학술논문

A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines
Document Type
article
Source
Cell Systems. 5(5)
Subject
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
Language
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.