1
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Sun Z, Chung D, Neelon B, Millar-Wilson A, Ethier SP, Xiao F, Zheng Y, Wallace K, Hardiman G. A Bayesian framework for pathway-guided identification of cancer subgroups by integrating multiple types of genomic data. Stat Med 2023; 42:5266-5284. [PMID: 37715500 DOI: 10.1002/sim.9911] [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: 03/19/2021] [Revised: 07/15/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023]
Abstract
In recent years, comprehensive cancer genomics platforms, such as The Cancer Genome Atlas (TCGA), provide access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alterations, DNA methylation, and somatic mutation. While the integration of these multi-omics datasets has the potential to provide novel insights that can lead to personalized medicine, most existing approaches only focus on gene-level analysis and lack the ability to facilitate biological findings at the pathway-level. In this article, we propose Bayes-InGRiD (Bayesian Integrative Genomics Robust iDentification of cancer subgroups), a novel pathway-guided Bayesian sparse latent factor model for the simultaneous identification of cancer patient subgroups (clustering) and key molecular features (variable selection) within a unified framework, based on the joint analysis of continuous, binary, and count data. By utilizing pathway (gene set) information, Bayes-InGRiD does not only enhance the accuracy and robustness of cancer patient subgroup and key molecular feature identification, but also promotes biological understanding and interpretation. Finally, to facilitate an efficient posterior sampling, an alternative Gibbs sampler for logistic and negative binomial models is proposed using Pólya-Gamma mixtures of normal to represent latent variables for binary and count data, which yields a conditionally Gaussian representation of the posterior. The R package "INGRID" implementing the proposed approach is currently available in our research group GitHub webpage (https://dongjunchung.github.io/INGRID/).
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Affiliation(s)
- Zequn Sun
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio
- The Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Brian Neelon
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | | | - Stephen P Ethier
- Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, South Carolina
| | - Feifei Xiao
- Department of Biostatistics, University of Florida, Gainesville, Florida
| | - Yinan Zheng
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois
| | - Kristin Wallace
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Gary Hardiman
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
- Faculty of Medicine, Health and Life Sciences, School of Biological Sciences and Institute for Global Food Security, Queen's University Belfast, Belfast, UK
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2
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Song J, Kim D, Lee S, Jung J, Joo JWJ, Jang W. Integrative transcriptome-wide analysis of atopic dermatitis for drug repositioning. Commun Biol 2022; 5:615. [PMID: 35729261 PMCID: PMC9213508 DOI: 10.1038/s42003-022-03564-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 06/07/2022] [Indexed: 12/13/2022] Open
Abstract
Atopic dermatitis (AD) is one of the most common inflammatory skin diseases, which significantly impact the quality of life. Transcriptome-wide association study (TWAS) was conducted to estimate both transcriptomic and genomic features of AD and detected significant associations between 31 expression quantitative loci and 25 genes. Our results replicated well-known genetic markers for AD, as well as 4 novel associated genes. Next, transcriptome meta-analysis was conducted with 5 studies retrieved from public databases and identified 5 additional novel susceptibility genes for AD. Applying the connectivity map to the results from TWAS and meta-analysis, robustly enriched perturbations were identified and their chemical or functional properties were analyzed. Here, we report the first research on integrative approaches for an AD, combining TWAS and transcriptome meta-analysis. Together, our findings could provide a comprehensive understanding of the pathophysiologic mechanisms of AD and suggest potential drug candidates as alternative treatment options. Integrative genomic and transcriptomic analyses on publicly available data-sets together with in silico drug repositioning identifies alternative therapeutic options to treat atopic dermatitis.
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Affiliation(s)
- Jaeseung Song
- Department of Life Sciences, Dongguk University-Seoul, 04620, Seoul, Republic of Korea
| | - Daeun Kim
- Department of Life Sciences, Dongguk University-Seoul, 04620, Seoul, Republic of Korea
| | - Sora Lee
- Department of Life Sciences, Dongguk University-Seoul, 04620, Seoul, Republic of Korea
| | - Junghyun Jung
- Department of Life Sciences, Dongguk University-Seoul, 04620, Seoul, Republic of Korea.,Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, 1985 Zonal Avenue, Los Angeles, CA, 90089, USA
| | - Jong Wha J Joo
- Department of Computer Science and Engineering, Dongguk University-Seoul, 04620, Seoul, Republic of Korea
| | - Wonhee Jang
- Department of Life Sciences, Dongguk University-Seoul, 04620, Seoul, Republic of Korea.
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3
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Zhang ZR, Jiang ZR. GraphDPA: predicting drug-pathway associations by graph convolutional networks. Comput Biol Chem 2022; 99:107719. [DOI: 10.1016/j.compbiolchem.2022.107719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 05/26/2022] [Accepted: 06/22/2022] [Indexed: 11/03/2022]
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4
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Ghulam A, Lei X, Zhang Y, Wu Z. Human Drug-Pathway Association Prediction Based on Network Consistency Projection. Comput Biol Chem 2022; 97:107624. [DOI: 10.1016/j.compbiolchem.2022.107624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 11/24/2021] [Accepted: 01/05/2022] [Indexed: 11/26/2022]
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5
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Wang J, Wu Z, Peng Y, Li W, Liu G, Tang Y. Pathway-Based Drug Repurposing with DPNetinfer: A Method to Predict Drug-Pathway Associations via Network-Based Approaches. J Chem Inf Model 2021; 61:2475-2485. [PMID: 33900090 DOI: 10.1021/acs.jcim.1c00009] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Identification of drug-pathway associations plays an important role in pathway-based drug repurposing. However, it is time-consuming and costly to uncover new drug-pathway associations experimentally. The drug-induced transcriptomics data provide a global view of cellular pathways and tell how these pathways change under different treatments. These data enable computational approaches for large-scale prediction of drug-pathway associations. Here we introduced DPNetinfer, a novel computational method to predict potential drug-pathway associations based on substructure-drug-pathway networks via network-based approaches. The results demonstrated that DPNetinfer performed well in a pan-cancer network with an AUC (area under curve) = 0.9358. Meanwhile, DPNetinfer was shown to have a good capability of generalization on two external validation sets (AUC = 0.8519 and 0.7494, respectively). As a case study, DPNetinfer was used in pathway-based drug repurposing for cancer therapy. Unexpected anticancer activities of some nononcology drugs were then identified on the PI3K-Akt pathway. Considering tumor heterogeneity, seven primary site-based models were constructed by DPNetinfer in different drug-pathway networks. In a word, DPNetinfer provides a powerful tool for large-scale prediction of drug-pathway associations in pathway-based drug repurposing. A web tool for DPNetinfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.
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Affiliation(s)
- Jiye Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yayuan Peng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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6
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Wang CC, Zhao Y, Chen X. Drug-pathway association prediction: from experimental results to computational models. Brief Bioinform 2020; 22:5835554. [PMID: 32393976 DOI: 10.1093/bib/bbaa061] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/16/2020] [Accepted: 03/26/2020] [Indexed: 12/14/2022] Open
Abstract
Effective drugs are urgently needed to overcome human complex diseases. However, the research and development of novel drug would take long time and cost much money. Traditional drug discovery follows the rule of one drug-one target, while some studies have demonstrated that drugs generally perform their task by affecting related pathway rather than targeting single target. Thus, the new strategy of drug discovery, namely pathway-based drug discovery, have been proposed. Obviously, identifying associations between drugs and pathways plays a key role in the development of pathway-based drug discovery. Revealing the drug-pathway associations by experiment methods would take much time and cost. Therefore, some computational models were established to predict potential drug-pathway associations. In this review, we first introduced the background of drug and the concept of drug-pathway associations. Then, some publicly accessible databases and web servers about drug-pathway associations were listed. Next, we summarized some state-of-the-art computational methods in the past years for inferring drug-pathway associations and divided these methods into three classes, namely Bayesian spare factor-based, matrix decomposition-based and other machine learning methods. In addition, we introduced several evaluation strategies to estimate the predictive performance of various computational models. In the end, we discussed the advantages and limitations of existing computational methods and provided some suggestions about the future directions of the data collection and the calculation models development.
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Dai LY, Zheng CH, Liu JX, Zhu R, Yuan SS, Wang J, Kong XZ. Integrative graph regularized matrix factorization for drug-pathway associations analysis. Comput Biol Chem 2019; 78:474-480. [DOI: 10.1016/j.compbiolchem.2018.11.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 11/21/2018] [Accepted: 11/30/2018] [Indexed: 02/06/2023]
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8
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Jiménez J, Sabbadin D, Cuzzolin A, Martínez-Rosell G, Gora J, Manchester J, Duca J, De Fabritiis G. PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks. J Chem Inf Model 2019; 59:1172-1181. [PMID: 30586501 DOI: 10.1021/acs.jcim.8b00711] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Drug discovery suffers from high attrition because compounds initially deemed as promising can later show ineffectiveness or toxicity resulting from a poor understanding of their activity profile. In this work, we describe a deep self-normalizing neural network model for the prediction of molecular pathway association and evaluate its performance, showing an AUC ranging from 0.69 to 0.91 on a set of compounds extracted from ChEMBL and from 0.81 to 0.83 on an external data set provided by Novartis. We finally discuss the applicability of the proposed model in the domain of lead discovery. A usable application is available via PlayMolecule.org .
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Affiliation(s)
- José Jiménez
- Computational Science Laboratory , Universitat Pompeu Fabra , Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain
| | - Davide Sabbadin
- Computational Science Laboratory , Universitat Pompeu Fabra , Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain
| | - Alberto Cuzzolin
- Acellera , Barcelona Biomedical Research Park (PRBB) , Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain
| | - Gerard Martínez-Rosell
- Acellera , Barcelona Biomedical Research Park (PRBB) , Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain
| | - Jacob Gora
- Global Discovery Chemistry , Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States.,Department of Mathematics and Computer Science , Freie Universität Berlin , Takustr. 9 , 14195 Berlin , Germany
| | - John Manchester
- Global Discovery Chemistry , Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States
| | - José Duca
- Global Discovery Chemistry , Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States
| | - Gianni De Fabritiis
- Computational Science Laboratory , Universitat Pompeu Fabra , Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain.,Acellera , Barcelona Biomedical Research Park (PRBB) , Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA) , Passeig Lluis Companys 23 , 08010 Barcelona , Spain
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9
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Liu JX, Wang DQ, Zheng CH, Gao YL, Wu SS, Shang JL. Identifying drug-pathway association pairs based on L 2,1-integrative penalized matrix decomposition. BMC SYSTEMS BIOLOGY 2017; 11:119. [PMID: 29297378 PMCID: PMC5770056 DOI: 10.1186/s12918-017-0480-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
BACKGROUND Traditional drug identification methods follow the "one drug-one target" thought. But those methods ignore the natural characters of human diseases. To overcome this limitation, many identification methods of drug-pathway association pairs have been developed, such as the integrative penalized matrix decomposition (iPaD) method. The iPaD method imposes the L1-norm penalty on the regularization term. However, lasso-type penalties have an obvious disadvantage, that is, the sparsity produced by them is too dispersive. RESULTS Therefore, to improve the performance of the iPaD method, we propose a novel method named L2,1-iPaD to identify paired drug-pathway associations. In the L2,1-iPaD model, we use the L2,1-norm penalty to replace the L1-norm penalty since the L2,1-norm penalty can produce row sparsity. CONCLUSIONS By applying the L2,1-iPaD method to the CCLE and NCI-60 datasets, we demonstrate that the performance of L2,1-iPaD method is superior to existing methods. And the proposed method can achieve better enrichment in terms of discovering validated drug-pathway association pairs than the iPaD method by performing permutation test. The results on the two real datasets prove that our method is effective.
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Affiliation(s)
- Jin-Xing Liu
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Dong-Qin Wang
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Chun-Hou Zheng
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China.
| | - Ying-Lian Gao
- Library of Qufu Normal University, Qufu Normal University, Rizhao, China.
| | - Sha-Sha Wu
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Jun-Liang Shang
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
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10
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Wang DQ, Gao YL, Liu JX, Zheng CH, Kong XZ. Identifying drug-pathway association pairs based on L1L2,1-integrative penalized matrix decomposition. Oncotarget 2017; 8:48075-48085. [PMID: 28624800 PMCID: PMC5564627 DOI: 10.18632/oncotarget.18254] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 05/01/2017] [Indexed: 01/27/2023] Open
Abstract
The traditional methods of drug discovery follow the "one drug-one target" approach, which ignores the cellular and physiological environment of the action mechanism of drugs. However, pathway-based drug discovery methods can overcome this limitation. This kind of method, such as the Integrative Penalized Matrix Decomposition (iPaD) method, identifies the drug-pathway associations by taking the lasso-type penalty on the regularization term. Moreover, instead of imposing the L1-norm regularization, the L2,1-Integrative Penalized Matrix Decomposition (L2,1-iPaD) method imposes the L2,1-norm penalty on the regularization term. In this paper, based on the iPaD and L2,1-iPaD methods, we propose a novel method named L1L2,1-iPaD (L1L2,1-Integrative Penalized Matrix Decomposition), which takes the sum of the L1-norm and L2,1-norm penalties on the regularization term. Besides, we perform permutation test to assess the significance of the identified drug-pathway association pairs and compute the P-values. Compared with the existing methods, our method can identify more drug-pathway association pairs which have been validated in the CancerResource database. In order to identify drug-pathway associations which are not validated in the CancerResource database, we retrieve published papers to prove these associations. The results on two real datasets prove that our method can achieve better enrichment for identified association pairs than the iPaD and L2,1-iPaD methods.
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Affiliation(s)
- Dong-Qin Wang
- 1 School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Ying-Lian Gao
- 2 Library of Qufu Normal University, Qufu Normal University, Rizhao, China
| | - Jin-Xing Liu
- 1 School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Chun-Hou Zheng
- 1 School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Xiang-Zhen Kong
- 1 School of Information Science and Engineering, Qufu Normal University, Rizhao, China
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11
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Chen FS, Jiang HY, Jiang Z. Prediction of drug–pathway interaction pairs with a disease-combined LSA-PU-KNN method. MOLECULAR BIOSYSTEMS 2017; 13:2583-2591. [DOI: 10.1039/c7mb00441a] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
This paper proposes a prediction of potential associations between drugs and pathways based on a disease-related LSA-PU-KNN method.
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Affiliation(s)
- Fan-Shu Chen
- Shanghai Key Laboratory of Multidimensional Information Processing
- East China Normal University
- Shanghai 200262
- China
- Department of Computer Science and Technology
| | - Hui-Yan Jiang
- Shanghai Key Laboratory of Multidimensional Information Processing
- East China Normal University
- Shanghai 200262
- China
- Department of Computer Science and Technology
| | - Zhenran Jiang
- Shanghai Key Laboratory of Multidimensional Information Processing
- East China Normal University
- Shanghai 200262
- China
- Department of Computer Science and Technology
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12
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Song M, Jiang Z. Inferring Association between Compound and Pathway with an Improved Ensemble Learning Method. Mol Inform 2015; 34:753-60. [DOI: 10.1002/minf.201500033] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Accepted: 07/03/2015] [Indexed: 12/20/2022]
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13
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Shen Y, Rahman M, Piccolo SR, Gusenleitner D, El-Chaar NN, Cheng L, Monti S, Bild AH, Johnson WE. ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways. ACTA ACUST UNITED AC 2015; 31:1745-53. [PMID: 25617415 DOI: 10.1093/bioinformatics/btv031] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2014] [Accepted: 01/14/2015] [Indexed: 12/20/2022]
Abstract
MOTIVATION Although gene-expression signature-based biomarkers are often developed for clinical diagnosis, many promising signatures fail to replicate during validation. One major challenge is that biological samples used to generate and validate the signature are often from heterogeneous biological contexts-controlled or in vitro samples may be used to generate the signature, but patient samples may be used for validation. In addition, systematic technical biases from multiple genome-profiling platforms often mask true biological variation. Addressing such challenges will enable us to better elucidate disease mechanisms and provide improved guidance for personalized therapeutics. RESULTS Here, we present a pathway profiling toolkit, Adaptive Signature Selection and InteGratioN (ASSIGN), which enables robust and context-specific pathway analyses by efficiently capturing pathway activity in heterogeneous sets of samples and across profiling technologies. The ASSIGN framework is based on a flexible Bayesian factor analysis approach that allows for simultaneous profiling of multiple correlated pathways and for the adaptation of pathway signatures into specific disease. We demonstrate the robustness and versatility of ASSIGN in estimating pathway activity in simulated data, cell lines perturbed pathways and in primary tissues samples including The Cancer Genome Atlas breast carcinoma samples and liver samples exposed to genotoxic carcinogens. AVAILABILITY AND IMPLEMENTATION Software for our approach is available for download at: http://www.bioconductor.org/packages/release/bioc/html/ASSIGN.html and https://github.com/wevanjohnson/ASSIGN.
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Affiliation(s)
- Ying Shen
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA
| | - Mumtahena Rahman
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA
| | - Stephen R Piccolo
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA
| | - Daniel Gusenleitner
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA
| | - Nader N El-Chaar
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA
| | - Luis Cheng
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA
| | - Stefano Monti
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA
| | - Andrea H Bild
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA
| | - W Evan Johnson
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA
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15
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Pratanwanich N, Lio P. Exploring the complexity of pathway-drug relationships using latent Dirichlet allocation. Comput Biol Chem 2014; 53 Pt A:144-52. [PMID: 25218217 DOI: 10.1016/j.compbiolchem.2014.08.019] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2014] [Indexed: 12/20/2022]
Abstract
Analysis of cellular responses to diverse stimuli enables the exploration in the complexity of functional genomics. Typically, high-throughput microarray data allow us to identify genes that are differentially expressed under a phenomenon of interest. To extract the meanings from the long list of those differentially expressed genes, we present a new method "pathway-based LDA" to determine pathways/gene sets that are perturbed after exposure to different chemicals. In this study, a pathway is defined as a group of functionally related genes. Specifically, we have implemented a probabilistic Latent Dirichlet Allocation (LDA) model to learn drug-pathway-gene relations by taking known gene-pathway memberships as prior knowledge. We applied the pathway-based LDA model and 236 known pathways in order to determine pathway responsiveness to gene expression data of 1169 drugs. Our method yielded a better predictive performance on pathway responsiveness to drug treatments than the existing methods. Moreover, the pathway-based LDA also revealed genes contributing the most in each pre-defined pathway through a probabilistic distribution of genes. In achieving that, our method could provide a useful estimator of the pathway complexity of a genome.
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Affiliation(s)
- Naruemon Pratanwanich
- Computer Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0FD, United Kingdom.
| | - Pietro Lio
- Computer Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0FD, United Kingdom.
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16
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Pratanwanich N, Lió P. Pathway-based Bayesian inference of drug–disease interactions. ACTA ACUST UNITED AC 2014; 10:1538-48. [DOI: 10.1039/c4mb00014e] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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17
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Ma C, Chen HIH, Flores M, Huang Y, Chen Y. BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database. BMC SYSTEMS BIOLOGY 2013; 7 Suppl 5:S5. [PMID: 24564956 PMCID: PMC4029357 DOI: 10.1186/1752-0509-7-s5-s5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions. METHOD Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects. RESULT BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased. CONCLUSIONS The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates.
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Affiliation(s)
- Chifeng Ma
- Department of Electrical and Computer Engineering, the University of Texas at San Antonio, One UTSA Circle, San Antonio, Texas, USA
| | - Hung-I Harry Chen
- Greehey Children Cancer Research Institute, the University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Mario Flores
- Department of Electrical and Computer Engineering, the University of Texas at San Antonio, One UTSA Circle, San Antonio, Texas, USA
| | - Yufei Huang
- Department of Electrical and Computer Engineering, the University of Texas at San Antonio, One UTSA Circle, San Antonio, Texas, USA
- Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Yidong Chen
- Greehey Children Cancer Research Institute, the University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
- Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
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Ma H, Zhao H. Drug target inference through pathway analysis of genomics data. Adv Drug Deliv Rev 2013; 65:966-72. [PMID: 23369829 DOI: 10.1016/j.addr.2012.12.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2012] [Revised: 12/21/2012] [Accepted: 12/22/2012] [Indexed: 10/27/2022]
Abstract
Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues in this field, covering methodological developments, their pros and cons, as well as future research directions.
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