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Qian ZY, Pan YQ, Li XX, Chen YX, Wu HX, Liu ZX, Kosar M, Bartek J, Wang ZX, Xu RH. Modulator of TMB-associated immune infiltration (MOTIF) predicts immunotherapy response and guides combination therapy. Sci Bull (Beijing) 2024; 69:803-822. [PMID: 38320897 DOI: 10.1016/j.scib.2024.01.025] [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: 08/01/2023] [Revised: 11/04/2023] [Accepted: 12/07/2023] [Indexed: 02/08/2024]
Abstract
Patients with high tumor mutational burden (TMB) levels do not consistently respond to immune checkpoint inhibitors (ICIs), possibly because a high TMB level does not necessarily result in adequate infiltration of CD8+ T cells. Using bulk ribonucleic acid sequencing (RNA-seq) data from 9311 tumor samples across 30 cancer types, we developed a novel tool called the modulator of TMB-associated immune infiltration (MOTIF), which comprises genes that can determine the extent of CD8+ T cell infiltration prompted by a certain TMB level. We confirmed that MOTIF can accurately reflect the integrity and defects of the cancer-immunity cycle. By analyzing 84 human single-cell RNA-seq datasets from 32 types of solid tumors, we revealed that MOTIF can provide insights into the diverse roles of various cell types in the modulation of CD8+ T cell infiltration. Using pretreatment RNA-seq data from 13 ICI-treated cohorts, we validated the use of MOTIF in predicting CD8+ T cell infiltration and ICI efficacy. Among the components of MOTIF, we identified EMC3 as a negative regulator of CD8+ T cell infiltration, which was validated via in vivo studies. Additionally, MOTIF provided guidance for the potential combinations of programmed death 1 blockade with certain immunostimulatory drugs to facilitate CD8+ T cell infiltration and improve ICI efficacy.
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Affiliation(s)
- Zheng-Yu Qian
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, China
| | - Yi-Qian Pan
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, China
| | - Xue-Xin Li
- Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm S-171 21, Sweden; Department of General Surgery, The Fourth Affiliated Hospital, China Medical University, Shenyang 110032, China
| | - Yan-Xing Chen
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, China
| | - Hao-Xiang Wu
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, China
| | - Ze-Xian Liu
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, China; Bioinformatics Platform, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Laboratory of Artificial Intelligence and Data Science, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Martin Kosar
- Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm S-171 21, Sweden; Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Haining 314400, China; Edinburgh Medical School, Biomedical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh EH1 1LT, UK
| | - Jiri Bartek
- Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm S-171 21, Sweden; Danish Cancer Society Research Center, Copenhagen DK-2100, Denmark.
| | - Zi-Xian Wang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, China; Laboratory of Artificial Intelligence and Data Science, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
| | - Rui-Hua Xu
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, China; Laboratory of Artificial Intelligence and Data Science, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
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Atri P, Shah A, Natarajan G, Rachagani S, Rauth S, Ganguly K, Carmicheal J, Ghersi D, Cox JL, Smith LM, Jain M, Kumar S, Ponnusamy MP, Seshacharyulu P, Batra SK. Connectivity mapping-based identification of pharmacological inhibitor targeting HDAC6 in aggressive pancreatic ductal adenocarcinoma. NPJ Precis Oncol 2024; 8:66. [PMID: 38454151 PMCID: PMC10920818 DOI: 10.1038/s41698-024-00562-5] [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: 08/21/2023] [Accepted: 02/23/2024] [Indexed: 03/09/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) remains highly lethal due to limited therapeutic options and expensive/burdensome drug discovery processes. Utilizing genomic-data-driven Connectivity Mapping (CMAP) to identify a drug closer to real-world PC targeting may improve pancreatic cancer (PC) patient outcomes. Initially, we mapped CMAP data to gene expression from 106 PC patients, identifying nine negatively connected drugs. These drugs were further narrowed down using a similar analysis for PC cell lines, human tumoroids, and patient-derived xenografts datasets, where ISOX emerged as the most potent agent to target PC. We used human and mouse syngeneic PC cells, human and mouse tumoroids, and in vivo mice to assess the ability of ISOX alone and in combination with 5FU to inhibit tumor growth. Global transcriptomic and pathway analysis of the ISOX-LINCS signature identified HDAC 6/cMyc as the target axis for ISOX. Specifically, we discovered that genetic and pharmacological targeting of HDAC 6 affected non-histone protein cMyc acetylation, leading to cMyc instability, thereby disrupting PC growth and metastasis by affecting cancer stemness. Finally, KrasG12D harboring tumoroids and mice responded effectively against ISOX and 5FU treatment by enhancing survival and controlling metastasis incidence. Overall, our data validate ISOX as a new drug to treat advanced PC patients without toxicity to normal cells. Our study supports the clinical utility of ISOX along with 5FU in future PC clinical trials.
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Affiliation(s)
- Pranita Atri
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Ashu Shah
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Gopalakrishnan Natarajan
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Satyanarayana Rachagani
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Sanchita Rauth
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Koelina Ganguly
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Joseph Carmicheal
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Dario Ghersi
- School of Interdisciplinary Informatics, College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA
| | - Jesse L Cox
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Lynette M Smith
- Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA
| | - Maneesh Jain
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Sushil Kumar
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Moorthy P Ponnusamy
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | | | - Surinder K Batra
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA.
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA.
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, USA.
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3
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Pang Z, Cravatt BF, Ye L. Deciphering Drug Targets and Actions with Single-Cell and Spatial Resolution. Annu Rev Pharmacol Toxicol 2024; 64:507-526. [PMID: 37722721 DOI: 10.1146/annurev-pharmtox-033123-123610] [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] [Indexed: 09/20/2023]
Abstract
Recent advances in chemical, molecular, and genetic approaches have provided us with an unprecedented capacity to identify drug-target interactions across the whole proteome and genome. Meanwhile, rapid developments of single-cell and spatial omics technologies are revolutionizing our understanding of the molecular architecture of biological systems. However, a significant gap remains in how we align our understanding of drug actions, traditionally based on molecular affinities, with the in vivo cellular and spatial tissue heterogeneity revealed by these newer techniques. Here, we review state-of-the-art methods for profiling drug-target interactions and emerging multiomics tools to delineate the tissue heterogeneity at single-cell resolution. Highlighting the recent technical advances enabling high-resolution, multiplexable in situ small-molecule drug imaging (clearing-assisted tissue click chemistry, or CATCH), we foresee the integration of single-cell and spatial omics platforms, data, and concepts into the future framework of defining and understanding in vivo drug-target interactions and mechanisms of actions.
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Affiliation(s)
- Zhengyuan Pang
- Department of Neuroscience, The Scripps Research Institute, La Jolla, California, USA;
| | - Benjamin F Cravatt
- Department of Chemistry, The Scripps Research Institute, La Jolla, California, USA;
| | - Li Ye
- Department of Neuroscience, The Scripps Research Institute, La Jolla, California, USA;
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California, USA
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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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5
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Hou D, Lin H, Feng Y, Zhou K, Li X, Yang Y, Wang S, Yang X, Wang J, Zhao H, Zhang X, Fan J, Lu S, Wang D, Zhu L, Ju D, Chen YZ, Zeng X. CMAUP database update 2024: extended functional and association information of useful plants for biomedical research. Nucleic Acids Res 2024; 52:D1508-D1518. [PMID: 37897343 PMCID: PMC10767869 DOI: 10.1093/nar/gkad921] [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: 09/05/2023] [Revised: 09/23/2023] [Accepted: 10/10/2023] [Indexed: 10/30/2023] Open
Abstract
Knowledge of the collective activities of individual plants together with the derived clinical effects and targeted disease associations is useful for plant-based biomedical research. To provide the information in complement to the established databases, we introduced a major update of CMAUP database, previously featured in NAR. This update includes (i) human transcriptomic changes overlapping with 1152 targets of 5765 individual plants, covering 74 diseases from 20 027 patient samples; (ii) clinical information for 185 individual plants in 691 clinical trials; (iii) drug development information for 4694 drug-producing plants with metabolites developed into approved or clinical trial drugs; (iv) plant and human disease associations (428 737 associations by target, 220 935 reversion of transcriptomic changes, 764 and 154121 associations by clinical trials of individual plants and plant ingredients); (v) the location of individual plants in the phylogenetic tree for navigating taxonomic neighbors, (vi) DNA barcodes of 3949 plants, (vii) predicted human oral bioavailability of plant ingredients by the established SwissADME and HobPre algorithm, (viii) 21-107% increase of CMAUP data over the previous version to cover 60 222 chemical ingredients, 7865 plants, 758 targets, 1399 diseases, 238 KEGG human pathways, 3013 gene ontologies and 1203 disease ontologies. CMAUP update version is freely accessible at https://bidd.group/CMAUP/index.html.
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Affiliation(s)
- Dongyue Hou
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Hanbo Lin
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Yuhan Feng
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Kaicheng Zhou
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Xingxiu Li
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Yuan Yang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Shuaiqi Wang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Xue Yang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Jiayu Wang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Hui Zhao
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Xuyao Zhang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Jiajun Fan
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - SongLin Lu
- The State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Dan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Lyuhan Zhu
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Dianwen Ju
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Yu Zong Chen
- The State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
- Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Xian Zeng
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
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Bhowmick C, Rahaman M, Bhattacharya S, Mukherjee M, Chakravorty N, Dutta PK, Mahadevappa M. Identification of hub genes to determine drug-disease correlation in breast carcinomas. Med Oncol 2023; 41:36. [PMID: 38153604 DOI: 10.1007/s12032-023-02246-9] [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: 08/26/2023] [Accepted: 11/11/2023] [Indexed: 12/29/2023]
Abstract
The exact molecular mechanism underlying the heterogeneous drug response against breast carcinoma remains to be fully understood. It is urgently required to identify key genes that are intricately associated with varied clinical response of standard anti-cancer drugs, clinically used to treat breast cancer patients. In the present study, the utility of transcriptomic data of breast cancer patients in discerning the clinical drug response using machine learning-based approaches were evaluated. Here, a computational framework has been developed which can be used to identify key genes that can be linked with clinical drug response and progression of cancer, offering an immense opportunity to predict potential prognostic biomarkers and therapeutic targets. The framework concerned utilizes DeSeq2, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape, and machine learning techniques to find these crucial genes. Total RNA extraction and qRT-PCR were performed to quantify relative expression of few hub genes selected from the networks. In our study, we have experimentally checked the expression of few key hub genes like APOA2, DLX5, APOC3, CAMK2B, and PAK6 that were predicted to play an immense role in breast cancer tumorigenesis and progression in response to anti-cancer drug Paclitaxel. However, further experimental validations will be required to get mechanistic insights of these genes in regulating the drug response and cancer progression which will likely to play pivotal role in cancer treatment and precision oncology.
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Affiliation(s)
- Chiranjib Bhowmick
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Medinipur, Kharagpur, West Bengal, 721302, India
| | - Motiur Rahaman
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Medinipur, Kharagpur, West Bengal, 721302, India
| | - Shatarupa Bhattacharya
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Medinipur, Kharagpur, West Bengal, 721302, India
| | - Mandrita Mukherjee
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Medinipur, Kharagpur, West Bengal, 721302, India
| | - Nishant Chakravorty
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Medinipur, Kharagpur, West Bengal, 721302, India
| | - Pranab Kumar Dutta
- Department of Electrical Engineering, Indian Institute of Technology Kharagpur, West Medinipur, Kharagpur, West Bengal, 721302, India
| | - Manjunatha Mahadevappa
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Medinipur, Kharagpur, West Bengal, 721302, India.
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Caputo WL, de Souza MC, Basso CR, Pedrosa VDA, Seiva FRF. Comprehensive Profiling and Therapeutic Insights into Differentially Expressed Genes in Hepatocellular Carcinoma. Cancers (Basel) 2023; 15:5653. [PMID: 38067357 PMCID: PMC10705715 DOI: 10.3390/cancers15235653] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/02/2023] [Accepted: 11/04/2023] [Indexed: 02/16/2024] Open
Abstract
Background: Drug repurposing is a strategy that complements the conventional approach of developing new drugs. Hepatocellular carcinoma (HCC) is a highly prevalent type of liver cancer, necessitating an in-depth understanding of the underlying molecular alterations for improved treatment. Methods: We searched for a vast array of microarray experiments in addition to RNA-seq data. Through rigorous filtering processes, we have identified highly representative differentially expressed genes (DEGs) between tumor and non-tumor liver tissues and identified a distinct class of possible new candidate drugs. Results: Functional enrichment analysis revealed distinct biological processes associated with metal ions, including zinc, cadmium, and copper, potentially implicating chronic metal ion exposure in tumorigenesis. Conversely, up-regulated genes are associated with mitotic events and kinase activities, aligning with the relevance of kinases in HCC. To unravel the regulatory networks governing these DEGs, we employed topological analysis methods, identifying 25 hub genes and their regulatory transcription factors. In the pursuit of potential therapeutic options, we explored drug repurposing strategies based on computational approaches, analyzing their potential to reverse the expression patterns of key genes, including AURKA, CCNB1, CDK1, RRM2, and TOP2A. Potential therapeutic chemicals are alvocidib, AT-7519, kenpaullone, PHA-793887, JNJ-7706621, danusertibe, doxorubicin and analogues, mitoxantrone, podofilox, teniposide, and amonafide. Conclusion: This multi-omic study offers a comprehensive view of DEGs in HCC, shedding light on potential therapeutic targets and drug repurposing opportunities.
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Affiliation(s)
- Wesley Ladeira Caputo
- Post Graduation Program in Experimental Pathology, State University of Londrina (UEL), Londrina 86057-970, PR, Brazil; (W.L.C.); (M.C.d.S.)
| | - Milena Cremer de Souza
- Post Graduation Program in Experimental Pathology, State University of Londrina (UEL), Londrina 86057-970, PR, Brazil; (W.L.C.); (M.C.d.S.)
| | - Caroline Rodrigues Basso
- Department of Chemical and Biological Sciences, Institute of Bioscience, São Paulo State University (UNESP), Botucatu 18610-034, SP, Brazil; (C.R.B.); (V.d.A.P.)
| | - Valber de Albuquerque Pedrosa
- Department of Chemical and Biological Sciences, Institute of Bioscience, São Paulo State University (UNESP), Botucatu 18610-034, SP, Brazil; (C.R.B.); (V.d.A.P.)
| | - Fábio Rodrigues Ferreira Seiva
- Post Graduation Program in Experimental Pathology, State University of Londrina (UEL), Londrina 86057-970, PR, Brazil; (W.L.C.); (M.C.d.S.)
- Department of Chemical and Biological Sciences, Institute of Bioscience, São Paulo State University (UNESP), Botucatu 18610-034, SP, Brazil; (C.R.B.); (V.d.A.P.)
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He C, Xu Y, Zhou Y, Fan J, Cheng C, Meng R, Gamazon ER, Zhou D. Integrating population-level and cell-based signatures for drug repositioning. bioRxiv 2023:2023.10.25.564079. [PMID: 37961219 PMCID: PMC10634827 DOI: 10.1101/2023.10.25.564079] [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] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Furthermore, drugs with genetic evidence are more likely to progress successfully through clinical trials towards FDA approval. Exploiting these developments, single gene-based drug repositioning methods have been implemented, but approaches leveraging the entire spectrum of molecular signatures are critically underexplored. Most multi-gene-based approaches rely on differential gene expression (DGE) analysis, which is prone to identify the molecular consequence of disease and renders causal inference challenging. We propose a framework TReD (Transcriptome-informed Reversal Distance) that integrates population-level disease signatures robust to reverse causality and cell-based drug-induced transcriptome response profiles. TReD embeds the disease signature and drug profile in a high-dimensional normed space, quantifying the reversal potential of candidate drugs in a disease-related cell screen assay. The robustness is ensured by evaluation in additional cell screens. For an application, we implement the framework to identify potential drugs against COVID-19. Taking transcriptome-wide association study (TWAS) results from four relevant tissues and three DGE results as disease features, we identify 37 drugs showing potential reversal roles in at least four of the seven disease signatures. Notably, over 70% (27/37) of the drugs have been linked to COVID-19 from other studies, and among them, eight drugs are supported by ongoing/completed clinical trials. For example, TReD identifies the well-studied JAK1/JAK2 inhibitor baricitinib, the first FDA-approved immunomodulatory treatment for COVID-19. Novel potential candidates, including enzastaurin, a selective inhibitor of PKC-beta which can be activated by SARS-CoV-2, are also identified. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screens that can accelerate the search for new therapeutic strategies.
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Ji X, Williams KP, Zheng W. Applying a Gene Reversal Rate Computational Methodology to Identify Drugs for a Rare Cancer: Inflammatory Breast Cancer. Cancer Inform 2023; 22:11769351231202588. [PMID: 37846218 PMCID: PMC10576937 DOI: 10.1177/11769351231202588] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/01/2023] [Indexed: 10/18/2023] Open
Abstract
The aim of this study was to utilize a computational methodology based on Gene Reversal Rate (GRR) scoring to repurpose existing drugs for a rare and understudied cancer: inflammatory breast cancer (IBC). This method uses IBC-related gene expression signatures (GES) and drug-induced gene expression profiles from the LINCS database to calculate a GRR score for each candidate drug, and is based on the idea that a compound that can counteract gene expression changes of a disease may have potential therapeutic applications for that disease. Genes related to IBC with associated differential expression data (265 up-regulated and 122 down-regulated) were collated from PubMed-indexed publications. Drug-induced gene expression profiles were downloaded from the LINCS database and candidate drugs to treat IBC were predicted using their GRR scores. Thirty-two (32) drug perturbations that could potentially reverse the pre-compiled list of 297 IBC genes were obtained using the LINCS Canvas Browser (LCB) analysis. Binary combinations of the 32 perturbations were assessed computationally to identify combined perturbations with the highest GRR scores, and resulted in 131 combinations with GRR greater than 80%, that reverse up to 264 of the 297 genes in the IBC-GES. The top 35 combinations involve 20 unique individual drug perturbations, and 19 potential drug candidates. A comprehensive literature search confirmed 17 of the 19 known drugs as having either anti-cancer or anti-inflammatory activities. AZD-7545, BMS-754807, and nimesulide target known IBC relevant genes: PDK, Met, and COX, respectively. AG-14361, butalbital, and clobenpropit are known to be functionally relevant in DNA damage, cell cycle, and apoptosis, respectively. These findings support the use of the GRR approach to identify drug candidates and potential combination therapies that could be used to treat rare diseases such as IBC.
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Affiliation(s)
- Xiaojia Ji
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
| | - Kevin P Williams
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
| | - Weifan Zheng
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
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10
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Tian S, Li Y, Xu J, Zhang L, Zhang J, Lu J, Xu X, Luan X, Zhao J, Zhang W. COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming. Brief Bioinform 2023; 24:bbad346. [PMID: 37816138 PMCID: PMC10564268 DOI: 10.1093/bib/bbad346] [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: 06/29/2023] [Revised: 08/16/2023] [Accepted: 09/13/2023] [Indexed: 10/12/2023] Open
Abstract
Immune evasion and metabolism reprogramming have been regarded as two vital hallmarks of the mechanism of carcinogenesis. Thus, targeting the immune microenvironment and the reprogrammed metabolic processes will aid in developing novel anti-cancer drugs. In recent decades, herbal medicine has been widely utilized to treat cancer through the modulation of the immune microenvironment and reprogrammed metabolic processes. However, labor-based herbal ingredient screening is time consuming, laborious and costly. Luckily, some computational approaches have been proposed to screen candidates for drug discovery rapidly. Yet, it has been challenging to develop methods to screen drug candidates exclusively targeting specific pathways, especially for herbal ingredients which exert anti-cancer effects by multiple targets, multiple pathways and synergistic ways. Meanwhile, currently employed approaches cannot quantify the contribution of the specific pathway to the overall curative effect of herbal ingredients. Hence, to address this problem, this study proposes a new computational framework to infer the contribution of the immune microenvironment and metabolic reprogramming (COIMMR) in herbal ingredients against human cancer and specifically screen herbal ingredients targeting the immune microenvironment and metabolic reprogramming. Finally, COIMMR was applied to identify isoliquiritigenin that specifically regulates the T cells in stomach adenocarcinoma and cephaelin hydrochloride that specifically targets metabolic reprogramming in low-grade glioma. The in silico results were further verified using in vitro experiments. Taken together, our approach opens new possibilities for repositioning drugs targeting immune and metabolic dysfunction in human cancer and provides new insights for drug development in other diseases. COIMMR is available at https://github.com/LYN2323/COIMMR.
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Affiliation(s)
- Saisai Tian
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Yanan Li
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Jia Xu
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
- College of Pharmacy, Henan University, Kaifeng 475000, China
| | - Lijun Zhang
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
| | - Jinbo Zhang
- Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China Department of Pharmacy, Tianjin Rehabilitation Center of Joint Logistics Support Force, Tianjin, 300110, China
| | - Jinyuan Lu
- College of Pharmacy, Anhui University of Chinese Medicine, Anhui 230012, China
| | - Xike Xu
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Xin Luan
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
| | - Jing Zhao
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
| | - Weidong Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
- The Research Center for Traditional Chinese Medicine, Shanghai Institute of Infectious Diseases and Biosafety, Institute of Interdisciplinary Integrative Medicine
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11
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Gusev A. Germline mechanisms of immunotherapy toxicities in the era of genome-wide association studies. Immunol Rev 2023; 318:138-156. [PMID: 37515388 DOI: 10.1111/imr.13253] [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: 03/14/2023] [Accepted: 06/29/2023] [Indexed: 07/30/2023]
Abstract
Cancer immunotherapy has revolutionized the treatment of advanced cancers and is quickly becoming an option for early-stage disease. By reactivating the host immune system, immunotherapy harnesses patients' innate defenses to eradicate the tumor. By putatively similar mechanisms, immunotherapy can also substantially increase the risk of toxicities or immune-related adverse events (irAEs). Severe irAEs can lead to hospitalization, treatment discontinuation, lifelong immune complications, or even death. Many irAEs present with similar symptoms to heritable autoimmune diseases, suggesting that germline genetics may contribute to their onset. Recently, genome-wide association studies (GWAS) of irAEs have identified common germline associations and putative mechanisms, lending support to this hypothesis. A wide range of well-established GWAS methods can potentially be harnessed to understand the etiology of irAEs specifically and immunotherapy outcomes broadly. This review summarizes current findings regarding germline effects on immunotherapy outcomes and discusses opportunities and challenges for leveraging germline genetics to understand, predict, and treat irAEs.
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Affiliation(s)
- Alexander Gusev
- Division of Population Sciences, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA
- Division of Genetics, Brigham & Women's Hospital, Boston, Massachusetts, USA
- The Broad Institute, Cambridge, Massachusetts, USA
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12
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Giudice LC, Oskotsky TT, Falako S, Opoku‐Anane J, Sirota M. Endometriosis in the era of precision medicine and impact on sexual and reproductive health across the lifespan and in diverse populations. FASEB J 2023; 37:e23130. [PMID: 37641572 PMCID: PMC10503213 DOI: 10.1096/fj.202300907] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 07/26/2023] [Indexed: 08/31/2023]
Abstract
Endometriosis is a common estrogen-dependent disorder wherein uterine lining tissue (endometrium) is found mainly in the pelvis where it causes inflammation, chronic pelvic pain, pain with intercourse and menses, and infertility. Recent evidence also supports a systemic inflammatory component that underlies associated co-morbidities, e.g., migraines and cardiovascular and autoimmune diseases. Genetics and environment contribute significantly to disease risk, and with the explosion of omics technologies, underlying mechanisms of symptoms are increasingly being elucidated, although novel and effective therapeutics for pain and infertility have lagged behind these advances. Moreover, there are stark disparities in diagnosis, access to care, and treatment among persons of color and transgender/nonbinary identity, socioeconomically disadvantaged populations, and adolescents, and a disturbing low awareness among health care providers, policymakers, and the lay public about endometriosis, which, if left undiagnosed and under-treated can lead to significant fibrosis, infertility, depression, and markedly diminished quality of life. This review summarizes endometriosis epidemiology, compelling evidence for its pathogenesis, mechanisms underlying its pathophysiology in the age of precision medicine, recent biomarker discovery, novel therapeutic approaches, and issues around reproductive justice for marginalized populations with this disorder spanning the past 100 years. As we enter the next revolution in health care and biomedical research, with rich molecular and clinical datasets, single-cell omics, and population-level data, endometriosis is well positioned to benefit from data-driven research leveraging computational and artificial intelligence approaches integrating data and predicting disease risk, diagnosis, response to medical and surgical therapies, and prognosis for recurrence.
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Affiliation(s)
- Linda C. Giudice
- UCSF Stanford Endometriosis Center for Innovation, Training, and Community Outreach (ENACT)University of California, San FranciscoSan FranciscoCaliforniaUSA
- Center for Reproductive SciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Tomiko T. Oskotsky
- UCSF Stanford Endometriosis Center for Innovation, Training, and Community Outreach (ENACT)University of California, San FranciscoSan FranciscoCaliforniaUSA
- Bakar Computational Health Sciences InstituteUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Simileoluwa Falako
- UCSF Stanford Endometriosis Center for Innovation, Training, and Community Outreach (ENACT)University of California, San FranciscoSan FranciscoCaliforniaUSA
- Columbia University Vagelos College of Physicians and SurgeonsNew YorkNew YorkUSA
| | - Jessica Opoku‐Anane
- UCSF Stanford Endometriosis Center for Innovation, Training, and Community Outreach (ENACT)University of California, San FranciscoSan FranciscoCaliforniaUSA
- Division of Gynecologic Specialty SurgeryColumbia UniversityNew YorkNew YorkUSA
| | - Marina Sirota
- UCSF Stanford Endometriosis Center for Innovation, Training, and Community Outreach (ENACT)University of California, San FranciscoSan FranciscoCaliforniaUSA
- Bakar Computational Health Sciences InstituteUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of PediatricsUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
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13
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Toh H, Smolentsev A, Sadjadi R, Clegg D, Yan J, Stewart R, Thomson JA, Jiang P. Transcriptomic clock predicts vascular changes of prodromal diabetic retinopathy. Sci Rep 2023; 13:12968. [PMID: 37563287 PMCID: PMC10415264 DOI: 10.1038/s41598-023-40328-w] [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: 03/31/2023] [Accepted: 08/08/2023] [Indexed: 08/12/2023] Open
Abstract
Diabetic retinopathy is a common complication of long-term diabetes and that could lead to vision loss. Unfortunately, early diabetic retinopathy remains poorly understood. There is no effective way to prevent or treat early diabetic retinopathy until patients develop later stages of diabetic retinopathy. Elevated acellular capillary density is considered a reliable quantitative trait present in the early development of retinopathy. Hence, in this study, we interrogated whole retinal vascular transcriptomic changes via a Nile rat model to better understand the early pathogenesis of diabetic retinopathy. We uncovered the complexity of associations between acellular capillary density and the joint factors of blood glucose, diet, and sex, which was modeled through a Bayesian network. Using segmented regressions, we have identified different gene expression patterns and enriched Gene Ontology (GO) terms associated with acellular capillary density increasing. We developed a random forest regression model based on expression patterns of 14 genes to predict the acellular capillary density. Since acellular capillary density is a reliable quantitative trait in early diabetic retinopathy, and thus our model can be used as a transcriptomic clock to measure the severity of the progression of early retinopathy. We also identified NVP-TAE684, geldanamycin, and NVP-AUY922 as the top three potential drugs which can potentially attenuate the early DR. Although we need more in vivo studies in the future to support our re-purposed drugs, we have provided a data-driven approach to drug discovery.
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Affiliation(s)
- Huishi Toh
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Alexander Smolentsev
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Ryan Sadjadi
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Dennis Clegg
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, USA
- Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Jingqi Yan
- Department of Biological, Geological and Environmental Sciences, Cleveland State University, Cleveland, OH, 44115, USA
- Center for Gene Regulation in Health and Disease, Cleveland State University, Cleveland, OH, 44115, USA
| | - Ron Stewart
- Morgridge Institute For Research, Madison, WI, 53706, USA
| | - James A Thomson
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, USA
- Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
- Morgridge Institute For Research, Madison, WI, 53706, USA
| | - Peng Jiang
- Department of Biological, Geological and Environmental Sciences, Cleveland State University, Cleveland, OH, 44115, USA.
- Center for Gene Regulation in Health and Disease, Cleveland State University, Cleveland, OH, 44115, USA.
- Center for RNA Science and Therapeutics, School of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.
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14
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Cüvitoğlu A, Isik Z. Network neighborhood operates as a drug repositioning method for cancer treatment. PeerJ 2023; 11:e15624. [PMID: 37456868 PMCID: PMC10340098 DOI: 10.7717/peerj.15624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 06/01/2023] [Indexed: 07/18/2023] Open
Abstract
Computational drug repositioning approaches are important, as they cost less compared to the traditional drug development processes. This study proposes a novel network-based drug repositioning approach, which computes similarities between disease-causing genes and drug-affected genes in a network topology to suggest candidate drugs with highest similarity scores. This new method aims to identify better treatment options by integrating systems biology approaches. It uses a protein-protein interaction network that is the main topology to compute a similarity score between candidate drugs and disease-causing genes. The disease-causing genes were mapped on this network structure. Transcriptome profiles of drug candidates were taken from the LINCS project and mapped individually on the network structure. The similarity of these two networks was calculated by different network neighborhood metrics, including Adamic-Adar, PageRank and neighborhood scoring. The proposed approach identifies the best candidates by choosing the drugs with significant similarity scores. The method was experimented on melanoma, colorectal, and prostate cancers. Several candidate drugs were predicted by applying AUC values of 0.6 or higher. Some of the predictions were approved by clinical phase trials or other in-vivo studies found in literature. The proposed drug repositioning approach would suggest better treatment options with integration of functional information between genes and transcriptome level effects of drug perturbations and diseases.
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Affiliation(s)
- Ali Cüvitoğlu
- The Graduate School of Natural and Applied Sciences, Dokuz Eylül University, Izmir, Turkiye
| | - Zerrin Isik
- Computer Engineering Department, Engineering Faculty, Dokuz Eylül University, Izmir, Turkiye
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15
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Obermayer AN, Chang D, Nobles G, Teng M, Tan AC, Wang X, Chen YA, Eschrich S, Rodriguez PC, Grass GD, Meshinchi S, Tarhini A, Chen DT, Shaw TI. PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing. BMC Bioinformatics 2023; 24:266. [PMID: 37380943 DOI: 10.1186/s12859-023-05393-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: 12/28/2022] [Accepted: 06/19/2023] [Indexed: 06/30/2023] Open
Abstract
Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.
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Affiliation(s)
- Alyssa N Obermayer
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Darwin Chang
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Gabrielle Nobles
- Morsani College of Medicine, University of South Florida, Tampa, FL, 33612, USA
| | - Mingxiang Teng
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Aik-Choon Tan
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, 84112, USA
| | - Xuefeng Wang
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Y Ann Chen
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Steven Eschrich
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Paulo C Rodriguez
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - G Daniel Grass
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Soheil Meshinchi
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Children's Oncology Group, Monrovia, CA, USA
| | - Ahmad Tarhini
- Department of Cutaneous Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Dung-Tsa Chen
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Timothy I Shaw
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA.
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16
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Adikusuma W, Zakaria ZA, Irham LM, Nopitasari BL, Pradiningsih A, Firdayani F, Septama AW, Chong R. Transcriptomics-driven drug repositioning for the treatment of diabetic foot ulcer. Sci Rep 2023; 13:10032. [PMID: 37340026 DOI: 10.1038/s41598-023-37120-1] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 06/15/2023] [Indexed: 06/22/2023] Open
Abstract
Diabetic foot ulcers (DFUs) are a common complication of diabetes and can lead to severe disability and even amputation. Despite advances in treatment, there is currently no cure for DFUs and available drugs for treatment are limited. This study aimed to identify new candidate drugs and repurpose existing drugs to treat DFUs based on transcriptomics analysis. A total of 31 differentially expressed genes (DEGs) were identified and used to prioritize the biological risk genes for DFUs. Further investigation using the database DGIdb revealed 12 druggable target genes among 50 biological DFU risk genes, corresponding to 31 drugs. Interestingly, we highlighted that two drugs (urokinase and lidocaine) are under clinical investigation for DFU and 29 drugs are potential candidates to be repurposed for DFU therapy. The top 5 potential biomarkers for DFU from our findings are IL6ST, CXCL9, IL1R1, CXCR2, and IL10. This study highlights IL1R1 as a highly promising biomarker for DFU due to its high systemic score in functional annotations, that can be targeted with an existing drug, Anakinra. Our study proposed that the integration of transcriptomic and bioinformatic-based approaches has the potential to drive drug repurposing for DFUs. Further research will further examine the mechanisms by which targeting IL1R1 can be used to treat DFU.
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Affiliation(s)
- Wirawan Adikusuma
- Borneo Research on Algesia, Inflammation, and Neurodegeneration (BRAIN) Group, Department of Biomedical Sciences, Faculty of Medicines and Health Sciences, University Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia.
- Departement of Pharmacy, University of Muhammadiyah Mataram, Mataram, Indonesia.
- Research Center for Vaccine and Drugs, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia.
| | - Zainul Amiruddin Zakaria
- Borneo Research on Algesia, Inflammation, and Neurodegeneration (BRAIN) Group, Department of Biomedical Sciences, Faculty of Medicines and Health Sciences, University Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia.
| | - Lalu Muhammad Irham
- Faculty of Pharmacy, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
- Research Center for Pharmaceutical Ingredients and Traditional Medicine, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
| | | | - Anna Pradiningsih
- Departement of Pharmacy, University of Muhammadiyah Mataram, Mataram, Indonesia
| | - Firdayani Firdayani
- Research Center for Vaccine and Drugs, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
| | - Abdi Wira Septama
- Research Center for Pharmaceutical Ingredients and Traditional Medicine, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
| | - Rockie Chong
- Department of Chemistry and Biochemistry, University of California, Los Angeles, USA
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17
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Yu K, Basu A, Yau C, Wolf DM, Goodarzi H, Bandyopadhyay S, Korkola JE, Hirst GL, Asare S, DeMichele A, Hylton N, Yee D, Esserman L, van ‘t Veer L, Sirota M. Computational drug repositioning for the identification of new agents to sensitize drug-resistant breast tumors across treatments and receptor subtypes. Front Oncol 2023; 13:1192208. [PMID: 37384294 PMCID: PMC10294228 DOI: 10.3389/fonc.2023.1192208] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 05/25/2023] [Indexed: 06/30/2023] Open
Abstract
Introduction Drug resistance is a major obstacle in cancer treatment and can involve a variety of different factors. Identifying effective therapies for drug resistant tumors is integral for improving patient outcomes. Methods In this study, we applied a computational drug repositioning approach to identify potential agents to sensitize primary drug resistant breast cancers. We extracted drug resistance profiles from the I-SPY 2 TRIAL, a neoadjuvant trial for early stage breast cancer, by comparing gene expression profiles of responder and non-responder patients stratified into treatments within HR/HER2 receptor subtypes, yielding 17 treatment-subtype pairs. We then used a rank-based pattern-matching strategy to identify compounds in the Connectivity Map, a database of cell line derived drug perturbation profiles, that can reverse these signatures in a breast cancer cell line. We hypothesize that reversing these drug resistance signatures will sensitize tumors to treatment and prolong survival. Results We found that few individual genes are shared among the drug resistance profiles of different agents. At the pathway level, however, we found enrichment of immune pathways in the responders in 8 treatments within the HR+HER2+, HR+HER2-, and HR-HER2- receptor subtypes. We also found enrichment of estrogen response pathways in the non-responders in 10 treatments primarily within the hormone receptor positive subtypes. Although most of our drug predictions are unique to treatment arms and receptor subtypes, our drug repositioning pipeline identified the estrogen receptor antagonist fulvestrant as a compound that can potentially reverse resistance across 13/17 of the treatments and receptor subtypes including HR+ and triple negative. While fulvestrant showed limited efficacy when tested in a panel of 5 paclitaxel resistant breast cancer cell lines, it did increase drug response in combination with paclitaxel in HCC-1937, a triple negative breast cancer cell line. Conclusion We applied a computational drug repurposing approach to identify potential agents to sensitize drug resistant breast cancers in the I-SPY 2 TRIAL. We identified fulvestrant as a potential drug hit and showed that it increased response in a paclitaxel-resistant triple negative breast cancer cell line, HCC-1937, when treated in combination with paclitaxel.
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Affiliation(s)
- Katharine Yu
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Amrita Basu
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Christina Yau
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Denise M. Wolf
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Hani Goodarzi
- University of California, San Francisco, San Francisco, CA, United States
| | | | - James E. Korkola
- Oregon Health and Science University, Portland, OR, United States
| | - Gillian L. Hirst
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Smita Asare
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- QuantumLeap Healthcare Collaborative, San Francisco, CA, United States
| | | | - Nola Hylton
- University of California, San Francisco, San Francisco, CA, United States
| | - Douglas Yee
- University of Minnesota, Minneapolis, MN, United States
| | - Laura Esserman
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Laura van ‘t Veer
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
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18
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Gopalan V, Hannenhalli S. Towards a Synthesis of the Non-Genetic and Genetic Views of Cancer in Understanding Pancreatic Ductal Adenocarcinoma Initiation and Prevention. Cancers (Basel) 2023; 15:cancers15072159. [PMID: 37046820 PMCID: PMC10093726 DOI: 10.3390/cancers15072159] [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: 02/07/2023] [Revised: 03/22/2023] [Accepted: 03/27/2023] [Indexed: 04/14/2023] Open
Abstract
While much of the research in oncogenesis and cancer therapy has focused on mutations in key cancer driver genes, more recent work suggests a complementary non-genetic paradigm. This paradigm focuses on how transcriptional and phenotypic heterogeneity, even in clonally derived cells, can create sub-populations associated with oncogenesis, metastasis, and therapy resistance. We discuss this complementary paradigm in the context of pancreatic ductal adenocarcinoma. A better understanding of cellular transcriptional heterogeneity and its association with oncogenesis can lead to more effective therapies that prevent tumor initiation and slow progression.
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Affiliation(s)
- Vishaka Gopalan
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | - Sridhar Hannenhalli
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA
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19
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Groenewald W, Lund AH, Gay DM. The Role of WNT Pathway Mutations in Cancer Development and an Overview of Therapeutic Options. Cells 2023; 12:cells12070990. [PMID: 37048063 PMCID: PMC10093220 DOI: 10.3390/cells12070990] [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: 02/27/2023] [Revised: 03/21/2023] [Accepted: 03/23/2023] [Indexed: 04/14/2023] Open
Abstract
It is well established that mutations in the canonical WNT-signalling pathway play a major role in various cancers. Critical to developing new therapeutic strategies is understanding which cancers are driven by WNT pathway activation and at what level these mutations occur within the pathway. Some cancers harbour mutations in genes whose protein products operate at the receptor level of the WNT pathway. For instance, tumours with RNF43 or RSPO mutations, still require exogenous WNT ligands to drive WNT signalling (ligand-dependent mutations). Conversely, mutations within the cytoplasmic segment of the Wnt pathway, such as in APC and CTNNB1, lead to constitutive WNT pathway activation even in the absence of WNT ligands (ligand-independent). Here, we review the predominant driving mutations found in cancer that lead to WNT pathway activation, as well as explore some of the therapeutic interventions currently available against tumours harbouring either ligand-dependent or ligand-independent mutations. Finally, we discuss a potentially new therapeutic avenue by targeting the translational apparatus downstream from WNT signalling.
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Affiliation(s)
- Wibke Groenewald
- Biotech Research and Innovation Centre, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Anders H Lund
- Biotech Research and Innovation Centre, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - David Michael Gay
- Biotech Research and Innovation Centre, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
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20
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Foo RJK, Tian S, Tan EY, Goh WWB. A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods. Comput Biol Chem 2023; 104:107845. [PMID: 36889140 DOI: 10.1016/j.compbiolchem.2023.107845] [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: 09/09/2022] [Revised: 01/06/2023] [Accepted: 03/01/2023] [Indexed: 03/08/2023]
Abstract
The robustness of a breast cancer gene signature, the super-proliferation set (SPS), is initially tested and investigated on breast cancer cell lines from the Cancer Cell Line Encyclopaedia (CCLE). Previously, SPS was derived via a meta-analysis of 47 independent breast cancer gene signatures, benchmarked on survival information from clinical data in the NKI dataset. Here, relying on the stability of cell line data and associative prior knowledge, we first demonstrate through Principal Component Analysis (PCA) that SPS prioritizes survival information over secondary subtype information, surpassing both PAM50 and Boruta, an artificial intelligence-based feature-selection algorithm, in this regard. We can also extract higher resolution 'progression' information using SPS, dividing survival outcomes into several clinically relevant stages ('good', 'intermediate', and 'bad) based on different quadrants of the PCA scatterplot. Furthermore, by transferring these 'progression' annotations onto independent clinical datasets, we demonstrate the generalisability of our method on actual patient data. Finally, via the characteristic genetic profiles of each quadrant/stage, we identified efficacious drugs using their gene reversal scores that can shift signatures across quadrants/stages, in a process known as gene signature reversal. This confirms the power of meta-analytical approaches for gene signature inference in breast cancer, as well as the clinical benefit in translating these inferences onto real-world patient data for more targeted therapies.
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Affiliation(s)
- Reuben Jyong Kiat Foo
- School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore
| | - Siqi Tian
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore
| | - Ern Yu Tan
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Tan Tock Seng Hospital, Singapore
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore; Centre for Biomedical Informatics, Nanyang Technological University, Singapore.
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21
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Torricelli F, Sauta E, Manicardi V, Mandato VD, Palicelli A, Ciarrocchi A, Manzotti G. An Innovative Drug Repurposing Approach to Restrain Endometrial Cancer Metastatization. Cells 2023; 12:cells12050794. [PMID: 36899930 PMCID: PMC10001006 DOI: 10.3390/cells12050794] [Citation(s) in RCA: 1] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/22/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
BACKGROUND Endometrial cancer (EC) is the most common gynecologic tumor and the world's fourth most common cancer in women. Most patients respond to first-line treatments and have a low risk of recurrence, but refractory patients, and those with metastatic cancer at diagnosis, remain with no treatment options. Drug repurposing aims to discover new clinical indications for existing drugs with known safety profiles. It provides ready-to-use new therapeutic options for highly aggressive tumors for which standard protocols are ineffective, such as high-risk EC. METHODS Here, we aimed at defining new therapeutic opportunities for high-risk EC using an innovative and integrated computational drug repurposing approach. RESULTS We compared gene-expression profiles, from publicly available databases, of metastatic and non-metastatic EC patients being metastatization the most severe feature of EC aggressiveness. A comprehensive analysis of transcriptomic data through a two-arm approach was applied to obtain a robust prediction of drug candidates. CONCLUSIONS Some of the identified therapeutic agents are already successfully used in clinical practice to treat other types of tumors. This highlights the potential to repurpose them for EC and, therefore, the reliability of the proposed approach.
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Affiliation(s)
- Federica Torricelli
- Laboratory of Translational Research, Azienda USL-IRCCS di Reggio Emilia, Viale Risorgimento 80, 42123 Reggio Emilia, Italy
| | - Elisabetta Sauta
- Laboratory of Translational Research, Azienda USL-IRCCS di Reggio Emilia, Viale Risorgimento 80, 42123 Reggio Emilia, Italy
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
| | - Veronica Manicardi
- Laboratory of Translational Research, Azienda USL-IRCCS di Reggio Emilia, Viale Risorgimento 80, 42123 Reggio Emilia, Italy
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
- Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Vincenzo Dario Mandato
- Unit of Obstetrics and Gynaecology, Azienda USL-IRCCS di Reggio Emilia, 42123 Reggio Emilia, Italy
| | - Andrea Palicelli
- Pathology Unit, Department of Oncology and Advanced Technologies, Azienda USL-IRCCS di Reggio Emilia, 42123 Reggio Emilia, Italy
| | - Alessia Ciarrocchi
- Laboratory of Translational Research, Azienda USL-IRCCS di Reggio Emilia, Viale Risorgimento 80, 42123 Reggio Emilia, Italy
| | - Gloria Manzotti
- Laboratory of Translational Research, Azienda USL-IRCCS di Reggio Emilia, Viale Risorgimento 80, 42123 Reggio Emilia, Italy
- Correspondence: ; Tel.: +39-05-2229-5477
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22
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Liu Y, Song F, Li Z, Chen L, Xu Y, Sun H, Chang Y. A comprehensive tool for tumor precision medicine with pharmaco-omics data analysis. Front Pharmacol 2023; 14:1085765. [PMID: 36713829 PMCID: PMC9878337 DOI: 10.3389/fphar.2023.1085765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/04/2023] [Indexed: 01/14/2023] Open
Abstract
Background: Cancer precision medicine is an effective strategy to fight cancers by bridging genomics and drug discovery to provide specific treatment for patients with different genetic characteristics. Although some public databases and modelling frameworks have been developed through studies on drug response, most of them only considered the ramifications of the drug on the cell line and the effects on the patient still require a huge amount of work to integrate data from various databases and calculations, especially concerning precision treatment. Furthermore, not only efficacy but also the adverse effects of drugs on patients should be taken into account during cancer treatment. However, the adverse effects as essential indicators of drug safety assessment are always neglected. Method: A holistic estimation explores various drugs' efficacy levels by calculating their potency both in reversing and enhancing cancer-associated gene expression change. And a method for bridging the gap between cell culture and living tissue estimates the effectiveness of a drug on individual patients through the mappings of various cell lines to each person according to their genetic mutation similarities. Result: We predicted the efficacy of FDA-recommended drugs, taking into account both efficacy and toxicity, and obtained consistent results. We also provided an intuitive and easy-to-use web server called DBPOM (http://www.dbpom.net/, a comprehensive database of pharmaco-omics for cancer precision medicine), which not only integrates the above methods but also provides calculation results on more than 10,000 small molecule compounds and drugs. As a one-stop web server, clinicians and drug researchers can also analyze the overall effect of a drug or a drug combination on cancer patients as well as the biological functions that they target. DBPOM is now public, free to use with no login requirement, and contains all the data and code. Conclusion: Both the positive and negative effects of drugs during precision treatment are essential for practical application of drugs. DBPOM based on the two effects will become a vital resource and analysis platform for drug development, drug mechanism studies and the discovery of new therapies.
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Affiliation(s)
- Yijun Liu
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Fuhu Song
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Zhi Li
- Medical Oncology Department, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Liang Chen
- Department of Computer Science, College of Engineering, Shantou University, Shantou, China,Key Laboratory of Intelligent Manufacturing Technology of Ministry of Education, Shantou University, Shantou, China
| | - Ying Xu
- Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, The University of Georgia, Athens, GA, United States
| | - Huiyan Sun
- School of Artificial Intelligence, Jilin University, Changchun, China,International Center of Future Science, Jilin University, Changchun, China,*Correspondence: Huiyan Sun, ; Yi Chang,
| | - Yi Chang
- School of Artificial Intelligence, Jilin University, Changchun, China,International Center of Future Science, Jilin University, Changchun, China,*Correspondence: Huiyan Sun, ; Yi Chang,
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23
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Bruggemann L, Falls Z, Mangione W, Schwartz SA, Battaglia S, Aalinkeel R, Mahajan SD, Samudrala R. Multiscale Analysis and Validation of Effective Drug Combinations Targeting Driver KRAS Mutations in Non-Small Cell Lung Cancer. Int J Mol Sci 2023; 24:ijms24020997. [PMID: 36674513 PMCID: PMC9867122 DOI: 10.3390/ijms24020997] [Citation(s) in RCA: 1] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/04/2022] [Accepted: 11/06/2022] [Indexed: 01/06/2023] Open
Abstract
Pharmacogenomics is a rapidly growing field with the goal of providing personalized care to every patient. Previously, we developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform for multiscale therapeutic discovery to screen optimal compounds for any indication/disease by performing analytics on their interactions using large protein libraries. We implemented a comprehensive precision medicine drug discovery pipeline within the CANDO platform to determine which drugs are most likely to be effective against mutant phenotypes of non-small cell lung cancer (NSCLC) based on the supposition that drugs with similar interaction profiles (or signatures) will have similar behavior and therefore show synergistic effects. CANDO predicted that osimertinib, an EGFR inhibitor, is most likely to synergize with four KRAS inhibitors.Validation studies with cellular toxicity assays confirmed that osimertinib in combination with ARS-1620, a KRAS G12C inhibitor, and BAY-293, a pan-KRAS inhibitor, showed a synergistic effect on decreasing cellular proliferation by acting on mutant KRAS. Gene expression studies revealed that MAPK expression is strongly correlated with decreased cellular proliferation following treatment with KRAS inhibitor BAY-293, but not treatment with ARS-1620 or osimertinib. These results indicate that our precision medicine pipeline may be used to identify compounds capable of synergizing with inhibitors of KRAS G12C, and to assess their likelihood of becoming drugs by understanding their behavior at the proteomic/interactomic scales.
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Affiliation(s)
- Liana Bruggemann
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA
| | - Zackary Falls
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA
| | - William Mangione
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA
| | | | | | | | - Supriya D. Mahajan
- Department of Medicine, University at Buffalo, Buffalo, NY 14260, USA
- Correspondence: (S.D.M.); (R.S.)
| | - Ram Samudrala
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA
- Correspondence: (S.D.M.); (R.S.)
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24
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Papikinos T, Krokidis MG, Vrahatis A, Vlamos P, Exarchos TP. Signature-Based Computational Drug Repurposing for Amyotrophic Lateral Sclerosis. Adv Exp Med Biol 2023; 1424:201-211. [PMID: 37486495 DOI: 10.1007/978-3-031-31982-2_22] [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] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Amyotrophic lateral sclerosis (ALS) is a late-onset fatal neurodegenerative disease characterized by progressive loss of the upper and lower motor neurons. There are currently limited approved drugs for the disorder, and for this reason the strategy of repositioning already approved therapeutics could exhibit a successful outcome. Herein, we used CMAP and L1000CDS2 databases which include gene expression profiles datasets (genomic signatures) to identify potent compounds and classes of compounds which reverse disease's signature which could in turn reverse its phenotype. ALS signature was obtained by comparing gene expression of muscle biopsy specimens between diseased and healthy individuals. Statistical analysis was conducted to explore differentially transcripts in patients' samples. Then, the list of upregulated and downregulated genes was used to query both databases in order to determine molecules which downregulate the genes which are upregulated by ALS and vice versa. These compounds, based on their chemical structure along with known treatments, were clustered to reveal drugs with novel and potentially more effective mode of action with most of them predicted to affect pathways heavily involved in ALS. This evidence suggests that these compounds are strong candidates for moving to the next phase of the drug repurposing pipeline which is in vitro and in vivo experimental evaluation.
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Affiliation(s)
- Thomas Papikinos
- Department of Informatics, Bioinformatics and Human Electrophysiology Laboratory, Ionian University, Corfu, Greece.
| | - Marios G Krokidis
- Department of Informatics, Bioinformatics and Human Electrophysiology Laboratory, Ionian University, Corfu, Greece
| | - Aris Vrahatis
- Department of Informatics, Bioinformatics and Human Electrophysiology Laboratory, Ionian University, Corfu, Greece
| | - Panagiotis Vlamos
- Department of Informatics, Bioinformatics and Human Electrophysiology Laboratory, Ionian University, Corfu, Greece
| | - Themis P Exarchos
- Department of Informatics, Bioinformatics and Human Electrophysiology Laboratory, Ionian University, Corfu, Greece
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25
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Ko M, Oh JM, Kim IW. Drug repositioning prediction for psoriasis using the adverse event reporting database. Front Med (Lausanne) 2023; 10:1159453. [PMID: 37035327 PMCID: PMC10076533 DOI: 10.3389/fmed.2023.1159453] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 02/28/2023] [Indexed: 04/11/2023] Open
Abstract
Introduction Inverse signals produced from disproportional analyses using spontaneous drug adverse event reports can be used for drug repositioning purposes. The purpose of this study is to predict drug candidates using a computational method that integrates reported drug adverse event data, disease-specific gene expression profiles, and drug-induced gene expression profiles. Methods Drug and adverse events from 2015 through 2020 were downloaded from the United States Food and Drug Administration Adverse Event Reporting System (FAERS). The reporting odds ratio (ROR), information component (IC) and empirical Bayes geometric mean (EBGM) were used to calculate the inverse signals. Psoriasis was selected as the target disease. Disease specific gene expression profiles were obtained by the meta-analysis of the Gene Expression Omnibus (GEO). The reverse gene expression scores were calculated using the Library of Integrated Network-based Cellular Signatures (LINCS) and their correlations with the inverse signals were obtained. Results Reversal genes and the candidate compounds were identified. Additionally, these correlations were validated using the relationship between the reverse gene expression scores and the half-maximal inhibitory concentration (IC50) values from the Chemical European Molecular Biology Laboratory (ChEMBL). Conclusion Inverse signals produced from a disproportional analysis can be used for drug repositioning and to predict drug candidates against psoriasis.
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Affiliation(s)
- Minoh Ko
- College of Pharmacy, Seoul National University, Seoul, Republic of Korea
| | - Jung Mi Oh
- College of Pharmacy, Seoul National University, Seoul, Republic of Korea
- Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - In-Wha Kim
- Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
- *Correspondence: In-Wha Kim,
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26
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Koudijs KKM, Böhringer S, Guchelaar HJ. Validation of transcriptome signature reversion for drug repurposing in oncology. Brief Bioinform 2022; 24:6850563. [PMID: 36445193 PMCID: PMC9851289 DOI: 10.1093/bib/bbac490] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 09/21/2022] [Accepted: 10/15/2022] [Indexed: 11/30/2022] Open
Abstract
Transcriptome signature reversion (TSR) has been extensively proposed and used to discover new indications for existing drugs (i.e. drug repositioning, drug repurposing) for various cancer types. TSR relies on the assumption that a drug that can revert gene expression changes induced by a disease back to original, i.e. healthy, levels is likely to be therapeutically active in treating the disease. Here, we aimed to validate the concept of TSR using the PRISM repurposing data set, which is-as of writing-the largest pharmacogenomic data set. The predictive utility of the TSR approach as it has currently been used appears to be much lower than previously reported and is completely nullified after the drug gene expression signatures are adjusted for the general anti-proliferative downstream effects of drug-induced decreased cell viability. Therefore, TSR mainly relies on generic anti-proliferative drug effects rather than on targeting cancer pathways specifically upregulated in tumor types.
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Affiliation(s)
- Karel K M Koudijs
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center (LUMC); 2333 ZA Leiden, The Netherlands
| | - Stefan Böhringer
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center (LUMC); 2333 ZA Leiden, The Netherlands,Department of Biomedical Data Sciences, Leiden University Medical Center (LUMC); 2333 ZA Leiden, The Netherlands
| | - Henk-Jan Guchelaar
- Corresponding author: Henk-Jan Guchelaar, Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center (LUMC), 2333 ZA Leiden, The Netherlands. Tel.: +31-71-526-4018; Fax: +31-71-526-6980; E-mail:
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27
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Singha M, Pu L, Stanfield BA, Uche IK, Rider PJF, Kousoulas KG, Ramanujam J, Brylinski M. Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors. BMC Cancer 2022; 22:1211. [PMID: 36434556 PMCID: PMC9694576 DOI: 10.1186/s12885-022-10293-0] [Citation(s) in RCA: 4] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 11/07/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Vast amounts of rapidly accumulating biological data related to cancer and a remarkable progress in the field of artificial intelligence (AI) have paved the way for precision oncology. Our recent contribution to this area of research is CancerOmicsNet, an AI-based system to predict the therapeutic effects of multitargeted kinase inhibitors across various cancers. This approach was previously demonstrated to outperform other deep learning methods, graph kernel models, molecular docking, and drug binding pocket matching. METHODS CancerOmicsNet integrates multiple heterogeneous data by utilizing a deep graph learning model with sophisticated attention propagation mechanisms to extract highly predictive features from cancer-specific networks. The AI-based system was devised to provide more accurate and robust predictions than data-driven therapeutic discovery using gene signature reversion. RESULTS Selected CancerOmicsNet predictions obtained for "unseen" data are positively validated against the biomedical literature and by live-cell time course inhibition assays performed against breast, pancreatic, and prostate cancer cell lines. Encouragingly, six molecules exhibited dose-dependent antiproliferative activities, with pan-CDK inhibitor JNJ-7706621 and Src inhibitor PP1 being the most potent against the pancreatic cancer cell line Panc 04.03. CONCLUSIONS CancerOmicsNet is a promising AI-based platform to help guide the development of new approaches in precision oncology involving a variety of tumor types and therapeutics.
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Affiliation(s)
- Manali Singha
- grid.64337.350000 0001 0662 7451Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803 USA
| | - Limeng Pu
- grid.64337.350000 0001 0662 7451Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803 USA
| | - Brent A. Stanfield
- grid.64337.350000 0001 0662 7451Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803 USA
| | - Ifeanyi K. Uche
- grid.64337.350000 0001 0662 7451Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803 USA ,grid.64337.350000 0001 0662 7451Division of Biotechnology and Molecular Medicine, Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803 USA ,grid.279863.10000 0000 8954 1233School of Medicine, Louisiana State University Health Sciences Center, New Orleans, LA 70112 USA
| | - Paul J. F. Rider
- grid.64337.350000 0001 0662 7451Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803 USA ,grid.64337.350000 0001 0662 7451Division of Biotechnology and Molecular Medicine, Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803 USA
| | - Konstantin G. Kousoulas
- grid.64337.350000 0001 0662 7451Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803 USA ,grid.64337.350000 0001 0662 7451Division of Biotechnology and Molecular Medicine, Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803 USA
| | - J. Ramanujam
- grid.64337.350000 0001 0662 7451Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803 USA ,grid.64337.350000 0001 0662 7451Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803 USA
| | - Michal Brylinski
- grid.64337.350000 0001 0662 7451Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803 USA ,grid.64337.350000 0001 0662 7451Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803 USA
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28
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Jang HY, Oh JM, Kim IW. Drug repurposing using meta-analysis of gene expression in Alzheimer's disease. Front Neurosci 2022; 16:989174. [PMID: 36440278 PMCID: PMC9684643 DOI: 10.3389/fnins.2022.989174] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 10/19/2022] [Indexed: 01/25/2023] Open
Abstract
INTRODUCTION Alzheimer's disease and other forms of dementia are disease that bring an increased global burden. However, the medicine developed to date remains limited. The purpose of this study is to predict drug repositioning candidates using a computational method that integrates gene expression profiles on Alzheimer's disease and compound-induced changes in gene expression levels. METHODS Gene expression data on Alzheimer's disease were obtained from the Gene Expression Omnibus (GEO) and we conducted a meta-analysis of their gene expression levels. The reverse scores of compound-induced gene expressions were computed based on the reversal relationship between disease and drug gene expression profiles. RESULTS Reversal genes and the candidate compounds were identified by the leave-one-out cross-validation procedure. Additionally, the half-maximal inhibitory concentration (IC50) values and the blood-brain barrier (BBB) permeability of candidate compounds were obtained from ChEMBL and PubChem, respectively. CONCLUSION New therapeutic target genes and drug candidates against Alzheimer's disease were identified by means of drug repositioning.
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Affiliation(s)
- Ha Young Jang
- Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, South Korea
| | - Jung Mi Oh
- Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, South Korea,College of Pharmacy, Seoul National University, Seoul, South Korea
| | - In-Wha Kim
- Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, South Korea,*Correspondence: In-Wha Kim,
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29
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Jiménez‐Santos MJ, García‐Martín S, Fustero‐Torre C, Di Domenico T, Gómez‐López G, Al‐Shahrour F. Bioinformatics roadmap for therapy selection in cancer genomics. Mol Oncol 2022; 16:3881-3908. [PMID: 35811332 PMCID: PMC9627786 DOI: 10.1002/1878-0261.13286] [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: 04/26/2022] [Revised: 06/22/2022] [Accepted: 07/08/2022] [Indexed: 12/24/2022] Open
Abstract
Tumour heterogeneity is one of the main characteristics of cancer and can be categorised into inter- or intratumour heterogeneity. This heterogeneity has been revealed as one of the key causes of treatment failure and relapse. Precision oncology is an emerging field that seeks to design tailored treatments for each cancer patient according to epidemiological, clinical and omics data. This discipline relies on bioinformatics tools designed to compute scores to prioritise available drugs, with the aim of helping clinicians in treatment selection. In this review, we describe the current approaches for therapy selection depending on which type of tumour heterogeneity is being targeted and the available next-generation sequencing data. We cover intertumour heterogeneity studies and individual treatment selection using genomics variants, expression data or multi-omics strategies. We also describe intratumour dissection through clonal inference and single-cell transcriptomics, in each case providing bioinformatics tools for tailored treatment selection. Finally, we discuss how these therapy selection workflows could be integrated into the clinical practice.
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Affiliation(s)
| | | | - Coral Fustero‐Torre
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Tomás Di Domenico
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Gonzalo Gómez‐López
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Fátima Al‐Shahrour
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
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30
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Zhao G, Newbury P, Ishi Y, Chekalin E, Zeng B, Glicksberg BS, Wen A, Paithankar S, Sasaki T, Suri A, Nazarian J, Pacold ME, Brat DJ, Nicolaides T, Chen B, Hashizume R. Reversal of cancer gene expression identifies repurposed drugs for diffuse intrinsic pontine glioma. Acta Neuropathol Commun 2022; 10:150. [PMID: 36274161 PMCID: PMC9590174 DOI: 10.1186/s40478-022-01463-z] [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: 03/19/2022] [Accepted: 10/13/2022] [Indexed: 11/25/2022] Open
Abstract
Diffuse intrinsic pontine glioma (DIPG) is an aggressive incurable brainstem tumor that targets young children. Complete resection is not possible, and chemotherapy and radiotherapy are currently only palliative. This study aimed to identify potential therapeutic agents using a computational pipeline to perform an in silico screen for novel drugs. We then tested the identified drugs against a panel of patient-derived DIPG cell lines. Using a systematic computational approach with publicly available databases of gene signature in DIPG patients and cancer cell lines treated with a library of clinically available drugs, we identified drug hits with the ability to reverse a DIPG gene signature to one that matches normal tissue background. The biological and molecular effects of drug treatment was analyzed by cell viability assay and RNA sequence. In vivo DIPG mouse model survival studies were also conducted. As a result, two of three identified drugs showed potency against the DIPG cell lines Triptolide and mycophenolate mofetil (MMF) demonstrated significant inhibition of cell viability in DIPG cell lines. Guanosine rescued reduced cell viability induced by MMF. In vivo, MMF treatment significantly inhibited tumor growth in subcutaneous xenograft mice models. In conclusion, we identified clinically available drugs with the ability to reverse DIPG gene signatures and anti-DIPG activity in vitro and in vivo. This novel approach can repurpose drugs and significantly decrease the cost and time normally required in drug discovery.
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Affiliation(s)
- Guisheng Zhao
- grid.137628.90000 0004 1936 8753Department of Pediatrics, New York University Langone Health, 160 East 32nd St., New York, NY 10016 USA
| | - Patrick Newbury
- grid.17088.360000 0001 2150 1785Department of Pediatrics and Human Development, Michigan State University, Secchia Center, Room 732, 15 Michigan St. NE, Grand Rapids, MI 49503 USA
| | - Yukitomo Ishi
- grid.16753.360000 0001 2299 3507Department of Pediatrics, Northwestern University Feinberg School of Medicine, 303 East Superior St., Simpson Querrey 4-514, Chicago, IL 60611 USA ,grid.413808.60000 0004 0388 2248Division of Hematology, Oncology, Neuro-Oncology & Stem Cell Transplantation, Ann & Robert H. Lurie Children’s Hospital of Chicago, 225 East Chicago Avenue, Box 205, Chicago, IL 60611 USA
| | - Eugene Chekalin
- grid.17088.360000 0001 2150 1785Department of Pediatrics and Human Development, Michigan State University, Secchia Center, Room 732, 15 Michigan St. NE, Grand Rapids, MI 49503 USA
| | - Billy Zeng
- grid.17088.360000 0001 2150 1785Department of Pediatrics and Human Development, Michigan State University, Secchia Center, Room 732, 15 Michigan St. NE, Grand Rapids, MI 49503 USA
| | - Benjamin S. Glicksberg
- grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029 USA ,grid.416167.30000 0004 0442 1996Icahn School of Medicine at Mount Sinai, Hasso Plattner Institute for Digital Health at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029 USA
| | - Anita Wen
- grid.17088.360000 0001 2150 1785Department of Pediatrics and Human Development, Michigan State University, Secchia Center, Room 732, 15 Michigan St. NE, Grand Rapids, MI 49503 USA
| | - Shreya Paithankar
- grid.17088.360000 0001 2150 1785Department of Pediatrics and Human Development, Michigan State University, Secchia Center, Room 732, 15 Michigan St. NE, Grand Rapids, MI 49503 USA
| | - Takahiro Sasaki
- grid.16753.360000 0001 2299 3507Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, 303 East Superior St., Chicago, IL 60611 USA ,grid.412857.d0000 0004 1763 1087Department of Neurological Surgery, Wakayama Medical University, 811-1 Kimiidera, Wakayama, Japan
| | - Amreena Suri
- grid.16753.360000 0001 2299 3507Department of Pediatrics, Northwestern University Feinberg School of Medicine, 303 East Superior St., Simpson Querrey 4-514, Chicago, IL 60611 USA ,grid.413808.60000 0004 0388 2248Division of Hematology, Oncology, Neuro-Oncology & Stem Cell Transplantation, Ann & Robert H. Lurie Children’s Hospital of Chicago, 225 East Chicago Avenue, Box 205, Chicago, IL 60611 USA
| | - Javad Nazarian
- grid.239560.b0000 0004 0482 1586Children’s National Medical Center, 111 Michigan Avenue NW, Washington, DC 20010 USA ,grid.412341.10000 0001 0726 4330University Children’s Hospital Zurich, Steinwiesstrasse 75, 8032 Zurich, Switzerland
| | - Michael E. Pacold
- grid.137628.90000 0004 1936 8753Department of Radiation Oncology, New York University Langone Health, 550 First Avenue, New York, NY 10016 USA
| | - Daniel J. Brat
- grid.16753.360000 0001 2299 3507Department of Pathology, Robert H. Lurie Cancer Center, Northwestern University Feinberg School of Medicine, 303 E. Chicago Ave., Chicago, IL 60611 USA
| | - Theodore Nicolaides
- grid.137628.90000 0004 1936 8753Department of Pediatrics, New York University Langone Health, 160 East 32nd St., New York, NY 10016 USA
| | - Bin Chen
- Department of Pediatrics and Human Development, Michigan State University, Secchia Center, Room 732, 15 Michigan St. NE, Grand Rapids, MI, 49503, USA. .,Department of Pharmacology and Toxicology, Michigan State University, 1355 Bogue St, East Lansing, MI, 48824, USA. .,Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, MI, 48824, USA.
| | - Rintaro Hashizume
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, 303 East Superior St., Simpson Querrey 4-514, Chicago, IL, 60611, USA. .,Division of Hematology, Oncology, Neuro-Oncology & Stem Cell Transplantation, Ann & Robert H. Lurie Children's Hospital of Chicago, 225 East Chicago Avenue, Box 205, Chicago, IL, 60611, USA. .,Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, 303 East Superior St., Chicago, IL, 60611, USA.
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Xing J, Shankar R, Ko M, Zhang K, Zhang S, Drelich A, Paithankar S, Chekalin E, Chua MS, Rajasekaran S, Kent Tseng CT, Zheng M, Kim S, Chen B. Deciphering COVID-19 host transcriptomic complexity and variations for therapeutic discovery against new variants. iScience 2022; 25:105068. [PMID: 36093376 PMCID: PMC9439871 DOI: 10.1016/j.isci.2022.105068] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/11/2022] [Accepted: 08/30/2022] [Indexed: 12/04/2022] Open
Abstract
The molecular manifestations of host cells responding to SARS-CoV-2 and its evolving variants of infection are vastly different across the studied models and conditions, imposing challenges for host-based antiviral drug discovery. Based on the postulation that antiviral drugs tend to reverse the global host gene expression induced by viral infection, we retrospectively evaluated hundreds of signatures derived from 1,700 published host transcriptomic profiles of SARS/MERS/SARS-CoV-2 infection using an iterative data-driven approach. A few of these signatures could be reversed by known anti-SARS-CoV-2 inhibitors, suggesting the potential of extrapolating the biology for new variant research. We discovered IMD-0354 as a promising candidate to reverse the signatures globally with nanomolar IC50 against SARS-CoV-2 and its five variants. IMD-0354 stimulated type I interferon antiviral response, inhibited viral entry, and down-regulated hijacked proteins. This study demonstrates that the conserved coronavirus signatures and the transcriptomic reversal approach that leverages polypharmacological effects could guide new variant therapeutic discovery.
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Affiliation(s)
- Jing Xing
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
| | - Rama Shankar
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
| | - Meehyun Ko
- Zoonotic Virus Laboratory, Institut Pasteur Korea, Seongnam-si, Gyeonggi-do, 13488, Korea
| | - Keke Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Sulin Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Aleksandra Drelich
- Departments of Microbiology and Immunology, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Shreya Paithankar
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
| | - Eugene Chekalin
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
| | - Mei-Sze Chua
- Department of Surgery, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Surender Rajasekaran
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
- Helen DeVos Children’s Hospital, Grand Rapids, MI 49503, USA
| | - Chien-Te Kent Tseng
- Departments of Microbiology and Immunology, The University of Texas Medical Branch, Galveston, TX 77555, USA
- Center of Biodefense and Emerging Infectious Diseases, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Seungtaek Kim
- Zoonotic Virus Laboratory, Institut Pasteur Korea, Seongnam-si, Gyeonggi-do, 13488, Korea
| | - Bin Chen
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
- Department of Pharmacology and Toxicology, Michigan State University, Grand Rapids, MI 49503, USA
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
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Kakoti BB, Bezbaruah R, Ahmed N. Therapeutic drug repositioning with special emphasis on neurodegenerative diseases: Threats and issues. Front Pharmacol 2022; 13:1007315. [PMID: 36263141 PMCID: PMC9574100 DOI: 10.3389/fphar.2022.1007315] [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: 07/30/2022] [Accepted: 09/12/2022] [Indexed: 11/21/2022] Open
Abstract
Drug repositioning or repurposing is the process of discovering leading-edge indications for authorized or declined/abandoned molecules for use in different diseases. This approach revitalizes the traditional drug discovery method by revealing new therapeutic applications for existing drugs. There are numerous studies available that highlight the triumph of several drugs as repurposed therapeutics. For example, sildenafil to aspirin, thalidomide to adalimumab, and so on. Millions of people worldwide are affected by neurodegenerative diseases. According to a 2021 report, the Alzheimer's disease Association estimates that 6.2 million Americans are detected with Alzheimer's disease. By 2030, approximately 1.2 million people in the United States possibly acquire Parkinson's disease. Drugs that act on a single molecular target benefit people suffering from neurodegenerative diseases. Current pharmacological approaches, on the other hand, are constrained in their capacity to unquestionably alter the course of the disease and provide patients with inadequate and momentary benefits. Drug repositioning-based approaches appear to be very pertinent, expense- and time-reducing strategies for the enhancement of medicinal opportunities for such diseases in the current era. Kinase inhibitors, for example, which were developed for various oncology indications, demonstrated significant neuroprotective effects in neurodegenerative diseases. This review expounds on the classical and recent examples of drug repositioning at various stages of drug development, with a special focus on neurodegenerative disorders and the aspects of threats and issues viz. the regulatory, scientific, and economic aspects.
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Affiliation(s)
- Bibhuti Bhusan Kakoti
- Department of Pharmaceutical Sciences, Faculty of Science and Engineering, Dibrugarh University, Dibrugarh, India
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Gentili M, Martini L, Sponziello M, Becchetti L. Biological Random Walks: multi-omics integration for disease gene prioritization. Bioinformatics 2022; 38:4145-4152. [PMID: 35792834 DOI: 10.1093/bioinformatics/btac446] [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: 11/17/2021] [Revised: 06/22/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Over the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets. This is all the more important, since experimental investigation of potential gene candidates is an expensive task, thus not always a feasible option. On the other hand, many sources of biological information exist beyond the interactome and an important research direction is the design of effective techniques for their integration. RESULTS In this work, we introduce the Biological Random Walks (BRW) approach for disease gene prioritization in the human interactome. The proposed framework leverages multiple biological sources within an integrated framework. We perform an extensive, comparative study of BRW's performance against well-established baselines. AVAILABILITY AND IMPLEMENTATION All codes are publicly available and can be downloaded at https://github.com/LeoM93/BiologicalRandomWalks. We used publicly available datasets, details on their retrieval and preprocessing are provided in the Supplementary Material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Michele Gentili
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Leonardo Martini
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Marialuisa Sponziello
- Translational and Precision Medicine Department, Sapienza University of Rome, Rome, Italy
| | - Luca Becchetti
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
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Truong TTT, Bortolasci CC, Kidnapillai S, Spolding B, Panizzutti B, Liu ZSJ, Kim JH, Dean OM, Richardson MF, Berk M, Walder K. Integrative Analyses of Transcriptomes to Explore Common Molecular Effects of Antipsychotic Drugs. Int J Mol Sci 2022; 23:7508. [PMID: 35886854 PMCID: PMC9325239 DOI: 10.3390/ijms23147508] [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: 06/14/2022] [Revised: 07/04/2022] [Accepted: 07/04/2022] [Indexed: 01/27/2023] Open
Abstract
There is little understanding of the underlying molecular mechanism(s) involved in the clinical efficacy of antipsychotics for schizophrenia. This study integrated schizophrenia-associated transcriptional perturbations with antipsychotic-induced gene expression profiles to detect potentially relevant therapeutic targets shared by multiple antipsychotics. Human neuronal-like cells (NT2-N) were treated for 24 h with one of the following antipsychotic drugs: amisulpride, aripiprazole, clozapine, risperidone, or vehicle controls. Drug-induced gene expression patterns were compared to schizophrenia-associated transcriptional data in post-mortem brain tissues. Genes regulated by each of four antipsychotic drugs in the reverse direction to schizophrenia were identified as potential therapeutic-relevant genes. A total of 886 genes were reversely expressed between at least one drug treatment (versus vehicle) and schizophrenia (versus healthy control), in which 218 genes were commonly regulated by all four antipsychotic drugs. The most enriched biological pathways include Wnt signaling and action potential regulation. The protein-protein interaction (PPI) networks found two main clusters having schizophrenia expression quantitative trait loci (eQTL) genes such as PDCD10, ANK2, and AKT3, suggesting further investigation on these genes as potential novel treatment targets.
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Chen R, Wang X, Deng X, Chen L, Liu Z, Li D. CPDR: An R Package of Recommending Personalized Drugs for Cancer Patients by Reversing the Individual’s Disease-Related Signature. Front Pharmacol 2022; 13:904909. [PMID: 35795573 PMCID: PMC9252520 DOI: 10.3389/fphar.2022.904909] [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: 03/26/2022] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
Due to cancer heterogeneity, only some patients can benefit from drug therapy. The personalized drug usage is important for improving the treatment response rate of cancer patients. The value of the transcriptome of patients has been recently demonstrated in guiding personalized drug use, and the Connectivity Map (CMAP) is a reliable computational approach for drug recommendation. However, there is still no personalized drug recommendation tool based on transcriptomic profiles of patients and CMAP. To fill this gap, here, we proposed such a feasible workflow and a user-friendly R package—Cancer-Personalized Drug Recommendation (CPDR). CPDR has three features. 1) It identifies the individual disease signature by using the patient subgroup with transcriptomic profiles similar to those of the input patient. 2) Transcriptomic profile purification is supported for the subgroup with high infiltration of non-cancerous cells. 3) It supports in silico drug efficacy assessment using drug sensitivity data on cancer cell lines. We demonstrated the workflow of CPDR with the aid of a colorectal cancer dataset from GEO and performed the in silico validation of drug efficacy. We further assessed the performance of CPDR by a pancreatic cancer dataset with clinical response to gemcitabine. The results showed that CPDR can recommend promising therapeutic agents for the individual patient. The CPDR R package is available at https://github.com/AllenSpike/CPDR.
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Affiliation(s)
| | | | | | | | | | - Dong Li
- *Correspondence: Zhongyang Liu, ; Dong Li,
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36
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Salame N, Fooks K, El-Hachem N, Bikorimana JP, Mercier FE, Rafei M. Recent Advances in Cancer Drug Discovery Through the Use of Phenotypic Reporter Systems, Connectivity Mapping, and Pooled CRISPR Screening. Front Pharmacol 2022; 13:852143. [PMID: 35795568 PMCID: PMC9250974 DOI: 10.3389/fphar.2022.852143] [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: 01/10/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
Multi-omic approaches offer an unprecedented overview of the development, plasticity, and resistance of cancer. However, the translation from anti-cancer compounds identified in vitro to clinically active drugs have a notoriously low success rate. Here, we review how technical advances in cell culture, robotics, computational biology, and development of reporter systems have transformed drug discovery, enabling screening approaches tailored to clinically relevant functional readouts (e.g., bypassing drug resistance). Illustrating with selected examples of “success stories,” we describe the process of phenotype-based high-throughput drug screening to target malignant cells or the immune system. Second, we describe computational approaches that link transcriptomic profiling of cancers with existing pharmaceutical compounds to accelerate drug repurposing. Finally, we review how CRISPR-based screening can be applied for the discovery of mechanisms of drug resistance and sensitization. Overall, we explore how the complementary strengths of each of these approaches allow them to transform the paradigm of pre-clinical drug development.
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Affiliation(s)
- Natasha Salame
- Department of Biomedical Sciences, Université de Montréal, Montreal, QC, Canada
| | - Katharine Fooks
- Lady Davis Institute for Medical Research, Montreal, QC, Canada
- Department of Medicine, McGill University, Montreal, QC, Canada
| | - Nehme El-Hachem
- Department of Pharmacology and Physiology, Université de Montréal, Montreal, QC, Canada
| | - Jean-Pierre Bikorimana
- Department of Microbiology, Infectious Diseases and Immunology, Université de Montréal, Montreal, QC, Canada
| | - François E. Mercier
- Lady Davis Institute for Medical Research, Montreal, QC, Canada
- Department of Medicine, McGill University, Montreal, QC, Canada
- *Correspondence: François E. Mercier, ; Moutih Rafei,
| | - Moutih Rafei
- Department of Pharmacology and Physiology, Université de Montréal, Montreal, QC, Canada
- Department of Microbiology, Infectious Diseases and Immunology, Université de Montréal, Montreal, QC, Canada
- Molecular Biology Program, Université de Montréal, Montreal, QC, Canada
- *Correspondence: François E. Mercier, ; Moutih Rafei,
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Kim IW, Jang H, Kim JH, Kim MG, Kim S, Oh JM. Retraction Note: Computational Drug Repositioning for Gastric Cancer using Reversal Gene Expression Profiles. Sci Rep 2022; 12:9726. [PMID: 35697726 DOI: 10.1038/s41598-022-13460-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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Cong Y, Shintani M, Imanari F, Osada N, Endo T. A New Approach to Drug Repurposing with Two-Stage Prediction, Machine Learning, and Unsupervised Clustering of Gene Expression. OMICS 2022; 26:339-347. [PMID: 35666246 PMCID: PMC9245788 DOI: 10.1089/omi.2022.0026] [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] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Drug repurposing has broad importance in planetary health for therapeutics innovation in infectious diseases as well as common or rare chronic human diseases. Drug repurposing has also proved important to develop interventions against the COVID-19 pandemic. We propose a new approach for drug repurposing involving two-stage prediction and machine learning. First, diseases are clustered by gene expression on the premise that similar patterns of altered gene expression imply critical pathways shared in different disease conditions. Next, drug efficacy is assessed by the reversibility of abnormal gene expression, and results are clustered to identify repurposing targets. To cluster similar diseases, gene expression data from 262 cases of 31 diseases and 268 controls were analyzed by Uniform Manifold Approximation and Projection for Dimension Reduction followed by k-means to optimize the number of clusters. For evaluation, we examined disease-specific gene expression data for inclusion, body myositis, polymyositis, and dermatomyositis (DM), and used LINCS L1000 characteristic direction signatures search engine (L1000CDS2) to obtain lists of small-molecule compounds that reversed the expression patterns of these specifically altered genes as candidates for drug repurposing. Finally, the functions of affected genes were analyzed by Gene Set Enrichment Analysis to examine consistency with expected drug efficacy. Consequently, we found disease-specific gene expression, and importantly, identified 20 drugs such as BMS-387032, phorbol-12-myristate-13-acetate, mitoxantrone, alvocidib, and vorinostat as candidates for repurposing. These were previously noted to be effective against two of the three diseases, and have a high probability of being effective against the other. That is, inclusion body myositis and DM. The two-stage prediction approach to drug repurposing presented here offers innovation to inform future drug discovery and clinical trials in a variety of human diseases.
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Affiliation(s)
- Yi Cong
- Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Misaki Shintani
- Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Fuga Imanari
- Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Naoki Osada
- Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Toshinori Endo
- Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan
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Vincent F, Nueda A, Lee J, Schenone M, Prunotto M, Mercola M. Phenotypic drug discovery: recent successes, lessons learned and new directions. Nat Rev Drug Discov. [DOI: 10.1038/s41573-022-00472-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/14/2022] [Indexed: 12/29/2022]
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40
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Berthelet J, Foroutan M, Bhuva DD, Whitfield HJ, El-saafin F, Cursons J, Serrano A, Merdas M, Lim E, Charafe-jauffret E, Ginestier C, Ernst M, Hollande F, Anderson RL, Pal B, Yeo B, Davis MJ, Merino D. Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin. Cancers (Basel) 2022; 14:2404. [PMID: 35626009 PMCID: PMC9139442 DOI: 10.3390/cancers14102404] [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: 04/11/2022] [Revised: 04/29/2022] [Accepted: 05/02/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary Using a collection of publicly available drug screening resources, we identified different partners of genes associated with either sensitivity or resistance to 90 anti-cancer therapies. When subsequently applying these signatures to multiple datasets, we found that these predictive models could predict a large range of drug responses in patient samples. In particular, we discovered a new gene signature to identify breast cancer tumors that are likely to respond to cisplatin in the absence of BRCA1 mutations. This work constitutes an important advance to accelerate the application of platinum-based therapies in patient groups that are not routinely treated with these drugs. In the future, this approach may help to guide the choice of drugs based on the molecular profile of the tumors. Abstract The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine.
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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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42
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Barsi S, Papp H, Valdeolivas A, Tóth DJ, Kuczmog A, Madai M, Hunyady L, Várnai P, Saez-Rodriguez J, Jakab F, Szalai B. Computational drug repurposing against SARS-CoV-2 reveals plasma membrane cholesterol depletion as key factor of antiviral drug activity. PLoS Comput Biol 2022; 18:e1010021. [PMID: 35404937 PMCID: PMC9022874 DOI: 10.1371/journal.pcbi.1010021] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 04/21/2022] [Accepted: 03/15/2022] [Indexed: 01/09/2023] Open
Abstract
Comparing SARS-CoV-2 infection-induced gene expression signatures to drug treatment-induced gene expression signatures is a promising bioinformatic tool to repurpose existing drugs against SARS-CoV-2. The general hypothesis of signature-based drug repurposing is that drugs with inverse similarity to a disease signature can reverse disease phenotype and thus be effective against it. However, in the case of viral infection diseases, like SARS-CoV-2, infected cells also activate adaptive, antiviral pathways, so that the relationship between effective drug and disease signature can be more ambiguous. To address this question, we analysed gene expression data from in vitro SARS-CoV-2 infected cell lines, and gene expression signatures of drugs showing anti-SARS-CoV-2 activity. Our extensive functional genomic analysis showed that both infection and treatment with in vitro effective drugs leads to activation of antiviral pathways like NFkB and JAK-STAT. Based on the similarity-and not inverse similarity-between drug and infection-induced gene expression signatures, we were able to predict the in vitro antiviral activity of drugs. We also identified SREBF1/2, key regulators of lipid metabolising enzymes, as the most activated transcription factors by several in vitro effective antiviral drugs. Using a fluorescently labeled cholesterol sensor, we showed that these drugs decrease the cholesterol levels of plasma-membrane. Supplementing drug-treated cells with cholesterol reversed the in vitro antiviral effect, suggesting the depleting plasma-membrane cholesterol plays a key role in virus inhibitory mechanism. Our results can help to more effectively repurpose approved drugs against SARS-CoV-2, and also highlights key mechanisms behind their antiviral effect.
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Affiliation(s)
- Szilvia Barsi
- Semmelweis University, Faculty of Medicine, Department of Physiology, Budapest, Hungary
| | - Henrietta Papp
- National Laboratory of Virology, University of Pécs, Pécs, Hungary
- Institute of Biology, Faculty of Sciences, University of Pécs, Pécs, Hungary
| | - Alberto Valdeolivas
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Dániel J. Tóth
- Semmelweis University, Faculty of Medicine, Department of Physiology, Budapest, Hungary
| | - Anett Kuczmog
- National Laboratory of Virology, University of Pécs, Pécs, Hungary
- Institute of Biology, Faculty of Sciences, University of Pécs, Pécs, Hungary
| | - Mónika Madai
- National Laboratory of Virology, University of Pécs, Pécs, Hungary
- Institute of Biology, Faculty of Sciences, University of Pécs, Pécs, Hungary
| | - László Hunyady
- Semmelweis University, Faculty of Medicine, Department of Physiology, Budapest, Hungary
- MTA-SE Laboratory of Molecular Physiology, Budapest, Hungary
- Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Péter Várnai
- Semmelweis University, Faculty of Medicine, Department of Physiology, Budapest, Hungary
- MTA-SE Laboratory of Molecular Physiology, Budapest, Hungary
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Ferenc Jakab
- National Laboratory of Virology, University of Pécs, Pécs, Hungary
- Institute of Biology, Faculty of Sciences, University of Pécs, Pécs, Hungary
| | - Bence Szalai
- Semmelweis University, Faculty of Medicine, Department of Physiology, Budapest, Hungary
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Lee S, Jeon S, Kim HS. A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus. Endocrinol Metab (Seoul) 2022; 37:195-207. [PMID: 35413782 PMCID: PMC9081315 DOI: 10.3803/enm.2022.1404] [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] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/21/2022] [Indexed: 11/11/2022] Open
Abstract
Drug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in the future. Reports on drug repositioning, retrieved from PubMed from January 2011 to December 2020, were classified based on an analysis of the methodology and reviewed by experts. Among various drug repositioning methods, the network-based approach was the most common (38.0%, 186/490 cases), followed by machine learning/deep learningbased (34.3%, 168/490 cases), text mining-based (7.1%, 35/490 cases), semantic-based (5.3%, 26/490 cases), and others (15.3%, 75/490 cases). Although drug repositioning offers several advantages, its implementation is curtailed by the need for prior, conclusive clinical proof. This approach requires the construction of various databases, and a deep understanding of the process underlying repositioning is quintessential. An in-depth understanding of drug repositioning could reduce the time, cost, and risks inherent to early drug development, providing reliable scientific evidence. Furthermore, regarding patient safety, drug repurposing might allow the discovery of new relationships between drugs and diseases.
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Affiliation(s)
- Suehyun Lee
- Department of Biomedical Informatics, Konyang University College of Medicine, Daejeon, Korea
- Health Care Data Science Center, Konyang University Hospital, Daejeon, Korea
| | - Seongwoo Jeon
- Health Care Data Science Center, Konyang University Hospital, Daejeon, Korea
| | - Hun-Sung Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Corresponding author: Hun-Sung Kim Department of Medical Informatics, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea Tel: +82-2-2258-8262, Fax: +82-2-2258-8297, E-mail:
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Abstract
Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.
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Affiliation(s)
- Jennifer L. Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Emma F. Jones
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Victoria L. Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Avery S. Williams
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Elizabeth J. Ramsey
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
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Abstract
The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms. In this chapter, we present a survey of data resources and computational tools available for drug repositioning.
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Yang C, Zhang H, Chen M, Wang S, Qian R, Zhang L, Huang X, Wang J, Liu Z, Qin W, Wang C, Hang H, Wang H. A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer. eLife 2022; 11:71880. [PMID: 35191375 PMCID: PMC8893721 DOI: 10.7554/elife.71880] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [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: 07/02/2021] [Accepted: 02/16/2022] [Indexed: 12/24/2022] Open
Abstract
Pharmacologic perturbation projects, such as Connectivity Map (CMap) and Library of Integrated Network-based Cellular Signatures (LINCS), have produced many perturbed expression data, providing enormous opportunities for computational therapeutic discovery. However, there is no consensus on which methodologies and parameters are the most optimal to conduct such analysis. Aiming to fill this gap, new benchmarking standards were developed to quantitatively evaluate drug retrieval performance. Investigations of potential factors influencing drug retrieval were conducted based on these standards. As a result, we determined an optimal approach for LINCS data-based therapeutic discovery. With this approach, homoharringtonine (HHT) was identified to be a candidate agent with potential therapeutic and preventive effects on liver cancer. The antitumor and antifibrotic activity of HHT was validated experimentally using subcutaneous xenograft tumor model and carbon tetrachloride (CCL4)-induced liver fibrosis model, demonstrating the reliability of the prediction results. In summary, our findings will not only impact the future applications of LINCS data but also offer new opportunities for therapeutic intervention of liver cancer.
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Affiliation(s)
- Chen Yang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Hailin Zhang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Mengnuo Chen
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Siying Wang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Ruolan Qian
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Linmeng Zhang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaowen Huang
- Division of Gastroenterology and Hepatology, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Wang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Zhicheng Liu
- Hepatic Surgery Center, Huazhong University of Science and Technology, Wuhan, China
| | - Wenxin Qin
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Cun Wang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Hualian Hang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
| | - Hui Wang
- Department of Liver Surgery, Shanghai Jiao Tong University, Shanghai, China
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Yang JJ, Gessner CR, Duerksen JL, Biber D, Binder JL, Ozturk M, Foote B, McEntire R, Stirling K, Ding Y, Wild DJ. Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination. BMC Bioinformatics 2022; 23:37. [PMID: 35021991 PMCID: PMC8756622 DOI: 10.1186/s12859-021-04530-9] [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: 01/12/2021] [Accepted: 12/13/2021] [Indexed: 11/12/2022] Open
Abstract
Background LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. Results Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. Conclusions The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04530-9.
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Li Y, Yang C, Liu Z, Du S, Can S, Zhang H, Zhang L, Huang X, Xiao Z, Li X, Fang J, Qin W, Sun C, Wang C, Chen J, Chen H. Integrative analysis of CRISPR screening data uncovers new opportunities for optimizing cancer immunotherapy. Mol Cancer 2022; 21:2. [PMID: 34980132 PMCID: PMC8722047 DOI: 10.1186/s12943-021-01462-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [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: 09/29/2021] [Accepted: 11/11/2021] [Indexed: 12/15/2022] Open
Abstract
Background In recent years, the application of functional genetic immuno-oncology screens has showcased the striking ability to identify potential regulators engaged in tumor-immune interactions. Although these screens have yielded substantial data, few studies have attempted to systematically aggregate and analyze them. Methods In this study, a comprehensive data collection of tumor immunity-associated functional screens was performed. Large-scale genomic data sets were exploited to conduct integrative analyses. Results We identified 105 regulator genes that could mediate resistance or sensitivity to immune cell-induced tumor elimination. Further analysis identified MON2 as a novel immune-oncology target with considerable therapeutic potential. In addition, based on the 105 genes, a signature named CTIS (CRISPR screening-based tumor-intrinsic immune score) for predicting response to immune checkpoint blockade (ICB) and several immunomodulatory agents with the potential to augment the efficacy of ICB were also determined. Conclusion Overall, our findings provide insights into immune oncology and open up novel opportunities for improving the efficacy of current immunotherapy agents. Supplementary Information The online version contains supplementary material available at 10.1186/s12943-021-01462-z.
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Affiliation(s)
- Yan Li
- State Key Laboratory for Oncogenes and Related Genes; Key Laboratory of Gastroenterology & Hepatology, Ministry of Health; Division of Gastroenterology and Hepatology; Shanghai Institute of Digestive Disease; Renji Hospital, Shanghai Jiao Tong University School of Medicine, 145 Middle Shandong Road, Shanghai, 200001, China.,Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Chen Yang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200001, China
| | - Zhicheng Liu
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Shangce Du
- Immune Regulation in Cancer Group, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
| | - Susan Can
- Immune Regulation in Cancer Group, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
| | - Hailin Zhang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200001, China
| | - Linmeng Zhang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200001, China
| | - Xiaowen Huang
- State Key Laboratory for Oncogenes and Related Genes; Key Laboratory of Gastroenterology & Hepatology, Ministry of Health; Division of Gastroenterology and Hepatology; Shanghai Institute of Digestive Disease; Renji Hospital, Shanghai Jiao Tong University School of Medicine, 145 Middle Shandong Road, Shanghai, 200001, China
| | - Zhenyu Xiao
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Xiaobo Li
- State Key Laboratory for Oncogenes and Related Genes; Key Laboratory of Gastroenterology & Hepatology, Ministry of Health; Division of Gastroenterology and Hepatology; Shanghai Institute of Digestive Disease; Renji Hospital, Shanghai Jiao Tong University School of Medicine, 145 Middle Shandong Road, Shanghai, 200001, China
| | - Jingyuan Fang
- State Key Laboratory for Oncogenes and Related Genes; Key Laboratory of Gastroenterology & Hepatology, Ministry of Health; Division of Gastroenterology and Hepatology; Shanghai Institute of Digestive Disease; Renji Hospital, Shanghai Jiao Tong University School of Medicine, 145 Middle Shandong Road, Shanghai, 200001, China
| | - Wenxin Qin
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200001, China
| | - Chong Sun
- Immune Regulation in Cancer Group, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany.
| | - Cun Wang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200001, China.
| | - Jun Chen
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China. .,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China. .,Key Laboratory of Tropical Disease Control of the Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China. .,Guangdong Engineering & Technology Research Center for Disease-Model Animals, Laboratory Animal Center, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China. .,Center for Precision Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Huimin Chen
- State Key Laboratory for Oncogenes and Related Genes; Key Laboratory of Gastroenterology & Hepatology, Ministry of Health; Division of Gastroenterology and Hepatology; Shanghai Institute of Digestive Disease; Renji Hospital, Shanghai Jiao Tong University School of Medicine, 145 Middle Shandong Road, Shanghai, 200001, China.
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Periyasamy L, Muruganantham B, Park WY, Muthusami S. Phyto-targeting the CEMIP Expression as a Strategy to Prevent Pancreatic Cancer Metastasis. Curr Pharm Des 2022; 28:922-946. [PMID: 35236267 DOI: 10.2174/1381612828666220302153201] [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: 04/08/2021] [Accepted: 12/16/2021] [Indexed: 11/22/2022]
Abstract
INTRODUCTION Metastasis of primary pancreatic cancer (PC) to adjacent or distant organs is responsible for the poor survival rate of affected individuals. Chemotherapy, radiotherapy, and immunotherapy are currently being prescribed to treat PC in addition to surgical resection. Surgical resection is the preferred treatment for PC that leads to 20% of 5-year survival, but only less than 20% of patients are eligible for surgical resection because of the poor prognosis. To improve the prognosis and clinical outcome, early diagnostic markers need to be identified, and targeting them would be of immense benefit to increase the efficiency of the treatment. Cell migration-inducing hyaluronan-binding protein (CEMIP) is identified as an important risk factor for the metastasis of various cancers, including PC. Emerging studies have pointed out the crucial role of CEMIP in the regulation of various signaling mechanisms, leading to enhanced migration and metastasis of PC. METHODS The published findings on PC metastasis, phytoconstituents, and CEMIP were retrieved from Pubmed, ScienceDirect, and Cochrane Library. Computational tools, such as gene expression profiling interactive analysis (GEPIA) and Kaplan-Meier (KM) plotter, were used to study the relationship between CEMIP expression and survival of PC individuals. RESULTS Gene expression analysis using the GEPIA database identified a stupendous increase in the CEMIP transcript in PC compared to adjacent normal tissues. KM plotter analysis revealed the impact of CEMIP on the overall survival (OS) and disease-free survival (DFS) among PC patients. Subsequently, several risk factors associated with PC development were screened, and their ability to regulate CEMIP gene expression was analyzed using computational tools. CONCLUSION The current review is focused on gathering information regarding the regulatory role of phytocomponents in PC migration and exploring their possible impact on the CEMIP expression.
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Affiliation(s)
- Loganayaki Periyasamy
- Department of Biochemistry, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, 641 021, India
| | - Bharathi Muruganantham
- Karpagam Cancer Research Centre, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, 641 021, India
| | - Woo-Yoon Park
- Department of Radiation Oncology, Chungbuk National University College of Medicine, Cheongju 28644, Republic of Korea
| | - Sridhar Muthusami
- Department of Biochemistry, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, 641 021, India
- Karpagam Cancer Research Centre, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, 641 021, India
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Laganà A. The Architecture of a Precision Oncology Platform. Advances in Experimental Medicine and Biology 2022; 1361:1-22. [DOI: 10.1007/978-3-030-91836-1_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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