Search

Your search keyword '"Norman, Thea"' showing total 267 results
Start Over Author "Norman, Thea"
267 results on '"Norman, Thea"'

1. Causes and consequences of child growth faltering in low-resource settings.

Author

Mertens, Andrew, Benjamin-Chung, Jade, Colford, John M, Coyle, Jeremy, van der Laan, Mark J, Hubbard, Alan E, Rosete, Sonali, Malenica, Ivana, Hejazi, Nima, Sofrygin, Oleg, Cai, Wilson, Li, Haodong, Nguyen, Anna, Pokpongkiat, Nolan N, Djajadi, Stephanie, Seth, Anmol, Jung, Esther, Chung, Esther O, Jilek, Wendy, Subramoney, Vishak, Hafen, Ryan, Häggström, Jonas, Norman, Thea, Brown, Kenneth H, Christian, Parul, Arnold, Benjamin F, and Ki Child Growth Consortium

Subjects
General Science & Technology
Abstract

Growth faltering in children (low length for age or low weight for length) during the first 1,000 days of life (from conception to 2 years of age) influences short-term and long-term health and survival1,2. Interventions such as nutritional supplementation during pregnancy and the postnatal period could help prevent growth faltering, but programmatic action has been insufficient to eliminate the high burden of stunting and wasting in low- and middle-income countries. Identification of age windows and population subgroups on which to focus will benefit future preventive efforts. Here we use a population intervention effects analysis of 33 longitudinal cohorts (83,671 children, 662,763 measurements) and 30 separate exposures to show that improving maternal anthropometry and child condition at birth accounted for population increases in length-for-age z-scores of up to 0.40 and weight-for-length z-scores of up to 0.15 by 24 months of age. Boys had consistently higher risk of all forms of growth faltering than girls. Early postnatal growth faltering predisposed children to subsequent and persistent growth faltering. Children with multiple growth deficits exhibited higher mortality rates from birth to 2 years of age than children without growth deficits (hazard ratios 1.9 to 8.7). The importance of prenatal causes and severe consequences for children who experienced early growth faltering support a focus on pre-conception and pregnancy as a key opportunity for new preventive interventions.

Published
2023

2. Child wasting and concurrent stunting in low- and middle-income countries.

Author

Mertens, Andrew, Benjamin-Chung, Jade, Colford, John M, Hubbard, Alan E, van der Laan, Mark J, Coyle, Jeremy, Sofrygin, Oleg, Cai, Wilson, Jilek, Wendy, Rosete, Sonali, Nguyen, Anna, Pokpongkiat, Nolan N, Djajadi, Stephanie, Seth, Anmol, Jung, Esther, Chung, Esther O, Malenica, Ivana, Hejazi, Nima, Li, Haodong, Hafen, Ryan, Subramoney, Vishak, Häggström, Jonas, Norman, Thea, Christian, Parul, Brown, Kenneth H, Arnold, Benjamin F, and Ki Child Growth Consortium

Subjects
General Science & Technology
Abstract

Sustainable Development Goal 2.2-to end malnutrition by 2030-includes the elimination of child wasting, defined as a weight-for-length z-score that is more than two standard deviations below the median of the World Health Organization standards for child growth1. Prevailing methods to measure wasting rely on cross-sectional surveys that cannot measure onset, recovery and persistence-key features that inform preventive interventions and estimates of disease burden. Here we analyse 21 longitudinal cohorts and show that wasting is a highly dynamic process of onset and recovery, with incidence peaking between birth and 3 months. Many more children experience an episode of wasting at some point during their first 24 months than prevalent cases at a single point in time suggest. For example, at the age of 24 months, 5.6% of children were wasted, but by the same age (24 months), 29.2% of children had experienced at least one wasting episode and 10.0% had experienced two or more episodes. Children who were wasted before the age of 6 months had a faster recovery and shorter episodes than did children who were wasted at older ages; however, early wasting increased the risk of later growth faltering, including concurrent wasting and stunting (low length-for-age z-score), and thus increased the risk of mortality. In diverse populations with high seasonal rainfall, the population average weight-for-length z-score varied substantially (more than 0.5 z in some cohorts), with the lowest mean z-scores occurring during the rainiest months; this indicates that seasonally targeted interventions could be considered. Our results show the importance of establishing interventions to prevent wasting from birth to the age of 6 months, probably through improved maternal nutrition, to complement current programmes that focus on children aged 6-59 months.

Published
2023

3. Early-childhood linear growth faltering in low- and middle-income countries.

Author

Benjamin-Chung, Jade, Mertens, Andrew, Colford, John M, Hubbard, Alan E, van der Laan, Mark J, Coyle, Jeremy, Sofrygin, Oleg, Cai, Wilson, Nguyen, Anna, Pokpongkiat, Nolan N, Djajadi, Stephanie, Seth, Anmol, Jilek, Wendy, Jung, Esther, Chung, Esther O, Rosete, Sonali, Hejazi, Nima, Malenica, Ivana, Li, Haodong, Hafen, Ryan, Subramoney, Vishak, Häggström, Jonas, Norman, Thea, Brown, Kenneth H, Christian, Parul, Arnold, Benjamin F, and Ki Child Growth Consortium

Subjects
General Science & Technology
Abstract

Globally, 149 million children under 5 years of age are estimated to be stunted (length more than 2 standard deviations below international growth standards)1,2. Stunting, a form of linear growth faltering, increases the risk of illness, impaired cognitive development and mortality. Global stunting estimates rely on cross-sectional surveys, which cannot provide direct information about the timing of onset or persistence of growth faltering-a key consideration for defining critical windows to deliver preventive interventions. Here we completed a pooled analysis of longitudinal studies in low- and middle-income countries (n = 32 cohorts, 52,640 children, ages 0-24 months), allowing us to identify the typical age of onset of linear growth faltering and to investigate recurrent faltering in early life. The highest incidence of stunting onset occurred from birth to the age of 3 months, with substantially higher stunting at birth in South Asia. From 0 to 15 months, stunting reversal was rare; children who reversed their stunting status frequently relapsed, and relapse rates were substantially higher among children born stunted. Early onset and low reversal rates suggest that improving children's linear growth will require life course interventions for women of childbearing age and a greater emphasis on interventions for children under 6 months of age.

Published
2023

4. A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery

Author

Creason, Allison, Haan, David, Dang, Kristen, Chiotti, Kami E, Inkman, Matthew, Lamb, Andrew, Yu, Thomas, Hu, Yin, Norman, Thea C, Buchanan, Alex, van Baren, Marijke J, Spangler, Ryan, Rollins, M Rick, Spellman, Paul T, Rozanov, Dmitri, Zhang, Jin, Maher, Christopher A, Caloian, Cristian, Watson, John D, Uhrig, Sebastian, Haas, Brian J, Jain, Miten, Akeson, Mark, Ahsen, Mehmet Eren, Participants, SMC-RNA Challenge, Zhang, Hongjiu, Wang, Yifan, Guan, Yuanfang, Nguyen, Cu, Sugai, Christopher, Jha, Alokkumar, Li, Jing Woei, Dobin, Alexander, Stolovitzky, Gustavo, Guinney, Justin, Boutros, Paul C, Stuart, Joshua M, and Ellrott, Kyle

Subjects
Genetics, Cancer, Human Genome, Humans, Neoplasms, Protein Isoforms, RNA, RNA-Seq, Sequence Analysis, RNA, SMC-RNA Challenge Participants, Cloud compute, DREAM Challenge, RNA fusion, RNA-seq, benchmark, crowd-sourced, evaluation, isoform quantification, Biochemistry and Cell Biology
Abstract

The accurate identification and quantitation of RNA isoforms present in the cancer transcriptome is key for analyses ranging from the inference of the impacts of somatic variants to pathway analysis to biomarker development and subtype discovery. The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge was a crowd-sourced effort to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing (RNA-seq) data. It concluded in 2018 with a comparison of 77 fusion detection entries and 65 isoform quantification entries on 51 synthetic tumors and 32 cell lines with spiked-in fusion constructs. We report the entries used to build this benchmark, the leaderboard results, and the experimental features associated with the accurate prediction of RNA species. This challenge required submissions to be in the form of containerized workflows, meaning each of the entries described is easily reusable through CWL and Docker containers at https://github.com/SMC-RNA-challenge. A record of this paper's transparent peer review process is included in the supplemental information.

Published
2021

5. Author Correction: Child wasting and concurrent stunting in low- and middle-income countries

Author

Mertens, Andrew, Benjamin-Chung, Jade, Colford, Jr, John M., Hubbard, Alan E., van der Laan, Mark J., Coyle, Jeremy, Sofrygin, Oleg, Cai, Wilson, Jilek, Wendy, Rosete, Sonali, Nguyen, Anna, Pokpongkiat, Nolan N., Djajadi, Stephanie, Seth, Anmol, Jung, Esther, Chung, Esther O., Malenica, Ivana, Hejazi, Nima, Li, Haodong, Hafen, Ryan, Subramoney, Vishak, Häggström, Jonas, Norman, Thea, Christian, Parul, Brown, Kenneth H., and Arnold, Benjamin F.

Published
2023
Full Text
View/download PDF

6. Author Correction: Early-childhood linear growth faltering in low- and middle-income countries

Author

Benjamin-Chung, Jade, Mertens, Andrew, Colford, Jr, John M., Hubbard, Alan E., van der Laan, Mark J., Coyle, Jeremy, Sofrygin, Oleg, Cai, Wilson, Nguyen, Anna, Pokpongkiat, Nolan N., Djajadi, Stephanie, Seth, Anmol, Jilek, Wendy, Jung, Esther, Chung, Esther O., Rosete, Sonali, Hejazi, Nima, Malenica, Ivana, Li, Haodong, Hafen, Ryan, Subramoney, Vishak, Häggström, Jonas, Norman, Thea, Brown, Kenneth H., Christian, Parul, and Arnold, Benjamin F.

Published
2023
Full Text
View/download PDF

7. Early childhood linear growth faltering in low- and middle-income countries

Author

Benjamin-Chung, Jade, Mertens, Andrew, Colford, John M, Hubbard, Alan E, van der Laan, Mark J, Coyle, Jeremy, Sofrygin, Oleg, Cai, Wilson, Nguyen, Anna, Pokpongkiat, Nolan N, Djajadi, Stephanie, Seth, Anmol, Jilek, Wendy M, Jung, Esther, Chung, Esther O, Dayal, Sonali, Hejazi, Nima, Malenica, Ivana, Li, Haodong, Hafen, Ryan, Subramoney, Vishak, Häggström, Jonas, Norman, Thea, Brown, Kenneth H, Christian, Parul, and Arnold, Benjamin F

Subjects
Prevention, Pediatric Research Initiative, Pediatric, Nutrition
Abstract

Globally 149 million children under five are estimated to be stunted (length more than 2 standard deviations below international growth standards). Stunting, a form of linear growth faltering, increases risk of illness, impaired cognitive development, and mortality. Global stunting estimates rely on cross-sectional surveys, which cannot provide direct information about the timing of onset or persistence of growth faltering- a key consideration for defining critical windows to deliver preventive interventions. We performed the largest pooled analysis of longitudinal studies in low- and middle-income countries to date (n=32 cohorts, 52,640 children, ages 0-24 months), allowing us to identify the typical age of linear growth faltering onset and to investigate recurrent faltering in early life. The highest incidence of stunting onset occurred from birth to age 3 months. From 0 to 15 months, less than 5% of children per month reversed their stunting status, and among those who did, stunting relapse was common. Early timing and low reversal rates emphasize the importance of preventive intervention delivery within the prenatal and early postnatal phases coupled with continued delivery of postnatal interventions through the first 1000 days of life.

Published
2020

8. Child wasting and concurrent stunting in low- and middle-income countries

Author

Mertens, Andrew, Benjamin-Chung, Jade, Colford, John M, Hubbard, Alan E, van der Laan, Mark J, Coyle, Jeremy, Sofrygin, Oleg, Cai, Wilson, Jilek, Wendy, Rosete, Sonali, Nguyen, Anna, Pokpongkiat, Nolan N, Djajadi, Stephanie, Seth, Anmol, Jung, Esther, Chung, Esther O, Malenica, Ivana, Hejazi, Nima, Li, Haodong, Hafen, Ryan, Subramoney, Vishak, Häggström, Jonas, Norman, Thea, Christian, Parul, Brown, Kenneth H, and Arnold, Benjamin F

Subjects
Prevention, Pediatric, Nutrition, Pediatric Research Initiative, 2.4 Surveillance and distribution
Abstract

SummarySustainable Development Goal 2.2.2, to end malnutrition by 2030, measures progress through elimination of child wasting, defined as weight-for-length more than 2 standard deviations below international standards. Prevailing methods to measure wasting rely on cross-sectional surveys that cannot measure onset, recovery, and persistence — key features of wasting epidemiology that could inform preventive interventions and disease burden estimates. Here, we show through an analysis of 21 longitudinal cohorts that wasting is a highly dynamic process of onset and recovery, and incidence peaks between birth and 3 months — far earlier than peak prevalence at 12-15 months. By age 24 months 29.2% of children had experienced at least one wasting episode, more than 5-fold higher than point prevalence (5.6%), demonstrating that wasting incidence is far higher than cross-sectional surveys suggest. Children wasted before 6 months were more likely to experience concurrent wasting and stunting (low height-for-age) later, increasing their risk of mortality. In diverse populations with seasonal rainfall, population average weight-for-length varied substantially (>0.5 z in some cohorts), with the lowest mean Z-scores during the rainiest months, creating potential for seasonally targeted interventions. Our results motivate a new focus on extending preventive interventions for wasting to pregnant and lactating mothers, and for preventive and therapeutic interventions to include children below age 6 months in addition to current targets of ages 6-59 months.

Published
2020

9. Risk factors and impacts of child growth faltering in low- and middle-income countries

Author

Mertens, Andrew, Benjamin-Chung, Jade, Colford, John M, Coyle, Jeremy, van der Laan, Mark J, Hubbard, Alan E, Rosete, Sonali, Malenica, Ivana, Hejazi, Nima, Sofrygin, Oleg, Cai, Wilson, Li, Haodong, Nguyen, Anna, Pokpongkiat, Nolan N, Djajadi, Stephanie, Seth, Anmol, Jung, Esther, Chung, Esther O, Jilek, Wendy, Subramoney, Vishak, Hafen, Ryan, Häggström, Jonas, Norman, Thea, Brown, Kenneth H, Christian, Parul, and Arnold, Benjamin F

Subjects
Clinical Research, Conditions Affecting the Embryonic and Fetal Periods, Infant Mortality, Nutrition, Pediatric, Prevention, Perinatal Period - Conditions Originating in Perinatal Period, Pediatric Research Initiative, Reproductive health and childbirth
Abstract

SummaryGrowth faltering (low length-for-age or weight-for length) in the first 1000 days — from conception to two years of age — influences both short and long-term health and survival. Evidence for interventions to prevent growth faltering such as nutritional supplementation during pregnancy and the postnatal period has increasingly accumulated, but programmatic action has been insufficient to eliminate the high burden of stunting and wasting in low- and middle-income countries. In addition, there is need to better understand age-windows and population subgroups in which to focus future preventive efforts. Here, we show using a population intervention effects analysis of 33 longitudinal cohorts (83,671 children) and 30 separate exposures that improving maternal anthropometry and child condition at birth, in particular child length-at-birth, accounted for population increases by age 24 months in length-for-age Z of 0.04 to 0.40 and weight-for-length Z by 0.02 to 0.15. Boys had consistently higher risk of all forms of growth faltering than girls, and early growth faltering predisposed children to subsequent and persistent growth faltering. Children with multiple growth deficits had higher mortality rates from birth to two years than those without deficits (hazard ratios 1.9 to 8.7). The importance of prenatal causes, and severe consequences for children who experienced early growth faltering, support a focus on pre-conception and pregnancy as key opportunities for new preventive interventions.

Published
2020

10. Causes and consequences of child growth faltering in low- and middle-income countries

Author

Mertens, Andrew, Benjamin-Chung, Jade, Colford, John, Coyle, Jeremy, van der Laan, Mark, Hubbard, Alan, Rosete, Sonali, Malenica, Ivana, Hejazi, Nima, Sofrygin, Oleg, Cai, Wilson, Li, Haodong, Nguyen, Anna, Pokpongkiat, Nolan, Djajadi, Stephanie, Seth, Anmol, Jung, Esther, Chung, Esther, Jilek, Wendy, Subramoney, Vishak, Hafen, Ryan, Häggström, Jonas, Norman, Thea, Brown, Kenneth, Christian, Parul, Arnold, Benjamin, and members of the ki Child Growth Consortium

Subjects
members of the ki Child Growth Consortium, Nutrition, Perinatal Period - Conditions Originating in Perinatal Period, Pediatric, Conditions Affecting the Embryonic and Fetal Periods, Pediatric Research Initiative, Prevention, Aetiology, 2.1 Biological and endogenous factors, Reproductive health and childbirth, Good Health and Well Being
Abstract

Summary Growth faltering (low length-for-age or weight-for-length) in the first 1000 days — from conception to two years of age — influences short and long-term health and survival. Interventions such as nutritional supplementation during pregnancy and the postnatal period could help prevent growth faltering, but programmatic action has been insufficient to eliminate the high burden of stunting and wasting in low- and middle-income countries. Future preventive efforts will benefit from understanding age-windows and population subgroups in which to focus. Here, we use a population intervention effects analysis of 33 longitudinal cohorts (83,671 children, 662,763 measurements) and 30 separate exposures to show that improving maternal anthropometry and child condition at birth accounted for population increases in length-for-age Z of up to 0.40 and weight-for-length Z of up to 0.15 by age 24 months. Boys had consistently higher risk of all forms of growth faltering than girls. Early post-natal growth faltering predisposed children to subsequent and persistent growth faltering. Children with multiple growth deficits had higher mortality rates from birth to two years than those without deficits (hazard ratios 1.9 to 8.7). The importance of prenatal causes, and severe consequences for children who experienced early growth faltering, support a focus on pre-conception and pregnancy as key opportunities for new preventive interventions.

Published
2020

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

Author

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

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

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Published
2019

12. Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach.

Author

Kueffner, Robert, Zach, Neta, Bronfeld, Maya, Norel, Raquel, Atassi, Nazem, Balagurusamy, Venkat, Di Camillo, Barbara, Chio, Adriano, Cudkowicz, Merit, Dillenberger, Donna, Garcia-Garcia, Javier, Hardiman, Orla, Hoff, Bruce, Knight, Joshua, Leitner, Melanie L, Li, Guang, Mangravite, Lara, Norman, Thea, Wang, Liuxia, ALS Stratification Consortium, Xiao, Jinfeng, Fang, Wen-Chieh, Peng, Jian, Yang, Chen, Chang, Huan-Jui, and Stolovitzky, Gustavo

Subjects
ALS Stratification Consortium
Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.

Published
2019

13. Germline contamination and leakage in whole genome somatic single nucleotide variant detection

Author

Sendorek, Dorota H, Caloian, Cristian, Ellrott, Kyle, Bare, J Christopher, Yamaguchi, Takafumi N, Ewing, Adam D, Houlahan, Kathleen E, Norman, Thea C, Margolin, Adam A, Stuart, Joshua M, and Boutros, Paul C

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Cancer, Generic health relevance, Good Health and Well Being, Algorithms, Genome, Human, Germ Cells, Humans, Internet, Neoplasms, Polymorphism, Single Nucleotide, User-Computer Interface, Whole Genome Sequencing, Cancer genomics, Next-generation sequencing, Mutation calling, Germline contamination, Germline leakage, Patient identifiability, Single nucleotide variant, SNV, Mathematical Sciences, Information and Computing Sciences, Bioinformatics, Biological sciences, Information and computing sciences, Mathematical sciences
Abstract

BackgroundThe clinical sequencing of cancer genomes to personalize therapy is becoming routine across the world. However, concerns over patient re-identification from these data lead to questions about how tightly access should be controlled. It is not thought to be possible to re-identify patients from somatic variant data. However, somatic variant detection pipelines can mistakenly identify germline variants as somatic ones, a process called "germline leakage". The rate of germline leakage across different somatic variant detection pipelines is not well-understood, and it is uncertain whether or not somatic variant calls should be considered re-identifiable. To fill this gap, we quantified germline leakage across 259 sets of whole-genome somatic single nucleotide variant (SNVs) predictions made by 21 teams as part of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge.ResultsThe median somatic SNV prediction set contained 4325 somatic SNVs and leaked one germline polymorphism. The level of germline leakage was inversely correlated with somatic SNV prediction accuracy and positively correlated with the amount of infiltrating normal cells. The specific germline variants leaked differed by tumour and algorithm. To aid in quantitation and correction of leakage, we created a tool, called GermlineFilter, for use in public-facing somatic SNV databases.ConclusionsThe potential for patient re-identification from leaked germline variants in somatic SNV predictions has led to divergent open data access policies, based on different assessments of the risks. Indeed, a single, well-publicized re-identification event could reshape public perceptions of the values of genomic data sharing. We find that modern somatic SNV prediction pipelines have low germline-leakage rates, which can be further reduced, especially for cloud-sharing, using pre-filtering software.

Published
2018

14. Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection

Author

Lee, Anna Y, Ewing, Adam D, Ellrott, Kyle, Hu, Yin, Houlahan, Kathleen E, Bare, J Christopher, Espiritu, Shadrielle Melijah G, Huang, Vincent, Dang, Kristen, Chong, Zechen, Caloian, Cristian, Yamaguchi, Takafumi N, Kellen, Michael R, Chen, Ken, Norman, Thea C, Friend, Stephen H, Guinney, Justin, Stolovitzky, Gustavo, Haussler, David, Margolin, Adam A, Stuart, Joshua M, and Boutros, Paul C

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Bioinformatics and Computational Biology, Genetics, Oncology and Carcinogenesis, Cancer, Human Genome, Generic health relevance, Algorithms, Benchmarking, Computer Simulation, Crowdsourcing, Databases, Genetic, Genetic Variation, Genome, Human, Genomics, High-Throughput Nucleotide Sequencing, Humans, Neoplasms, Software, Somatic mutations, Simulation, Structural variants, Cancer genomics, Whole-genome sequencing, ICGC-TCGA DREAM Somatic Mutation Calling Challenge Participants, Environmental Sciences, Information and Computing Sciences, Bioinformatics
Abstract

BackgroundThe phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness, and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries; however, there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold standards, extensive resource requirements, and difficulties arising from the need to share personal genomic information.ResultsTo facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowdsourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches.ConclusionsThe synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available at https://github.com/adamewing/bamsurgeon .

Published
2018

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

Author

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

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

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

Published
2017

17. Reducing overfitting in challenge-based competitions

Author

Neto, Elias Chaibub, Hoff, Bruce R, Bare, Chris, Bot, Brian M, Yu, Thomas, Magravite, Lara, Trister, Andrew D, Norman, Thea, Meyer, Pablo, Saez-Rodrigues, Julio, Costello, James C, Guinney, Justin, and Stolovitzky, Gustavo

Subjects
Statistics - Applications
Abstract

Over-fitting is a dreaded foe in challenge-based competitions. Because participants rely on public leaderboards to evaluate and refine their models, there is always the danger they might over-fit to the holdout data supporting the leaderboard. The recently published Ladder algorithm aims to address this problem by preventing the participants from exploiting willingly or inadvertently minor fluctuations in public leaderboard scores during model refinement. In this paper, we report a vulnerability of the Ladder that induces severe over-fitting of the leaderboard when the sample size is small. To circumvent this attack, we propose a variation of the Ladder that releases a bootstrapped estimate of the public leaderboard score instead of providing participants with a direct measure of performance. We also extend the scope of the Ladder to arbitrary performance metrics by relying on a more broadly applicable testing procedure based on the Bayesian bootstrap. Our method makes it possible to use a leaderboard, with the technical and social advantages that it provides, even in cases where data is scant.

Published
2016

18. Inferring causal molecular networks: empirical assessment through a community-based effort

Author

Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V., Favorov, Alexander V., Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W., Long, Byron L., Noren, David P., Bisberg, Alexander J., Mills, Gordon B., Gray, Joe W., Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A., Fertig, Elana J., Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M., Spellman, Paul T., Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, Mukherjee, Sach, Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V., Favorov, Alexander V., Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W., Long, Byron L., Noren, David P., Bisberg, Alexander J., Mills, Gordon B., Gray, Joe W., Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A., Fertig, Elana J., Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M., Spellman, Paul T., Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, and Mukherjee, Sach

Abstract

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Published
2024

19. Action for Increasing Diversity, Market Access, and Capacity in Oncology Registration Trials—Is Africa the Answer? Report From a Satellite Session of the Accelerating Anti-Cancer Agent Development and Validation Workshop

Author

Kizub, Darya, Manner, Cathyryne K., Graef, Katy, Abubakar, Bello, Orem, Jackson, Odedina, Folakemi, Adeyeye, Mojisola Christianah, Nakigudde, Gertrude, Ayalew, Kassa, Kalidas, Chitkala, Lyerly, Herbert Kim, Norman, Thea, Fashoyin-Aje, Lola, Freedman, Jamie, Dent, Jennifer, Cance, Bill, and Gralow, Julie

Published
2022
Full Text
View/download PDF

21. A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis.

Author

Noren, David P, Long, Byron L, Norel, Raquel, Rrhissorrakrai, Kahn, Hess, Kenneth, Hu, Chenyue Wendy, Bisberg, Alex J, Schultz, Andre, Engquist, Erik, Liu, Li, Lin, Xihui, Chen, Gregory M, Xie, Honglei, Hunter, Geoffrey AM, Boutros, Paul C, Stepanov, Oleg, DREAM 9 AML-OPC Consortium, Norman, Thea, Friend, Stephen H, Stolovitzky, Gustavo, Kornblau, Steven, and Qutub, Amina A

Subjects
DREAM 9 AML-OPC Consortium, Humans, Amyotrophic Lateral Sclerosis, Proteome, Treatment Outcome, Risk Assessment, Sensitivity and Specificity, Reproducibility of Results, Algorithms, Outcome and Process Assessment (Health Care), Crowdsourcing, Biomarkers, Outcome and Process Assessment, Health Care, Outcome and Process Assessment, Health Care, Mathematical Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response.

Published
2016

22. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Author

Allen, Genevera I, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher J, Beaton, Derek, Bellotti, Roberto, Bennett, David A, Boehme, Kevin L, Boutros, Paul C, Caberlotto, Laura, Caloian, Cristian, Campbell, Frederick, Neto, Elias Chaibub, Chang, Yu‐Chuan, Chen, Beibei, Chen, Chien‐Yu, Chien, Ting‐Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard JB, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David W, Friend, Stephen H, Fröhlich, Holger, Gan, Jessica, St George‐Hyslop, Peter, Ghosh, Satrajit S, Glaab, Enrico, Green, Robert C, Guan, Yuanfang, Hong, Ming‐Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Iyappan, Anandhi, Jiang, Qijia, Katsumata, Yuriko, Kauwe, John SK, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mario, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjamin A, Lovestone, Simon, Ma, Tsung‐wei, Malhotra, Ashutosh, Mangravite, Lara M, Maxwell, Taylor J, Merrill, Emily, Nagorski, John, Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Newhouse, Stephen J, Norman, Thea C, Nurtdinov, Ramil N, Oyang, Yen‐Jen, Pawitan, Yudi, Peng, Shengwen, Peters, Mette A, Piccolo, Stephen R, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica Y, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi‐An, Tustison, Nicholas J, Varol, Erdem, Vradenburg, George, Weiner, Michael W, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, and Zhu, Hongtu

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Acquired Cognitive Impairment, Prevention, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Alzheimer's Disease, Aging, Brain Disorders, Neurological, Alzheimer Disease, Apolipoproteins E, Biomarkers, Cognition Disorders, Computational Biology, Databases, Bibliographic, Humans, Predictive Value of Tests, Azheimer's disease, Crowdsource, Big data, Bioinformatics, Cognitive decline, Imaging, Genetics, Alzheimer's Disease Neuroimaging Initiative, Geriatrics, Clinical sciences, Biological psychology
Abstract

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

Published
2016

23. Inferring causal molecular networks: empirical assessment through a community-based effort

Author

Hill, Steven M, Heiser, Laura M, Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K, Carlin, Daniel E, Zhang, Yang, Sokolov, Artem, Paull, Evan O, Wong, Chris K, Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V, Favorov, Alexander V, Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W, Long, Byron L, Noren, David P, Bisberg, Alexander J, Mills, Gordon B, Gray, Joe W, Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A, Fertig, Elana J, Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M, Spellman, Paul T, Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, and Mukherjee, Sach

Subjects
Biochemistry and Cell Biology, Biological Sciences, Networking and Information Technology R&D (NITRD), Bioengineering, Cancer, 2.1 Biological and endogenous factors, Generic health relevance, Algorithms, Causality, Computational Biology, Computer Simulation, Gene Expression Profiling, Gene Regulatory Networks, Humans, Models, Biological, Neoplasms, Protein Interaction Mapping, Signal Transduction, Software, Systems Biology, Tumor Cells, Cultured, HPN-DREAM Consortium, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Published
2016

25. Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection

Author

Ewing, Adam D, Houlahan, Kathleen E, Hu, Yin, Ellrott, Kyle, Caloian, Cristian, Yamaguchi, Takafumi N, Bare, J Christopher, P'ng, Christine, Waggott, Daryl, Sabelnykova, Veronica Y, Kellen, Michael R, Norman, Thea C, Haussler, David, Friend, Stephen H, Stolovitzky, Gustavo, Margolin, Adam A, Stuart, Joshua M, and Boutros, Paul C

Subjects
Genetics, Cancer, Human Genome, Algorithms, Benchmarking, Crowdsourcing, Genome, Humans, Neoplasms, Polymorphism, Single Nucleotide, ICGC-TCGA DREAM Somatic Mutation Calling Challenge participants, Biological Sciences, Technology, Medical and Health Sciences, Developmental Biology
Abstract

The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.

Published
2015

27. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data

Author

Abdallah, Kald, Aittokallio, Tero, Airola, Antti, Anghe, Catalina, Azima, Helia, Baertsch, Robert, Ballester, Pedro J, Bare, Chris, Bhandari, Vinayak, Bot, Brian M, Dang, Cuong C, Dunbar, Maria Bekker-Nielsen, Buchardt, Ann-Sophie, Buturovic, Ljubomir, Cao, Da, Chalise, Prabhakar, Cho, Junwoo, Chu, Tzu-Ming, Coley, R Yates, Conjeti, Sailesh, Correia, Sara, Costello, James C, Dai, Ziwei, Dai, Junqiang, Dargatz, Philip, Delavarkhan, Sam, Deng, Detian, Dhanik, Ankur, Du, Yu, Elangovan, Aparna, Ellis, Shellie, Elo, Laura L, Espiritu, Shadrielle M, Fan, Fan, Farshi, Ashkan B, Freitas, Ana, Fridley, Brooke, Friend, Stephen, Fuchs, Christiane, Gofer, Eyal, Peddinti, Gopalacharyulu, Graw, Stefan, Greiner, Russ, Guan, Yuanfang, Guinney, Justin, Guo, Jing, Gupta, Pankaj, Guyer, Anna I, Han, Jiawei, Hansen, Niels R, Chang, Billy HW, Hirvonen, Outi, Huang, Barbara, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph G, Jayaswa, Vivek, Jeon, Jouhyun, Ji, Zhicheng, Juvvadi, Deekshith, Jyrkkiö, Sirkku, Kanigel-Winner, Kimberly, Katouzian, Amin, Kazanov, Marat D, Khan, Suleiman A, Khayyer, Shahin, Kim, Dalho, Golinska, Agnieszka K, Koestler, Devin, Kokowicz, Fernanda, Kondofersky, Ivan, Krautenbacher, Norbert, Krstajic, Damjan, Kumar, Luke, Kurz, Christoph, Kyan, Matthew, Laajala, Teemu D, Laimighofer, Michael, Lee, Eunjee, Lesinski, Wojciech, Li, Miaozhu, Li, Ye, Lian, Qiuyu, Liang, Xiaotao, Lim, Minseong, Lin, Henry, Lin, Xihui, Lu, Jing, Mahmoudian, Mehrad, Manshaei, Roozbeh, Meier, Richard, Miljkovic, Dejan, Mirtti, Tuomas, Mnich, Krzysztof, Navab, Nassir, Neto, Elias C, Newton, Yulia, Norman, Thea, Pahikkala, Tapio, Pal, Subhabrata, Park, Byeongju, Patel, Jaykumar, Pathak, Swetabh, Pattin, Alejandrina, Ankerst, Donna P, Peng, Jian, Petersen, Anne H, Philip, Robin, Piccolo, Stephen R, Pölsterl, Sebastian, Polewko-Klim, Aneta, Rao, Karthik, Ren, Xiang, Rocha, Miguel, Rudnicki, Witold R., Ryan, Charles J, Ryu, Hyunnam, Sartor, Oliver, Scherb, Hagen, Sehgal, Raghav, Seyednasrollah, Fatemeh, Shang, Jingbo, Shao, Bin, Shen, Liji, Sher, Howard, Shiga, Motoki, Sokolov, Artem, Söllner, Julia F, Song, Lei, Soule, Howard, Stolovitzky, Gustavo, Stuart, Josh, Sun, Ren, Sweeney, Christopher J, Tahmasebi, Nazanin, Tan, Kar-Tong, Tomaziu, Lisbeth, Usset, Joseph, Vang, Yeeleng S, Vega, Roberto, Vieira, Vitor, Wang, David, Wang, Difei, Wang, Junmei, Wang, Lichao, Wang, Sheng, Wang, Tao, Wang, Yue, Wolfinger, Russ, Wong, Chris, Wu, Zhenke, Xiao, Jinfeng, Xie, Xiaohui, Xie, Yang, Xin, Doris, Yang, Hojin, Yu, Nancy, Yu, Thomas, Yu, Xiang, Zahedi, Sulmaz, Zanin, Massimiliano, Zhang, Chihao, Zhang, Jingwen, Zhang, Shihua, Zhang, Yanchun, Zhou, Fang Liz, Zhu, Hongtu, Zhu, Shanfeng, Zhu, Yuxin, Winner, Kimberly Kanigel, Bare, J Christopher, Neto, Elias Chaibub, Peddinti, Gopal, and Scher, Howard I

Published
2017
Full Text
View/download PDF

29. Leveraging crowdsourcing to accelerate global health solutions

Author

Davis, Sage, Button-Simons, Katrina, Bensellak, Taoufik, Ahsen, Eren Mehmet, Checkley, Lisa, Foster, Gabriel J., Su, Xinzhuan, Moussa, Ahmed, Mapiye, Darlington, Khoo, Sok Kean, Nosten, Francois, Anderson, Timothy J. C., Vendrely, Katelyn, Bletz, Julie, Yu, Thomas, Panji, Sumir, Ghouila, Amel, Mulder, Nicola, Norman, Thea, Kern, Steven, Meyer, Pablo, Stolovitzky, Gustavo, Ferdig, Michael T., and Siwo, Geoffrey H.

Published
2019
Full Text
View/download PDF

32. A Maturity Model for the Scientific Review of Clinical Trial Designs and Their Informativeness

Author

Dolley, Shawn, Norman, Thea, McNair, Doug, and Hartman, Dan

Subjects
health_policy_and_services_185
Abstract

Many clinical trials end uninformatively. Informativeness, in the context of clinical trials, defines whether a study’s results definitively answer its research questions with meaningful next steps. One subset of these trials are those focused on global health set in low-resource settings. Global health clinical trials benefitting people in low-resource settings are funded primarily by a limited number of large foundations, pharmaceutical firms (“industry”), and national governments. While clinical trial protocols are required to go through reviews in regulatory and ethical domains, outside of industry-funded trials, funders rarely require focused scientific design reviews. There are no documented standards and processes, or even best practices, for funders to perform scientific reviews after the funding commitment. Considering the investment in and standardization of ethical and regulatory reviews, and the prevalence of studies ending without clarity or never finishing, it may be that scientific reviews of trial designs with a focus on informativeness offer the best chance for improved outcomes and return on investments in clinical trials. A maturity model is a helpful tool for knowledge transfer to help grow capabilities in a new area, or for those looking to perform a self-assessment in an existing area. Such a model is offered for scientific design reviews of clinical trial protocols: a valuable and often-neglected governance step for funders or sponsors, among others. This maturity model includes 11 process areas and 5 maturity levels. Each of the 55 process area levels is populated with descriptions on a continuum toward an optimal state to improve trial protocols in the area of risk of failure. This tool allows for prescriptive guidance on next investments to improve attributes of post-funding reviews of trials, with a focus on informativeness.

Published
2023

33. Optimized clinical performance of growth hormone with an expanded genetic code

Author

Cho, Ho, Daniel, Tom, Buechler, Ying Ji, Litzinger, David C., Maio, Zhenwei, Putnam, Anna-Maria Hays, Kraynov, Vadim S., Sim, Bee-Cheng, Bussell, Stuart, Javahishvili, Tsotne, Kaphle, Sami, Viramontes, Guillermo, Ong, Mike, Chu, Stephanie, GC, Becky, Lieu, Ricky, Knudsen, Nick, Castiglioni, Paola, Norman, Thea C., Axelrod, Douglas W., Hoffman, Andrew R., Schultz, Peter G., DiMarchi, Richard D., and Kimmel, Bruce E.

Published
2011

34. Hvorfor blir flere unge voksne mottakere av uføretrygd?

Author

Norman, Thea and Blekesaune, Morten

Abstract

Min oppgave tar for seg undersøkelser av mulige årsaker til økningen i andel og antall unge voksne mottakere av uføretrygd, særlig i tidsperioden 2009 til 2018. Bakgrunnen for valget av tematikken er den økte bekymringen i samfunnet tilknyttet at det foreligger en økning i unge uføre. En vekst i unge mottakere av uføretrygd kan være negativt for velferdsstaten, og videre en utfordring i henhold til arbeidslinjas formål som innebærer flere mennesker i jobb og færre på trygd. At det foreligger økning i unge voksne på uføretrygd kan også indikere samfunnsmessige utfordringer med å inkludere unge voksne med helsesvikt inn i arbeidslivet, og videre bli i en del av sysselsettingen. Å foreta en undersøkelser av økningen, og videre finne mulige årsaker til denne økningen, kan bidra til å bevisstgjøre og skape ideer til ulike tiltak som kan iverksettes for å motvirke økningen. Hovedproblemstillingen min oppgave reiser er: Hva er årsakene til at det foreligger en økning i både andel og antall unge voksne personer som får innvilget uføretrygd i tidsperioden fra 2009 og frem til 2018? For å undersøke og videre belyse mulige årsaker til økt overgang til uføretrygd blant unge voksne, vil jeg undersøke følgende to muligheter: 1.Kan økningen i uføretrygding av unge voksne skyldes at flere unge voksner har helseproblemer? 2.Kan økningen i uføretrygding skyldes forskyvninger mellom forskjellige forsørgelsesformer? I løpet av oppgaven foretar jeg undersøkelser av tre ulike kvantitative data. Jeg foretar undersøkelser av endringer og utviklingen mottak av uføretrygd og andre forsørgelsesmetoder, jeg anvender analyse av forløpene frem mot uføretrygd ved 30 års alder og undersøker geografiske variasjoner. Mine funn indikerer at den økte andelen av unge uføre i tidsperioden 2009 til 2018 må ses i lys av flere forhold; utdanningsnivå, tilknytning til arbeidsmarkedet, oppvekstsvilkår, endringer i krav og forventninger på arbeidsmarkedet, regelverksendringer (særlig AAP-regelverket), forvaltningspraksis og helsetilstanden til unge. Antall ord i oppgaven (eksl. litteraturliste, sammendrag og innholdsfortegnelse): 29600.

Published
2022

35. A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery

Author

Zhang, Hongjiu, Wang, Yifan, Guan, Yuanfang, Nguyen, Cu, Sugai, Christopher, Jha, Alokkumar, Li, Jing Woei, Dobin, Alexander, Creason, Allison, Haan, David, Dang, Kristen, Chiotti, Kami E., Inkman, Matthew, Lamb, Andrew, Yu, Thomas, Hu, Yin, Norman, Thea C., Buchanan, Alex, van Baren, Marijke J., Spangler, Ryan, Rollins, M. Rick, Spellman, Paul T., Rozanov, Dmitri, Zhang, Jin, Maher, Christopher A., Caloian, Cristian, Watson, John D., Uhrig, Sebastian, Haas, Brian J., Jain, Miten, Akeson, Mark, Ahsen, Mehmet Eren, Stolovitzky, Gustavo, Guinney, Justin, Boutros, Paul C., Stuart, Joshua M., and Ellrott, Kyle

Published
2021
Full Text
View/download PDF

38. FGF21 Analogs of Sustained Action Enabled by Orthogonal Biosynthesis Demonstrate Enhanced Antidiabetic Pharmacology in Rodents

Author

Mu, James, Pinkstaff, Jason, Li, Zhihua, Skidmore, Lillian, Li, Nina, Myler, Heather, Dallas-Yang, Qing, Putnam, Anna-Maria, Yao, Jun, Bussell, Stuart, Wu, Margaret, Norman, Thea C., Rodriguez, Carlos G., Kimmel, Bruce, Metzger, Joseph M., Manibusan, Anthony, Lee, Darin, Zaller, Dennis M., Zhang, Bei B., DiMarchi, Richard D., Berger, Joel P., and Axelrod, Douglas W.

Published
2012
Full Text
View/download PDF

40. Penicillium Genomics

Author

Royer, John C., primary, Madden, Kevin T., additional, Norman, Thea C., additional, and LoBuglio, Katherine F., additional

Published
2004
Full Text
View/download PDF

41. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

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

Subjects
health care economics and organizations
Abstract

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. AstraZeneca European Union Horizon 2020 research [668858 PrECISE] Joint Research Center for Computational Biomedicine (Bayer AG) National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences Wellcome Trust [102696, 206194] We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).

Published
2019

42. Assessment of network module identification across complex diseases

Author

Choobdar, Sarvenaz, Ahsen, Mehmet E., Natoli, Ted, Lysenko, Artem, Ma, Tianle, Mall, Raghvendra, Marbach, Daniel, Mattia, Tomasoni, Medvedovic, Mario, Menche, Jörg, Mercer, Johnathan, Micarelli, Elisa, Monaco, Alfonso, Narayan, Rajiv, Müller, Felix, Narykov, Oleksandr, Norman, Thea, Park, Sungjoon, Perfetto, Livia, Perrin, Dimitri, Pirrò, Stefano, Przytycka, Teresa M., DREAM Module Identification Challenge Consortium, Qian, Xiaoning, Raman, Karthik, Ramazzotti, Daniele, Ramsahai, Emilie, Ravindran, Balaraman, Rennert, Philip, Sáez Rodríguez, Julio, Schärfe, Charlotta, Sharan, Roded, Shi, Ning, Subramanian, Aravind, Shin, Wonho, Shu, Hai, Sinha, Himanshu, Slonim, Donna K., Spinelli, Lionel, Srinivasan, Suhas, Suver, Christine, Szklarczyk, Damian, Tangaro, Sabina, Zhang, Jitao D., Thiagarajan, Suresh, Tichit, Laurent, Tiede, Thorsten, Tripathi, Beethika, Tsherniak, Aviad, Tsunoda, Tatsuhiko, Türei, Dénes, Ullah, Ehsan, Vahedi, Golnaz, Valdeolivas, Alberto, Stolovitzky, Gustavo, Vivek, Jayaswal, von Mering, Christian, Waagmeester, Andra, Wang, Bo, Wang, Yijie, Weir, Barbara A., White, Shana, Winkler, Sebastian, Xu, Ke, Xu, Taosheng, Kutalik, Zoltán, Yan, Chunhua, Yang, Liuqing, Yu, Kaixian, Yu, Xiangtian, Zaffaroni, Gaia, Zaslavskiy, Mikhail, Zeng, Tao, Zhang, Lu, Zhang, Weijia, Lage, Kasper, Zhang, Lixia, Zhang, Xinyu, Zhang, Junpeng, Zhou, Xin, Zhou, Jiarui, Zhu, Hongtu, Zhu, Junjie, Zuccon, Guido, Crawford, Jake, Cowen, Lenore J., Bergmann, Sven, Aicheler, Fabian, Amoroso, Nicola, Arenas, Alex, Azhagesan, Karthik, Baker, Aaron, Banf, Michael, Batzoglou, Serafim, Tomasoni, Mattia, Baudot, Anaïs, Bellotti, Roberto, Boroevich, Keith A., Brun, Christine, Cai, Stanley, Caldera, Michael, Calderone, Alberto, Cesareni, Gianni, Chen, Weiqi, Fang, Tao, Chichester, Christine, Cowen, Lenore, Cui, Hongzhu, Dao, Phuong, De Domenico, Manlio, Dhroso, Andi, Didier, Gilles, Divine, Mathew, Lamparter, David, Del Sol, Antonio, Feng, Xuyang, Flores-Canales, Jose C., Fortunato, Santo, Gitter, Anthony, Gorska, Anna, Guan, Yuanfang, Guénoche, Alain, Gómez, Sergio, Lin, Junyuan, Hamza, Hatem, Hartmann, András, He, Shan, Heijs, Anton, Heinrich, Julian, Hescott, Benjamin, Hu, Xiaozhe, Hu, Ying, Huang, Xiaoqing, Hughitt, V. Keith, Jeon, Minji, Jeub, Lucas, Johnson, Nathan T., Joo, Keehyoung, Joung, InSuk, Jung, Sascha, Kalko, Susana G., Kamola, Piotr J., Kang, Jaewoo, Kaveelerdpotjana, Benjapun, Kim, Minjun, Kim, Yoo-Ah, Kohlbacher, Oliver, Korkin, Dmitry, Krzysztof, Kiryluk, Kunji, Khalid, Kutalik, Zoltàn, Lang-Brown, Sean, Le, Thuc Duy, Lee, Jooyoung, Lee, Sunwon, Lee, Juyong, Li, Dong, Li, Jiuyong, Liu, Lin, Loizou, Antonis, Luo, Zhenhua, Choobdar, Sarvenaz, Ahsen, Mehmet E., Crawford, Jake, Tomasoni, Mattia, Le, Thuc Duy, Li, Jiuyong, Liu, Lin, Zhang, W, Marbach, D, The DREAM Module Identification Challenge Consortium, Choobdar, S, Ahsen, M, Crawford, J, Tomasoni, M, Fang, T, Lamparter, D, Lin, J, Hescott, B, Hu, X, Mercer, J, Natoli, T, Narayan, R, Aicheler, F, Amoroso, N, Arenas, A, Azhagesan, K, Baker, A, Banf, M, Batzoglou, S, Baudot, A, Bellotti, R, Bergmann, S, Boroevich, K, Brun, C, Cai, S, Caldera, M, Calderone, A, Cesareni, G, Chen, W, Chichester, C, Cowen, L, Cui, H, Dao, P, De Domenico, M, Dhroso, A, Didier, G, Divine, M, del Sol, A, Feng, X, Flores-Canales, J, Fortunato, S, Gitter, A, Gorska, A, Guan, Y, Guenoche, A, Gomez, S, Hamza, H, Hartmann, A, He, S, Heijs, A, Heinrich, J, Hu, Y, Huang, X, Hughitt, V, Jeon, M, Jeub, L, Johnson, N, Joo, K, Joung, I, Jung, S, Kalko, S, Kamola, P, Kang, J, Kaveelerdpotjana, B, Kim, M, Kim, Y, Kohlbacher, O, Korkin, D, Krzysztof, K, Kunji, K, Kutalik, Z, Lage, K, Lang-Brown, S, Le, T, Lee, J, Lee, S, Li, D, Li, J, Liu, L, Loizou, A, Luo, Z, Lysenko, A, Ma, T, Mall, R, Mattia, T, Medvedovic, M, Menche, J, Micarelli, E, Monaco, A, Muller, F, Narykov, O, Norman, T, Park, S, Perfetto, L, Perrin, D, Pirro, S, Przytycka, T, Qian, X, Raman, K, Ramazzotti, D, Ramsahai, E, Ravindran, B, Rennert, P, Saez-Rodriguez, J, Scharfe, C, Sharan, R, Shi, N, Shin, W, Shu, H, Sinha, H, Slonim, D, Spinelli, L, Srinivasan, S, Subramanian, A, Suver, C, Szklarczyk, D, Tangaro, S, Thiagarajan, S, Tichit, L, Tiede, T, Tripathi, B, Tsherniak, A, Tsunoda, T, Turei, D, Ullah, E, Vahedi, G, Valdeolivas, A, Vivek, J, von Mering, C, Waagmeester, A, Wang, B, Wang, Y, Weir, B, White, S, Winkler, S, Xu, K, Xu, T, Yan, C, Yang, L, Yu, K, Yu, X, Zaffaroni, G, Zaslavskiy, M, Zeng, T, Zhang, J, Zhang, L, Zhang, X, Zhou, X, Zhou, J, Zhu, H, Zhu, J, Zuccon, G, Stolovitzky, G, Spinelli, Lionel, Institut de Mathématiques de Marseille (I2M), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Marseille medical genetics - Centre de génétique médicale de Marseille (MMG), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Theories and Approaches of Genomic Complexity (TAGC), DREAM Module Identification Challenge Consortium, Aicheler, F., Amoroso, N., Arenas, A., Azhagesan, K., Baker, A., Banf, M., Batzoglou, S., Baudot, A., Bellotti, R., Bergmann, S., Boroevich, K.A., Brun, C., Cai, S., Caldera, M., Calderone, A., Cesareni, G., Chen, W., Chichester, C., Choobdar, S., Cowen, L., Crawford, J., Cui, H., Dao, P., De Domenico, M., Dhroso, A., Didier, G., Divine, M., Del Sol, A., Fang, T., Feng, X., Flores-Canales, J.C., Fortunato, S., Gitter, A., Gorska, A., Guan, Y., Guénoche, A., Gómez, S., Hamza, H., Hartmann, A., He, S., Heijs, A., Heinrich, J., Hescott, B., Hu, X., Hu, Y., Huang, X., Hughitt, V.K., Jeon, M., Jeub, L., Johnson, N.T., Joo, K., Joung, I., Jung, S., Kalko, S.G., Kamola, P.J., Kang, J., Kaveelerdpotjana, B., Kim, M., Kim, Y.A., Kohlbacher, O., Korkin, D., Krzysztof, K., Kunji, K., Kutalik, Z., Lage, K., Lamparter, D., Lang-Brown, S., Le, T.D., Lee, J., Lee, S., Li, D., Li, J., Lin, J., Liu, L., Loizou, A., Luo, Z., Lysenko, A., Ma, T., Mall, R., Marbach, D., Mattia, T., Medvedovic, M., Menche, J., Mercer, J., Micarelli, E., Monaco, A., Müller, F., Narayan, R., Narykov, O., Natoli, T., Norman, T., Park, S., Perfetto, L., Perrin, D., Pirrò, S., Przytycka, T.M., Qian, X., Raman, K., Ramazzotti, D., Ramsahai, E., Ravindran, B., Rennert, P., Saez-Rodriguez, J., Schärfe, C., Sharan, R., Shi, N., Shin, W., Shu, H., Sinha, H., Slonim, D.K., Spinelli, L., Srinivasan, S., Subramanian, A., Suver, C., Szklarczyk, D., Tangaro, S., Thiagarajan, S., Tichit, L., Tiede, T., Tripathi, B., Tsherniak, A., Tsunoda, T., Türei, D., Ullah, E., Vahedi, G., Valdeolivas, A., Vivek, J., von Mering, C., Waagmeester, A., Wang, B., Wang, Y., Weir, B.A., White, S., Winkler, S., Xu, K., Xu, T., Yan, C., Yang, L., Yu, K., Yu, X., Zaffaroni, G., Zaslavskiy, M., Zeng, T., Zhang, J.D., Zhang, L., Zhang, W., Zhang, X., Zhang, J., Zhou, X., Zhou, J., Zhu, H., Zhu, J., and Zuccon, G.

Subjects
Identification methods, Cellular signalling networks, Computer science, Population genetics, [SDV]Life Sciences [q-bio], Quantitative Trait Loci, Gene regulatory network, DREAM challenge, network, modules, predictions, Genome-wide association study, Computational biology, Biochemistry, Models, Biological, Polymorphism, Single Nucleotide, Gene regulatory networks, Functional clustering, 03 medical and health sciences, Human disease, Humans, Disease, ddc:610, Protein Interaction Maps, Molecular Biology, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, Network module, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Network topology, Gene Expression Profiling, Computational Biology, Cell Biology, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Gene expression profiling, [SDV] Life Sciences [q-bio], Molecular network, Phenotype, Protein network, Network Module Identification, Analysis, Algorithms, Biotechnology, Genome-Wide Association Study
Abstract

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology., In this DREAM challenge, 75 methods for the identification of disease-relevant modules from molecular networks are compared and validated with GWAS data. The authors provide practical guidelines for users and establish benchmarks for network analysis.

Published
2019

43. Synthesis of petrosins C and D

Author

Heathcock, Clayton H., Brown, Richard C.D., and Norman, Thea C.

Subjects
Organic compounds -- Synthesis, Alkaloids -- Research, Pyridine -- Research, Cyclic compounds -- Research, Biological sciences, Chemistry
Abstract

Petrosins C and D were synthesized and characterized. The procedure involved the production of a 16-membered bis-pyridine intermediate, followed by the formation of the macrocycle using Z double bonds in the six-carbon chains conecting the two pyridine rings. Bisannulation was then consummated via hydroboration and transformation of the primary alcohols into methanesulphonate substituents, which cyclized to produce bis-pyridinium derivatives.

Published
1998

44. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

Gönen, Mehmet (ORCID 0000-0002-2483-075X & YÖK ID 237468), Menden, Michael P.; Wang, Dennis; Mason, Mike J.; Szalai, Bence; Bulusu, Krishna C.; Guan, Yuanfang; Yu, Thomas; Kang, Jaewoo; Jeon, Minji; Wolfinger, Russ; Nguyen, Tin; Zaslavskiy, Mikhail; Jang, In Sock; Ghazoui, Zara; Ahsen, Mehmet Eren; Vogel, Robert; Neto, Elias Chaibub; Norman, Thea; Tang, Eric K. Y.; Garnett, Mathew J.; Di Veroli, Giovanni Y.; Fawell, Stephen; Stolovitzky, Gustavo; Guinney, Justin; Dry, Jonathan R.; Saez-Rodriguez, Julio; Abante, Jordi; Abecassis, Barbara Schmitz; Aben, Nanne; Aghamirzaie, Delasa; Aittokallio, Tero; Akhtari, Farida S.; Al-lazikani, Bissan; Alam, Tanvir; Allam, Amin; Allen, Chad; de Almeida, Mariana Pelicano; Altarawy, Doaa; Alves, Vinicius; Amadoz, Alicia; Anchang, Benedict; Antolin, Albert A.; Ash, Jeremy R.; Romeo Aznar, Victoria; Ba-alawi, Wail; Bagheri, Moeen; Bajic, Vladimir; Ball, Gordon; Ballester, Pedro J.; Baptista, Delora; Bare, Christopher; Bateson, Mathilde; Bender, Andreas; Bertrand, Denis; Wijayawardena, Bhagya; Boroevich, Keith A.; Bosdriesz, Evert; Bougouffa, Salim; Bounova, Gergana; Brouwer, Thomas; Bryant, Barbara; Calaza, Manuel; Calderone, Alberto; Calza, Stefano; Capuzzi, Stephen; Carbonell-Caballero, Jose; Carlin, Daniel; Carter, Hannah; Castagnoli, Luisa; Celebi, Remzi; Cesareni, Gianni; Chang, Hyeokyoon; Chen, Guocai; Chen, Haoran; Chen, Huiyuan; Cheng, Lijun; Chernomoretz, Ariel; Chicco, Davide; Cho, Kwang-Hyun; Cho, Sunghwan; Choi, Daeseon; Choi, Jaejoon; Choi, Kwanghun; Choi, Minsoo; De Cock, Martine; Coker, Elizabeth; Cortes-Ciriano, Isidro; Cserzo, Miklos; Cubuk, Cankut; Curtis, Christina; Van Daele, Dries; Dang, Cuong C.; Dijkstra, Tjeerd; Dopazo, Joaquin; Draghici, Sorin; Drosou, Anastasios; Dumontier, Michel; Ehrhart, Friederike; Eid, Fatma-Elzahraa; ElHefnawi, Mahmoud; Elmarakeby, Haitham; van Engelen, Bo; Engin, Hatice Billur; de Esch, Iwan; Evelo, Chris; Falcao, Andre O.; Farag, Sherif; Fernandez-Lozano, Carlos; Fisch, Kathleen; Flobak, Asmund; Fornari, Chiara; Foroushani, Amir B. K.; Fotso, College of Engineering, Department of Industrial Engineering, Gönen, Mehmet (ORCID 0000-0002-2483-075X & YÖK ID 237468), Menden, Michael P.; Wang, Dennis; Mason, Mike J.; Szalai, Bence; Bulusu, Krishna C.; Guan, Yuanfang; Yu, Thomas; Kang, Jaewoo; Jeon, Minji; Wolfinger, Russ; Nguyen, Tin; Zaslavskiy, Mikhail; Jang, In Sock; Ghazoui, Zara; Ahsen, Mehmet Eren; Vogel, Robert; Neto, Elias Chaibub; Norman, Thea; Tang, Eric K. Y.; Garnett, Mathew J.; Di Veroli, Giovanni Y.; Fawell, Stephen; Stolovitzky, Gustavo; Guinney, Justin; Dry, Jonathan R.; Saez-Rodriguez, Julio; Abante, Jordi; Abecassis, Barbara Schmitz; Aben, Nanne; Aghamirzaie, Delasa; Aittokallio, Tero; Akhtari, Farida S.; Al-lazikani, Bissan; Alam, Tanvir; Allam, Amin; Allen, Chad; de Almeida, Mariana Pelicano; Altarawy, Doaa; Alves, Vinicius; Amadoz, Alicia; Anchang, Benedict; Antolin, Albert A.; Ash, Jeremy R.; Romeo Aznar, Victoria; Ba-alawi, Wail; Bagheri, Moeen; Bajic, Vladimir; Ball, Gordon; Ballester, Pedro J.; Baptista, Delora; Bare, Christopher; Bateson, Mathilde; Bender, Andreas; Bertrand, Denis; Wijayawardena, Bhagya; Boroevich, Keith A.; Bosdriesz, Evert; Bougouffa, Salim; Bounova, Gergana; Brouwer, Thomas; Bryant, Barbara; Calaza, Manuel; Calderone, Alberto; Calza, Stefano; Capuzzi, Stephen; Carbonell-Caballero, Jose; Carlin, Daniel; Carter, Hannah; Castagnoli, Luisa; Celebi, Remzi; Cesareni, Gianni; Chang, Hyeokyoon; Chen, Guocai; Chen, Haoran; Chen, Huiyuan; Cheng, Lijun; Chernomoretz, Ariel; Chicco, Davide; Cho, Kwang-Hyun; Cho, Sunghwan; Choi, Daeseon; Choi, Jaejoon; Choi, Kwanghun; Choi, Minsoo; De Cock, Martine; Coker, Elizabeth; Cortes-Ciriano, Isidro; Cserzo, Miklos; Cubuk, Cankut; Curtis, Christina; Van Daele, Dries; Dang, Cuong C.; Dijkstra, Tjeerd; Dopazo, Joaquin; Draghici, Sorin; Drosou, Anastasios; Dumontier, Michel; Ehrhart, Friederike; Eid, Fatma-Elzahraa; ElHefnawi, Mahmoud; Elmarakeby, Haitham; van Engelen, Bo; Engin, Hatice Billur; de Esch, Iwan; Evelo, Chris; Falcao, Andre O.; Farag, Sherif; Fernandez-Lozano, Carlos; Fisch, Kathleen; Flobak, Asmund; Fornari, Chiara; Foroushani, Amir B. K.; Fotso, College of Engineering, and Department of Industrial Engineering

Abstract

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells., AstraZeneca; European Union Horizon 2020 Research; European Union (European Union); Horizon 2020; Joint Research Center for Computational Biomedicine; National Institute for Health Research (NIHR) Sheffield Biomedical Research Center; Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences; Wellcome Trust

Published
2019

45. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

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

Abstract

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Published
2019

46. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Author

Sieberts, Solveig K., Zhu, Fan, García-García, Javier, Stahl, Eli, Pratap, Abhishek, Pandey, Gaurav, Pappas, Dimitrios, Aguilar, Daniel, Anton, Bernat, Bonet, Jaume, Eksi, Ridvan, Fornés, Oriol, Guney, Emre, Li, Hongdong, Marín, Manuel Alejandro, Panwar, Bharat, Planas-Iglesias, Joan, Poglayen, Daniel, Cui, Jing, Falcao, Andre O., Suver, Christine, Hoff, Bruce, Balagurusamy, Venkat S. K., Dillenberger, Donna, Neto, Elias Chaibub, Norman, Thea, Aittokallio, Tero, Ammad-ud-din, Muhammad, Azencott, Chloe-Agathe, Bellón, Víctor, Boeva, Valentina, Bunte, Kerstin, Chheda, Himanshu, Cheng, Lu, Corander, Jukka, Dumontier, Michel, Goldenberg, Anna, Gopalacharyulu, Peddinti, Hajiloo, Mohsen, Hidru, Daniel, Jaiswal, Alok, Kaski, Samuel, Khalfaoui, Beyrem, Khan, Suleiman Ali, Kramer, Eric R., Marttinen, Pekka, Mezlini, Aziz M., Molparia, Bhuvan, Pirinen, Matti, Saarela, Janna, Samwald, Matthias, Stoven, Véronique, Tang, Hao, Tang, Jing, Torkamani, Ali, Vert, Jean-Phillipe, Wang, Bo, Wang, Tao, Wennerberg, Krister, Wineinger, Nathan E., Xiao, Guanghua, Xie, Yang, Yeung, Rae, Zhan, Xiaowei, Zhao, Cheng, Calaza, Manuel, Elmarakeby, Haitham, Heath, Lenwood S., Long, Quan, Moore, Jonathan D., Opiyo, Stephen Obol, Savage, Richard S., Zhu, Jun, Greenberg, Jeff, Kremer, Joel, Michaud, Kaleb, Barton, Anne, Coenen, Marieke, Mariette, Xavier, Miceli, Corinne, Shadick, Nancy, Weinblatt, Michael, de Vries, Niek, Tak, Paul P., Gerlag, Danielle, Huizinga, Tom W. J., Kurreeman, Fina, Allaart, Cornelia F., Louis Bridges Jr., S., Criswell, Lindsey, Moreland, Larry, Klareskog, Lars, Saevarsdottir, Saedis, Padyukov, Leonid, Gregersen, Peter K., Friend, Stephen, Plenge, Robert, Stolovitzky, Gustavo, Oliva, Baldo, Guan, Yuanfang, Mangravite, Lara M., Bridges, S. Louis, Intelligent Systems, Institute for Molecular Medicine Finland, Department of Mathematics and Statistics, Helsinki Institute for Information Technology, Jukka Corander / Principal Investigator, Department of Computer Science, Janna Saarela / Principal Investigator, Krister Wennerberg / Principal Investigator, Biostatistics Helsinki, Statistical and population genetics, AII - Amsterdam institute for Infection and Immunity, Clinical Immunology and Rheumatology, Zhejiang University, Universidad del Pais Vasco / Euskal Herriko Unibertsitatea [Espagne] (UPV/EHU), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), DYNBIO LEGOS, Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Institut d'Ecologia Aquàtica, Universitat de Girona (UdG), Department of Medical Genetics, Faculty of Medicine-Child and Family Research Institute-Centre for Molecular Medicine and Therapeutics-University of British Columbia (UBC), China Agricultural University (CAU), Computational Intelligence Research Group (CA3), Centre of Technology and Systems (CTS), Faculdade de Ciências e Tecnologia = School of Science & Technology (FCT NOVA), Universidade Nova de Lisboa = NOVA University Lisbon (NOVA)-Universidade Nova de Lisboa = NOVA University Lisbon (NOVA)-Faculdade de Ciências e Tecnologia = School of Science & Technology (FCT NOVA), Universidade Nova de Lisboa = NOVA University Lisbon (NOVA)-Universidade Nova de Lisboa = NOVA University Lisbon (NOVA), 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), Hefei University of Technology (HFUT), Stanford Center for BioMedical Informatics Research (BMIR), Stanford University, Service de génétique [Rouen], CHU Rouen, Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU), Technical Research Centre of Finland, VTT Technical Research Centre of Finland (VTT), U.S. Food and Drug Administration (FDA), Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Profium Oy [Helsinki], Chinese Academy of Agricultural Sciences (CAAS), Business School [Hohai University], Hohai University, Institute of Industrial Economics [Hohai University], State Key Laboratory of Medical Genetics of China, Hunan Medical College, State Key Laboratory of Advanced Special Steel, Shanghai University, Quantitative Biomedical Research Center (QBRC), University of Texas Southwestern Medical Center [Dallas], Harold C. Simmons Comprehensive Cancer Center [Dallas, TX, États-Unis], Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique (LHEEA), École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), China National Research Center of Intelligent Equipment for Agriculture [Beijing] (NRCIEA), Centro Nacional de Análisi Genómico (CNAG), Centro Nacional de Análisis Genómico, Gregor Mendel Institute of Molecular Plant Biology (GMI), Austrian Academy of Sciences (OeAW), Department of Mechanical and Aerospace Engineering [Univ California Davis] (MAE - UC Davis), University of California [Davis] (UC Davis), University of California (UC)-University of California (UC), Tongji University, Laboratory for Measles and Rubella, Laboratoire National de Santé [Luxembourg] (LNS), Department of Human Genetics, Radboud University Medical Center [Nijmegen], INSERM U1012, AP-HP Hôpital Bicêtre (Le Kremlin-Bicêtre), School of biosciences and biotechnology, University of Camerino, Università degli Studi di Camerino = University of Camerino (UNICAM), Division of Rheumatology, Immunology and Allergy and Partners Asthma Center, Brigham and Women's Hospital [Boston], Department of Health Education and Promotion, Maastricht University [Maastricht], University of Amsterdam [Amsterdam] (UvA), Department of rheumatology, Leiden University Medical Center (LUMC), Universiteit Leiden-Universiteit Leiden, Rheumatology Unit, Department of Medicine, University of California [San Francisco] (UC San Francisco), University of California (UC), Rheumatology Unit, Karolinska Institutet [Stockholm], Merck & Co. Inc, Structural Bioinformatics Group (GRIB), Barcelona Research Park of Biomedicine (PRBB), Universitat Pompeu Fabra [Barcelona] (UPF), Capital Normal University [Beijing], Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), MINES ParisTech - École nationale supérieure des mines de Paris, California Institute of Technology (CALTECH)-NASA, Department of Mechanical and Aerospace Engineering [Davis], University of California-University of California, Università degli Studi di Camerino (UNICAM), University of California [San Francisco] (UCSF), University of California, Sage Bionetworks, University of Michigan, Ann Arbor, Pompeu Fabra University, Icahn School of Medicine at Mount Sinai, Corrona LLC, Northeastern University, Harvard University, University of Lisbon, IBM, INSERM U900, University of Helsinki, University of Toronto, Scripps Research Institute, Medical University of Vienna, University of Texas at Dallas, New York University, Albany Medical College, Arthritis Research Center Foundation, Radboud University Nijmegen, INSERM U1184, University of Amsterdam, Glaxo Smith Kline, Leiden University, University of Alabama at Birmingham, University of California San Francisco, University of Pittsburgh, Karolinska Institutet, North Shore-Long Island Jewish Health System, Merck, Aalto-yliopisto, and Aalto University

Subjects
Male, 0301 basic medicine, General Physics and Astronomy, Arthritis, Genome-wide association study, Bioinformatics, DISEASE, Arthritis, Rheumatoid, Cohort Studies, Missing heritability problem, MISSING HERITABILITY, Medicine, Certolizumab pegol, RISK, Multidisciplinary, Antibodies, Monoclonal, Middle Aged, Prognosis, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, STACKED GENERALIZATION, Treatment Outcome, Antirheumatic Agents, Crowdsourcing, Biomarker (medicine), Female, Erratum, medicine.drug, Adult, Science, Context (language use), Antibodies, Monoclonal, Humanized, Polymorphism, Single Nucleotide, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, INFLIXIMAB, Journal Article, Humans, SNP, Genetic Predisposition to Disease, GENOME-WIDE ASSOCIATION, METAANALYSIS, Aged, ta113, Tumor Necrosis Factor-alpha, business.industry, NETWORK INFERENCE, COMPLEX TRAITS, Artritis reumatoide -- Tractament, CLINICAL-RESPONSE, General Chemistry, 113 Computer and information sciences, medicine.disease, Infliximab, 030104 developmental biology, 3121 General medicine, internal medicine and other clinical medicine, Certolizumab Pegol, business, RC
Abstract

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h2=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data., Rheumatoid arthritis patients respond differently to anti-TNF treatment. Using community-based challenge, the authors show that currently available data does not reveal meaningful genetic predictors of response to anti-TNF therapy, thus confirming clinical observations.

Published
2016

47. Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases

Author

Choobdar, Sarvenaz, Ahsen, Mehmet E., Crawford, Jake, Tomasoni, Mattia, Fang, Tao, Lamparter, David, Lin, Junyuan, Hescott, Benjamin, Hu, Xiaozhe, Mercer, Johnathan, Natoli, Ted, Narayan, Rajiv, Subramanian, Aravind, Zhang, Jitao D., Stolovitzky, Gustavo, Kutalik, Zoltán, Lage, Kasper, Slonim, Donna K., Saez-Rodriguez, Julio, Cowen, Lenore J., Bergmann, Sven, Marbach, Daniel, Aicheler, Fabian, Amoroso, Nicola, Arenas, Alex, Azhagesan, Karthik, Baker, Aaron, Banf, Michael, Batzoglou, Serafim, Baudot, Anaïs, Bellotti, Roberto, Boroevich, Keith A., Brun, Christine, Cai, Stanley, Caldera, Michael, Calderone, Alberto, Cesareni, Gianni, Chen, Weiqi, Chichester, Christine, Cowen, Lenore, Cui, Hongzhu, Dao, Phuong, Domenico, Manlio De, Dhroso, Andi, Didier, Gilles, Divine, Mathew, Sol, Antonio del, Feng, Xuyang, Flores-Canales, Jose C., Fortunato, Santo, Gitter, Anthony, Gorska, Anna, Guan, Yuanfang, Guénoche, Alain, Gómez, Sergio, Hamza, Hatem, Hartmann, András, He, Shan, Heijs, Anton, Heinrich, Julian, Hu, Ying, Huang, Xiaoqing, Hughitt, V. Keith, Jeon, Minji, Jeub, Lucas, Johnson, Nathan, Joo, Keehyoung, Joung, InSuk, Jung, Sascha, Kalko, Susana G., Kamola, Piotr J., Kang, Jaewoo, Kaveelerdpotjana, Benjapun, Kim, Minjun, Kim, Yoo-Ah, Kohlbacher, Oliver, Korkin, Dmitry, Krzysztof, Kiryluk, Kunji, Khalid, Kutalik, Zoltàn, Lang-Brown, Sean, Le, Thuc Duy, Lee, Jooyoung, Lee, Sunwon, Lee, Juyong, Li, Dong, Li, Jiuyong, Liu, Lin, Loizou, Antonis, Luo, Zhenhua, Lysenko, Artem, Ma, Tianle, Mall, Raghvendra, Mattia, Tomasoni, Medvedovic, Mario, Menche, Jörg, Micarelli, Elisa, Monaco, Alfonso, Müller, Felix, Narykov, Oleksandr, Norman, Thea, Park, Sungjoon, Perfetto, Livia, Perrin, Dimitri, Pirrò, Stefano, Przytycka, Teresa M., Qian, Xiaoning, Raman, Karthik, Ramazzotti, Daniele, Ramsahai, Emilie, Ravindran, Balaraman, Rennert, Philip, Schärfe, Charlotta, Sharan, Roded, Shi, Ning, Shin, Wonho, Shu, Hai, Sinha, Himanshu, Spinelli, Lionel, Srinivasan, Suhas, Suver, Christine, Szklarczyk, Damian, Tangaro, Sabina, Thiagarajan, Suresh, Tichit, Laurent, Tiede, Thorsten, Tripathi, Beethika, Tsherniak, Aviad, Tsunoda, Tatsuhiko, Türei, Dénes, Ullah, Ehsan, Vahedi, Golnaz, Valdeolivas, Alberto, Vivek, Jayaswal, Mering, Christian von, Waagmeester, Andra, Wang, Bo, Wang, Yijie, Weir, Barbara A., White, Shana, Winkler, Sebastian, Xu, Ke, Xu, Taosheng, Yan, Chunhua, Yang, Liuqing, Yu, Kaixian, Yu, Xiangtian, Zaffaroni, Gaia, Zaslavskiy, Mikhail, Zeng, Tao, Zhang, Lu, Zhang, Weijia, Zhang, Lixia, Zhang, Xinyu, Zhang, Junpeng, Zhou, Xin, Zhou, Jiarui, Zhu, Hongtu, Zhu, Junjie, and Zuccon, Guido

Subjects
Identification methods, Molecular network, Computer science, Association (object-oriented programming), Key (cryptography), Open community, Genome-wide association study, Identification (biology), Computational biology, Disease
Abstract

Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of gene and protein networks remains poorly understood. We launched the “Disease Module Identification DREAM Challenge”, an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). Our critical assessment of 75 contributed module identification methods reveals novel top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).

Published
2018
Full Text
View/download PDF

48. Additional file 4: of Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection

Author

Lee, Anna, Ewing, Adam, Ellrott, Kyle, Hu, Yin, Houlahan, Kathleen, J. Bare, Shadrielle Espiritu, Huang, Vincent, Dang, Kristen, Zechen Chong, Caloian, Cristian, Yamaguchi, Takafumi, Kellen, Michael, Chen, Ken, Norman, Thea, Friend, Stephen, Guinney, Justin, Stolovitzky, Gustavo, Haussler, David, Margolin, Adam, Stuart, Joshua, and Boutros, Paul

Subjects
ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Hardware_ARITHMETICANDLOGICSTRUCTURES, GeneralLiterature_MISCELLANEOUS, ComputingMilieux_MISCELLANEOUS
Abstract

Descriptions of pipelines used to generate submissions. (PDF 3875 kb)

Published
2018
Full Text
View/download PDF

49. Additional file 1: of Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection

Author

Lee, Anna, Ewing, Adam, Ellrott, Kyle, Hu, Yin, Houlahan, Kathleen, J. Bare, Shadrielle Espiritu, Huang, Vincent, Dang, Kristen, Zechen Chong, Caloian, Cristian, Yamaguchi, Takafumi, Kellen, Michael, Chen, Ken, Norman, Thea, Friend, Stephen, Guinney, Justin, Stolovitzky, Gustavo, Haussler, David, Margolin, Adam, Stuart, Joshua, and Boutros, Paul

Abstract

Figures S1-S26. (PDF 3614 kb)

Published
2018
Full Text
View/download PDF

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

Author

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

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

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

Published
2017

Catalog

Books, media, physical & digital resources
See catalog results