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Chi C, Ye Y, Chen B, Huang H. Bipartite graph-based approach for clustering of cell lines by gene expression-drug response associations. Bioinformatics 2021; 37:2617-2626. [PMID: 33682877 PMCID: PMC8428606 DOI: 10.1093/bioinformatics/btab143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 02/16/2021] [Accepted: 03/01/2021] [Indexed: 01/29/2023] Open
Abstract
MOTIVATION In pharmacogenomic studies, the biological context of cell lines influences the predictive ability of drug-response models and the discovery of biomarkers. Thus, similar cell lines are often studied together based on prior knowledge of biological annotations. However, this selection approach is not scalable with the number of annotations, and the relationship between gene-drug association patterns and biological context may not be obvious. RESULTS We present a procedure to compare cell lines based on their gene-drug association patterns. Starting with a grouping of cell lines from biological annotation, we model gene-drug association patterns for each group as a bipartite graph between genes and drugs. This is accomplished by applying sparse canonical correlation analysis (SCCA) to extract the gene-drug associations, and using the canonical vectors to construct the edge weights. Then, we introduce a nuclear norm-based dissimilarity measure to compare the bipartite graphs. Accompanying our procedure is a permutation test to evaluate the significance of similarity of cell line groups in terms of gene-drug associations. In the pharmacogenomics datasets CTRP2, GDSC2, and CCLE, hierarchical clustering of carcinoma groups based on this dissimilarity measure uniquely reveals clustering patterns driven by carcinoma subtype rather than primary site. Next, we show that the top associated drugs or genes from SCCA can be used to characterize the clustering patterns of haematopoietic and lymphoid malignancies. Finally, we confirm by simulation that when drug responses are linearly-dependent on expression, our approach is the only one that can effectively infer the true hierarchy compared to existing approaches. AVAILABILITY Bipartite graph-based hierarchical clustering is implemented in R and can be obtained from CRAN: https://CRAN.R-project.org/package=hierBipartite. The source code is available at https://github.com/CalvinTChi/hierBipartite. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Calvin Chi
- Center for Computational Biology, University of California, Berkeley, CA 94720, USA
| | - Yuting Ye
- Division of Biostatistics, University of California, Berkeley, CA 94720, USA
| | - Bin Chen
- Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI 48912, USA.,Department of Pharmacology and Toxicology, Michigan State University, Grand Rapids, MI 48824, USA
| | - Haiyan Huang
- Center for Computational Biology, University of California, Berkeley, CA 94720, USA.,Department of Statistics, University of California, Berkeley, CA 94720, USA
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2
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Lloyd JP, Soellner MB, Merajver SD, Li JZ. Impact of between-tissue differences on pan-cancer predictions of drug sensitivity. PLoS Comput Biol 2021; 17:e1008720. [PMID: 33630864 PMCID: PMC7906305 DOI: 10.1371/journal.pcbi.1008720] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 01/18/2021] [Indexed: 11/24/2022] Open
Abstract
Increased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines, each with MEK inhibitor (MEKi) response and mRNA expression, point mutation, and copy number variation data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ρ = 0.88–0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ρ = 0.11–0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as exclusion of between-tissue signals leads to a decrease in Spearman’s ρ from a range of 0.43–0.62 to 0.30–0.51. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference. One of the central goals for precision oncology is to tailor treatment of individual tumors by their molecular characteristics. While drug response predictions have traditionally been sought within each cancer type, it has long been hoped to develop more robust predictions by jointly considering diverse cancer types. While such pan-cancer approaches have improved in recent years, it remains unclear whether between-tissue differences are contributing to the reported pan-cancer prediction performance. This concern stems from the observation that, when cancer types differ in both molecular features and drug response, strong predictive information can come mainly from differences among tissue types. Our study finds that both between- and within-cancer type signals provide substantial contributions to pan-cancer drug response prediction models, and about half of the cancer types examined are poorly predicted despite strong overall performance across all cancer types. We also find that pan-cancer prediction models perform similarly or better than cancer type-specific models, and in many cases the advantage of pan-cancer models is due to the larger number of samples available for pan-cancer analysis. Our results highlight tissue-of-origin as a key consideration for pan-cancer drug response prediction models, and recommend cancer type-specific considerations when translating pan-cancer prediction models for clinical use.
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Affiliation(s)
- John P Lloyd
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America.,Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America.,Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Matthew B Soellner
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America.,Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Sofia D Merajver
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America.,Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jun Z Li
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America.,Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan, United States of America
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Li K, Du Y, Li L, Wei DQ. Bioinformatics Approaches for Anti-cancer Drug Discovery. Curr Drug Targets 2021; 21:3-17. [PMID: 31549592 DOI: 10.2174/1389450120666190923162203] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 07/17/2019] [Accepted: 07/26/2019] [Indexed: 12/23/2022]
Abstract
Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.
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Affiliation(s)
- Kening Li
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuxin Du
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lu Li
- Department of Bioinformatics, Nanjing Medical University, Nanjing 211166, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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Wang R, Han Y, Zhao Z, Yang F, Chen T, Zhou W, Wang X, Qi L, Zhao W, Guo Z, Gu Y. Link synthetic lethality to drug sensitivity of cancer cells. Brief Bioinform 2020; 20:1295-1307. [PMID: 29300844 DOI: 10.1093/bib/bbx172] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 11/22/2017] [Indexed: 12/16/2022] Open
Abstract
Synthetic lethal (SL) interactions occur when alterations in two genes lead to cell death but alteration in only one of them is not lethal. SL interactions provide a new strategy for molecular-targeted cancer therapy. Currently, there are few drugs targeting SL interactions that entered into clinical trials. Therefore, it is necessary to investigate the link between SL interactions and drug sensitivity of cancer cells systematically for drug development purpose. We identified SL interactions by integrating the high-throughput data from The Cancer Genome Atlas, small hairpin RNA data and genetic interactions of yeast. By integrating SL interactions from other studies, we tested whether the SL pairs that consist of drug target genes and the genes with genomic alterations are related with drug sensitivity of cancer cells. We found that only 6.26%∼34.61% of SL interactions showed the expected significant drug sensitivity using the pooled cancer cell line data from different tissues, but the proportion increased significantly to approximately 90% using the cancer cell line data for each specific tissue. From an independent pharmacogenomics data of 41 breast cancer cell lines, we found three SL interactions (ABL1-IFI16, ABL1-SLC50A1 and ABL1-SYT11) showed significantly better prognosis for the patients with both genes being altered than the patients with only one gene being altered, which partially supports the SL effect between the gene pairs. Our study not only provides a new way for unraveling the complex mechanisms of drug sensitivity but also suggests numerous potentially important drug targets for cancer therapy.
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Yao F, Madani Tonekaboni SA, Safikhani Z, Smirnov P, El-Hachem N, Freeman M, Manem VSK, Haibe-Kains B. Tissue specificity of in vitro drug sensitivity. J Am Med Inform Assoc 2019; 25:158-166. [PMID: 29016819 DOI: 10.1093/jamia/ocx062] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 05/22/2017] [Indexed: 12/11/2022] Open
Abstract
Objectives We sought to investigate the tissue specificity of drug sensitivities in large-scale pharmacological studies and compare these associations to those found in drug clinical indications. Materials and Methods We leveraged the curated cell line response data from PharmacoGx and applied an enrichment algorithm on drug sensitivity values' area under the drug dose-response curves (AUCs) with and without adjustment for general level of drug sensitivity. Results We observed tissue specificity in 63% of tested drugs, with 8% of total interactions deemed significant (false discovery rate <0.05). By restricting the drug-tissue interactions to those with AUC > 0.2, we found that in 52% of interactions, the tissue was predictive of drug sensitivity (concordance index > 0.65). When compared with clinical indications, the observed overlap was weak (Matthew correlation coefficient, MCC = 0.0003, P > .10). Discussion While drugs exhibit significant tissue specificity in vitro, there is little overlap with clinical indications. This can be attributed to factors such as underlying biological differences between in vitro models and patient tumors, or the inability of tissue-specific drugs to bring additional benefits beyond gold standard treatments during clinical trials. Conclusion Our meta-analysis of pan-cancer drug screening datasets indicates that most tested drugs exhibit tissue-specific sensitivities in a large panel of cancer cell lines. However, the observed preclinical results do not translate to the clinical setting. Our results suggest that additional research into showing parallels between preclinical and clinical data is required to increase the translational potential of in vitro drug screening.
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Affiliation(s)
- Fupan Yao
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Seyed Ali Madani Tonekaboni
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Zhaleh Safikhani
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Petr Smirnov
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Nehme El-Hachem
- Integrative Systems Biology, Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada.,Department of Medicine, University of Montreal, Montréal, Quebec, Canada
| | - Mark Freeman
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Venkata Satya Kumar Manem
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute of Cancer Research, Toronto, Ontario, Canada
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Azuaje F. Computational models for predicting drug responses in cancer research. Brief Bioinform 2017; 18:820-829. [PMID: 27444372 PMCID: PMC5862310 DOI: 10.1093/bib/bbw065] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Indexed: 02/06/2023] Open
Abstract
The computational prediction of drug responses based on the analysis of multiple types of genome-wide molecular data is vital for accomplishing the promise of precision medicine in oncology. This will benefit cancer patients by matching their tumor characteristics to the most effective therapy available. As larger and more diverse layers of patient-related data become available, further demands for new bioinformatics approaches and expertise will arise. This article reviews key strategies, resources and techniques for the prediction of drug sensitivity in cell lines and patient-derived samples. It discusses major advances and challenges associated with the different model development steps. This review highlights major trends in this area, and will assist researchers in the assessment of recent progress and in the selection of approaches to emerging applications in oncology.
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Affiliation(s)
- Francisco Azuaje
- NorLux Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
- Corresponding author: Francisco Azuaje, NorLux Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health (LIH), Luxembourg L-1526, Luxembourg. Tel.: +352-26970875; Fax: +352-26970396; E-mail:
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Ammad-ud-din M, Khan SA, Wennerberg K, Aittokallio T. Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression. Bioinformatics 2017; 33:i359-i368. [PMID: 28881998 PMCID: PMC5870540 DOI: 10.1093/bioinformatics/btx266] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
MOTIVATION A prime challenge in precision cancer medicine is to identify genomic and molecular features that are predictive of drug treatment responses in cancer cells. Although there are several computational models for accurate drug response prediction, these often lack the ability to infer which feature combinations are the most predictive, particularly for high-dimensional molecular datasets. As increasing amounts of diverse genome-wide data sources are becoming available, there is a need to build new computational models that can effectively combine these data sources and identify maximally predictive feature combinations. RESULTS We present a novel approach that leverages on systematic integration of data sources to identify response predictive features of multiple drugs. To solve the modeling task we implement a Bayesian linear regression method. To further improve the usefulness of the proposed model, we exploit the known human cancer kinome for identifying biologically relevant feature combinations. In case studies with a synthetic dataset and two publicly available cancer cell line datasets, we demonstrate the improved accuracy of our method compared to the widely used approaches in drug response analysis. As key examples, our model identifies meaningful combinations of features for the well known EGFR, ALK, PLK and PDGFR inhibitors. AVAILABILITY AND IMPLEMENTATION The source code of the method is available at https://github.com/suleimank/mvlr . CONTACT muhammad.ammad-ud-din@helsinki.fi or suleiman.khan@helsinki.fi. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Muhammad Ammad-ud-din
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland
| | - Suleiman A Khan
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland
| | - Krister Wennerberg
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
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9
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Ammad-ud-din M, Khan SA, Malani D, Murumägi A, Kallioniemi O, Aittokallio T, Kaski S. Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization. Bioinformatics 2016; 32:i455-i463. [DOI: 10.1093/bioinformatics/btw433] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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10
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Hwang W, Choi J, Kwon M, Lee D. Context-specific functional module based drug efficacy prediction. BMC Bioinformatics 2016; 17 Suppl 6:275. [PMID: 27490093 PMCID: PMC4965733 DOI: 10.1186/s12859-016-1078-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Background It is necessary to evaluate the efficacy of individual drugs on patients to realize personalized medicine. Testing drugs on patients in clinical trial is the only way to evaluate the efficacy of drugs. The approach is labour intensive and requires overwhelming costs and a number of experiments. Therefore, preclinical model system has been intensively investigated for predicting the efficacy of drugs. Current computational drug sensitivity prediction approaches use general biological network modules as their prediction features. Therefore, they miss indirect effectors or the effects from tissue-specific interactions. Results We developed cell line specific functional modules. Enriched scores of functional modules are utilized as cell line specific features to predict the efficacy of drugs. Cell line specific functional modules are clusters of genes, which have similar biological functions in cell line specific networks. We used linear regression for drug efficacy prediction. We assessed the prediction performance in leave-one-out cross-validation (LOOCV). Our method was compared with elastic net model, which is a popular model for drug efficacy prediction. In addition, we analysed drug sensitivity-associated functions of five drugs - lapatinib, erlotinib, raloxifene, tamoxifen and gefitinib- by our model. Conclusions Our model can provide cell line specific drug efficacy prediction and also provide functions which are associated with drug sensitivity. Therefore, we could utilize drug sensitivity associated functions for drug repositioning or for suggesting secondary drugs for overcoming drug resistance. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1078-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Woochang Hwang
- Department of Bio and Brain Engineering, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Jaejoon Choi
- Department of Bio and Brain Engineering, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Mijin Kwon
- Department of Bio and Brain Engineering, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Doheon Lee
- Department of Bio and Brain Engineering, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea. .,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.
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