Synergistic drug combinations from electronic health records and gene expression

Yen S Low, Aaron C Daugherty, Elizabeth A Schroeder, William Chen, Tina Seto, Susan Weber, Michael Lim, Trevor Hastie, Maya Mathur, Manisha Desai, Carl Farrington, Andrew A Radin, Marina Sirota, Pragati Kenkare, Caroline A Thompson, Peter P Yu, Scarlett L Gomez, George W Sledge Jr, Allison W Kurian, Nigam H Shah, Yen S Low, Aaron C Daugherty, Elizabeth A Schroeder, William Chen, Tina Seto, Susan Weber, Michael Lim, Trevor Hastie, Maya Mathur, Manisha Desai, Carl Farrington, Andrew A Radin, Marina Sirota, Pragati Kenkare, Caroline A Thompson, Peter P Yu, Scarlett L Gomez, George W Sledge Jr, Allison W Kurian, Nigam H Shah

Abstract

Objective: Using electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding.

Method: We applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis.

Results: From EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence.

Conclusions: This is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing.

Keywords: breast cancer; combination therapies; drug discovery; drug interactions; drug repurposing; electronic health records.

© The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Figures

Figure 1.
Figure 1.
Method overview of (A) scoring EHR-based synergistic drug pairs, (B) scoring gene expression–based synergistic drug pairs, and (C) gene set enrichment analysis–like analysis of enrichment of EHR-based drug class pairs among gene expression–based drug pairs.
Figure 2.
Figure 2.
Odds ratios of factors (excluding pairwise interactions) most associated with 5-year mortality (see also Supplementary Table S3).
Figure 3.
Figure 3.
Variables (nodes) that synergistically interact such that they are associated with lower mortality (blue edges) or higher mortality (red edges, also see Table 2). Variable nodes that tend to have synergistically beneficial interactions (blue edges) also tend to be factors associated with lower mortality (eg, Stage I), while those with synergistically risky interactions (red) tend to be risk factors on their own (eg, Stage IV). Nodes are grouped together (eg, by categorical level, ATC class) to facilitate visual comparison within a group (eg, Stages I and II have many synergistically beneficial interactions while Stages III and IV have many synergistically risky interactions). Case studies described in the Discussion section are highlighted with thicker edges.
Figure 4.
Figure 4.
Enrichment analysis of EHR-based synergistic drug class pairs (A) anti-inflammatories/antirheumatics with lipid modifiers, (B) anti-inflammatories/antirheumatics with hormone antagonists, and (C) lipid modifiers and drugs for obstructed airways among gene expression–based synergistic drug pairs. All possible pairs of drugs from DrugBank v. 4.0 were scored on their association with genes differentially expressed in breast cancer (shaded area). A GSEA-based analysis was then performed to score the enrichment of pairs of drugs derived from the respective EHR-based classes (derived drug pairs represented by black vertical lines, running enrichment represented by red bold line) and compared to a randomly sampled null distribution (10 000 iterations) to assess significance and fold enrichment.

References

    1. Xu H, Aldrich MC, Chen Q et al. . Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. J. Am. Med. Inform. Assoc. 2015;22:179–91.
    1. Jung K, LePendu P, Chen WS et al. . Automated detection of off-label drug use. PLoS One. 2014;9:e89324.
    1. Yao L, Zhang Y, Li Y et al. . Electronic health records: Implications for drug discovery. Drug Discov. Today. 2011;16:594–99.
    1. Sirota M, Dudley JT, Kim J et al. . Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci. Transl. Med. 2011;3:96ra77.
    1. Iyer S V, Harpaz R, LePendu P et al. . Mining clinical text for signals of adverse drug-drug interactions. J. Am. Med. Inform. Assoc. 2014;21:353–62.
    1. Tatonetti NP, Ye PP, Daneshjou R et al. . Data-driven prediction of drug effects and interactions. Sci. Transl. Med. 2012;4:125ra31.
    1. Hurle MR, Yang L, Xie Q et al. . Computational drug repositioning: from data to therapeutics. Clin. Pharmacol. Ther. 2013;93:335–41.
    1. Shameer K, Readhead B, Dudley JT. Computational and experimental advances in drug repositioning for accelerated therapeutic stratification. Curr. Top. Med. Chem. 2015;15:5–20.
    1. Issa NT, Byers SW, Dakshanamurthy S. Drug repurposing: translational pharmacology, chemistry, computers and the clinic. Curr. Top. Med. Chem. 2013;13:2328–36.
    1. Lega IC, Austin PC, Gruneir A et al. . Association between metformin therapy and mortality after breast cancer: a population-based study. Diabetes Care. 2013;36:3018–26.
    1. Noto H, Goto A, Tsujimoto T et al. . Cancer risk in diabetic patients treated with metformin: a systematic review and meta-analysis. PLoS One. 2012;7:e33411.
    1. Col NF, Ochs L, Springmann V et al. . Metformin and breast cancer risk: a meta-analysis and critical literature review. Breast Cancer Res. Treat. 2012;135:639–46.
    1. Dowling RJO, Goodwin PJ, Stambolic V. Understanding the benefit of metformin use in cancer treatment. BMC Med. 2011;9:33.
    1. Bo S, Benso A, Durazzo M et al. . Does use of metformin protect against cancer in Type 2 diabetes mellitus? J. Endocrinol. Invest. 2012;35:231–35.
    1. DeCensi A, Puntoni M, Goodwin P et al. . Metformin and cancer risk in diabetic patients: A systematic review and meta-analysis. Cancer Prev. Res. 2010;3:1451–61.
    1. National Cancer Institute. Metformin: can a diabetes drug help prevent cancer? 2013. . Accessed April 23, 2015.
    1. Berenbaum MC. What is synergy? Pharmacol. Rev. 1989;41:93–141.
    1. Schmider W, Boulenc X. Drug-Drug Interaction Studies. In: Drug Discovery and Evaluation: Methods in Clinical Pharmacology. Springer: Berlin Heidelberg; 2011: 119–31.
    1. Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 2004;3:673–83.
    1. Cokol M, Chua HN, Tasan M et al. . Systematic exploration of synergistic drug pairs. Mol. Syst. Biol. 2011;7:544.
    1. Guimerà R, Sales-Pardo M. A network inference method for large-scale unsupervised identification of novel drug-drug interactions. PLoS Comput. Biol. 2013;9:e1003374.
    1. Kurian AW, Mitani A, Desai M et al. . Breast cancer treatment across health care systems: linking electronic medical records and state registry data to enable outcomes research. Cancer. 2013;120:1–9.
    1. Bennett CC. Utilizing RxNorm to support practical computing applications: Capturing medication history in live electronic health records. J. Biomed. Inform. 2012;45:634–41.
    1. World Health Organization Collaborating Centre for Drug Statistics Methodology. Guidelines for ATC Classification and DDD Assignment. Oslo: 2015.
    1. Tibshirani R. Regression shrinkage and selection via the lasso. J.R. Stat. Soc. Ser. B. 1996;58:267–88.
    1. Lim M, Hastie T. Learning interactions via hierarchical group-lasso regularization. J. Comput. Graph Stat. 2015;24:627–54.
    1. Efron B, Tibshirani R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1986;1:54–75.
    1. Brazma A, Parkinson H, Sarkans U et al. . ArrayExpress: a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 2003;31:68–71.
    1. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30:207–10.
    1. Bolstad BM, Irizarry RA, Astrand M et al. . A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003;19:185–93.
    1. Kauffmann A, Gentleman R, Huber W. arrayQualityMetrics: a bioconductor package for quality assessment of microarray data. Bioinformatics. 2009;25:415–16.
    1. Clark NR, Hu KS, Feldmann AS et al. . The characteristic direction: a geometrical approach to identify differentially expressed genes. BMC Bioinformatics. 2014;15:79.
    1. Trim Galore! Version 0.4.1. . Accessed February 5, 2016.
    1. Bray NL, Pimentel H, Melsted P et al. . Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 2016;34:525–27.
    1. Law V, Knox C, Djoumbou Y et al. . DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2014;42:D1091–97.
    1. Sugaya N, Furuya T Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions. BMC Bioinformatics. 2011;12:50.
    1. Taylor IW, Linding R, Warde-Farley D et al. . Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat. Biotechnol. 2009;27:199–204.
    1. Chuang HY, Lee E, Liu YT et al. . Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 2007;3:140.
    1. Subramanian A, Tamayo P, Mootha VK et al. . Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U.S.A. 2005;102:15545–50.
    1. Desantis C, Ma J, Bryan L et al. . Breast cancer statistics, 2013. CA Cancer J. Clin. 2014;64:52–62.
    1. Yost K, Perkins C, Cohen R et al. . Socioeconomic status and breast cancer incidence in California for different race/ethnic groups. Cancer Causes Control. 2001;12:703–11.
    1. Yeo W, Chan. Antiemetic therapy options for chemotherapy-induced nausea and vomiting in breast cancer patients. Breast Cancer Targets Ther. 2011;2011:151–60.
    1. Xiao H, Yang CS. Combination regimen with statins and NSAIDs: A promising strategy for cancer chemoprevention. Int. J. Cancer. 2008;123:983–90.
    1. Lochhead P, Chan AT. Statins and colorectal cancer. Clin. Gastroenterol. Hepatol. 2013;11:109–18.
    1. Hoffmeister M, Chang-Claude J, Brenner H. Individual and joint use of statins and low-dose aspirin and risk of colorectal cancer: a population-based case-control study. Int. J. Cancer. 2007;121:1325–30.
    1. Moon H, Hill MM, Roberts MJ et al. . Statins: protectors or pretenders in prostate cancer? Trends Endocrinol. Metab. 2014;25:188–96.
    1. Holmes MD, Chen WY. Hiding in plain view: the potential for commonly used drugs to reduce breast cancer mortality. Breast Cancer Res. 2012;14:216.
    1. Hiľovska L, Jendželovsky R, Fedoročko P. Potency of non-steroidal anti-inflammatory drugs in chemotherapy (Review). Mol. Clin. Oncol. 2015;3:3–12.
    1. Altomare DA, Testa JR. Perturbations of the AKT signaling pathway in human cancer. Oncogene. 2005;24:7455–64.
    1. Qiao Y, He H, Jonsson P et al. . AP-1 is a key regulator of proinflammatory cytokine TNFα-mediated triple-negative breast cancer progression. J. Biol. Chem. 2016;291:5068–79.
    1. Atwood AA, Sealy L. Regulation of C/EBPbeta1 by Ras in mammary epithelial cells and the role of C/EBPbeta1 in oncogene-induced senescence. Oncogene. 2010;29:6004–15.
    1. Duan L, Sterba K, Kolomeichuk S et al. . Inducible overexpression of c-Jun in MCF7 cells causes resistance to vinblastine via inhibition of drug-induced apoptosis and senescence at a step subsequent to mitotic arrest. Biochem. Pharmacol. 2007;73:481–90.
    1. Reiss K, Del Valle L, Lassak A et al. . Nuclear IRS-1 and cancer. J. Cell Physiol. 2012;227:2992–3000.
    1. Porter HA, Perry A, Kingsley C et al. . IRS1 is highly expressed in localized breast tumors and regulates the sensitivity of breast cancer cells to chemotherapy, while IRS2 is highly expressed in invasive breast tumors. Cancer Lett. 2013;338:239–48.
    1. Migliaccio I, Wu MF, Gutierrez C et al. . Nuclear IRS-1 predicts tamoxifen response in patients with early breast cancer. Breast Cancer Res. Treat. 2010;123:651–60.
    1. Malorni L, Giuliano M, Migliaccio I et al. . Blockade of AP-1 potentiates endocrine therapy and overcomes resistance. Mol. Cancer Res. 2016;14:470–81.
    1. Zhao Y, Zhu S, Li X et al. . Association between NSAIDs use and breast cancer risk: a systematic review and meta-analysis. Breast Cancer Res. Treat. 2009;117:141–50.
    1. Arun B, Goss P. The role of COX-2 inhibition in breast cancer treatment and prevention. Semin. Oncol. 2004;31:22–29.
    1. Falandry C, Canney PA, Freyer G et al. . Role of combination therapy with aromatase and cyclooxygenase-2 inhibitors in patients with metastatic breast cancer. Ann. Oncol. 2009;20:615–20.
    1. Liu H, Talalay P. Relevance of anti-inflammatory and antioxidant activities of exemestane and synergism with sulforaphane for disease prevention. Proc. Natl. Acad. Sci. U.S.A. 2013;110:19065–70.
    1. Brandão RD, Veeck J, Van de Vijver KK et al. . A randomised controlled phase II trial of pre-operative celecoxib treatment reveals anti-tumour transcriptional response in primary breast cancer. Breast Cancer Res. 2013;15:R29.
    1. Mercier I, Casimiro MC, Zhou J et al. . Genetic ablation of caveolin-1 drives estrogen-hypersensitivity and the development of DCIS-like mammary lesions. Am. J. Pathol. 2009;174:1172–90.
    1. Wang R, He W, Li Z et al. . Caveolin-1 functions as a key regulator of 17beta-estradiol-mediated autophagy and apoptosis in BT474 breast cancer cells. Int. J. Mol. Med. 2014;34:822–27.
    1. Barrios-Garcia T, Gomez-Romero V, Tecalco-Cruz A et al. . Nuclear tristetraprolin acts as a corepressor of multiple steroid nuclear receptors in breast cancer cells. Mol. Genet. Metab. Rep 2016;7:20–26.
    1. Barrios-Garcia T, Tecalco-Cruz A, Gomez-Romero V et al. . Tristetraprolin represses estrogen receptor alpha transactivation in breast cancer cells. J. Biol. Chem. 2014;289:15554–65.
    1. Gold M, Dunn LB, Phoenix B et al. . Co-occurrence of anxiety and depressive symptoms following breast cancer surgery and its impact on quality of life. Eur. J. Oncol. Nurs. 2016;20:97–105.
    1. Goeman JJ. L1 penalized estimation in the Cox proportional hazards model. Biometrical J. 2010;52:70–84.
    1. Sartori S. Penalized Regression: Bootstrap Confidence Intervals and Variable Selection for High Dimensional Data Sets. 2011. PhD dissertation from Universitá degli Studi di Milano.
    1. Overhage JM, Overhage LM. Sensible use of observational clinical data. Stat. Methods Med. Res. 2013;22:7–13.
    1. Thompson CA, Kurian AW, Luft HS. Linking electronic health records to better understand breast cancer patient pathways within and between two health systems. Generating Evid. Methods to Improv Patient Outcomes. 2015;3:Article 5.
    1. Williams AJ, Ekins S, Tkachenko V. Towards a gold standard: regarding quality in public domain chemistry databases and approaches to improving the situation. Drug Discov. Today. 2012;17:685–701.
    1. Moore JH, Gilbert JC, Tsai C-T et al. . A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J. Theor. Biol. 2006;241:252–61.

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