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169 results on '"Menden, Michael P."'

3. The Oncology Biomarker Discovery framework reveals cetuximab and bevacizumab response patterns in metastatic colorectal cancer

Author

Ohnmacht, Alexander J., Stahler, Arndt, Stintzing, Sebastian, Modest, Dominik P., Holch, Julian W., Westphalen, C. Benedikt, Hölzel, Linus, Schübel, Marisa K., Galhoz, Ana, Farnoud, Ali, Ud-Dean, Minhaz, Vehling-Kaiser, Ursula, Decker, Thomas, Moehler, Markus, Heinig, Matthias, Heinemann, Volker, and Menden, Michael P.

Published
2023
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6. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.

Author

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, AstraZeneca-Sanger Drug Combination DREAM Consortium, Jang, In Sock, Ghazoui, Zara, Ahsen, Mehmet Eren, Vogel, Robert, Neto, Elias Chaibub, Norman, Thea, Tang, Eric KY, Garnett, Mathew J, Veroli, Giovanni Y Di, Fawell, Stephen, Stolovitzky, Gustavo, Guinney, Justin, Dry, Jonathan R, and Saez-Rodriguez, Julio

Subjects
AstraZeneca-Sanger Drug Combination DREAM Consortium, Cell Line, Tumor, Humans, Neoplasms, Antineoplastic Combined Chemotherapy Protocols, Treatment Outcome, Computational Biology, Genomics, Pharmacogenetics, Drug Antagonism, Drug Synergism, Drug Resistance, Neoplasm, Mutation, Benchmarking, Phosphatidylinositol 3-Kinases, Molecular Targeted Therapy, Datasets as Topic, Biomarkers, Tumor, ADAM17 Protein, Phosphoinositide-3 Kinase Inhibitors, Genetics, Cancer, Human Genome, Patient Safety, 5.1 Pharmaceuticals
Abstract

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.

Published
2019

7. Infection Control Measures and Prevalence of SARS-CoV-2 IgG among 4,554 University Hospital Employees, Munich, Germany

Author

Erber, Johanna, Kappler, Verena, Haller, Bernhard, Mijocevic, Hrvoje, Galhoz, Ana, da Costa, Clarissa Prazeres, Gebhardt, Friedemann, Graf, Natalia, Hoffmann, Dieter, Thaler, Markus, Lorenz, Elke, Roggendorf, Hedwig, Kohlmayer, Florian, Henkel, Andreas, Menden, Michael P., Ruland, Jurgen, Spinner, Christoph D., Protzer, Ulrike, Knolle, Percy, and Lingor, Paul

Subjects
Infection control -- Methods, Hospitals, University -- Safety and security measures, Medical personnel -- Health aspects, Health
Abstract

Healthcare workers (HCWs) are exposed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the private context, as well as professionally with varying exposure risk depending on their workplace. Prevalence [...]

Published
2022
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9. A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines

Author

Gönen, Mehmet, Weir, Barbara A, Cowley, Glenn S, Vazquez, Francisca, Guan, Yuanfang, Jaiswal, Alok, Karasuyama, Masayuki, Uzunangelov, Vladislav, Wang, Tao, Tsherniak, Aviad, Howell, Sara, Marbach, Daniel, Hoff, Bruce, Norman, Thea C, Airola, Antti, Bivol, Adrian, Bunte, Kerstin, Carlin, Daniel, Chopra, Sahil, Deran, Alden, Ellrott, Kyle, Gopalacharyulu, Peddinti, Graim, Kiley, Kaski, Samuel, Khan, Suleiman A, Newton, Yulia, Ng, Sam, Pahikkala, Tapio, Paull, Evan, Sokolov, Artem, Tang, Hao, Tang, Jing, Wennerberg, Krister, Xie, Yang, Zhan, Xiaowei, Zhu, Fan, Community, Broad-DREAM, Afsari, Bahman, Aittokallio, Tero, Boehm, Jesse S, Chang, Yu-Chuan, Chen, Tenghui, Chong, Zechen, Elmarakeby, Haitham, Fertig, Elana J, Gonçalves, Emanuel, Gong, Pinghua, Hafemeister, Christoph, Hahn, William C, Heath, Lenwood, Kędziorski, Łukasz, Khemka, Niraj, King, Erh-kan, Lauria, Mario, Liu, Mark, Machado, Daniel, Mamitsuka, Hiroshi, Margolin, Adam A, Mazurkiewicz, Mateusz, Menden, Michael P, Migacz, Szymon, Nie, Zhi, Praveen, Paurush, Priami, Corrado, Rizzetto, Simone, Rocha, Miguel, Root, David E, Rudd, Cameron, Rudnicki, Witold R, Saez-Rodriguez, Julio, Song, Lei, Stolovitzky, Gustavo, Stuart, Joshua M, Sun, Duanchen, and Szalai, Bence

Subjects
Biological Sciences, Genetics, Biotechnology, Prevention, Cancer, Human Genome, Algorithms, Cell Line, Tumor, Gene Expression, Genes, Essential, Genomics, Humans, RNA, Small Interfering, Broad-DREAM Community, cancer genomics, community challenge, crowdsourcing, functional screen, machine learning, oncogene, Biochemistry and Cell Biology, Biochemistry and cell biology
Abstract

We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.

Published
2017

10. Spatial transcriptomics reveals altered lipid metabolism and inflammation-related gene expression of sebaceous glands in psoriasis and atopic dermatitis

Author

Seiringer, Peter, primary, Hillig, Christina, additional, Schäbitz, Alexander, additional, Jargosch, Manja, additional, Pilz, Anna Caroline, additional, Eyerich, Stefanie, additional, Szegedi, Andrea, additional, Sochorová, Michaela, additional, Gruber, Florian, additional, Zouboulis, Christos C., additional, Biedermann, Tilo, additional, Menden, Michael P., additional, Eyerich, Kilian, additional, and Törőcsik, Daniel, additional

Published
2024
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13. CXCL17 induces activation of human mast cells via MRGPRX2.

Author

Ding, Jie, Hillig, Christina, White, Carl W., Fernandopulle, Nithya A., Anderton, Holly, Kern, Johannes S., Menden, Michael P., and Mackay, Graham A.

Subjects
TRYPTASE, MAST cells, ITCHING, MYELOID-derived suppressor cells, G protein coupled receptors, REGULATORY T cells, ANTIMICROBIAL peptides
Abstract

This article discusses the activation of mast cells (MCs) through the Mas-related G protein-coupled receptor X2 (MRGPRX2) pathway. The study focuses on the chemokine CXCL17, which is expressed in mucosal tissues and has antimicrobial properties. The researchers found that CXCL17 activates human MCs via the MRGPRX2 pathway, and this activation may be important in inflammatory conditions such as psoriasis. The study also provides evidence of increased expression of CXCL17 in psoriatic skin, particularly in areas proximal to MRGPRX2-positive MCs. Further research is needed to understand the role of CXCL17-induced MC activation in psoriasis and other inflammatory skin diseases. [Extracted from the article]

Published
2024
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17. Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines

Author

Guo, Tiannan, Luna, Augustin, Rajapakse, Vinodh N., Koh, Ching Chiek, Wu, Zhicheng, Liu, Wei, Sun, Yaoting, Gao, Huanhuan, Menden, Michael P., Xu, Chao, Calzone, Laurence, Martignetti, Loredana, Auwerx, Chiara, Buljan, Marija, Banaei-Esfahani, Amir, Ori, Alessandro, Iskar, Murat, Gillet, Ludovic, Bi, Ran, Zhang, Jiangnan, Zhang, Huanhuan, Yu, Chenhuan, Zhong, Qing, Varma, Sudhir, Schmitt, Uwe, Qiu, Peng, Zhang, Qiushi, Zhu, Yi, Wild, Peter J., Garnett, Mathew J., Bork, Peer, Beck, Martin, Liu, Kexin, Saez-Rodriguez, Julio, Elloumi, Fathi, Reinhold, William C., Sander, Chris, Pommier, Yves, and Aebersold, Ruedi

Published
2019
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18. Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties

Author

Menden, Michael P., Iorio, Francesco, Garnett, Mathew, McDermott, Ultan, Benes, Cyril, Ballester, Pedro J., and Saez-Rodriguez, Julio

Subjects
Quantitative Biology - Genomics, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Learning, Quantitative Biology - Cell Behavior
Abstract

Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC50 values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC50 values in a 8-fold cross-validation and an independent blind test with coefficient of determination R2 of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R2 of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC50 values rather than experimentally measure them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity., Comment: 26 pages, 7 figures, including supplemental information, presented by Michael Menden at the 5th annual RECOMB Conference on Regulatory and Systems Genomics with DREAM Challenges; accepted in PLOS ONE

Published
2012
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19. Refining first-line treatment decision in RAS wildtype (RAS-WT) metastatic colorectal cancer (mCRC) by combining clinical biomarkers: Results of the randomized phase 3 trial FIRE-3 (AIO KRK0306).

Author

Holch, Julian Walter, Ohnmacht, Alexander, Stintzing, Sebastian, Heinrich, Kathrin, Westphalen, Benedikt, Weiss, Lena, von Weikersthal, Ludwig, Decker, Thomas, Kiani, Alexander, Kaiser, Florian, Heintges, Tobias, Kahl, Christoph, Kullmann, Frank, Scheithauer, Werner, Link, Hartmut, Hoeffkes, Heinz-Gert, Moehler, Markus H., Modest, Dominik Paul, Menden, Michael, and Heinemann, Volker

Published
2024
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22. Nfe2l1-mediated proteasome function controls muscle energy metabolism in obesity

Author

Lemmer, Imke L., primary, Haas, Daniel T., additional, Willemsen, Nienke, additional, Kotschi, Stefan, additional, Toksoez, Irmak, additional, Gjika, Ejona, additional, Khani, Sajjad, additional, Rohm, Maria, additional, Diercksen, Nick, additional, Nguyen, Phong B.H., additional, Menden, Michael P., additional, Egu, Desalegn T., additional, Waschke, Jens, additional, Larsen, Steen, additional, Ma, Tao, additional, Gerhart-Hines, Zachary, additional, Herzig, Stephan, additional, Dyar, Kenneth, additional, Krahmer, Natalie, additional, and Bartelt, Alexander, additional

Published
2023
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23. Suppression of ferroptosis by vitamin A or antioxidants is essential for neuronal development

Author

Tschuck, Juliane, primary, Padmanabhan Nair, Vidya, additional, Galhoz, Ana, additional, Ciceri, Gabriele, additional, Rothenaigner, Ina, additional, Tchieu, Jason, additional, Tai, Hin-Man, additional, Stockwell, Brent R., additional, Studer, Lorenz, additional, Menden, Michael P., additional, Vincendeau, Michelle, additional, and Hadian, Kamyar, additional

Published
2023
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24. Prediction of polyneuropathy in recent-onset diabetes: A machine learning algorithm using blood-based protein biomarkers and standard demographic and clinical features

Author

Maalmi, Haifa, additional, Nguyen, Phong BH, additional, Strom, Alexander, additional, Zaharia, Oana Patricia, additional, Straßburger, Klaus, additional, Bönhof, Gidon J., additional, Rathmann, Wolfgang, additional, Trenkamp, Sandra, additional, Burkart, Volker, additional, Szendrödi, Julia, additional, Menden, Michael P, additional, Ziegler, Dan, additional, Roden, Michael, additional, and Herder, Christian, additional

Published
2023
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25. Generative artificial intelligence empowers digital twins in drug discovery and clinical trials

Author

Bordukova, Maria, Makarov, Nikita, Rodriguez-Esteban, Raul, Schmich, Fabian, and Menden, Michael P.

Abstract

ABSTRACTIntroductionThe concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug discovery and development by digitalizing processes associated with high economic, ethical, or social burden. The impact is multifaceted: DT models sharpen disease understanding, support biomarker discovery and accelerate drug development, thus advancing precision medicine. One way to realize DTs is by generative artificial intelligence (AI), a cutting-edge technology that enables the creation of novel, realistic and complex data with desired properties.Areas coveredThe authors provide a brief introduction to generative AI and describe how it facilitates the modeling of DTs. In addition, they compare existing implementations of generative AI for DTs in drug discovery and clinical trials. Finally, they discuss technical and regulatory challenges that should be addressed before DTs can transform drug discovery and clinical trials.Expert opinionThe current state of DTs in drug discovery and clinical trials does not exploit the entire power of generative AI yet and is limited to simulation of a small number of characteristics. Nonetheless, generative AI has the potential to transform the field by leveraging recent developments in deep learning and customizing models for the needs of scientists, physicians and patients.

Published
2024
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27. WT1 and DNMT3A play essential roles in the growth of certain patient AML cells in mice

Author

Ghalandary, Maryam, primary, Gao, Yuqiao, additional, Amend, Diana, additional, Kutkaite, Ginte, additional, Vick, Binje, additional, Spiekermann, Karsten, additional, Rothenberg-Thurley, Maja, additional, Metzeler, Klaus H., additional, Marcinek, Anetta, additional, Subklewe, Marion, additional, Menden, Michael P., additional, Jurinovic, Vindi, additional, Bahrami, Ehsan, additional, and Jeremias, Irmela, additional

Published
2023
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28. Numerical and Machine Learning Analysis of the Parameters Affecting the Regionally Delivered Nasal Dose of Nano- and Micro-Sized Aerosolized Drugs

Author

Farnoud, Ali, primary, Tofighian, Hesam, additional, Baumann, Ingo, additional, Ahookhosh, Kaveh, additional, Pourmehran, Oveis, additional, Cui, Xinguang, additional, Heuveline, Vincent, additional, Song, Chen, additional, Vreugde, Sarah, additional, Wormald, Peter-John, additional, Menden, Michael P., additional, and Schmid, Otmar, additional

Published
2023
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29. Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin

Author

Keutzer, Lina, You, Huifang, Farnoud, Ali, Nyberg, Joakim, Wicha, Sebastian G, Maher-Edwards, Gareth, Vlasakakis, Georgios, Moghaddam, Gita Khalili, Svensson, Elin M, Menden, Michael P, Simonsson, Ulrika SH, Consortium, On Behalf Of The Unite Tb, You, Huifang [0000-0003-1497-9136], Farnoud, Ali [0000-0002-9298-5497], Vlasakakis, Georgios [0000-0002-7390-9712], Moghaddam, Gita Khalili [0000-0003-2099-8026], Menden, Michael P [0000-0003-0267-5792], Simonsson, Ulrika SH [0000-0002-3424-9686], and Apollo - University of Cambridge Repository

Subjects
machine learning, feature selection, population pharmacokinetics, rifampicin, simulation, pharmacokinetics, pharmacometrics
Abstract

Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but offer less mechanistic insights. The opportunity of using ML predictions of drug PK as input for a PKPD model could strongly accelerate analysis efforts. Here exemplified by rifampicin, a widely used antibiotic, we explore the ability of different ML algorithms to predict drug PK. Based on simulated data, we trained linear regressions (LASSO), Gradient Boosting Machines, XGBoost and Random Forest to predict the plasma concentration-time series and rifampicin area under the concentration-versus-time curve from 0-24 h (AUC0-24h) after repeated dosing. XGBoost performed best for prediction of the entire PK series (R2: 0.84, root mean square error (RMSE): 6.9 mg/L, mean absolute error (MAE): 4.0 mg/L) for the scenario with the largest data size. For AUC0-24h prediction, LASSO showed the highest performance (R2: 0.97, RMSE: 29.1 h·mg/L, MAE: 18.8 h·mg/L). Increasing the number of plasma concentrations per patient (0, 2 or 6 concentrations per occasion) improved model performance. For example, for AUC0-24h prediction using LASSO, the R2 was 0.41, 0.69 and 0.97 when using predictors only (no plasma concentrations), 2 or 6 plasma concentrations per occasion as input, respectively. Run times for the ML models ranged from 1.0 s to 8 min, while the run time for the PM model was more than 3 h. Furthermore, building a PM model is more time- and labor-intensive compared with ML. ML predictions of drug PK could thus be used as input into a PKPD model, enabling time-efficient analysis.

Published
2022
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31. Multi‐omic landscaping of human midbrains identifies disease‐relevant molecular targets and pathways in advanced‐stage Parkinson's disease

Author

Caldi Gomes, Lucas, primary, Galhoz, Ana, additional, Jain, Gaurav, additional, Roser, Anna‐Elisa, additional, Maass, Fabian, additional, Carboni, Eleonora, additional, Barski, Elisabeth, additional, Lenz, Christof, additional, Lohmann, Katja, additional, Klein, Christine, additional, Bähr, Mathias, additional, Fischer, André, additional, Menden, Michael P., additional, and Lingor, Paul, additional

Published
2022
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32. Pharmacogenomic agreement between two cancer cell line data sets

Author

Stransky, Nicolas, Ghandi, Mahmoud, Kryukov, Gregory V., Garraway, Levi A., Lehár, Joseph, Liu, Manway, Sonkin, Dmitriy, Kauffmann, Audrey, Venkatesan, Kavitha, Edelman, Elena J., Riester, Markus, Barretina, Jordi, Caponigro, Giordano, Schlegel, Robert, Sellers, William R., Stegmeier, Frank, Morrissey, Michael, Amzallag, Arnaud, Pruteanu-Malinici, Iulian, Haber, Daniel A., Ramaswamy, Sridhar, Benes, Cyril H., Menden, Michael P., Iorio, Francesco, Stratton, Michael R., McDermott, Ultan, Garnett, Mathew J., and Saez-Rodriguez, Julio

Published
2015
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35. Multi-omic landscaping of human midbrains identifies neuroinflammation as major disease mechanism in advanced-stage Parkinson’s disease

Author

Gomes, Lucas Caldi, primary, Galhoz, Ana, additional, Jain, Gaurav, additional, Roser, Anna-Elisa, additional, Maass, Fabian, additional, Carboni, Eleonora, additional, Barski, Elisabeth, additional, Lenz, Christof, additional, Lohmann, Katja, additional, Klein, Christine, additional, Bähr, Mathias, additional, Fischer, André, additional, Menden, Michael P., additional, and Lingor, Paul, additional

Published
2021
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37. A statistical framework for assessing pharmacological response and biomarkers with confidence

Author

Wang, Dennis, Hensman, James, Kutkaite, Ginte, Toh, Tzen S., Dry, Jonathan R, Saez-Rodriguez, Julio, Garnett, Mathew J., Menden, Michael P., and Dondelinger, Frank

Abstract

Drug high-throughput screenings across large molecular-characterised cancer cell line panels enable the discovery of biomarkers, and thereby, cancer precision medicine. The ability to experimentally generate drug response data has accelerated. However, this data is typically quantified by a summary statistic from a best-fit dose response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage this uncertainty for identifying associated biomarkers with a new statistical framework based on Bayesian testing. Applied to the Genomics of Drug Sensitivity in Cancer, in vitro screening data on 265 compounds across 1,074 cell lines, our uncertainty models identified 24 clinically established drug response biomarkers, and in addition provided evidence for 6 novel biomarkers. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to drug high-throughput screens without replicates, and enables robust biomarker discovery for new cancer therapies.

Published
2020
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39. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

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 A., 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, Gonen, Mehmet, 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 T., 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. P., Westerman, Bart A., White, Simon Richard, Willighagen, Egon, Wurdinger, Tom, Xie, Lei, Xie, Shuilian, Xu, Hua, Yadav, Bhagwan, Yau, Christopher, Yeerna, Huwate, Yin, Jia Wei, Yu, Michael, Yu, MinHwan, Yun, So Jeong, Zakharov, Alexey, Zamichos, Alexandros, Zanin, Massimiliano, Zeng, Li, Zenil, Hector, Zhang, Frederick, Zhang, Pengyue, Zhang, Wei, Zhao, Hongyu, Zhao, Lan, Zheng, Wenjin, Zoufir, Azedine, Zucknick, Manuela, and Computer Science

Subjects
health care economics and organizations
Abstract

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. AstraZeneca European Union Horizon 2020 research [668858 PrECISE] Joint Research Center for Computational Biomedicine (Bayer AG) National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences Wellcome Trust [102696, 206194] 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).

Published
2019

41. Community assessment of cancer drug combination screens identifies strategies for synergy prediction

Author

Menden, Michael P, Wang, Dennis, Guan, Yuanfang, Mason, Michael, Szalai, Bence, Bulusu, Krishna C, Yu, Thomas, Kang, Jaewoo, Jeon, Minji, Wolfinger, Russ, Nguyen, Tin, Zaskavskiy, Mikhail, DREAM consortium, Jang, In Sock, Ghazoui, Zara, Ahsen, Mehmet Eren, Vogel, Robert, Neto, Elias Chaibub, Norman, Thea, Tang, Eric KY, Garnett, Matthew J, Di Veroli, Giovanni, Fawell, Steve, Stolovitzky, Gustavo, Guinney, Justin, Dry, Jonathan R., and Saez-Rodriguez, Julio

Subjects
Copy number events, genomics, gene expression, mutations
Abstract

In the last decade advances in genomics, uptake of targeted therapies, and the advent of personalized treatment has fueled a step change in cancer care. However the effectiveness of most targeted therapies is short lived, as tumors evolve and develop resistance.Combinations of drugs offer the potential to overcome resistance. The space of possible combinations is vast, and significant advances are required to effectively find optimal treatment regimens tailored to a patient’s tumor. DREAM and AstraZeneca hosted a challenge open to the scientific community aimed at computational prediction of synergistic drug combinations and associated predictive biomarkers. We released a data set comprising ~11,500 experimentally tested drug combinations, coupled to deep molecular characterization of the respective 85 cancer cell lines. Of 150 submitted approaches, methods that incorporated prior knowledge of putative drug targets outperformed other approaches in predicting drug synergy across independent data. Genomic features of winning models revealed putative mechanisms of drug synergy for multiple drugs in combination with PI3K/AKT pathway inhibitors.

Published
2017

46. Rapid proteotyping reveals cancer biology and drug response determinants in the NCI-60 cells

Author

Guo, Tiannan, primary, Luna, Augustin, additional, Rajapakse, Vinodh N, additional, Koh, Ching Chiek, additional, Wu, Zhicheng, additional, Menden, Michael P, additional, Cheng, Yongran, additional, Calzone, Laurence, additional, Martignetti, Loredana, additional, Ori, Alessandro, additional, Iskar, Murat, additional, Gillet, Ludovic, additional, Zhong, Qing, additional, Varma, Sudhir, additional, Schmitt, Uwe, additional, Qiu, Peng, additional, Sun, Yaoting, additional, Zhu, Yi, additional, Wild, Peter J, additional, Garnett, Mathew J, additional, Bork, Peer, additional, Beck, Martin, additional, Saez-Rodriguez, Julio, additional, Reinhold, William C., additional, Sander, Chris, additional, Pommier, Yves, additional, and Aebersold, Ruedi, additional

Published
2018
Full Text
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48. Consistency of drug profiles and predictors in large-scale cancer cell line data

Author

Stransky, Nicolas, Ghandi, Mahmoud, Kryukov, Gregory V., Garraway, Levi A., Amzallag, Arnaud, Pruteanu-Malinici, Iulian, Haber, Daniel A., Ramaswamy, Sridhar, Benes, Cyril H., Lehár, Joseph, Liu, Manway, Sonkin, Dmitriy, Kauffmann, Audrey, Venkatesan, Kavitha, Edelman, Elena J., Riester, Markus, Barretina, Jordi, Caponigro, Giordano, Schlegel, Robert, Sellers, William, Stegmeier, Frank, Morrissey, Michael, Menden, Michael P., Iorio, Francesco, Stratton, Michael R., McDermott, Ultan, Saez-Rodriguez, Julio, and Garnett, Mathew J.

Subjects
Inhibitory Concentration 50, Databases, Factual, Pharmacogenetics, Cell Line, Tumor, Neoplasms, Datasets as Topic, Humans, Reproducibility of Results, Article
Abstract

Large cancer cell line collections broadly capture the genomic diversity of human cancers and provide valuable insight into anti-cancer drug response. Here we show substantial agreement and biological consilience between drug sensitivity measurements and their associated genomic predictors from two publicly available large-scale pharmacogenomics resources: The Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer databases.

Published
2015

49. A community computational challenge to predict the activity of pairs of compounds

Author

Bansal, M, Yang, J, Karan, C, Menden, Mp, Costello, Jc, Tang, H, Xiao, G, Li, Y, Allen, J, Zhong, R, Chen, B, Kim, M, Wang, T, Heiser, Lm, Realubit, R, Mattioli, M, Alvarez, Mj, Shen, Y, Gallahan, D, Singer, D, Saez Rodriguez, J, Xie, Y, Stolovitzky, G, Califano, A, NCI DREAM Community: Jean Paul Abbuehl, NCI DREAM C. o. m. m. u. n. i. t. y., Jeffrey, Allen, Altman, Russ B., Shawn, Balcome, Mukesh, Bansal, Ana, Bell, Andreas, Bender, Bonnie, Berger, Jonathan, Bernard, Bieberich, Andrew A., Giorgos, Borboudakis, Andrea, Califano, Christina, Chan–, Beibei, Chen, Ting Huei Chen, Jaejoon, Choi, Luis Pedro Coelho, Costello, James C., Creighton, Chad J., Will, Dampier, Jo Davisson, V., Raamesh, Deshpande, Lixia, Diao, DI CAMILLO, Barbara, Murat, Dundar, Adam, Ertel, Cellworks, Group, Daniel, Gallahan, Goswami, Chirayu P., Assaf, Gottlieb, Gould, Michael N., Jonathan, Goya, Michael, Grau, Gray, Joe W., Heiser, Laura M., Hejase, Hussein A., Hoffmann, Michael F., Krisztian, Homicsko, Max, Homilius, Woochang, Hwang, Ijzerman, Adriaan P., Olli, Kallioniemi, Bilge, Karacali, Charles, Karan, Samuel, Kaski, Junho, Kim, Minsoo, Kim, Arjun, Krishnan, Junehawk, Lee, Young Suk Lee, Lenselink, Eelke B., Peter, Lenz, Lang, Li, Jun, Li, Yajuan, Li, Han, Liang, Michela, Mattioli, Menden, Michael P., John Patrick Mpindi, Myers, Chad L., Newton, Michael A., Overington, John P., Juuso, Parkkinen, Prill, Robert J., Jian, Peng, Richard, Pestell, Peng, Qiu, Bartek, Rajwa, Ronald, Realubit, Anguraj, Sadanandam, Julio Saez Rodriguez, Sambo, Francesco, Dinah, Singer, Gustavo, Stolovitzky, Arvind, Sridhar, Wei, Sun, Hao, Tang, Toffolo, GIANNA MARIA, Aydin, Tozeren, Troyanskaya, Olga G., Ioannis, Tsamardinos, van Vlijmen, Herman W. T., Tao, Wang, Wen, Wang, Wegner, Joerg K., Krister, Wennerberg, van Westen, Gerard J. P., Tian, Xia, Guanghua, Xiao, Yang, Xie, Jichen, Yang, Yang, Yang, Victoria, Yao, Yuan, Yuan, Haoyang, Zeng, Shihua, Zhang, Junfei, Zhao, Jian, Zhou, Rui, Zhong, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Zeng, Haoyang, TR11527, Karaçalı, Bilge, and Izmir Institute of Technology. Electronics and Communication Engineering

Subjects
Computer science, In silico, Synergistic combinations, Biomedical Engineering, Bioengineering, Computational biology, Bioinformatics, Applied Microbiology and Biotechnology, Article, Drug synergism, Multiple time, Humans, Computer Simulation, Computational challenges, B-Lymphocytes, Extramural, Drug combinations, Rank (computer programming), Drug Synergism, Scoring metrics, Drug Combinations, Molecular Medicine, Gene expression, Algorithms, Forecasting, Biotechnology
Abstract

Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction., Multiscale Analysis of Genomic and Cellular Networks (MAGNet 5U54CA121852-08); Library of Integrated Network-based Cellular Signatures Program (LINCS 1U01CA164184-02--3U01HL111566-02); National Institutes of Health (NIH 5R01CA152301); Cancer Prevention and Research Institute of Texas (CPRIT RP101251); NIH, NCI (U54 CA112970)

Published
2014

50. A community effort to assess and improve drug sensitivity prediction algorithms

Author

Costello, James C, Heiser, Laura M, Georgii, Elisabeth, Gönen, Mehmet, Menden, Michael P, Wang, Nicholas J, Bansal, Mukesh, Ammad-ud-din, Muhammad, Hintsanen, Petteri, Khan, Suleiman A, Mpindi, John-Patrick, Kallioniemi, Olli, Honkela, Antti, Aittokallio, Tero, Wennerberg, Krister, Abbuehl, Jean-Paul, Allen, Jeffrey, Altman, Russ B, Balcome, Shawn, Battle, Alexis, Bender, Andreas, Berger, Bonnie, Bernard, Jonathan, Bhattacharjee, Madhuchhanda, Bhuvaneshwar, Krithika, Bieberich, Andrew A, Boehm, Fred, Califano, Andrea, Chan, Christina, Chen, Beibei, Chen, Ting-Huei, Choi, Jaejoon, Coelho, Luis Pedro, Cokelaer, Thomas, Collins, James C, Creighton, Chad J, Cui, Jike, Dampier, Will, Davisson, V Jo, De Baets, Bernard, Deshpande, Raamesh, DiCamillo, Barbara, Dundar, Murat, Duren, Zhana, Ertel, Adam, Fan, Haoyang, Fang, Hongbin, Gallahan, Dan, Gauba, Robinder, Gottlieb, Assaf, Grau, Michael, Gray, Joe W, Gusev, Yuriy, Ha, Min Jin, Han, Leng, Harris, Michael, Henderson, Nicholas, Hejase, Hussein A, Homicsko, Krisztian, Hou, Jack P, Hwang, Woochang, IJzerman, Adriaan P, Karacali, Bilge, Kaski, Samuel, Keles, Sunduz, Kendziorski, Christina, Kim, Junho, Kim, Min, Kim, Youngchul, Knowles, David A, Koller, Daphne, Lee, Junehawk, Lee, Jae K, Lenselink, Eelke B, Li, Biao, Li, Bin, Li, Jun, Liang, Han, Ma, Jian, Madhavan, Subha, Mooney, Sean, Myers, Chad L, Newton, Michael A, Overington, John P, Pal, Ranadip, Peng, Jian, Pestell, Richard, Prill, Robert J, Qiu, Peng, Rajwa, Bartek, Sadanandam, Anguraj, Saez-Rodriguez, Julio, Sambo, Francesco, Shin, Hyunjin, Singer, Dinah, Song, Jiuzhou, Song, Lei, Sridhar, Arvind, Stock, Michiel, Stolovitzky, Gustavo, Sun, Wei, Ta, Tram, Tadesse, Mahlet, Tan, Ming, Tang, Hao, Theodorescu, Dan, Toffolo, Gianna Maria, Tozeren, Aydin, Trepicchio, William, Varoquaux, Nelle, Vert, Jean-Philippe, Waegeman, Willem, Walter, Thomas, Wan, Qian, Wang, Difei, Wang, Wen, Wang, Yong, Wang, Zhishi, Wegner, Joerg K, Wu, Tongtong, Xia, Tian, Xiao, Guanghua, Xie, Yang, Xu, Yanxun, Yang, Jichen, Yuan, Yuan, Zhang, Shihua, Zhang, Xiang-Sun, Zhao, Junfei, Zuo, Chandler, van Vlijmen, Herman W T, van Westen, Gerard J P, Collins, James J, National Centre for Plasma Science and Technology (NCPST), Dublin City University [Dublin] ( DCU ), Institut Lumière Matière [Villeurbanne] ( ILM ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Mécanique des Contacts et des Structures [Villeurbanne] ( LaMCoS ), Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ), Helsinki Institute for Information Technology, University of Westminster [London] ( UOW ), Stanford Center for BioMedical Informatics Research ( BMIR ), Stanford University [Stanford], DSTO, Equipe NEMESIS - Centre de Recherches de l'Institut du Cerveau et de la Moelle épinière ( NEMESIS-CRICM ), Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière ( CRICM ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland, Brisbane, QLD 4072, Nanjing University of Information Science & Technology, Department of Physics [Stockholm], Stockholm University, Chercheur indépendant, Instituto de Engenharia de Sistemas e Computadores ( INESC ), European Bioinformatics Institute [Hinxton] ( EMBL-EBI ), European Molecular Biology Laboratory [Hinxton], Department of Agronomy, Tianjin Agricultural University ( TJAU ), Medicine Faculty, Erciyes University, Faculty of anima Medicine l, Northeast Agricultural University [Harbin], Oxford e-Research Centre [Oxford], University of Oxford [Oxford], Department of Computer Science [Alabama], University of Alabama [Tuscaloosa] ( UA ), Roberval, Université de Technologie de Compiègne ( UTC ), Institut de Mathématiques de Jussieu ( IMJ ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Centre for Inflammation Research, University of Edinburgh-Queen's Medical Research Institute, Division of Medicinal Chemistry, Universiteit Leiden [Leiden], University of Pittsburgh [Pittsburg], University of Pittsburgh, Department of Chemistry and Nano Science, EWHA Womans University ( EWHA ), Institute of Materials Chemistry, Technical University of Vienna [Vienna] ( TU WIEN ), Leiden Academic Center for Drug Research, Peking Univ, Coll Urban & Environm Sci, Lab Earth Surface Proc, Beijing 100871, Peoples R China, Queensland Research Lab, National ICT Australia [Sydney] ( NICTA ), Institut des Systèmes Intelligents et de Robotique ( ISIR ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS ), Centre for Plant Integrative Biology [Nothingham] ( CPIB ), University of Nottingham, UK ( UON ), Department of Electrical and Computer Engineering, University of Utah, SUN Yatsen University, Sidney Kimmel Cancer Center, Jefferson (Philadelphia University + Thomas Jefferson University), Molecular Carcinogenesis [Sutton], Institute of cancer research, Centre d'études et de recherches appliquées à la gestion (Grenoble), Centre d'études et de recherches appliquées à la gestion ( CERAG ), Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Grenoble Alpes ( UGA ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Grenoble Alpes ( UGA ), China Meteorological Administration, Equipe de Recherche Interdisciplinaire sur le Tourisme ( IUKB ), Institut Universitaire Kurt Bösch, University of Leeds, Dipartimento di Chimica, Università degli Studi di Roma 'La Sapienza' [Rome], Centre d'élaboration de matériaux et d'études structurales ( CEMES ), Institut National des Sciences Appliquées - Toulouse ( INSA Toulouse ), Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Toulouse III - Paul Sabatier ( UPS ), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -INSTITUT CURIE, Centre de Bioinformatique ( CBIO ), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University ( PSL ), Computer Graphics Group, Department of Computer Science [Hong Kong], City University of Hong Kong [Hong Kong] ( CUHK ) -City University of Hong Kong [Hong Kong] ( CUHK ), Ingénierie Moléculaire et Physiopathologie Articulaire ( IMoPA ), Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Computing [London], Biomedical Image Analysis Group [London] ( BioMedIA ), Imperial College London-Imperial College London, State Key Laboratory of Virology, Wuhan University [China], MoNOS, Huygens Laboratory, Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China ( IMP ), University of Science and Technology Beijing [Beijing] ( USTB ), Laboratoire de Chimie Physique - Matière et Rayonnement ( LCPMR ), Dalian University of Technology, Chalmers University of Technology [Göteborg], Advanced Photon Source [ANL] ( APS ), Argonne National Laboratory [Lemont] ( ANL ) -University of Chicago-US Department of Energy, Beth Israel Deaconess Medical Center, Harvard Medical School [Boston] ( HMS ), Dublin City University [Dublin] (DCU), Helsinki Institute for Information Technology (HIIT), Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Aalto University, University of Westminster [London] (UOW), Stanford Center for BioMedical Informatics Research (BMIR), Stanford University, Equipe NEMESIS - Centre de Recherches de l'Institut du Cerveau et de la Moelle épinière (NEMESIS-CRICM), Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière (CRICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institute for Molecular Bioscience, University of Queensland [Brisbane], Nanjing University of Information Science and Technology (NUIST), Instituto de Engenharia de Sistemas e Computadores (INESC), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Tianjin Agricultural University (TJAU), University of Oxford, University of Alabama [Tuscaloosa] (UA), Roberval (Roberval), Université de Technologie de Compiègne (UTC), University of Edinburgh-Queen's Medical Researche Institute, University of Edinburgh, Universiteit Leiden, University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), EWHA Womans University (EWHA), Vienna University of Technology (TU Wien), National ICT Australia [Sydney] (NICTA), Centre for Plant Integrative Biology [Nothingham] (CPIB), University of Nottingham, UK (UON), Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Bioinformatique (CBIO), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Computer Graphics Group [Hong Kong], City University of Hong Kong [Hong Kong] (CUHK)-City University of Hong Kong [Hong Kong] (CUHK), Biomedical Image Analysis Group [London] (BioMedIA), Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China (IMP), University of Science and Technology Beijing [Beijing] (USTB), Advanced Photon Source [ANL] (APS), Argonne National Laboratory [Lemont] (ANL)-University of Chicago-US Department of Energy, Harvard Medical School [Boston] (HMS), Aalto University-University of Helsinki, Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], MINES ParisTech - École nationale supérieure des mines de Paris, TR11527, Karaçalı, Bilge, and Izmir Institute of Technology. Electronics and Communication Engineering

Subjects
Epigenomics, Proteomics, Biological pathways, Inference, computer.software_genre, Genomic information, Applied Microbiology and Biotechnology, 0302 clinical medicine, Neoplasms, Computational models, Profiling (information science), ta518, [ SDV.BIBS ] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ta515, ComputingMilieux_MISCELLANEOUS, 0303 health sciences, Computational model, ta213, Genomics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, Gene Expression Regulation, Neoplastic, 030220 oncology & carcinogenesis, Molecular Medicine, Algorithms, Biotechnology, Data integration, Bayesian probability, Biomedical Engineering, Antineoplastic Agents, Bioengineering, Biology, Machine learning, Article, 03 medical and health sciences, Humans, 030304 developmental biology, ta113, ta112, Proteomic Profiling, business.industry, Gene Expression Profiling, Precision medicine, Drug Resistance, Neoplasm, ta5141, Gene expression, Artificial intelligence, business, computer, Forecasting
Abstract

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods., MaGNeT grant 5U54CA121852-08; National Institutes of Health, National Cancer Institute (U54 CA 112970); Stand Up To Cancer-American Association for Cancer Research Dream Team Translational Cancer Research (SU2C-AACR-DT0409); Prospect Creek Foundation; Howard Hughes Medical Institute (HHMI); Academy of Finland (Finnish Center of Excellence in Computational Inference Research COIN) (251170--140057)

Published
2014

Catalog

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