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Cell Syst. 2017 Nov 22;5(5):485-497.e3. doi: 10.1016/j.cels.2017.09.004. Epub 2017 Oct 4.

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

Author information

1
Department 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.
2
Cancer Program, The Broad Institute, Boston, MA, USA.
3
Genetic Perturbation Platform, The Broad Institute, Boston, MA, USA; Janssen R&D US, Spring House, PA, USA.
4
Cancer Program, The Broad Institute, Boston, MA, USA; Dana-Farber Cancer Institute, Boston, MA, USA.
5
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
6
Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.
7
Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan.
8
Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.
9
Quantitative 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.
10
Cancer Program, The Broad Institute, Boston, MA, USA; Brandeis University, Waltham, MA, USA.
11
Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
12
Sage Bionetworks, Seattle, WA, USA.
13
Department of Information Technology, University of Turku, Turku, Finland.
14
Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland; School of Computer Science, The University of Birmingham, Birmingham, UK.
15
Department of Bioengineering, University of California, San Diego, CA, USA.
16
Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA.
17
Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA; Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA.
18
Helsinki 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.
19
Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
20
Quantitative 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.
21
Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland; Department of Mathematics and Statistics, University of Turku, Turku, Finland.
22
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.
23
Genetic Perturbation Platform, The Broad Institute, Boston, MA, USA.
24
Quantitative 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.
25
Computational 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.
26
Cancer Program, The Broad Institute, Boston, MA, USA; Dana-Farber Cancer Institute, Boston, MA, USA. Electronic address: william_hahn@dfci.harvard.edu.
27
Department 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.

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.

KEYWORDS:

cancer genomics; community challenge; crowdsourcing; functional screen; machine learning; oncogene

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