in a dose-dependent manner, and topical NFX-179 application penetrates human skin and inhibits MAPK signaling in human cSCC explants. Together, our data provide a compelling rationale for using topical MEK inhibition through the application of NFX-179 gel as an effective strategy for cSCC chemoprevention.
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Affiliation(s)
- Kavita Y Sarin
- Department of Dermatology, Stanford University Medical Center, Stanford, CA 94063, USA
| | | | - Brittney Sell
- Department of Tumor Biology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | | | | | | | - Wenchao Sun
- Department of Dermatology, Stanford University Medical Center, Stanford, CA 94063, USA
| | - Karol Prieto
- Department of Tumor Biology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Omar Chavez-Chiang
- Department of Tumor Biology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Carlos de Moran Segura
- Department of Pathology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Jonathan Nguyen
- Department of Pathology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Roderick T Bronson
- Department of Immunology, Rodent Histopathology Core, Harvard Medical School, Boston, MA 02115, USA
| | | | | | - Peter Fenn
- NFlection Therapeutics, Boston, MA 02116, USA
| | | | | | | | - Kenneth Y Tsai
- Department of Tumor Biology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
- Department of Pathology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
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Kwak MS, Hwang CI, Cha JM, Jeon JW, Yoon JY, Park SB. Single-Cell Network-Based Drug Repositioning for Discovery of Therapies against Anti-Tumour Necrosis Factor-Resistant Crohn's Disease. Int J Mol Sci 2023; 24:14099. [PMID: 37762402 PMCID: PMC10531326 DOI: 10.3390/ijms241814099] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/11/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Primary and secondary non-response affects approximately 50% of patients with Crohn's disease treated with anti-tumour necrosis factor (TNF) monoclonal antibodies. To date, very little single cell research exists regarding drug repurposing in Crohn's disease. We aimed to elucidate the cellular phenomena underlying resistance to anti-TNF therapy in patients with Crohn's disease and to identify potential drug candidates for these patients. Single-cell transcriptome analyses were performed using data (GSE134809) from the Gene Expression Omnibus and Library of Integrated Network-Based Cellular Signatures L1000 Project. Data aligned to the Genome Reference Consortium Human Build 38 reference genome using the Cell Ranger software were processed using the Seurat package. To capture significant functional terms, gene ontology functional enrichment analysis was performed on the marker genes. For biological analysis, 93,893 cells were retained (median 20,163 genes). Through marker genes, seven major cell lineages were identified: B-cells, T-cells, natural killer cells, monocytes, endothelial cells, epithelial cells, and tissue stem cells. In the anti-TNF-resistant samples, the top 10 differentially expressed genes were HLA-DQB-1, IGHG1, RPS23, RPL7A, ARID5B, LTB, STAT1, NAMPT, COTL1, ISG20, IGHA1, IGKC, and JCHAIN, which were robustly distributed in all cell lineages, mainly in B-cells. Through molecular function analyses, we found that the biological functions of both monocyte and T-cell groups mainly involved immune-mediated functions. According to multi-cluster drug repurposing prediction, vorinostat is the top drug candidate for patients with anti-TNF-refractory Crohn's disease. Differences in cell populations and immune-related activity within tissues may influence the responsiveness of Crohn's disease to anti-TNF agents. Vorinostat may serve as a promising novel therapy for anti-TNF-resistant Crohn's disease.
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Affiliation(s)
- Min Seob Kwak
- Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul 05278, Republic of Korea
| | - Chang-Il Hwang
- Department of Microbiology and Molecular Genetics, College of Biological Sciences, University of California Davis, Davis, CA 95616, USA;
| | - Jae Myung Cha
- Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul 05278, Republic of Korea
| | - Jung Won Jeon
- Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul 05278, Republic of Korea
| | - Jin Young Yoon
- Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul 05278, Republic of Korea
| | - Su Bee Park
- Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul 05278, Republic of Korea
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14
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Aprile M, Cataldi S, Perfetto C, Federico A, Ciccodicola A, Costa V. Targeting metabolism by B-raf inhibitors and diclofenac restrains the viability of BRAF-mutated thyroid carcinomas with Hif-1α-mediated glycolytic phenotype. Br J Cancer 2023; 129:249-265. [PMID: 37198319 PMCID: PMC10338540 DOI: 10.1038/s41416-023-02282-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 04/03/2023] [Accepted: 04/14/2023] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND B-raf inhibitors (BRAFi) are effective for BRAF-mutated papillary (PTC) and anaplastic (ATC) thyroid carcinomas, although acquired resistance impairs tumour cells' sensitivity and/or limits drug efficacy. Targeting metabolic vulnerabilities is emerging as powerful approach in cancer. METHODS In silico analyses identified metabolic gene signatures and Hif-1α as glycolysis regulator in PTC. BRAF-mutated PTC, ATC and control thyroid cell lines were exposed to HIF1A siRNAs or chemical/drug treatments (CoCl2, EGF, HGF, BRAFi, MEKi and diclofenac). Genes/proteins expression, glucose uptake, lactate quantification and viability assays were used to investigate the metabolic vulnerability of BRAF-mutated cells. RESULTS A specific metabolic gene signature was identified as a hallmark of BRAF-mutated tumours, which display a glycolytic phenotype, characterised by enhanced glucose uptake, lactate efflux and increased expression of Hif-1α-modulated glycolytic genes. Indeed, Hif-1α stabilisation counteracts the inhibitory effects of BRAFi on these genes and on cell viability. Interestingly, targeting metabolic routes with BRAFi and diclofenac combination we could restrain the glycolytic phenotype and synergistically reduce tumour cells' viability. CONCLUSION The identification of a metabolic vulnerability of BRAF-mutated carcinomas and the capacity BRAFi and diclofenac combination to target metabolism open new therapeutic perspectives in maximising drug efficacy and reducing the onset of secondary resistance and drug-related toxicity.
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Affiliation(s)
- Marianna Aprile
- Institute of Genetics and Biophysics "Adriano Buzzati-Traverso", CNR, Via P. Castellino 111, 80131, Naples, Italy.
| | - Simona Cataldi
- Institute of Genetics and Biophysics "Adriano Buzzati-Traverso", CNR, Via P. Castellino 111, 80131, Naples, Italy
| | - Caterina Perfetto
- Institute of Genetics and Biophysics "Adriano Buzzati-Traverso", CNR, Via P. Castellino 111, 80131, Naples, Italy
| | - Antonio Federico
- Institute of Genetics and Biophysics "Adriano Buzzati-Traverso", CNR, Via P. Castellino 111, 80131, Naples, Italy
- Tampere Institute for Advanced Study (IAS), Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)-Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Alfredo Ciccodicola
- Institute of Genetics and Biophysics "Adriano Buzzati-Traverso", CNR, Via P. Castellino 111, 80131, Naples, Italy
- Department of Science and Technology, University of Naples "Parthenope", Naples, Italy
| | - Valerio Costa
- Institute of Genetics and Biophysics "Adriano Buzzati-Traverso", CNR, Via P. Castellino 111, 80131, Naples, Italy.
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15
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Erdem C, Gross SM, Heiser LM, Birtwistle MR. MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms. Nat Commun 2023; 14:3991. [PMID: 37414767 PMCID: PMC10326020 DOI: 10.1038/s41467-023-39729-2] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 06/27/2023] [Indexed: 07/08/2023] Open
Abstract
Robust identification of context-specific network features that control cellular phenotypes remains a challenge. We here introduce MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with cellular phenotypes and pathways. First, we use MOBILE to nominate mechanisms of interferon-γ (IFNγ) regulated PD-L1 expression. Our analyses suggest that IFNγ-controlled PD-L1 expression involves BST2, CLIC2, FAM83D, ACSL5, and HIST2H2AA3 genes, which were supported by prior literature. We also compare networks activated by related family members transforming growth factor-beta 1 (TGFβ1) and bone morphogenetic protein 2 (BMP2) and find that differences in ligand-induced changes in cell size and clustering properties are related to differences in laminin/collagen pathway activity. Finally, we demonstrate the broad applicability and adaptability of MOBILE by analyzing publicly available molecular datasets to investigate breast cancer subtype specific networks. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly useful for identification of context-specific molecular features and pathways.
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Affiliation(s)
- Cemal Erdem
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Sean M Gross
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Laura M Heiser
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
| | - Marc R Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA.
- Department of Bioengineering, Clemson University, Clemson, SC, USA.
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16
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Coria-Rodríguez H, Ochoa S, de Anda-Jáuregui G, Hernández-Lemus E. Drug repurposing for Basal breast cancer subpopulations using modular network signatures. Comput Biol Chem 2023; 105:107902. [PMID: 37348299 DOI: 10.1016/j.compbiolchem.2023.107902] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 05/30/2023] [Accepted: 06/02/2023] [Indexed: 06/24/2023]
Abstract
Breast cancer is characterized as being a heterogeneous pathology with a broad phenotype variability. Breast cancer subtypes have been developed in order to capture some of this heterogeneity. Each of these breast cancer subtypes, in turns retains varied characteristic features impacting diagnostic, prognostic and therapeutics. Basal breast tumors, in particular have been challenging in these regards. Basal breast cancer is often more aggressive, of rapid evolution and no tailor-made targeted therapies are available yet to treat it. Arguably, epigenetic variability is behind some of these intricacies. It is possible to further classify basal breast tumor in groups based on their non-coding transcriptome and methylome profiles. It is expected that these groups will have differences in survival as well as in sensitivity to certain classes of drugs. With this in mind, we implemented a computational learning approach to infer different subpopulations of basal breast cancer (from TCGA multi-omic data) based on their epigenetic signatures. Such epigenomic signatures were associated with different survival profiles; we then identified their associated gene co-expression network structure, extracted a signature based on modules within these networks, and use these signatures to find and prioritize drugs (in the LINCS dataset) that may be used to target these types of cancer. In this way we are introducing the analytical workflow for an epigenomic signature-based drug repurposing structure.
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Affiliation(s)
- Hiram Coria-Rodríguez
- Computational Genomics Division, National Institute of Genomic Medicine, Periferico Sur 4809, Mexico City, 14610, Mexico
| | - Soledad Ochoa
- Computational Genomics Division, National Institute of Genomic Medicine, Periferico Sur 4809, Mexico City, 14610, Mexico
| | - Guillermo de Anda-Jáuregui
- Computational Genomics Division, National Institute of Genomic Medicine, Periferico Sur 4809, Mexico City, 14610, Mexico; Center for Complexity Sciences, Universidad Nacional Autonoma de Mexico, Circuito Exterior, Mexico City, 04510, Mexico; Catedras Conacyt, National Council on Science and Technology, Insurgentes Sur, Mexico City, 03940, Mexico.
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Periferico Sur 4809, Mexico City, 14610, Mexico; Center for Complexity Sciences, Universidad Nacional Autonoma de Mexico, Circuito Exterior, Mexico City, 04510, Mexico.
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Chien W, Tyner JW, Gery S, Zheng Y, Li LY, Gopinatha Pillai MS, Nam C, Bhowmick NA, Lin DC, Koeffler HP. Treatment for ovarian clear cell carcinoma with combined inhibition of WEE1 and ATR. J Ovarian Res 2023; 16:80. [PMID: 37087441 PMCID: PMC10122390 DOI: 10.1186/s13048-023-01160-y] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 04/10/2023] [Indexed: 04/24/2023] Open
Abstract
BACKGROUND Standard platinum-based therapy for ovarian cancer is inefficient against ovarian clear cell carcinoma (OCCC). OCCC is a distinct subtype of epithelial ovarian cancer. OCCC constitutes 25% of ovarian cancers in East Asia (Japan, Korea, China, Singapore) and 6-10% in Europe and North America. The cancer is characterized by frequent inactivation of ARID1A and 10% of cases of endometriosis progression to OCCC. The aim of this study was to identify drugs that are either FDA-approved or in clinical trials for the treatment of OCCC. RESULTS High throughput screening of 166 compounds that are either FDA-approved, in clinical trials or are in pre-clinical studies identified several cytotoxic compounds against OCCC. ARID1A knockdown cells were more sensitive to inhibitors of either mTOR (PP242), dual mTOR/PI3K (GDC0941), ATR (AZD6738) or MDM2 (RG7388) compared to control cells. Also, compounds targeting BH3 domain (AZD4320) and SRC (AZD0530) displayed preferential cytotoxicity against ARID1A mutant cell lines. In addition, WEE1 inhibitor (AZD1775) showed broad cytotoxicity toward OCCC cell lines, irrespective of ARID1A status. CONCLUSIONS In a selection of 166 compounds we showed that inhibitors of ATR and WEE1 were cytotoxic against a panel of OCCC cell lines. These two drugs are already in other clinical trials, making them ideal candidates for treatment of OCCC.
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Affiliation(s)
- Wenwen Chien
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA, 90048, USA.
| | - Jeffrey W Tyner
- Knight Cancer Institute, Oregon Health & Science University, Oregon Health and Science University, 2720 S.W. Moody Avenue, Portland, OR, 97201, USA
| | - Sigal Gery
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA, 90048, USA
| | - Yueyuan Zheng
- Clinical Big Data Research Center, Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, P. R. China
| | - Li-Yan Li
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, Guandong Province, P. R. China
| | - Mohan Shankar Gopinatha Pillai
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA, 90048, USA
| | - Chehyun Nam
- Center for Craniofacial Molecular Biology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Neil A Bhowmick
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA, 90048, USA
| | - De-Chen Lin
- Center for Craniofacial Molecular Biology, University of Southern California, Los Angeles, CA, 90089, USA
| | - H Phillip Koeffler
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA, 90048, USA
- Department of Hematology-Oncology, National University Cancer Institute of Singapore, National University Hospital, Singapore, 119074, Singapore
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Quille RVE, de Almeida FV, Ohara MY, Corrêa PLP, de Freitas LG, Alves-Souza SN, de Almeida JR, Davis M, Prakash G. Architecture of a Data Portal for Publishing and Delivering Open Data for Atmospheric Measurement. Int J Environ Res Public Health 2023; 20:5374. [PMID: 37047988 PMCID: PMC10094644 DOI: 10.3390/ijerph20075374] [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] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/12/2023] [Accepted: 03/14/2023] [Indexed: 06/19/2023]
Abstract
Atmospheric data are collected by researchers every day. Campaigns such as GOAmazon 2014/2015 and the Amazon Tall Tower Observatory collect essential data on aerosols, gases, cloud properties, and meteorological parameters in the Brazilian Amazon basin. These data products provide insights and essential information for analyzing and predicting natural processes. However, in Brazil, it is estimated that more than 80% of the scientific data collected are not published due to the lack of web portals that collect and store these data. This makes it difficult, or even impossible, to access and integrate the data, which can result in the loss of significant amounts of information and significantly affect the understanding of the overall data. To address this problem, we propose a data portal architecture and open data deployment that enable Big Data processing, human interaction, and download-oriented approaches with tools that help users catalog, publish and visualize atmospheric data. Thus, we describe the architecture developed, based on the experience of the Atmospheric Radiation Measurement Data Center, which incorporates the principles of FAIR, the infrastructure and content management system for managing scientific data. The portal partial results were tested with environmental data from contaminated areas at the University of São Paulo. Overall, this data portal creates more shared knowledge about atmospheric processes by providing users with access to open environmental data.
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Affiliation(s)
- Rosa Virginia Encinas Quille
- School of Arts, Sciences and Humanities, University of São Paulo, Rua Arlindo Béttio, 1000-Ermelino Matarazzo, São Paulo 03828-000, Brazil
- Residues and Contaminated Areas Laboratory (LARC), Institute for Technological Research (IPT), Av. Prof. Almeida Prado, 532-Butantã, São Paulo 05508-901, Brazil
| | - Felipe Valencia de Almeida
- Polytechnic School, University of São Paulo, Av. Prof. Luciano Gualberto, 380-Butantã, São Paulo 05508-010, Brazil; (F.V.d.A.)
| | - Mauro Yuji Ohara
- Polytechnic School, University of São Paulo, Av. Prof. Luciano Gualberto, 380-Butantã, São Paulo 05508-010, Brazil; (F.V.d.A.)
| | - Pedro Luiz Pizzigatti Corrêa
- School of Arts, Sciences and Humanities, University of São Paulo, Rua Arlindo Béttio, 1000-Ermelino Matarazzo, São Paulo 03828-000, Brazil
- Polytechnic School, University of São Paulo, Av. Prof. Luciano Gualberto, 380-Butantã, São Paulo 05508-010, Brazil; (F.V.d.A.)
| | - Leandro Gomes de Freitas
- Residues and Contaminated Areas Laboratory (LARC), Institute for Technological Research (IPT), Av. Prof. Almeida Prado, 532-Butantã, São Paulo 05508-901, Brazil
| | - Solange Nice Alves-Souza
- School of Arts, Sciences and Humanities, University of São Paulo, Rua Arlindo Béttio, 1000-Ermelino Matarazzo, São Paulo 03828-000, Brazil
- Polytechnic School, University of São Paulo, Av. Prof. Luciano Gualberto, 380-Butantã, São Paulo 05508-010, Brazil; (F.V.d.A.)
| | - Jorge Rady de Almeida
- Polytechnic School, University of São Paulo, Av. Prof. Luciano Gualberto, 380-Butantã, São Paulo 05508-010, Brazil; (F.V.d.A.)
| | - Maggie Davis
- Environmental Sciences Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, USA
| | - Giri Prakash
- Environmental Sciences Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, USA
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Takahashi S, Ferdousi F, Yamamoto S, Hirano A, Nukaga S, Nozaki H, Isoda H. Botryococcus terribilis Ethanol Extract Exerts Anti-inflammatory Effects on Murine RAW264 Cells. Int J Mol Sci 2023; 24:ijms24076666. [PMID: 37047640 PMCID: PMC10095501 DOI: 10.3390/ijms24076666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/23/2023] [Accepted: 03/28/2023] [Indexed: 04/05/2023] Open
Abstract
The present study aimed to evaluate the effects of Botryococcus terribilis ethanol extract (BTEE) on lipopolysaccharide (LPS)-induced inflammation in RAW264 cells. BTEE significantly attenuated LPS-induced nitric oxide production and inflammatory cytokines release, including Ccl2, Cox2, and Il6. On the other hand, several anti-inflammatory mediators, such as Pgc1β and Socs1, were increased in BTEE-treated cells. Further, we performed an untargeted whole-genome microarray analysis to explore the anti-inflammatory molecular mechanism of BTEE. Enrichment analysis showed BTEE significantly downregulated ‘response to stimulus’, ‘locomotion’, and ‘immune system response’ and upregulated ‘cell cycle’ gene ontologies in both 6- and 17-h post-LPS stimulation conditions. Pathway analysis revealed BTEE could downregulate the expressions of chemokines of the CC and CXC subfamily, and cytokines of the TNF family, TGFβ family, IL1-like, and class I helical. PPI analysis showed AXL receptor tyrosine kinase (Axl), a receptor tyrosine kinase from the TAM family, and its upstream transcription factors were downregulated in both conditions. Node neighborhood analysis showed several Axl coexpressed genes were also downregulated. Further, kinase enrichment and chemical perturbation analyses supported Axl inhibition in BTEE-treated conditions. Altogether, these findings suggest anti-inflammatory effects of BTEE that are mediated via the suppression of pro-inflammatory cytokines and predict its potential as an Axl inhibitor.
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Affiliation(s)
- Shinya Takahashi
- Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Japan
- Alliance for Research on the Mediterranean and North Africa (ARENA), University of Tsukuba, Tsukuba 305-8572, Japan
| | - Farhana Ferdousi
- Alliance for Research on the Mediterranean and North Africa (ARENA), University of Tsukuba, Tsukuba 305-8572, Japan
| | - Seri Yamamoto
- Graduate School of Science and Technology, University of Tsukuba, Tsukuba 305-8572, Japan
| | - Atsushi Hirano
- Tokyo Electric Power Company Holdings, Inc., Tokyo 100-8560, Japan
| | - Sachiko Nukaga
- Tokyo Electric Power Company Holdings, Inc., Tokyo 100-8560, Japan
| | - Hiroyuki Nozaki
- Tokyo Electric Power Company Holdings, Inc., Tokyo 100-8560, Japan
| | - Hiroko Isoda
- Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Japan
- Alliance for Research on the Mediterranean and North Africa (ARENA), University of Tsukuba, Tsukuba 305-8572, Japan
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20
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Morello G, La Cognata V, Guarnaccia M, D'Agata V, Cavallaro S. Cracking the Code of Neuronal Cell Fate. Cells 2023; 12:cells12071057. [PMID: 37048129 PMCID: PMC10093029 DOI: 10.3390/cells12071057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/27/2023] [Accepted: 03/29/2023] [Indexed: 04/03/2023] Open
Abstract
Transcriptional regulation is fundamental to most biological processes and reverse-engineering programs can be used to decipher the underlying programs. In this review, we describe how genomics is offering a systems biology-based perspective of the intricate and temporally coordinated transcriptional programs that control neuronal apoptosis and survival. In addition to providing a new standpoint in human pathology focused on the regulatory program, cracking the code of neuronal cell fate may offer innovative therapeutic approaches focused on downstream targets and regulatory networks. Similar to computers, where faults often arise from a software bug, neuronal fate may critically depend on its transcription program. Thus, cracking the code of neuronal life or death may help finding a patch for neurodegeneration and cancer.
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Affiliation(s)
- Giovanna Morello
- Institute for Biomedical Research and Innovation, National Research Council (CNR-IRIB), 95126 Catania, Italy
| | - Valentina La Cognata
- Institute for Biomedical Research and Innovation, National Research Council (CNR-IRIB), 95126 Catania, Italy
| | - Maria Guarnaccia
- Institute for Biomedical Research and Innovation, National Research Council (CNR-IRIB), 95126 Catania, Italy
| | - Velia D'Agata
- Section of Human Anatomy and Histology, Department of Biomedical and Biotechnological Sciences, University of Catania, 95124 Catania, Italy
| | - Sebastiano Cavallaro
- Institute for Biomedical Research and Innovation, National Research Council (CNR-IRIB), 95126 Catania, Italy
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21
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Graves OK, Kim W, Özcan M, Ashraf S, Turkez H, Yuan M, Zhang C, Mardinoglu A, Li X. Discovery of drug targets and therapeutic agents based on drug repositioning to treat lung adenocarcinoma. Biomed Pharmacother 2023; 161:114486. [PMID: 36906970 DOI: 10.1016/j.biopha.2023.114486] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/20/2023] [Accepted: 03/07/2023] [Indexed: 03/12/2023] Open
Abstract
BACKGROUND Lung adenocarcinoma (LUAD) is the one of the most common subtypes in lung cancer. Although various targeted therapies have been used in the clinical practice, the 5-year overall survival rate of patients is still low. Thus, it is urgent to identify new therapeutic targets and develop new drugs for the treatment of the LUAD patients. METHODS Survival analysis was used to identify the prognostic genes. Gene co-expression network analysis was used to identify the hub genes driving the tumor development. A profile-based drug repositioning approach was used to repurpose the potentially useful drugs for targeting the hub genes. MTT and LDH assay were used to measure the cell viability and drug cytotoxicity, respectively. Western blot was used to detect the expression of the proteins. FINDINGS We identified 341 consistent prognostic genes from two independent LUAD cohorts, whose high expression was associated with poor survival outcomes of patients. Among them, eight genes were identified as hub genes due to their high centrality in the key functional modules in the gene-co-expression network analysis and these genes were associated with the various hallmarks of cancer (e.g., DNA replication and cell cycle). We performed drug repositioning analysis for three of the eight genes (CDCA8, MCM6, and TTK) based on our drug repositioning approach. Finally, we repurposed five drugs for inhibiting the protein expression level of each target gene and validated the drug efficacy by performing in vitro experiments. INTERPRETATION We found the consensus targetable genes for the treatment of LUAD patients with different races and geographic characteristics. We also proved the feasibility of our drug repositioning approach for the development of new drugs for disease treatment.
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Affiliation(s)
| | - Woonghee Kim
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden.
| | - Mehmet Özcan
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden.
| | - Sajda Ashraf
- Trustlife Labs, Drug Research & Development Center, 34774 Istanbul, Turkey.
| | - Hasan Turkez
- Trustlife Labs, Drug Research & Development Center, 34774 Istanbul, Turkey.
| | - Meng Yuan
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden.
| | - Cheng Zhang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden.
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK.
| | - Xiangyu Li
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden; Bash Biotech Inc, 600 West Broadway, Suite 700, San Diego, CA 92101, USA; Guangzhou Laboratory, Guangzhou 510005, China.
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22
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Wu L, Gao J, Zhang Y, Sui B, Wen Y, Wu Q, Liu K, He S, Bo X. A hybrid deep forest-based method for predicting synergistic drug combinations. Cell Rep Methods 2023; 3:100411. [PMID: 36936075 PMCID: PMC10014304 DOI: 10.1016/j.crmeth.2023.100411] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/27/2022] [Accepted: 01/27/2023] [Indexed: 02/23/2023]
Abstract
Combination therapy is a promising approach in treating multiple complex diseases. However, the large search space of available drug combinations exacerbates challenge for experimental screening. To predict synergistic drug combinations in different cancer cell lines, we propose an improved deep forest-based method, ForSyn, and design two forest types embedded in ForSyn. ForSyn handles imbalanced and high-dimensional data in medium-/small-scale datasets, which are inherent characteristics of drug combination datasets. Compared with 12 state-of-the-art methods, ForSyn ranks first on four metrics for eight datasets with different feature combinations. We conduct a systematic analysis to identify the most appropriate configuration parameters. We validate the predictive value of ForSyn with cell-based experiments on several previously unexplored drug combinations. Finally, a systematic analysis of feature importance is performed on the top contributing features extracted by ForSyn. The resulting key genes may play key roles on corresponding cancers.
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Affiliation(s)
- Lianlian Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Jie Gao
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Yixin Zhang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Binsheng Sui
- School of Film, Xiamen University, Xiamen 361005, China
| | - Yuqi Wen
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Qingqiang Wu
- School of Film, Xiamen University, Xiamen 361005, China
| | - Kunhong Liu
- School of Film, Xiamen University, Xiamen 361005, China
| | - Song He
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
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23
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Matlock AD, Vaibhav V, Holewinski R, Venkatraman V, Dardov V, Manalo DM, Shelley B, Ornelas L, Banuelos M, Mandefro B, Escalante-Chong R, Li J, Finkbeiner S, Fraenkel E, Rothstein J, Thompson L, Sareen D, Svendsen CN, Van Eyk JE; NIH NeuroLINCS Consortium. NeuroLINCS Proteomics: Defining human-derived iPSC proteomes and protein signatures of pluripotency. Sci Data 2023; 10:24. [PMID: 36631473 DOI: 10.1038/s41597-022-01687-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 09/07/2022] [Indexed: 01/13/2023] Open
Abstract
The National Institute of Health (NIH) Library of integrated network-based cellular signatures (LINCS) program is premised on the generation of a publicly available data resource of cell-based biochemical responses or "signatures" to genetic or environmental perturbations. NeuroLINCS uses human inducible pluripotent stem cells (hiPSCs), derived from patients and healthy controls, and differentiated into motor neuron cell cultures. This multi-laboratory effort strives to establish i) robust multi-omic workflows for hiPSC and differentiated neuronal cultures, ii) public annotated data sets and iii) relevant and targetable biological pathways of spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). Here, we focus on the proteomics and the quality of the developed workflow of hiPSC lines from 6 individuals, though epigenomics and transcriptomics data are also publicly available. Known and commonly used markers representing 73 proteins were reproducibly quantified with consistent expression levels across all hiPSC lines. Data quality assessments, data levels and metadata of all 6 genetically diverse human iPSCs analysed by DIA-MS are parsable and available as a high-quality resource to the public.
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24
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Srinivasan S, Maurya MR, Ramachandran S, Fahy E, Subramaniam S. MetGENE: gene-centric metabolomics information retrieval tool. Gigascience 2022; 12:giad089. [PMID: 37983749 PMCID: PMC10659118 DOI: 10.1093/gigascience/giad089] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 06/14/2023] [Accepted: 10/01/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND Biomedical research often involves contextual integration of multimodal and multiomic data in search of mechanisms for improved diagnosis, treatment, and monitoring. Researchers need to access information from diverse sources, comprising data in various and sometimes incongruent formats. The downstream processing of the data to decipher mechanisms by reconstructing networks and developing quantitative models warrants considerable effort. RESULTS MetGENE is a knowledge-based, gene-centric data aggregator that hierarchically retrieves information about the gene(s), their related pathway(s), reaction(s), metabolite(s), and metabolomic studies from standard data repositories under one dashboard to enable ease of access through centralization of relevant information. We note that MetGENE focuses only on those genes that encode for proteins directly associated with metabolites. All other gene-metabolite associations are beyond the current scope of MetGENE. Further, the information can be contextualized by filtering by species, anatomy (tissue), and condition (disease or phenotype). CONCLUSIONS MetGENE is an open-source tool that aggregates metabolite information for a given gene(s) and presents them in different computable formats (e.g., JSON) for further integration with other omics studies. MetGENE is available at https://bdcw.org/MetGENE/index.php.
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Affiliation(s)
- Sumana Srinivasan
- University of California San Diego, Department of Bioengineering, 9500 Gilman Dr, La Jolla, CA 92093, United States
| | - Mano R Maurya
- University of California San Diego, Department of Bioengineering, 9500 Gilman Dr, La Jolla, CA 92093, United States
| | - Srinivasan Ramachandran
- University of California San Diego, Department of Bioengineering, 9500 Gilman Dr, La Jolla, CA 92093, United States
| | - Eoin Fahy
- University of California San Diego, Department of Bioengineering, 9500 Gilman Dr, La Jolla, CA 92093, United States
| | - Shankar Subramaniam
- University of California San Diego, Department of Bioengineering, 9500 Gilman Dr, La Jolla, CA 92093, United States
- University of California San Diego, San Diego Supercomputer Center, Department of Computer Science and Engineering, Department of Cellular and Molecular Medicine, 9500 Gilman Drive, La Jolla, CA 92093, United States
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25
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de Lanna CA, da Silva BNM, de Melo AC, Bonamino MH, Alves LDB, Pinto LFR, Cardoso AS, Antunes HS, Boroni M, Cohen Goldemberg D. Oral Lichen Planus and Oral Squamous Cell Carcinoma share key oncogenic signatures. Sci Rep 2022; 12:20645. [PMID: 36450755 PMCID: PMC9712651 DOI: 10.1038/s41598-022-24801-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [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: 08/15/2022] [Accepted: 11/21/2022] [Indexed: 12/03/2022] Open
Abstract
To investigate similarities in the gene profile of Oral Lichen Planus and Oral Squamous Cell Carcinoma that may justify a carcinogenic potential, we analyzed the gene expression signatures of Oral Lichen Planus and Oral Squamous Cell Carcinoma in early and advanced stages. Based on gene expression data from public databases, we used a bioinformatics approach to compare expression profiles, estimate immune infiltrate composition, identify differentially and co-expressed genes, and propose putative therapeutic targets and associated drugs. Our results revealed gene expression patterns related to processes of keratinization, keratinocyte differentiation, cell proliferation and immune response in common between Oral Lichen Planus and early and advanced Oral Squamous Cell Carcinoma, with the cornified envelope formation and antigen processing cross-presentation pathways in common between Oral Lichen Planus and early Oral Squamous Cell Carcinoma. Together, these results reveal that key tumor suppressors and oncogenes such as PI3, SPRR1B and KRT17, as well as genes associated with different immune processes such as CXCL13, HIF1A and IL1B are dysregulated in OLP.
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Affiliation(s)
- Cristóvão Antunes de Lanna
- grid.419166.dLaboratory of Bioinformatics and Computational Biology, Division of Experimental and Translational Research, Brazilian National Cancer Institute (INCA), Rio de Janeiro, 20231-050 Brazil
| | - Beatriz Nascimento Monteiro da Silva
- grid.419166.dDivision of Clinical Research and Technological Development of the National Cancer Institute José Alencar Gomes da Silva (INCA), Rio de Janeiro, RJ Brazil
| | - Andreia Cristina de Melo
- grid.419166.dDivision of Clinical Research and Technological Development of the National Cancer Institute José Alencar Gomes da Silva (INCA), Rio de Janeiro, RJ Brazil
| | - Martín H. Bonamino
- grid.419166.dImmunology and Tumor Biology Program-Research Coordination, Brazilian National Cancer Institute (INCA), Rio de Janeiro, Brazil ,grid.418068.30000 0001 0723 0931Presidency of Research and Biological Collections (VPPCB), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
| | - Lísia Daltro Borges Alves
- grid.419166.dDivision of Clinical Research and Technological Development of the National Cancer Institute José Alencar Gomes da Silva (INCA), Rio de Janeiro, RJ Brazil
| | - Luis Felipe Ribeiro Pinto
- grid.419166.dMolecular Carcinogenesis Program, Brazilian National Cancer Institute (INCA), Rio de Janeiro, Brazil
| | - Abel Silveira Cardoso
- grid.8536.80000 0001 2294 473XDepartment of Oral Pathology and Oral Diagnosis, School of Dentistry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Héliton Spíndola Antunes
- grid.419166.dDivision of Clinical Research and Technological Development of the National Cancer Institute José Alencar Gomes da Silva (INCA), Rio de Janeiro, RJ Brazil
| | - Mariana Boroni
- grid.419166.dLaboratory of Bioinformatics and Computational Biology, Division of Experimental and Translational Research, Brazilian National Cancer Institute (INCA), Rio de Janeiro, 20231-050 Brazil ,grid.411087.b0000 0001 0723 2494Experimental Medicine Research Cluster (EMRC), University of Campinas (UNICAMP), Campinas, 13083-970 Brazil
| | - Daniel Cohen Goldemberg
- grid.419166.dDivision of Clinical Research and Technological Development of the National Cancer Institute José Alencar Gomes da Silva (INCA), Rio de Janeiro, RJ Brazil ,grid.83440.3b0000000121901201Latin American Cooperative Oncology Group (LACOG)-Head and Neck, University College London (UCL), London, UK
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Missner AA, Johns JD, Gu S, Hoa M. Repurposable Drugs That Interact with Steroid Responsive Gene Targets for Inner Ear Disease. Biomolecules 2022; 12:1641. [PMID: 36358991 PMCID: PMC9687275 DOI: 10.3390/biom12111641] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/25/2022] [Accepted: 11/02/2022] [Indexed: 10/28/2023] Open
Abstract
Corticosteroids, oral or transtympanic, remain the mainstay for inner ear diseases characterized by hearing fluctuation or sudden changes in hearing, including sudden sensorineural hearing loss (SSNHL), Meniere's disease (MD), and autoimmune inner ear disease (AIED). Despite their use across these diseases, the rate of complete recovery remains low, and results across the literature demonstrates significant heterogeneity with respect to the effect of corticosteroids, suggesting a need to identify more efficacious treatment options. Previously, our group has cross-referenced steroid-responsive genes in the cochlea with published single-cell and single-nucleus transcriptome datasets to demonstrate that steroid-responsive differentially regulated genes are expressed in spiral ganglion neurons (SGN) and stria vascularis (SV) cell types. These differentially regulated genes represent potential druggable gene targets. We utilized multiple gene target databases (DrugBank, Pharos, and LINCS) to identify orally administered, FDA approved medications that potentially target these genes. We identified 42 candidate drugs that have been shown to interact with these genes, with an emphasis on safety profile, and tolerability. This study utilizes multiple databases to identify drugs that can target a number of druggable genes in otologic disorders that are commonly treated with steroids, providing a basis for establishing novel repurposing treatment trials.
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Affiliation(s)
| | - James Dixon Johns
- Department of Otolaryngology-Head and Neck Surgery, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Shoujun Gu
- Auditory Development and Restoration Program, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael Hoa
- Department of Otolaryngology-Head and Neck Surgery, Georgetown University Medical Center, Washington, DC 20007, USA
- Auditory Development and Restoration Program, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD 20892, USA
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27
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Ahmed F, Gi Ho S, Samantasinghar A, Memon FH, Rahim CSA, Soomro AM, Pratibha, Sunildutt N, Kim KH, Choi KH. Drug repurposing in psoriasis, performed by reversal of disease-associated gene expression profiles. Comput Struct Biotechnol J 2022; 20:6097-6107. [DOI: 10.1016/j.csbj.2022.10.046] [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] [Received: 09/18/2022] [Revised: 10/09/2022] [Accepted: 10/30/2022] [Indexed: 11/10/2022] Open
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28
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Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE. PubChem 2023 update. Nucleic Acids Res 2022; 51:D1373-D1380. [PMID: 36305812 PMCID: PMC9825602 DOI: 10.1093/nar/gkac956] [Citation(s) in RCA: 391] [Impact Index Per Article: 195.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 01/30/2023] Open
Abstract
PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves a wide range of use cases. In the past two years, a number of changes were made to PubChem. Data from more than 120 data sources was added to PubChem. Some major highlights include: the integration of Google Patents data into PubChem, which greatly expanded the coverage of the PubChem Patent data collection; the creation of the Cell Line and Taxonomy data collections, which provide quick and easy access to chemical information for a given cell line and taxon, respectively; and the update of the bioassay data model. In addition, new functionalities were added to the PubChem programmatic access protocols, PUG-REST and PUG-View, including support for target-centric data download for a given protein, gene, pathway, cell line, and taxon and the addition of the 'standardize' option to PUG-REST, which returns the standardized form of an input chemical structure. A significant update was also made to PubChemRDF. The present paper provides an overview of these changes.
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Affiliation(s)
- Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jie Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Asta Gindulyte
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jia He
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Siqian He
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Qingliang Li
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Benjamin A Shoemaker
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Paul A Thiessen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Bo Yu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Leonid Zaslavsky
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jian Zhang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Evan E Bolton
- To whom correspondence should be addressed. Tel: +1 301 451 1811; Fax: +1 301 480 4559;
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Lu L, Qin J, Chen J, Yu N, Miyano S, Deng Z, Li C. Recent computational drug repositioning strategies against SARS-CoV-2. Comput Struct Biotechnol J 2022; 20:5713-5728. [PMID: 36277237 PMCID: PMC9575573 DOI: 10.1016/j.csbj.2022.10.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 10/12/2022] [Accepted: 10/12/2022] [Indexed: 11/08/2022] Open
Abstract
We performed a comprehensive review of computational drug repositioning methods applied to COVID-19 based on differing data types including sequence data, expression data, structure data and interaction data. We found that graph theory and neural network were the most used strategies for drug repositioning in the case of COVID-19. Integrating different levels of data may improve the success rate for drug repositioning.
Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.
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Affiliation(s)
- Lu Lu
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,Zhejiang Provincial Key Laboratory of Genetic & Developmental Disorders, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiale Qin
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Hangzhou, China
| | - Jiandong Chen
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,School of Public Health, Undergraduate School of Zhejiang University, Hangzhou, China
| | - Na Yu
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Zhenzhong Deng
- Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China,Corresponding authors at: Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China (C. Li).
| | - Chen Li
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,Zhejiang Provincial Key Laboratory of Genetic & Developmental Disorders, Zhejiang University School of Medicine, Hangzhou, China,Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China,Corresponding authors at: Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China (C. Li).
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30
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Chen D, Liu Z, Wang J, Yang C, Pan C, Tang Y, Zhang P, Liu N, Li G, Li Y, Wu Z, Xia F, Zhang C, Nie H, Tang Z. Integrative genomic analysis facilitates precision strategies for glioblastoma treatment. iScience 2022; 25:105276. [PMID: 36300002 PMCID: PMC9589211 DOI: 10.1016/j.isci.2022.105276] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 08/29/2022] [Accepted: 09/30/2022] [Indexed: 11/13/2022] Open
Abstract
Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a dismal prognosis. Currently, the standard treatments for GBM rarely achieve satisfactory results, which means that current treatments are not individualized and precise enough. In this study, a multiomics-based GBM classification was established and three subclasses (GPA, GPB, and GPC) were identified, which have different molecular features both in bulk samples and at single-cell resolution. A robust GBM poor prognostic signature (GPS) score model was then developed using machine learning method, manifesting an excellent ability to predict the survival of GBM. NVP−BEZ235, GDC−0980, dasatinib and XL765 were ultimately identified to have subclass-specific efficacy targeting patients with a high risk of poor prognosis. Furthermore, the GBM classification and GPS score model could be considered as potential biomarkers for immunotherapy response. In summary, an integrative genomic analysis was conducted to advance individual-based therapies in GBM. A multiomics-based classification of GBM was established Single-cell transcriptomic profiling of GBM subclasses was revealed using Scissor A robust prognostic risk model was developed for GBM by machine learning method Prediction of potential agents based on molecular and prognostic risk stratification
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Affiliation(s)
- Danyang Chen
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhicheng Liu
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jingxuan Wang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Chen Yang
- State Key Laboratory of Oncogenes and Related Genes, Department of Liver Surgery and Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200032, China
| | - Chao Pan
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yingxin Tang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ping Zhang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Na Liu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Gaigai Li
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yan Li
- State Key Laboratory of Oncogenes and Related Genes, Department of Liver Surgery and Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200032, China,Department of Immunology, Sun Yat-Sen University, Zhongshan School of Medicine, Guangzhou, Guangdong 510080, China
| | - Zhuojin Wu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Feng Xia
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Cuntai Zhang
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Hao Nie
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China,Corresponding author
| | - Zhouping Tang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China,Corresponding author
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31
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Metzger JJ, Pereda C, Adhikari A, Haremaki T, Galgoczi S, Siggia ED, Brivanlou AH, Etoc F. Deep-learning analysis of micropattern-based organoids enables high-throughput drug screening of Huntington's disease models. Cell Rep Methods 2022; 2:100297. [PMID: 36160045 PMCID: PMC9500000 DOI: 10.1016/j.crmeth.2022.100297] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 02/06/2022] [Accepted: 08/19/2022] [Indexed: 11/19/2022]
Abstract
Organoids are carrying the promise of modeling complex disease phenotypes and serving as a powerful basis for unbiased drug screens, potentially offering a more efficient drug-discovery route. However, unsolved technical bottlenecks of reproducibility and scalability have prevented the use of current organoids for high-throughput screening. Here, we present a method that overcomes these limitations by using deep-learning-driven analysis for phenotypic drug screens based on highly standardized micropattern-based neural organoids. This allows us to distinguish between disease and wild-type phenotypes in complex tissues with extremely high accuracy as well as quantify two predictors of drug success: efficacy and adverse effects. We applied our approach to Huntington's disease (HD) and discovered that bromodomain inhibitors revert complex phenotypes induced by the HD mutation. This work demonstrates the power of combining machine learning with phenotypic drug screening and its successful application to reveal a potentially new druggable target for HD.
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Affiliation(s)
- Jakob J. Metzger
- Laboratory of Stem Cell Biology and Molecular Embryology, The Rockefeller University, New York, NY 10065, USA
- Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 10065, USA
| | - Carlota Pereda
- RUMI Scientific, Alexandria LaunchLabs, New York, NY 10016, USA
| | - Arjun Adhikari
- RUMI Scientific, Alexandria LaunchLabs, New York, NY 10016, USA
| | - Tomomi Haremaki
- Laboratory of Stem Cell Biology and Molecular Embryology, The Rockefeller University, New York, NY 10065, USA
- RUMI Scientific, Alexandria LaunchLabs, New York, NY 10016, USA
| | - Szilvia Galgoczi
- Laboratory of Stem Cell Biology and Molecular Embryology, The Rockefeller University, New York, NY 10065, USA
| | - Eric D. Siggia
- Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 10065, USA
| | - Ali H. Brivanlou
- Laboratory of Stem Cell Biology and Molecular Embryology, The Rockefeller University, New York, NY 10065, USA
| | - Fred Etoc
- Laboratory of Stem Cell Biology and Molecular Embryology, The Rockefeller University, New York, NY 10065, USA
- RUMI Scientific, Alexandria LaunchLabs, New York, NY 10016, USA
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32
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Leung RWT, Jiang X, Zong X, Zhang Y, Hu X, Hu Y, Qin J. CORN-Condition Orientated Regulatory Networks: bridging conditions to gene networks. Brief Bioinform 2022; 23:6702670. [PMID: 36124777 PMCID: PMC9677472 DOI: 10.1093/bib/bbac402] [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: 05/24/2022] [Revised: 08/18/2022] [Accepted: 08/19/2022] [Indexed: 12/14/2022] Open
Abstract
A transcriptional regulatory network (TRN) is a collection of transcription regulators with their associated downstream genes, which is highly condition-specific. Understanding how cell states can be programmed through small molecules/drugs or conditions by modulating the whole gene expression system granted us the potential to amend abnormal cells and cure diseases. Condition Orientated Regulatory Networks (CORN, https://qinlab.sysu.edu.cn/home) is a library of condition (small molecule/drug treatments and gene knockdowns)-based transcriptional regulatory sub-networks (TRSNs) that come with an online TRSN matching tool. It allows users to browse condition-associated TRSNs or match those TRSNs by inputting transcriptomic changes of interest. CORN utilizes transcriptomic changes data after specific conditional treatment in cells, and in vivo transcription factor (TF) binding data in cells, by combining TF binding information and calculations of significant expression alterations of TFs and genes after the conditional treatments, TRNs under the effect of different conditions were constructed. In short, CORN associated 1805 different types of specific conditions (small molecule/drug treatments and gene knockdowns) to 9553 TRSNs in 25 human cell lines, involving 204TFs. By linking and curating specific conditions to responsive TRNs, the scientific community can now perceive how TRNs are altered and controlled by conditions alone in an organized manner for the first time. This study demonstrated with examples that CORN can aid the understanding of molecular pathology, pharmacology and drug repositioning, and screened drugs with high potential for cancer and coronavirus disease 2019 (COVID-19) treatments.
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Affiliation(s)
| | | | | | - Yanhong Zhang
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China
| | - Xinlin Hu
- College of Mathematics and Statistics, Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China,Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Yaohua Hu
- Corresponding authors: Yaohua Hu, College of Mathematics and Statistics, Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China; Jing Qin, School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China. E-mail:
| | - Jing Qin
- Corresponding authors: Yaohua Hu, College of Mathematics and Statistics, Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China; Jing Qin, School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China. E-mail:
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33
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Pilarczyk M, Fazel-Najafabadi M, Kouril M, Shamsaei B, Vasiliauskas J, Niu W, Mahi N, Zhang L, Clark NA, Ren Y, White S, Karim R, Xu H, Biesiada J, Bennett MF, Davidson SE, Reichard JF, Roberts K, Stathias V, Koleti A, Vidovic D, Clarke DJB, Schürer SC, Ma'ayan A, Meller J, Medvedovic M. Connecting omics signatures and revealing biological mechanisms with iLINCS. Nat Commun 2022; 13:4678. [PMID: 35945222 PMCID: PMC9362980 DOI: 10.1038/s41467-022-32205-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 07/21/2022] [Indexed: 11/21/2022] Open
Abstract
There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here we present iLINCS ( http://ilincs.org ), an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates mining and re-analysis of the large collection of omics datasets (>34,000), pre-computed signatures (>200,000), and their connections, as well as the analysis of user-submitted omics signatures of diseases and cellular perturbations. iLINCS analysis workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures. iLINCS user-friendly interfaces enable execution of sophisticated analyses of omics signatures, mechanism of action analysis, and signature-driven drug repositioning. We illustrate the utility of iLINCS with three use cases involving analysis of cancer proteogenomic signatures, COVID 19 transcriptomic signatures and mTOR signaling.
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Affiliation(s)
- Marcin Pilarczyk
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Mehdi Fazel-Najafabadi
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Michal Kouril
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Behrouz Shamsaei
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Juozas Vasiliauskas
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Wen Niu
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Naim Mahi
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Lixia Zhang
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Nicholas A Clark
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Yan Ren
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Shana White
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Rashid Karim
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - Huan Xu
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Jacek Biesiada
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - Mark F Bennett
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Sarah E Davidson
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - John F Reichard
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Kurt Roberts
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - Vasileios Stathias
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Amar Koleti
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Dusica Vidovic
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Daniel J B Clarke
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Stephan C Schürer
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Avi Ma'ayan
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jarek Meller
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - Mario Medvedovic
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA.
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA.
- LINCS Data Coordination and Integration Center (DCIC), New York, USA.
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA.
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34
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Xie Z, Kropiwnicki E, Wojciechowicz ML, Jagodnik KM, Shu I, Bailey A, Clarke DJB, Jeon M, Evangelista JE, Kuleshov M, Lachmann A, Parigi AA, Sanchez JM, Jenkins SL, Ma’ayan A. Getting Started with LINCS Datasets and Tools. Curr Protoc 2022; 2:e487. [PMID: 35876555 PMCID: PMC9326873 DOI: 10.1002/cpz1.487] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The Library of Integrated Network-based Cellular Signatures (LINCS) was an NIH Common Fund program that aimed to expand our knowledge about human cellular responses to chemical, genetic, and microenvironment perturbations. Responses to perturbations were measured by transcriptomics, proteomics, cellular imaging, and other high content assays. The second phase of the LINCS program, which lasted 7 years, involved the engagement of six data and signature generation centers (DSGCs) and one data coordination and integration center (DCIC). The DSGCs and the DCIC developed several digital resources, including tools, databases, and workflows that aim to facilitate the use of the LINCS data and integrate this data with other publicly available data. The digital resources developed by the DSGCs and the DCIC can be used to gain new biological and pharmacological insights that can lead to the development of novel therapeutics. This protocol provides step-by-step instructions for processing the LINCS data into signatures, and utilizing the digital resources developed by the LINCS consortia for hypothesis generation and knowledge discovery. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Navigating L1000 tools and data in CLUE.io Basic Protocol 2: Computing signatures from the L1000 data with the CD method Basic Protocol 3: Analyzing lists of differentially expressed genes and querying them against the L1000 data with BioJupies and the Bulk RNA-seq Appyter Basic Protocol 4: Utilizing the L1000FWD resource for drug discovery Basic Protocol 5: KINOMEscan and the KINOMEscan Appyter Basic Protocol 6: LINCS P100 and GCP Proteomics Assays Basic Protocol 7: The LINCS Joint Project (LJP) Basic Protocol 8: The LINCS Data Portals and SigCom LINCS Basic Protocol 9: Creating and analyzing signatures with iLINCS.
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Affiliation(s)
- Zhuorui Xie
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Eryk Kropiwnicki
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Megan L. Wojciechowicz
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Kathleen M. Jagodnik
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Ingrid Shu
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Allison Bailey
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Daniel J. B. Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Minji Jeon
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - John Erol Evangelista
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Maxim Kuleshov
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Alexander Lachmann
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Abhijna A. Parigi
- School of Veterinary Medicine, University of California Davis, Davis, CA 95616, USA
| | - Jose M. Sanchez
- School of Veterinary Medicine, University of California Davis, Davis, CA 95616, USA
| | - Sherry L. Jenkins
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Avi Ma’ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
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Trastulla L, Noorbakhsh J, Vazquez F, McFarland J, Iorio F. Computational estimation of quality and clinical relevance of cancer cell lines. Mol Syst Biol 2022; 18:e11017. [PMID: 35822563 PMCID: PMC9277610 DOI: 10.15252/msb.202211017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 12/12/2022] Open
Abstract
Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome‐wide editing screenings have facilitated the discovery of clinically relevant gene–drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine‐learning‐based directions that could resolve some of the arising discrepancies.
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Affiliation(s)
- Lucia Trastulla
- Human Technopole, Milano, Italy.,Open Targets, Cambridge, UK
| | | | - Francisca Vazquez
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Francesco Iorio
- Human Technopole, Milano, Italy.,Open Targets, Cambridge, UK
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Gao H, Baylis RA, Luo L, Kojima Y, Bell CF, Ross EG, Wang F, Leeper NJ. Clustering cancers by shared transcriptional risk reveals novel targets for cancer therapy. Mol Cancer 2022; 21:116. [PMID: 35585548 PMCID: PMC9115915 DOI: 10.1186/s12943-022-01592-y] [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] [Received: 02/22/2022] [Accepted: 05/10/2022] [Indexed: 05/31/2023] Open
Affiliation(s)
- Hua Gao
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.,Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA
| | - Richard A Baylis
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Lingfeng Luo
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.,Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA
| | - Yoko Kojima
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.,Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA
| | - Caitlin F Bell
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA.,Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Biomedical Innovations Building, 240 Pasteur Drive, #3654, Stanford, CA, 94305, USA
| | - Elsie G Ross
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Fudi Wang
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.,Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA
| | - Nicholas J Leeper
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA. .,Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA. .,Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Biomedical Innovations Building, 240 Pasteur Drive, #3654, Stanford, CA, 94305, USA.
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Evangelista JE, Clarke DJB, Xie Z, Lachmann A, Jeon M, Chen K, Jagodnik KM, Jenkins SL, Kuleshov MV, Wojciechowicz ML, Schürer SC, Medvedovic M, Ma'ayan A. SigCom LINCS: data and metadata search engine for a million gene expression signatures. Nucleic Acids Res 2022; 50:W697-W709. [PMID: 35524556 PMCID: PMC9252724 DOI: 10.1093/nar/gkac328] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [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: 02/22/2022] [Revised: 04/04/2022] [Accepted: 04/20/2022] [Indexed: 12/13/2022] Open
Abstract
Millions of transcriptome samples were generated by the Library of Integrated Network-based Cellular Signatures (LINCS) program. When these data are processed into searchable signatures along with signatures extracted from Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO), connections between drugs, genes, pathways and diseases can be illuminated. SigCom LINCS is a webserver that serves over a million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO. SigCom LINCS is built with Signature Commons, a cloud-agnostic skeleton Data Commons with a focus on serving searchable signatures. SigCom LINCS provides a rapid signature similarity search for mimickers and reversers given sets of up and down genes, a gene set, a single gene, or any search term. Additionally, users of SigCom LINCS can perform a metadata search to find and analyze subsets of signatures and find information about genes and drugs. SigCom LINCS is findable, accessible, interoperable, and reusable (FAIR) with metadata linked to standard ontologies and vocabularies. In addition, all the data and signatures within SigCom LINCS are available via a well-documented API. In summary, SigCom LINCS, available at https://maayanlab.cloud/sigcom-lincs, is a rich webserver resource for accelerating drug and target discovery in systems pharmacology.
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Affiliation(s)
- John Erol Evangelista
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Alexander Lachmann
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Minji Jeon
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Kerwin Chen
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Kathleen M Jagodnik
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Sherry L Jenkins
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Maxim V Kuleshov
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Megan L Wojciechowicz
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Stephan C Schürer
- Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Mario Medvedovic
- Department of Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Avi Ma'ayan
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
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Zheng Z, Xie W, Chen X, Wang F, Huang L, Li X, Lin Q, Wong KC. Subclass-specific Prognosis and Treatment Efficacy Inference in Head and Neck Squamous Carcinoma. IEEE J Biomed Health Inform 2022; 26:4303-4313. [PMID: 35439152 DOI: 10.1109/jbhi.2022.3168289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Exploring the prognostic classification and biomarkers in Head and Neck Squamous Carcinoma (HNSC) is of great clinical significance. We hybridized three prominent strategies to comprehensively characterize the molecular features of HNSC. We constructed a 15-gene signature to predict patients death risk with an average AUC of 0.744 for 1-, 3-, and 5-year on TCGA-HNSC training set, and average AUCs of 0.636, 0.584, 0.755 in GSE65858, GSE-112026, CPTAC-HNSCC datasets, respectively. By combined with NMF clustering and consensus clustering of fraction of tumor immune cell infiltration (ICI) in the tumor microenvironment (TME), we captured a more refined biological characteristics of HNSC, and observed a prognosis heterogeneity in high tumor immunity patients. By matching tumor subset-specific expression signatures to drug-induced cell line expression profiles from large-scale pharmacogenomic databases in the OCTAD workspace, we identified a group of HNSC patients featured with poor prognosis and demonstrated that the individuals in this group are likely to receive increased drug sensitivity to reverse differentially expressed disease signature genes. This trend is especially highlighted among those with higher death risk and tumour immunity.
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Federico A, Fratello M, Scala G, Möbus L, Pavel A, Del Giudice G, Ceccarelli M, Costa V, Ciccodicola A, Fortino V, Serra A, Greco D. Integrated Network Pharmacology Approach for Drug Combination Discovery: A Multi-Cancer Case Study. Cancers (Basel) 2022; 14:2043. [PMID: 35454948 DOI: 10.3390/cancers14082043] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/04/2022] [Accepted: 04/13/2022] [Indexed: 12/20/2022] Open
Abstract
Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed a multilayer network-based computational framework integrating perturbational signatures of the disease as well as intrinsic characteristics of the drugs, such as their mechanism of action and chemical structure. We present five case studies carried out on public data from The Cancer Genome Atlas, including invasive breast cancer, colon adenocarcinoma, lung squamous cell carcinoma, hepatocellular carcinoma and prostate adenocarcinoma. Our results highlight paclitaxel as a suitable drug for combination therapy for many of the considered cancer types. In addition, several non-cancer-related genes representing unusual drug targets were identified as potential candidates for pharmacological treatment of cancer.
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Li X, Shong K, Kim W, Yuan M, Yang H, Sato Y, Kume H, Ogawa S, Turkez H, Shoaie S, Boren J, Nielsen J, Uhlen M, Zhang C, Mardinoglu A. Prediction of drug candidates for clear cell renal cell carcinoma using a systems biology-based drug repositioning approach. EBioMedicine 2022; 78:103963. [PMID: 35339898 PMCID: PMC8960981 DOI: 10.1016/j.ebiom.2022.103963] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 03/09/2022] [Accepted: 03/09/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The response rates of the clinical chemotherapies are still low in clear cell renal cell carcinoma (ccRCC). Computational drug repositioning is a promising strategy to discover new uses for existing drugs to treat patients who cannot get benefits from clinical drugs. METHODS We proposed a systematic approach which included the target prediction based on the co-expression network analysis of transcriptomics profiles of ccRCC patients and drug repositioning for cancer treatment based on the analysis of shRNA- and drug-perturbed signature profiles of human kidney cell line. FINDINGS First, based on the gene co-expression network analysis, we identified two types of gene modules in ccRCC, which significantly enriched with unfavorable and favorable signatures indicating poor and good survival outcomes of patients, respectively. Then, we selected four genes, BUB1B, RRM2, ASF1B and CCNB2, as the potential drug targets based on the topology analysis of modules. Further, we repurposed three most effective drugs for each target by applying the proposed drug repositioning approach. Finally, we evaluated the effects of repurposed drugs using an in vitro model and observed that these drugs inhibited the protein levels of their corresponding target genes and cell viability. INTERPRETATION These findings proved the usefulness and efficiency of our approach to improve the drug repositioning researches for cancer treatment and precision medicine. FUNDING This study was funded by Knut and Alice Wallenberg Foundation and Bash Biotech Inc., San Diego, CA, USA.
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Affiliation(s)
- Xiangyu Li
- Bash Biotech Inc, 600 est Broadway, Suite 700, San Diego, CA 92101, USA; Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Koeun Shong
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Woonghee Kim
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Meng Yuan
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Hong Yang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Yusuke Sato
- Department of Pathology and Tumor Biology, Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto 606-8501, Japan; Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan
| | - Haruki Kume
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto 606-8501, Japan; Centre for Hematology and Regenerative Medicine, Department of Medicine, Karolinska Institute, Stockholm SE-17177, Sweden
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum 25240, Turkey
| | - Saeed Shoaie
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg SE-41345, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-41296, Sweden; BioInnovation Institute, Copenhagen N DK-2200, Denmark
| | - Mathias Uhlen
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden; Key Laboratory of Advanced Drug Preparation Technologies, School of Pharmaceutical Sciences, Ministry of Education, Zhengzhou University, Zhengzhou 450001, China.
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK.
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Barazandeh M, Kriti D, Nislow C, Giaever G. The cellular response to drug perturbation is limited: comparison of large-scale chemogenomic fitness signatures. BMC Genomics 2022; 23:197. [PMID: 35277135 PMCID: PMC8915488 DOI: 10.1186/s12864-022-08395-x] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 02/17/2022] [Indexed: 11/25/2022] Open
Abstract
Background Chemogenomic profiling is a powerful approach for understanding the genome-wide cellular response to small molecules. First developed in Saccharomyces cerevisiae, chemogenomic screens provide direct, unbiased identification of drug target candidates as well as genes required for drug resistance. While many laboratories have performed chemogenomic fitness assays, few have been assessed for reproducibility and accuracy. Here we analyze the two largest independent yeast chemogenomic datasets comprising over 35 million gene-drug interactions and more than 6000 unique chemogenomic profiles; the first from our own academic laboratory (HIPLAB) and the second from the Novartis Institute of Biomedical Research (NIBR). Results Despite substantial differences in experimental and analytical pipelines, the combined datasets revealed robust chemogenomic response signatures, characterized by gene signatures, enrichment for biological processes and mechanisms of drug action. We previously reported that the cellular response to small molecules is limited and can be described by a network of 45 chemogenomic signatures. In the present study, we show that the majority of these signatures (66%) are also found in the companion dataset, providing further support for their biological relevance as conserved systems-level, small molecule response systems. Conclusions Our results demonstrate the robustness of chemogenomic fitness profiling in yeast, while offering guidelines for performing other high-dimensional comparisons including parallel CRISPR screens in mammalian cells. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08395-x.
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Guala D, Sonnhammer ELL. Network Crosstalk as a Basis for Drug Repurposing. Front Genet 2022; 13:792090. [PMID: 35350247 PMCID: PMC8958038 DOI: 10.3389/fgene.2022.792090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 01/27/2022] [Indexed: 11/23/2022] Open
Abstract
The need for systematic drug repurposing has seen a steady increase over the past decade and may be particularly valuable to quickly remedy unexpected pandemics. The abundance of functional interaction data has allowed mapping of substantial parts of the human interactome modeled using functional association networks, favoring network-based drug repurposing. Network crosstalk-based approaches have never been tested for drug repurposing despite their success in the related and more mature field of pathway enrichment analysis. We have, therefore, evaluated the top performing crosstalk-based approaches for drug repurposing. Additionally, the volume of new interaction data as well as more sophisticated network integration approaches compelled us to construct a new benchmark for performance assessment of network-based drug repurposing tools, which we used to compare network crosstalk-based methods with a state-of-the-art technique. We find that network crosstalk-based drug repurposing is able to rival the state-of-the-art method and in some cases outperform it.
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Affiliation(s)
- Dimitri Guala
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
- Merck AB, Solna, Sweden
| | - Erik L. L. Sonnhammer
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
- *Correspondence: Erik L. L. Sonnhammer,
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Vulliard L, Hancock J, Kamnev A, Fell CW, Ferreira da Silva J, Loizou JI, Nagy V, Dupré L, Menche J. BioProfiling.jl: profiling biological perturbations with high-content imaging in single cells and heterogeneous populations. Bioinformatics 2022; 38:1692-1699. [PMID: 34935929 PMCID: PMC8896612 DOI: 10.1093/bioinformatics/btab853] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [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: 06/15/2021] [Revised: 12/15/2021] [Accepted: 12/16/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION High-content imaging screens provide a cost-effective and scalable way to assess cell states across diverse experimental conditions. The analysis of the acquired microscopy images involves assembling and curating raw cellular measurements into morphological profiles suitable for testing biological hypotheses. Despite being a critical step, general-purpose and adaptable tools for morphological profiling are lacking and no solution is available for the high-performance Julia programming language. RESULTS Here, we introduce BioProfiling.jl, an efficient end-to-end solution for compiling and filtering informative morphological profiles in Julia. The package contains all the necessary data structures to curate morphological measurements and helper functions to transform, normalize and visualize profiles. Robust statistical distances and permutation tests enable quantification of the significance of the observed changes despite the high fraction of outliers inherent to high-content screens. This package also simplifies visual artifact diagnostics, thus streamlining a bottleneck of morphological analyses. We showcase the features of the package by analyzing a chemical imaging screen, in which the morphological profiles prove to be informative about the compounds' mechanisms of action and can be conveniently integrated with the network localization of molecular targets. AVAILABILITY AND IMPLEMENTATION The Julia package is available on GitHub: https://github.com/menchelab/BioProfiling.jl. We also provide Jupyter notebooks reproducing our analyses: https://github.com/menchelab/BioProfilingNotebooks. The data underlying this article are available from FigShare, at https://doi.org/10.6084/m9.figshare.14784678.v2. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Loan Vulliard
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna 1090, Austria
- Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Vienna 1030, Austria
| | - Joel Hancock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna 1090, Austria
- Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Vienna 1030, Austria
| | - Anton Kamnev
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna 1090, Austria
- Department of Dermatology, Medical University of Vienna, Vienna 1090, Austria
| | - Christopher W Fell
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna 1090, Austria
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna 1090, Austria
- Department of Neurology, Medical University of Vienna, Vienna 1090, Austria
| | - Joana Ferreira da Silva
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna 1090, Austria
- Institute of Cancer Research, Department of Medicine I, Medical University of Vienna and Comprehensive Cancer Center, Vienna 1090, Austria
| | - Joanna I Loizou
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna 1090, Austria
- Institute of Cancer Research, Department of Medicine I, Medical University of Vienna and Comprehensive Cancer Center, Vienna 1090, Austria
| | - Vanja Nagy
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna 1090, Austria
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna 1090, Austria
- Department of Neurology, Medical University of Vienna, Vienna 1090, Austria
| | - Loïc Dupré
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna 1090, Austria
- Department of Dermatology, Medical University of Vienna, Vienna 1090, Austria
- Toulouse Institute for Infectious and Inflammatory Diseases (INFINITy), INSERM UMR1291, CNRS UMR5051, Toulouse III Paul Sabatier University, Toulouse 31024, France
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Abstract
Single-cell sequencing technologies are revolutionizing cancer research and are poised to become the standard for translational cancer studies. Rapidly decreasing costs and increasing throughput and resolution are paving the way for the adoption of single-cell technologies in clinical settings for personalized medicine applications. In this chapter, we review the state of the art of single-cell DNA and RNA sequencing technologies, the computational tools to analyze the data, and their potential application to precision oncology. We also discuss the advantages of single-cell over bulk sequencing for the dissection of intra-tumor heterogeneity and the characterization of subclonal cell populations, the implementation of targeted drug repurposing approaches, and describe advanced methodologies for multi-omics data integration and to assess cell signaling at single-cell resolution.
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Affiliation(s)
- David T Melnekoff
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alessandro Laganà
- Department of Genetics and Genomic Sciences, Department of Oncological Sciences, Mount Sinai Icahn School of Medicine, New York, NY, USA.
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Garcia-moreno A, López-domínguez R, Villatoro-garcía JA, Ramirez-mena A, Aparicio-puerta E, Hackenberg M, Pascual-montano A, Carmona-saez P. Functional Enrichment Analysis of Regulatory Elements. Biomedicines 2022; 10:590. [PMID: 35327392 PMCID: PMC8945021 DOI: 10.3390/biomedicines10030590] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/22/2022] [Accepted: 02/25/2022] [Indexed: 01/27/2023] Open
Abstract
Statistical methods for enrichment analysis are important tools to extract biological information from omics experiments. Although these methods have been widely used for the analysis of gene and protein lists, the development of high-throughput technologies for regulatory elements demands dedicated statistical and bioinformatics tools. Here, we present a set of enrichment analysis methods for regulatory elements, including CpG sites, miRNAs, and transcription factors. Statistical significance is determined via a power weighting function for target genes and tested by the Wallenius noncentral hypergeometric distribution model to avoid selection bias. These new methodologies have been applied to the analysis of a set of miRNAs associated with arrhythmia, showing the potential of this tool to extract biological information from a list of regulatory elements. These new methods are available in GeneCodis 4, a web tool able to perform singular and modular enrichment analysis that allows the integration of heterogeneous information.
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Barker CG, Petsalaki E, Giudice G, Sero J, Ekpenyong EN, Bakal C, Petsalaki E. Identification of phenotype-specific networks from paired gene expression-cell shape imaging data. Genome Res 2022; 32:750-765. [PMID: 35197309 PMCID: PMC8997347 DOI: 10.1101/gr.276059.121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 07/29/2021] [Accepted: 02/17/2022] [Indexed: 11/24/2022]
Abstract
The morphology of breast cancer cells is often used as an indicator of tumor severity and prognosis. Additionally, morphology can be used to identify more fine-grained, molecular developments within a cancer cell, such as transcriptomic changes and signaling pathway activity. Delineating the interface between morphology and signaling is important to understand the mechanical cues that a cell processes in order to undergo epithelial-to-mesenchymal transition and consequently metastasize. However, the exact regulatory systems that define these changes remain poorly characterized. In this study, we used a network-systems approach to integrate imaging data and RNA-seq expression data. Our workflow allowed the discovery of unbiased and context-specific gene expression signatures and cell signaling subnetworks relevant to the regulation of cell shape, rather than focusing on the identification of previously known, but not always representative, pathways. By constructing a cell-shape signaling network from shape-correlated gene expression modules and their upstream regulators, we found central roles for developmental pathways such as WNT and Notch, as well as evidence for the fine control of NF-kB signaling by numerous kinase and transcriptional regulators. Further analysis of our network implicates a gene expression module enriched in the RAP1 signaling pathway as a mediator between the sensing of mechanical stimuli and regulation of NF-kB activity, with specific relevance to cell shape in breast cancer.
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Zahoránszky-Kőhalmi G, Siramshetty VB, Kumar P, Gurumurthy M, Grillo B, Mathew B, Metaxatos D, Backus M, Mierzwa T, Simon R, Grishagin I, Brovold L, Mathé EA, Hall MD, Michael SG, Godfrey AG, Mestres J, Jensen LJ, Oprea TI. A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research. J Chem Inf Model 2022; 62:718-729. [PMID: 35057621 PMCID: PMC10790216 DOI: 10.1021/acs.jcim.1c00431] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen, and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semiautomated integration of rapidly emerging data sets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 63 278 host-host protein, and 1221 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is made publicly accessible via a web interface and via API calls based on the Bolt protocol. Details for accessing the database are provided on a landing page (https://neo4covid19.ncats.io/). We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.
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Affiliation(s)
| | - Vishal B. Siramshetty
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Praveen Kumar
- Department of Internal Medicine, University of New Mexico School of Medicine, 1 University of New Mexico, Albuquerque, NM 87131, USA
- Department of Computer Science, University of New Mexico, 1 University of New Mexico Albuquerque, NM 87131, USA
| | - Manideep Gurumurthy
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Busola Grillo
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Biju Mathew
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Dimitrios Metaxatos
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Mark Backus
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Tim Mierzwa
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Reid Simon
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Ivan Grishagin
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
- Rancho BioSciences LLC., 16955 Via Del Campo Suite 200, San Diego, CA 92127, USA
| | - Laura Brovold
- Rancho BioSciences LLC., 16955 Via Del Campo Suite 200, San Diego, CA 92127, USA
| | - Ewy A. Mathé
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Matthew D. Hall
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Samuel G. Michael
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Alexander G. Godfrey
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Jordi Mestres
- Research Group on Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
| | - Lars J. Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences,University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
| | - Tudor I. Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, 1 University of New Mexico, Albuquerque, NM 87131, USA
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences,University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
- UNM Comprehensive Cancer Center, 1201 Camino de Salud NE, Albuquerque, NM 87102, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Box 480, 40530 Gothenburg, Sweden
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48
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Huang T, Li J, Wang SM. Etiological roles of core promoter variation in triple-negative breast cancer. Genes Dis 2022; 10:228-238. [PMID: 37013029 PMCID: PMC10066267 DOI: 10.1016/j.gendis.2022.01.003] [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] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 12/26/2021] [Accepted: 01/12/2022] [Indexed: 10/19/2022] Open
Abstract
Abnormal gene expression plays key role in cancer development. A core promoter is located around the transcriptional start site. Through interaction between core promoter sequences and transcriptional factors, core promoter controls transcriptional initiation. We hypothesized that in cancer, core promoter sequences could be mutated to interfere the interaction with transcriptional factors, resulting in altered transcriptional initiation and abnormal gene expression and cancer development. We used triple-negative breast cancer (TNBC) as a model to test our hypothesis. We collected genome-wide core promoter variants from 279 TNBC genomes. After extensive filtering of normal genomic polymorphism, we identified 19,427 recurrent somatic variants in 1,238 core promoters of 1,274 genes and 1,694 recurrent germline variants in 272 core promoters of 294 genes. Many of the affected genes were oncogenes and tumor suppressors. Analysis of RNA-seq data from the same patient cohort identified increased or decreased gene expression in 439 somatic and 85 germline variants-affected genes, and the results were validated by luciferase reporter assay. By comparing with the core promoter variation data from 610 unclassified breast cancer, we observed that core promoter variants in TNBC were highly TNBC-specific. We further identified the drugs targeting the genes with core promoter variation. Our study demonstrates that core promoter is highly mutable in cancer, and can play etiological roles in TNBC and other types of cancer through influencing transcriptional initiation.
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Yeh SJ, Chung YC, Chen BS. Investigating the Role of Obesity in Prostate Cancer and Identifying Biomarkers for Drug Discovery: Systems Biology and Deep Learning Approaches. Molecules 2022; 27:molecules27030900. [PMID: 35164166 PMCID: PMC8840188 DOI: 10.3390/molecules27030900] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/22/2022] [Accepted: 01/26/2022] [Indexed: 12/21/2022]
Abstract
Prostate cancer (PCa) is the second most frequently diagnosed cancer for men and is viewed as the fifth leading cause of death worldwide. The body mass index (BMI) is taken as a vital criterion to elucidate the association between obesity and PCa. In this study, systematic methods are employed to investigate how obesity influences the noncutaneous malignancies of PCa. By comparing the core signaling pathways of lean and obese patients with PCa, we are able to investigate the relationships between obesity and pathogenic mechanisms and identify significant biomarkers as drug targets for drug discovery. Regarding drug design specifications, we take drug–target interaction, drug regulation ability, and drug toxicity into account. One deep neural network (DNN)-based drug–target interaction (DTI) model is trained in advance for predicting drug candidates based on the identified biomarkers. In terms of the application of the DNN-based DTI model and the consideration of drug design specifications, we suggest two potential multiple-molecule drugs to prevent PCa (covering lean and obese PCa) and obesity-specific PCa, respectively. The proposed multiple-molecule drugs (apigenin, digoxin, and orlistat) not only help to prevent PCa, suppressing malignant metastasis, but also result in lower production of fatty acids and cholesterol, especially for obesity-specific PCa.
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Zauner R, Wimmer M, Dorfer S, Ablinger M, Koller U, Piñón Hofbauer J, Guttmann-Gruber C, Bauer JW, Wally V. Transcriptome-Guided Drug Repurposing for Aggressive SCCs. Int J Mol Sci 2022; 23:ijms23021007. [PMID: 35055192 PMCID: PMC8780441 DOI: 10.3390/ijms23021007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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: 12/03/2021] [Revised: 01/03/2022] [Accepted: 01/13/2022] [Indexed: 02/04/2023] Open
Abstract
Despite a significant rise in the incidence of cutaneous squamous cell carcinoma (SCC) in recent years, most SCCs are well treatable. However, against the background of pre-existing risk factors such as immunosuppression upon organ transplantation, or conditions such as recessive dystrophic epidermolysis bullosa (RDEB), SCCs arise more frequently and follow a particularly aggressive course. Notably, such SCC types display molecular similarities, despite their differing etiologies. We leveraged the similarities in transcriptomes between tumors from organ transplant recipients and RDEB-patients, augmented with data from more common head and neck (HN)-SCCs, to identify drugs that can be repurposed to treat these SCCs. The in silico approach used is based on the assumption that SCC-derived transcriptome profiles reflect critical tumor pathways that, if reversed towards healthy tissue, will attenuate the malignant phenotype. We determined tumor-specific signatures based on differentially expressed genes, which were then used to mine drug-perturbation data. By leveraging recent efforts in the systematic profiling and cataloguing of thousands of small molecule compounds, we identified drugs including selumetinib that specifically target key molecules within the MEK signaling cascade, representing candidates with the potential to be effective in the treatment of these rare and aggressive SCCs.
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Affiliation(s)
- Roland Zauner
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, 5020 Salzburg, Austria; (M.W.); (S.D.); (M.A.); (U.K.); (J.P.H.); (C.G.-G.); (J.W.B.); (V.W.)
- Correspondence:
| | - Monika Wimmer
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, 5020 Salzburg, Austria; (M.W.); (S.D.); (M.A.); (U.K.); (J.P.H.); (C.G.-G.); (J.W.B.); (V.W.)
| | - Sonja Dorfer
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, 5020 Salzburg, Austria; (M.W.); (S.D.); (M.A.); (U.K.); (J.P.H.); (C.G.-G.); (J.W.B.); (V.W.)
| | - Michael Ablinger
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, 5020 Salzburg, Austria; (M.W.); (S.D.); (M.A.); (U.K.); (J.P.H.); (C.G.-G.); (J.W.B.); (V.W.)
| | - Ulrich Koller
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, 5020 Salzburg, Austria; (M.W.); (S.D.); (M.A.); (U.K.); (J.P.H.); (C.G.-G.); (J.W.B.); (V.W.)
| | - Josefina Piñón Hofbauer
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, 5020 Salzburg, Austria; (M.W.); (S.D.); (M.A.); (U.K.); (J.P.H.); (C.G.-G.); (J.W.B.); (V.W.)
| | - Christina Guttmann-Gruber
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, 5020 Salzburg, Austria; (M.W.); (S.D.); (M.A.); (U.K.); (J.P.H.); (C.G.-G.); (J.W.B.); (V.W.)
| | - Johann W. Bauer
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, 5020 Salzburg, Austria; (M.W.); (S.D.); (M.A.); (U.K.); (J.P.H.); (C.G.-G.); (J.W.B.); (V.W.)
- Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University, 5020 Salzburg, Austria
| | - Verena Wally
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, 5020 Salzburg, Austria; (M.W.); (S.D.); (M.A.); (U.K.); (J.P.H.); (C.G.-G.); (J.W.B.); (V.W.)
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