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

Research output: Contribution to journalArticleScientificpeer-review

Researchers

  • Mehmet Gönen
  • Barbara A. Weir
  • Glenn S. Cowley
  • Francisca Vazquez
  • Yuanfang Guan
  • Alok Jaiswal
  • Masayuki Karasuyama
  • Vladislav Uzunangelov
  • Tao Wang
  • Aviad Tsherniak
  • Sara Howell
  • Daniel Marbach
  • Bruce Hoff
  • Thea C. Norman
  • Antti Airola
  • Adrian Bivol
  • Kerstin Bunte
  • Daniel Carlin
  • Sahil Chopra
  • Alden Deran
  • Kyle Ellrott
  • Peddinti Gopalacharyulu
  • Kiley Graim
  • Suleiman A. Khan
  • Yulia Newton
  • Sam Ng
  • Tapio Pahikkala
  • Evan Paull
  • Artem Sokolov
  • Hao Tang
  • Jing Tang
  • Krister Wennerberg
  • Yang Xie
  • Xiaowei Zhan
  • Fan Zhu
  • Tero Aittokallio
  • Joshua M. Stuart
  • Jesse S. Boehm
  • David E. Root
  • Guanghua Xiao
  • Gustavo Stolovitzky
  • William C. Hahn
  • Adam A. Margolin

Research units

  • Koc University
  • Oregon Health and Science University
  • Broad Institute
  • Janssen R and D
  • University of Michigan, Ann Arbor
  • University of Helsinki
  • Nagoya Institute of Technology
  • University of California at Santa Cruz
  • University of Texas Southwestern Medical Center
  • Brandeis University
  • Swiss Institute of Bioinformatics
  • Sage Bionetworks
  • University of Turku
  • University of Birmingham
  • University of California at San Diego
  • Stanford University
  • Kyoto University
  • Icahn School of Medicine at Mount Sinai
  • Dana-Farber Cancer Institute

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. Gönen et al. report the results of an open-participation DREAM challenge to critically assess the ability to predict gene essentiality on a novel functional screening dataset of 149 cancer cell lines. 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.

Details

Original languageEnglish
Pages (from-to)485-497
JournalCell Systems
Volume5
Issue number5
Publication statusPublished - 2017
MoE publication typeA1 Journal article-refereed

    Research areas

  • Cancer genomics, Community challenge, Crowdsourcing, Functional screen, Machine learning, Oncogene

ID: 15791613