Search

Your search keyword '"Costello, James C."' showing total 7 results
Start Over Author "Costello, James C." Topic humans
7 results on '"Costello, James C."'

1. Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

Author

Golob, Jonathan L, Oskotsky, Tomiko T, Tang, Alice S, Roldan, Alennie, Chung, Verena, Ha, Connie WY, Wong, Ronald J, Flynn, Kaitlin J, Parraga-Leo, Antonio, Wibrand, Camilla, Minot, Samuel S, Oskotsky, Boris, Andreoletti, Gaia, Kosti, Idit, Bletz, Julie, Nelson, Amber, Gao, Jifan, Wei, Zhoujingpeng, Chen, Guanhua, Tang, Zheng-Zheng, Novielli, Pierfrancesco, Romano, Donato, Pantaleo, Ester, Amoroso, Nicola, Monaco, Alfonso, Vacca, Mirco, De Angelis, Maria, Bellotti, Roberto, Tangaro, Sabina, Kuntzleman, Abigail, Bigcraft, Isaac, Techtmann, Stephen, Bae, Daehun, Kim, Eunyoung, Jeon, Jongbum, Joe, Soobok, Community, The Preterm Birth DREAM, Theis, Kevin R, Ng, Sherrianne, Lee, Yun S, Diaz-Gimeno, Patricia, Bennett, Phillip R, MacIntyre, David A, Stolovitzky, Gustavo, Lynch, Susan V, Albrecht, Jake, Gomez-Lopez, Nardhy, Romero, Roberto, Stevenson, David K, Aghaeepour, Nima, Tarca, Adi L, Costello, James C, and Sirota, Marina

Subjects
Paediatrics, Biomedical and Clinical Sciences, Infant Mortality, Prevention, Genetics, Perinatal Period - Conditions Originating in Perinatal Period, Pediatric, Human Genome, Preterm, Low Birth Weight and Health of the Newborn, Reproductive health and childbirth, Good Health and Well Being, Pregnancy, Female, Infant, Newborn, Humans, Premature Birth, Crowdsourcing, Phylogeny, Vagina, Microbiota, Preterm Birth DREAM Community, 16S harmonization, DREAM challenge, crowdsourced, machine learning, microbiome, predictive modeling, preterm birth, vaginal microbiome, Biomedical and clinical sciences
Abstract

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB;

Published
2024

2. Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth

Author

Tarca, Adi L, Pataki, Bálint Ármin, Romero, Roberto, Sirota, Marina, Guan, Yuanfang, Kutum, Rintu, Gomez-Lopez, Nardhy, Done, Bogdan, Bhatti, Gaurav, Yu, Thomas, Andreoletti, Gaia, Chaiworapongsa, Tinnakorn, Consortium, The DREAM Preterm Birth Prediction Challenge, Hassan, Sonia S, Hsu, Chaur-Dong, Aghaeepour, Nima, Stolovitzky, Gustavo, Csabai, Istvan, and Costello, James C

Subjects
Paediatrics, Reproductive Medicine, Biomedical and Clinical Sciences, Infant Mortality, Contraception/Reproduction, Clinical Research, Preterm, Low Birth Weight and Health of the Newborn, Pediatric, Perinatal Period - Conditions Originating in Perinatal Period, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Reproductive health and childbirth, Good Health and Well Being, Adult, Asymptomatic Diseases, Biomarkers, Blood Proteins, Cell-Free Nucleic Acids, Crowdsourcing, Female, Gestational Age, Humans, Infant, Newborn, Longitudinal Studies, Pre-Eclampsia, Pregnancy, Premature Birth, Proteomics, ROC Curve, Transcriptome, DREAM Preterm Birth Prediction Challenge Consortium, aptamers, collaborative competition, human transcriptome arrays, machine learning, plasma proteomics, predictive modeling, preterm labor and delivery, spontaneous preterm birth, whole blood transcriptomics, Biomedical and clinical sciences
Abstract

Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r = 0.83) and, using data collected before 37 weeks of gestation, also predicts the delivery date in both normal pregnancies (r = 0.86) and those with spontaneous preterm birth (r = 0.75). Based on samples collected before 33 weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC = 0.76 versus AUROC = 0.6 at 27-33 weeks of gestation).

Published
2021

3. Mesenchymal and MAPK Expression Signatures Associate with Telomerase Promoter Mutations in Multiple Cancers

Author

Stern, Josh Lewis, Hibshman, Grace, Hu, Kevin, Ferrara, Sarah E, Costello, James C, Kim, William, Tamayo, Pablo, Cech, Thomas R, and Huang, Franklin W

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Cancer, Genetics, 2.1 Biological and endogenous factors, Aetiology, Generic health relevance, Cell Line, Tumor, Chromatin Immunoprecipitation, Epithelial-Mesenchymal Transition, Extracellular Signal-Regulated MAP Kinases, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Humans, Mutation, Neoplasms, Promoter Regions, Genetic, Sequence Analysis, RNA, Small Molecule Libraries, Telomerase, Tumor Microenvironment, Oncology and Carcinogenesis, Developmental Biology, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

In a substantial fraction of cancers TERT promoter (TERTp) mutations drive expression of the catalytic subunit of telomerase, contributing to their proliferative immortality. We conducted a pan-cancer analysis of cell lines and find a TERTp mutation expression signature dominated by epithelial-to-mesenchymal transition and MAPK signaling. These data indicate that TERTp mutants are likely to generate distinctive tumor microenvironments and intercellular interactions. Analysis of high-throughput screening tests of 546 small molecules on cell line growth indicated that TERTp mutants displayed heightened sensitivity to specific drugs, including RAS pathway inhibitors, and we found that inhibition of MEK1 and 2, key RAS/MAPK pathway effectors, inhibited TERT mRNA expression. Consistent with an enrichment of mesenchymal states in TERTp mutants, cell lines and some patient tumors displayed low expression of the central adherens junction protein E-cadherin, and we provide evidence that its expression in these cells is regulated by MEK1/2. Several mesenchymal transcription factors displayed elevated expression in TERTp mutants including ZEB1 and 2, TWIST1 and 2, and SNAI1. Of note, the developmental transcription factor SNAI2/SLUG was conspicuously elevated in a significant majority of TERTp-mutant cell lines, and knock-down experiments suggest that it promotes TERT expression. IMPLICATIONS: Cancers harboring TERT promoter mutations are often more lethal, but the basis for this higher mortality remains unknown. Our study identifies that TERTp mutants, as a class, associate with a distinct gene and protein expression signature likely to impact their biological and clinical behavior and provide new directions for investigating treatment approaches for these cancers.

Published
2020

4. Combined administration of testosterone plus an ornithine decarboxylase inhibitor as a selective prostate‐sparing anabolic therapy

Author

Jasuja, Ravi, Costello, James C, Singh, Rajan, Gupta, Vandana, Spina, Catherine S, Toraldo, Gianluca, Jang, Hyeran, Li, Hu, Serra, Carlo, Guo, Wen, Chauhan, Pratibha, Narula, Navjot S, Guarneri, Tyler, Ergun, Ayla, Travison, Thomas G, Collins, James J, and Bhasin, Shalender

Subjects
Prostate Cancer, Cancer, Biotechnology, Urologic Diseases, Aging, Anabolic Agents, Animals, Body Weight, Eflornithine, Enzyme Inhibitors, Follistatin, Humans, Male, Mice, Mice, Inbred C57BL, Mice, Transgenic, Muscle, Skeletal, Oligonucleotide Array Sequence Analysis, Organ Size, Organ Sparing Treatments, Ornithine Decarboxylase, Ornithine Decarboxylase Inhibitors, Prostate, Recombinant Proteins, Signal Transduction, Testosterone, aging, anti-aging, sarcopenia, sex hormones, skeletal muscle, steroids, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Because of its anabolic effects on muscle, testosterone is being explored as a function-promoting anabolic therapy for functional limitations associated with aging; however, concerns about testosterone's adverse effects on prostate have inspired efforts to develop strategies that selectively increase muscle mass while sparing the prostate. Testosterone's promyogenic effects are mediated through upregulation of follistatin. We show here that the administration of recombinant follistatin (rFst) increased muscle mass in mice, but had no effect on prostate mass. Consistent with the results of rFst administration, follistatin transgenic mice with constitutively elevated follistatin levels displayed greater muscle mass than controls, but had similar prostate weights. To elucidate signaling pathways regulated differentially by testosterone and rFst in prostate and muscle, we performed microarray analysis of mRNAs from prostate and levator ani of castrated male mice treated with vehicle, testosterone, or rFst. Testosterone and rFst shared the regulation of many transcripts in levator ani; however, in prostate, 593 transcripts in several growth-promoting pathways were differentially expressed after testosterone treatment, while rFst showed a negligible effect with only 9 transcripts differentially expressed. Among pathways that were differentially responsive to testosterone in prostate, we identified ornithine decarboxylase (Odc1), an enzyme in polyamine biosynthesis, as a testosterone-responsive gene that is unresponsive to rFst. Accordingly, we administered testosterone with and without α-difluoromethylornithine (DFMO), an Odc1 inhibitor, to castrated mice. DFMO selectively blocked testosterone's effects on prostate, but did not affect testosterone's anabolic effects on muscle. Co-administration of testosterone and Odc1 inhibitor presents a novel therapeutic strategy for prostate-sparing anabolic therapy.

Published
2014

5. Differential splicing across immune system lineages

Author

Ergun, Ayla, Doran, Graeme, Costello, James C, Paik, Henry H, Collins, James J, Mathis, Diane, Benoist, Christophe, Blair, David A, Dustin, Michael L, Shinton, Susan A, Hardy, Richard R, Shay, Tal, Regevc, Aviv, Cohen, Nadia, Brennan, Patrick, Brenner, Michael, Kim, Francis, Rao, Tata Nageswara, Wagers, Amy, Heng, Tracy, Ericson, Jeffrey, Rothamel, Katherine, Ortiz-Lopez, Adriana, Kreslavsky, Taras, Fletcher, Anne, Elpek, Kutlu, Bellemare-Pelletier, Angelique, Malhotra, Deepali, Turley, Shannon, Miller, Jennifer, Brown, Brian, Merad, Miriam, Gautier, Emmanuel L, Jakubzick, Claudia, Randolph, Gwendalyn J, Monach, Paul, Best, Adam J, Knell, Jamie, Goldrath, Ananda, Jojic, Vladimir, Koller, Daphne, Laidlaw, David, Collins, Jim, Gazit, Roi, Rossi, Derrick J, Malhotra, Nidhi, Sylvia, Katelyn, Kang, Joonsoo, Bezman, Natalie A, Sun, Joseph C, Min-Oo, Gundula, Kim, Charlie C, and Lanier, Lewis L

Subjects
Genetics, Human Genome, 1.1 Normal biological development and functioning, Underpinning research, Inflammatory and immune system, Alternative Splicing, B-Lymphocytes, Cell Lineage, Humans, T-Lymphocytes, ImmGen Consortium
Abstract

Alternative splicing (AS) allows increased diversity and orthogonal regulation of the transcriptional products of mammalian genomes. To assess the distribution and variation of alternative splicing across cell lineages of the immune system, we comprehensively analyzed RNA sequencing and microarray data generated by the Immunological Genome Project Consortium. AS is pervasive: 60% of genes showed frequent AS isoforms in T or B lymphocytes, with 7,599 previously unreported isoforms. Distinct cell specificity was observed, with differential exon skipping in 5% of genes otherwise coexpressed in both B and T cells. The distribution of isoforms was mostly all or none, suggesting on/off switching as a frequent mode of AS regulation in lymphocytes. From the identification of differential exon use in the microarray data, clustering of exon inclusion/exclusion patterns across all Immunological Genome Project cell types showed that ∼70% of AS exons are distributed along a common pattern linked to lineage differentiation and cell cycling. Other AS events distinguished myeloid from lymphoid cells or affected only a small set of exons without clear lineage specificity (e.g., Ptprc). Computational analysis predicted specific associations between AS exons and splicing regulators, which were verified by detection of the hnRPLL/Ptprc connection.

Published
2013

6. 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

7. 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

Books, media, physical & digital resources
See catalog results