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Your search keyword '"Motsinger-Reif, Alison A"' showing total 12 results
Start Over Author "Motsinger-Reif, Alison A" Topic pharmacogenetics
12 results on '"Motsinger-Reif, Alison A"'

2. Genome-wide association studies in pharmacogenomics: successes and lessons.

Author

Motsinger-Reif AA, Jorgenson E, Relling MV, Kroetz DL, Weinshilboum R, Cox NJ, and Roden DM

Subjects
Case-Control Studies, Chromosome Mapping, Cohort Studies, Diagnosis, Genotype, Humans, Linear Models, Pharmacokinetics, Phenotype, Genome-Wide Association Study, Pharmacogenetics, Therapeutics
Abstract

Objective: As genotyping technology has progressed, genome-wide association studies (GWAS) have matured into efficient and effective tools for mapping genes underlying human phenotypes., Methods: Recent studies have shown the utility of the GWAS approach for examining pharmacogenomic traits, including drug metabolism, efficacy, and toxicity., Results: Application of GWAS to pharmacogenomic outcomes presents unique challenges and opportunities., Conclusion: In the current review, we discuss the potential promises and potential caveats of this approach specifically as it relates to pharmacogenomic studies. Concerns with study design, power and sample size, and analysis are reviewed. We further examine the features of successful pharmacogenomic GWAS, and describe consortia efforts that are likely to expand the reach of pharmacogenomic GWAS in the future.

Published
2013
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3. Institutional profile. UNC Institute for Pharmacogenomics and Individualized Therapy: interdisciplinary research for individual care.

Author

Rakhra-Burris TK, Auman JT, Deverka P, Dressler LG, Evans JP, Goldberg RM, Havener TM, Hoskins JM, Jonas DE, Long KM, Motsinger-Reif AA, Irvin WJ, Richards KL, Roederer MW, Valgus JM, Riper Mv, Vernon JA, Zamboni WC, Wagner MJ, Walko CM, Weck KE, Wiltshire T, and McLeod HL

Subjects
Biomedical Research, Breast Neoplasms drug therapy, Breast Neoplasms genetics, Female, Genotype, Humans, Molecular Biology, North Carolina, RNA, Small Interfering, Randomized Controlled Trials as Topic, Tamoxifen therapeutic use, Warfarin therapeutic use, Academies and Institutes, Pharmacogenetics education, Precision Medicine
Abstract

The Institute for Pharmacogenomics and Individualized Therapy (IPIT) at the University of North Carolina at Chapel Hill (NC, USA) is a collaborative, multidisciplinary unit that brings together faculty from different disciplines and crosses the traditional departmental/school structure to perform pharmacogenomics research. IPIT investigators work together towards the goal of developing therapies to enable the delivery of individualized medical care. The NIH-supported Comprehensive Research on Expressed Alleles in Therapeutic Evaluation (CREATE) group leads the field in the evaluation of pathways regulating drug activity, and also provides a foundation for future IPIT research. IPIT members perform bench research, clinical cohort analysis and prospective clinical intervention studies, research on the integration of pharmacogenomic therapy into practice and research to foster global health pharmacogenomics application through the Pharmacogenetics for Every Nation Initiative. IPIT Investigators are actively incorporating a pharmacogenomics curriculum into existing teaching programs at all levels.

Published
2010
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4. Sphingolipid Metabolic Pathway Impacts Thiazide Diuretics Blood Pressure Response: Insights From Genomics, Metabolomics, and Lipidomics

Author

Shahin, Mohamed H, Gong, Yan, Frye, Reginald F, Rotroff, Daniel M, Beitelshees, Amber L, Baillie, Rebecca A, Chapman, Arlene B, Gums, John G, Turner, Stephen T, Boerwinkle, Eric, Motsinger‐Reif, Alison, Fiehn, Oliver, Cooper‐DeHoff, Rhonda M, Han, Xianlin, Kaddurah‐Daouk, Rima, and Johnson, Julie A

Subjects
Human Genome, Genetics, Hypertension, Biotechnology, 2.1 Biological and endogenous factors, Aetiology, Cardiovascular, Adult, Blood Pressure, Female, Genomics, Humans, Hydrochlorothiazide, Lipid Metabolism, Male, Metabolic Networks and Pathways, Metabolomics, Middle Aged, Nitriles, Pharmacogenetics, Prognosis, Serine C-Palmitoyltransferase, Siloxanes, Sodium Chloride Symporter Inhibitors, Sphingolipids, Treatment Outcome, blood pressure, lipid metabolites, metabolomics, pharmacogenetics, thiazide diuretics, Cardiorespiratory Medicine and Haematology
Abstract

Although hydrochlorothiazide (HCTZ) is a well-established first-line antihypertensive in the United States,

Published
2018

5. A Genetic Response Score for Hydrochlorothiazide Use

Author

Shahin, Mohamed H, Gong, Yan, McDonough, Caitrin W, Rotroff, Daniel M, Beitelshees, Amber L, Garrett, Timothy J, Gums, John G, Motsinger-Reif, Alison, Chapman, Arlene B, Turner, Stephen T, Boerwinkle, Eric, Frye, Reginald F, Fiehn, Oliver, Cooper-DeHoff, Rhonda M, Kaddurah-Daouk, Rima, and Johnson, Julie A

Subjects
Medical Biochemistry and Metabolomics, Biomedical and Clinical Sciences, Patient Safety, Biotechnology, Hypertension, Genetics, Clinical Research, Human Genome, AMP-Activated Protein Kinases, Adult, Aged, Antihypertensive Agents, Blood Pressure Monitoring, Ambulatory, Cohort Studies, DCC Receptor, Epoxide Hydrolases, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Hydrochlorothiazide, Male, Metabolomics, Middle Aged, Pharmacogenetics, Polymorphism, Genetic, Prospective Studies, Receptors, Cell Surface, Severity of Illness Index, Signal Transduction, Treatment Outcome, Tumor Suppressor Proteins, White People, hydrochlorothiazide, hypertension, genome-wide association study, metabolomics, pharmacogenetics, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Public Health and Health Services, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

Hydrochlorothiazide is among the most commonly prescribed antihypertensives; yet,

Published
2016

6. Genome-wide association and pharmacological profiling of 29 anticancer agents using lymphoblastoid cell lines

Author

Brown, Chad C, Havener, Tammy M, Medina, Marisa W, Jack, John R, Krauss, Ronald M, McLeod, Howard L, and Motsinger-Reif, Alison A

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Medical Biotechnology, Pharmacology and Pharmaceutical Sciences, Human Genome, Cancer, Biotechnology, Good Health and Well Being, Antineoplastic Agents, Biomarkers, Pharmacological, Cell Line, Tumor, Chromosome Mapping, Genome-Wide Association Study, Genotype, Histone Deacetylases, Humans, Pharmacogenetics, Repressor Proteins, Medical and Health Sciences, Pharmacology & Pharmacy, Pharmacology and pharmaceutical sciences
Abstract

AimAssociation mapping with lymphoblastoid cell lines (LCLs) is a promising approach in pharmacogenomics research, and in the current study we utilized LCLs to perform association mapping for 29 chemotherapy drugs.Materials & methodsCurrently, we use LCLs to perform genome-wide association mapping of the cytotoxic response of 520 European-Americans to 29 different anticancer drugs; the largest LCL study to date. A novel association approach using a multivariate analysis of covariance design was employed with the software program MAGWAS, testing for differences in the dose-response profiles between genotypes without making assumptions about the response curve or the biologic mode of association. Additionally, by classifying 25 of the 29 drugs into eight families according to structural and mechanistic relationships, MAGWAS was used to test for associations that were shared across each drug family. Finally, a unique algorithm using multivariate responses and multiple linear regressions across pairs of response curves was used for unsupervised clustering of drugs.ResultsAmong the single-drug studies, suggestive associations were obtained for 18 loci, 12 within/near genes. Three of these, MED12L, CHN2 and MGMT, have been previously implicated in cancer pharmacogenomics. The drug family associations resulted in four additional suggestive loci (three contained within/near genes). One of these genes, HDAC4, associated with the DNA alkylating agents, shows possible clinical interactions with temozolomide. For the drug clustering analysis, 18 of 25 drugs clustered into the appropriate family.ConclusionThis study demonstrates the utility of LCLs in identifying genes that have clinical importance in drug response and for assigning unclassified agents to specific drug families, and proposes new candidate genes for follow-up in a large number of chemotherapy drugs.

Published
2014

9. Accuracy of SNPs to predict risk of HLA alleles associated with drug-induced hypersensitivity events across racial groups.

Author

He, Yijing, Hoskins, Janelle M, Clark, Scott, Campbell, Nathan H, Wagner, Kim, Motsinger-Reif, Alison A, and McLeod, Howard L

Abstract

Aim: To evaluate the potential usefulness of selected SNPs to predict specific HLA alleles that are associated with drug-induced hypersensitivity reactions (HSR) in different ethnic groups. Methods & results: Five specific HLA alleles known to predict HSR were tagged by seven SNPs (rs1061235- HLA-A*31:01; rs2395029- HLA-B*57:01; rs3909184- HLA-B*15:02; rs9469003- HLA-B*58:01; rs3117583- HLA-B*58:01; rs9270986- HLA-DQA1*01:02 and rs3129900- HLA-DQA1*01:02). DNA from 24 African-Americans, 56 Asian, 44 Caucasians and 36 Hispanics of known high resolution HLA-A, B and DQA1 status were genotyped for tagSNPs using TaqMan. Sensitivity and specificity were considered the primary end points and were 100% across the four populations for rs2395029- HLA-B*57:01. SNP prediction of HLA-A*31:01 had 100% sensitivity and 84% specificity. Conclusion: This study demonstrates the utility of SNP tagging as a 'real time' approach to predict or exclude the presence of specific HLA alleles of known importance to HSR across diverse ethnic groups. Original submitted 24 April 2014; Revision submitted 2 April 2015 [ABSTRACT FROM AUTHOR]

Published
2015
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10. Assessing the utility of whole genome amplified DNA as a template for DMET Plus array.

Author

He, Yi Jing, Misher, Anne D., Irvin, William, Motsinger-Reif, Alison, McLeod, Howard L., and Hoskins, Janelle M.

Subjects
GENETIC research, GENETIC polymorphisms, MOLECULAR weights, CELL culture, BREAST cancer patients, PHARMACOGENOMICS
Abstract

Background: Large amounts of high quality DNA are typically required for high-throughput genotyping arrays but sometimes study participant DNA is in limited supply. Multiple displacement amplification (MDA)-based whole genome amplification is an in vitro technique that permits the genetic analysis of limited amounts of high molecular weight genomic DNA (gDNA). Methods: The performance of MDA-whole genome amplified DNA (wgaDNA) as a template for DMET Plus (Affymetrix) was assessed. wgaDNA was generated from gDNA from three HapMap CEU cell lines and 11 breast cancer patients. One HapMap sample and three patient samples were randomly selected for replication to assess reproducibility. Accuracy was assessed by comparing the wgaDNA genotypes with gDNA genotypes. The kappa (κ) statistic was used to measure genotype concordance between paired gDNA-wgaDNA and wgaDNA-wgaDNA samples. Copy number variants (CNV) were not included in concordance analysis in this study. Results: A good genotype call rate of 98.8%±1.06% (mean±standard deviation, 1931 markers) was observed for all 18 wgaDNA samples with three samples having call rates lower than 98%. High genotype concordance rates were observed between four HapMap wgaDNA-gDNA pairs (98.5%, κ=0.9817, p<0.0001, 1931 markers) and 14 patient wgaDNA-gDNA pairs (100%, κ=1.00, p<0.0001, 19 markers among CYP2D6 and CYP2C19). Excellent genotype concordance was also observed between four independently amplified duplicate samples (98.0%, κ=0.9745; p<0.0001, 1931 markers). Conclusions: MDA-produced wgaDNA provides accurate and reproducible genotypes with the DMET Plus array and is therefore a suitable template for this targeted pharmacogenetic genotyping array. [ABSTRACT FROM AUTHOR]

Published
2012
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11. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

Menden M, Wang D, Mason M, Szalai B, Bulusu K, Guan Y, Yu T, Kang J, Jeon M, Wolfinger R, Nguyen T, Zaslavskiy M, Jang I, Ghazoui Z, Ahsen M, Vogel R, Neto E, Norman T, Tang E, Garnett M, Di Veroli G, Fawell S, Stolovitzky G, Guinney J, Dry J, Saez-Rodriguez J, Abante J, Abecassis B, Aben N, Aghamirzaie D, Aittokallio T, Akhtari F, Al-lazikani B, Alam T, Allam A, Allen C, de Almeida M, Altarawy D, Alves V, Amadoz A, Anchang B, Antolin A, Ash J, Aznar V, Ba-alawi W, Bagheri M, Bajic V, Ball G, Ballester P, Baptista D, Bare C, Bateson M, Bender A, Bertrand D, Wijayawardena B, Boroevich K, Bosdriesz E, Bougouffa S, Bounova G, Brouwer T, Bryant B, Calaza M, Calderone A, Calza S, Capuzzi S, Carbonell-Caballero J, Carlin D, Carter H, Castagnoli L, Celebi R, Cesareni G, Chang H, Chen G, Chen H, Cheng L, Chernomoretz A, Chicco D, Cho K, Cho S, Choi D, Choi J, Choi K, Choi M, De Cock M, Coker E, Cortes-Ciriano I, Cserzo M, Cubuk C, Curtis C, Van Daele D, Dang C, Dijkstra T, Dopazo J, Draghici S, Drosou A, Dumontier M, Ehrhart F, Eid F, ElHefnawi M, Elmarakeby H, van Engelen B, Engin H, de Esch I, Evelo C, Falcao A, Farag S, Fernandez-Lozano C, Fisch K, Flobak A, Fornari C, Foroushani A, Fotso D, Fourches D, Friend S, Frigessi A, Gao F, Gao X, Gerold J, Gestraud P, Ghosh S, Gillberg J, Godoy-Lorite A, Godynyuk L, Godzik A, Goldenberg A, Gomez-Cabrero D, Gonen M, de Graaf C, Gray H, Grechkin M, Guimera R, Guney E, Haibe-Kains B, Han Y, Hase T, He D, He L, Heath L, Hellton K, Helmer-Citterich M, Hidalgo M, Hidru D, Hill S, Hochreiter S, Hong S, Hovig E, Hsueh Y, Hu Z, Huang J, Huang R, Hunyady L, Hwang J, Hwang T, Hwang W, Hwang Y, Isayev O, Walk O, Jack J, Jahandideh S, Ji J, Jo Y, Kamola P, Kanev G, Karacosta L, Karimi M, Kaski S, Kazanov M, Khamis A, Khan S, Kiani N, Kim A, Kim J, Kim K, Kim S, Kim Y, Kirk P, Kitano H, Klambauer G, Knowles D, Ko M, Kohn-Luque A, Kooistra A, Kuenemann M, Kuiper M, Kurz C, Kwon M, van Laarhoven T, Laegreid A, Lederer S, Lee H, Lee J, Lee Y, Leppaho E, Lewis R, Li J, Li L, Liley J, Lim W, Lin C, Liu Y, Lopez Y, Low J, Lysenko A, Machado D, Madhukar N, De Maeyer D, Malpartida A, Mamitsuka H, Marabita F, Marchal K, Marttinen P, Mason D, Mazaheri A, Mehmood A, Mehreen A, Michaut M, Miller R, Mitsopoulos C, Modos D, Van Moerbeke M, Moo K, Motsinger-Reif A, Movva R, Muraru S, Muratov E, Mushthofa M, Nagarajan N, Nakken S, Nath A, Neuvial P, Newton R, Ning Z, De Niz C, Oliva B, Olsen C, Palmeri A, Panesar B, Papadopoulos S, Park J, Park S, Pawitan Y, Peluso D, Pendyala S, Peng J, Perfetto L, Pirro S, Plevritis S, Politi R, Poon H, Porta E, Prellner I, Preuer K, Pujana M, Ramnarine R, Reid J, Reyal F, Richardson S, Ricketts C, Rieswijk L, Rocha M, Rodriguez-Gonzalvez C, Roell K, Rotroff D, de Ruiter J, Rukawa P, Sadacca B, Safikhani Z, Safitri F, Sales-Pardo M, Sauer S, Schlichting M, Seoane J, Serra J, Shang M, Sharma A, Sharma H, Shen Y, Shiga M, Shin M, Shkedy Z, Shopsowitz K, Sinai S, Skola D, Smirnov P, Soerensen I, Soerensen P, Song J, Song S, Soufan O, Spitzmueller A, Steipe B, Suphavilai C, Tamayo S, Tamborero D, Tang J, Tanoli Z, Tarres-Deulofeu M, Tegner J, Thommesen L, Tonekaboni S, Tran H, De Troyer E, Truong A, Tsunoda T, Turu G, Tzeng G, Verbeke L, Videla S, Vis D, Voronkov A, Votis K, Wang A, Wang H, Wang P, Wang S, Wang W, Wang X, Wennerberg K, Wernisch L, Wessels L, van Westen G, Westerman B, White S, Willighagen E, Wurdinger T, Xie L, Xie S, Xu H, Yadav B, Yau C, Yeerna H, Yin J, Yu M, Yun S, Zakharov A, Zamichos A, Zanin M, Zeng L, Zenil H, Zhang F, Zhang P, Zhang W, Zhao H, Zhao L, Zheng W, Zoufir A, Zucknick M, AstraZeneca-Sanger Drug Combinatio, Ege Üniversitesi, Gönen, Mehmet (ORCID 0000-0002-2483-075X & YÖK ID 237468), Menden, Michael P., Wang, Dennis, Mason, Mike J., Szalai, Bence, Bulusu, Krishna C., Guan, Yuanfang, Yu, Thomas, Kang, Jaewoo, Jeon, Minji, Wolfinger, Russ, Nguyen, Tin, Zaslavskiy, Mikhail, Jang, In Sock, Ghazoui, Zara, Ahsen, Mehmet Eren, Vogel, Robert, Neto, Elias Chaibub, Norman, Thea, Tang, Eric K. Y., Garnett, Mathew J., Di Veroli, Giovanni Y., Fawell, Stephen, Stolovitzky, Gustavo, Guinney, Justin, Dry, Jonathan R., Saez-Rodriguez, Julio, Abante, Jordi, Abecassis, Barbara Schmitz, Aben, Nanne, Aghamirzaie, Delasa, Aittokallio, Tero, Akhtari, Farida S., Al-lazikani, Bissan, Alam, Tanvir, Allam, Amin, Allen, Chad, de Almeida, Mariana Pelicano, Altarawy, Doaa, Alves, Vinicius, Amadoz, Alicia, Anchang, Benedict, Antolin, Albert A., Ash, Jeremy R., Romeo Aznar, Victoria, Ba-alawi, Wail, Bagheri, Moeen, Bajic, Vladimir, Ball, Gordon, Ballester, Pedro J., Baptista, Delora, Bare, Christopher, Bateson, Mathilde, Bender, Andreas, Bertrand, Denis, Wijayawardena, Bhagya, Boroevich, Keith A., Bosdriesz, Evert, Bougouffa, Salim, Bounova, Gergana, Brouwer, Thomas, Bryant, Barbara, Calaza, Manuel, Calderone, Alberto, Calza, Stefano, Capuzzi, Stephen, Carbonell-Caballero, Jose, Carlin, Daniel, Carter, Hannah, Castagnoli, Luisa, Celebi, Remzi, Cesareni, Gianni, Chang, Hyeokyoon, Chen, Guocai, Chen, Haoran, Chen, Huiyuan, Cheng, Lijun, Chernomoretz, Ariel, Chicco, Davide, Cho, Kwang-Hyun, Cho, Sunghwan, Choi, Daeseon, Choi, Jaejoon, Choi, Kwanghun, Choi, Minsoo, De Cock, Martine, Coker, Elizabeth, Cortes-Ciriano, Isidro, Cserzo, Miklos, Cubuk, Cankut, Curtis, Christina, Van Daele, Dries, Dang, Cuong C., Dijkstra, Tjeerd, Dopazo, Joaquin, Draghici, Sorin, Drosou, Anastasios, Dumontier, Michel, Ehrhart, Friederike, Eid, Fatma-Elzahraa, ElHefnawi, Mahmoud, Elmarakeby, Haitham, van Engelen, Bo, Engin, Hatice Billur, de Esch, Iwan, Evelo, Chris, Falcao, Andre O., Farag, Sherif, Fernandez-Lozano, Carlos, Fisch, Kathleen, Flobak, Asmund, Fornari, Chiara, Foroushani, Amir B. K., Fotso, Donatien Chedom, Fourches, Denis, Friend, Stephen, Frigessi, Arnoldo, Gao, Feng, Gao, Xiaoting, Gerold, Jeffrey M., Gestraud, Pierre, Ghosh, Samik, Gillberg, Jussi, Godoy-Lorite, Antonia, Godynyuk, Lizzy, Godzik, Adam, Goldenberg, Anna, Gomez-Cabrero, David, de Graaf, Chris, Gray, Harry, Grechkin, Maxim, Guimera, Roger, Guney, Emre, Haibe-Kains, Benjamin, Han, Younghyun, Hase, Takeshi, He, Di, He, Liye, Heath, Lenwood S., Hellton, Kristoffer H., Helmer-Citterich, Manuela, Hidalgo, Marta R., Hidru, Daniel, Hill, Steven M., Hochreiter, Sepp, Hong, Seungpyo, Hovig, Eivind, Hsueh, Ya-Chih, Hu, Zhiyuan, Huang, Justin K., Huang, R. Stephanie, Hunyady, Laszlo, Hwang, Jinseub, Hwang, Tae Hyun, Hwang, Woochang, Hwang, Yongdeuk, Isayev, Olexandr, Walk, Oliver Bear Don't, Jack, John, Jahandideh, Samad, Ji, Jiadong, Jo, Yousang, Kamola, Piotr J., Kanev, Georgi K., Karacosta, Loukia, Karimi, Mostafa, Kaski, Samuel, Kazanov, Marat, Khamis, Abdullah M., Khan, Suleiman Ali, Kiani, Narsis A., Kim, Allen, Kim, Jinhan, Kim, Juntae, Kim, Kiseong, Kim, Kyung, Kim, Sunkyu, Kim, Yongsoo, Kim, Yunseong, Kirk, Paul D. W., Kitano, Hiroaki, Klambauer, Gunter, Knowles, David, Ko, Melissa, Kohn-Luque, Alvaro, Kooistra, Albert J., Kuenemann, Melaine A., Kuiper, Martin, Kurz, Christoph, Kwon, Mijin, van Laarhoven, Twan, Laegreid, Astrid, Lederer, Simone, Lee, Heewon, Lee, Jeon, Lee, Yun Woo, Leppaho, Eemeli, Lewis, Richard, Li, Jing, Li, Lang, Liley, James, Lim, Weng Khong, Lin, Chieh, Liu, Yiyi, Lopez, Yosvany, Low, Joshua, Lysenko, Artem, Machado, Daniel, Madhukar, Neel, De Maeyer, Dries, Malpartida, Ana Belen, Mamitsuka, Hiroshi, Marabita, Francesco, Marchal, Kathleen, Marttinen, Pekka, Mason, Daniel, Mazaheri, Alireza, Mehmood, Arfa, Mehreen, Ali, Michaut, Magali, Miller, Ryan A., Mitsopoulos, Costas, Modos, Dezso, Van Moerbeke, Marijke, Moo, Keagan, Motsinger-Reif, Alison, Movva, Rajiv, Muraru, Sebastian, Muratov, Eugene, Mushthofa, Mushthofa, Nagarajan, Niranjan, Nakken, Sigve, Nath, Aritro, Neuvial, Pierre, Newton, Richard, Ning, Zheng, De Niz, Carlos, Oliva, Baldo, Olsen, Catharina, Palmeri, Antonio, Panesar, Bhawan, Papadopoulos, Stavros, Park, Jaesub, Park, Seonyeong, Park, Sungjoon, Pawitan, Yudi, Peluso, Daniele, Pendyala, Sriram, Peng, Jian, Perfetto, Livia, Pirro, Stefano, Plevritis, Sylvia, Politi, Regina, Poon, Hoifung, Porta, Eduard, Prellner, Isak, Preuer, Kristina, Angel Pujana, Miguel, Ramnarine, Ricardo, Reid, John E., Reyal, Fabien, Richardson, Sylvia, Ricketts, Camir, Rieswijk, Linda, Rocha, Miguel, Rodriguez-Gonzalvez, Carmen, Roell, Kyle, Rotroff, Daniel, de Ruiter, Julian R., Rukawa, Ploy, Sadacca, Benjamin, Safikhani, Zhaleh, Safitri, Fita, Sales-Pardo, Marta, Sauer, Sebastian, Schlichting, Moritz, Seoane, Jose A., Serra, Jordi, Shang, Ming-Mei, Sharma, Alok, Sharma, Hari, Shen, Yang, Shiga, Motoki, Shin, Moonshik, Shkedy, Ziv, Shopsowitz, Kevin, Sinai, Sam, Skola, Dylan, Smirnov, Petr, Soerensen, Izel Fourie, Soerensen, Peter, Song, Je-Hoon, Song, Sang Ok, Soufan, Othman, Spitzmueller, Andreas, Steipe, Boris, Suphavilai, Chayaporn, Tamayo, Sergio Pulido, Tamborero, David, Tang, Jing, Tanoli, Zia-ur-Rehman, Tarres-Deulofeu, Marc, Tegner, Jesper, Thommesen, Liv, Tonekaboni, Seyed Ali Madani, Tran, Hong, De Troyer, Ewoud, Truong, Amy, Tsunoda, Tatsuhiko, Turu, Gabor, Tzeng, Guang-Yo, Verbeke, Lieven, Videla, Santiago, Vis, Daniel, Voronkov, Andrey, Votis, Konstantinos, Wang, Ashley, Wang, Hong-Qiang Horace, Wang, Po-Wei, Wang, Sheng, Wang, Wei, Wang, Xiaochen, Wang, Xin, Wennerberg, Krister, Wernisch, Lorenz, Wessels, Lodewyk, van Westen, Gerard J. 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[0000-0003-0267-5792], Mason, Mike J. [0000-0002-5652-7739], Yu, Thomas [0000-0002-5841-0198], Kang, Jaewoo [0000-0001-6798-9106], Nguyen, Tin [0000-0001-8001-9470], Ahsen, Mehmet Eren [0000-0002-4907-0427], Stolovitzky, Gustavo [0000-0002-9618-2819], Guinney, Justin [0000-0003-1477-1888], Saez-Rodriguez, Julio [0000-0002-8552-8976], Apollo - University of Cambridge Repository, Menden, Michael P [0000-0003-0267-5792], Mason, Mike J [0000-0002-5652-7739], Pathology, CCA - Cancer biology and immunology, Medical oncology laboratory, Neurosurgery, Chemistry and Pharmaceutical Sciences, AIMMS, Medicinal chemistry, Universidade do Minho, Department of Computer Science, Professorship Marttinen P., Aalto-yliopisto, and Aalto University

Subjects
Drug Resistance, 02 engineering and technology, 13, PATHWAY, Antineoplastic Combined Chemotherapy Protocols, Molecular Targeted Therapy, Càncer, lcsh:Science, media_common, Cancer, Tumor, Settore BIO/18, Settore BIO/11, Drug combinations, High-throughput screening, Drug Synergism, purl.org/becyt/ford/1.2 [https], Genomics, Machine Learning, predictions, 3. Good health, ddc, Technologie de l'environnement, contrôle de la pollution, Benchmarking, 5.1 Pharmaceuticals, Cancer treatment, Farmacogenètica, Science & Technology - Other Topics, Development of treatments and therapeutic interventions, 0210 nano-technology, Human, Drug, media_common.quotation_subject, Science, 49/23, ADAM17 Protein, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, SDG 3 - Good Health and Well-being, RESOURCE, Machine learning, Genetics, Chimie, Humans, BREAST-CANCER, CELL, 49/98, Science & Technology, Antineoplastic Combined Chemotherapy Protocol, 45, MUTATIONS, Computational Biology, Androgen receptor, Breast-cancer, Gene, Cell, Inhibition, Resistance, Pathway, Mutations, Landscape, Resource, 631/114/1305, medicine.disease, Drug synergy, 49, 030104 developmental biology, Pharmacogenetics, Mutation, Ciências Médicas::Biotecnologia Médica, lcsh:Q, 631/154/1435/2163, Biomarkers, RESISTANCE, 0301 basic medicine, ING-INF/06 - BIOINGEGNERIA ELETTRONICA E INFORMATICA, Statistical methods, Computer science, General Physics and Astronomy, Datasets as Topic, Drug resistance, purl.org/becyt/ford/1 [https], Phosphatidylinositol 3-Kinases, Biotecnologia Médica [Ciências Médicas], Neoplasms, Science and technology, Phosphoinositide-3 Kinase Inhibitors, Multidisciplinary, Biomarkers, Tumor, Cell Line, Tumor, Drug Antagonism, Drug Resistance, Neoplasm, Treatment Outcome, Pharmacogenetic, article, ANDROGEN RECEPTOR, 49/39, 631/114/2415, 021001 nanoscience & nanotechnology, 692/4028/67, Multidisciplinary Sciences, 317 Pharmacy, Patient Safety, Systems biology, 3122 Cancers, INHIBITION, Computational biology, Cell Line, medicine, LANDSCAPE, Physique, Human Genome, Data Science, General Chemistry, AstraZeneca-Sanger Drug Combination DREAM Consortium, Astronomie, GENE, Good Health and Well Being, Pharmacogenomics, Genomic, Neoplasm, 631/553, Phosphatidylinositol 3-Kinase
Abstract

PubMed: 31209238, The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. © 2019, The Author(s)., National Institute for Health Research, NIHR Wellcome Trust, WT: 102696, 206194 Magyar Tudományos Akadémia, MTA Bayer 668858 PrECISE AstraZeneca, We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194)., Competing interests: K.C.B., Z.G., G.Y.D., E.K.Y.T., S.F., and J.R.D. are AstraZeneca employees. K.C.B., Z.G., E.K.Y.T., S.F., and J.R.D. are AstraZeneca shareholders. Y.G. receives personal compensation from Eli Lilly and Company, is a shareholder of Cleerly, Inc., and Ann Arbor Algorithms, Inc. M.G. receives research funding from AstraZeneca and has performed consultancy for Sanofi. The remaining authors declare no competing interests.

Published
2019
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12. A genome-wide association analysis of temozolomide response using lymphoblastoid cell lines shows a clinically relevant association with MGMT.

Author

Brown, Chad C., Havener, Tammy M., Medina, Marisa W., Auman, J. Todd, Mangravite, Lara M., Krauss, Ronald M., Mcleod, Howard L., and Motsinger-Reif, Alison A.

Abstract

Recently, lymphoblastoid cell lines (LCLs) have emerged as an innovative model system for mapping gene variants that predict the dose response to chemotherapy drugs.In the current study, this strategy was expanded to the in-vitro genome-wide association approach, using 516 LCLs derived from a White cohort to assess the cytotoxic response to temozolomide.Genome-wide association analysis using ∼2.1 million quality-controlled single-nucleotide polymorphisms (SNPs) identified a statistically significant association (P<10-8) with SNPs in the O6-methylguanine-DNA methyltransferase (MGMT) gene. We also show that the primary SNP in this region is significantly associated with the differential gene expression of MGMT (P<10-26) in LCLs and differential methylation in glioblastoma samples from The Cancer Genome Atlas.The previously documented clinical and functional relationships between MGMT and temozolomide response highlight the potential of well-powered genome-wide association studies of the LCL model system to identify meaningful genetic associations. [ABSTRACT FROM AUTHOR]

Published
2012
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