Dataset Information

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

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.

SUBMITTER: Gonen M

PROVIDER: S-EPMC5814247 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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

Gönen Mehmet M Weir Barbara A BA Cowley Glenn S GS Vazquez Francisca F Guan Yuanfang Y Jaiswal Alok A Karasuyama Masayuki M Uzunangelov Vladislav V Wang Tao T Tsherniak Aviad A Howell Sara S Marbach Daniel D Hoff Bruce B Norman Thea C TC Airola Antti A Bivol Adrian A Bunte Kerstin K Carlin Daniel D Chopra Sahil S Deran Alden A Ellrott Kyle K Gopalacharyulu Peddinti P Graim Kiley K Kaski Samuel S Khan Suleiman A SA Newton Yulia Y Ng Sam S Pahikkala Tapio T Paull Evan E Sokolov Artem A Tang Hao H Tang Jing J Wennerberg Krister K Xie Yang Y Zhan Xiaowei X Zhu Fan F Aittokallio Tero T Mamitsuka Hiroshi H Stuart Joshua M JM Boehm Jesse S JS Root David E DE Xiao Guanghua G Stolovitzky Gustavo G Hahn William C WC Margolin Adam A AA

Cell systems 20171004 5

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 ...[more]

PMID: 28988802

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Project description:Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships.

Dataset Information

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

Publications

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

Similar Datasets

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