1
|
Duo L, Liu Y, Ren J, Tang B, Hirst JD. Artificial intelligence for small molecule anticancer drug discovery. Expert Opin Drug Discov 2024; 19:933-948. [PMID: 39074493 DOI: 10.1080/17460441.2024.2367014] [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: 04/22/2024] [Accepted: 06/07/2024] [Indexed: 07/31/2024]
Abstract
INTRODUCTION The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships. AREA COVERED In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research. EXPERT OPINION The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.
Collapse
Affiliation(s)
- Lihui Duo
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Yu Liu
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Jianfeng Ren
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Bencan Tang
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Jonathan D Hirst
- School of Chemistry, University of Nottingham University Park, Nottingham, UK
| |
Collapse
|
2
|
Pan T, Gao Y, Xu G, Yu L, Xu Q, Yu J, Liu M, Zhang C, Ma Y, Li Y. Widespread transcriptomic alterations of transient receptor potential channel genes in cancer. Brief Funct Genomics 2024; 23:214-227. [PMID: 37288496 DOI: 10.1093/bfgp/elad023] [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: 02/09/2023] [Revised: 05/17/2023] [Accepted: 05/22/2023] [Indexed: 06/09/2023] Open
Abstract
Ion channels, in particular transient-receptor potential (TRP) channels, are essential genes that play important roles in many physiological processes. Emerging evidence has demonstrated that TRP genes are involved in a number of diseases, including various cancer types. However, we still lack knowledge about the expression alterations landscape of TRP genes across cancer types. In this review, we comprehensively reviewed and summarised the transcriptomes from more than 10 000 samples in 33 cancer types. We found that TRP genes were widespreadly transcriptomic dysregulated in cancer, which was associated with clinical survival of cancer patients. Perturbations of TRP genes were associated with a number of cancer pathways across cancer types. Moreover, we reviewed the functions of TRP family gene alterations in a number of diseases reported in recent studies. Taken together, our study comprehensively reviewed TRP genes with extensive transcriptomic alterations and their functions will directly contribute to cancer therapy and precision medicine.
Collapse
Affiliation(s)
- Tao Pan
- Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, Hainan Provincial Clinical Research Center for Thalassemia, Key Laboratory of Reproductive Health Diseases Research and Translation (Hainan Medical University), Ministry of Education, Department of Reproductive Medicine, the First Affliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, Hainan 571199, China
| | - Yueying Gao
- Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, Hainan Provincial Clinical Research Center for Thalassemia, Key Laboratory of Reproductive Health Diseases Research and Translation (Hainan Medical University), Ministry of Education, Department of Reproductive Medicine, the First Affliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, Hainan 571199, China
| | - Gang Xu
- Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, Hainan Provincial Clinical Research Center for Thalassemia, Key Laboratory of Reproductive Health Diseases Research and Translation (Hainan Medical University), Ministry of Education, Department of Reproductive Medicine, the First Affliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, Hainan 571199, China
| | | | - Qi Xu
- Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, Hainan Provincial Clinical Research Center for Thalassemia, Key Laboratory of Reproductive Health Diseases Research and Translation (Hainan Medical University), Ministry of Education, Department of Reproductive Medicine, the First Affliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, Hainan 571199, China
| | - Jinyang Yu
- Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, Hainan Provincial Clinical Research Center for Thalassemia, Key Laboratory of Reproductive Health Diseases Research and Translation (Hainan Medical University), Ministry of Education, Department of Reproductive Medicine, the First Affliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, Hainan 571199, China
| | - Meng Liu
- Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, Hainan Provincial Clinical Research Center for Thalassemia, Key Laboratory of Reproductive Health Diseases Research and Translation (Hainan Medical University), Ministry of Education, Department of Reproductive Medicine, the First Affliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, Hainan 571199, China
| | - Can Zhang
- Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, Hainan Provincial Clinical Research Center for Thalassemia, Key Laboratory of Reproductive Health Diseases Research and Translation (Hainan Medical University), Ministry of Education, Department of Reproductive Medicine, the First Affliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, Hainan 571199, China
| | - Yanlin Ma
- Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, Hainan Provincial Clinical Research Center for Thalassemia, Key Laboratory of Reproductive Health Diseases Research and Translation (Hainan Medical University), Ministry of Education, Department of Reproductive Medicine, the First Affliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, Hainan 571199, China
| | - Yongsheng Li
- Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, Hainan Provincial Clinical Research Center for Thalassemia, Key Laboratory of Reproductive Health Diseases Research and Translation (Hainan Medical University), Ministry of Education, Department of Reproductive Medicine, the First Affliated Hospital of Hainan Medical University, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, Hainan 571199, China
| |
Collapse
|
3
|
B S N, P K KN, Akey KS, Sankaran S, Raman RK, Natarajan J, Selvaraj J. Vitamin D analog calcitriol for breast cancer therapy; an integrated drug discovery approach. J Biomol Struct Dyn 2023; 41:11017-11043. [PMID: 37054526 DOI: 10.1080/07391102.2023.2199866] [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: 07/25/2022] [Accepted: 12/11/2022] [Indexed: 04/15/2023]
Abstract
As breast cancer remains leading cause of cancer death globally, it is essential to develop an affordable breast cancer therapy in underdeveloped countries. Drug repurposing offers potential to address gaps in breast cancer treatment. Molecular networking studies were performed for drug repurposing approach by using heterogeneous data. The PPI networks were built to select the target genes from the EGFR overexpression signaling pathway and its associated family members. The selected genes EGFR, ErbB2, ErbB4 and ErbB3 were allowed to interact with 2637 drugs, leads to PDI network construction of 78, 61, 15 and 19 drugs, respectively. As drugs approved for treating non cancer-related diseases or disorders are clinically safe, effective, and affordable, these drugs were given considerable attention. Calcitriol had shown significant binding affinities with all four receptors than standard neratinib. The RMSD, RMSF, and H-bond analysis of protein-ligand complexes from molecular dynamics simulation (100 ns), confirmed the stable binding of calcitriol with ErbB2 and EGFR receptors. In addition, MMGBSA and MMP BSA also affirmed the docking results. These in-silico results were validated with in-vitro cytotoxicity studies in SK-BR-3 and Vero cells. The IC50 value of calcitriol (43.07 mg/ml) was found to be lower than neratinib (61.50 mg/ml) in SK-BR-3 cells. In Vero cells the IC50 value of calcitriol (431.05 mg/ml) was higher than neratinib (404.95 mg/ml). It demonstrates that calcitriol suggestively downregulated the SK-BR-3 cell viability in a dose-dependent manner. These implications revealed calcitriol has shown better cytotoxicity and decreased the proliferation rate of breast cancer cells than neratinib.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Nagaraj B S
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Krishnan Namboori P K
- Amrita Molecular Modeling and Synthesis (AMMAS) Research lab, Amrita Vishwavidyapeetham, Coimbatore, Tamilnadu, India
| | - Krishna Swaroop Akey
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Sathianarayanan Sankaran
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Karpagam Academy of Higher Education, Coimbatore, Tamilnadu, India
| | - Rajesh Kumar Raman
- Department of Pharmaceutical Biotechnology, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Jawahar Natarajan
- Department of Pharmaceutics, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Jubie Selvaraj
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| |
Collapse
|
4
|
Zong N, Li N, Wen A, Ngo V, Yu Y, Huang M, Chowdhury S, Jiang C, Fu S, Weinshilboum R, Jiang G, Hunter L, Liu H. BETA: a comprehensive benchmark for computational drug-target prediction. Brief Bioinform 2022; 23:6596989. [PMID: 35649342 PMCID: PMC9294420 DOI: 10.1093/bib/bbac199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/10/2022] [Accepted: 04/29/2022] [Indexed: 11/14/2022] Open
Abstract
Internal validation is the most popular evaluation strategy used for drug-target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug-drug and protein-protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.
Collapse
Affiliation(s)
- Nansu Zong
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| | - Ning Li
- Center for Structure Biology, Center for Cancer Research, National Cancer Institute, Frederick, MD
| | - Andrew Wen
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| | - Victoria Ngo
- Betty Irene Moore School of Nursing, University of California Davis Health, Sacramento, CA.,Stanford Health Policy, Stanford School of Medicine and Freeman Spogli Institute for International Studies, Palo Alto, CA
| | - Yue Yu
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| | - Ming Huang
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| | - Shaika Chowdhury
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| | - Chao Jiang
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL
| | - Sunyang Fu
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN
| | - Guoqian Jiang
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| | - Lawrence Hunter
- Department of Pharmacology, University of Colorado Denver, Aurora, CO
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN
| |
Collapse
|
5
|
Pan-cancer analyses reveal the genetic and pharmacogenomic landscape of transient receptor potential channels. NPJ Genom Med 2022; 7:32. [PMID: 35614079 PMCID: PMC9132893 DOI: 10.1038/s41525-022-00304-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 05/05/2022] [Indexed: 12/15/2022] Open
Abstract
Transient-receptor potential (TRP) channels comprise a diverse family of ion channels, which play important roles in regulation of intracellular calcium. Emerging evidence has revealed the critical roles of TRP channels in tumor development and progression. However, we still lack knowledge about the genetic and pharmacogenomics landscape of TRP genes across cancer types. Here, we comprehensively characterized the genetic and transcriptome alterations of TRP genes across >10,000 patients of 33 cancer types. We revealed prevalent somatic mutations and copy number variation in TRP genes. In particular, mutations located in transmembrane regions of TRP genes were likely to be deleterious mutations (p-values < 0.001). Genetic alterations were correlated with transcriptome dysregulation of TRP genes, and we found that TRPM2, TRPM8, and TPRA1 showed extent dysregulation in cancer. Patients with TRP gene alterations were with significantly higher hypoxia scores, tumor mutation burdens, tumor stages and grades, and poor survival. The alterations of TRP genes were significantly associated with the activity of cancer-related pathways. Moreover, we found that the expression of TRP genes were potentially useful for development of targeted therapies. Our study provided the landscape of genomic and transcriptomic alterations of TPRs across 33 cancer types, which is a comprehensive resource for guiding both mechanistic and therapeutic analyses of the roles of TRP genes in cancer. Identifying the TRP genes with extensive genetic alterations will directly contribute to cancer therapy in the context of predictive, preventive, and personalized medicine.
Collapse
|
6
|
You Y, Lai X, Pan Y, Zheng H, Vera J, Liu S, Deng S, Zhang L. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7:156. [PMID: 35538061 PMCID: PMC9090746 DOI: 10.1038/s41392-022-00994-0] [Citation(s) in RCA: 74] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
Collapse
Affiliation(s)
- Yujie You
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Room D513, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, China
| | - Huiru Zheng
- School of Computing, Ulster University, Belfast, BT15 1ED, UK
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Suran Liu
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Senyi Deng
- Institute of Thoracic Oncology, Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610065, China.
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
| |
Collapse
|
7
|
Timilsina M, Kernan DPM, Yang H, d'Aquin M. Synergy Between Embedding and Protein Functional Association Networks for Drug Label Prediction Using Harmonic Function. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1203-1213. [PMID: 33064647 DOI: 10.1109/tcbb.2020.3031696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Semi-Supervised Learning (SSL)is an approach to machine learning that makes use of unlabeled data for training with a small amount of labeled data. In the context of molecular biology and pharmacology, one can take advantage of unlabeled data. For instance, to identify drugs and targets where a few genes are known to be associated with a specific target for drugs and considered as labeled data. Labeling the genes requires laboratory verification and validation. This process is usually very time consuming and expensive. Thus, it is useful to estimate the functional role of drugs from unlabeled data using computational methods. To develop such a model, we used openly available data resources to create (i)drugs and genes, (ii)genes and disease, bipartite graphs. We constructed the genetic embedding graph from the two bipartite graphs using Tensor Factorization methods. We integrated the genetic embedding graph with the publicly available protein functional association network. Our results show the usefulness of the integration by effectively predicting drug labels.
Collapse
|
8
|
Feng Z, Zhang J, Zheng Y, Liu J, Duan T, Tian T. Overexpression of abnormal spindle-like microcephaly-associated (ASPM) increases tumor aggressiveness and predicts poor outcome in patients with lung adenocarcinoma. Transl Cancer Res 2022; 10:983-997. [PMID: 35116426 PMCID: PMC8798794 DOI: 10.21037/tcr-20-2570] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 12/04/2020] [Indexed: 12/25/2022]
Abstract
Background Cumulative evidence points to abnormal spindle-like microcephaly-associated (ASPM) protein being overexpressed in various cancers, and the aberrant expression of ASPM has been shown to promote cancer tumorigenicity and progression. However, its role and clinical significance in lung adenocarcinoma (LUAD) remains unclear. This study aimed to determine the expression patterns of ASPM and its clinical significance in LUAD. Methods In total, 4 original worldwide LUAD microarray mRNA expression datasets (N=1,116) with clinical and follow-up annotations were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The expression of ASPM protein in LUAD patients was detected by immunohistochemistry. Survival analysis and Cox regression analysis were used to examine the prognostic value of ASPM expression. Gene set enrichment analysis (GSEA) was performed to investigate the relationship between ASPM and LUAD. Results Dataset analyses and immunohistochemistry revealed that ASPM expression was significantly higher in the LUAD tissues compared with normal lung tissues, especially in the advanced tumor stage. Additionally, overexpression of ASPM was significantly correlated with shorter overall survival (OS) and relapse-free survival (RFS) in LUAD. Univariate and multivariate Cox regression analyses revealed that the overexpression of ASPM was a potential independent predictor of poor OS and RFS. However, ASPM overexpression was not significantly associated with predicting OS in lung squamous cell carcinoma. GSEA analysis demonstrated that ASPM was significantly enriched in the cell cycle, DNA replication, homologous recombination, RNA degradation, mismatch repair, and p53 signaling pathways. Conclusions These findings demonstrate the important role of ASPM in the tumorigenesis and progression of LUAD.
Collapse
Affiliation(s)
- Zhenxing Feng
- Department of Radiation Oncology, Tianjin Chest Hospital, Tianjin Cardiovascular Disease Research Institute, Tianjin, China.,Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Jiao Zhang
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Yafang Zheng
- Department of Radiation Oncology, Tianjin Chest Hospital, Tianjin Cardiovascular Disease Research Institute, Tianjin, China
| | - Jianchao Liu
- Department of Radiation Oncology, Tianjin Chest Hospital, Tianjin Cardiovascular Disease Research Institute, Tianjin, China
| | - Tianyu Duan
- Department of Radiation Oncology, Tianjin Chest Hospital, Tianjin Cardiovascular Disease Research Institute, Tianjin, China
| | - Tieshuan Tian
- Department of Radiation Oncology, Tianjin Chest Hospital, Tianjin Cardiovascular Disease Research Institute, Tianjin, China
| |
Collapse
|
9
|
Zhao R, Sa X, Ouyang N, Zhang H, Yang J, Pan J, Gu J, Zhou Y. A Pan-Cancer Analysis of Transcriptome and Survival Reveals Prognostic Differentially Expressed LncRNAs and Predicts Novel Drugs for Glioblastoma Multiforme Therapy. Front Genet 2021; 12:723725. [PMID: 34759954 PMCID: PMC8575119 DOI: 10.3389/fgene.2021.723725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 07/23/2021] [Indexed: 11/13/2022] Open
Abstract
Numerous studies have identified various prognostic long non-coding RNAs (LncRNAs) in a specific cancer type, but a comprehensive pan-cancer analysis for prediction of LncRNAs that may serve as prognostic biomarkers is of great significance to be performed. Glioblastoma multiforme (GBM) is the most common and aggressive malignant adult primary brain tumor. There is an urgent need to identify novel therapies for GBM due to its poor prognosis and universal recurrence. Using available LncRNA expression data of 12 cancer types and survival data of 30 cancer types from online databases, we identified 48 differentially expressed LncRNAs in cancers as potential pan-cancer prognostic biomarkers. Two candidate LncRNAs were selected for validation in GBM. By the expression detection in GBM cell lines and survival analysis in GBM patients, we demonstrated the reliability of the list of pan-cancer prognostic LncRNAs obtained above. By constructing LncRNA-mRNA-drug network in GBM, we predicted novel drug-target interactions for GBM correlated LncRNA. This analysis has revealed common prognostic LncRNAs among cancers, which may provide insights into cancer pathogenesis and novel drug target in GBM.
Collapse
Affiliation(s)
- Rongchuan Zhao
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Heifei, China.,Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xiaohan Sa
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Heifei, China.,Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Nan Ouyang
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Heifei, China.,Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Hong Zhang
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Jiao Yang
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Jinlin Pan
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Heifei, China.,Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Jinhui Gu
- Department of Anorectum, Suzhou Hospital of Traditional Chinese Medicine, Suzhou, China
| | - Yuanshuai Zhou
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| |
Collapse
|
10
|
Chen P, Bao T, Yu X, Liu Z. A drug repositioning algorithm based on a deep autoencoder and adaptive fusion. BMC Bioinformatics 2021; 22:532. [PMID: 34717542 PMCID: PMC8556784 DOI: 10.1186/s12859-021-04406-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 09/27/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Drug repositioning has caught the attention of scholars at home and abroad due to its effective reduction of the development cost and time of new drugs. However, existing drug repositioning methods that are based on computational analysis are limited by sparse data and classic fusion methods; thus, we use autoencoders and adaptive fusion methods to calculate drug repositioning. RESULTS In this study, a drug repositioning algorithm based on a deep autoencoder and adaptive fusion was proposed to mitigate the problems of decreased precision and low-efficiency multisource data fusion caused by data sparseness. Specifically, a drug is repositioned by fusing drug-disease associations, drug target proteins, drug chemical structures and drug side effects. First, drug feature data integrated by drug target proteins and chemical structures were processed with dimension reduction via a deep autoencoder to characterize feature representations more densely and abstractly. Then, disease similarity was computed using drug-disease association data, while drug similarity was calculated with drug feature and drug-side effect data. Predictions of drug-disease associations were also calculated using a top-k neighbor method that is commonly used in predictive drug repositioning studies. Finally, a predicted matrix for drug-disease associations was acquired after fusing a wide variety of data via adaptive fusion. Based on experimental results, the proposed algorithm achieves a higher precision and recall rate than the DRCFFS, SLAMS and BADR algorithms with the same dataset. CONCLUSION The proposed algorithm contributes to investigating the novel uses of drugs, as shown in a case study of Alzheimer's disease. Therefore, the proposed algorithm can provide an auxiliary effect for clinical trials of drug repositioning.
Collapse
Affiliation(s)
- Peng Chen
- College of Computer and Information Technology, China Three Gorges University, Hubei, China
| | - Tianjiazhi Bao
- College of Computer and Information Technology, China Three Gorges University, Hubei, China
| | - Xiaosheng Yu
- College of Computer and Information Technology, China Three Gorges University, Hubei, China
| | - Zhongtu Liu
- College of Computer and Information Technology, China Three Gorges University, Hubei, China
| |
Collapse
|
11
|
Fang J, Wu Q, Ye F, Cai C, Xu L, Gu Y, Wang Q, Liu AL, Tan W, Du GH. Network-Based Identification and Experimental Validation of Drug Candidates Toward SARS-CoV-2 via Targeting Virus-Host Interactome. Front Genet 2021; 12:728960. [PMID: 34539756 PMCID: PMC8440948 DOI: 10.3389/fgene.2021.728960] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 08/06/2021] [Indexed: 12/24/2022] Open
Abstract
Despite that several therapeutic agents have exhibited promising prevention or treatment on Coronavirus disease-2019 (COVID-19), there is no specific drug discovered for this pandemic. Targeting virus-host interactome provides a more effective strategy for antivirus drug discovery compared with targeting virus proteins. In this study, we developed a network-based infrastructure to prioritize promising drug candidates from natural products and approved drugs via targeting host proteins of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). We firstly measured the network distances between drug targets and COVID-19 disease module utilizing the network proximity approach, and identified 229 approved drugs as well as 432 natural products had significant associations with SARS-CoV-2. After searching for previous literature evidence, we found that 60.7% (139/229) of approved drugs and 39.6% (171/432) of natural products were confirmed with antivirus or anti-inflammation. We further integrated our network-based predictions and validated anti-SARS-CoV-2 activities of some compounds. Four drug candidates, including hesperidin, isorhapontigenin, salmeterol, and gallocatechin-7-gallate, have exhibited activity on SARS-COV-2 virus-infected Vero cells. Finally, we showcased the mechanism of actions of isorhapontigenin and salmeterol via network analysis. Overall, this study offers forceful approaches for in silico identification of drug candidates on COVID-19, which may facilitate the discovery of antiviral drug therapies.
Collapse
Affiliation(s)
- Jiansong Fang
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qihui Wu
- Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou, China
| | - Fei Ye
- MHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, Beijing, China
| | - Chuipu Cai
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Lvjie Xu
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Gu
- Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou, China
| | - Qi Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ai-lin Liu
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenjie Tan
- MHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, Beijing, China
| | - Guan-hua Du
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| |
Collapse
|
12
|
Al-Taie Z, Liu D, Mitchem JB, Papageorgiou C, Kaifi JT, Warren WC, Shyu CR. Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential. J Biomed Inform 2021; 118:103792. [PMID: 33915273 DOI: 10.1016/j.jbi.2021.103792] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 03/26/2021] [Accepted: 04/21/2021] [Indexed: 01/02/2023]
Abstract
Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. This shows the importance of developing data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Contrast pattern mining and network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The proposed method represents a human-in-the-loop framework, where medical experts use the data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. Colorectal cancer (CRC) was used as a case study. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups identified by medical experts showed that most of these drugs are cancer-related, and most of them have the potential to be a CRC regimen based on studies in the literature.
Collapse
Affiliation(s)
- Zainab Al-Taie
- Institute for Data Science & Informatics, University of Missouri, Columbia, MO 65211, USA; Department of Computer Science, College of Science for Women, University of Baghdad, Baghdad, Iraq
| | - Danlu Liu
- Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65211, USA
| | - Jonathan B Mitchem
- Institute for Data Science & Informatics, University of Missouri, Columbia, MO 65211, USA; Department of Surgery, School of Medicine, University of Missouri, Columbia, MO 65212, USA; Harry S. Truman Memorial Veterans' Hospital, Columbia, MO 65201, USA.
| | - Christos Papageorgiou
- Department of Surgery, School of Medicine, University of Missouri, Columbia, MO 65212, USA
| | - Jussuf T Kaifi
- Department of Surgery, School of Medicine, University of Missouri, Columbia, MO 65212, USA; Harry S. Truman Memorial Veterans' Hospital, Columbia, MO 65201, USA
| | - Wesley C Warren
- Institute for Data Science & Informatics, University of Missouri, Columbia, MO 65211, USA; Department of Surgery, School of Medicine, University of Missouri, Columbia, MO 65212, USA; Department of Animal Sciences, Bond Life Sciences Center, University of Missouri, 1201 Rollins Street, Columbia, MO 65211, USA
| | - Chi-Ren Shyu
- Institute for Data Science & Informatics, University of Missouri, Columbia, MO 65211, USA; Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65211, USA; Department of Medicine, School of Medicine, University of Missouri, Columbia, MO 65212, USA.
| |
Collapse
|
13
|
Xia C, Xu X, Ding Y, Yu C, Qiao J, Liu P. Abnormal spindle-like microcephaly-associated protein enhances cell invasion through Wnt/β-catenin-dependent regulation of epithelial-mesenchymal transition in non-small cell lung cancer cells. J Thorac Dis 2021; 13:2460-2474. [PMID: 34012593 PMCID: PMC8107535 DOI: 10.21037/jtd-21-566] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Background Lung cancer is one of the most common cancer worldwide, invasion and metastasis are still the bottleneck in the clinical setting. More diagnostic markers and drug targets need to be clarified. Therefore, we screened abnormal spindle-like microcephaly-associated protein (ASPM) as our candidate gene, which is associated with the poor prognosis. The aim of the present study was to understand the roles of ASPM in cell invasion in non-small cell lung cancer (NSCLC). Methods Gene Expression Omnibus (GEO) datamining was used to identify ASPM. Transwell invasion assay, quantitative reverse transcription polymerase chain reaction (qRT-PCR), and Western blot analysis were performed to discover the molecular functions of ASPM. Overexpression and small interfering mediated knockdown techniques have been used to study the cell invasion hallmarks of cancer. Results ASPM stood out among all the candidate genes from GEO datamining. ASPM in lung cancer tissues has been associated with poor overall survival rate. The protein levels of ASPM has been validated using lung cancer patients’ tissues, which upregulation of ASPM expression has been found in lung cancer patients. Silencing of ASPM decreased the cell invasion reflected by epithelial-mesenchymal transition (EMT) biomarkers: downregulation of vimentin and upregulation of E-cadherin. Matrix metalloproteinase (MMP) 2/9 protein levels were also affected upon transient knockdown of ASPM. Furthermore, the suppression of ASPM markedly inhibited the Wnt/β-catenin signaling pathway in vitro. The ectopic expression of ASPM had the opposite effect. The inhibition of β-catenin in ASPM-overexpressing lung cancer cells reduced the expression of EMT markers. The inhibitory effects on the Wnt/β-catenin signaling pathway were attenuated in cancer cells when ASPM was silenced. These findings demonstrated that the silencing of ASPM strongly reduced cell invasion in lung cancer. Conclusions ASPM promoted NSCLC invasion through EMT and by affecting the MMP family of proteins. The Wnt/β-catenin signaling pathway played an indispensable role in the ASPM-mediated NSCLC EMT-invasion cascade.
Collapse
Affiliation(s)
- Chunwei Xia
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.,Department of Respiratory Medicine, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaofeng Xu
- Department of Respiratory Medicine, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yiyan Ding
- Department of Respiratory Medicine, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Cunjun Yu
- Department of Respiratory Medicine, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jianbing Qiao
- Department of Respiratory Medicine, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Ping Liu
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| |
Collapse
|
14
|
Cui C, Ding X, Wang D, Chen L, Xiao F, Xu T, Zheng M, Luo X, Jiang H, Chen K. Drug repurposing against breast cancer by integrating drug-exposure expression profiles and drug-drug links based on graph neural network. Bioinformatics 2021; 37:2930-2937. [PMID: 33739367 PMCID: PMC8479657 DOI: 10.1093/bioinformatics/btab191] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION Breast cancer is one of the leading causes of cancer deaths among women worldwide. It is necessary to develop new breast cancer drugs because of the shortcomings of existing therapies. The traditional discovery process is time-consuming and expensive. Repositioning of clinically approved drugs has emerged as a novel approach for breast cancer therapy. However, serendipitous or experiential repurposing cannot be used as a routine method. RESULTS In this study, we proposed a graph neural network model GraphRepur based on GraphSAGE for drug repurposing against breast cancer. GraphRepur integrated two major classes of computational methods, drug network-based and drug signature-based. The differentially expressed genes of disease, drug-exposure gene expression data and the drug-drug links information were collected. By extracting the drug signatures and topological structure information contained in the drug relationships, GraphRepur can predict new drugs for breast cancer, outperforming previous state-of-the-art approaches and some classic machine learning methods. The high-ranked drugs have indeed been reported as new uses for breast cancer treatment recently. AVAILABILITYAND IMPLEMENTATION The source code of our model and datasets are available at: https://github.com/cckamy/GraphRepur and https://figshare.com/articles/software/GraphRepur_Breast_Cancer_Drug_Repurposing/14220050. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Chen Cui
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoyu Ding
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dingyan Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lifan Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fu Xiao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - 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
- University of Chinese Academy of Sciences, Beijing 100049, China
- To whom correspondence should be addressed. or
| | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- To whom correspondence should be addressed. or
| | - Hualiang Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China
| | - Kaixian Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China
| |
Collapse
|
15
|
Cabral de Carvalho Corrêa D, Dias Oliveira I, Mascaro Cordeiro B, Silva FA, de Seixas Alves MT, Saba-Silva N, Capellano AM, Dastoli P, Cavalheiro S, Caminada de Toledo SR. Abnormal spindle-like microcephaly-associated (ASPM) gene expression in posterior fossa brain tumors of childhood and adolescence. Childs Nerv Syst 2021; 37:137-145. [PMID: 32591873 DOI: 10.1007/s00381-020-04740-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 06/11/2020] [Indexed: 11/30/2022]
Abstract
PURPOSE In neurogenesis, ASPM (abnormal spindle-like microcephaly-associated) gene is expressed mainly in the ventricular zone of posterior fossa and is the major determinant in the cerebral cortex. Besides its role in embryonic development, ASPM overexpression promotes tumor growth, including central nervous system (CNS) tumors. This study aims to investigate ASPM expression levels in most frequent posterior fossa brain tumors of childhood and adolescence: medulloblastoma (MB), ependymoma (EPN), and astrocytoma (AS), correlating them with clinicopathological characteristics and tumor solid portion size. METHODS Quantitative reverse transcription (qRT-PCR) is used to quantify ASPM mRNA levels in 80 pre-treatment tumor samples: 28 MB, 22 EPN, and 30 AS. The tumor solid portion size was determined by IOP-GRAACC Diagnostic Imaging Center. We correlated these findings with clinicopathological characteristics and tumor solid portion size. RESULTS Our results demonstrated that ASPM gene was overexpressed in MB (p = 0.007) and EPN (p = 0.0260) samples. ASPM high expression was significantly associated to MB samples from patients with worse overall survival (p = 0.0123) and death due to disease progression (p = 0.0039). Interestingly, two patients with AS progressed toward higher grade showed ASPM overexpression (p = 0.0046). No correlation was found between the tumor solid portion size and ASPM expression levels in MB (p = 0.1154 and r = - 0.4825) and EPN (p = 0.1108 and r = - 0.3495) samples. CONCLUSION Taking in account that ASPM gene has several functions to support cell proliferation, as mitotic defects and premature differentiation, we suggest that its overexpression, presumably, plays a critical role in disease progression of posterior fossa brain tumors of childhood and adolescence.
Collapse
Affiliation(s)
- Débora Cabral de Carvalho Corrêa
- Department of Pediatrics, Pediatric Oncology Institute-GRAACC, Federal University of São Paulo, São Paulo, SP, Brazil.,Department of Morphology and Genetics, Division of Genetics, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Indhira Dias Oliveira
- Department of Pediatrics, Pediatric Oncology Institute-GRAACC, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Bruna Mascaro Cordeiro
- Department of Pediatrics, Pediatric Oncology Institute-GRAACC, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Frederico Adolfo Silva
- Department of Pediatrics, Pediatric Oncology Institute-GRAACC, Federal University of São Paulo, São Paulo, SP, Brazil.,Department of Imaging Diagnosis, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Maria Teresa de Seixas Alves
- Department of Pediatrics, Pediatric Oncology Institute-GRAACC, Federal University of São Paulo, São Paulo, SP, Brazil.,Department of Pathology, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Nasjla Saba-Silva
- Department of Pediatrics, Pediatric Oncology Institute-GRAACC, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Andrea Maria Capellano
- Department of Pediatrics, Pediatric Oncology Institute-GRAACC, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Patrícia Dastoli
- Department of Pediatrics, Pediatric Oncology Institute-GRAACC, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Sergio Cavalheiro
- Department of Pediatrics, Pediatric Oncology Institute-GRAACC, Federal University of São Paulo, São Paulo, SP, Brazil.,Department of Neurology, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Silvia Regina Caminada de Toledo
- Department of Pediatrics, Pediatric Oncology Institute-GRAACC, Federal University of São Paulo, São Paulo, SP, Brazil. .,Department of Morphology and Genetics, Division of Genetics, Federal University of São Paulo, São Paulo, SP, Brazil.
| |
Collapse
|
16
|
Xia C, He Z, Cai Y. Quantitative proteomics analysis of differentially expressed proteins induced by astragaloside IV in cervical cancer cell invasion. Cell Mol Biol Lett 2020; 25:25. [PMID: 32265995 PMCID: PMC7110762 DOI: 10.1186/s11658-020-00218-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 03/17/2020] [Indexed: 12/16/2022] Open
Abstract
Background Cervical cancer remains the second leading cause of mortality in women in developing countries. While surgery, chemotherapy, radiotherapy, and vaccine therapy are being applied for its treatment, individually or in combination, the survival rate in advanced cervical cancer patients is still very low. Traditional Chinese medicine has been found to be effective in the treatment of cervical cancer. Astragaloside IV (AS-IV), a compound belonging to Astragalus polysaccharides, shows anticancer activity through several cell signaling pathways. However, the detailed molecular mechanism governing the anticancer activity of AS-IV remains unknown. Material and methods In our study, we performed tumor xenograft analysis, transwell cell migration and invasion assay, Western blot analysis, and iTRAQ combination by parallel reaction monitoring (PRM) analysis to study the molecular mechanism of AS-IV in the suppression of cervical cancer cell invasion. Results Our results showed that AS-IV suppressed cervical cancer cell invasion and induced autophagy in them, with the tumor growth curve increasing slowly. We also identified 32 proteins that were differentially expressed in the SiHa cells when treated with AS-IV, with 16 of them involved in the upregulation and 16 in the downregulation of these cells. These differentially expressed proteins, which were predominantly actin–myosin complexes, controlled cell proliferation and cell development by steroid binding and altering the composition of the cell cytoskeleton. DCP1A and TMSB4X, the two proteins regulating autophagy, increased in cervical cancer cells when treated with AS-IV. Conclusions We conclude that AS-IV could inhibit cervical cancer invasion by inducing autophagy in cervical cancer cells. Since iTRAQ combination by PRM has been observed to be useful in identifying macromolecular target compounds, it may be considered as a novel strategy in the screening of anticancer compounds used in the treatment of cervical cancer.
Collapse
Affiliation(s)
- Chenglai Xia
- 1Foshan Maternal and Child Health Research Institute, South Medical University Affiliated Maternal & Child Health Hospital of Foshan, 11 Renmin Xi Street, Foshan, 528000 China
| | - Zhihong He
- 1Foshan Maternal and Child Health Research Institute, South Medical University Affiliated Maternal & Child Health Hospital of Foshan, 11 Renmin Xi Street, Foshan, 528000 China
| | - Yantao Cai
- 2Department of Dermatology and Pheumatology, South Medical University Affiliated Maternal & Child Health Hospital of Foshan, 11 Renmin Xi Street, Foshan, 528000 China
| |
Collapse
|
17
|
Liu C, Zhao J, Lu W, Dai Y, Hockings J, Zhou Y, Nussinov R, Eng C, Cheng F. Individualized genetic network analysis reveals new therapeutic vulnerabilities in 6,700 cancer genomes. PLoS Comput Biol 2020; 16:e1007701. [PMID: 32101536 PMCID: PMC7062285 DOI: 10.1371/journal.pcbi.1007701] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 03/09/2020] [Accepted: 01/30/2020] [Indexed: 02/06/2023] Open
Abstract
Tumor-specific genomic alterations allow systematic identification of genetic interactions that promote tumorigenesis and tumor vulnerabilities, offering novel strategies for development of targeted therapies for individual patients. We develop an Individualized Network-based Co-Mutation (INCM) methodology by inspecting over 2.5 million nonsynonymous somatic mutations derived from 6,789 tumor exomes across 14 cancer types from The Cancer Genome Atlas. Our INCM analysis reveals a higher genetic interaction burden on the significantly mutated genes, experimentally validated cancer genes, chromosome regulatory factors, and DNA damage repair genes, as compared to human pan-cancer essential genes identified by CRISPR-Cas9 screenings on 324 cancer cell lines. We find that genes involved in the cancer type-specific genetic subnetworks identified by INCM are significantly enriched in established cancer pathways, and the INCM-inferred putative genetic interactions are correlated with patient survival. By analyzing drug pharmacogenomics profiles from the Genomics of Drug Sensitivity in Cancer database, we show that the network-predicted putative genetic interactions (e.g., BRCA2-TP53) are significantly correlated with sensitivity/resistance of multiple therapeutic agents. We experimentally validated that afatinib has the strongest cytotoxic activity on BT474 (IC50 = 55.5 nM, BRCA2 and TP53 co-mutant) compared to MCF7 (IC50 = 7.7 μM, both BRCA2 and TP53 wild type) and MDA-MB-231 (IC50 = 7.9 μM, BRCA2 wild type but TP53 mutant). Finally, drug-target network analysis reveals several potential druggable genetic interactions by targeting tumor vulnerabilities. This study offers a powerful network-based methodology for identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential pharmacogenomics biomarkers for development of personalized cancer medicine. Recent efforts to map genetic interactions in tumor cells have suggested that tumor vulnerabilities can be exploited for development of novel targeted therapies. Tumor-specific genomic alterations derived from multi-center cancer genome projects allow identification of genetic interactions that promote tumor vulnerabilities, offering novel strategies for development of targeted cancer therapies. This study develops a novel Individualized Network-based Co-Mutation (termed INCM) methodology for quantifying the putative genetic interactions in cancer. Trained on over 2.5 million nonsynonymous somatic mutations derived from 6,789 tumor exomes across 14 cancer type, we found that genes identified in the cancer type-specific genetic subnetworks were significantly enriched in established cancer pathways. The network-predicted putative genetic interactions are correlated with patient survival. By analyzing drug pharmacogenomics profiles, we showed that the network-predicted putative genetic interactions (e.g., BRCA2-TP53) were significantly correlated with sensitivity/resistance of anticancer drugs (e.g., afatinib) and we experimentally validated it in breast cancer cell lines. Finally, drug-target network analysis reveals several potential druggable genetic interactions (e.g., PIK3CA-PTEN) by targeting tumor vulnerabilities. This study offers a generalizable network-based approach for comprehensive identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential prognostic and pharmacogenomics biomarkers for development of personalized cancer medicine.
Collapse
Affiliation(s)
- Chuang Liu
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Junfei Zhao
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- Department of Biomedical Informatics, Columbia University, New York, New York, United States of America
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Yao Dai
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Jennifer Hockings
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland, United States of America
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- * E-mail:
| |
Collapse
|
18
|
Jia P, Zhao Z. Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types. Cell Rep 2020; 26:496-506.e3. [PMID: 30625331 PMCID: PMC7375892 DOI: 10.1016/j.celrep.2018.12.066] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 08/07/2018] [Accepted: 12/14/2018] [Indexed: 11/30/2022] Open
Abstract
We systematically investigated the landscape of tumor-suppressor gene (TSG) inactivation events in 33 cancer types by quantitatively measuring their global and local genomic features and their transcriptional and signaling footprints. Using The Cancer Genome Atlas data, we identified with high confidence 337 TSG × cancer events in 30 cancer types, of which 277 were unique events. The majority (91.0%) of these events had a significant downstream impact measured by reduced expression of the TSG itself (cis-effect), disturbance of the transcriptome (trans-effect), or combinatorial effects. Importantly, the transcriptomic changes associated with TSG inactivation events were stronger than the cancer lineage difference, and the same TSGs inactivated in different cancer types tended to cluster together. Several TSGs (e.g., RB1, TP53, and CDKN2A) involved in the regulation of the cell-cycle-formed clusters. Finally, we constructed subnetworks of the TSG × cancer inactivation events, including the local genes frequently disturbed upon the inactivation events.
Collapse
Affiliation(s)
- Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
| |
Collapse
|
19
|
Zong N, Wong RSN, Yu Y, Wen A, Huang M, Li N. Drug-target prediction utilizing heterogeneous bio-linked network embeddings. Brief Bioinform 2019; 22:568-580. [PMID: 31885036 DOI: 10.1093/bib/bbz147] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 10/11/2019] [Accepted: 10/29/2019] [Indexed: 11/12/2022] Open
Abstract
To enable modularization for network-based prediction, we conducted a review of known methods conducting the various subtasks corresponding to the creation of a drug-target prediction framework and associated benchmarking to determine the highest-performing approaches. Accordingly, our contributions are as follows: (i) from a network perspective, we benchmarked the association-mining performance of 32 distinct subnetwork permutations, arranging based on a comprehensive heterogeneous biomedical network derived from 12 repositories; (ii) from a methodological perspective, we identified the best prediction strategy based on a review of combinations of the components with off-the-shelf classification, inference methods and graph embedding methods. Our benchmarking strategy consisted of two series of experiments, totaling six distinct tasks from the two perspectives, to determine the best prediction. We demonstrated that the proposed method outperformed the existing network-based methods as well as how combinatorial networks and methodologies can influence the prediction. In addition, we conducted disease-specific prediction tasks for 20 distinct diseases and showed the reliability of the strategy in predicting 75 novel drug-target associations as shown by a validation utilizing DrugBank 5.1.0. In particular, we revealed a connection of the network topology with the biological explanations for predicting the diseases, 'Asthma' 'Hypertension', and 'Dementia'. The results of our benchmarking produced knowledge on a network-based prediction framework with the modularization of the feature selection and association prediction, which can be easily adapted and extended to other feature sources or machine learning algorithms as well as a performed baseline to comprehensively evaluate the utility of incorporating varying data sources.
Collapse
Affiliation(s)
- Nansu Zong
- Department of Health Sciences Research, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA
| | - Rachael Sze Nga Wong
- Department of Bioengineering, UC San Diego, 9500 Gilman Drive, San Diego, CA 92093-0412, USA
| | - Yue Yu
- Department of Health Sciences Research, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA
| | - Andrew Wen
- Department of Health Sciences Research, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA
| | - Ming Huang
- Department of Health Sciences Research, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA
| | - Ning Li
- Scripps Research Institute, 10550 North Torrey Pines Road, San Diego, CA, 92037, USA
| |
Collapse
|
20
|
Guo P, Cai C, Wu X, Fan X, Huang W, Zhou J, Wu Q, Huang Y, Zhao W, Zhang F, Wang Q, Zhang Y, Fang J. An Insight Into the Molecular Mechanism of Berberine Towards Multiple Cancer Types Through Systems Pharmacology. Front Pharmacol 2019; 10:857. [PMID: 31447670 PMCID: PMC6691338 DOI: 10.3389/fphar.2019.00857] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 07/04/2019] [Indexed: 12/14/2022] Open
Abstract
Over the past several decades, natural products with poly-pharmacological profiles have demonstrated promise as novel therapeutics for various complex diseases, including cancer. Berberine (PubChem CID: 2353), a soliloquies quaternary alkaloid, has been validated to exert powerful effects in many cancers. However, the underlying molecular mechanism is not yet fully elucidated. In this study, we summarized the molecular effects of berberine against multiple cancers based on current available literatures. Furthermore, a systems pharmacology infrastructure was developed to discover new cancer indications of berberine and explore their molecular mechanisms. Specifically, we incorporated 289 high-quality protein targets of berberine by integrating experimental drug-target interactions (DTIs) extracted from literatures and computationally predicted DTIs inferred by network-based inference approach. Statistical network models were developed for identification of new cancer indications of berberine through integration of DTIs and curated cancer significantly mutated genes (SMGs). High accuracy was yielded for our statistical models. We further discussed three typical cancer indications (hepatocarcinoma, lung adenocarcinoma, and bladder carcinoma) of berberine with new mechanisms of actions (MOAs) based on our systems pharmacology framework. In summary, this study systematically provides a powerful strategy to identify potential anti-cancer effects of berberine with novel mechanisms from a systems pharmacology perspective.
Collapse
Affiliation(s)
- Pengfei Guo
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China.,Laboratory of Experimental Animal, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Chuipu Cai
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China.,School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiaoqin Wu
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Xiude Fan
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Wei Huang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jingwei Zhou
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qihui Wu
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yujie Huang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Wei Zhao
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Fengxue Zhang
- School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qi Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China.,Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yongbin Zhang
- Laboratory of Experimental Animal, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China.,Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States.,Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, China
| |
Collapse
|
21
|
Cheng F, Lu W, Liu C, Fang J, Hou Y, Handy DE, Wang R, Zhao Y, Yang Y, Huang J, Hill DE, Vidal M, Eng C, Loscalzo J. A genome-wide positioning systems network algorithm for in silico drug repurposing. Nat Commun 2019; 10:3476. [PMID: 31375661 PMCID: PMC6677722 DOI: 10.1038/s41467-019-10744-6] [Citation(s) in RCA: 120] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 05/26/2019] [Indexed: 01/28/2023] Open
Abstract
Recent advances in DNA/RNA sequencing have made it possible to identify new targets rapidly and to repurpose approved drugs for treating heterogeneous diseases by the 'precise' targeting of individualized disease modules. In this study, we develop a Genome-wide Positioning Systems network (GPSnet) algorithm for drug repurposing by specifically targeting disease modules derived from individual patient's DNA and RNA sequencing profiles mapped to the human protein-protein interactome network. We investigate whole-exome sequencing and transcriptome profiles from ~5,000 patients across 15 cancer types from The Cancer Genome Atlas. We show that GPSnet-predicted disease modules can predict drug responses and prioritize new indications for 140 approved drugs. Importantly, we experimentally validate that an approved cardiac arrhythmia and heart failure drug, ouabain, shows potential antitumor activities in lung adenocarcinoma by uniquely targeting a HIF1α/LEO1-mediated cell metabolism pathway. In summary, GPSnet offers a network-based, in silico drug repurposing framework for more efficacious therapeutic selections.
Collapse
Affiliation(s)
- Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Chuang Liu
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, 311121, Hangzhou, China
| | - Jiansong Fang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Diane E Handy
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Ruisheng Wang
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Yuzheng Zhao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 200237, Shanghai, China
- Synthetic Biology and Biotechnology Laboratory, State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing Technology, 200237, Shanghai, China
| | - Yi Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 200237, Shanghai, China
- Synthetic Biology and Biotechnology Laboratory, State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing Technology, 200237, Shanghai, China
| | - Jin Huang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 200237, Shanghai, China
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| |
Collapse
|
22
|
Kim P, Park A, Han G, Sun H, Jia P, Zhao Z. TissGDB: tissue-specific gene database in cancer. Nucleic Acids Res 2019; 46:D1031-D1038. [PMID: 29036590 PMCID: PMC5753286 DOI: 10.1093/nar/gkx850] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2017] [Accepted: 09/15/2017] [Indexed: 12/11/2022] Open
Abstract
Tissue-specific gene expression is critical in understanding biological processes, physiological conditions, and disease. The identification and appropriate use of tissue-specific genes (TissGenes) will provide important insights into disease mechanisms and organ-specific therapeutic targets. To better understand the tissue-specific features for each cancer type and to advance the discovery of clinically relevant genes or mutations, we built TissGDB (Tissue specific Gene DataBase in cancer) available at http://zhaobioinfo.org/TissGDB. We collected and curated 2461 tissue specific genes (TissGenes) across 22 tissue types that matched the 28 cancer types of The Cancer Genome Atlas (TCGA) from three representative tissue-specific gene expression resources: The Human Protein Atlas (HPA), Tissue-specific Gene Expression and Regulation (TiGER), and Genotype-Tissue Expression (GTEx). For these 2461 TissGenes, we performed gene expression, somatic mutation, and prognostic marker-based analyses across 28 cancer types using TCGA data. Our analyses identified hundreds of TissGenes, including genes that universally kept or lost tissue-specific gene expression, with other features: cancer type-specific isoform expression, fusion with oncogenes or tumor suppressor genes, and markers for protective or risk prognosis. TissGDB provides seven categories of annotations: TissGeneSummary, TissGeneExp, TissGene-miRNA, TissGeneMut, TissGeneNet, TissGeneProg, TissGeneClin.
Collapse
Affiliation(s)
- Pora Kim
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Aekyung Park
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,College of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Suncheon 57922, Korea
| | - Guangchun Han
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Hua Sun
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| |
Collapse
|
23
|
de Anda‐Jáuregui G, McGregor BA, Guo K, Hur J. A Network Pharmacology Approach for the Identification of Common Mechanisms of Drug-Induced Peripheral Neuropathy. CPT Pharmacometrics Syst Pharmacol 2019; 8:211-219. [PMID: 30762308 PMCID: PMC6482281 DOI: 10.1002/psp4.12383] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 12/27/2018] [Indexed: 01/06/2023] Open
Abstract
Drug-induced peripheral neuropathy is a side effect of a variety of therapeutic agents that can affect therapeutic adherence and lead to regimen modifications, impacting patient quality of life. The molecular mechanisms involved in the development of this condition have yet to be completely described in the literature. We used a computational network pharmacology approach to explore the Connectivity Map, a large collection of transcriptional profiles from drug perturbation experiments to identify common genes affected by peripheral neuropathy-inducing drugs. Consensus profiles for 98 of these drugs were used to construct a drug-gene perturbation network. We identified 27 genes significantly associated with neuropathy-inducing drugs. These genes may have a potential role in the action of neuropathy-inducing drugs. Our results suggest that molecular mechanisms, including alterations in mitochondrial function, microtubule and cytoskeleton function, ion channels, transcriptional regulation including epigenetic mechanisms, signal transduction, and wound healing, may play a critical role in drug-induced peripheral neuropathy.
Collapse
Affiliation(s)
- Guillermo de Anda‐Jáuregui
- Department of Biomedical SciencesSchool of Medicine & Health SciencesUniversity of North DakotaGrand ForksNorth DakotaUSA
- Present address:
Computational Genomics DivisionNational Institute of Genomic MedicineColonia Arenal TepepanDelegación TlalpanMéxico DFMexico
| | - Brett A. McGregor
- Department of Biomedical SciencesSchool of Medicine & Health SciencesUniversity of North DakotaGrand ForksNorth DakotaUSA
| | - Kai Guo
- Department of Biomedical SciencesSchool of Medicine & Health SciencesUniversity of North DakotaGrand ForksNorth DakotaUSA
| | - Junguk Hur
- Department of Biomedical SciencesSchool of Medicine & Health SciencesUniversity of North DakotaGrand ForksNorth DakotaUSA
| |
Collapse
|
24
|
Ding Y, Wang H, Zheng H, Wang L, Zhang G, Yang J, Lu X, Bai Y, Zhang H, Li J, Gao W, Chen F, Hu S, Wu J, Xu L. Evaluation of drug efficacy based on the spatial position comparison of drug–target interaction centers. Brief Bioinform 2019; 21:762-776. [DOI: 10.1093/bib/bbz024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 01/28/2019] [Accepted: 02/09/2019] [Indexed: 02/06/2023] Open
Abstract
Abstract
The spatial position and interaction of drugs and their targets is the most important characteristics for understanding a drug’s pharmacological effect, and it could help both in finding new and more precise treatment targets for diseases and in exploring the targeting effects of the new drugs. In this work, we develop a computational pipeline to confirm the spatial interaction relationship of the drugs and their targets and compare the drugs’ efficacies based on the interaction centers. First, we produce a 100-sample set to reconstruct a stable docking model of the confirmed drug–target pairs. Second, we set 5.5 Å as the maximum distance threshold for the drug–amino acid residue atom interaction and construct 3-dimensional interaction surface models. Third, by calculating the spatial position of the 3-dimensional interaction surface center, we develop a comparison strategy for estimating the efficacy of different drug–target pairs. For the 1199 drug–target interactions of the 649 drugs and 355 targets, the drugs that have similar interaction center positions tend to have similar efficacies in disease treatment, especially in the analysis of the 37 targeted relationships between the 15 known anti-cancer drugs and 10 target molecules. Furthermore, the analysis of the unpaired anti-cancer drug and target molecules suggests that there is a potential application for discovering new drug actions using the sampling molecular docking and analyzing method. The comparison of the drug–target interaction center spatial position method better reflect the drug–target interaction situations and could support the discovery of new efficacies among the known anti-cancer drugs.
Collapse
Affiliation(s)
- Yu Ding
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Hong Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Hewei Zheng
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Lianzong Wang
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Guosi Zhang
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Jiaxin Yang
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Xiaoyan Lu
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Yu Bai
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Haotian Zhang
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Jing Li
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Wenyan Gao
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Fukun Chen
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Shui Hu
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Jingqi Wu
- Harbin Medical University, Harbin, P. R. China
- Wenzhou Medical University, Wenzhou
| | - Liangde Xu
- School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou
- Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin
| |
Collapse
|
25
|
Cheng F, Liang H, Butte AJ, Eng C, Nussinov R. Personal Mutanomes Meet Modern Oncology Drug Discovery and Precision Health. Pharmacol Rev 2019; 71:1-19. [PMID: 30545954 PMCID: PMC6294046 DOI: 10.1124/pr.118.016253] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Recent remarkable advances in genome sequencing have enabled detailed maps of identified and interpreted genomic variation, dubbed "mutanomes." The availability of thousands of exome/genome sequencing data has prompted the emergence of new challenges in the identification of novel druggable targets and therapeutic strategies. Typically, mutanomes are viewed as one- or two-dimensional. The three-dimensional protein structural view of personal mutanomes sheds light on the functional consequences of clinically actionable mutations revealed in tumor diagnosis and followed up in personalized treatments, in a mutanome-informed manner. In this review, we describe the protein structural landscape of personal mutanomes and provide expert opinions on rational strategies for more streamlined oncological drug discovery and molecularly targeted therapies for each individual and each tumor. We provide the structural mechanism of orthosteric versus allosteric drugs at the atom-level via targeting specific somatic alterations for combating drug resistance and the "undruggable" challenges in solid and hematologic neoplasias. We discuss computational biophysics strategies for innovative mutanome-informed cancer immunotherapies and combination immunotherapies. Finally, we highlight a personal mutanome infrastructure for the emerging development of personalized cancer medicine using a breast cancer case study.
Collapse
Affiliation(s)
- Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute (F.C., C.E.) and Taussig Cancer Institute (C.E.), Cleveland Clinic, Cleveland, Ohio; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio (F.C., C.E.); CASE Comprehensive Cancer Center (F.C., C.E.) and Department of Genetics and Genome Sciences (C.E.), Case Western Reserve University School of Medicine, Cleveland, Ohio; Departments of Bioinformatics and Computational Biology and Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas (H.L.); Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California (A.J.B.); Center for Data-Driven Insights and Innovation, University of California Health, Oakland, California (A.J.B.); Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland (R.N.); and Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (R.N.)
| | - Han Liang
- Genomic Medicine Institute, Lerner Research Institute (F.C., C.E.) and Taussig Cancer Institute (C.E.), Cleveland Clinic, Cleveland, Ohio; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio (F.C., C.E.); CASE Comprehensive Cancer Center (F.C., C.E.) and Department of Genetics and Genome Sciences (C.E.), Case Western Reserve University School of Medicine, Cleveland, Ohio; Departments of Bioinformatics and Computational Biology and Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas (H.L.); Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California (A.J.B.); Center for Data-Driven Insights and Innovation, University of California Health, Oakland, California (A.J.B.); Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland (R.N.); and Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (R.N.)
| | - Atul J Butte
- Genomic Medicine Institute, Lerner Research Institute (F.C., C.E.) and Taussig Cancer Institute (C.E.), Cleveland Clinic, Cleveland, Ohio; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio (F.C., C.E.); CASE Comprehensive Cancer Center (F.C., C.E.) and Department of Genetics and Genome Sciences (C.E.), Case Western Reserve University School of Medicine, Cleveland, Ohio; Departments of Bioinformatics and Computational Biology and Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas (H.L.); Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California (A.J.B.); Center for Data-Driven Insights and Innovation, University of California Health, Oakland, California (A.J.B.); Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland (R.N.); and Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (R.N.)
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute (F.C., C.E.) and Taussig Cancer Institute (C.E.), Cleveland Clinic, Cleveland, Ohio; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio (F.C., C.E.); CASE Comprehensive Cancer Center (F.C., C.E.) and Department of Genetics and Genome Sciences (C.E.), Case Western Reserve University School of Medicine, Cleveland, Ohio; Departments of Bioinformatics and Computational Biology and Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas (H.L.); Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California (A.J.B.); Center for Data-Driven Insights and Innovation, University of California Health, Oakland, California (A.J.B.); Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland (R.N.); and Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (R.N.)
| | - Ruth Nussinov
- Genomic Medicine Institute, Lerner Research Institute (F.C., C.E.) and Taussig Cancer Institute (C.E.), Cleveland Clinic, Cleveland, Ohio; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio (F.C., C.E.); CASE Comprehensive Cancer Center (F.C., C.E.) and Department of Genetics and Genome Sciences (C.E.), Case Western Reserve University School of Medicine, Cleveland, Ohio; Departments of Bioinformatics and Computational Biology and Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas (H.L.); Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California (A.J.B.); Center for Data-Driven Insights and Innovation, University of California Health, Oakland, California (A.J.B.); Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland (R.N.); and Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (R.N.)
| |
Collapse
|
26
|
Abstract
Network-aided in silico approaches have been widely used for prediction of drug-target interactions and evaluation of drug safety to increase the clinical efficiency and productivity during drug discovery and development. Here we review the advances and new progress in this field and summarize the translational applications of several new network-aided in silico approaches we developed recently. In addition, we describe the detailed protocols for a network-aided drug repositioning infrastructure for identification of new targets for old drugs, failed drugs in clinical trials, and new chemical entities. These state-of-the-art network-aided in silico approaches have been used for the discovery and development of broad-acting and targeted clinical therapies for various complex diseases, in particular for oncology drug repositioning. In this chapter, the described network-aided in silico protocols are appropriate for target-centric drug repositioning to various complex diseases, but expertise is still necessary to perform the specific oncology projects based on the cancer targets of interest.
Collapse
|
27
|
Fang J, Cai C, Chai Y, Zhou J, Huang Y, Gao L, Wang Q, Cheng F. Quantitative and systems pharmacology 4. Network-based analysis of drug pleiotropy on coronary artery disease. Eur J Med Chem 2018; 161:192-204. [PMID: 30359818 DOI: 10.1016/j.ejmech.2018.10.020] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 09/26/2018] [Accepted: 10/09/2018] [Indexed: 12/14/2022]
Abstract
Despite recent advance of therapeutic development, coronary artery disease (CAD) remains one of the major issues to public health. The use of genomics and systems biology approaches to inform drug discovery and development have offered the possibilities for new target identification and in silico drug repurposing. In this study, we propose a network-based, systems pharmacology framework for target identification and drug repurposing in pharmacologic treatment and chemoprevention of CAD. Specifically, we build in silico models by integrating known drug-target interactions, CAD genes derived from the genetic and genomic studies, and the human protein-protein interactome. We demonstrate that the proposed in silico models can successfully uncover approved drugs and novel natural products in potentially treating and preventing CAD. In case studies, we highlight several approved drugs (e.g., fasudil, parecoxib, and dexamethasone) or natural products (e.g., resveratrol, luteolin, daidzein and caffeic acid) with new mechanism-of-action in chemical intervention of CAD by network analysis. In summary, this study offers a powerful systems pharmacology approach for target identification and in silico drug repurposing on CAD.
Collapse
Affiliation(s)
- Jiansong Fang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Chuipu Cai
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Yanting Chai
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Jingwei Zhou
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Yujie Huang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Li Gao
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
| | - Qi Wang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; CASE Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.
| |
Collapse
|
28
|
Khalid Z, Sezerman OU. Computational drug repurposing to predict approved and novel drug-disease associations. J Mol Graph Model 2018; 85:91-96. [PMID: 30130693 DOI: 10.1016/j.jmgm.2018.08.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 08/07/2018] [Accepted: 08/10/2018] [Indexed: 11/24/2022]
Abstract
The Drug often binds to more than one targets defined as polypharmacology, one application of which is drug repurposing also referred as drug repositioning or therapeutic switching. The traditional drug discovery and development is a high-priced and tedious process, thus making drug repurposing a popular alternate strategy. We proposed an integrative method based on similarity scheme that predicts approved and novel Drug targets with new disease associations. We combined PPI, biological pathways, binding site structural similarities and disease-disease similarity measures. The results showed 94% Accuracy with 0.93 Recall and 0.94 Precision measure in predicting the approved and novel targets surpassing the existing methods. All these parameters help in elucidating the unknown associations between drug and diseases for finding the new uses for old drugs.
Collapse
|
29
|
Dai E, Wang J, Yang F, Zhou X, Song Q, Wang S, Yu X, Liu D, Yang Q, Dai H, Jiang W, Ling H. Accurate prediction and elucidation of drug resistance based on the robust and reproducible chemoresponse communities. Int J Cancer 2018; 142:1427-1439. [PMID: 29143332 DOI: 10.1002/ijc.31158] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 11/07/2017] [Indexed: 01/28/2023]
Abstract
Selecting the available treatment for each cancer patient from genomic context is a core goal of precision medicine, but innovative approaches with mechanism interpretation and improved performance are still highly needed. Through utilizing in vitro chemotherapy response data coupled with gene and miRNA expression profiles, we applied a network-based approach that identified markers not as individual molecules but as functional groups extracted from the integrated transcription factor and miRNA regulatory network. Based on the identified chemoresponse communities, the predictors of drug resistance achieved high accuracy in cross-validation and were more robust and reproducible than conventional single-molecule markers. Meanwhile, as candidate communities not only enriched abundant cellular process but also covered a variety of drug enzymes, transporters, and targets, these resulting chemoresponse communities furnished novel models to interpret multiple kinds of potential regulatory mechanism, such as dysregulation of cancer cell apoptosis or disturbance of drug metabolism. Moreover, compounds were linked based on the enrichment of their common chemoresponse communities to uncover undetected response patterns and possible multidrug resistance phenotype. Finally, an omnibus repository named ChemoCommunity (http://www.jianglab.cn/ChemoCommunity/) was constructed, which furnished a user-friendly interface for a convenient retrieval of the detailed information on chemoresponse communities. Taken together, our method, and the accompanying database, improved the performance of classifiers for drug resistance and provided novel model to uncover the possible regulatory mechanism of individual response to drug.
Collapse
Affiliation(s)
- Enyu Dai
- Department of Microbiology, Wu Lien-Teh Institute, Harbin Medical University, Harbin, 150081, People's Republic of China.,Department of Parasitology, Harbin Medical University, Harbin, 150081, People's Republic of China.,Heilongjiang Provincial Key Laboratory of Infection and Immunity, Harbin, 150081, People's Republic of China.,Key Laboratory of Pathogen Biology, Harbin, 150081, People's Republic of China.,College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Jing Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Feng Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Xu Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Qian Song
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Shuyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Xuexin Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Dianming Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Qian Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Hong Dai
- The 2nd Affiliated Hospital, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Wei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China.,Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, People's Republic of China
| | - Hong Ling
- Department of Microbiology, Wu Lien-Teh Institute, Harbin Medical University, Harbin, 150081, People's Republic of China.,Department of Parasitology, Harbin Medical University, Harbin, 150081, People's Republic of China.,Heilongjiang Provincial Key Laboratory of Infection and Immunity, Harbin, 150081, People's Republic of China.,Key Laboratory of Pathogen Biology, Harbin, 150081, People's Republic of China
| |
Collapse
|
30
|
Cheng F, Hong H, Yang S, Wei Y. Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era. Brief Bioinform 2017; 18:682-697. [PMID: 27296652 DOI: 10.1093/bib/bbw051] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Indexed: 12/12/2022] Open
Abstract
Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.
Collapse
|
31
|
Wu Z, Lu W, Yu W, Wang T, Li W, Liu G, Zhang H, Pang X, Huang J, Liu M, Cheng F, Tang Y. Quantitative and systems pharmacology 2. In silico polypharmacology of G protein-coupled receptor ligands via network-based approaches. Pharmacol Res 2017; 129:400-413. [PMID: 29133212 DOI: 10.1016/j.phrs.2017.11.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 11/07/2017] [Accepted: 11/07/2017] [Indexed: 02/06/2023]
Abstract
G protein-coupled receptors (GPCRs) are the largest super family with more than 800 membrane receptors. Currently, over 30% of the approved drugs target human GPCRs. However, only approximately 30 human GPCRs have been resolved three-dimensional crystal structures, which limits traditional structure-based drug discovery. Recent advances in network-based systems pharmacology approaches have demonstrated powerful strategies for identifying new targets of GPCR ligands. In this study, we proposed a network-based systems pharmacology framework for comprehensive identification of new drug-target interactions on GPCRs. Specifically, we reconstructed both global and local drug-target interaction networks for human GPCRs. Network analysis on the known drug-target networks showed rational strategies for designing new GPCR ligands and evaluating side effects of the approved GPCR drugs. We further built global and local network-based models for predicting new targets of the known GPCR ligands. The area under the receiver operating characteristic curve of more than 0.96 was obtained for the best network-based models in cross validation. In case studies, we identified that several network-predicted GPCR off-targets (e.g. ADRA2A, ADRA2C and CHRM2) were associated with cardiovascular complications (e.g. bradycardia and palpitations) of the approved GPCR drugs via an integrative analysis of drug-target and off-target-adverse drug event networks. Importantly, we experimentally validated that two newly predicted compounds, AM966 and Ki16425, showed high binding affinities on prostaglandin E2 receptor EP4 subtype with IC50=2.67μM and 6.34μM, respectively. In summary, this study offers powerful network-based tools for identifying polypharmacology of GPCR ligands in drug discovery and development.
Collapse
Affiliation(s)
- Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Weiwei Yu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Tianduanyi Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Hankun Zhang
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Xiufeng Pang
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Jin Huang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Mingyao Liu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China; Institute of Biosciences and Technology, Department of Molecular and Cellular Medicine, Texas A&M University Health Science Center, Houston, TX 77030, USA.
| | - Feixiong Cheng
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA; Center for Complex Networks Research, Northeastern University, Boston, MA 02115, USA.
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
| |
Collapse
|
32
|
Fang J, Gao L, Ma H, Wu Q, Wu T, Wu J, Wang Q, Cheng F. Quantitative and Systems Pharmacology 3. Network-Based Identification of New Targets for Natural Products Enables Potential Uses in Aging-Associated Disorders. Front Pharmacol 2017; 8:747. [PMID: 29093681 PMCID: PMC5651538 DOI: 10.3389/fphar.2017.00747] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 10/03/2017] [Indexed: 12/27/2022] Open
Abstract
Aging that refers the accumulation of genetic and physiology changes in cells and tissues over a lifetime has been shown a high risk of developing various complex diseases, such as neurodegenerative disease, cardiovascular disease and cancer. Over the past several decades, natural products have been demonstrated as anti-aging interveners via extending lifespan and preventing aging-associated disorders. In this study, we developed an integrated systems pharmacology infrastructure to uncover new indications for aging-associated disorders by natural products. Specifically, we incorporated 411 high-quality aging-associated human genes or human-orthologous genes from mus musculus (MM), saccharomyces cerevisiae (SC), caenorhabditis elegans (CE), and drosophila melanogaster (DM). We constructed a global drug-target network of natural products by integrating both experimental and computationally predicted drug-target interactions (DTI). We further built the statistical network models for identification of new anti-aging indications of natural products through integration of the curated aging-associated genes and drug-target network of natural products. High accuracy was achieved on the network models. We showcased several network-predicted anti-aging indications of four typical natural products (caffeic acid, metformin, myricetin, and resveratrol) with new mechanism-of-actions. In summary, this study offers a powerful systems pharmacology infrastructure to identify natural products for treatment of aging-associated disorders.
Collapse
Affiliation(s)
- Jiansong Fang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Li Gao
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
| | - Huili Ma
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qihui Wu
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Tian Wu
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jun Wu
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qi Wang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Feixiong Cheng
- Department of Cancer Biology, Center for Cancer Systems Biology, Harvard Medical School, Dana-Farber Cancer Institute, Boston, MA, United States.,Center for Complex Networks Research, Northeastern University, Boston, MA, United States
| |
Collapse
|
33
|
Luo Y, Zhao X, Zhou J, Yang J, Zhang Y, Kuang W, Peng J, Chen L, Zeng J. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat Commun 2017; 8:573. [PMID: 28924171 PMCID: PMC5603535 DOI: 10.1038/s41467-017-00680-8] [Citation(s) in RCA: 394] [Impact Index Per Article: 56.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 07/19/2017] [Indexed: 02/05/2023] Open
Abstract
The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug-target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug-target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs.Network-based data integration for drug-target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.
Collapse
Affiliation(s)
- Yunan Luo
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Xinbin Zhao
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Jingtian Zhou
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Jinglin Yang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China
| | - Yanqing Zhang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China
| | - Wenhua Kuang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Jian Peng
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Ligong Chen
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
- Collaborative Innovation Center for Biotherapy, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, 610041, China.
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China.
| |
Collapse
|
34
|
Zhang W, Chien J, Yong J, Kuang R. Network-based machine learning and graph theory algorithms for precision oncology. NPJ Precis Oncol 2017; 1:25. [PMID: 29872707 PMCID: PMC5871915 DOI: 10.1038/s41698-017-0029-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Revised: 06/28/2017] [Accepted: 06/29/2017] [Indexed: 01/07/2023] Open
Abstract
Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.
Collapse
Affiliation(s)
- Wei Zhang
- 1Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN USA
| | - Jeremy Chien
- 2Department of Cancer Biology, University of Kansas Medical Center, Kansas City, KS USA
| | - Jeongsik Yong
- 3Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities, Minneapolis, MN USA
| | - Rui Kuang
- 1Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN USA
| |
Collapse
|
35
|
Fang J, Liu C, Wang Q, Lin P, Cheng F. In silico polypharmacology of natural products. Brief Bioinform 2017; 19:1153-1171. [DOI: 10.1093/bib/bbx045] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Indexed: 12/16/2022] Open
|
36
|
Fang J, Cai C, Wang Q, Lin P, Zhao Z, Cheng F. Systems Pharmacology-Based Discovery of Natural Products for Precision Oncology Through Targeting Cancer Mutated Genes. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 6:177-187. [PMID: 28294568 PMCID: PMC5356618 DOI: 10.1002/psp4.12172] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Revised: 01/09/2017] [Accepted: 01/10/2017] [Indexed: 02/05/2023]
Abstract
Massive cancer genomics data have facilitated the rapid revolution of a novel oncology drug discovery paradigm through targeting clinically relevant driver genes or mutations for the development of precision oncology. Natural products with polypharmacological profiles have been demonstrated as promising agents for the development of novel cancer therapies. In this study, we developed an integrated systems pharmacology framework that facilitated identifying potential natural products that target mutated genes across 15 cancer types or subtypes in the realm of precision medicine. High performance was achieved for our systems pharmacology framework. In case studies, we computationally identified novel anticancer indications for several US Food and Drug Administration-approved or clinically investigational natural products (e.g., resveratrol, quercetin, genistein, and fisetin) through targeting significantly mutated genes in multiple cancer types. In summary, this study provides a powerful tool for the development of molecularly targeted cancer therapies through targeting the clinically actionable alterations by exploiting the systems pharmacology of natural products.
Collapse
Affiliation(s)
- J Fang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, P.R. China
| | - C Cai
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, P.R. China
| | - Q Wang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, P.R. China
| | - P Lin
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, P.R. China
| | - Z Zhao
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA.,Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - F Cheng
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, P.R. China.,Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.,Center for Complex Networks Research, Northeastern University, Boston, Massachusetts, USA
| |
Collapse
|
37
|
Mounika Inavolu S, Renbarger J, Radovich M, Vasudevaraja V, Kinnebrew GH, Zhang S, Cheng L. IODNE: An integrated optimization method for identifying the deregulated subnetwork for precision medicine in cancer. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 6:168-176. [PMID: 28266149 PMCID: PMC5351413 DOI: 10.1002/psp4.12167] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 01/05/2017] [Accepted: 01/06/2017] [Indexed: 12/18/2022]
Abstract
Subnetwork analysis can explore complex patterns of entire molecular pathways for the purpose of drug target identification. In this article, the gene expression profiles of a cohort of patients with breast cancer are integrated with protein‐protein interaction (PPI) networks using, simultaneously, both edge scoring and node scoring. A novel optimization algorithm, integrated optimization method to identify deregulated subnetwork (IODNE), is developed to search for the optimal dysregulated subnetwork of the merged gene and protein network. IODNE is applied to select subnetworks for Luminal‐A breast cancer from The Cancer Genome Atlas (TCGA) data. A large fraction of cancer‐related genes and the well‐known clinical targets, ER1/PR and HER2, are found by IODNE. This validates the utility of IODNE. When applying IODNE to the triple‐negative breast cancer (TNBC) subtype data, we identified subnetworks that contain genes such as ERBB2, HRAS, PGR, CAD, POLE, and SLC2A1.
Collapse
Affiliation(s)
- S Mounika Inavolu
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - J Renbarger
- Department of Pediatrics, Hematology/Oncology, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - M Radovich
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - V Vasudevaraja
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - G H Kinnebrew
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - S Zhang
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - L Cheng
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Pediatrics, Hematology/Oncology, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| |
Collapse
|
38
|
Proteome-Scale Investigation of Protein Allosteric Regulation Perturbed by Somatic Mutations in 7,000 Cancer Genomes. Am J Hum Genet 2017; 100:5-20. [PMID: 27939638 DOI: 10.1016/j.ajhg.2016.09.020] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Accepted: 09/27/2016] [Indexed: 02/05/2023] Open
Abstract
The allosteric regulation triggering the protein's functional activity via conformational changes is an intrinsic function of protein under many physiological and pathological conditions, including cancer. Identification of the biological effects of specific somatic variants on allosteric proteins and the phenotypes that they alter during tumor initiation and progression is a central challenge for cancer genomes in the post-genomic era. Here, we mapped more than 47,000 somatic missense mutations observed in approximately 7,000 tumor-normal matched samples across 33 cancer types into protein allosteric sites to prioritize the mutated allosteric proteins and we tested our prediction in cancer cell lines. We found that the deleterious mutations identified in cancer genomes were more significantly enriched at protein allosteric sites than tolerated mutations, suggesting a critical role for protein allosteric variants in cancer. Next, we developed a statistical approach, namely AlloDriver, and further identified 15 potential mutated allosteric proteins during pan-cancer and individual cancer-type analyses. More importantly, we experimentally confirmed that p.Pro360Ala on PDE10A played a potential oncogenic role in mediating tumorigenesis in non-small cell lung cancer (NSCLC). In summary, these findings shed light on the role of allosteric regulation during tumorigenesis and provide a useful tool for the timely development of targeted cancer therapies.
Collapse
|
39
|
Wu Z, Lu W, Wu D, Luo A, Bian H, Li J, Li W, Liu G, Huang J, Cheng F, Tang Y. In silico prediction of chemical mechanism of action via an improved network-based inference method. Br J Pharmacol 2016; 173:3372-3385. [PMID: 27646592 DOI: 10.1111/bph.13629] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2016] [Revised: 08/26/2016] [Accepted: 09/10/2016] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND PURPOSE Deciphering chemical mechanism of action (MoA) enables the development of novel therapeutics (e.g. drug repositioning) and evaluation of drug side effects. Development of novel computational methods for chemical MoA assessment under a systems pharmacology framework would accelerate drug discovery and development with greater efficiency and low cost. EXPERIMENTAL APPROACH In this study, we proposed an improved network-based inference method, balanced substructure-drug-target network-based inference (bSDTNBI), to predict MoA for old drugs, clinically failed drugs and new chemical entities. Specifically, three parameters were introduced into network-based resource diffusion processes to adjust the initial resource allocation of different node types, the weighted values of different edge types and the influence of hub nodes. The performance of the method was systematically validated by benchmark datasets and bioassays. KEY RESULTS High performance was yielded for bSDTNBI in both 10-fold and leave-one-out cross validations. A global drug-target network was built to explore MoA of anticancer drugs and repurpose old drugs for 15 cancer types/subtypes. In a case study, 27 predicted candidates among 56 commercially available compounds were experimentally validated to have binding affinities on oestrogen receptor α with IC50 or EC50 values ≤10 μM. Furthermore, two dual ligands with both agonistic and antagonistic activities ≤1 μM would provide potential lead compounds for the development of novel targeted therapy in breast cancer or osteoporosis. CONCLUSION AND IMPLICATIONS In summary, bSDTNBI would provide a powerful tool for the MoA assessment on both old drugs and novel compounds in drug discovery and development.
Collapse
Affiliation(s)
- Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Dang Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Anqi Luo
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Hanping Bian
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jie Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jin Huang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Feixiong Cheng
- State Key Laboratory of Biotherapy/Collaborative Innovation Center for Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, China.,Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.,Center for Complex Networks Research, Northeastern University, Boston, Massachusetts, USA
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| |
Collapse
|
40
|
Cheng F, Zhao J, Hanker AB, Brewer MR, Arteaga CL, Zhao Z. Transcriptome- and proteome-oriented identification of dysregulated eIF4G, STAT3, and Hippo pathways altered by PIK3CA H1047R in HER2/ER-positive breast cancer. Breast Cancer Res Treat 2016; 160:457-474. [PMID: 27771839 PMCID: PMC10183099 DOI: 10.1007/s10549-016-4011-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 10/05/2016] [Indexed: 01/25/2023]
Abstract
PURPOSE Phosphatidylinositol 3-kinase (PI3K)/AKT pathway aberrations are common in human breast cancer. Furthermore, PIK3CA mutations are commonly associated with resistance to anti-epidermal growth factor receptor 2 (HER2) or anti-estrogen receptor (ER) agents in HER2 or ER positive (HER2+/ER+) breast cancer. Hence, deciphering the underlying mechanisms of PIK3CA mutations in HER2+/ER+ breast cancer would provide novel insights into elucidating resistance to anti-HER2/ER therapies. METHODS In this study, we systematically investigated the biological consequences of PIK3CA H1047R in HER2+/ER+ breast cancer by uniquely incorporating mRNA transcriptomic data from The Cancer Genome Atlas and proteomic data from reverse-phase protein arrays. RESULTS Our integrative bioinformatics analyses revealed that several important pathways such as STAT3 and VEGF/hypoxia were selectively altered by PIK3CA H1047R in HER2+/ER+ breast cancer. Protein differential expression analysis indicated that an elevated eIF4G might promote tumor angiogenesis and growth via regulation of the hypoxia-activated switch in HER2+ PIK3CA H1047R breast cancer. We observed hypo-phosphorylation of EGFR in HER2+ PIK3CA H1047R breast cancer versus HER2+PIK3CAwild-type (PIK3CA WT). In addition, ER and PIK3CA H1047R might cooperate to activate STAT3, MAPK, AKT, and Hippo pathways in ER+ PIK3CA H1047R breast cancer. A higher YAPpS127 level was observed in ER+ PIK3CA H1047R patients than that in an ER+ PIK3CA WT subgroup. By examining breast cancer cell lines having both microarray gene expression and drug treatment data from the Genomics of Drug Sensitivity in Cancer and the Stand Up to Cancer datasets, we found that the elevated YAP1 mRNA expression was associated with the resistance of BCL-2 family inhibitors, but with the sensitivity to MEK/MAPK inhibitors in breast cancer cells. CONCLUSIONS In summary, these findings shed light on the functional consequences of PIK3CA H1047R-driven breast tumorigenesis and resistance to the existing therapeutic agents in HER2+/ER+ breast cancer.
Collapse
Affiliation(s)
- Feixiong Cheng
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.,Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02215, USA.,Center for Complex Networks Research, Northeastern University, Boston, MA, 02115, USA
| | - Junfei Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Ariella B Hanker
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Monica Red Brewer
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Carlos L Arteaga
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Department of Cancer Biology, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Breast Cancer Research Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
| | - Zhongming Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA. .,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. .,Department of Cancer Biology, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Breast Cancer Research Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
| |
Collapse
|
41
|
Ung MH, Varn FS, Cheng C. In silico frameworks for systematic pre-clinical screening of potential anti-leukemia therapeutics. Expert Opin Drug Discov 2016; 11:1213-1222. [PMID: 27689915 DOI: 10.1080/17460441.2016.1243524] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Leukemia is a collection of highly heterogeneous cancers that arise from neoplastic transformation and clonal expansion of immature hematopoietic cells. Post-treatment recurrence is high, especially among elderly patients, thus necessitating more effective treatment modalities. Development of novel anti-leukemic compounds relies heavily on traditional in vitro screens which require extensive resources and time. Therefore, integration of in silico screens prior to experimental validation can improve the efficiency of pre-clinical drug development. Areas covered: This article reviews different methods and frameworks used to computationally screen for anti-leukemic agents. In particular, three approaches are discussed including molecular docking, transcriptomic integration, and network analysis. Expert opinion: Today's data deluge presents novel opportunities to develop computational tools and pipelines to screen for likely therapeutic candidates in the treatment of leukemia. Formal integration of these methodologies can accelerate and improve the efficiency of modern day anti-leukemic drug discovery and ease the economic and healthcare burden associated with it.
Collapse
Affiliation(s)
- Matthew H Ung
- a Department of Molecular and Systems Biology , Geisel School of Medicine at Dartmouth , Hanover , NH , USA
| | - Frederick S Varn
- a Department of Molecular and Systems Biology , Geisel School of Medicine at Dartmouth , Hanover , NH , USA
| | - Chao Cheng
- a Department of Molecular and Systems Biology , Geisel School of Medicine at Dartmouth , Hanover , NH , USA.,b Department of Biomedical Data Science , Geisel School of Medicine at Dartmouth , Lebanon , NH , USA.,c Norris Cotton Cancer Center , Lebanon , NH , USA
| |
Collapse
|
42
|
Frey LJ, Bernstam EV, Denny JC. Precision medicine informatics. J Am Med Inform Assoc 2016; 23:668-70. [PMID: 27274018 DOI: 10.1093/jamia/ocw053] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Accepted: 03/15/2016] [Indexed: 12/15/2022] Open
Affiliation(s)
- Lewis J Frey
- Biomedical Informatics Center, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA Health Equity and Rural Outreach Innovation Center, Ralph H. Johnson Department of Veterans Affairs Medical Center, Charleston, SC, USA
| | - Elmer V Bernstam
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA Division of General Internal Medicine, Department of Internal Medicine, Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA Department of Medicine, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|