1
|
Deng EZ, Marino GB, Clarke DJB, Diamant I, Resnick AC, Ma W, Wang P, Ma'ayan A. Multiomics2Targets identifies targets from cancer cohorts profiled with transcriptomics, proteomics, and phosphoproteomics. CELL REPORTS METHODS 2024; 4:100839. [PMID: 39127042 PMCID: PMC11384097 DOI: 10.1016/j.crmeth.2024.100839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 06/06/2024] [Accepted: 07/19/2024] [Indexed: 08/12/2024]
Abstract
The availability of data from profiling of cancer patients with multiomics is rapidly increasing. However, integrative analysis of such data for personalized target identification is not trivial. Multiomics2Targets is a platform that enables users to upload transcriptomics, proteomics, and phosphoproteomics data matrices collected from the same cohort of cancer patients. After uploading the data, Multiomics2Targets produces a report that resembles a research publication. The uploaded matrices are processed, analyzed, and visualized using the tools Enrichr, KEA3, ChEA3, Expression2Kinases, and TargetRanger to identify and prioritize proteins, genes, and transcripts as potential targets. Figures and tables, as well as descriptions of the methods and results, are automatically generated. Reports include an abstract, introduction, methods, results, discussion, conclusions, and references and are exportable as citable PDFs and Jupyter Notebooks. Multiomics2Targets is applied to analyze version 3 of the Clinical Proteomic Tumor Analysis Consortium (CPTAC3) pan-cancer cohort, identifying potential targets for each CPTAC3 cancer subtype. Multiomics2Targets is available from https://multiomics2targets.maayanlab.cloud/.
Collapse
Affiliation(s)
- Eden Z Deng
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Ido Diamant
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Adam C Resnick
- Center for Data Driven Discovery in Biomedicine, Division of Neurosurgery, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Weiping Ma
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498, New York, NY 10029, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498, New York, NY 10029, USA
| | - Avi Ma'ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.
| |
Collapse
|
2
|
Yeh SJ, Paithankar S, Chen R, Xing J, Sun M, Liu K, Zhou J, Chen B. TransCell: In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzad008. [PMID: 39240541 PMCID: PMC11378636 DOI: 10.1093/gpbjnl/qzad008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 06/30/2023] [Accepted: 09/20/2023] [Indexed: 09/07/2024]
Abstract
Gene expression profiling of new or modified cell lines becomes routine today; however, obtaining comprehensive molecular characterization and cellular responses for a variety of cell lines, including those derived from underrepresented groups, is not trivial when resources are minimal. Using gene expression to predict other measurements has been actively explored; however, systematic investigation of its predictive power in various measurements has not been well studied. Here, we evaluated commonly used machine learning methods and presented TransCell, a two-step deep transfer learning framework that utilized the knowledge derived from pan-cancer tumor samples to predict molecular features and responses. Among these models, TransCell had the best performance in predicting metabolite, gene effect score (or genetic dependency), and drug sensitivity, and had comparable performance in predicting mutation, copy number variation, and protein expression. Notably, TransCell improved the performance by over 50% in drug sensitivity prediction and achieved a correlation of 0.7 in gene effect score prediction. Furthermore, predicted drug sensitivities revealed potential repurposing candidates for new 100 pediatric cancer cell lines, and predicted gene effect scores reflected BRAF resistance in melanoma cell lines. Together, we investigated the predictive power of gene expression in six molecular measurement types and developed a web portal (http://apps.octad.org/transcell/) that enables the prediction of 352,000 genomic and cellular response features solely from gene expression profiles.
Collapse
Affiliation(s)
- Shan-Ju Yeh
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
| | - Shreya Paithankar
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
| | - Ruoqiao Chen
- Department of Pharmacology and Toxicology, Michigan State University, Grand Rapids, MI 49503, USA
| | - Jing Xing
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
| | - Mengying Sun
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Ke Liu
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 49503, USA
| | - Jiayu Zhou
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - 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
| |
Collapse
|
3
|
Gao H, Zhang M, Baylis RA, Wang F, Björkegren JLM, Kovacic JJ, Ruusalepp A, Leeper NJ. Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases. STAR Protoc 2024; 5:102883. [PMID: 38354084 PMCID: PMC10876979 DOI: 10.1016/j.xpro.2024.102883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/29/2023] [Accepted: 01/26/2024] [Indexed: 02/16/2024] Open
Abstract
The accumulation of omics and biobank resources allows for a genome-wide understanding of the shared pathologic mechanisms between diseases and for strategies to identify drugs that could be repurposed as novel treatments. Here, we present a computational protocol, implemented as a Snakemake workflow, to identify shared transcriptional processes and screen compounds that could result in mutual benefit. This protocol also includes a description of a pharmacovigilance study designed to validate the effect of compounds using electronic health records. For complete details on the use and execution of this protocol, please refer to Gao et al.1 and Baylis et al.2.
Collapse
Affiliation(s)
- Hua Gao
- Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cardiovascular Institute, Stanford, CA 94305, USA.
| | - Mao Zhang
- Stanford Cardiovascular Institute, Stanford, CA 94305, USA
| | - Richard A Baylis
- Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cardiovascular Institute, Stanford, CA 94305, USA; Department of Medicine, Division of Cardiology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Fudi Wang
- Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cardiovascular Institute, Stanford, CA 94305, USA
| | - Johan L M Björkegren
- Department of Medicine, Karolinska Institute, Huddinge, Sweden; Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jason J Kovacic
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia; St. Vincent's Clinical School, University of NSW, Sydney, NSW, Australia
| | - Arno Ruusalepp
- Department of Cardiac Surgery and The Heart Clinic, Tartu University Hospital and Department of Cardiology, Institute of Clinical Medicine, Tartu University, Tartu, Estonia
| | - Nicholas J Leeper
- Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cardiovascular Institute, Stanford, CA 94305, USA; Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
| |
Collapse
|
4
|
Mishra S, Chaudhury P, Tripathy HK, Sahoo KS, Jhanjhi NZ, Hassan Elnour AA, Abdelmaboud A. Enhancing health care through medical cognitive virtual agents. Digit Health 2024; 10:20552076241256732. [PMID: 39165388 PMCID: PMC11334247 DOI: 10.1177/20552076241256732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/07/2024] [Indexed: 08/22/2024] Open
Abstract
Objective The modern era of cognitive intelligence in clinical space has led to the rise of 'Medical Cognitive Virtual Agents' (MCVAs) which are labeled as intelligent virtual assistants interacting with users in a context-sensitive and ambient manner. They aim to augment users' cognitive capabilities thereby helping both patients and medical experts in providing personalized healthcare like remote health tracking, emergency healthcare and robotic diagnosis of critical illness, among others. The objective of this study is to explore the technical aspects of MCVA and their relevance in modern healthcare. Methods In this study, a comprehensive and interpretable analysis of MCVAs are presented and their impacts are discussed. A novel system framework prototype based on artificial intelligence for MCVA is presented. Architectural workflow of potential applications of functionalities of MCVAs are detailed. A novel MCVA relevance survey analysis was undertaken during March-April 2023 at Bhubaneswar, Odisha, India to understand the current position of MCVA in society. Results Outcome of the survey delivered constructive results. Majority of people associated with healthcare showed their inclination towards MCVA. The curiosity for MCVA in Urban zone was more than in rural areas. Also, elderly citizens preferred using MCVA more as compared to youths. Medical decision support emerged as the most preferred application of MCVA. Conclusion The article established and validated the relevance of MCVA in modern healthcare. The study showed that MCVA is likely to grow in future and can prove to be an effective assistance to medical experts in coming days.
Collapse
Affiliation(s)
- Sushruta Mishra
- School of Computer Engineering, Kalinga Institute of Industrial Technology, deemed to be University, Bhubaneswar, India
| | - Pamela Chaudhury
- Department of Computer Science and Engineering, Silicon Institute of Technology, Bhubaneswar, India
| | - Hrudaya Kumar Tripathy
- School of Computer Engineering, Kalinga Institute of Industrial Technology, deemed to be University, Bhubaneswar, India
| | - Kshira Sagar Sahoo
- Department of Computer Science and Engineering, SRM University, Amaravati, India
| | - NZ Jhanjhi
- School of Computer Science, SCS Taylor's University, Subang Jaya, Malaysia
| | - Asma Abbas Hassan Elnour
- Computer Department, Applied College_ Girls Section, King Khalid University, Muhayel Aseer, Saudi Arabia
| | | |
Collapse
|
5
|
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] [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.
Collapse
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.)
| |
Collapse
|
6
|
Liu R, Su S, Xing J, Liu K, Zhao Y, Stangis M, Jacho DP, Yildirim-Ayan ED, Gatto-Weis CM, Chen B, Li X. Tumor removal limits prostate cancer cell dissemination in bone and osteoblasts induce cancer cell dormancy through focal adhesion kinase. J Exp Clin Cancer Res 2023; 42:264. [PMID: 37821954 PMCID: PMC10566127 DOI: 10.1186/s13046-023-02849-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 10/02/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Disseminated tumor cells (DTCs) can enter a dormant state and cause no symptoms in cancer patients. On the other hand, the dormant DTCs can reactivate and cause metastases progression and lethal relapses. In prostate cancer (PCa), relapse can happen after curative treatments such as primary tumor removal. The impact of surgical removal on PCa dissemination and dormancy remains elusive. Furthermore, as dormant DTCs are asymptomatic, dormancy-induction can be an operational cure for preventing metastases and relapse of PCa patients. METHODS We used a PCa subcutaneous xenograft model and species-specific PCR to survey the DTCs in various organs at different time points of tumor growth and in response to tumor removal. We developed in vitro 2D and 3D co-culture models to recapitulate the dormant DTCs in the bone microenvironment. Proliferation assays, fluorescent cell cycle reporter, qRT-PCR, and Western Blot were used to characterize the dormancy phenotype. We performed RNA sequencing to determine the dormancy signature of PCa. A drug repurposing algorithm was applied to predict dormancy-inducing drugs and a top candidate was validated for the efficacy and the mechanism of dormancy induction. RESULTS We found DTCs in almost all mouse organs examined, including bones, at week 2 post-tumor cell injections. Surgical removal of the primary tumor reduced the overall DTC abundance, but the DTCs were enriched only in the bones. We found that osteoblasts, but not other cells of the bones, induced PCa cell dormancy. RNA-Seq revealed the suppression of mitochondrial-related biological processes in osteoblast-induced dormant PCa cells. Importantly, the mitochondrial-related biological processes were found up-regulated in both circulating tumor cells and bone metastases from PCa patients' data. We predicted and validated the dormancy-mimicking effect of PF-562,271 (PF-271), an inhibitor of focal adhesion kinase (FAK) in vitro. Decreased FAK phosphorylation and increased nuclear translocation were found in both co-cultured and PF-271-treated C4-2B cells, suggesting that FAK plays a key role in osteoblast-induced PCa dormancy. CONCLUSIONS Our study provides the first insights into how primary tumor removal enriches PCa cell dissemination in the bones, defines a unique osteoblast-induced PCa dormancy signature, and identifies FAK as a PCa cell dormancy gatekeeper.
Collapse
Affiliation(s)
- Ruihua Liu
- Department of Cell and Cancer Biology, College of Medicine and Life Sciences, the University of Toledo, 3000 Transverse Drive, Toledo, OH, 43614, USA
| | - Shang Su
- Department of Cell and Cancer Biology, College of Medicine and Life Sciences, the University of Toledo, 3000 Transverse Drive, Toledo, OH, 43614, USA
| | - Jing Xing
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, 49503, USA
| | - Ke Liu
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, 49503, USA
| | - Yawei Zhao
- Department of Cell and Cancer Biology, College of Medicine and Life Sciences, the University of Toledo, 3000 Transverse Drive, Toledo, OH, 43614, USA
| | - Mary Stangis
- Department of Cell and Cancer Biology, College of Medicine and Life Sciences, the University of Toledo, 3000 Transverse Drive, Toledo, OH, 43614, USA
| | - Diego P Jacho
- Bioengineering Department, the University of Toledo, Toledo, OH, 43606, USA
| | | | - Cara M Gatto-Weis
- Department of Pathology, College of Medicine and Life Sciences, the University of Toledo, Toledo, OH, 43614, USA
| | - Bin Chen
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, 49503, USA.
- Department of Pharmacology and Toxicology, Michigan State University, Grand Rapids, MI, 49503, USA.
| | - Xiaohong Li
- Department of Cell and Cancer Biology, College of Medicine and Life Sciences, the University of Toledo, 3000 Transverse Drive, Toledo, OH, 43614, USA.
| |
Collapse
|
7
|
Baylis RA, Gao H, Wang F, Bell CF, Luo L, Björkegren JL, Leeper NJ. Identifying shared transcriptional risk patterns between atherosclerosis and cancer. iScience 2023; 26:107513. [PMID: 37636064 PMCID: PMC10448075 DOI: 10.1016/j.isci.2023.107513] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/18/2023] [Accepted: 07/27/2023] [Indexed: 08/29/2023] Open
Abstract
Cancer and cardiovascular disease (CVD) are the leading causes of death worldwide. Numerous overlapping pathophysiologic mechanisms have been hypothesized to drive the development of both diseases. Further investigation of these common pathways could allow for the identification of mutually detrimental processes and therapeutic targeting to derive mutual benefit. In this study, we intersect transcriptomic datasets correlated with disease severity or patient outcomes for both cancer and atherosclerotic CVD. These analyses confirmed numerous pathways known to underlie both diseases, such as inflammation and hypoxia, but also identified several novel shared pathways. We used these to explore common translational targets by applying the drug prediction software, OCTAD, to identify compounds that simultaneously reverse the gene expression signature for both diseases. These analyses suggest that certain tumor-specific therapeutic approaches may be implemented so that they avoid cardiovascular consequences, and in some cases may even be used to simultaneously target co-prevalent cancer and atherosclerosis.
Collapse
Affiliation(s)
- Richard A. Baylis
- Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford, CA, USA
- Department of Medicine, Division of Cardiology, University of California, San Francisco, CA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Hua Gao
- Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford, CA, USA
| | - Fudi Wang
- Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford, CA, USA
| | - Caitlin F. Bell
- Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Lingfeng Luo
- Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford, CA, USA
| | - Johan L.M. Björkegren
- Department of Medicine, Karolinska Institute, Huddinge, Sweden
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicholas J. Leeper
- Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| |
Collapse
|
8
|
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] [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.
Collapse
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
| |
Collapse
|
9
|
Marino G, Ngai M, Clarke DB, Fleishman R, Deng E, Xie Z, Ahmed N, Ma’ayan A. GeneRanger and TargetRanger: processed gene and protein expression levels across cells and tissues for target discovery. Nucleic Acids Res 2023; 51:W213-W224. [PMID: 37166966 PMCID: PMC10320068 DOI: 10.1093/nar/gkad399] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 04/23/2023] [Accepted: 05/02/2023] [Indexed: 05/12/2023] Open
Abstract
Several atlasing efforts aim to profile human gene and protein expression across tissues, cell types and cell lines in normal physiology, development and disease. One utility of these resources is to examine the expression of a single gene across all cell types, tissues and cell lines in each atlas. However, there is currently no centralized place that integrates data from several atlases to provide this type of data in a uniform format for visualization, analysis and download, and via an application programming interface. To address this need, GeneRanger is a web server that provides access to processed data about gene and protein expression across normal human cell types, tissues and cell lines from several atlases. At the same time, TargetRanger is a related web server that takes as input RNA-seq data from profiled human cells and tissues, and then compares the uploaded input data to expression levels across the atlases to identify genes that are highly expressed in the input and lowly expressed across normal human cell types and tissues. Identified targets can be filtered by transmembrane or secreted proteins. The results from GeneRanger and TargetRanger are visualized as box and scatter plots, and as interactive tables. GeneRanger and TargetRanger are available from https://generanger.maayanlab.cloud and https://targetranger.maayanlab.cloud, respectively.
Collapse
Affiliation(s)
- Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael Ngai
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Reid H Fleishman
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Eden Z Deng
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nasheath Ahmed
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Avi Ma’ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| |
Collapse
|
10
|
He B, Xiao Y, Liang H, Huang Q, Du Y, Li Y, Garmire D, Sun D, Garmire LX. ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs. Nat Commun 2023; 14:993. [PMID: 36813801 PMCID: PMC9945835 DOI: 10.1038/s41467-023-36637-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 02/10/2023] [Indexed: 02/24/2023] Open
Abstract
Single-cell RNA sequencing technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD) that defines a drug score to recommend drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. ASGARD shows significantly better average accuracy on single-drug therapy compared to two bulk-cell-based drug repurposing methods. We also demonstrated that it performs considerably better than other cell cluster-level predicting methods. In addition, we validate ASGARD using the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. We find that many top-ranked drugs are either approved by the Food and Drug Administration or in clinical trials treating corresponding diseases. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD .
Collapse
Affiliation(s)
- Bing He
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA
| | - Yao Xiao
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA
| | - Haodong Liang
- Department of Statistics, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, USA
| | - Qianhui Huang
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA
| | - Yuheng Du
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA
| | - Yijun Li
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA
| | - David Garmire
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Duxin Sun
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, USA
| | - Lana X Garmire
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA.
| |
Collapse
|
11
|
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: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [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.
Collapse
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.
| |
Collapse
|
12
|
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] [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.
Collapse
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
| |
Collapse
|
13
|
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: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [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.
Collapse
Affiliation(s)
| | | | | | | | | | - Dong Li
- *Correspondence: Zhongyang Liu, ; Dong Li,
| |
Collapse
|
14
|
Gao H, Baylis RA, Luo L, Kojima Y, Bell CF, Ross EG, Wang F, Leeper NJ. Clustering cancers by shared transcriptional risk reveals novel targets for cancer therapy. Mol Cancer 2022; 21:116. [PMID: 35585548 PMCID: PMC9115915 DOI: 10.1186/s12943-022-01592-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/10/2022] [Indexed: 06/09/2024] Open
Affiliation(s)
- Hua Gao
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA
| | - Richard A Baylis
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Lingfeng Luo
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA
| | - Yoko Kojima
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA
| | - Caitlin F Bell
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Biomedical Innovations Building, 240 Pasteur Drive, #3654, Stanford, CA, 94305, USA
| | - Elsie G Ross
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Fudi Wang
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA
| | - Nicholas J Leeper
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA.
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Biomedical Innovations Building, 240 Pasteur Drive, #3654, Stanford, CA, 94305, USA.
| |
Collapse
|
15
|
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: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [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.
Collapse
|
16
|
Chen X, Cheng G, Wang FL, Tao X, Xie H, Xu L. Machine and cognitive intelligence for human health: systematic review. Brain Inform 2022; 9:5. [PMID: 35150379 PMCID: PMC8840949 DOI: 10.1186/s40708-022-00153-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 01/25/2022] [Indexed: 12/27/2022] Open
Abstract
Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.
Collapse
Affiliation(s)
- Xieling Chen
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China
| | - Gary Cheng
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China.
| | - Fu Lee Wang
- School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, China
| | - Xiaohui Tao
- School of Sciences, University of Southern Queensland, Toowoomba, Australia
| | - Haoran Xie
- Department of Computing and Decision Sciences, Lingnan University, Hong Kong SAR, China
| | - Lingling Xu
- School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, China
| |
Collapse
|
17
|
Misek SA, Newbury PA, Chekalin E, Paithankar S, Doseff AI, Chen B, Gallo KA, Neubig RR. Ibrutinib Blocks YAP1 Activation and Reverses BRAF Inhibitor Resistance in Melanoma Cells. Mol Pharmacol 2022; 101:1-12. [PMID: 34732527 PMCID: PMC11037454 DOI: 10.1124/molpharm.121.000331] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 10/01/2021] [Indexed: 11/22/2022] Open
Abstract
Most B-Raf proto-oncogene (BRAF)-mutant melanoma tumors respond initially to BRAF inhibitor (BRAFi)/mitogen-activated protein kinase kinase 1 inhibitor (MEKi) therapy, although few patients have durable long-term responses to these agents. The goal of this study was to use an unbiased computational approach to identify inhibitors that reverse an experimentally derived BRAFi resistance gene expression signature. Using this approach, we found that ibrutinib effectively reverses this signature, and we demonstrate experimentally that ibrutinib resensitizes a subset of BRAFi-resistant melanoma cells to vemurafenib. Ibrutinib is used clinically as an inhibitor of the Src family kinase Bruton tyrosine kinase (BTK); however, neither BTK deletion nor treatment with acalabrutinib, another BTK inhibitor with reduced off-target activity, resensitized cells to vemurafenib. These data suggest that ibrutinib acts through a BTK-independent mechanism in vemurafenib resensitization. To better understand this mechanism, we analyzed the transcriptional profile of ibrutinib-treated BRAFi-resistant melanoma cells and found that the transcriptional profile of ibrutinib was highly similar to that of multiple Src proto-oncogene kinase inhibitors. Since ibrutinib, but not acalabrutinib, has appreciable off-target activity against multiple Src family kinases, it suggests that ibrutinib may be acting through this mechanism. Furthermore, genes that are differentially expressed in ibrutinib-treated cells are enriched in Yes1-associated transcriptional regulator (YAP1) target genes, and we showed that ibrutinib, but not acalabrutinib, reduces YAP1 activity in BRAFi-resistant melanoma cells. Taken together, these data suggest that ibrutinib, or other Src family kinase inhibitors, may be useful for treating some BRAFi/MEKi-refractory melanoma tumors. SIGNIFICANCE STATEMENT: MAPK-targeted therapies provide dramatic initial responses, but resistance develops rapidly; a subset of these tumors may be rendered sensitive again by treatment with an approved Src family kinase inhibitor-ibrutinub-potentially providing improved clinical outcomes.
Collapse
Affiliation(s)
- Sean A Misek
- Departments of Physiology (S.A.M., A.I.D., K.A.G.), Pediatrics and Human Development (P.A.N., E.C., S.P., B.C.), and Pharmacology (A.I.D., B.C., R.R.N.), Michigan State University, East Lansing, Michigan
| | - Patrick A Newbury
- Departments of Physiology (S.A.M., A.I.D., K.A.G.), Pediatrics and Human Development (P.A.N., E.C., S.P., B.C.), and Pharmacology (A.I.D., B.C., R.R.N.), Michigan State University, East Lansing, Michigan
| | - Evgenii Chekalin
- Departments of Physiology (S.A.M., A.I.D., K.A.G.), Pediatrics and Human Development (P.A.N., E.C., S.P., B.C.), and Pharmacology (A.I.D., B.C., R.R.N.), Michigan State University, East Lansing, Michigan
| | - Shreya Paithankar
- Departments of Physiology (S.A.M., A.I.D., K.A.G.), Pediatrics and Human Development (P.A.N., E.C., S.P., B.C.), and Pharmacology (A.I.D., B.C., R.R.N.), Michigan State University, East Lansing, Michigan
| | - Andrea I Doseff
- Departments of Physiology (S.A.M., A.I.D., K.A.G.), Pediatrics and Human Development (P.A.N., E.C., S.P., B.C.), and Pharmacology (A.I.D., B.C., R.R.N.), Michigan State University, East Lansing, Michigan
| | - Bin Chen
- Departments of Physiology (S.A.M., A.I.D., K.A.G.), Pediatrics and Human Development (P.A.N., E.C., S.P., B.C.), and Pharmacology (A.I.D., B.C., R.R.N.), Michigan State University, East Lansing, Michigan
| | - Kathleen A Gallo
- Departments of Physiology (S.A.M., A.I.D., K.A.G.), Pediatrics and Human Development (P.A.N., E.C., S.P., B.C.), and Pharmacology (A.I.D., B.C., R.R.N.), Michigan State University, East Lansing, Michigan
| | - Richard R Neubig
- Departments of Physiology (S.A.M., A.I.D., K.A.G.), Pediatrics and Human Development (P.A.N., E.C., S.P., B.C.), and Pharmacology (A.I.D., B.C., R.R.N.), Michigan State University, East Lansing, Michigan
| |
Collapse
|
18
|
Carvalho RF, do Canto LM, Cury SS, Frøstrup Hansen T, Jensen LH, Rogatto SR. Drug Repositioning Based on the Reversal of Gene Expression Signatures Identifies TOP2A as a Therapeutic Target for Rectal Cancer. Cancers (Basel) 2021; 13:5492. [PMID: 34771654 PMCID: PMC8583090 DOI: 10.3390/cancers13215492] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/21/2021] [Accepted: 10/28/2021] [Indexed: 12/12/2022] Open
Abstract
Rectal cancer is a common disease with high mortality rates and limited therapeutic options. Here we combined the gene expression signatures of rectal cancer patients with the reverse drug-induced gene-expression profiles to identify drug repositioning candidates for cancer therapy. Among the predicted repurposable drugs, topoisomerase II inhibitors (doxorubicin, teniposide, idarubicin, mitoxantrone, and epirubicin) presented a high potential to reverse rectal cancer gene expression signatures. We showed that these drugs effectively reduced the growth of colorectal cancer cell lines closely representing rectal cancer signatures. We also found a clear correlation between topoisomerase 2A (TOP2A) gene copy number or expression levels with the sensitivity to topoisomerase II inhibitors. Furthermore, CRISPR-Cas9 and shRNA screenings confirmed that loss-of-function of the TOP2A has the highest efficacy in reducing cellular proliferation. Finally, we observed significant TOP2A copy number gains and increased expression in independent cohorts of rectal cancer patients. These findings can be translated into clinical practice to evaluate TOP2A status for targeted and personalized therapies based on topoisomerase II inhibitors in rectal cancer patients.
Collapse
Affiliation(s)
- Robson Francisco Carvalho
- Department of Clinical Genetics, University Hospital of Southern Denmark, 7100 Vejle, Denmark;
- Institute of Regional Health Research, University of Southern Denmark, 5230 Odense, Denmark
- Department of Functional and Structural Biology—Institute of Bioscience, São Paulo State University (UNESP), Botucatu 18618-689, Brazil;
| | - Luisa Matos do Canto
- Department of Clinical Genetics, University Hospital of Southern Denmark, 7100 Vejle, Denmark;
- Institute of Regional Health Research, University of Southern Denmark, 5230 Odense, Denmark
| | - Sarah Santiloni Cury
- Department of Functional and Structural Biology—Institute of Bioscience, São Paulo State University (UNESP), Botucatu 18618-689, Brazil;
| | - Torben Frøstrup Hansen
- Department of Oncology, University Hospital of Southern Denmark, 7100 Vejle, Denmark; (T.F.H.); (L.H.J.)
- Danish Colorectal Cancer Center South, 7100 Vejle, Denmark
| | - Lars Henrik Jensen
- Department of Oncology, University Hospital of Southern Denmark, 7100 Vejle, Denmark; (T.F.H.); (L.H.J.)
- Danish Colorectal Cancer Center South, 7100 Vejle, Denmark
| | - Silvia Regina Rogatto
- Department of Clinical Genetics, University Hospital of Southern Denmark, 7100 Vejle, Denmark;
- Institute of Regional Health Research, University of Southern Denmark, 5230 Odense, Denmark
- Danish Colorectal Cancer Center South, 7100 Vejle, Denmark
| |
Collapse
|
19
|
Xing J, Paithankar S, Liu K, Uhl K, Li X, Ko M, Kim S, Haskins J, Chen B. Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals. Brief Bioinform 2021; 22:6318177. [PMID: 34245241 PMCID: PMC8344595 DOI: 10.1093/bib/bbab249] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
The global efforts in the past year have led to the discovery of nearly 200 drug repurposing candidates for COVID-19. Gaining more insights into their mechanisms of action could facilitate a better understanding of infection and the development of therapeutics. Leveraging large-scale drug-induced gene expression profiles, we found 36% of the active compounds regulate genes related to cholesterol homeostasis and microtubule cytoskeleton organization. Following bioinformatics analyses revealed that the expression of these genes is associated with COVID-19 patient severity and has predictive power on anti-SARS-CoV-2 efficacy in vitro. Monensin, a top new compound that regulates these genes, was further confirmed as an inhibitor of SARS-CoV-2 replication in Vero-E6 cells. Interestingly, drugs co-targeting cholesterol homeostasis and microtubule cytoskeleton organization processes more likely present a synergistic effect with antivirals. Therefore, potential therapeutics could be centered around combinations of targeting these processes and viral proteins.
Collapse
Affiliation(s)
- Jing Xing
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
| | - Shreya Paithankar
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
| | - Ke Liu
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
| | - Katie Uhl
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
| | - Xiaopeng Li
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
| | - Meehyun Ko
- Zoonotic Virus Laboratory, Institut Pasteur Korea, Seongnam, South Korea
| | - Seungtaek Kim
- Zoonotic Virus Laboratory, Institut Pasteur Korea, Seongnam, South Korea
| | - Jeremy Haskins
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
| | - Bin Chen
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA.,Department of Pharmacology and Toxicology, Michigan State University, Grand Rapids, Michigan, USA
| |
Collapse
|
20
|
Sun M, Shankar R, Ko M, Chang CD, Yeh SJ, Li S, Liu K, Zhou G, Xing J, VanVelsen A, VanVelsen T, Paithankar S, Feng BY, Young K, Strug M, Turco L, Wang Z, Schadt E, Chen R, Li X, Oskotsky T, Sirota M, Glicksberg BS, Nadkarni GN, Moeser AJ, Li L, Kim S, Zhou J, Chen B. Sex differences in viral entry protein expression and host transcript responses to SARS-CoV-2. RESEARCH SQUARE 2020:rs.3.rs-100914. [PMID: 33173861 PMCID: PMC7654875 DOI: 10.21203/rs.3.rs-100914/v1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Epidemiological studies suggest that men exhibit a higher mortality rate to COVID-19 than women, yet the underlying biology is largely unknown. Here, we seek to delineate sex differences in the gene expression of viral entry proteins ACE2 and TMPRSS2, and host transcriptional responses to SARS-CoV-2 through large-scale analysis of genomic and clinical data. We first compiled 220,000 human gene expression profiles from three databases and completed the meta-information through machine learning and manual annotation. Large scale analysis of these profiles indicated that male samples show higher expression levels of ACE2 and TMPRSS2 than female samples, especially in the older group (>60 years) and in the kidney. Subsequent analysis of 6,031 COVID-19 patients at Mount Sinai Health System revealed that men have significantly higher creatinine levels, an indicator of impaired kidney function. Further analysis of 782 COVID-19 patient gene expression profiles taken from upper airway and blood suggested men and women present distinct expression changes. Computational deconvolution analysis of these profiles revealed male COVID-19 patients have enriched kidney-specific mesangial cells in blood compared to healthy patients. Together, this study suggests biological differences in the kidney between sexes may contribute to sex disparity in COVID-19.
Collapse
Affiliation(s)
- Mengying Sun
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Rama Shankar
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
| | - Meehyun Ko
- Zoonotic Virus Laboratory, Institut Pasteur Korea, Seongnam, Korea
| | | | - Shan-Ju Yeh
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | | | - Ke Liu
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
| | - Guoli Zhou
- Biomedical Research Informatics Core, Clinical & Translational Sciences Institute, Michigan State University, East Lansing, Michigan, USA
| | - Jing Xing
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
| | - Austin VanVelsen
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
| | - Tyler VanVelsen
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
| | - Shreya Paithankar
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
| | - Benjamin Y. Feng
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
| | - Krista Young
- College of Osteopathic Medicine, Michigan State University, East Lansing, Michigan, USA
| | - Michael Strug
- Department of Obstetrics and Gynecology, Spectrum Health, Grand Rapids, Michigan, USA
- Department of Obstetrics, Gynecology and Reproductive Biology, Michigan State University, Grand Rapids, Michigan, USA
| | - Lauren Turco
- Emergency Medicine Residency, Spectrum Health, Grand Rapids, Michigan, USA
| | | | - Eric Schadt
- Sema4, Stamford, CT, Connecticut, USA
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Rong Chen
- Sema4, Stamford, CT, Connecticut, USA
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Xiaohong Li
- Van Andel Research Institute, Grand Rapids, Michigan, USA
| | - Tomiko Oskotsky
- Department of Pediatrics and Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Marina Sirota
- Department of Pediatrics and Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Benjamin S. Glicksberg
- The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N. Nadkarni
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adam J. Moeser
- Large Animal Clinical Sciences and Department of Physiology, Michigan State University, East Lansing, Michigan, USA
| | - Li Li
- Sema4, Stamford, CT, Connecticut, USA
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Seungtaek Kim
- Zoonotic Virus Laboratory, Institut Pasteur Korea, Seongnam, Korea
| | - Jiayu Zhou
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Bin Chen
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan, USA
- Department of Pharmacology and Toxicology, Michigan State University, Grand Rapids, Michigan, USA
- Correspondence to Bin Chen:
| |
Collapse
|