47 results on '"Menden, Michael P."'
Search Results
2. Loss of NEDD8 in cancer cells causes vulnerability to immune checkpoint blockade in triple-negative breast cancer
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Papakyriacou, Irineos, Kutkaite, Ginte, Rúbies Bedós, Marta, Nagarajan, Divya, Alford, Liam P., Menden, Michael P., and Mao, Yumeng
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- 2024
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3. The Oncology Biomarker Discovery framework reveals cetuximab and bevacizumab response patterns in metastatic colorectal cancer
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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.
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- 2023
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4. The pharmacoepigenomic landscape of cancer cell lines reveals the epigenetic component of drug sensitivity
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Ohnmacht, Alexander Joschua, Rajamani, Anantharamanan, Avar, Göksu, Kutkaite, Ginte, Gonçalves, Emanuel, Saur, Dieter, and Menden, Michael Patrick
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- 2023
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5. Oncogene-induced MALT1 protease activity drives posttranscriptional gene expression in malignant lymphomas
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Wimberger, Nicole, Ober, Franziska, Avar, Göksu, Grau, Michael, Xu, Wendan, Lenz, Georg, Menden, Michael P., and Krappmann, Daniel
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- 2023
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6. Infection Control Measures and Prevalence of SARS-CoV-2 IgG among 4,554 University Hospital Employees, Munich, Germany
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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
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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 [...]
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- 2022
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7. CXCL17 induces activation of human mast cells via MRGPRX2.
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Ding, Jie, Hillig, Christina, White, Carl W., Fernandopulle, Nithya A., Anderton, Holly, Kern, Johannes S., Menden, Michael P., and Mackay, Graham A.
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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]
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- 2024
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8. Identification of Intrinsic Drug Resistance and Its Biomarkers in High-Throughput Pharmacogenomic and CRISPR Screens
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Ayestaran, Iñigo, Galhoz, Ana, Spiegel, Elmar, Sidders, Ben, Dry, Jonathan R., Dondelinger, Frank, Bender, Andreas, McDermott, Ultan, Iorio, Francesco, and Menden, Michael P.
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- 2020
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9. Applying synergy metrics to combination screening data: agreements, disagreements and pitfalls
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Vlot, Anna H.C., Aniceto, Natália, Menden, Michael P., Ulrich-Merzenich, Gudrun, and Bender, Andreas
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- 2019
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10. Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines
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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
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- 2019
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11. Spatial transcriptomics reveals altered lipid metabolism and inflammation-related gene expression of sebaceous glands in psoriasis and atopic dermatitis.
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Seiringer, Peter, Hillig, Christina, Schäbitz, Alexander, Jargosch, Manja, Pilz, Anna Caroline, Eyerich, Stefanie, Szegedi, Andrea, Sochorová, Michaela, Gruber, Florian, Zouboulis, Christos C., Biedermann, Tilo, Menden, Michael P., Eyerich, Kilian, and Törőcsik, Daniel
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SEBACEOUS glands ,LIPID metabolism ,ATOPIC dermatitis ,TRANSCRIPTOMES ,GENE expression ,PSORIATIC arthritis - Abstract
Sebaceous glands drive acne, however, their role in other inflammatory skin diseases remains unclear. To shed light on their potential contribution to disease development, we investigated the spatial transcriptome of sebaceous glands in psoriasis and atopic dermatitis patients across lesional and nonlesional human skin samples. Both atopic dermatitis and psoriasis sebaceous glands expressed genes encoding key proteins for lipid metabolism and transport such as ALOX15B, APOC1, FABP7, FADS1/2, FASN, PPARG, and RARRES1. Also, inflammation-related SAA1 was identified as a common spatially variable gene. In atopic dermatitis, genes mainly related to lipid metabolism (e.g. ACAD8, FADS6, or EBP) as well as disease-specific genes, i.e., Th2 inflammation-related lipid-regulating HSD3B1 were differentially expressed. On the contrary, in psoriasis, more inflammation-related spatially variable genes (e.g. SERPINF1, FKBP5, IFIT1/3, DDX58) were identified. Other psoriasis-specific enriched pathways included lipid metabolism (e.g. ACOT4, S1PR3), keratinization (e.g. LCE5A, KRT5/7/16), neutrophil degranulation, and antimicrobial peptides (e.g. LTF, DEFB4A, S100A7-9). In conclusion, our results show that sebaceous glands contribute to skin homeostasis with a cell type-specific lipid metabolism, which is influenced by the inflammatory microenvironment. These findings further support that sebaceous glands are not bystanders in inflammatory skin diseases, but can actively and differentially modulate inflammation in a disease-specific manner. [ABSTRACT FROM AUTHOR]
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- 2024
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12. 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).
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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
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- 2024
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13. Stratification and prediction of drug synergy based on target functional similarity
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Yang, Mi, Jaaks, Patricia, Dry, Jonathan, Garnett, Mathew, Menden, Michael P., and Saez-Rodriguez, Julio
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- 2020
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14. Defining subpopulations of differential drug response to reveal novel target populations
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Keshava, Nirmal, Toh, Tzen S., Yuan, Haobin, Yang, Bingxun, Menden, Michael P., and Wang, Dennis
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- 2019
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15. The impact of the cardiovascular component and somatic mutations on ageing.
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Garger, Daniel, Meinel, Martin, Dietl, Tamina, Hillig, Christina, Garzorz‐Stark, Natalie, Eyerich, Kilian, de Angelis, Martin Hrabě, Eyerich, Stefanie, and Menden, Michael P.
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SOMATIC mutation ,HEART beat ,PHENOTYPES ,AGING - Abstract
Mechanistic insight into ageing may empower prolonging the lifespan of humans; however, a complete understanding of this process is still lacking despite a plethora of ageing theories. In order to address this, we investigated the association of lifespan with eight phenotypic traits, that is, litter size, body mass, female and male sexual maturity, somatic mutation, heart, respiratory, and metabolic rate. In support of the somatic mutation theory, we analysed 15 mammalian species and their whole‐genome sequencing deriving somatic mutation rate, which displayed the strongest negative correlation with lifespan. All remaining phenotypic traits showed almost equivalent strong associations across this mammalian cohort, however, resting heart rate explained additional variance in lifespan. Integrating somatic mutation and resting heart rate boosted the prediction of lifespan, thus highlighting that resting heart rate may either directly influence lifespan, or represents an epiphenomenon for additional lower‐level mechanisms, for example, metabolic rate, that are associated with lifespan. [ABSTRACT FROM AUTHOR]
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- 2023
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16. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
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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 K. Y., Garnett, Mathew J., Veroli, Giovanni Y. Di, Fawell, Stephen, Stolovitzky, Gustavo, Guinney, Justin, Dry, Jonathan R., and Saez-Rodriguez, Julio
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- 2019
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17. WT1 and DNMT3A play essential roles in the growth of certain patient AML cells in mice
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Ghalandary, Maryam, Gao, Yuqiao, Amend, Diana, Kutkaite, Ginte, Vick, Binje, Spiekermann, Karsten, Rothenberg-Thurley, Maja, Metzeler, Klaus H., Marcinek, Anetta, Subklewe, Marion, Menden, Michael P., Jurinovic, Vindi, Bahrami, Ehsan, and Jeremias, Irmela
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- 2023
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18. Numerical and Machine Learning Analysis of the Parameters Affecting the Regionally Delivered Nasal Dose of Nano- and Micro-Sized Aerosolized Drugs.
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Farnoud, Ali, Tofighian, Hesam, Baumann, Ingo, Ahookhosh, Kaveh, Pourmehran, Oveis, Cui, Xinguang, Heuveline, Vincent, Song, Chen, Vreugde, Sarah, Wormald, Peter-John, Menden, Michael P., and Schmid, Otmar
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MACHINE learning ,NASAL mucosa ,RESPIRATORY organs ,DRUG delivery systems ,PARTIAL differential equations ,OLFACTORY bulb - Abstract
The nasal epithelium is an important target for drug delivery to the nose and secondary organs such as the brain via the olfactory bulb. For both topical and brain delivery, the targeting of specific nasal regions such as the olfactory epithelium (brain) is essential, yet challenging. In this study, a numerical model was developed to predict the regional dose as mass per surface area (for an inhaled mass of 2.5 mg), which is the biologically most relevant dose metric for drug delivery in the respiratory system. The role of aerosol diameter (particle diameter: 1 nm to 30 µm) and inhalation flow rate (4, 15 and 30 L/min) in optimal drug delivery to the vestibule, nasal valve, olfactory and nasopharynx is assessed. To obtain the highest doses in the olfactory region, we suggest aerosols with a diameter of 20 µm and a medium inlet air flow rate of 15 L/min. High deposition on the olfactory epithelium was also observed for nanoparticles below 1 nm, as was high residence time (slow flow rate of 4 L/min), but the very low mass of 1 nm nanoparticles is prohibitive for most therapeutic applications. Moreover, high flow rates (30 L/min) and larger micro-aerosols lead to highest doses in the vestibule and nasal valve regions. On the other hand, the highest drug doses in the nasopharynx are observed for nano-aerosol (1 nm) and fine microparticles (1–20 µm) with a relatively weak dependence on flow rate. Furthermore, using the 45 different inhalation scenarios generated by numerical models, different machine learning models with five-fold cross-validation are trained to predict the delivered dose and avoid partial differential equation solvers for future predictions. Random forest and gradient boosting models resulted in R
2 scores of 0.89 and 0.96, respectively. The aerosol diameter and region of interest are the most important features affecting delivered dose, with an approximate importance of 42% and 47%, respectively. [ABSTRACT FROM AUTHOR]- Published
- 2023
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19. Pharmacogenomic agreement between two cancer cell line data sets
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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
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- 2015
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20. Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin.
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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., and Simonsson, Ulrika S. H.
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ANTIBIOTIC residues ,MACHINE learning ,PHARMACOKINETICS ,STANDARD deviations ,RIFAMPIN - 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 (AUC
0–24h ) 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. [ABSTRACT FROM AUTHOR]- Published
- 2022
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21. A statistical framework for assessing pharmacological response and biomarkers with confidence
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Wang, Dennis, Hensman, James, Kutkaite, Ginte, Toh, Tzen S., Dry, Jonathan R, Saez-Rodriguez, Julio, Garnett, Mathew J., Menden, Michael P., and Dondelinger, Frank
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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.
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- 2020
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22. Multi‐omic landscaping of human midbrains identifies disease‐relevant molecular targets and pathways in advanced‐stage Parkinson's disease.
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Caldi Gomes, Lucas, Galhoz, Ana, Jain, Gaurav, Roser, Anna‐Elisa, Maass, Fabian, Carboni, Eleonora, Barski, Elisabeth, Lenz, Christof, Lohmann, Katja, Klein, Christine, Bähr, Mathias, Fischer, André, Menden, Michael P., and Lingor, Paul
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PARKINSON'S disease ,DRUG target ,MESENCEPHALON ,MASS spectrometry ,RNA sequencing ,SUBTHALAMIC nucleus - Abstract
Background: Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence is rapidly increasing worldwide. The molecular mechanisms underpinning the pathophysiology of sporadic PD remain incompletely understood. Therefore, causative therapies are still elusive. To obtain a more integrative view of disease‐mediated alterations, we investigated the molecular landscape of PD in human post‐mortem midbrains, a region that is highly affected during the disease process. Methods: Tissue from 19 PD patients and 12 controls were obtained from the Parkinson's UK Brain Bank and subjected to multi‐omic analyses: small and total RNA sequencing was performed on an Illumina's HiSeq4000, while proteomics experiments were performed in a hybrid triple quadrupole‐time of flight mass spectrometer (TripleTOF5600+) following quantitative sequential window acquisition of all theoretical mass spectra. Differential expression analyses were performed with customized frameworks based on DESeq2 (for RNA sequencing) and with Perseus v.1.5.6.0 (for proteomics). Custom pipelines in R were used for integrative studies. Results: Our analyses revealed multiple deregulated molecular targets linked to known disease mechanisms in PD as well as to novel processes. We have identified and experimentally validated (quantitative real‐time polymerase chain reaction/western blotting) several PD‐deregulated molecular candidates, including miR‐539‐3p, miR‐376a‐5p, miR‐218‐5p and miR‐369‐3p, the valid miRNA‐mRNA interacting pairs miR‐218‐5p/RAB6C and miR‐369‐3p/GTF2H3, as well as multiple proteins, such as CHI3L1, HSPA1B, FNIP2 and TH. Vertical integration of multi‐omic analyses allowed validating disease‐mediated alterations across different molecular layers. Next to the identification of individual molecular targets in all explored omics layers, functional annotation of differentially expressed molecules showed an enrichment of pathways related to neuroinflammation, mitochondrial dysfunction and defects in synaptic function. Conclusions: This comprehensive assessment of PD‐affected and control human midbrains revealed multiple molecular targets and networks that are relevant to the disease mechanism of advanced PD. The integrative analyses of multiple omics layers underscore the importance of neuroinflammation, immune response activation, mitochondrial and synaptic dysfunction as putative therapeutic targets for advanced PD. [ABSTRACT FROM AUTHOR]
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- 2022
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23. A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates.
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Dennis Wang, Hensman, James, Kutkaite, Ginte, Toh, Tzen S., Galhoz, Ana, Dry, Jonathan R., Saez-Rodriguez, Julio, Garnett, Mathew J., Menden, Michael P., and Dondelinger, Frank
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- 2020
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24. Consistency of drug profiles and predictors in large-scale cancer cell line data
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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.
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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.
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- 2015
25. A community computational challenge to predict the activity of pairs of compounds
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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
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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)
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- 2014
26. A community effort to assess and improve drug sensitivity prediction algorithms
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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
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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)
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- 2014
27. GDSCTools for mining pharmacogenomic interactions in cancer.
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Cokelaer, Thomas, Chen, Elisabeth, Iorio, Francesco, Menden, Michael P, Lightfoot, Howard, Saez-Rodriguez, Julio, and Garnett, Mathew J
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PHARMACOGENOMICS ,CANCER genetics ,CANCER cells ,COMPUTATIONAL biology ,USER interfaces - Abstract
Motivation: Large pharmacogenomic screenings integrate heterogeneous cancer genomic datasets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces. Results: We have developed GDSCTools: a software aimed at the identification of clinically relevant genomic markers of drug response. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) integrates heterogeneous cancer genomic datasets as well as anticancer drug responses on a thousand cancer cell lines. Including statistical tools (analysis of variance) and predictive methods (Elastic Net), as well as common data structures, GDSCTools allows users to reproduce published results from GDSC and to implement new analytical methods. In addition, non-GDSC data resources can also be analysed since drug responses and genomic features can be encoded as CSV files. [ABSTRACT FROM AUTHOR]
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- 2018
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28. Prediction of human population responses to toxic compounds by a collaborative competition.
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Eduati, Federica, Mangravite, Lara M, Wang, Tao, Tang, Hao, Bare, J Christopher, Huang, Ruili, Norman, Thea, Kellen, Mike, Menden, Michael P, Yang, Jichen, Zhan, Xiaowei, Zhong, Rui, Xiao, Guanghua, Xia, Menghang, Abdo, Nour, and Kosyk, Oksana
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TOXIC substance exposure ,ADVERSE health care events ,CELL-mediated cytotoxicity ,LYMPHOBLASTOID cell lines ,ENVIRONMENTAL toxicology - Abstract
The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal. [ABSTRACT FROM AUTHOR]
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- 2015
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29. A community computational challenge to predict the activity of pairs of compounds.
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Bansal, Mukesh, Alvarez, Mariano J, Shen, Yao, Gallahan, Daniel, Singer, Dinah, Stolovitzky, Gustavo, Califano, Andrea, Yang, Jichen, Tang, Hao, Xiao, Guanghua, Allen, Jeffrey, Zhong, Rui, Chen, Beibei, Wang, Tao, Karan, Charles, Realubit, Ronald, Menden, Michael P, Saez-Rodriguez, Julio, Costello, James C, and Li, Yajuan
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COMPUTATIONAL biology ,BIOACTIVE compounds ,DRUG development ,THERAPEUTICS research ,B cells ,GENE expression profiling ,THERAPEUTICS - 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. [ABSTRACT FROM AUTHOR]
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- 2014
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30. A Landscape of Pharmacogenomic Interactions in Cancer
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Iorio, Francesco, Knijnenburg, Theo A., Vis, Daniel J., Bignell, Graham R., Menden, Michael P., Schubert, Michael, Aben, Nanne, Gonçalves, Emanuel, Barthorpe, Syd, Lightfoot, Howard, Cokelaer, Thomas, Greninger, Patricia, van Dyk, Ewald, Chang, Han, de Silva, Heshani, Heyn, Holger, Deng, Xianming, Egan, Regina K., Liu, Qingsong, Mironenko, Tatiana, Mitropoulos, Xeni, Richardson, Laura, Wang, Jinhua, Zhang, Tinghu, Moran, Sebastian, Sayols, Sergi, Soleimani, Maryam, Tamborero, David, Lopez-Bigas, Nuria, Ross-Macdonald, Petra, Esteller, Manel, Gray, Nathanael S., Haber, Daniel A., Stratton, Michael R., Benes, Cyril H., Wessels, Lodewyk F.A., Saez-Rodriguez, Julio, McDermott, Ultan, and Garnett, Mathew J.
- Abstract
Summary Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.
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- 2016
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31. Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties.
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Menden, Michael P., Iorio, Francesco, Garnett, Mathew, McDermott, Ultan, Benes, Cyril H., Ballester, Pedro J., and Saez-Rodriguez, Julio
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CANCER treatment , *CANCER cells , *ONCOLOGY , *DRUG use testing , *MEDICAL genetics , *GENOMICS , *COMPUTATIONAL biology - 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 measuring 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. [ABSTRACT FROM AUTHOR]
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- 2013
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32. Adaptive informatics for multifactorial and high-content biological data.
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Millard, Bjorn L, Niepel, Mario, Menden, Michael P, Muhlich, Jeremy L, and Sorger, Peter K
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Whereas genomic data are universally machine-readable, data from imaging, multiplex biochemistry, flow cytometry and other cell- and tissue-based assays usually reside in loosely organized files of poorly documented provenance. This arises because the relational databases used in genomic research are difficult to adapt to rapidly evolving experimental designs, data formats and analytic algorithms. Here we describe an adaptive approach to managing experimental data based on semantically typed data hypercubes (SDCubes) that combine hierarchical data format 5 (HDF5) and extensible markup language (XML) file types. We demonstrate the application of SDCube-based storage using ImageRail, a software package for high-throughput microscopy. Experimental design and its day-to-day evolution, not rigid standards, determine how ImageRail data are organized in SDCubes. We applied ImageRail to collect and analyze drug dose-response landscapes in human cell lines at single-cell resolution. [ABSTRACT FROM AUTHOR]
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- 2011
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33. Inferred Ancestral Origin of Cancer Cell Lines Associates with Differential Drug Response.
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Nguyen, Phong B. H., Ohnmacht, Alexander J., Sharifli, Samir, Garnett, Mathew J., and Menden, Michael P.
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CELL lines ,HIGH throughput screening (Drug development) ,SURVIVAL rate ,ORGAN donors ,CANCER cells ,INDIVIDUALIZED medicine - Abstract
Disparities between risk, treatment outcomes and survival rates in cancer patients across the world may be attributed to socioeconomic factors. In addition, the role of ancestry is frequently discussed. In preclinical studies, high-throughput drug screens in cancer cell lines have empowered the identification of clinically relevant molecular biomarkers of drug sensitivity; however, the genetic ancestry from tissue donors has been largely neglected in this setting. In order to address this, here, we show that the inferred ancestry of cancer cell lines is conserved and may impact drug response in patients as a predictive covariate in high-throughput drug screens. We found that there are differential drug responses between European and East Asian ancestries, especially when treated with PI3K/mTOR inhibitors. Our finding emphasizes a new angle in precision medicine, as cancer intervention strategies should consider the germline landscape, thereby reducing the failure rate of clinical trials. [ABSTRACT FROM AUTHOR]
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- 2021
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34. Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles.
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Naulaerts, Stefan, Menden, Michael P., and Ballester, Pedro J.
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SMALL cell lung cancer , *FORENSIC genetics , *DNA fingerprinting , *FEATURE selection , *PHARMACOGENOMICS - Abstract
In silico models to predict which tumors will respond to a given drug are necessary for Precision Oncology. However, predictive models are only available for a handful of cases (each case being a given drug acting on tumors of a specific cancer type). A way to generate predictive models for the remaining cases is with suitable machine learning algorithms that are yet to be applied to existing in vitro pharmacogenomics datasets. Here, we apply XGBoost integrated with a stringent feature selection approach, which is an algorithm that is advantageous for these high-dimensional problems. Thus, we identified and validated 118 predictive models for 62 drugs across five cancer types by exploiting four molecular profiles (sequence mutations, copy-number alterations, gene expression, and DNA methylation). Predictive models were found in each cancer type and with every molecular profile. On average, no omics profile or cancer type obtained models with higher predictive accuracy than the rest. However, within a given cancer type, some molecular profiles were overrepresented among predictive models. For instance, CNA profiles were predictive in breast invasive carcinoma (BRCA) cell lines, but not in small cell lung cancer (SCLC) cell lines where gene expression (GEX) and DNA methylation profiles were the most predictive. Lastly, we identified the best XGBoost model per cancer type and analyzed their selected features. For each model, some of the genes in the selected list had already been found to be individually linked to the response to that drug, providing additional evidence of the usefulness of these models and the merits of the feature selection scheme. [ABSTRACT FROM AUTHOR]
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- 2020
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35. Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines
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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
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3. Good health - Abstract
iScience 21, 664-680 (2019). doi:10.1016/j.isci.2019.10.059, Published by Elsevier, Amsterdam
36. GDSCTools for Mining Pharmacogenomic Interactions in Cancer
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Cokelaer, Thomas, Chen, Elisabeth, Iorio, Francesco, Menden, Michael P., Lightfoot, Howard, Saez-Rodriguez, Julio, and Garnett, Mathew J.
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3. Good health - Abstract
Bioinformatics (2017). doi:10.1093/bioinformatics/btx744, Published by Oxford Univ. Press, Oxford
37. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
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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 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., Aznar, Victoria Romeo, 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, Cserzö, Miklós, 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, 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, László, Hwang, Jinseub, Hwang, Tae Hyun, Hwang, Woochang, Hwang, Yongdeuk, Isayev, Olexandr, Don't Walk, Oliver Bear, 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, Lepp Aho, 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, Pujana, Miguel Angel, 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, Gábor, 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, and Zucknick, Manuela
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3. Good health - Abstract
Nat Commun 10(1), 2674 (2019). doi:10.1038/s41467-019-09799-2
38. The germline genetic component of drug sensitivity in cancer cell lines
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Menden, Michael P., Casale, Francesco Paolo, Stephan, Johannes, Bignell, Graham R., Iorio, Francesco, McDermott, Ultan, Garnett, Mathew J., Saez-Rodriguez, Julio, and Stegle, Oliver
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3. Good health - Abstract
Nature Communications 9(1), 3385 (2018). doi:10.1038/s41467-018-05811-3
39. The germline genetic component of drug sensitivity in cancer cell lines.
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Menden, Michael P., Casale, Francesco Paolo, Stephan, Johannes, Bignell, Graham R., Iorio, Francesco, McDermott, Ultan, Garnett, Mathew J., Saez-Rodriguez, Julio, and Stegle, Oliver
- Abstract
Patients with seemingly the same tumour can respond very differently to treatment. There are strong, well-established effects of somatic mutations on drug efficacy, but there is at-most anecdotal evidence of a germline component to drug response. Here, we report a systematic survey of how inherited germline variants affect drug susceptibility in cancer cell lines. We develop a joint analysis approach that leverages both germline and somatic variants, before applying it to screening data from 993 cell lines and 265 drugs. Surprisingly, we find that the germline contribution to variation in drug susceptibility can be as large or larger than effects due to somatic mutations. Several of the associations identified have a direct relationship to the drug target. Finally, using 17-AAG response as an example, we show how germline effects in combination with transcriptomic data can be leveraged for improved patient stratification and to identify new markers for drug sensitivity. [ABSTRACT FROM AUTHOR]
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- 2018
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40. Erratum: Prediction of human population responses to toxic compounds by a collaborative competition.
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Eduati, Federica, Mangravite, Lara M, Wang, Tao, Tang, Hao, Bare, J Christopher, Huang, Ruili, Norman, Thea, Kellen, Mike, Menden, Michael P, Yang, Jichen, Zhan, Xiaowei, Zhong, Rui, Xiao, Guanghua, Xia, Menghang, Abdo, Nour, Kosyk, Oksana, Friend, Stephen, Dearry, Allen, Simeonov, Anton, and Tice, Raymond R
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PHYSIOLOGICAL effects of poisons ,TOXIC substance exposure - Abstract
A correction to the article "Prediction of human population responses to toxic compounds by a collaborative competition" by Federica Eduati and colleagues, which was published in August 10, 2015 issue of the journal is presented.
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- 2015
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41. Epigenetic regulation of cell state by H2AFY governs immunogenicity in high-risk neuroblastoma.
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Nagarajan D, Parracho RT, Corujo D, Xie M, Kutkaite G, Olsen TK, Rúbies Bedós M, Salehi M, Baryawno N, Menden MP, Chen X, Buschbeck M, and Mao Y
- Abstract
Childhood neuroblastoma with MYCN-amplification is classified as high-risk and often relapses after intensive treatments. Immune checkpoint blockade therapy against the PD-1/L1 axis shows limited efficacy in neuroblastoma patients and the cancer intrinsic immune regulatory network is poorly understood. Here, we leverage genome-wide CRISPR/Cas9 screens and identify H2AFY as a resistance gene to the clinically approved PD-1 blocking antibody, nivolumab. Analysis of single-cell RNA sequencing datasets reveals that H2AFY mRNA is enriched in adrenergic cancer cells and is associated with worse patient survival. Genetic deletion of H2afy in MYCN-driven neuroblastoma cells reverts in vivo resistance to PD-1 blockade by eliciting activation of the adaptive and innate immunity. Mapping of the epigenetic and translational landscape demonstrates that H2afy deletion promotes cell transition to a mesenchymal-like state. With a multi-omics approach, we uncover H2AFY-associated genes that are functionally relevant and prognostic in patients. Altogether, our study elucidates the role of H2AFY as an epigenetic gatekeeper for cell states and immunogenicity in high-risk neuroblastoma.
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- 2024
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42. Generative artificial intelligence empowers digital twins in drug discovery and clinical trials.
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Bordukova M, Makarov N, Rodriguez-Esteban R, Schmich F, and Menden MP
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- Humans, Computer Simulation, Drug Development, Drug Discovery, Clinical Trials as Topic, Artificial Intelligence, Biomedical Research
- Abstract
Introduction: The 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 Covered: The 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 Opinion: The 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.
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- 2024
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43. Can artificial intelligence accelerate preclinical drug discovery and precision medicine?
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Farnoud A, Ohnmacht AJ, Meinel M, and Menden MP
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- Drug Discovery, Humans, Machine Learning, Artificial Intelligence, Precision Medicine
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- 2022
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44. Artificial intelligence in early drug discovery enabling precision medicine.
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Boniolo F, Dorigatti E, Ohnmacht AJ, Saur D, Schubert B, and Menden MP
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- Algorithms, Drug Design, Drug Discovery, Humans, Artificial Intelligence, Precision Medicine
- Abstract
Introduction : Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence. Areas covered : In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design. Expert opinion : Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice.
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- 2021
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45. A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates.
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Wang D, Hensman J, Kutkaite G, Toh TS, Galhoz A, Dry JR, Saez-Rodriguez J, Garnett MJ, Menden MP, and Dondelinger F
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- Cell Line, Tumor, High-Throughput Screening Assays methods, High-Throughput Screening Assays standards, Humans, Antineoplastic Agents, Biomarkers, Tumor analysis, Drug Discovery methods, Drug Discovery standards, Statistics as Topic methods
- Abstract
High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells' response to a drug 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 uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. 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 any dose-response data without replicates, and improves biomarker discovery for precision medicine., Competing Interests: DW, GK, TT, AG, JS, MG, MM No competing interests declared, JH James Hensman is an employee of Amazon.com. The author has no competing financial interests to declare. JD Jonathan Dry is affiliated with AstraZeneca and Tempus. The author has no competing financial interests to declare. FD Frank Dondelinger is an employee of Roche. The author has no competing financial interests to declare., (© 2020, Wang et al.)
- Published
- 2020
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46. DREAMTools: a Python package for scoring collaborative challenges.
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Cokelaer T, Bansal M, Bare C, Bilal E, Bot BM, Chaibub Neto E, Eduati F, Gönen M, Hill SM, Hoff B, Karr JR, Küffner R, Menden MP, Meyer P, Norel R, Pratap A, Prill RJ, Weirauch MT, Costello JC, Stolovitzky G, and Saez-Rodriguez J
- Abstract
Unlabelled: DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of September 2015, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website http://dreamchallenges.org and on the Synapse platform https://www.synapse.org., Availability: DREAMTools is a Python package. Releases and documentation are available at http://pypi.python.org/pypi/dreamtools. The source code is available at http://github.com/dreamtools.
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- 2015
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47. A community effort to assess and improve drug sensitivity prediction algorithms.
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Costello JC, Heiser LM, Georgii E, Gönen M, Menden MP, Wang NJ, Bansal M, Ammad-ud-din M, Hintsanen P, Khan SA, Mpindi JP, Kallioniemi O, Honkela A, Aittokallio T, Wennerberg K, Collins JJ, Gallahan D, Singer D, Saez-Rodriguez J, Kaski S, Gray JW, and Stolovitzky G
- Subjects
- Algorithms, Antineoplastic Agents adverse effects, Epigenomics methods, Gene Expression Regulation, Neoplastic drug effects, Genomics methods, Humans, Neoplasms genetics, Proteomics methods, Antineoplastic Agents therapeutic use, Drug Resistance, Neoplasm genetics, Gene Expression Profiling, Neoplasms drug therapy
- 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.
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- 2014
- Full Text
- View/download PDF
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