ArticleCell systems2017
A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines.
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Article in Cell systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- TARGET-SL: precision essential gene prediction using driver prioritisation and synthetic lethality.Briefings in bioinformatics · 2025Article
- Robust evaluation of deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data.Scientific reports · 2024Article
- Predicting gene knockout effects from expression data.BMC medical genomics · 2023Article
- Fast and interpretable genomic data analysis using multiple approximate kernel learning.Bioinformatics (Oxford, England) · 2022Article
- PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning.BMC bioinformatics · 2021Article
- Predicting and characterizing a cancer dependency map of tumors with deep learning.Science advances · 2021Article
- Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth.Cell reports. Medicine · 2021Article
- Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge.PLoS computational biology · 2021Article
- High-throughput synthetic rescue for exhaustive characterization of suppressor mutations in human genes.Cellular and molecular life sciences : CMLS · 2020Review
- Signatures of cell death and proliferation in perturbation transcriptomics data-from confounding factor to effective prediction.Nucleic acids research · 2019Article
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Authors and funding
46 authors.
Mehmet GönenDepartment of Industrial Engineering, College of Engineering, Koç University, İstanbul, Turkey; School of Medicine, Koç University, İstanbul, Turkey; Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
Barbara A WeirCancer Program, The Broad Institute, Boston, MA, USA.
Glenn S CowleyGenetic Perturbation Platform, The Broad Institute, Boston, MA, USA; Janssen R&D US, Spring House, PA, USA.
Francisca VazquezCancer Program, The Broad Institute, Boston, MA, USA; Dana-Farber Cancer Institute, Boston, MA, USA.
Yuanfang GuanDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Alok JaiswalInstitute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.
Masayuki KarasuyamaDepartment of Computer Science, Nagoya Institute of Technology, Nagoya, Japan.
Vladislav UzunangelovDepartment of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.
Tao WangQuantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Aviad TsherniakCancer Program, The Broad Institute, Boston, MA, USA.
Sara HowellCancer Program, The Broad Institute, Boston, MA, USA; Brandeis University, Waltham, MA, USA.
Daniel MarbachDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
Bruce HoffSage Bionetworks, Seattle, WA, USA.
Thea C NormanSage Bionetworks, Seattle, WA, USA.
Antti AirolaDepartment of Information Technology, University of Turku, Turku, Finland.
Adrian BivolDepartment of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.
Kerstin BunteHelsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland; School of Computer Science, The University of Birmingham, Birmingham, UK.
Daniel CarlinDepartment of Bioengineering, University of California, San Diego, CA, USA.
Sahil ChopraDepartment of Biomolecular Engineering, University of California, Santa Cruz, CA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA.
Alden DeranDepartment of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.
Kyle EllrottDepartment of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA; Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.
Peddinti GopalacharyuluInstitute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.
Kiley GraimDepartment of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.
Samuel KaskiHelsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland; Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.
Suleiman A KhanInstitute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.
Yulia NewtonDepartment of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.
Sam NgDepartment of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.
Tapio PahikkalaDepartment of Information Technology, University of Turku, Turku, Finland.
Evan PaullDepartment of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.
Artem SokolovDepartment of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.
Hao TangQuantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Jing TangInstitute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.
Krister WennerbergInstitute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.
Yang XieQuantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Simons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Xiaowei ZhanQuantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Fan ZhuDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Broad-DREAM Community
Tero AittokallioInstitute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland; Department of Mathematics and Statistics, University of Turku, Turku, Finland.
Hiroshi MamitsukaBioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.
Joshua M StuartDepartment of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.
Jesse S BoehmCancer Program, The Broad Institute, Boston, MA, USA.
David E RootGenetic Perturbation Platform, The Broad Institute, Boston, MA, USA.
Guanghua XiaoQuantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Simons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Gustavo StolovitzkyComputational Biology Center, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA; Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: gustavo@us.ibm.com.
William C HahnCancer Program, The Broad Institute, Boston, MA, USA; Dana-Farber Cancer Institute, Boston, MA, USA. Electronic address: william_hahn@dfci.harvard.edu.
Adam A MargolinDepartment of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA; Computational Biology Program, Oregon Health & Science University, Portland, OR, USA. Electronic address: margolin@ohsu.edu.
Funding
Signatures of Kinase Activation in Cancer (MIT consortium)U54CA112962 · NCI · DANA-FARBER CANCER INSTITUTE · PI GOLUB, TODD R. · 2004 to 2014
$24.2MPharmaco Response Signatures and Disease MechanismU54HL127365 · NHLBI · HARVARD MEDICAL SCHOOL · PI SORGER, PETER KARL · 2014 to 2019
$13.1MUCSC-Buck Inst. Genome Data Analysis Center for TCGA Research Network (GDAC)U24CA143858 · NCI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI BENZ, CHRISTOPHER, HAUSSLER, DAVID H · 2009 to 2016
$8.9MThe Dana-Farber Cancer Institute Cancer Target Discovery and Development CenterU01CA176058 · NCI · DANA-FARBER CANCER INST · PI HAHN, WILLIAM C. · 2013 to 2021
$7.8MFunctional Genomic Discovery of Pathway Targeted and Immune Modulatory Therapeutic Combinations in Hematologic MalignanciesU01CA217862 · NCI · OREGON HEALTH & SCIENCE UNIVERSITY · PI DRUKER, BRIAN J, TYNER, JEFFREY WALLACE · 2017 to 2021
$5.3MAn integrated computational and functional genomics discovery engine for precliniU01CA176303 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI KEMP, CHRISTOPHER J · 2013 to 2016
$4.1MSystematic identification of oncogenic KRAS synthetic lethal interactionsU01CA199253 · NCI · BROAD INSTITUTE, INC. · PI HAHN, WILLIAM C. · 2015 to 2019
$3.7MBIGDATA: Mid-Scale DCM: DA: ESCE: Discovering Molecular ProcessesR01CA180778 · NCI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI STUART, JOSHUA MICHAEL · 2013 to 2017
$3.5MNew Integrative Pathway Analysis Methods to Predict Biomedical OutcomesR01GM109031 · NIGMS · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI STUART, JOSHUA MICHAEL · 2014 to 2018
$2.9MIntegrative Analysis to Identify Therapeutic Targets for Lung CancerR01CA172211 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI XIAO, GUANGHUA · 2013 to 2017
$1.6MPredicting Adjuvant Chemotherapy Response in Lung CancerR01CA152301 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI XIE, YANG · 2010 to 2014
$1.5MNCI NIH HHS R01 CA152301NCI NIH HHS R01 CA172211NCI NIH HHS R01 CA180778NCI NIH HHS U01 CA176058NCI NIH HHS U01 CA176303NCI NIH HHS U01 CA199253NCI NIH HHS U01 CA217862NCI NIH HHS U24 CA143858NCI NIH HHS U54 CA112962NHLBI NIH HHS U54 HL127365NIGMS NIH HHS R01 GM109031
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.
Indexed as
AlgorithmsCell Line, TumorGene ExpressionGenes, EssentialGenomicsHumansRNA, Small InterferingRNA, Small Interferingcancer genomicscommunity challengecrowdsourcingfunctional screenmachine learningoncogene
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.